{ "metadata": { "name": "", "signature": "sha256:f778a5c1ca3cfb0656c452780fb9a5567be5ff757bb305f94299ee7587a2e48e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#IV-[SciPy](http://www.scipy.org/) - Scientific Python\n", "\n", "\n", "Lecturer:*Jos\u00e9 Pedro Silva*[1](http://www-num.math.uni-wuppertal.de/en/amna/people/jose-pedro-silva.html) - [silva_at_math.uni-wuppertal.de](mailto:silva_at_math.uni-wuppertal.de)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Index\n", "\n", "- [Quadrature](#quadrature)\n", "- [ODE Integrate](#integrate)\n", " - [Way 1](#odeint)\n", " - [Way 2](#ode)\n", "- [Linear Algebra](#linearalgebra)\n", "- [Optimize](#optimize)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import Image, YouTubeVideo" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy as scp\n", "%pylab" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Using matplotlib backend: Qt4Agg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "help(scp);" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Help on package scipy:\n", "\n", "NAME\n", " scipy\n", "\n", "FILE\n", " /home/jpsilva/anaconda/lib/python2.7/site-packages/scipy/__init__.py\n", "\n", "DESCRIPTION\n", " SciPy: A scientific computing package for Python\n", " ================================================\n", " \n", " Documentation is available in the docstrings and\n", " online at http://docs.scipy.org.\n", " \n", " Contents\n", " --------\n", " SciPy imports all the functions from the NumPy namespace, and in\n", " addition provides:\n", " \n", " Subpackages\n", " -----------\n", " Using any of these subpackages requires an explicit import. For example,\n", " ``import scipy.cluster``.\n", " \n", " ::\n", " \n", " cluster --- Vector Quantization / Kmeans\n", " fftpack --- Discrete Fourier Transform algorithms\n", " integrate --- Integration routines\n", " interpolate --- Interpolation Tools\n", " io --- Data input and output\n", " lib --- Python wrappers to external libraries\n", " lib.lapack --- Wrappers to LAPACK library\n", " linalg --- Linear algebra routines\n", " misc --- Various utilities that don't have\n", " another home.\n", " ndimage --- n-dimensional image package\n", " odr --- Orthogonal Distance Regression\n", " optimize --- Optimization Tools\n", " signal --- Signal Processing Tools\n", " sparse --- Sparse Matrices\n", " sparse.linalg --- Sparse Linear Algebra\n", " sparse.linalg.dsolve --- Linear Solvers\n", " sparse.linalg.dsolve.umfpack --- :Interface to the UMFPACK library:\n", " Conjugate Gradient Method (LOBPCG)\n", " sparse.linalg.eigen.lobpcg --- Locally Optimal Block Preconditioned\n", " Conjugate Gradient Method (LOBPCG) [*]\n", " special --- Airy Functions [*]\n", " lib.blas --- Wrappers to BLAS library [*]\n", " sparse.linalg.eigen --- Sparse Eigenvalue Solvers [*]\n", " stats --- Statistical Functions [*]\n", " lib --- Python wrappers to external libraries\n", " [*]\n", " lib.lapack --- Wrappers to LAPACK library [*]\n", " integrate --- Integration routines [*]\n", " ndimage --- n-dimensional image package [*]\n", " linalg --- Linear algebra routines [*]\n", " spatial --- Spatial data structures and algorithms\n", " special --- Airy Functions\n", " stats --- Statistical Functions\n", " \n", " Utility tools\n", " -------------\n", " ::\n", " \n", " test --- Run scipy unittests\n", " show_config --- Show scipy build configuration\n", " show_numpy_config --- Show numpy build configuration\n", " __version__ --- Scipy version string\n", " __numpy_version__ --- Numpy version string\n", "\n", "PACKAGE CONTENTS\n", " __config__\n", " _build_utils (package)\n", " cluster (package)\n", " constants (package)\n", " fftpack (package)\n", " integrate (package)\n", " interpolate (package)\n", " io (package)\n", " lib (package)\n", " linalg (package)\n", " misc (package)\n", " ndimage (package)\n", " odr (package)\n", " optimize (package)\n", " setup\n", " signal (package)\n", " sparse (package)\n", " spatial (package)\n", " special (package)\n", " stats (package)\n", " version\n", " weave (package)\n", "\n", "CLASSES\n", " __builtin__.object\n", " numpy.broadcast\n", " numpy.busdaycalendar\n", " numpy.dtype\n", " numpy.flatiter\n", " numpy.generic\n", " numpy.bool_\n", " numpy.datetime64\n", " numpy.flexible\n", " numpy.character\n", " numpy.string_(__builtin__.str, numpy.character)\n", " numpy.unicode_(__builtin__.unicode, numpy.character)\n", " numpy.void\n", " numpy.core.records.record\n", " numpy.number\n", " numpy.inexact\n", " numpy.complexfloating\n", " numpy.complex128(numpy.complexfloating, __builtin__.complex)\n", " numpy.complex256\n", " numpy.complex64\n", " numpy.floating\n", " numpy.float128\n", " numpy.float16\n", " numpy.float32\n", " numpy.float64(numpy.floating, __builtin__.float)\n", " numpy.integer\n", " numpy.signedinteger\n", " numpy.int16\n", " numpy.int32\n", " numpy.int64(numpy.signedinteger, __builtin__.int)\n", " numpy.int64(numpy.signedinteger, __builtin__.int)\n", " numpy.int8\n", " numpy.timedelta64\n", " numpy.unsignedinteger\n", " numpy.uint16\n", " numpy.uint32\n", " numpy.uint64\n", " numpy.uint64\n", " numpy.uint8\n", " numpy.object_\n", " numpy.ndarray\n", " numpy.core.defchararray.chararray\n", " numpy.core.memmap.memmap\n", " numpy.core.records.recarray\n", " numpy.matrixlib.defmatrix.matrix\n", " numpy.nditer\n", " numpy.ufunc\n", " numpy._import_tools.PackageLoader\n", " numpy.core.getlimits.finfo\n", " numpy.core.getlimits.iinfo\n", " numpy.core.machar.MachAr\n", " numpy.core.numeric.errstate\n", " numpy.lib._datasource.DataSource\n", " numpy.lib.function_base.vectorize\n", " numpy.lib.index_tricks.ndenumerate\n", " numpy.lib.index_tricks.ndindex\n", " numpy.lib.polynomial.poly1d\n", " __builtin__.str(__builtin__.basestring)\n", " numpy.string_(__builtin__.str, numpy.character)\n", " __builtin__.unicode(__builtin__.basestring)\n", " numpy.unicode_(__builtin__.unicode, numpy.character)\n", " exceptions.DeprecationWarning(exceptions.Warning)\n", " numpy.ModuleDeprecationWarning\n", " exceptions.RuntimeWarning(exceptions.Warning)\n", " numpy.core.numeric.ComplexWarning\n", " exceptions.UserWarning(exceptions.Warning)\n", " numpy.VisibleDeprecationWarning\n", " numpy.lib.polynomial.RankWarning\n", " numpy.core.records.format_parser\n", " \n", " class ComplexWarning(exceptions.RuntimeWarning)\n", " | The warning raised when casting a complex dtype to a real dtype.\n", " | \n", " | As implemented, casting a complex number to a real discards its imaginary\n", " | part, but this behavior may not be what the user actually wants.\n", " | \n", " | Method resolution order:\n", " | ComplexWarning\n", " | exceptions.RuntimeWarning\n", " | exceptions.Warning\n", " | exceptions.Exception\n", " | exceptions.BaseException\n", " | __builtin__.object\n", " | \n", " | Data descriptors defined here:\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from exceptions.RuntimeWarning:\n", " | \n", " | __init__(...)\n", " | x.__init__(...) initializes x; see help(type(x)) for signature\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes inherited from exceptions.RuntimeWarning:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from exceptions.BaseException:\n", " | \n", " | __delattr__(...)\n", " | x.__delattr__('name') <==> del x.name\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __setattr__(...)\n", " | x.__setattr__('name', value) <==> x.name = value\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __unicode__(...)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from exceptions.BaseException:\n", " | \n", " | __dict__\n", " | \n", " | args\n", " | \n", " | message\n", " \n", " class DataSource(__builtin__.object)\n", " | DataSource(destpath='.')\n", " | \n", " | A generic data source file (file, http, ftp, ...).\n", " | \n", " | DataSources can be local files or remote files/URLs. The files may\n", " | also be compressed or uncompressed. DataSource hides some of the\n", " | low-level details of downloading the file, allowing you to simply pass\n", " | in a valid file path (or URL) and obtain a file object.\n", " | \n", " | Parameters\n", " | ----------\n", " | destpath : str or None, optional\n", " | Path to the directory where the source file gets downloaded to for\n", " | use. If `destpath` is None, a temporary directory will be created.\n", " | The default path is the current directory.\n", " | \n", " | Notes\n", " | -----\n", " | URLs require a scheme string (``http://``) to be used, without it they\n", " | will fail::\n", " | \n", " | >>> repos = DataSource()\n", " | >>> repos.exists('www.google.com/index.html')\n", " | False\n", " | >>> repos.exists('http://www.google.com/index.html')\n", " | True\n", " | \n", " | Temporary directories are deleted when the DataSource is deleted.\n", " | \n", " | Examples\n", " | --------\n", " | ::\n", " | \n", " | >>> ds = DataSource('/home/guido')\n", " | >>> urlname = 'http://www.google.com/index.html'\n", " | >>> gfile = ds.open('http://www.google.com/index.html') # remote file\n", " | >>> ds.abspath(urlname)\n", " | '/home/guido/www.google.com/site/index.html'\n", " | \n", " | >>> ds = DataSource(None) # use with temporary file\n", " | >>> ds.open('/home/guido/foobar.txt')\n", " | \n", " | >>> ds.abspath('/home/guido/foobar.txt')\n", " | '/tmp/tmpy4pgsP/home/guido/foobar.txt'\n", " | \n", " | Methods defined here:\n", " | \n", " | __del__(self)\n", " | \n", " | __init__(self, destpath='.')\n", " | Create a DataSource with a local path at destpath.\n", " | \n", " | abspath(self, path)\n", " | Return absolute path of file in the DataSource directory.\n", " | \n", " | If `path` is an URL, then `abspath` will return either the location\n", " | the file exists locally or the location it would exist when opened\n", " | using the `open` method.\n", " | \n", " | Parameters\n", " | ----------\n", " | path : str\n", " | Can be a local file or a remote URL.\n", " | \n", " | Returns\n", " | -------\n", " | out : str\n", " | Complete path, including the `DataSource` destination directory.\n", " | \n", " | Notes\n", " | -----\n", " | The functionality is based on `os.path.abspath`.\n", " | \n", " | exists(self, path)\n", " | Test if path exists.\n", " | \n", " | Test if `path` exists as (and in this order):\n", " | \n", " | - a local file.\n", " | - a remote URL that has been downloaded and stored locally in the\n", " | `DataSource` directory.\n", " | - a remote URL that has not been downloaded, but is valid and\n", " | accessible.\n", " | \n", " | Parameters\n", " | ----------\n", " | path : str\n", " | Can be a local file or a remote URL.\n", " | \n", " | Returns\n", " | -------\n", " | out : bool\n", " | True if `path` exists.\n", " | \n", " | Notes\n", " | -----\n", " | When `path` is an URL, `exists` will return True if it's either\n", " | stored locally in the `DataSource` directory, or is a valid remote\n", " | URL. `DataSource` does not discriminate between the two, the file\n", " | is accessible if it exists in either location.\n", " | \n", " | open(self, path, mode='r')\n", " | Open and return file-like object.\n", " | \n", " | If `path` is an URL, it will be downloaded, stored in the\n", " | `DataSource` directory and opened from there.\n", " | \n", " | Parameters\n", " | ----------\n", " | path : str\n", " | Local file path or URL to open.\n", " | mode : {'r', 'w', 'a'}, optional\n", " | Mode to open `path`. Mode 'r' for reading, 'w' for writing,\n", " | 'a' to append. Available modes depend on the type of object\n", " | specified by `path`. Default is 'r'.\n", " | \n", " | Returns\n", " | -------\n", " | out : file object\n", " | File object.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " \n", " class MachAr(__builtin__.object)\n", " | Diagnosing machine parameters.\n", " | \n", " | Attributes\n", " | ----------\n", " | ibeta : int\n", " | Radix in which numbers are represented.\n", " | it : int\n", " | Number of base-`ibeta` digits in the floating point mantissa M.\n", " | machep : int\n", " | Exponent of the smallest (most negative) power of `ibeta` that,\n", " | added to 1.0, gives something different from 1.0\n", " | eps : float\n", " | Floating-point number ``beta**machep`` (floating point precision)\n", " | negep : int\n", " | Exponent of the smallest power of `ibeta` that, substracted\n", " | from 1.0, gives something different from 1.0.\n", " | epsneg : float\n", " | Floating-point number ``beta**negep``.\n", " | iexp : int\n", " | Number of bits in the exponent (including its sign and bias).\n", " | minexp : int\n", " | Smallest (most negative) power of `ibeta` consistent with there\n", " | being no leading zeros in the mantissa.\n", " | xmin : float\n", " | Floating point number ``beta**minexp`` (the smallest [in\n", " | magnitude] usable floating value).\n", " | maxexp : int\n", " | Smallest (positive) power of `ibeta` that causes overflow.\n", " | xmax : float\n", " | ``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]\n", " | usable floating value).\n", " | irnd : int\n", " | In ``range(6)``, information on what kind of rounding is done\n", " | in addition, and on how underflow is handled.\n", " | ngrd : int\n", " | Number of 'guard digits' used when truncating the product\n", " | of two mantissas to fit the representation.\n", " | epsilon : float\n", " | Same as `eps`.\n", " | tiny : float\n", " | Same as `xmin`.\n", " | huge : float\n", " | Same as `xmax`.\n", " | precision : float\n", " | ``- int(-log10(eps))``\n", " | resolution : float\n", " | ``- 10**(-precision)``\n", " | \n", " | Parameters\n", " | ----------\n", " | float_conv : function, optional\n", " | Function that converts an integer or integer array to a float\n", " | or float array. Default is `float`.\n", " | int_conv : function, optional\n", " | Function that converts a float or float array to an integer or\n", " | integer array. Default is `int`.\n", " | float_to_float : function, optional\n", " | Function that converts a float array to float. Default is `float`.\n", " | Note that this does not seem to do anything useful in the current\n", " | implementation.\n", " | float_to_str : function, optional\n", " | Function that converts a single float to a string. Default is\n", " | ``lambda v:'%24.16e' %v``.\n", " | title : str, optional\n", " | Title that is printed in the string representation of `MachAr`.\n", " | \n", " | See Also\n", " | --------\n", " | finfo : Machine limits for floating point types.\n", " | iinfo : Machine limits for integer types.\n", " | \n", " | References\n", " | ----------\n", " | .. [1] Press, Teukolsky, Vetterling and Flannery,\n", " | \"Numerical Recipes in C++,\" 2nd ed,\n", " | Cambridge University Press, 2002, p. 31.\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, float_conv=, int_conv=, float_to_float=, float_to_str=>, title='Python floating point number')\n", " | float_conv - convert integer to float (array)\n", " | int_conv - convert float (array) to integer\n", " | float_to_float - convert float array to float\n", " | float_to_str - convert array float to str\n", " | title - description of used floating point numbers\n", " | \n", " | __str__(self)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " \n", " class ModuleDeprecationWarning(exceptions.DeprecationWarning)\n", " | Module deprecation warning.\n", " | \n", " | The nose tester turns ordinary Deprecation warnings into test failures.\n", " | That makes it hard to deprecate whole modules, because they get\n", " | imported by default. So this is a special Deprecation warning that the\n", " | nose tester will let pass without making tests fail.\n", " | \n", " | Method resolution order:\n", " | ModuleDeprecationWarning\n", " | exceptions.DeprecationWarning\n", " | exceptions.Warning\n", " | exceptions.Exception\n", " | exceptions.BaseException\n", " | __builtin__.object\n", " | \n", " | Data descriptors defined here:\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from exceptions.DeprecationWarning:\n", " | \n", " | __init__(...)\n", " | x.__init__(...) initializes x; see help(type(x)) for signature\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes inherited from exceptions.DeprecationWarning:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from exceptions.BaseException:\n", " | \n", " | __delattr__(...)\n", " | x.__delattr__('name') <==> del x.name\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __setattr__(...)\n", " | x.__setattr__('name', value) <==> x.name = value\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __unicode__(...)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from exceptions.BaseException:\n", " | \n", " | __dict__\n", " | \n", " | args\n", " | \n", " | message\n", " \n", " class PackageLoader(__builtin__.object)\n", " | Methods defined here:\n", " | \n", " | __call__(self, *packages, **options)\n", " | Load one or more packages into parent package top-level namespace.\n", " | \n", " | This function is intended to shorten the need to import many\n", " | subpackages, say of scipy, constantly with statements such as\n", " | \n", " | import scipy.linalg, scipy.fftpack, scipy.etc...\n", " | \n", " | Instead, you can say:\n", " | \n", " | import scipy\n", " | scipy.pkgload('linalg','fftpack',...)\n", " | \n", " | or\n", " | \n", " | scipy.pkgload()\n", " | \n", " | to load all of them in one call.\n", " | \n", " | If a name which doesn't exist in scipy's namespace is\n", " | given, a warning is shown.\n", " | \n", " | Parameters\n", " | ----------\n", " | *packages : arg-tuple\n", " | the names (one or more strings) of all the modules one\n", " | wishes to load into the top-level namespace.\n", " | verbose= : integer\n", " | verbosity level [default: -1].\n", " | verbose=-1 will suspend also warnings.\n", " | force= : bool\n", " | when True, force reloading loaded packages [default: False].\n", " | postpone= : bool\n", " | when True, don't load packages [default: False]\n", " | \n", " | __init__(self, verbose=False, infunc=False)\n", " | Manages loading packages.\n", " | \n", " | error(self, mess)\n", " | \n", " | get_pkgdocs(self)\n", " | Return documentation summary of subpackages.\n", " | \n", " | log(self, mess)\n", " | \n", " | warn(self, mess)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " \n", " class RankWarning(exceptions.UserWarning)\n", " | Issued by `polyfit` when the Vandermonde matrix is rank deficient.\n", " | \n", " | For more information, a way to suppress the warning, and an example of\n", " | `RankWarning` being issued, see `polyfit`.\n", " | \n", " | Method resolution order:\n", " | RankWarning\n", " | exceptions.UserWarning\n", " | exceptions.Warning\n", " | exceptions.Exception\n", " | exceptions.BaseException\n", " | __builtin__.object\n", " | \n", " | Data descriptors defined here:\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from exceptions.UserWarning:\n", " | \n", " | __init__(...)\n", " | x.__init__(...) initializes x; see help(type(x)) for signature\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes inherited from exceptions.UserWarning:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from exceptions.BaseException:\n", " | \n", " | __delattr__(...)\n", " | x.__delattr__('name') <==> del x.name\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __setattr__(...)\n", " | x.__setattr__('name', value) <==> x.name = value\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __unicode__(...)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from exceptions.BaseException:\n", " | \n", " | __dict__\n", " | \n", " | args\n", " | \n", " | message\n", " \n", " class VisibleDeprecationWarning(exceptions.UserWarning)\n", " | Visible deprecation warning.\n", " | \n", " | By default, python will not show deprecation warnings, so this class\n", " | can be used when a very visible warning is helpful, for example because\n", " | the usage is most likely a user bug.\n", " | \n", " | Method resolution order:\n", " | VisibleDeprecationWarning\n", " | exceptions.UserWarning\n", " | exceptions.Warning\n", " | exceptions.Exception\n", " | exceptions.BaseException\n", " | __builtin__.object\n", " | \n", " | Data descriptors defined here:\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from exceptions.UserWarning:\n", " | \n", " | __init__(...)\n", " | x.__init__(...) initializes x; see help(type(x)) for signature\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes inherited from exceptions.UserWarning:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from exceptions.BaseException:\n", " | \n", " | __delattr__(...)\n", " | x.__delattr__('name') <==> del x.name\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __setattr__(...)\n", " | x.__setattr__('name', value) <==> x.name = value\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __unicode__(...)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from exceptions.BaseException:\n", " | \n", " | __dict__\n", " | \n", " | args\n", " | \n", " | message\n", " \n", " bool8 = class bool_(generic)\n", " | Numpy's Boolean type. Character code: ``?``. Alias: bool8\n", " | \n", " | Method resolution order:\n", " | bool_\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class bool_(generic)\n", " | Numpy's Boolean type. Character code: ``?``. Alias: bool8\n", " | \n", " | Method resolution order:\n", " | bool_\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class broadcast(__builtin__.object)\n", " | Produce an object that mimics broadcasting.\n", " | \n", " | Parameters\n", " | ----------\n", " | in1, in2, ... : array_like\n", " | Input parameters.\n", " | \n", " | Returns\n", " | -------\n", " | b : broadcast object\n", " | Broadcast the input parameters against one another, and\n", " | return an object that encapsulates the result.\n", " | Amongst others, it has ``shape`` and ``nd`` properties, and\n", " | may be used as an iterator.\n", " | \n", " | Examples\n", " | --------\n", " | Manually adding two vectors, using broadcasting:\n", " | \n", " | >>> x = np.array([[1], [2], [3]])\n", " | >>> y = np.array([4, 5, 6])\n", " | >>> b = np.broadcast(x, y)\n", " | \n", " | >>> out = np.empty(b.shape)\n", " | >>> out.flat = [u+v for (u,v) in b]\n", " | >>> out\n", " | array([[ 5., 6., 7.],\n", " | [ 6., 7., 8.],\n", " | [ 7., 8., 9.]])\n", " | \n", " | Compare against built-in broadcasting:\n", " | \n", " | >>> x + y\n", " | array([[5, 6, 7],\n", " | [6, 7, 8],\n", " | [7, 8, 9]])\n", " | \n", " | Methods defined here:\n", " | \n", " | __iter__(...)\n", " | x.__iter__() <==> iter(x)\n", " | \n", " | next(...)\n", " | x.next() -> the next value, or raise StopIteration\n", " | \n", " | reset(...)\n", " | reset()\n", " | \n", " | Reset the broadcasted result's iterator(s).\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> y = np.array([[4], [5], [6]]\n", " | >>> b = np.broadcast(x, y)\n", " | >>> b.index\n", " | 0\n", " | >>> b.next(), b.next(), b.next()\n", " | ((1, 4), (2, 4), (3, 4))\n", " | >>> b.index\n", " | 3\n", " | >>> b.reset()\n", " | >>> b.index\n", " | 0\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | index\n", " | current index in broadcasted result\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[1], [2], [3]])\n", " | >>> y = np.array([4, 5, 6])\n", " | >>> b = np.broadcast(x, y)\n", " | >>> b.index\n", " | 0\n", " | >>> b.next(), b.next(), b.next()\n", " | ((1, 4), (1, 5), (1, 6))\n", " | >>> b.index\n", " | 3\n", " | \n", " | iters\n", " | tuple of iterators along ``self``'s \"components.\"\n", " | \n", " | Returns a tuple of `numpy.flatiter` objects, one for each \"component\"\n", " | of ``self``.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.flatiter\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> y = np.array([[4], [5], [6]])\n", " | >>> b = np.broadcast(x, y)\n", " | >>> row, col = b.iters\n", " | >>> row.next(), col.next()\n", " | (1, 4)\n", " | \n", " | nd\n", " | Number of dimensions of broadcasted result.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> y = np.array([[4], [5], [6]])\n", " | >>> b = np.broadcast(x, y)\n", " | >>> b.nd\n", " | 2\n", " | \n", " | numiter\n", " | Number of iterators possessed by the broadcasted result.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> y = np.array([[4], [5], [6]])\n", " | >>> b = np.broadcast(x, y)\n", " | >>> b.numiter\n", " | 2\n", " | \n", " | shape\n", " | Shape of broadcasted result.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> y = np.array([[4], [5], [6]])\n", " | >>> b = np.broadcast(x, y)\n", " | >>> b.shape\n", " | (3, 3)\n", " | \n", " | size\n", " | Total size of broadcasted result.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> y = np.array([[4], [5], [6]])\n", " | >>> b = np.broadcast(x, y)\n", " | >>> b.size\n", " | 9\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " \n", " class busdaycalendar(__builtin__.object)\n", " | busdaycalendar(weekmask='1111100', holidays=None)\n", " | \n", " | A business day calendar object that efficiently stores information\n", " | defining valid days for the busday family of functions.\n", " | \n", " | The default valid days are Monday through Friday (\"business days\").\n", " | A busdaycalendar object can be specified with any set of weekly\n", " | valid days, plus an optional \"holiday\" dates that always will be invalid.\n", " | \n", " | Once a busdaycalendar object is created, the weekmask and holidays\n", " | cannot be modified.\n", " | \n", " | .. versionadded:: 1.7.0\n", " | \n", " | Parameters\n", " | ----------\n", " | weekmask : str or array_like of bool, optional\n", " | A seven-element array indicating which of Monday through Sunday are\n", " | valid days. May be specified as a length-seven list or array, like\n", " | [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n", " | like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n", " | weekdays, optionally separated by white space. Valid abbreviations\n", " | are: Mon Tue Wed Thu Fri Sat Sun\n", " | holidays : array_like of datetime64[D], optional\n", " | An array of dates to consider as invalid dates, no matter which\n", " | weekday they fall upon. Holiday dates may be specified in any\n", " | order, and NaT (not-a-time) dates are ignored. This list is\n", " | saved in a normalized form that is suited for fast calculations\n", " | of valid days.\n", " | \n", " | Returns\n", " | -------\n", " | out : busdaycalendar\n", " | A business day calendar object containing the specified\n", " | weekmask and holidays values.\n", " | \n", " | See Also\n", " | --------\n", " | is_busday : Returns a boolean array indicating valid days.\n", " | busday_offset : Applies an offset counted in valid days.\n", " | busday_count : Counts how many valid days are in a half-open date range.\n", " | \n", " | Attributes\n", " | ----------\n", " | Note: once a busdaycalendar object is created, you cannot modify the\n", " | weekmask or holidays. The attributes return copies of internal data.\n", " | weekmask : (copy) seven-element array of bool\n", " | holidays : (copy) sorted array of datetime64[D]\n", " | \n", " | Examples\n", " | --------\n", " | >>> # Some important days in July\n", " | ... bdd = np.busdaycalendar(\n", " | ... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])\n", " | >>> # Default is Monday to Friday weekdays\n", " | ... bdd.weekmask\n", " | array([ True, True, True, True, True, False, False], dtype='bool')\n", " | >>> # Any holidays already on the weekend are removed\n", " | ... bdd.holidays\n", " | array(['2011-07-01', '2011-07-04'], dtype='datetime64[D]')\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(...)\n", " | x.__init__(...) initializes x; see help(type(x)) for signature\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | holidays\n", " | A copy of the holiday array indicating additional invalid days.\n", " | \n", " | weekmask\n", " | A copy of the seven-element boolean mask indicating valid days.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " \n", " byte = class int8(signedinteger)\n", " | 8-bit integer. Character code ``b``. C char compatible.\n", " | \n", " | Method resolution order:\n", " | int8\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " bytes_ = class string_(__builtin__.str, character)\n", " | Method resolution order:\n", " | string_\n", " | __builtin__.str\n", " | __builtin__.basestring\n", " | character\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.str:\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __format__(...)\n", " | S.__format__(format_spec) -> string\n", " | \n", " | Return a formatted version of S as described by format_spec.\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __mod__(...)\n", " | x.__mod__(y) <==> x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(n) <==> x*n\n", " | \n", " | __rmod__(...)\n", " | x.__rmod__(y) <==> y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(n) <==> n*x\n", " | \n", " | __sizeof__(...)\n", " | S.__sizeof__() -> size of S in memory, in bytes\n", " | \n", " | capitalize(...)\n", " | S.capitalize() -> string\n", " | \n", " | Return a copy of the string S with only its first character\n", " | capitalized.\n", " | \n", " | center(...)\n", " | S.center(width[, fillchar]) -> string\n", " | \n", " | Return S centered in a string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | count(...)\n", " | S.count(sub[, start[, end]]) -> int\n", " | \n", " | Return the number of non-overlapping occurrences of substring sub in\n", " | string S[start:end]. Optional arguments start and end are interpreted\n", " | as in slice notation.\n", " | \n", " | decode(...)\n", " | S.decode([encoding[,errors]]) -> object\n", " | \n", " | Decodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n", " | as well as any other name registered with codecs.register_error that is\n", " | able to handle UnicodeDecodeErrors.\n", " | \n", " | encode(...)\n", " | S.encode([encoding[,errors]]) -> object\n", " | \n", " | Encodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n", " | 'xmlcharrefreplace' as well as any other name registered with\n", " | codecs.register_error that is able to handle UnicodeEncodeErrors.\n", " | \n", " | endswith(...)\n", " | S.endswith(suffix[, start[, end]]) -> bool\n", " | \n", " | Return True if S ends with the specified suffix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | suffix can also be a tuple of strings to try.\n", " | \n", " | expandtabs(...)\n", " | S.expandtabs([tabsize]) -> string\n", " | \n", " | Return a copy of S where all tab characters are expanded using spaces.\n", " | If tabsize is not given, a tab size of 8 characters is assumed.\n", " | \n", " | find(...)\n", " | S.find(sub [,start [,end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | format(...)\n", " | S.format(*args, **kwargs) -> string\n", " | \n", " | Return a formatted version of S, using substitutions from args and kwargs.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | index(...)\n", " | S.index(sub [,start [,end]]) -> int\n", " | \n", " | Like S.find() but raise ValueError when the substring is not found.\n", " | \n", " | isalnum(...)\n", " | S.isalnum() -> bool\n", " | \n", " | Return True if all characters in S are alphanumeric\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isalpha(...)\n", " | S.isalpha() -> bool\n", " | \n", " | Return True if all characters in S are alphabetic\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isdigit(...)\n", " | S.isdigit() -> bool\n", " | \n", " | Return True if all characters in S are digits\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | islower(...)\n", " | S.islower() -> bool\n", " | \n", " | Return True if all cased characters in S are lowercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | isspace(...)\n", " | S.isspace() -> bool\n", " | \n", " | Return True if all characters in S are whitespace\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | istitle(...)\n", " | S.istitle() -> bool\n", " | \n", " | Return True if S is a titlecased string and there is at least one\n", " | character in S, i.e. uppercase characters may only follow uncased\n", " | characters and lowercase characters only cased ones. Return False\n", " | otherwise.\n", " | \n", " | isupper(...)\n", " | S.isupper() -> bool\n", " | \n", " | Return True if all cased characters in S are uppercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | join(...)\n", " | S.join(iterable) -> string\n", " | \n", " | Return a string which is the concatenation of the strings in the\n", " | iterable. The separator between elements is S.\n", " | \n", " | ljust(...)\n", " | S.ljust(width[, fillchar]) -> string\n", " | \n", " | Return S left-justified in a string of length width. Padding is\n", " | done using the specified fill character (default is a space).\n", " | \n", " | lower(...)\n", " | S.lower() -> string\n", " | \n", " | Return a copy of the string S converted to lowercase.\n", " | \n", " | lstrip(...)\n", " | S.lstrip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with leading whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | partition(...)\n", " | S.partition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, and return the part before it,\n", " | the separator itself, and the part after it. If the separator is not\n", " | found, return S and two empty strings.\n", " | \n", " | replace(...)\n", " | S.replace(old, new[, count]) -> string\n", " | \n", " | Return a copy of string S with all occurrences of substring\n", " | old replaced by new. If the optional argument count is\n", " | given, only the first count occurrences are replaced.\n", " | \n", " | rfind(...)\n", " | S.rfind(sub [,start [,end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | rindex(...)\n", " | S.rindex(sub [,start [,end]]) -> int\n", " | \n", " | Like S.rfind() but raise ValueError when the substring is not found.\n", " | \n", " | rjust(...)\n", " | S.rjust(width[, fillchar]) -> string\n", " | \n", " | Return S right-justified in a string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | rpartition(...)\n", " | S.rpartition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, starting at the end of S, and return\n", " | the part before it, the separator itself, and the part after it. If the\n", " | separator is not found, return two empty strings and S.\n", " | \n", " | rsplit(...)\n", " | S.rsplit([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in the string S, using sep as the\n", " | delimiter string, starting at the end of the string and working\n", " | to the front. If maxsplit is given, at most maxsplit splits are\n", " | done. If sep is not specified or is None, any whitespace string\n", " | is a separator.\n", " | \n", " | rstrip(...)\n", " | S.rstrip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with trailing whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | split(...)\n", " | S.split([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in the string S, using sep as the\n", " | delimiter string. If maxsplit is given, at most maxsplit\n", " | splits are done. If sep is not specified or is None, any\n", " | whitespace string is a separator and empty strings are removed\n", " | from the result.\n", " | \n", " | splitlines(...)\n", " | S.splitlines(keepends=False) -> list of strings\n", " | \n", " | Return a list of the lines in S, breaking at line boundaries.\n", " | Line breaks are not included in the resulting list unless keepends\n", " | is given and true.\n", " | \n", " | startswith(...)\n", " | S.startswith(prefix[, start[, end]]) -> bool\n", " | \n", " | Return True if S starts with the specified prefix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | prefix can also be a tuple of strings to try.\n", " | \n", " | strip(...)\n", " | S.strip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with leading and trailing\n", " | whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | swapcase(...)\n", " | S.swapcase() -> string\n", " | \n", " | Return a copy of the string S with uppercase characters\n", " | converted to lowercase and vice versa.\n", " | \n", " | title(...)\n", " | S.title() -> string\n", " | \n", " | Return a titlecased version of S, i.e. words start with uppercase\n", " | characters, all remaining cased characters have lowercase.\n", " | \n", " | translate(...)\n", " | S.translate(table [,deletechars]) -> string\n", " | \n", " | Return a copy of the string S, where all characters occurring\n", " | in the optional argument deletechars are removed, and the\n", " | remaining characters have been mapped through the given\n", " | translation table, which must be a string of length 256 or None.\n", " | If the table argument is None, no translation is applied and\n", " | the operation simply removes the characters in deletechars.\n", " | \n", " | upper(...)\n", " | S.upper() -> string\n", " | \n", " | Return a copy of the string S converted to uppercase.\n", " | \n", " | zfill(...)\n", " | S.zfill(width) -> string\n", " | \n", " | Pad a numeric string S with zeros on the left, to fill a field\n", " | of the specified width. The string S is never truncated.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " cdouble = class complex128(complexfloating, __builtin__.complex)\n", " | Composed of two 64 bit floats\n", " | \n", " | Method resolution order:\n", " | complex128\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.complex\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.complex:\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " \n", " cfloat = class complex128(complexfloating, __builtin__.complex)\n", " | Composed of two 64 bit floats\n", " | \n", " | Method resolution order:\n", " | complex128\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.complex\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.complex:\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " \n", " class character(flexible)\n", " | Method resolution order:\n", " | character\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class chararray(numpy.ndarray)\n", " | chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,\n", " | strides=None, order=None)\n", " | \n", " | Provides a convenient view on arrays of string and unicode values.\n", " | \n", " | .. note::\n", " | The `chararray` class exists for backwards compatibility with\n", " | Numarray, it is not recommended for new development. Starting from numpy\n", " | 1.4, if one needs arrays of strings, it is recommended to use arrays of\n", " | `dtype` `object_`, `string_` or `unicode_`, and use the free functions\n", " | in the `numpy.char` module for fast vectorized string operations.\n", " | \n", " | Versus a regular Numpy array of type `str` or `unicode`, this\n", " | class adds the following functionality:\n", " | \n", " | 1) values automatically have whitespace removed from the end\n", " | when indexed\n", " | \n", " | 2) comparison operators automatically remove whitespace from the\n", " | end when comparing values\n", " | \n", " | 3) vectorized string operations are provided as methods\n", " | (e.g. `.endswith`) and infix operators (e.g. ``\"+\", \"*\", \"%\"``)\n", " | \n", " | chararrays should be created using `numpy.char.array` or\n", " | `numpy.char.asarray`, rather than this constructor directly.\n", " | \n", " | This constructor creates the array, using `buffer` (with `offset`\n", " | and `strides`) if it is not ``None``. If `buffer` is ``None``, then\n", " | constructs a new array with `strides` in \"C order\", unless both\n", " | ``len(shape) >= 2`` and ``order='Fortran'``, in which case `strides`\n", " | is in \"Fortran order\".\n", " | \n", " | Methods\n", " | -------\n", " | astype\n", " | argsort\n", " | copy\n", " | count\n", " | decode\n", " | dump\n", " | dumps\n", " | encode\n", " | endswith\n", " | expandtabs\n", " | fill\n", " | find\n", " | flatten\n", " | getfield\n", " | index\n", " | isalnum\n", " | isalpha\n", " | isdecimal\n", " | isdigit\n", " | islower\n", " | isnumeric\n", " | isspace\n", " | istitle\n", " | isupper\n", " | item\n", " | join\n", " | ljust\n", " | lower\n", " | lstrip\n", " | nonzero\n", " | put\n", " | ravel\n", " | repeat\n", " | replace\n", " | reshape\n", " | resize\n", " | rfind\n", " | rindex\n", " | rjust\n", " | rsplit\n", " | rstrip\n", " | searchsorted\n", " | setfield\n", " | setflags\n", " | sort\n", " | split\n", " | splitlines\n", " | squeeze\n", " | startswith\n", " | strip\n", " | swapaxes\n", " | swapcase\n", " | take\n", " | title\n", " | tofile\n", " | tolist\n", " | tostring\n", " | translate\n", " | transpose\n", " | upper\n", " | view\n", " | zfill\n", " | \n", " | Parameters\n", " | ----------\n", " | shape : tuple\n", " | Shape of the array.\n", " | itemsize : int, optional\n", " | Length of each array element, in number of characters. Default is 1.\n", " | unicode : bool, optional\n", " | Are the array elements of type unicode (True) or string (False).\n", " | Default is False.\n", " | buffer : int, optional\n", " | Memory address of the start of the array data. Default is None,\n", " | in which case a new array is created.\n", " | offset : int, optional\n", " | Fixed stride displacement from the beginning of an axis?\n", " | Default is 0. Needs to be >=0.\n", " | strides : array_like of ints, optional\n", " | Strides for the array (see `ndarray.strides` for full description).\n", " | Default is None.\n", " | order : {'C', 'F'}, optional\n", " | The order in which the array data is stored in memory: 'C' ->\n", " | \"row major\" order (the default), 'F' -> \"column major\"\n", " | (Fortran) order.\n", " | \n", " | Examples\n", " | --------\n", " | >>> charar = np.chararray((3, 3))\n", " | >>> charar[:] = 'a'\n", " | >>> charar\n", " | chararray([['a', 'a', 'a'],\n", " | ['a', 'a', 'a'],\n", " | ['a', 'a', 'a']],\n", " | dtype='|S1')\n", " | \n", " | >>> charar = np.chararray(charar.shape, itemsize=5)\n", " | >>> charar[:] = 'abc'\n", " | >>> charar\n", " | chararray([['abc', 'abc', 'abc'],\n", " | ['abc', 'abc', 'abc'],\n", " | ['abc', 'abc', 'abc']],\n", " | dtype='|S5')\n", " | \n", " | Method resolution order:\n", " | chararray\n", " | numpy.ndarray\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __add__(self, other)\n", " | Return (self + other), that is string concatenation,\n", " | element-wise for a pair of array_likes of str or unicode.\n", " | \n", " | See also\n", " | --------\n", " | add\n", " | \n", " | __array_finalize__(self, obj)\n", " | \n", " | __eq__(self, other)\n", " | Return (self == other) element-wise.\n", " | \n", " | See also\n", " | --------\n", " | equal\n", " | \n", " | __ge__(self, other)\n", " | Return (self >= other) element-wise.\n", " | \n", " | See also\n", " | --------\n", " | greater_equal\n", " | \n", " | __getitem__(self, obj)\n", " | \n", " | __gt__(self, other)\n", " | Return (self > other) element-wise.\n", " | \n", " | See also\n", " | --------\n", " | greater\n", " | \n", " | __le__(self, other)\n", " | Return (self <= other) element-wise.\n", " | \n", " | See also\n", " | --------\n", " | less_equal\n", " | \n", " | __lt__(self, other)\n", " | Return (self < other) element-wise.\n", " | \n", " | See also\n", " | --------\n", " | less\n", " | \n", " | __mod__(self, i)\n", " | Return (self % i), that is pre-Python 2.6 string formatting\n", " | (iterpolation), element-wise for a pair of array_likes of `string_`\n", " | or `unicode_`.\n", " | \n", " | See also\n", " | --------\n", " | mod\n", " | \n", " | __mul__(self, i)\n", " | Return (self * i), that is string multiple concatenation,\n", " | element-wise.\n", " | \n", " | See also\n", " | --------\n", " | multiply\n", " | \n", " | __ne__(self, other)\n", " | Return (self != other) element-wise.\n", " | \n", " | See also\n", " | --------\n", " | not_equal\n", " | \n", " | __radd__(self, other)\n", " | Return (other + self), that is string concatenation,\n", " | element-wise for a pair of array_likes of `string_` or `unicode_`.\n", " | \n", " | See also\n", " | --------\n", " | add\n", " | \n", " | __rmod__(self, other)\n", " | \n", " | __rmul__(self, i)\n", " | Return (self * i), that is string multiple concatenation,\n", " | element-wise.\n", " | \n", " | See also\n", " | --------\n", " | multiply\n", " | \n", " | argsort(self, axis=-1, kind='quicksort', order=None)\n", " | \n", " | capitalize(self)\n", " | Return a copy of `self` with only the first character of each element\n", " | capitalized.\n", " | \n", " | See also\n", " | --------\n", " | char.capitalize\n", " | \n", " | center(self, width, fillchar=' ')\n", " | Return a copy of `self` with its elements centered in a\n", " | string of length `width`.\n", " | \n", " | See also\n", " | --------\n", " | center\n", " | \n", " | count(self, sub, start=0, end=None)\n", " | Returns an array with the number of non-overlapping occurrences of\n", " | substring `sub` in the range [`start`, `end`].\n", " | \n", " | See also\n", " | --------\n", " | char.count\n", " | \n", " | decode(self, encoding=None, errors=None)\n", " | Calls `str.decode` element-wise.\n", " | \n", " | See also\n", " | --------\n", " | char.decode\n", " | \n", " | encode(self, encoding=None, errors=None)\n", " | Calls `str.encode` element-wise.\n", " | \n", " | See also\n", " | --------\n", " | char.encode\n", " | \n", " | endswith(self, suffix, start=0, end=None)\n", " | Returns a boolean array which is `True` where the string element\n", " | in `self` ends with `suffix`, otherwise `False`.\n", " | \n", " | See also\n", " | --------\n", " | char.endswith\n", " | \n", " | expandtabs(self, tabsize=8)\n", " | Return a copy of each string element where all tab characters are\n", " | replaced by one or more spaces.\n", " | \n", " | See also\n", " | --------\n", " | char.expandtabs\n", " | \n", " | find(self, sub, start=0, end=None)\n", " | For each element, return the lowest index in the string where\n", " | substring `sub` is found.\n", " | \n", " | See also\n", " | --------\n", " | char.find\n", " | \n", " | index(self, sub, start=0, end=None)\n", " | Like `find`, but raises `ValueError` when the substring is not found.\n", " | \n", " | See also\n", " | --------\n", " | char.index\n", " | \n", " | isalnum(self)\n", " | Returns true for each element if all characters in the string\n", " | are alphanumeric and there is at least one character, false\n", " | otherwise.\n", " | \n", " | See also\n", " | --------\n", " | char.isalnum\n", " | \n", " | isalpha(self)\n", " | Returns true for each element if all characters in the string\n", " | are alphabetic and there is at least one character, false\n", " | otherwise.\n", " | \n", " | See also\n", " | --------\n", " | char.isalpha\n", " | \n", " | isdecimal(self)\n", " | For each element in `self`, return True if there are only\n", " | decimal characters in the element.\n", " | \n", " | See also\n", " | --------\n", " | char.isdecimal\n", " | \n", " | isdigit(self)\n", " | Returns true for each element if all characters in the string are\n", " | digits and there is at least one character, false otherwise.\n", " | \n", " | See also\n", " | --------\n", " | char.isdigit\n", " | \n", " | islower(self)\n", " | Returns true for each element if all cased characters in the\n", " | string are lowercase and there is at least one cased character,\n", " | false otherwise.\n", " | \n", " | See also\n", " | --------\n", " | char.islower\n", " | \n", " | isnumeric(self)\n", " | For each element in `self`, return True if there are only\n", " | numeric characters in the element.\n", " | \n", " | See also\n", " | --------\n", " | char.isnumeric\n", " | \n", " | isspace(self)\n", " | Returns true for each element if there are only whitespace\n", " | characters in the string and there is at least one character,\n", " | false otherwise.\n", " | \n", " | See also\n", " | --------\n", " | char.isspace\n", " | \n", " | istitle(self)\n", " | Returns true for each element if the element is a titlecased\n", " | string and there is at least one character, false otherwise.\n", " | \n", " | See also\n", " | --------\n", " | char.istitle\n", " | \n", " | isupper(self)\n", " | Returns true for each element if all cased characters in the\n", " | string are uppercase and there is at least one character, false\n", " | otherwise.\n", " | \n", " | See also\n", " | --------\n", " | char.isupper\n", " | \n", " | join(self, seq)\n", " | Return a string which is the concatenation of the strings in the\n", " | sequence `seq`.\n", " | \n", " | See also\n", " | --------\n", " | char.join\n", " | \n", " | ljust(self, width, fillchar=' ')\n", " | Return an array with the elements of `self` left-justified in a\n", " | string of length `width`.\n", " | \n", " | See also\n", " | --------\n", " | char.ljust\n", " | \n", " | lower(self)\n", " | Return an array with the elements of `self` converted to\n", " | lowercase.\n", " | \n", " | See also\n", " | --------\n", " | char.lower\n", " | \n", " | lstrip(self, chars=None)\n", " | For each element in `self`, return a copy with the leading characters\n", " | removed.\n", " | \n", " | See also\n", " | --------\n", " | char.lstrip\n", " | \n", " | partition(self, sep)\n", " | Partition each element in `self` around `sep`.\n", " | \n", " | See also\n", " | --------\n", " | partition\n", " | \n", " | replace(self, old, new, count=None)\n", " | For each element in `self`, return a copy of the string with all\n", " | occurrences of substring `old` replaced by `new`.\n", " | \n", " | See also\n", " | --------\n", " | char.replace\n", " | \n", " | rfind(self, sub, start=0, end=None)\n", " | For each element in `self`, return the highest index in the string\n", " | where substring `sub` is found, such that `sub` is contained\n", " | within [`start`, `end`].\n", " | \n", " | See also\n", " | --------\n", " | char.rfind\n", " | \n", " | rindex(self, sub, start=0, end=None)\n", " | Like `rfind`, but raises `ValueError` when the substring `sub` is\n", " | not found.\n", " | \n", " | See also\n", " | --------\n", " | char.rindex\n", " | \n", " | rjust(self, width, fillchar=' ')\n", " | Return an array with the elements of `self`\n", " | right-justified in a string of length `width`.\n", " | \n", " | See also\n", " | --------\n", " | char.rjust\n", " | \n", " | rpartition(self, sep)\n", " | Partition each element in `self` around `sep`.\n", " | \n", " | See also\n", " | --------\n", " | rpartition\n", " | \n", " | rsplit(self, sep=None, maxsplit=None)\n", " | For each element in `self`, return a list of the words in\n", " | the string, using `sep` as the delimiter string.\n", " | \n", " | See also\n", " | --------\n", " | char.rsplit\n", " | \n", " | rstrip(self, chars=None)\n", " | For each element in `self`, return a copy with the trailing\n", " | characters removed.\n", " | \n", " | See also\n", " | --------\n", " | char.rstrip\n", " | \n", " | split(self, sep=None, maxsplit=None)\n", " | For each element in `self`, return a list of the words in the\n", " | string, using `sep` as the delimiter string.\n", " | \n", " | See also\n", " | --------\n", " | char.split\n", " | \n", " | splitlines(self, keepends=None)\n", " | For each element in `self`, return a list of the lines in the\n", " | element, breaking at line boundaries.\n", " | \n", " | See also\n", " | --------\n", " | char.splitlines\n", " | \n", " | startswith(self, prefix, start=0, end=None)\n", " | Returns a boolean array which is `True` where the string element\n", " | in `self` starts with `prefix`, otherwise `False`.\n", " | \n", " | See also\n", " | --------\n", " | char.startswith\n", " | \n", " | strip(self, chars=None)\n", " | For each element in `self`, return a copy with the leading and\n", " | trailing characters removed.\n", " | \n", " | See also\n", " | --------\n", " | char.strip\n", " | \n", " | swapcase(self)\n", " | For each element in `self`, return a copy of the string with\n", " | uppercase characters converted to lowercase and vice versa.\n", " | \n", " | See also\n", " | --------\n", " | char.swapcase\n", " | \n", " | title(self)\n", " | For each element in `self`, return a titlecased version of the\n", " | string: words start with uppercase characters, all remaining cased\n", " | characters are lowercase.\n", " | \n", " | See also\n", " | --------\n", " | char.title\n", " | \n", " | translate(self, table, deletechars=None)\n", " | For each element in `self`, return a copy of the string where\n", " | all characters occurring in the optional argument\n", " | `deletechars` are removed, and the remaining characters have\n", " | been mapped through the given translation table.\n", " | \n", " | See also\n", " | --------\n", " | char.translate\n", " | \n", " | upper(self)\n", " | Return an array with the elements of `self` converted to\n", " | uppercase.\n", " | \n", " | See also\n", " | --------\n", " | char.upper\n", " | \n", " | zfill(self, width)\n", " | Return the numeric string left-filled with zeros in a string of\n", " | length `width`.\n", " | \n", " | See also\n", " | --------\n", " | char.zfill\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(subtype, shape, itemsize=1, unicode=False, buffer=None, offset=0, strides=None, order='C')\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from numpy.ndarray:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | a.__array__(|dtype) -> reference if type unchanged, copy otherwise.\n", " | \n", " | Returns either a new reference to self if dtype is not given or a new array\n", " | of provided data type if dtype is different from the current dtype of the\n", " | array.\n", " | \n", " | __array_prepare__(...)\n", " | a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n", " | \n", " | __array_wrap__(...)\n", " | a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __copy__(...)\n", " | a.__copy__([order])\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | If order is 'C' (False) then the result is contiguous (default).\n", " | If order is 'Fortran' (True) then the result has fortran order.\n", " | If order is 'Any' (None) then the result has fortran order\n", " | only if the array already is in fortran order.\n", " | \n", " | __deepcopy__(...)\n", " | a.__deepcopy__() -> Deep copy of array.\n", " | \n", " | Used if copy.deepcopy is called on an array.\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __delslice__(...)\n", " | x.__delslice__(i, j) <==> del x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __iadd__(...)\n", " | x.__iadd__(y) <==> x+=y\n", " | \n", " | __iand__(...)\n", " | x.__iand__(y) <==> x&=y\n", " | \n", " | __idiv__(...)\n", " | x.__idiv__(y) <==> x/=y\n", " | \n", " | __ifloordiv__(...)\n", " | x.__ifloordiv__(y) <==> x//y\n", " | \n", " | __ilshift__(...)\n", " | x.__ilshift__(y) <==> x<<=y\n", " | \n", " | __imod__(...)\n", " | x.__imod__(y) <==> x%=y\n", " | \n", " | __imul__(...)\n", " | x.__imul__(y) <==> x*=y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __ior__(...)\n", " | x.__ior__(y) <==> x|=y\n", " | \n", " | __ipow__(...)\n", " | x.__ipow__(y) <==> x**=y\n", " | \n", " | __irshift__(...)\n", " | x.__irshift__(y) <==> x>>=y\n", " | \n", " | __isub__(...)\n", " | x.__isub__(y) <==> x-=y\n", " | \n", " | __iter__(...)\n", " | x.__iter__() <==> iter(x)\n", " | \n", " | __itruediv__(...)\n", " | x.__itruediv__(y) <==> x/y\n", " | \n", " | __ixor__(...)\n", " | x.__ixor__(y) <==> x^=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | a.__reduce__()\n", " | \n", " | For pickling.\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | __setslice__(...)\n", " | x.__setslice__(i, j, y) <==> x[i:j]=y\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __setstate__(...)\n", " | a.__setstate__(version, shape, dtype, isfortran, rawdata)\n", " | \n", " | For unpickling.\n", " | \n", " | Parameters\n", " | ----------\n", " | version : int\n", " | optional pickle version. If omitted defaults to 0.\n", " | shape : tuple\n", " | dtype : data-type\n", " | isFortran : bool\n", " | rawdata : string or list\n", " | a binary string with the data (or a list if 'a' is an object array)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | a.all(axis=None, out=None)\n", " | \n", " | Returns True if all elements evaluate to True.\n", " | \n", " | Refer to `numpy.all` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.all : equivalent function\n", " | \n", " | any(...)\n", " | a.any(axis=None, out=None)\n", " | \n", " | Returns True if any of the elements of `a` evaluate to True.\n", " | \n", " | Refer to `numpy.any` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.any : equivalent function\n", " | \n", " | argmax(...)\n", " | a.argmax(axis=None, out=None)\n", " | \n", " | Return indices of the maximum values along the given axis.\n", " | \n", " | Refer to `numpy.argmax` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmax : equivalent function\n", " | \n", " | argmin(...)\n", " | a.argmin(axis=None, out=None)\n", " | \n", " | Return indices of the minimum values along the given axis of `a`.\n", " | \n", " | Refer to `numpy.argmin` for detailed documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmin : equivalent function\n", " | \n", " | argpartition(...)\n", " | a.argpartition(kth, axis=-1, kind='introselect', order=None)\n", " | \n", " | Returns the indices that would partition this array.\n", " | \n", " | Refer to `numpy.argpartition` for full documentation.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argpartition : equivalent function\n", " | \n", " | astype(...)\n", " | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n", " | \n", " | Copy of the array, cast to a specified type.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | Typecode or data-type to which the array is cast.\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout order of the result.\n", " | 'C' means C order, 'F' means Fortran order, 'A'\n", " | means 'F' order if all the arrays are Fortran contiguous,\n", " | 'C' order otherwise, and 'K' means as close to the\n", " | order the array elements appear in memory as possible.\n", " | Default is 'K'.\n", " | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n", " | Controls what kind of data casting may occur. Defaults to 'unsafe'\n", " | for backwards compatibility.\n", " | \n", " | * 'no' means the data types should not be cast at all.\n", " | * 'equiv' means only byte-order changes are allowed.\n", " | * 'safe' means only casts which can preserve values are allowed.\n", " | * 'same_kind' means only safe casts or casts within a kind,\n", " | like float64 to float32, are allowed.\n", " | * 'unsafe' means any data conversions may be done.\n", " | subok : bool, optional\n", " | If True, then sub-classes will be passed-through (default), otherwise\n", " | the returned array will be forced to be a base-class array.\n", " | copy : bool, optional\n", " | By default, astype always returns a newly allocated array. If this\n", " | is set to false, and the `dtype`, `order`, and `subok`\n", " | requirements are satisfied, the input array is returned instead\n", " | of a copy.\n", " | \n", " | Returns\n", " | -------\n", " | arr_t : ndarray\n", " | Unless `copy` is False and the other conditions for returning the input\n", " | array are satisfied (see description for `copy` input paramter), `arr_t`\n", " | is a new array of the same shape as the input array, with dtype, order\n", " | given by `dtype`, `order`.\n", " | \n", " | Notes\n", " | -----\n", " | Starting in NumPy 1.9, astype method now returns an error if the string\n", " | dtype to cast to is not long enough in 'safe' casting mode to hold the max\n", " | value of integer/float array that is being casted. Previously the casting\n", " | was allowed even if the result was truncated.\n", " | \n", " | Raises\n", " | ------\n", " | ComplexWarning\n", " | When casting from complex to float or int. To avoid this,\n", " | one should use ``a.real.astype(t)``.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 2.5])\n", " | >>> x\n", " | array([ 1. , 2. , 2.5])\n", " | \n", " | >>> x.astype(int)\n", " | array([1, 2, 2])\n", " | \n", " | byteswap(...)\n", " | a.byteswap(inplace)\n", " | \n", " | Swap the bytes of the array elements\n", " | \n", " | Toggle between low-endian and big-endian data representation by\n", " | returning a byteswapped array, optionally swapped in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | inplace : bool, optional\n", " | If ``True``, swap bytes in-place, default is ``False``.\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | The byteswapped array. If `inplace` is ``True``, this is\n", " | a view to self.\n", " | \n", " | Examples\n", " | --------\n", " | >>> A = np.array([1, 256, 8755], dtype=np.int16)\n", " | >>> map(hex, A)\n", " | ['0x1', '0x100', '0x2233']\n", " | >>> A.byteswap(True)\n", " | array([ 256, 1, 13090], dtype=int16)\n", " | >>> map(hex, A)\n", " | ['0x100', '0x1', '0x3322']\n", " | \n", " | Arrays of strings are not swapped\n", " | \n", " | >>> A = np.array(['ceg', 'fac'])\n", " | >>> A.byteswap()\n", " | array(['ceg', 'fac'],\n", " | dtype='|S3')\n", " | \n", " | choose(...)\n", " | a.choose(choices, out=None, mode='raise')\n", " | \n", " | Use an index array to construct a new array from a set of choices.\n", " | \n", " | Refer to `numpy.choose` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.choose : equivalent function\n", " | \n", " | clip(...)\n", " | a.clip(a_min, a_max, out=None)\n", " | \n", " | Return an array whose values are limited to ``[a_min, a_max]``.\n", " | \n", " | Refer to `numpy.clip` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.clip : equivalent function\n", " | \n", " | compress(...)\n", " | a.compress(condition, axis=None, out=None)\n", " | \n", " | Return selected slices of this array along given axis.\n", " | \n", " | Refer to `numpy.compress` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.compress : equivalent function\n", " | \n", " | conj(...)\n", " | a.conj()\n", " | \n", " | Complex-conjugate all elements.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | conjugate(...)\n", " | a.conjugate()\n", " | \n", " | Return the complex conjugate, element-wise.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | copy(...)\n", " | a.copy(order='C')\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout of the copy. 'C' means C-order,\n", " | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n", " | 'C' otherwise. 'K' means match the layout of `a` as closely\n", " | as possible. (Note that this function and :func:numpy.copy are very\n", " | similar, but have different default values for their order=\n", " | arguments.)\n", " | \n", " | See also\n", " | --------\n", " | numpy.copy\n", " | numpy.copyto\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n", " | \n", " | >>> y = x.copy()\n", " | \n", " | >>> x.fill(0)\n", " | \n", " | >>> x\n", " | array([[0, 0, 0],\n", " | [0, 0, 0]])\n", " | \n", " | >>> y\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | \n", " | >>> y.flags['C_CONTIGUOUS']\n", " | True\n", " | \n", " | cumprod(...)\n", " | a.cumprod(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative product of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumprod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumprod : equivalent function\n", " | \n", " | cumsum(...)\n", " | a.cumsum(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative sum of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumsum` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumsum : equivalent function\n", " | \n", " | diagonal(...)\n", " | a.diagonal(offset=0, axis1=0, axis2=1)\n", " | \n", " | Return specified diagonals. In NumPy 1.9 the returned array is a\n", " | read-only view instead of a copy as in previous NumPy versions. In\n", " | NumPy 1.10 the read-only restriction will be removed.\n", " | \n", " | Refer to :func:`numpy.diagonal` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.diagonal : equivalent function\n", " | \n", " | dot(...)\n", " | a.dot(b, out=None)\n", " | \n", " | Dot product of two arrays.\n", " | \n", " | Refer to `numpy.dot` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dot : equivalent function\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.eye(2)\n", " | >>> b = np.ones((2, 2)) * 2\n", " | >>> a.dot(b)\n", " | array([[ 2., 2.],\n", " | [ 2., 2.]])\n", " | \n", " | This array method can be conveniently chained:\n", " | \n", " | >>> a.dot(b).dot(b)\n", " | array([[ 8., 8.],\n", " | [ 8., 8.]])\n", " | \n", " | dump(...)\n", " | a.dump(file)\n", " | \n", " | Dump a pickle of the array to the specified file.\n", " | The array can be read back with pickle.load or numpy.load.\n", " | \n", " | Parameters\n", " | ----------\n", " | file : str\n", " | A string naming the dump file.\n", " | \n", " | dumps(...)\n", " | a.dumps()\n", " | \n", " | Returns the pickle of the array as a string.\n", " | pickle.loads or numpy.loads will convert the string back to an array.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | fill(...)\n", " | a.fill(value)\n", " | \n", " | Fill the array with a scalar value.\n", " | \n", " | Parameters\n", " | ----------\n", " | value : scalar\n", " | All elements of `a` will be assigned this value.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([1, 2])\n", " | >>> a.fill(0)\n", " | >>> a\n", " | array([0, 0])\n", " | >>> a = np.empty(2)\n", " | >>> a.fill(1)\n", " | >>> a\n", " | array([ 1., 1.])\n", " | \n", " | flatten(...)\n", " | a.flatten(order='C')\n", " | \n", " | Return a copy of the array collapsed into one dimension.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | Whether to flatten in C (row-major), Fortran (column-major) order,\n", " | or preserve the C/Fortran ordering from `a`.\n", " | The default is 'C'.\n", " | \n", " | Returns\n", " | -------\n", " | y : ndarray\n", " | A copy of the input array, flattened to one dimension.\n", " | \n", " | See Also\n", " | --------\n", " | ravel : Return a flattened array.\n", " | flat : A 1-D flat iterator over the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,2], [3,4]])\n", " | >>> a.flatten()\n", " | array([1, 2, 3, 4])\n", " | >>> a.flatten('F')\n", " | array([1, 3, 2, 4])\n", " | \n", " | getfield(...)\n", " | a.getfield(dtype, offset=0)\n", " | \n", " | Returns a field of the given array as a certain type.\n", " | \n", " | A field is a view of the array data with a given data-type. The values in\n", " | the view are determined by the given type and the offset into the current\n", " | array in bytes. The offset needs to be such that the view dtype fits in the\n", " | array dtype; for example an array of dtype complex128 has 16-byte elements.\n", " | If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n", " | between 0 and 12 bytes.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | The data type of the view. The dtype size of the view can not be larger\n", " | than that of the array itself.\n", " | offset : int\n", " | Number of bytes to skip before beginning the element view.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.diag([1.+1.j]*2)\n", " | >>> x[1, 1] = 2 + 4.j\n", " | >>> x\n", " | array([[ 1.+1.j, 0.+0.j],\n", " | [ 0.+0.j, 2.+4.j]])\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0.],\n", " | [ 0., 2.]])\n", " | \n", " | By choosing an offset of 8 bytes we can select the complex part of the\n", " | array for our view:\n", " | \n", " | >>> x.getfield(np.float64, offset=8)\n", " | array([[ 1., 0.],\n", " | [ 0., 4.]])\n", " | \n", " | item(...)\n", " | a.item(*args)\n", " | \n", " | Copy an element of an array to a standard Python scalar and return it.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments (variable number and type)\n", " | \n", " | * none: in this case, the method only works for arrays\n", " | with one element (`a.size == 1`), which element is\n", " | copied into a standard Python scalar object and returned.\n", " | \n", " | * int_type: this argument is interpreted as a flat index into\n", " | the array, specifying which element to copy and return.\n", " | \n", " | * tuple of int_types: functions as does a single int_type argument,\n", " | except that the argument is interpreted as an nd-index into the\n", " | array.\n", " | \n", " | Returns\n", " | -------\n", " | z : Standard Python scalar object\n", " | A copy of the specified element of the array as a suitable\n", " | Python scalar\n", " | \n", " | Notes\n", " | -----\n", " | When the data type of `a` is longdouble or clongdouble, item() returns\n", " | a scalar array object because there is no available Python scalar that\n", " | would not lose information. Void arrays return a buffer object for item(),\n", " | unless fields are defined, in which case a tuple is returned.\n", " | \n", " | `item` is very similar to a[args], except, instead of an array scalar,\n", " | a standard Python scalar is returned. This can be useful for speeding up\n", " | access to elements of the array and doing arithmetic on elements of the\n", " | array using Python's optimized math.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.item(3)\n", " | 2\n", " | >>> x.item(7)\n", " | 5\n", " | >>> x.item((0, 1))\n", " | 1\n", " | >>> x.item((2, 2))\n", " | 3\n", " | \n", " | itemset(...)\n", " | a.itemset(*args)\n", " | \n", " | Insert scalar into an array (scalar is cast to array's dtype, if possible)\n", " | \n", " | There must be at least 1 argument, and define the last argument\n", " | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster\n", " | than ``a[args] = item``. The item should be a scalar value and `args`\n", " | must select a single item in the array `a`.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments\n", " | If one argument: a scalar, only used in case `a` is of size 1.\n", " | If two arguments: the last argument is the value to be set\n", " | and must be a scalar, the first argument specifies a single array\n", " | element location. It is either an int or a tuple.\n", " | \n", " | Notes\n", " | -----\n", " | Compared to indexing syntax, `itemset` provides some speed increase\n", " | for placing a scalar into a particular location in an `ndarray`,\n", " | if you must do this. However, generally this is discouraged:\n", " | among other problems, it complicates the appearance of the code.\n", " | Also, when using `itemset` (and `item`) inside a loop, be sure\n", " | to assign the methods to a local variable to avoid the attribute\n", " | look-up at each loop iteration.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.itemset(4, 0)\n", " | >>> x.itemset((2, 2), 9)\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 0, 3],\n", " | [8, 5, 9]])\n", " | \n", " | max(...)\n", " | a.max(axis=None, out=None)\n", " | \n", " | Return the maximum along a given axis.\n", " | \n", " | Refer to `numpy.amax` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.amax : equivalent function\n", " | \n", " | mean(...)\n", " | a.mean(axis=None, dtype=None, out=None)\n", " | \n", " | Returns the average of the array elements along given axis.\n", " | \n", " | Refer to `numpy.mean` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.mean : equivalent function\n", " | \n", " | min(...)\n", " | a.min(axis=None, out=None)\n", " | \n", " | Return the minimum along a given axis.\n", " | \n", " | Refer to `numpy.amin` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.amin : equivalent function\n", " | \n", " | newbyteorder(...)\n", " | arr.newbyteorder(new_order='S')\n", " | \n", " | Return the array with the same data viewed with a different byte order.\n", " | \n", " | Equivalent to::\n", " | \n", " | arr.view(arr.dtype.newbytorder(new_order))\n", " | \n", " | Changes are also made in all fields and sub-arrays of the array data\n", " | type.\n", " | \n", " | \n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : string, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. `new_order` codes can be any of::\n", " | \n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_arr : array\n", " | New array object with the dtype reflecting given change to the\n", " | byte order.\n", " | \n", " | nonzero(...)\n", " | a.nonzero()\n", " | \n", " | Return the indices of the elements that are non-zero.\n", " | \n", " | Refer to `numpy.nonzero` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.nonzero : equivalent function\n", " | \n", " | prod(...)\n", " | a.prod(axis=None, dtype=None, out=None)\n", " | \n", " | Return the product of the array elements over the given axis\n", " | \n", " | Refer to `numpy.prod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.prod : equivalent function\n", " | \n", " | ptp(...)\n", " | a.ptp(axis=None, out=None)\n", " | \n", " | Peak to peak (maximum - minimum) value along a given axis.\n", " | \n", " | Refer to `numpy.ptp` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ptp : equivalent function\n", " | \n", " | put(...)\n", " | a.put(indices, values, mode='raise')\n", " | \n", " | Set ``a.flat[n] = values[n]`` for all `n` in indices.\n", " | \n", " | Refer to `numpy.put` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.put : equivalent function\n", " | \n", " | ravel(...)\n", " | a.ravel([order])\n", " | \n", " | Return a flattened array.\n", " | \n", " | Refer to `numpy.ravel` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ravel : equivalent function\n", " | \n", " | ndarray.flat : a flat iterator on the array.\n", " | \n", " | repeat(...)\n", " | a.repeat(repeats, axis=None)\n", " | \n", " | Repeat elements of an array.\n", " | \n", " | Refer to `numpy.repeat` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.repeat : equivalent function\n", " | \n", " | reshape(...)\n", " | a.reshape(shape, order='C')\n", " | \n", " | Returns an array containing the same data with a new shape.\n", " | \n", " | Refer to `numpy.reshape` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.reshape : equivalent function\n", " | \n", " | resize(...)\n", " | a.resize(new_shape, refcheck=True)\n", " | \n", " | Change shape and size of array in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | new_shape : tuple of ints, or `n` ints\n", " | Shape of resized array.\n", " | refcheck : bool, optional\n", " | If False, reference count will not be checked. Default is True.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | Raises\n", " | ------\n", " | ValueError\n", " | If `a` does not own its own data or references or views to it exist,\n", " | and the data memory must be changed.\n", " | \n", " | SystemError\n", " | If the `order` keyword argument is specified. This behaviour is a\n", " | bug in NumPy.\n", " | \n", " | See Also\n", " | --------\n", " | resize : Return a new array with the specified shape.\n", " | \n", " | Notes\n", " | -----\n", " | This reallocates space for the data area if necessary.\n", " | \n", " | Only contiguous arrays (data elements consecutive in memory) can be\n", " | resized.\n", " | \n", " | The purpose of the reference count check is to make sure you\n", " | do not use this array as a buffer for another Python object and then\n", " | reallocate the memory. However, reference counts can increase in\n", " | other ways so if you are sure that you have not shared the memory\n", " | for this array with another Python object, then you may safely set\n", " | `refcheck` to False.\n", " | \n", " | Examples\n", " | --------\n", " | Shrinking an array: array is flattened (in the order that the data are\n", " | stored in memory), resized, and reshaped:\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='C')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [1]])\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='F')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [2]])\n", " | \n", " | Enlarging an array: as above, but missing entries are filled with zeros:\n", " | \n", " | >>> b = np.array([[0, 1], [2, 3]])\n", " | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n", " | >>> b\n", " | array([[0, 1, 2],\n", " | [3, 0, 0]])\n", " | \n", " | Referencing an array prevents resizing...\n", " | \n", " | >>> c = a\n", " | >>> a.resize((1, 1))\n", " | Traceback (most recent call last):\n", " | ...\n", " | ValueError: cannot resize an array that has been referenced ...\n", " | \n", " | Unless `refcheck` is False:\n", " | \n", " | >>> a.resize((1, 1), refcheck=False)\n", " | >>> a\n", " | array([[0]])\n", " | >>> c\n", " | array([[0]])\n", " | \n", " | round(...)\n", " | a.round(decimals=0, out=None)\n", " | \n", " | Return `a` with each element rounded to the given number of decimals.\n", " | \n", " | Refer to `numpy.around` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.around : equivalent function\n", " | \n", " | searchsorted(...)\n", " | a.searchsorted(v, side='left', sorter=None)\n", " | \n", " | Find indices where elements of v should be inserted in a to maintain order.\n", " | \n", " | For full documentation, see `numpy.searchsorted`\n", " | \n", " | See Also\n", " | --------\n", " | numpy.searchsorted : equivalent function\n", " | \n", " | setfield(...)\n", " | a.setfield(val, dtype, offset=0)\n", " | \n", " | Put a value into a specified place in a field defined by a data-type.\n", " | \n", " | Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n", " | bytes into the field.\n", " | \n", " | Parameters\n", " | ----------\n", " | val : object\n", " | Value to be placed in field.\n", " | dtype : dtype object\n", " | Data-type of the field in which to place `val`.\n", " | offset : int, optional\n", " | The number of bytes into the field at which to place `val`.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | See Also\n", " | --------\n", " | getfield\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.eye(3)\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | >>> x.setfield(3, np.int32)\n", " | >>> x.getfield(np.int32)\n", " | array([[3, 3, 3],\n", " | [3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x\n", " | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]])\n", " | >>> x.setfield(np.eye(3), np.int32)\n", " | >>> x\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | \n", " | setflags(...)\n", " | a.setflags(write=None, align=None, uic=None)\n", " | \n", " | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.\n", " | \n", " | These Boolean-valued flags affect how numpy interprets the memory\n", " | area used by `a` (see Notes below). The ALIGNED flag can only\n", " | be set to True if the data is actually aligned according to the type.\n", " | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE\n", " | can only be set to True if the array owns its own memory, or the\n", " | ultimate owner of the memory exposes a writeable buffer interface,\n", " | or is a string. (The exception for string is made so that unpickling\n", " | can be done without copying memory.)\n", " | \n", " | Parameters\n", " | ----------\n", " | write : bool, optional\n", " | Describes whether or not `a` can be written to.\n", " | align : bool, optional\n", " | Describes whether or not `a` is aligned properly for its type.\n", " | uic : bool, optional\n", " | Describes whether or not `a` is a copy of another \"base\" array.\n", " | \n", " | Notes\n", " | -----\n", " | Array flags provide information about how the memory area used\n", " | for the array is to be interpreted. There are 6 Boolean flags\n", " | in use, only three of which can be changed by the user:\n", " | UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n", " | \n", " | WRITEABLE (W) the data area can be written to;\n", " | \n", " | ALIGNED (A) the data and strides are aligned appropriately for the hardware\n", " | (as determined by the compiler);\n", " | \n", " | UPDATEIFCOPY (U) this array is a copy of some other array (referenced\n", " | by .base). When this array is deallocated, the base array will be\n", " | updated with the contents of this array.\n", " | \n", " | All flags can be accessed using their first (upper case) letter as well\n", " | as the full name.\n", " | \n", " | Examples\n", " | --------\n", " | >>> y\n", " | array([[3, 1, 7],\n", " | [2, 0, 0],\n", " | [8, 5, 9]])\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : True\n", " | ALIGNED : True\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(write=0, align=0)\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : False\n", " | ALIGNED : False\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(uic=1)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: cannot set UPDATEIFCOPY flag to True\n", " | \n", " | sort(...)\n", " | a.sort(axis=-1, kind='quicksort', order=None)\n", " | \n", " | Sort an array, in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | axis : int, optional\n", " | Axis along which to sort. Default is -1, which means sort along the\n", " | last axis.\n", " | kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n", " | Sorting algorithm. Default is 'quicksort'.\n", " | order : list, optional\n", " | When `a` is an array with fields defined, this argument specifies\n", " | which fields to compare first, second, etc. Not all fields need be\n", " | specified.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.sort : Return a sorted copy of an array.\n", " | argsort : Indirect sort.\n", " | lexsort : Indirect stable sort on multiple keys.\n", " | searchsorted : Find elements in sorted array.\n", " | partition: Partial sort.\n", " | \n", " | Notes\n", " | -----\n", " | See ``sort`` for notes on the different sorting algorithms.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,4], [3,1]])\n", " | >>> a.sort(axis=1)\n", " | >>> a\n", " | array([[1, 4],\n", " | [1, 3]])\n", " | >>> a.sort(axis=0)\n", " | >>> a\n", " | array([[1, 3],\n", " | [1, 4]])\n", " | \n", " | Use the `order` keyword to specify a field to use when sorting a\n", " | structured array:\n", " | \n", " | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n", " | >>> a.sort(order='y')\n", " | >>> a\n", " | array([('c', 1), ('a', 2)],\n", " | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | tofile(...)\n", " | a.tofile(fid, sep=\"\", format=\"%s\")\n", " | \n", " | Write array to a file as text or binary (default).\n", " | \n", " | Data is always written in 'C' order, independent of the order of `a`.\n", " | The data produced by this method can be recovered using the function\n", " | fromfile().\n", " | \n", " | Parameters\n", " | ----------\n", " | fid : file or str\n", " | An open file object, or a string containing a filename.\n", " | sep : str\n", " | Separator between array items for text output.\n", " | If \"\" (empty), a binary file is written, equivalent to\n", " | ``file.write(a.tobytes())``.\n", " | format : str\n", " | Format string for text file output.\n", " | Each entry in the array is formatted to text by first converting\n", " | it to the closest Python type, and then using \"format\" % item.\n", " | \n", " | Notes\n", " | -----\n", " | This is a convenience function for quick storage of array data.\n", " | Information on endianness and precision is lost, so this method is not a\n", " | good choice for files intended to archive data or transport data between\n", " | machines with different endianness. Some of these problems can be overcome\n", " | by outputting the data as text files, at the expense of speed and file\n", " | size.\n", " | \n", " | tolist(...)\n", " | a.tolist()\n", " | \n", " | Return the array as a (possibly nested) list.\n", " | \n", " | Return a copy of the array data as a (nested) Python list.\n", " | Data items are converted to the nearest compatible Python type.\n", " | \n", " | Parameters\n", " | ----------\n", " | none\n", " | \n", " | Returns\n", " | -------\n", " | y : list\n", " | The possibly nested list of array elements.\n", " | \n", " | Notes\n", " | -----\n", " | The array may be recreated, ``a = np.array(a.tolist())``.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([1, 2])\n", " | >>> a.tolist()\n", " | [1, 2]\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> list(a)\n", " | [array([1, 2]), array([3, 4])]\n", " | >>> a.tolist()\n", " | [[1, 2], [3, 4]]\n", " | \n", " | tostring(...)\n", " | a.tostring(order='C')\n", " | \n", " | Construct Python bytes containing the raw data bytes in the array.\n", " | \n", " | Constructs Python bytes showing a copy of the raw contents of\n", " | data memory. The bytes object can be produced in either 'C' or 'Fortran',\n", " | or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n", " | unless the F_CONTIGUOUS flag in the array is set, in which case it\n", " | means 'Fortran' order.\n", " | \n", " | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', None}, optional\n", " | Order of the data for multidimensional arrays:\n", " | C, Fortran, or the same as for the original array.\n", " | \n", " | Returns\n", " | -------\n", " | s : bytes\n", " | Python bytes exhibiting a copy of `a`'s raw data.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | trace(...)\n", " | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n", " | \n", " | Return the sum along diagonals of the array.\n", " | \n", " | Refer to `numpy.trace` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.trace : equivalent function\n", " | \n", " | transpose(...)\n", " | a.transpose(*axes)\n", " | \n", " | Returns a view of the array with axes transposed.\n", " | \n", " | For a 1-D array, this has no effect. (To change between column and\n", " | row vectors, first cast the 1-D array into a matrix object.)\n", " | For a 2-D array, this is the usual matrix transpose.\n", " | For an n-D array, if axes are given, their order indicates how the\n", " | axes are permuted (see Examples). If axes are not provided and\n", " | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n", " | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n", " | \n", " | Parameters\n", " | ----------\n", " | axes : None, tuple of ints, or `n` ints\n", " | \n", " | * None or no argument: reverses the order of the axes.\n", " | \n", " | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n", " | `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n", " | \n", " | * `n` ints: same as an n-tuple of the same ints (this form is\n", " | intended simply as a \"convenience\" alternative to the tuple form)\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | View of `a`, with axes suitably permuted.\n", " | \n", " | See Also\n", " | --------\n", " | ndarray.T : Array property returning the array transposed.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> a\n", " | array([[1, 2],\n", " | [3, 4]])\n", " | >>> a.transpose()\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose((1, 0))\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose(1, 0)\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | \n", " | var(...)\n", " | a.var(axis=None, dtype=None, out=None, ddof=0)\n", " | \n", " | Returns the variance of the array elements, along given axis.\n", " | \n", " | Refer to `numpy.var` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.var : equivalent function\n", " | \n", " | view(...)\n", " | a.view(dtype=None, type=None)\n", " | \n", " | New view of array with the same data.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : data-type or ndarray sub-class, optional\n", " | Data-type descriptor of the returned view, e.g., float32 or int16. The\n", " | default, None, results in the view having the same data-type as `a`.\n", " | This argument can also be specified as an ndarray sub-class, which\n", " | then specifies the type of the returned object (this is equivalent to\n", " | setting the ``type`` parameter).\n", " | type : Python type, optional\n", " | Type of the returned view, e.g., ndarray or matrix. Again, the\n", " | default None results in type preservation.\n", " | \n", " | Notes\n", " | -----\n", " | ``a.view()`` is used two different ways:\n", " | \n", " | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n", " | of the array's memory with a different data-type. This can cause a\n", " | reinterpretation of the bytes of memory.\n", " | \n", " | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n", " | returns an instance of `ndarray_subclass` that looks at the same array\n", " | (same shape, dtype, etc.) This does not cause a reinterpretation of the\n", " | memory.\n", " | \n", " | For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n", " | bytes per entry than the previous dtype (for example, converting a\n", " | regular array to a structured array), then the behavior of the view\n", " | cannot be predicted just from the superficial appearance of ``a`` (shown\n", " | by ``print(a)``). It also depends on exactly how ``a`` is stored in\n", " | memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n", " | defined as a slice or transpose, etc., the view may give different\n", " | results.\n", " | \n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n", " | \n", " | Viewing array data using a different type and dtype:\n", " | \n", " | >>> y = x.view(dtype=np.int16, type=np.matrix)\n", " | >>> y\n", " | matrix([[513]], dtype=int16)\n", " | >>> print type(y)\n", " | \n", " | \n", " | Creating a view on a structured array so it can be used in calculations\n", " | \n", " | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n", " | >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n", " | >>> xv\n", " | array([[1, 2],\n", " | [3, 4]], dtype=int8)\n", " | >>> xv.mean(0)\n", " | array([ 2., 3.])\n", " | \n", " | Making changes to the view changes the underlying array\n", " | \n", " | >>> xv[0,1] = 20\n", " | >>> print x\n", " | [(1, 20) (3, 4)]\n", " | \n", " | Using a view to convert an array to a record array:\n", " | \n", " | >>> z = x.view(np.recarray)\n", " | >>> z.a\n", " | array([1], dtype=int8)\n", " | \n", " | Views share data:\n", " | \n", " | >>> x[0] = (9, 10)\n", " | >>> z[0]\n", " | (9, 10)\n", " | \n", " | Views that change the dtype size (bytes per entry) should normally be\n", " | avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n", " | \n", " | >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n", " | >>> y = x[:, 0:2]\n", " | >>> y\n", " | array([[1, 2],\n", " | [4, 5]], dtype=int16)\n", " | >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: new type not compatible with array.\n", " | >>> z = y.copy()\n", " | >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n", " | array([[(1, 2)],\n", " | [(4, 5)]], dtype=[('width', '>> x = np.array([[1.,2.],[3.,4.]])\n", " | >>> x\n", " | array([[ 1., 2.],\n", " | [ 3., 4.]])\n", " | >>> x.T\n", " | array([[ 1., 3.],\n", " | [ 2., 4.]])\n", " | >>> x = np.array([1.,2.,3.,4.])\n", " | >>> x\n", " | array([ 1., 2., 3., 4.])\n", " | >>> x.T\n", " | array([ 1., 2., 3., 4.])\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side.\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: C-struct side.\n", " | \n", " | base\n", " | Base object if memory is from some other object.\n", " | \n", " | Examples\n", " | --------\n", " | The base of an array that owns its memory is None:\n", " | \n", " | >>> x = np.array([1,2,3,4])\n", " | >>> x.base is None\n", " | True\n", " | \n", " | Slicing creates a view, whose memory is shared with x:\n", " | \n", " | >>> y = x[2:]\n", " | >>> y.base is x\n", " | True\n", " | \n", " | ctypes\n", " | An object to simplify the interaction of the array with the ctypes\n", " | module.\n", " | \n", " | This attribute creates an object that makes it easier to use arrays\n", " | when calling shared libraries with the ctypes module. The returned\n", " | object has, among others, data, shape, and strides attributes (see\n", " | Notes below) which themselves return ctypes objects that can be used\n", " | as arguments to a shared library.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | c : Python object\n", " | Possessing attributes data, shape, strides, etc.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ctypeslib\n", " | \n", " | Notes\n", " | -----\n", " | Below are the public attributes of this object which were documented\n", " | in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n", " | as well as documented private attributes):\n", " | \n", " | * data: A pointer to the memory area of the array as a Python integer.\n", " | This memory area may contain data that is not aligned, or not in correct\n", " | byte-order. The memory area may not even be writeable. The array\n", " | flags and data-type of this array should be respected when passing this\n", " | attribute to arbitrary C-code to avoid trouble that can include Python\n", " | crashing. User Beware! The value of this attribute is exactly the same\n", " | as self._array_interface_['data'][0].\n", " | \n", " | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the C-integer corresponding to dtype('p') on this\n", " | platform. This base-type could be c_int, c_long, or c_longlong\n", " | depending on the platform. The c_intp type is defined accordingly in\n", " | numpy.ctypeslib. The ctypes array contains the shape of the underlying\n", " | array.\n", " | \n", " | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the same as for the shape attribute. This ctypes array\n", " | contains the strides information from the underlying array. This strides\n", " | information is important for showing how many bytes must be jumped to\n", " | get to the next element in the array.\n", " | \n", " | * data_as(obj): Return the data pointer cast to a particular c-types object.\n", " | For example, calling self._as_parameter_ is equivalent to\n", " | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a\n", " | pointer to a ctypes array of floating-point data:\n", " | self.data_as(ctypes.POINTER(ctypes.c_double)).\n", " | \n", " | * shape_as(obj): Return the shape tuple as an array of some other c-types\n", " | type. For example: self.shape_as(ctypes.c_short).\n", " | \n", " | * strides_as(obj): Return the strides tuple as an array of some other\n", " | c-types type. For example: self.strides_as(ctypes.c_longlong).\n", " | \n", " | Be careful using the ctypes attribute - especially on temporary\n", " | arrays or arrays constructed on the fly. For example, calling\n", " | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory\n", " | that is invalid because the array created as (a+b) is deallocated\n", " | before the next Python statement. You can avoid this problem using\n", " | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will\n", " | hold a reference to the array until ct is deleted or re-assigned.\n", " | \n", " | If the ctypes module is not available, then the ctypes attribute\n", " | of array objects still returns something useful, but ctypes objects\n", " | are not returned and errors may be raised instead. In particular,\n", " | the object will still have the as parameter attribute which will\n", " | return an integer equal to the data attribute.\n", " | \n", " | Examples\n", " | --------\n", " | >>> import ctypes\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.ctypes.data\n", " | 30439712\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))\n", " | \n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents\n", " | c_long(0)\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents\n", " | c_longlong(4294967296L)\n", " | >>> x.ctypes.shape\n", " | \n", " | >>> x.ctypes.shape_as(ctypes.c_long)\n", " | \n", " | >>> x.ctypes.strides\n", " | \n", " | >>> x.ctypes.strides_as(ctypes.c_longlong)\n", " | \n", " | \n", " | data\n", " | Python buffer object pointing to the start of the array's data.\n", " | \n", " | dtype\n", " | Data-type of the array's elements.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | d : numpy dtype object\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dtype\n", " | \n", " | Examples\n", " | --------\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.dtype\n", " | dtype('int32')\n", " | >>> type(x.dtype)\n", " | \n", " | \n", " | flags\n", " | Information about the memory layout of the array.\n", " | \n", " | Attributes\n", " | ----------\n", " | C_CONTIGUOUS (C)\n", " | The data is in a single, C-style contiguous segment.\n", " | F_CONTIGUOUS (F)\n", " | The data is in a single, Fortran-style contiguous segment.\n", " | OWNDATA (O)\n", " | The array owns the memory it uses or borrows it from another object.\n", " | WRITEABLE (W)\n", " | The data area can be written to. Setting this to False locks\n", " | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE\n", " | from its base array at creation time, but a view of a writeable\n", " | array may be subsequently locked while the base array remains writeable.\n", " | (The opposite is not true, in that a view of a locked array may not\n", " | be made writeable. However, currently, locking a base object does not\n", " | lock any views that already reference it, so under that circumstance it\n", " | is possible to alter the contents of a locked array via a previously\n", " | created writeable view onto it.) Attempting to change a non-writeable\n", " | array raises a RuntimeError exception.\n", " | ALIGNED (A)\n", " | The data and all elements are aligned appropriately for the hardware.\n", " | UPDATEIFCOPY (U)\n", " | This array is a copy of some other array. When this array is\n", " | deallocated, the base array will be updated with the contents of\n", " | this array.\n", " | FNC\n", " | F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | FORC\n", " | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n", " | BEHAVED (B)\n", " | ALIGNED and WRITEABLE.\n", " | CARRAY (CA)\n", " | BEHAVED and C_CONTIGUOUS.\n", " | FARRAY (FA)\n", " | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | \n", " | Notes\n", " | -----\n", " | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n", " | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n", " | names are only supported in dictionary access.\n", " | \n", " | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by\n", " | the user, via direct assignment to the attribute or dictionary entry,\n", " | or by calling `ndarray.setflags`.\n", " | \n", " | The array flags cannot be set arbitrarily:\n", " | \n", " | - UPDATEIFCOPY can only be set ``False``.\n", " | - ALIGNED can only be set ``True`` if the data is truly aligned.\n", " | - WRITEABLE can only be set ``True`` if the array owns its own memory\n", " | or the ultimate owner of the memory exposes a writeable buffer\n", " | interface or is a string.\n", " | \n", " | Arrays can be both C-style and Fortran-style contiguous simultaneously.\n", " | This is clear for 1-dimensional arrays, but can also be true for higher\n", " | dimensional arrays.\n", " | \n", " | Even for contiguous arrays a stride for a given dimension\n", " | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n", " | or the array has no elements.\n", " | It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n", " | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n", " | Fortran-style contiguous arrays is true.\n", " | \n", " | flat\n", " | A 1-D iterator over the array.\n", " | \n", " | This is a `numpy.flatiter` instance, which acts similarly to, but is not\n", " | a subclass of, Python's built-in iterator object.\n", " | \n", " | See Also\n", " | --------\n", " | flatten : Return a copy of the array collapsed into one dimension.\n", " | \n", " | flatiter\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(1, 7).reshape(2, 3)\n", " | >>> x\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | >>> x.flat[3]\n", " | 4\n", " | >>> x.T\n", " | array([[1, 4],\n", " | [2, 5],\n", " | [3, 6]])\n", " | >>> x.T.flat[3]\n", " | 5\n", " | >>> type(x.flat)\n", " | \n", " | \n", " | An assignment example:\n", " | \n", " | >>> x.flat = 3; x\n", " | array([[3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x.flat[[1,4]] = 1; x\n", " | array([[3, 1, 3],\n", " | [3, 1, 3]])\n", " | \n", " | imag\n", " | The imaginary part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.imag\n", " | array([ 0. , 0.70710678])\n", " | >>> x.imag.dtype\n", " | dtype('float64')\n", " | \n", " | itemsize\n", " | Length of one array element in bytes.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1,2,3], dtype=np.float64)\n", " | >>> x.itemsize\n", " | 8\n", " | >>> x = np.array([1,2,3], dtype=np.complex128)\n", " | >>> x.itemsize\n", " | 16\n", " | \n", " | nbytes\n", " | Total bytes consumed by the elements of the array.\n", " | \n", " | Notes\n", " | -----\n", " | Does not include memory consumed by non-element attributes of the\n", " | array object.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3,5,2), dtype=np.complex128)\n", " | >>> x.nbytes\n", " | 480\n", " | >>> np.prod(x.shape) * x.itemsize\n", " | 480\n", " | \n", " | ndim\n", " | Number of array dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> x.ndim\n", " | 1\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.ndim\n", " | 3\n", " | \n", " | real\n", " | The real part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.real\n", " | array([ 1. , 0.70710678])\n", " | >>> x.real.dtype\n", " | dtype('float64')\n", " | \n", " | See Also\n", " | --------\n", " | numpy.real : equivalent function\n", " | \n", " | shape\n", " | Tuple of array dimensions.\n", " | \n", " | Notes\n", " | -----\n", " | May be used to \"reshape\" the array, as long as this would not\n", " | require a change in the total number of elements\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3, 4])\n", " | >>> x.shape\n", " | (4,)\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.shape\n", " | (2, 3, 4)\n", " | >>> y.shape = (3, 8)\n", " | >>> y\n", " | array([[ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.]])\n", " | >>> y.shape = (3, 6)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: total size of new array must be unchanged\n", " | \n", " | size\n", " | Number of elements in the array.\n", " | \n", " | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's\n", " | dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n", " | >>> x.size\n", " | 30\n", " | >>> np.prod(x.shape)\n", " | 30\n", " | \n", " | strides\n", " | Tuple of bytes to step in each dimension when traversing an array.\n", " | \n", " | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n", " | is::\n", " | \n", " | offset = sum(np.array(i) * a.strides)\n", " | \n", " | A more detailed explanation of strides can be found in the\n", " | \"ndarray.rst\" file in the NumPy reference guide.\n", " | \n", " | Notes\n", " | -----\n", " | Imagine an array of 32-bit integers (each 4 bytes)::\n", " | \n", " | x = np.array([[0, 1, 2, 3, 4],\n", " | [5, 6, 7, 8, 9]], dtype=np.int32)\n", " | \n", " | This array is stored in memory as 40 bytes, one after the other\n", " | (known as a contiguous block of memory). The strides of an array tell\n", " | us how many bytes we have to skip in memory to move to the next position\n", " | along a certain axis. For example, we have to skip 4 bytes (1 value) to\n", " | move to the next column, but 20 bytes (5 values) to get to the same\n", " | position in the next row. As such, the strides for the array `x` will be\n", " | ``(20, 4)``.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.lib.stride_tricks.as_strided\n", " | \n", " | Examples\n", " | --------\n", " | >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n", " | >>> y\n", " | array([[[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]],\n", " | [[12, 13, 14, 15],\n", " | [16, 17, 18, 19],\n", " | [20, 21, 22, 23]]])\n", " | >>> y.strides\n", " | (48, 16, 4)\n", " | >>> y[1,1,1]\n", " | 17\n", " | >>> offset=sum(y.strides * np.array((1,1,1)))\n", " | >>> offset/y.itemsize\n", " | 17\n", " | \n", " | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n", " | >>> x.strides\n", " | (32, 4, 224, 1344)\n", " | >>> i = np.array([3,5,2,2])\n", " | >>> offset = sum(i * x.strides)\n", " | >>> x[3,5,2,2]\n", " | 813\n", " | >>> offset / x.itemsize\n", " | 813\n", " \n", " clongdouble = class complex256(complexfloating)\n", " | Composed of two 128 bit floats\n", " | \n", " | Method resolution order:\n", " | complex256\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __complex__(...)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " clongfloat = class complex256(complexfloating)\n", " | Composed of two 128 bit floats\n", " | \n", " | Method resolution order:\n", " | complex256\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __complex__(...)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class complex128(complexfloating, __builtin__.complex)\n", " | Composed of two 64 bit floats\n", " | \n", " | Method resolution order:\n", " | complex128\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.complex\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.complex:\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " \n", " class complex256(complexfloating)\n", " | Composed of two 128 bit floats\n", " | \n", " | Method resolution order:\n", " | complex256\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __complex__(...)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class complex64(complexfloating)\n", " | Composed of two 32 bit floats\n", " | \n", " | Method resolution order:\n", " | complex64\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __complex__(...)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " complex_ = class complex128(complexfloating, __builtin__.complex)\n", " | Composed of two 64 bit floats\n", " | \n", " | Method resolution order:\n", " | complex128\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.complex\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.complex:\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " \n", " class complexfloating(inexact)\n", " | Method resolution order:\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " csingle = class complex64(complexfloating)\n", " | Composed of two 32 bit floats\n", " | \n", " | Method resolution order:\n", " | complex64\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __complex__(...)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class datetime64(generic)\n", " | Method resolution order:\n", " | datetime64\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " double = class float64(floating, __builtin__.float)\n", " | 64-bit floating-point number. Character code 'd'. Python float compatible.\n", " | \n", " | Method resolution order:\n", " | float64\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.float\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.float:\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getformat__(...)\n", " | float.__getformat__(typestr) -> string\n", " | \n", " | You probably don't want to use this function. It exists mainly to be\n", " | used in Python's test suite.\n", " | \n", " | typestr must be 'double' or 'float'. This function returns whichever of\n", " | 'unknown', 'IEEE, big-endian' or 'IEEE, little-endian' best describes the\n", " | format of floating point numbers used by the C type named by typestr.\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __setformat__(...)\n", " | float.__setformat__(typestr, fmt) -> None\n", " | \n", " | You probably don't want to use this function. It exists mainly to be\n", " | used in Python's test suite.\n", " | \n", " | typestr must be 'double' or 'float'. fmt must be one of 'unknown',\n", " | 'IEEE, big-endian' or 'IEEE, little-endian', and in addition can only be\n", " | one of the latter two if it appears to match the underlying C reality.\n", " | \n", " | Override the automatic determination of C-level floating point type.\n", " | This affects how floats are converted to and from binary strings.\n", " | \n", " | __trunc__(...)\n", " | Return the Integral closest to x between 0 and x.\n", " | \n", " | as_integer_ratio(...)\n", " | float.as_integer_ratio() -> (int, int)\n", " | \n", " | Return a pair of integers, whose ratio is exactly equal to the original\n", " | float and with a positive denominator.\n", " | Raise OverflowError on infinities and a ValueError on NaNs.\n", " | \n", " | >>> (10.0).as_integer_ratio()\n", " | (10, 1)\n", " | >>> (0.0).as_integer_ratio()\n", " | (0, 1)\n", " | >>> (-.25).as_integer_ratio()\n", " | (-1, 4)\n", " | \n", " | fromhex(...)\n", " | float.fromhex(string) -> float\n", " | \n", " | Create a floating-point number from a hexadecimal string.\n", " | >>> float.fromhex('0x1.ffffp10')\n", " | 2047.984375\n", " | >>> float.fromhex('-0x1p-1074')\n", " | -4.9406564584124654e-324\n", " | \n", " | hex(...)\n", " | float.hex() -> string\n", " | \n", " | Return a hexadecimal representation of a floating-point number.\n", " | >>> (-0.1).hex()\n", " | '-0x1.999999999999ap-4'\n", " | >>> 3.14159.hex()\n", " | '0x1.921f9f01b866ep+1'\n", " | \n", " | is_integer(...)\n", " | Return True if the float is an integer.\n", " \n", " class dtype(__builtin__.object)\n", " | dtype(obj, align=False, copy=False)\n", " | \n", " | Create a data type object.\n", " | \n", " | A numpy array is homogeneous, and contains elements described by a\n", " | dtype object. A dtype object can be constructed from different\n", " | combinations of fundamental numeric types.\n", " | \n", " | Parameters\n", " | ----------\n", " | obj\n", " | Object to be converted to a data type object.\n", " | align : bool, optional\n", " | Add padding to the fields to match what a C compiler would output\n", " | for a similar C-struct. Can be ``True`` only if `obj` is a dictionary\n", " | or a comma-separated string. If a struct dtype is being created,\n", " | this also sets a sticky alignment flag ``isalignedstruct``.\n", " | copy : bool, optional\n", " | Make a new copy of the data-type object. If ``False``, the result\n", " | may just be a reference to a built-in data-type object.\n", " | \n", " | See also\n", " | --------\n", " | result_type\n", " | \n", " | Examples\n", " | --------\n", " | Using array-scalar type:\n", " | \n", " | >>> np.dtype(np.int16)\n", " | dtype('int16')\n", " | \n", " | Record, one field name 'f1', containing int16:\n", " | \n", " | >>> np.dtype([('f1', np.int16)])\n", " | dtype([('f1', '>> np.dtype([('f1', [('f1', np.int16)])])\n", " | dtype([('f1', [('f1', '>> np.dtype([('f1', np.uint), ('f2', np.int32)])\n", " | dtype([('f1', '>> np.dtype([('a','f8'),('b','S10')])\n", " | dtype([('a', '>> np.dtype(\"i4, (2,3)f8\")\n", " | dtype([('f0', '>> np.dtype([('hello',(np.int,3)),('world',np.void,10)])\n", " | dtype([('hello', '>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))\n", " | dtype(('>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})\n", " | dtype([('gender', '|S1'), ('age', '|u1')])\n", " | \n", " | Offsets in bytes, here 0 and 25:\n", " | \n", " | >>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})\n", " | dtype([('surname', '|S25'), ('age', '|u1')])\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x*n\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(n) <==> n*x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new dtype with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : string, optional\n", " | Byte order to force; a value from the byte order\n", " | specifications below. The default value ('S') results in\n", " | swapping the current byte order.\n", " | `new_order` codes can be any of::\n", " | \n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | The code does a case-insensitive check on the first letter of\n", " | `new_order` for these alternatives. For example, any of '>'\n", " | or 'B' or 'b' or 'brian' are valid to specify big-endian.\n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New dtype object with the given change to the byte order.\n", " | \n", " | Notes\n", " | -----\n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | Examples\n", " | --------\n", " | >>> import sys\n", " | >>> sys_is_le = sys.byteorder == 'little'\n", " | >>> native_code = sys_is_le and '<' or '>'\n", " | >>> swapped_code = sys_is_le and '>' or '<'\n", " | >>> native_dt = np.dtype(native_code+'i2')\n", " | >>> swapped_dt = np.dtype(swapped_code+'i2')\n", " | >>> native_dt.newbyteorder('S') == swapped_dt\n", " | True\n", " | >>> native_dt.newbyteorder() == swapped_dt\n", " | True\n", " | >>> native_dt == swapped_dt.newbyteorder('S')\n", " | True\n", " | >>> native_dt == swapped_dt.newbyteorder('=')\n", " | True\n", " | >>> native_dt == swapped_dt.newbyteorder('N')\n", " | True\n", " | >>> native_dt == native_dt.newbyteorder('|')\n", " | True\n", " | >>> np.dtype('>> np.dtype('>> np.dtype('>i2') == native_dt.newbyteorder('>')\n", " | True\n", " | >>> np.dtype('>i2') == native_dt.newbyteorder('B')\n", " | True\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | alignment\n", " | The required alignment (bytes) of this data-type according to the compiler.\n", " | \n", " | More information is available in the C-API section of the manual.\n", " | \n", " | base\n", " | \n", " | byteorder\n", " | A character indicating the byte-order of this data-type object.\n", " | \n", " | One of:\n", " | \n", " | === ==============\n", " | '=' native\n", " | '<' little-endian\n", " | '>' big-endian\n", " | '|' not applicable\n", " | === ==============\n", " | \n", " | All built-in data-type objects have byteorder either '=' or '|'.\n", " | \n", " | Examples\n", " | --------\n", " | \n", " | >>> dt = np.dtype('i2')\n", " | >>> dt.byteorder\n", " | '='\n", " | >>> # endian is not relevant for 8 bit numbers\n", " | >>> np.dtype('i1').byteorder\n", " | '|'\n", " | >>> # or ASCII strings\n", " | >>> np.dtype('S2').byteorder\n", " | '|'\n", " | >>> # Even if specific code is given, and it is native\n", " | >>> # '=' is the byteorder\n", " | >>> import sys\n", " | >>> sys_is_le = sys.byteorder == 'little'\n", " | >>> native_code = sys_is_le and '<' or '>'\n", " | >>> swapped_code = sys_is_le and '>' or '<'\n", " | >>> dt = np.dtype(native_code + 'i2')\n", " | >>> dt.byteorder\n", " | '='\n", " | >>> # Swapped code shows up as itself\n", " | >>> dt = np.dtype(swapped_code + 'i2')\n", " | >>> dt.byteorder == swapped_code\n", " | True\n", " | \n", " | char\n", " | A unique character code for each of the 21 different built-in types.\n", " | \n", " | descr\n", " | Array-interface compliant full description of the data-type.\n", " | \n", " | The format is that required by the 'descr' key in the\n", " | `__array_interface__` attribute.\n", " | \n", " | fields\n", " | Dictionary of named fields defined for this data type, or ``None``.\n", " | \n", " | The dictionary is indexed by keys that are the names of the fields.\n", " | Each entry in the dictionary is a tuple fully describing the field::\n", " | \n", " | (dtype, offset[, title])\n", " | \n", " | If present, the optional title can be any object (if it is a string\n", " | or unicode then it will also be a key in the fields dictionary,\n", " | otherwise it's meta-data). Notice also that the first two elements\n", " | of the tuple can be passed directly as arguments to the ``ndarray.getfield``\n", " | and ``ndarray.setfield`` methods.\n", " | \n", " | See Also\n", " | --------\n", " | ndarray.getfield, ndarray.setfield\n", " | \n", " | Examples\n", " | --------\n", " | >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])\n", " | >>> print dt.fields\n", " | {'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}\n", " | \n", " | flags\n", " | Bit-flags describing how this data type is to be interpreted.\n", " | \n", " | Bit-masks are in `numpy.core.multiarray` as the constants\n", " | `ITEM_HASOBJECT`, `LIST_PICKLE`, `ITEM_IS_POINTER`, `NEEDS_INIT`,\n", " | `NEEDS_PYAPI`, `USE_GETITEM`, `USE_SETITEM`. A full explanation\n", " | of these flags is in C-API documentation; they are largely useful\n", " | for user-defined data-types.\n", " | \n", " | hasobject\n", " | Boolean indicating whether this dtype contains any reference-counted\n", " | objects in any fields or sub-dtypes.\n", " | \n", " | Recall that what is actually in the ndarray memory representing\n", " | the Python object is the memory address of that object (a pointer).\n", " | Special handling may be required, and this attribute is useful for\n", " | distinguishing data types that may contain arbitrary Python objects\n", " | and data-types that won't.\n", " | \n", " | isalignedstruct\n", " | Boolean indicating whether the dtype is a struct which maintains\n", " | field alignment. This flag is sticky, so when combining multiple\n", " | structs together, it is preserved and produces new dtypes which\n", " | are also aligned.\n", " | \n", " | isbuiltin\n", " | Integer indicating how this dtype relates to the built-in dtypes.\n", " | \n", " | Read-only.\n", " | \n", " | = ========================================================================\n", " | 0 if this is a structured array type, with fields\n", " | 1 if this is a dtype compiled into numpy (such as ints, floats etc)\n", " | 2 if the dtype is for a user-defined numpy type\n", " | A user-defined type uses the numpy C-API machinery to extend\n", " | numpy to handle a new array type. See\n", " | :ref:`user.user-defined-data-types` in the Numpy manual.\n", " | = ========================================================================\n", " | \n", " | Examples\n", " | --------\n", " | >>> dt = np.dtype('i2')\n", " | >>> dt.isbuiltin\n", " | 1\n", " | >>> dt = np.dtype('f8')\n", " | >>> dt.isbuiltin\n", " | 1\n", " | >>> dt = np.dtype([('field1', 'f8')])\n", " | >>> dt.isbuiltin\n", " | 0\n", " | \n", " | isnative\n", " | Boolean indicating whether the byte order of this dtype is native\n", " | to the platform.\n", " | \n", " | itemsize\n", " | The element size of this data-type object.\n", " | \n", " | For 18 of the 21 types this number is fixed by the data-type.\n", " | For the flexible data-types, this number can be anything.\n", " | \n", " | kind\n", " | A character code (one of 'biufcOSUV') identifying the general kind of data.\n", " | \n", " | = ======================\n", " | b boolean\n", " | i signed integer\n", " | u unsigned integer\n", " | f floating-point\n", " | c complex floating-point\n", " | O object\n", " | S (byte-)string\n", " | U Unicode\n", " | V void\n", " | = ======================\n", " | \n", " | metadata\n", " | \n", " | name\n", " | A bit-width name for this data-type.\n", " | \n", " | Un-sized flexible data-type objects do not have this attribute.\n", " | \n", " | names\n", " | Ordered list of field names, or ``None`` if there are no fields.\n", " | \n", " | The names are ordered according to increasing byte offset. This can be\n", " | used, for example, to walk through all of the named fields in offset order.\n", " | \n", " | Examples\n", " | --------\n", " | >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])\n", " | >>> dt.names\n", " | ('name', 'grades')\n", " | \n", " | num\n", " | A unique number for each of the 21 different built-in types.\n", " | \n", " | These are roughly ordered from least-to-most precision.\n", " | \n", " | shape\n", " | Shape tuple of the sub-array if this data type describes a sub-array,\n", " | and ``()`` otherwise.\n", " | \n", " | str\n", " | The array-protocol typestring of this data-type object.\n", " | \n", " | subdtype\n", " | Tuple ``(item_dtype, shape)`` if this `dtype` describes a sub-array, and\n", " | None otherwise.\n", " | \n", " | The *shape* is the fixed shape of the sub-array described by this\n", " | data type, and *item_dtype* the data type of the array.\n", " | \n", " | If a field whose dtype object has this attribute is retrieved,\n", " | then the extra dimensions implied by *shape* are tacked on to\n", " | the end of the retrieved array.\n", " | \n", " | type\n", " | The type object used to instantiate a scalar of this data-type.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " \n", " class errstate(__builtin__.object)\n", " | errstate(**kwargs)\n", " | \n", " | Context manager for floating-point error handling.\n", " | \n", " | Using an instance of `errstate` as a context manager allows statements in\n", " | that context to execute with a known error handling behavior. Upon entering\n", " | the context the error handling is set with `seterr` and `seterrcall`, and\n", " | upon exiting it is reset to what it was before.\n", " | \n", " | Parameters\n", " | ----------\n", " | kwargs : {divide, over, under, invalid}\n", " | Keyword arguments. The valid keywords are the possible floating-point\n", " | exceptions. Each keyword should have a string value that defines the\n", " | treatment for the particular error. Possible values are\n", " | {'ignore', 'warn', 'raise', 'call', 'print', 'log'}.\n", " | \n", " | See Also\n", " | --------\n", " | seterr, geterr, seterrcall, geterrcall\n", " | \n", " | Notes\n", " | -----\n", " | The ``with`` statement was introduced in Python 2.5, and can only be used\n", " | there by importing it: ``from __future__ import with_statement``. In\n", " | earlier Python versions the ``with`` statement is not available.\n", " | \n", " | For complete documentation of the types of floating-point exceptions and\n", " | treatment options, see `seterr`.\n", " | \n", " | Examples\n", " | --------\n", " | >>> from __future__ import with_statement # use 'with' in Python 2.5\n", " | >>> olderr = np.seterr(all='ignore') # Set error handling to known state.\n", " | \n", " | >>> np.arange(3) / 0.\n", " | array([ NaN, Inf, Inf])\n", " | >>> with np.errstate(divide='warn'):\n", " | ... np.arange(3) / 0.\n", " | ...\n", " | __main__:2: RuntimeWarning: divide by zero encountered in divide\n", " | array([ NaN, Inf, Inf])\n", " | \n", " | >>> np.sqrt(-1)\n", " | nan\n", " | >>> with np.errstate(invalid='raise'):\n", " | ... np.sqrt(-1)\n", " | Traceback (most recent call last):\n", " | File \"\", line 2, in \n", " | FloatingPointError: invalid value encountered in sqrt\n", " | \n", " | Outside the context the error handling behavior has not changed:\n", " | \n", " | >>> np.geterr()\n", " | {'over': 'warn', 'divide': 'warn', 'invalid': 'warn',\n", " | 'under': 'ignore'}\n", " | \n", " | Methods defined here:\n", " | \n", " | __enter__(self)\n", " | \n", " | __exit__(self, *exc_info)\n", " | \n", " | __init__(self, **kwargs)\n", " | # Note that we don't want to run the above doctests because they will fail\n", " | # without a from __future__ import with_statement\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " \n", " class finfo(__builtin__.object)\n", " | finfo(dtype)\n", " | \n", " | Machine limits for floating point types.\n", " | \n", " | Attributes\n", " | ----------\n", " | eps : float\n", " | The smallest representable positive number such that\n", " | ``1.0 + eps != 1.0``. Type of `eps` is an appropriate floating\n", " | point type.\n", " | epsneg : floating point number of the appropriate type\n", " | The smallest representable positive number such that\n", " | ``1.0 - epsneg != 1.0``.\n", " | iexp : int\n", " | The number of bits in the exponent portion of the floating point\n", " | representation.\n", " | machar : MachAr\n", " | The object which calculated these parameters and holds more\n", " | detailed information.\n", " | machep : int\n", " | The exponent that yields `eps`.\n", " | max : floating point number of the appropriate type\n", " | The largest representable number.\n", " | maxexp : int\n", " | The smallest positive power of the base (2) that causes overflow.\n", " | min : floating point number of the appropriate type\n", " | The smallest representable number, typically ``-max``.\n", " | minexp : int\n", " | The most negative power of the base (2) consistent with there\n", " | being no leading 0's in the mantissa.\n", " | negep : int\n", " | The exponent that yields `epsneg`.\n", " | nexp : int\n", " | The number of bits in the exponent including its sign and bias.\n", " | nmant : int\n", " | The number of bits in the mantissa.\n", " | precision : int\n", " | The approximate number of decimal digits to which this kind of\n", " | float is precise.\n", " | resolution : floating point number of the appropriate type\n", " | The approximate decimal resolution of this type, i.e.,\n", " | ``10**-precision``.\n", " | tiny : float\n", " | The smallest positive usable number. Type of `tiny` is an\n", " | appropriate floating point type.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : float, dtype, or instance\n", " | Kind of floating point data-type about which to get information.\n", " | \n", " | See Also\n", " | --------\n", " | MachAr : The implementation of the tests that produce this information.\n", " | iinfo : The equivalent for integer data types.\n", " | \n", " | Notes\n", " | -----\n", " | For developers of NumPy: do not instantiate this at the module level.\n", " | The initial calculation of these parameters is expensive and negatively\n", " | impacts import times. These objects are cached, so calling ``finfo()``\n", " | repeatedly inside your functions is not a problem.\n", " | \n", " | Methods defined here:\n", " | \n", " | __repr__(self)\n", " | \n", " | __str__(self)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(cls, dtype)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " \n", " class flatiter(__builtin__.object)\n", " | Flat iterator object to iterate over arrays.\n", " | \n", " | A `flatiter` iterator is returned by ``x.flat`` for any array `x`.\n", " | It allows iterating over the array as if it were a 1-D array,\n", " | either in a for-loop or by calling its `next` method.\n", " | \n", " | Iteration is done in C-contiguous style, with the last index varying the\n", " | fastest. The iterator can also be indexed using basic slicing or\n", " | advanced indexing.\n", " | \n", " | See Also\n", " | --------\n", " | ndarray.flat : Return a flat iterator over an array.\n", " | ndarray.flatten : Returns a flattened copy of an array.\n", " | \n", " | Notes\n", " | -----\n", " | A `flatiter` iterator can not be constructed directly from Python code\n", " | by calling the `flatiter` constructor.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(6).reshape(2, 3)\n", " | >>> fl = x.flat\n", " | >>> type(fl)\n", " | \n", " | >>> for item in fl:\n", " | ... print item\n", " | ...\n", " | 0\n", " | 1\n", " | 2\n", " | 3\n", " | 4\n", " | 5\n", " | \n", " | >>> fl[2:4]\n", " | array([2, 3])\n", " | \n", " | Methods defined here:\n", " | \n", " | __array__(...)\n", " | __array__(type=None) Get array from iterator\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __iter__(...)\n", " | x.__iter__() <==> iter(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | copy(...)\n", " | copy()\n", " | \n", " | Get a copy of the iterator as a 1-D array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(6).reshape(2, 3)\n", " | >>> x\n", " | array([[0, 1, 2],\n", " | [3, 4, 5]])\n", " | >>> fl = x.flat\n", " | >>> fl.copy()\n", " | array([0, 1, 2, 3, 4, 5])\n", " | \n", " | next(...)\n", " | x.next() -> the next value, or raise StopIteration\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | base\n", " | A reference to the array that is iterated over.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(5)\n", " | >>> fl = x.flat\n", " | >>> fl.base is x\n", " | True\n", " | \n", " | coords\n", " | An N-dimensional tuple of current coordinates.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(6).reshape(2, 3)\n", " | >>> fl = x.flat\n", " | >>> fl.coords\n", " | (0, 0)\n", " | >>> fl.next()\n", " | 0\n", " | >>> fl.coords\n", " | (0, 1)\n", " | \n", " | index\n", " | Current flat index into the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(6).reshape(2, 3)\n", " | >>> fl = x.flat\n", " | >>> fl.index\n", " | 0\n", " | >>> fl.next()\n", " | 0\n", " | >>> fl.index\n", " | 1\n", " \n", " class flexible(generic)\n", " | Method resolution order:\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class float128(floating)\n", " | 128-bit floating-point number. Character code: 'g'. C long float\n", " | compatible.\n", " | \n", " | Method resolution order:\n", " | float128\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class float16(floating)\n", " | Method resolution order:\n", " | float16\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class float32(floating)\n", " | 32-bit floating-point number. Character code 'f'. C float compatible.\n", " | \n", " | Method resolution order:\n", " | float32\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class float64(floating, __builtin__.float)\n", " | 64-bit floating-point number. Character code 'd'. Python float compatible.\n", " | \n", " | Method resolution order:\n", " | float64\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.float\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.float:\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getformat__(...)\n", " | float.__getformat__(typestr) -> string\n", " | \n", " | You probably don't want to use this function. It exists mainly to be\n", " | used in Python's test suite.\n", " | \n", " | typestr must be 'double' or 'float'. This function returns whichever of\n", " | 'unknown', 'IEEE, big-endian' or 'IEEE, little-endian' best describes the\n", " | format of floating point numbers used by the C type named by typestr.\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __setformat__(...)\n", " | float.__setformat__(typestr, fmt) -> None\n", " | \n", " | You probably don't want to use this function. It exists mainly to be\n", " | used in Python's test suite.\n", " | \n", " | typestr must be 'double' or 'float'. fmt must be one of 'unknown',\n", " | 'IEEE, big-endian' or 'IEEE, little-endian', and in addition can only be\n", " | one of the latter two if it appears to match the underlying C reality.\n", " | \n", " | Override the automatic determination of C-level floating point type.\n", " | This affects how floats are converted to and from binary strings.\n", " | \n", " | __trunc__(...)\n", " | Return the Integral closest to x between 0 and x.\n", " | \n", " | as_integer_ratio(...)\n", " | float.as_integer_ratio() -> (int, int)\n", " | \n", " | Return a pair of integers, whose ratio is exactly equal to the original\n", " | float and with a positive denominator.\n", " | Raise OverflowError on infinities and a ValueError on NaNs.\n", " | \n", " | >>> (10.0).as_integer_ratio()\n", " | (10, 1)\n", " | >>> (0.0).as_integer_ratio()\n", " | (0, 1)\n", " | >>> (-.25).as_integer_ratio()\n", " | (-1, 4)\n", " | \n", " | fromhex(...)\n", " | float.fromhex(string) -> float\n", " | \n", " | Create a floating-point number from a hexadecimal string.\n", " | >>> float.fromhex('0x1.ffffp10')\n", " | 2047.984375\n", " | >>> float.fromhex('-0x1p-1074')\n", " | -4.9406564584124654e-324\n", " | \n", " | hex(...)\n", " | float.hex() -> string\n", " | \n", " | Return a hexadecimal representation of a floating-point number.\n", " | >>> (-0.1).hex()\n", " | '-0x1.999999999999ap-4'\n", " | >>> 3.14159.hex()\n", " | '0x1.921f9f01b866ep+1'\n", " | \n", " | is_integer(...)\n", " | Return True if the float is an integer.\n", " \n", " float_ = class float64(floating, __builtin__.float)\n", " | 64-bit floating-point number. Character code 'd'. Python float compatible.\n", " | \n", " | Method resolution order:\n", " | float64\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.float\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.float:\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getformat__(...)\n", " | float.__getformat__(typestr) -> string\n", " | \n", " | You probably don't want to use this function. It exists mainly to be\n", " | used in Python's test suite.\n", " | \n", " | typestr must be 'double' or 'float'. This function returns whichever of\n", " | 'unknown', 'IEEE, big-endian' or 'IEEE, little-endian' best describes the\n", " | format of floating point numbers used by the C type named by typestr.\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __setformat__(...)\n", " | float.__setformat__(typestr, fmt) -> None\n", " | \n", " | You probably don't want to use this function. It exists mainly to be\n", " | used in Python's test suite.\n", " | \n", " | typestr must be 'double' or 'float'. fmt must be one of 'unknown',\n", " | 'IEEE, big-endian' or 'IEEE, little-endian', and in addition can only be\n", " | one of the latter two if it appears to match the underlying C reality.\n", " | \n", " | Override the automatic determination of C-level floating point type.\n", " | This affects how floats are converted to and from binary strings.\n", " | \n", " | __trunc__(...)\n", " | Return the Integral closest to x between 0 and x.\n", " | \n", " | as_integer_ratio(...)\n", " | float.as_integer_ratio() -> (int, int)\n", " | \n", " | Return a pair of integers, whose ratio is exactly equal to the original\n", " | float and with a positive denominator.\n", " | Raise OverflowError on infinities and a ValueError on NaNs.\n", " | \n", " | >>> (10.0).as_integer_ratio()\n", " | (10, 1)\n", " | >>> (0.0).as_integer_ratio()\n", " | (0, 1)\n", " | >>> (-.25).as_integer_ratio()\n", " | (-1, 4)\n", " | \n", " | fromhex(...)\n", " | float.fromhex(string) -> float\n", " | \n", " | Create a floating-point number from a hexadecimal string.\n", " | >>> float.fromhex('0x1.ffffp10')\n", " | 2047.984375\n", " | >>> float.fromhex('-0x1p-1074')\n", " | -4.9406564584124654e-324\n", " | \n", " | hex(...)\n", " | float.hex() -> string\n", " | \n", " | Return a hexadecimal representation of a floating-point number.\n", " | >>> (-0.1).hex()\n", " | '-0x1.999999999999ap-4'\n", " | >>> 3.14159.hex()\n", " | '0x1.921f9f01b866ep+1'\n", " | \n", " | is_integer(...)\n", " | Return True if the float is an integer.\n", " \n", " class floating(inexact)\n", " | Method resolution order:\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class format_parser\n", " | Class to convert formats, names, titles description to a dtype.\n", " | \n", " | After constructing the format_parser object, the dtype attribute is\n", " | the converted data-type:\n", " | ``dtype = format_parser(formats, names, titles).dtype``\n", " | \n", " | Attributes\n", " | ----------\n", " | dtype : dtype\n", " | The converted data-type.\n", " | \n", " | Parameters\n", " | ----------\n", " | formats : str or list of str\n", " | The format description, either specified as a string with\n", " | comma-separated format descriptions in the form ``'f8, i4, a5'``, or\n", " | a list of format description strings in the form\n", " | ``['f8', 'i4', 'a5']``.\n", " | names : str or list/tuple of str\n", " | The field names, either specified as a comma-separated string in the\n", " | form ``'col1, col2, col3'``, or as a list or tuple of strings in the\n", " | form ``['col1', 'col2', 'col3']``.\n", " | An empty list can be used, in that case default field names\n", " | ('f0', 'f1', ...) are used.\n", " | titles : sequence\n", " | Sequence of title strings. An empty list can be used to leave titles\n", " | out.\n", " | aligned : bool, optional\n", " | If True, align the fields by padding as the C-compiler would.\n", " | Default is False.\n", " | byteorder : str, optional\n", " | If specified, all the fields will be changed to the\n", " | provided byte-order. Otherwise, the default byte-order is\n", " | used. For all available string specifiers, see `dtype.newbyteorder`.\n", " | \n", " | See Also\n", " | --------\n", " | dtype, typename, sctype2char\n", " | \n", " | Examples\n", " | --------\n", " | >>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],\n", " | ... ['T1', 'T2', 'T3']).dtype\n", " | dtype([(('T1', 'col1'), '>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],\n", " | ... []).dtype\n", " | dtype([('col1', '>> np.format_parser(['f8', 'i4', 'a5'], [], []).dtype\n", " | dtype([('f0', ' abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " half = class float16(floating)\n", " | Method resolution order:\n", " | float16\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class iinfo(__builtin__.object)\n", " | iinfo(type)\n", " | \n", " | Machine limits for integer types.\n", " | \n", " | Attributes\n", " | ----------\n", " | min : int\n", " | The smallest integer expressible by the type.\n", " | max : int\n", " | The largest integer expressible by the type.\n", " | \n", " | Parameters\n", " | ----------\n", " | type : integer type, dtype, or instance\n", " | The kind of integer data type to get information about.\n", " | \n", " | See Also\n", " | --------\n", " | finfo : The equivalent for floating point data types.\n", " | \n", " | Examples\n", " | --------\n", " | With types:\n", " | \n", " | >>> ii16 = np.iinfo(np.int16)\n", " | >>> ii16.min\n", " | -32768\n", " | >>> ii16.max\n", " | 32767\n", " | >>> ii32 = np.iinfo(np.int32)\n", " | >>> ii32.min\n", " | -2147483648\n", " | >>> ii32.max\n", " | 2147483647\n", " | \n", " | With instances:\n", " | \n", " | >>> ii32 = np.iinfo(np.int32(10))\n", " | >>> ii32.min\n", " | -2147483648\n", " | >>> ii32.max\n", " | 2147483647\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, int_type)\n", " | \n", " | __repr__(self)\n", " | \n", " | __str__(self)\n", " | String representation.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " | \n", " | max\n", " | Maximum value of given dtype.\n", " | \n", " | min\n", " | Minimum value of given dtype.\n", " \n", " class inexact(number)\n", " | Method resolution order:\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " int0 = class int64(signedinteger, __builtin__.int)\n", " | 64-bit integer. Character code 'l'. Python int compatible.\n", " | \n", " | Method resolution order:\n", " | int64\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.int\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.int:\n", " | \n", " | __cmp__(...)\n", " | x.__cmp__(y) <==> cmp(x,y)\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __trunc__(...)\n", " | Truncating an Integral returns itself.\n", " | \n", " | bit_length(...)\n", " | int.bit_length() -> int\n", " | \n", " | Number of bits necessary to represent self in binary.\n", " | >>> bin(37)\n", " | '0b100101'\n", " | >>> (37).bit_length()\n", " | 6\n", " \n", " class int16(signedinteger)\n", " | 16-bit integer. Character code ``h``. C short compatible.\n", " | \n", " | Method resolution order:\n", " | int16\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class int32(signedinteger)\n", " | 32-bit integer. Character code 'i'. C int compatible.\n", " | \n", " | Method resolution order:\n", " | int32\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class int64(signedinteger, __builtin__.int)\n", " | 64-bit integer. Character code 'l'. Python int compatible.\n", " | \n", " | Method resolution order:\n", " | int64\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.int\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.int:\n", " | \n", " | __cmp__(...)\n", " | x.__cmp__(y) <==> cmp(x,y)\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __trunc__(...)\n", " | Truncating an Integral returns itself.\n", " | \n", " | bit_length(...)\n", " | int.bit_length() -> int\n", " | \n", " | Number of bits necessary to represent self in binary.\n", " | >>> bin(37)\n", " | '0b100101'\n", " | >>> (37).bit_length()\n", " | 6\n", " \n", " class int8(signedinteger)\n", " | 8-bit integer. Character code ``b``. C char compatible.\n", " | \n", " | Method resolution order:\n", " | int8\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " int_ = class int64(signedinteger, __builtin__.int)\n", " | 64-bit integer. Character code 'l'. Python int compatible.\n", " | \n", " | Method resolution order:\n", " | int64\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.int\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.int:\n", " | \n", " | __cmp__(...)\n", " | x.__cmp__(y) <==> cmp(x,y)\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __trunc__(...)\n", " | Truncating an Integral returns itself.\n", " | \n", " | bit_length(...)\n", " | int.bit_length() -> int\n", " | \n", " | Number of bits necessary to represent self in binary.\n", " | >>> bin(37)\n", " | '0b100101'\n", " | >>> (37).bit_length()\n", " | 6\n", " \n", " intc = class int32(signedinteger)\n", " | 32-bit integer. Character code 'i'. C int compatible.\n", " | \n", " | Method resolution order:\n", " | int32\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class integer(number)\n", " | Method resolution order:\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Data descriptors defined here:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " intp = class int64(signedinteger, __builtin__.int)\n", " | 64-bit integer. Character code 'l'. Python int compatible.\n", " | \n", " | Method resolution order:\n", " | int64\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.int\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.int:\n", " | \n", " | __cmp__(...)\n", " | x.__cmp__(y) <==> cmp(x,y)\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __trunc__(...)\n", " | Truncating an Integral returns itself.\n", " | \n", " | bit_length(...)\n", " | int.bit_length() -> int\n", " | \n", " | Number of bits necessary to represent self in binary.\n", " | >>> bin(37)\n", " | '0b100101'\n", " | >>> (37).bit_length()\n", " | 6\n", " \n", " longcomplex = class complex256(complexfloating)\n", " | Composed of two 128 bit floats\n", " | \n", " | Method resolution order:\n", " | complex256\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __complex__(...)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " longdouble = class float128(floating)\n", " | 128-bit floating-point number. Character code: 'g'. C long float\n", " | compatible.\n", " | \n", " | Method resolution order:\n", " | float128\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " longfloat = class float128(floating)\n", " | 128-bit floating-point number. Character code: 'g'. C long float\n", " | compatible.\n", " | \n", " | Method resolution order:\n", " | float128\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " longlong = class int64(signedinteger, __builtin__.int)\n", " | Method resolution order:\n", " | int64\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.int\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.int:\n", " | \n", " | __cmp__(...)\n", " | x.__cmp__(y) <==> cmp(x,y)\n", " | \n", " | __coerce__(...)\n", " | x.__coerce__(y) <==> coerce(x, y)\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __trunc__(...)\n", " | Truncating an Integral returns itself.\n", " | \n", " | bit_length(...)\n", " | int.bit_length() -> int\n", " | \n", " | Number of bits necessary to represent self in binary.\n", " | >>> bin(37)\n", " | '0b100101'\n", " | >>> (37).bit_length()\n", " | 6\n", " \n", " class matrix(numpy.ndarray)\n", " | matrix(data, dtype=None, copy=True)\n", " | \n", " | Returns a matrix from an array-like object, or from a string of data.\n", " | A matrix is a specialized 2-D array that retains its 2-D nature\n", " | through operations. It has certain special operators, such as ``*``\n", " | (matrix multiplication) and ``**`` (matrix power).\n", " | \n", " | Parameters\n", " | ----------\n", " | data : array_like or string\n", " | If `data` is a string, it is interpreted as a matrix with commas\n", " | or spaces separating columns, and semicolons separating rows.\n", " | dtype : data-type\n", " | Data-type of the output matrix.\n", " | copy : bool\n", " | If `data` is already an `ndarray`, then this flag determines\n", " | whether the data is copied (the default), or whether a view is\n", " | constructed.\n", " | \n", " | See Also\n", " | --------\n", " | array\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.matrix('1 2; 3 4')\n", " | >>> print a\n", " | [[1 2]\n", " | [3 4]]\n", " | \n", " | >>> np.matrix([[1, 2], [3, 4]])\n", " | matrix([[1, 2],\n", " | [3, 4]])\n", " | \n", " | Method resolution order:\n", " | matrix\n", " | numpy.ndarray\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __array_finalize__(self, obj)\n", " | \n", " | __getitem__(self, index)\n", " | \n", " | __imul__(self, other)\n", " | \n", " | __ipow__(self, other)\n", " | \n", " | __mul__(self, other)\n", " | \n", " | __pow__(self, other)\n", " | \n", " | __repr__(self)\n", " | \n", " | __rmul__(self, other)\n", " | \n", " | __rpow__(self, other)\n", " | \n", " | __str__(self)\n", " | \n", " | all(self, axis=None, out=None)\n", " | Test whether all matrix elements along a given axis evaluate to True.\n", " | \n", " | Parameters\n", " | ----------\n", " | See `numpy.all` for complete descriptions\n", " | \n", " | See Also\n", " | --------\n", " | numpy.all\n", " | \n", " | Notes\n", " | -----\n", " | This is the same as `ndarray.all`, but it returns a `matrix` object.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> y = x[0]; y\n", " | matrix([[0, 1, 2, 3]])\n", " | >>> (x == y)\n", " | matrix([[ True, True, True, True],\n", " | [False, False, False, False],\n", " | [False, False, False, False]], dtype=bool)\n", " | >>> (x == y).all()\n", " | False\n", " | >>> (x == y).all(0)\n", " | matrix([[False, False, False, False]], dtype=bool)\n", " | >>> (x == y).all(1)\n", " | matrix([[ True],\n", " | [False],\n", " | [False]], dtype=bool)\n", " | \n", " | any(self, axis=None, out=None)\n", " | Test whether any array element along a given axis evaluates to True.\n", " | \n", " | Refer to `numpy.any` for full documentation.\n", " | \n", " | Parameters\n", " | ----------\n", " | axis : int, optional\n", " | Axis along which logical OR is performed\n", " | out : ndarray, optional\n", " | Output to existing array instead of creating new one, must have\n", " | same shape as expected output\n", " | \n", " | Returns\n", " | -------\n", " | any : bool, ndarray\n", " | Returns a single bool if `axis` is ``None``; otherwise,\n", " | returns `ndarray`\n", " | \n", " | argmax(self, axis=None, out=None)\n", " | Indices of the maximum values along an axis.\n", " | \n", " | Parameters\n", " | ----------\n", " | See `numpy.argmax` for complete descriptions\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmax\n", " | \n", " | Notes\n", " | -----\n", " | This is the same as `ndarray.argmax`, but returns a `matrix` object\n", " | where `ndarray.argmax` would return an `ndarray`.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.argmax()\n", " | 11\n", " | >>> x.argmax(0)\n", " | matrix([[2, 2, 2, 2]])\n", " | >>> x.argmax(1)\n", " | matrix([[3],\n", " | [3],\n", " | [3]])\n", " | \n", " | argmin(self, axis=None, out=None)\n", " | Return the indices of the minimum values along an axis.\n", " | \n", " | Parameters\n", " | ----------\n", " | See `numpy.argmin` for complete descriptions.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmin\n", " | \n", " | Notes\n", " | -----\n", " | This is the same as `ndarray.argmin`, but returns a `matrix` object\n", " | where `ndarray.argmin` would return an `ndarray`.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = -np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, -1, -2, -3],\n", " | [ -4, -5, -6, -7],\n", " | [ -8, -9, -10, -11]])\n", " | >>> x.argmin()\n", " | 11\n", " | >>> x.argmin(0)\n", " | matrix([[2, 2, 2, 2]])\n", " | >>> x.argmin(1)\n", " | matrix([[3],\n", " | [3],\n", " | [3]])\n", " | \n", " | getA(self)\n", " | Return `self` as an `ndarray` object.\n", " | \n", " | Equivalent to ``np.asarray(self)``.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : ndarray\n", " | `self` as an `ndarray`\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.getA()\n", " | array([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | \n", " | getA1(self)\n", " | Return `self` as a flattened `ndarray`.\n", " | \n", " | Equivalent to ``np.asarray(x).ravel()``\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : ndarray\n", " | `self`, 1-D, as an `ndarray`\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.getA1()\n", " | array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n", " | \n", " | getH(self)\n", " | Returns the (complex) conjugate transpose of `self`.\n", " | \n", " | Equivalent to ``np.transpose(self)`` if `self` is real-valued.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : matrix object\n", " | complex conjugate transpose of `self`\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4)))\n", " | >>> z = x - 1j*x; z\n", " | matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],\n", " | [ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],\n", " | [ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])\n", " | >>> z.getH()\n", " | matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],\n", " | [ 1. +1.j, 5. +5.j, 9. +9.j],\n", " | [ 2. +2.j, 6. +6.j, 10.+10.j],\n", " | [ 3. +3.j, 7. +7.j, 11.+11.j]])\n", " | \n", " | getI(self)\n", " | Returns the (multiplicative) inverse of invertible `self`.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : matrix object\n", " | If `self` is non-singular, `ret` is such that ``ret * self`` ==\n", " | ``self * ret`` == ``np.matrix(np.eye(self[0,:].size)`` all return\n", " | ``True``.\n", " | \n", " | Raises\n", " | ------\n", " | numpy.linalg.LinAlgError: Singular matrix\n", " | If `self` is singular.\n", " | \n", " | See Also\n", " | --------\n", " | linalg.inv\n", " | \n", " | Examples\n", " | --------\n", " | >>> m = np.matrix('[1, 2; 3, 4]'); m\n", " | matrix([[1, 2],\n", " | [3, 4]])\n", " | >>> m.getI()\n", " | matrix([[-2. , 1. ],\n", " | [ 1.5, -0.5]])\n", " | >>> m.getI() * m\n", " | matrix([[ 1., 0.],\n", " | [ 0., 1.]])\n", " | \n", " | getT(self)\n", " | Returns the transpose of the matrix.\n", " | \n", " | Does *not* conjugate! For the complex conjugate transpose, use ``.H``.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : matrix object\n", " | The (non-conjugated) transpose of the matrix.\n", " | \n", " | See Also\n", " | --------\n", " | transpose, getH\n", " | \n", " | Examples\n", " | --------\n", " | >>> m = np.matrix('[1, 2; 3, 4]')\n", " | >>> m\n", " | matrix([[1, 2],\n", " | [3, 4]])\n", " | >>> m.getT()\n", " | matrix([[1, 3],\n", " | [2, 4]])\n", " | \n", " | max(self, axis=None, out=None)\n", " | Return the maximum value along an axis.\n", " | \n", " | Parameters\n", " | ----------\n", " | See `amax` for complete descriptions\n", " | \n", " | See Also\n", " | --------\n", " | amax, ndarray.max\n", " | \n", " | Notes\n", " | -----\n", " | This is the same as `ndarray.max`, but returns a `matrix` object\n", " | where `ndarray.max` would return an ndarray.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.max()\n", " | 11\n", " | >>> x.max(0)\n", " | matrix([[ 8, 9, 10, 11]])\n", " | >>> x.max(1)\n", " | matrix([[ 3],\n", " | [ 7],\n", " | [11]])\n", " | \n", " | mean(self, axis=None, dtype=None, out=None)\n", " | Returns the average of the matrix elements along the given axis.\n", " | \n", " | Refer to `numpy.mean` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.mean\n", " | \n", " | Notes\n", " | -----\n", " | Same as `ndarray.mean` except that, where that returns an `ndarray`,\n", " | this returns a `matrix` object.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3, 4)))\n", " | >>> x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.mean()\n", " | 5.5\n", " | >>> x.mean(0)\n", " | matrix([[ 4., 5., 6., 7.]])\n", " | >>> x.mean(1)\n", " | matrix([[ 1.5],\n", " | [ 5.5],\n", " | [ 9.5]])\n", " | \n", " | min(self, axis=None, out=None)\n", " | Return the minimum value along an axis.\n", " | \n", " | Parameters\n", " | ----------\n", " | See `amin` for complete descriptions.\n", " | \n", " | See Also\n", " | --------\n", " | amin, ndarray.min\n", " | \n", " | Notes\n", " | -----\n", " | This is the same as `ndarray.min`, but returns a `matrix` object\n", " | where `ndarray.min` would return an ndarray.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = -np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, -1, -2, -3],\n", " | [ -4, -5, -6, -7],\n", " | [ -8, -9, -10, -11]])\n", " | >>> x.min()\n", " | -11\n", " | >>> x.min(0)\n", " | matrix([[ -8, -9, -10, -11]])\n", " | >>> x.min(1)\n", " | matrix([[ -3],\n", " | [ -7],\n", " | [-11]])\n", " | \n", " | prod(self, axis=None, dtype=None, out=None)\n", " | Return the product of the array elements over the given axis.\n", " | \n", " | Refer to `prod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | prod, ndarray.prod\n", " | \n", " | Notes\n", " | -----\n", " | Same as `ndarray.prod`, except, where that returns an `ndarray`, this\n", " | returns a `matrix` object instead.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.prod()\n", " | 0\n", " | >>> x.prod(0)\n", " | matrix([[ 0, 45, 120, 231]])\n", " | >>> x.prod(1)\n", " | matrix([[ 0],\n", " | [ 840],\n", " | [7920]])\n", " | \n", " | ptp(self, axis=None, out=None)\n", " | Peak-to-peak (maximum - minimum) value along the given axis.\n", " | \n", " | Refer to `numpy.ptp` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ptp\n", " | \n", " | Notes\n", " | -----\n", " | Same as `ndarray.ptp`, except, where that would return an `ndarray` object,\n", " | this returns a `matrix` object.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.ptp()\n", " | 11\n", " | >>> x.ptp(0)\n", " | matrix([[8, 8, 8, 8]])\n", " | >>> x.ptp(1)\n", " | matrix([[3],\n", " | [3],\n", " | [3]])\n", " | \n", " | std(self, axis=None, dtype=None, out=None, ddof=0)\n", " | Return the standard deviation of the array elements along the given axis.\n", " | \n", " | Refer to `numpy.std` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.std\n", " | \n", " | Notes\n", " | -----\n", " | This is the same as `ndarray.std`, except that where an `ndarray` would\n", " | be returned, a `matrix` object is returned instead.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3, 4)))\n", " | >>> x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.std()\n", " | 3.4520525295346629\n", " | >>> x.std(0)\n", " | matrix([[ 3.26598632, 3.26598632, 3.26598632, 3.26598632]])\n", " | >>> x.std(1)\n", " | matrix([[ 1.11803399],\n", " | [ 1.11803399],\n", " | [ 1.11803399]])\n", " | \n", " | sum(self, axis=None, dtype=None, out=None)\n", " | Returns the sum of the matrix elements, along the given axis.\n", " | \n", " | Refer to `numpy.sum` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.sum\n", " | \n", " | Notes\n", " | -----\n", " | This is the same as `ndarray.sum`, except that where an `ndarray` would\n", " | be returned, a `matrix` object is returned instead.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix([[1, 2], [4, 3]])\n", " | >>> x.sum()\n", " | 10\n", " | >>> x.sum(axis=1)\n", " | matrix([[3],\n", " | [7]])\n", " | >>> x.sum(axis=1, dtype='float')\n", " | matrix([[ 3.],\n", " | [ 7.]])\n", " | >>> out = np.zeros((1, 2), dtype='float')\n", " | >>> x.sum(axis=1, dtype='float', out=out)\n", " | matrix([[ 3.],\n", " | [ 7.]])\n", " | \n", " | tolist(self)\n", " | Return the matrix as a (possibly nested) list.\n", " | \n", " | See `ndarray.tolist` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | ndarray.tolist\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.tolist()\n", " | [[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]\n", " | \n", " | var(self, axis=None, dtype=None, out=None, ddof=0)\n", " | Returns the variance of the matrix elements, along the given axis.\n", " | \n", " | Refer to `numpy.var` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.var\n", " | \n", " | Notes\n", " | -----\n", " | This is the same as `ndarray.var`, except that where an `ndarray` would\n", " | be returned, a `matrix` object is returned instead.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3, 4)))\n", " | >>> x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.var()\n", " | 11.916666666666666\n", " | >>> x.var(0)\n", " | matrix([[ 10.66666667, 10.66666667, 10.66666667, 10.66666667]])\n", " | >>> x.var(1)\n", " | matrix([[ 1.25],\n", " | [ 1.25],\n", " | [ 1.25]])\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(subtype, data, dtype=None, copy=True)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | A\n", " | Return `self` as an `ndarray` object.\n", " | \n", " | Equivalent to ``np.asarray(self)``.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : ndarray\n", " | `self` as an `ndarray`\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.getA()\n", " | array([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | \n", " | A1\n", " | Return `self` as a flattened `ndarray`.\n", " | \n", " | Equivalent to ``np.asarray(x).ravel()``\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : ndarray\n", " | `self`, 1-D, as an `ndarray`\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4))); x\n", " | matrix([[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]])\n", " | >>> x.getA1()\n", " | array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n", " | \n", " | H\n", " | Returns the (complex) conjugate transpose of `self`.\n", " | \n", " | Equivalent to ``np.transpose(self)`` if `self` is real-valued.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : matrix object\n", " | complex conjugate transpose of `self`\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.matrix(np.arange(12).reshape((3,4)))\n", " | >>> z = x - 1j*x; z\n", " | matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],\n", " | [ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],\n", " | [ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])\n", " | >>> z.getH()\n", " | matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],\n", " | [ 1. +1.j, 5. +5.j, 9. +9.j],\n", " | [ 2. +2.j, 6. +6.j, 10.+10.j],\n", " | [ 3. +3.j, 7. +7.j, 11.+11.j]])\n", " | \n", " | I\n", " | Returns the (multiplicative) inverse of invertible `self`.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : matrix object\n", " | If `self` is non-singular, `ret` is such that ``ret * self`` ==\n", " | ``self * ret`` == ``np.matrix(np.eye(self[0,:].size)`` all return\n", " | ``True``.\n", " | \n", " | Raises\n", " | ------\n", " | numpy.linalg.LinAlgError: Singular matrix\n", " | If `self` is singular.\n", " | \n", " | See Also\n", " | --------\n", " | linalg.inv\n", " | \n", " | Examples\n", " | --------\n", " | >>> m = np.matrix('[1, 2; 3, 4]'); m\n", " | matrix([[1, 2],\n", " | [3, 4]])\n", " | >>> m.getI()\n", " | matrix([[-2. , 1. ],\n", " | [ 1.5, -0.5]])\n", " | >>> m.getI() * m\n", " | matrix([[ 1., 0.],\n", " | [ 0., 1.]])\n", " | \n", " | T\n", " | Returns the transpose of the matrix.\n", " | \n", " | Does *not* conjugate! For the complex conjugate transpose, use ``.H``.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | ret : matrix object\n", " | The (non-conjugated) transpose of the matrix.\n", " | \n", " | See Also\n", " | --------\n", " | transpose, getH\n", " | \n", " | Examples\n", " | --------\n", " | >>> m = np.matrix('[1, 2; 3, 4]')\n", " | >>> m\n", " | matrix([[1, 2],\n", " | [3, 4]])\n", " | >>> m.getT()\n", " | matrix([[1, 3],\n", " | [2, 4]])\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __array_priority__ = 10.0\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from numpy.ndarray:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | a.__array__(|dtype) -> reference if type unchanged, copy otherwise.\n", " | \n", " | Returns either a new reference to self if dtype is not given or a new array\n", " | of provided data type if dtype is different from the current dtype of the\n", " | array.\n", " | \n", " | __array_prepare__(...)\n", " | a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n", " | \n", " | __array_wrap__(...)\n", " | a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __copy__(...)\n", " | a.__copy__([order])\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | If order is 'C' (False) then the result is contiguous (default).\n", " | If order is 'Fortran' (True) then the result has fortran order.\n", " | If order is 'Any' (None) then the result has fortran order\n", " | only if the array already is in fortran order.\n", " | \n", " | __deepcopy__(...)\n", " | a.__deepcopy__() -> Deep copy of array.\n", " | \n", " | Used if copy.deepcopy is called on an array.\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __delslice__(...)\n", " | x.__delslice__(i, j) <==> del x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __iadd__(...)\n", " | x.__iadd__(y) <==> x+=y\n", " | \n", " | __iand__(...)\n", " | x.__iand__(y) <==> x&=y\n", " | \n", " | __idiv__(...)\n", " | x.__idiv__(y) <==> x/=y\n", " | \n", " | __ifloordiv__(...)\n", " | x.__ifloordiv__(y) <==> x//y\n", " | \n", " | __ilshift__(...)\n", " | x.__ilshift__(y) <==> x<<=y\n", " | \n", " | __imod__(...)\n", " | x.__imod__(y) <==> x%=y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __ior__(...)\n", " | x.__ior__(y) <==> x|=y\n", " | \n", " | __irshift__(...)\n", " | x.__irshift__(y) <==> x>>=y\n", " | \n", " | __isub__(...)\n", " | x.__isub__(y) <==> x-=y\n", " | \n", " | __iter__(...)\n", " | x.__iter__() <==> iter(x)\n", " | \n", " | __itruediv__(...)\n", " | x.__itruediv__(y) <==> x/y\n", " | \n", " | __ixor__(...)\n", " | x.__ixor__(y) <==> x^=y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | a.__reduce__()\n", " | \n", " | For pickling.\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | __setslice__(...)\n", " | x.__setslice__(i, j, y) <==> x[i:j]=y\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __setstate__(...)\n", " | a.__setstate__(version, shape, dtype, isfortran, rawdata)\n", " | \n", " | For unpickling.\n", " | \n", " | Parameters\n", " | ----------\n", " | version : int\n", " | optional pickle version. If omitted defaults to 0.\n", " | shape : tuple\n", " | dtype : data-type\n", " | isFortran : bool\n", " | rawdata : string or list\n", " | a binary string with the data (or a list if 'a' is an object array)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | argpartition(...)\n", " | a.argpartition(kth, axis=-1, kind='introselect', order=None)\n", " | \n", " | Returns the indices that would partition this array.\n", " | \n", " | Refer to `numpy.argpartition` for full documentation.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argpartition : equivalent function\n", " | \n", " | argsort(...)\n", " | a.argsort(axis=-1, kind='quicksort', order=None)\n", " | \n", " | Returns the indices that would sort this array.\n", " | \n", " | Refer to `numpy.argsort` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argsort : equivalent function\n", " | \n", " | astype(...)\n", " | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n", " | \n", " | Copy of the array, cast to a specified type.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | Typecode or data-type to which the array is cast.\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout order of the result.\n", " | 'C' means C order, 'F' means Fortran order, 'A'\n", " | means 'F' order if all the arrays are Fortran contiguous,\n", " | 'C' order otherwise, and 'K' means as close to the\n", " | order the array elements appear in memory as possible.\n", " | Default is 'K'.\n", " | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n", " | Controls what kind of data casting may occur. Defaults to 'unsafe'\n", " | for backwards compatibility.\n", " | \n", " | * 'no' means the data types should not be cast at all.\n", " | * 'equiv' means only byte-order changes are allowed.\n", " | * 'safe' means only casts which can preserve values are allowed.\n", " | * 'same_kind' means only safe casts or casts within a kind,\n", " | like float64 to float32, are allowed.\n", " | * 'unsafe' means any data conversions may be done.\n", " | subok : bool, optional\n", " | If True, then sub-classes will be passed-through (default), otherwise\n", " | the returned array will be forced to be a base-class array.\n", " | copy : bool, optional\n", " | By default, astype always returns a newly allocated array. If this\n", " | is set to false, and the `dtype`, `order`, and `subok`\n", " | requirements are satisfied, the input array is returned instead\n", " | of a copy.\n", " | \n", " | Returns\n", " | -------\n", " | arr_t : ndarray\n", " | Unless `copy` is False and the other conditions for returning the input\n", " | array are satisfied (see description for `copy` input paramter), `arr_t`\n", " | is a new array of the same shape as the input array, with dtype, order\n", " | given by `dtype`, `order`.\n", " | \n", " | Notes\n", " | -----\n", " | Starting in NumPy 1.9, astype method now returns an error if the string\n", " | dtype to cast to is not long enough in 'safe' casting mode to hold the max\n", " | value of integer/float array that is being casted. Previously the casting\n", " | was allowed even if the result was truncated.\n", " | \n", " | Raises\n", " | ------\n", " | ComplexWarning\n", " | When casting from complex to float or int. To avoid this,\n", " | one should use ``a.real.astype(t)``.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 2.5])\n", " | >>> x\n", " | array([ 1. , 2. , 2.5])\n", " | \n", " | >>> x.astype(int)\n", " | array([1, 2, 2])\n", " | \n", " | byteswap(...)\n", " | a.byteswap(inplace)\n", " | \n", " | Swap the bytes of the array elements\n", " | \n", " | Toggle between low-endian and big-endian data representation by\n", " | returning a byteswapped array, optionally swapped in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | inplace : bool, optional\n", " | If ``True``, swap bytes in-place, default is ``False``.\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | The byteswapped array. If `inplace` is ``True``, this is\n", " | a view to self.\n", " | \n", " | Examples\n", " | --------\n", " | >>> A = np.array([1, 256, 8755], dtype=np.int16)\n", " | >>> map(hex, A)\n", " | ['0x1', '0x100', '0x2233']\n", " | >>> A.byteswap(True)\n", " | array([ 256, 1, 13090], dtype=int16)\n", " | >>> map(hex, A)\n", " | ['0x100', '0x1', '0x3322']\n", " | \n", " | Arrays of strings are not swapped\n", " | \n", " | >>> A = np.array(['ceg', 'fac'])\n", " | >>> A.byteswap()\n", " | array(['ceg', 'fac'],\n", " | dtype='|S3')\n", " | \n", " | choose(...)\n", " | a.choose(choices, out=None, mode='raise')\n", " | \n", " | Use an index array to construct a new array from a set of choices.\n", " | \n", " | Refer to `numpy.choose` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.choose : equivalent function\n", " | \n", " | clip(...)\n", " | a.clip(a_min, a_max, out=None)\n", " | \n", " | Return an array whose values are limited to ``[a_min, a_max]``.\n", " | \n", " | Refer to `numpy.clip` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.clip : equivalent function\n", " | \n", " | compress(...)\n", " | a.compress(condition, axis=None, out=None)\n", " | \n", " | Return selected slices of this array along given axis.\n", " | \n", " | Refer to `numpy.compress` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.compress : equivalent function\n", " | \n", " | conj(...)\n", " | a.conj()\n", " | \n", " | Complex-conjugate all elements.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | conjugate(...)\n", " | a.conjugate()\n", " | \n", " | Return the complex conjugate, element-wise.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | copy(...)\n", " | a.copy(order='C')\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout of the copy. 'C' means C-order,\n", " | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n", " | 'C' otherwise. 'K' means match the layout of `a` as closely\n", " | as possible. (Note that this function and :func:numpy.copy are very\n", " | similar, but have different default values for their order=\n", " | arguments.)\n", " | \n", " | See also\n", " | --------\n", " | numpy.copy\n", " | numpy.copyto\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n", " | \n", " | >>> y = x.copy()\n", " | \n", " | >>> x.fill(0)\n", " | \n", " | >>> x\n", " | array([[0, 0, 0],\n", " | [0, 0, 0]])\n", " | \n", " | >>> y\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | \n", " | >>> y.flags['C_CONTIGUOUS']\n", " | True\n", " | \n", " | cumprod(...)\n", " | a.cumprod(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative product of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumprod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumprod : equivalent function\n", " | \n", " | cumsum(...)\n", " | a.cumsum(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative sum of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumsum` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumsum : equivalent function\n", " | \n", " | diagonal(...)\n", " | a.diagonal(offset=0, axis1=0, axis2=1)\n", " | \n", " | Return specified diagonals. In NumPy 1.9 the returned array is a\n", " | read-only view instead of a copy as in previous NumPy versions. In\n", " | NumPy 1.10 the read-only restriction will be removed.\n", " | \n", " | Refer to :func:`numpy.diagonal` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.diagonal : equivalent function\n", " | \n", " | dot(...)\n", " | a.dot(b, out=None)\n", " | \n", " | Dot product of two arrays.\n", " | \n", " | Refer to `numpy.dot` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dot : equivalent function\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.eye(2)\n", " | >>> b = np.ones((2, 2)) * 2\n", " | >>> a.dot(b)\n", " | array([[ 2., 2.],\n", " | [ 2., 2.]])\n", " | \n", " | This array method can be conveniently chained:\n", " | \n", " | >>> a.dot(b).dot(b)\n", " | array([[ 8., 8.],\n", " | [ 8., 8.]])\n", " | \n", " | dump(...)\n", " | a.dump(file)\n", " | \n", " | Dump a pickle of the array to the specified file.\n", " | The array can be read back with pickle.load or numpy.load.\n", " | \n", " | Parameters\n", " | ----------\n", " | file : str\n", " | A string naming the dump file.\n", " | \n", " | dumps(...)\n", " | a.dumps()\n", " | \n", " | Returns the pickle of the array as a string.\n", " | pickle.loads or numpy.loads will convert the string back to an array.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | fill(...)\n", " | a.fill(value)\n", " | \n", " | Fill the array with a scalar value.\n", " | \n", " | Parameters\n", " | ----------\n", " | value : scalar\n", " | All elements of `a` will be assigned this value.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([1, 2])\n", " | >>> a.fill(0)\n", " | >>> a\n", " | array([0, 0])\n", " | >>> a = np.empty(2)\n", " | >>> a.fill(1)\n", " | >>> a\n", " | array([ 1., 1.])\n", " | \n", " | flatten(...)\n", " | a.flatten(order='C')\n", " | \n", " | Return a copy of the array collapsed into one dimension.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | Whether to flatten in C (row-major), Fortran (column-major) order,\n", " | or preserve the C/Fortran ordering from `a`.\n", " | The default is 'C'.\n", " | \n", " | Returns\n", " | -------\n", " | y : ndarray\n", " | A copy of the input array, flattened to one dimension.\n", " | \n", " | See Also\n", " | --------\n", " | ravel : Return a flattened array.\n", " | flat : A 1-D flat iterator over the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,2], [3,4]])\n", " | >>> a.flatten()\n", " | array([1, 2, 3, 4])\n", " | >>> a.flatten('F')\n", " | array([1, 3, 2, 4])\n", " | \n", " | getfield(...)\n", " | a.getfield(dtype, offset=0)\n", " | \n", " | Returns a field of the given array as a certain type.\n", " | \n", " | A field is a view of the array data with a given data-type. The values in\n", " | the view are determined by the given type and the offset into the current\n", " | array in bytes. The offset needs to be such that the view dtype fits in the\n", " | array dtype; for example an array of dtype complex128 has 16-byte elements.\n", " | If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n", " | between 0 and 12 bytes.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | The data type of the view. The dtype size of the view can not be larger\n", " | than that of the array itself.\n", " | offset : int\n", " | Number of bytes to skip before beginning the element view.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.diag([1.+1.j]*2)\n", " | >>> x[1, 1] = 2 + 4.j\n", " | >>> x\n", " | array([[ 1.+1.j, 0.+0.j],\n", " | [ 0.+0.j, 2.+4.j]])\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0.],\n", " | [ 0., 2.]])\n", " | \n", " | By choosing an offset of 8 bytes we can select the complex part of the\n", " | array for our view:\n", " | \n", " | >>> x.getfield(np.float64, offset=8)\n", " | array([[ 1., 0.],\n", " | [ 0., 4.]])\n", " | \n", " | item(...)\n", " | a.item(*args)\n", " | \n", " | Copy an element of an array to a standard Python scalar and return it.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments (variable number and type)\n", " | \n", " | * none: in this case, the method only works for arrays\n", " | with one element (`a.size == 1`), which element is\n", " | copied into a standard Python scalar object and returned.\n", " | \n", " | * int_type: this argument is interpreted as a flat index into\n", " | the array, specifying which element to copy and return.\n", " | \n", " | * tuple of int_types: functions as does a single int_type argument,\n", " | except that the argument is interpreted as an nd-index into the\n", " | array.\n", " | \n", " | Returns\n", " | -------\n", " | z : Standard Python scalar object\n", " | A copy of the specified element of the array as a suitable\n", " | Python scalar\n", " | \n", " | Notes\n", " | -----\n", " | When the data type of `a` is longdouble or clongdouble, item() returns\n", " | a scalar array object because there is no available Python scalar that\n", " | would not lose information. Void arrays return a buffer object for item(),\n", " | unless fields are defined, in which case a tuple is returned.\n", " | \n", " | `item` is very similar to a[args], except, instead of an array scalar,\n", " | a standard Python scalar is returned. This can be useful for speeding up\n", " | access to elements of the array and doing arithmetic on elements of the\n", " | array using Python's optimized math.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.item(3)\n", " | 2\n", " | >>> x.item(7)\n", " | 5\n", " | >>> x.item((0, 1))\n", " | 1\n", " | >>> x.item((2, 2))\n", " | 3\n", " | \n", " | itemset(...)\n", " | a.itemset(*args)\n", " | \n", " | Insert scalar into an array (scalar is cast to array's dtype, if possible)\n", " | \n", " | There must be at least 1 argument, and define the last argument\n", " | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster\n", " | than ``a[args] = item``. The item should be a scalar value and `args`\n", " | must select a single item in the array `a`.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments\n", " | If one argument: a scalar, only used in case `a` is of size 1.\n", " | If two arguments: the last argument is the value to be set\n", " | and must be a scalar, the first argument specifies a single array\n", " | element location. It is either an int or a tuple.\n", " | \n", " | Notes\n", " | -----\n", " | Compared to indexing syntax, `itemset` provides some speed increase\n", " | for placing a scalar into a particular location in an `ndarray`,\n", " | if you must do this. However, generally this is discouraged:\n", " | among other problems, it complicates the appearance of the code.\n", " | Also, when using `itemset` (and `item`) inside a loop, be sure\n", " | to assign the methods to a local variable to avoid the attribute\n", " | look-up at each loop iteration.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.itemset(4, 0)\n", " | >>> x.itemset((2, 2), 9)\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 0, 3],\n", " | [8, 5, 9]])\n", " | \n", " | newbyteorder(...)\n", " | arr.newbyteorder(new_order='S')\n", " | \n", " | Return the array with the same data viewed with a different byte order.\n", " | \n", " | Equivalent to::\n", " | \n", " | arr.view(arr.dtype.newbytorder(new_order))\n", " | \n", " | Changes are also made in all fields and sub-arrays of the array data\n", " | type.\n", " | \n", " | \n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : string, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. `new_order` codes can be any of::\n", " | \n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_arr : array\n", " | New array object with the dtype reflecting given change to the\n", " | byte order.\n", " | \n", " | nonzero(...)\n", " | a.nonzero()\n", " | \n", " | Return the indices of the elements that are non-zero.\n", " | \n", " | Refer to `numpy.nonzero` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.nonzero : equivalent function\n", " | \n", " | partition(...)\n", " | a.partition(kth, axis=-1, kind='introselect', order=None)\n", " | \n", " | Rearranges the elements in the array in such a way that value of the\n", " | element in kth position is in the position it would be in a sorted array.\n", " | All elements smaller than the kth element are moved before this element and\n", " | all equal or greater are moved behind it. The ordering of the elements in\n", " | the two partitions is undefined.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | Parameters\n", " | ----------\n", " | kth : int or sequence of ints\n", " | Element index to partition by. The kth element value will be in its\n", " | final sorted position and all smaller elements will be moved before it\n", " | and all equal or greater elements behind it.\n", " | The order all elements in the partitions is undefined.\n", " | If provided with a sequence of kth it will partition all elements\n", " | indexed by kth of them into their sorted position at once.\n", " | axis : int, optional\n", " | Axis along which to sort. Default is -1, which means sort along the\n", " | last axis.\n", " | kind : {'introselect'}, optional\n", " | Selection algorithm. Default is 'introselect'.\n", " | order : list, optional\n", " | When `a` is an array with fields defined, this argument specifies\n", " | which fields to compare first, second, etc. Not all fields need be\n", " | specified.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.partition : Return a parititioned copy of an array.\n", " | argpartition : Indirect partition.\n", " | sort : Full sort.\n", " | \n", " | Notes\n", " | -----\n", " | See ``np.partition`` for notes on the different algorithms.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([3, 4, 2, 1])\n", " | >>> a.partition(a, 3)\n", " | >>> a\n", " | array([2, 1, 3, 4])\n", " | \n", " | >>> a.partition((1, 3))\n", " | array([1, 2, 3, 4])\n", " | \n", " | put(...)\n", " | a.put(indices, values, mode='raise')\n", " | \n", " | Set ``a.flat[n] = values[n]`` for all `n` in indices.\n", " | \n", " | Refer to `numpy.put` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.put : equivalent function\n", " | \n", " | ravel(...)\n", " | a.ravel([order])\n", " | \n", " | Return a flattened array.\n", " | \n", " | Refer to `numpy.ravel` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ravel : equivalent function\n", " | \n", " | ndarray.flat : a flat iterator on the array.\n", " | \n", " | repeat(...)\n", " | a.repeat(repeats, axis=None)\n", " | \n", " | Repeat elements of an array.\n", " | \n", " | Refer to `numpy.repeat` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.repeat : equivalent function\n", " | \n", " | reshape(...)\n", " | a.reshape(shape, order='C')\n", " | \n", " | Returns an array containing the same data with a new shape.\n", " | \n", " | Refer to `numpy.reshape` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.reshape : equivalent function\n", " | \n", " | resize(...)\n", " | a.resize(new_shape, refcheck=True)\n", " | \n", " | Change shape and size of array in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | new_shape : tuple of ints, or `n` ints\n", " | Shape of resized array.\n", " | refcheck : bool, optional\n", " | If False, reference count will not be checked. Default is True.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | Raises\n", " | ------\n", " | ValueError\n", " | If `a` does not own its own data or references or views to it exist,\n", " | and the data memory must be changed.\n", " | \n", " | SystemError\n", " | If the `order` keyword argument is specified. This behaviour is a\n", " | bug in NumPy.\n", " | \n", " | See Also\n", " | --------\n", " | resize : Return a new array with the specified shape.\n", " | \n", " | Notes\n", " | -----\n", " | This reallocates space for the data area if necessary.\n", " | \n", " | Only contiguous arrays (data elements consecutive in memory) can be\n", " | resized.\n", " | \n", " | The purpose of the reference count check is to make sure you\n", " | do not use this array as a buffer for another Python object and then\n", " | reallocate the memory. However, reference counts can increase in\n", " | other ways so if you are sure that you have not shared the memory\n", " | for this array with another Python object, then you may safely set\n", " | `refcheck` to False.\n", " | \n", " | Examples\n", " | --------\n", " | Shrinking an array: array is flattened (in the order that the data are\n", " | stored in memory), resized, and reshaped:\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='C')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [1]])\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='F')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [2]])\n", " | \n", " | Enlarging an array: as above, but missing entries are filled with zeros:\n", " | \n", " | >>> b = np.array([[0, 1], [2, 3]])\n", " | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n", " | >>> b\n", " | array([[0, 1, 2],\n", " | [3, 0, 0]])\n", " | \n", " | Referencing an array prevents resizing...\n", " | \n", " | >>> c = a\n", " | >>> a.resize((1, 1))\n", " | Traceback (most recent call last):\n", " | ...\n", " | ValueError: cannot resize an array that has been referenced ...\n", " | \n", " | Unless `refcheck` is False:\n", " | \n", " | >>> a.resize((1, 1), refcheck=False)\n", " | >>> a\n", " | array([[0]])\n", " | >>> c\n", " | array([[0]])\n", " | \n", " | round(...)\n", " | a.round(decimals=0, out=None)\n", " | \n", " | Return `a` with each element rounded to the given number of decimals.\n", " | \n", " | Refer to `numpy.around` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.around : equivalent function\n", " | \n", " | searchsorted(...)\n", " | a.searchsorted(v, side='left', sorter=None)\n", " | \n", " | Find indices where elements of v should be inserted in a to maintain order.\n", " | \n", " | For full documentation, see `numpy.searchsorted`\n", " | \n", " | See Also\n", " | --------\n", " | numpy.searchsorted : equivalent function\n", " | \n", " | setfield(...)\n", " | a.setfield(val, dtype, offset=0)\n", " | \n", " | Put a value into a specified place in a field defined by a data-type.\n", " | \n", " | Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n", " | bytes into the field.\n", " | \n", " | Parameters\n", " | ----------\n", " | val : object\n", " | Value to be placed in field.\n", " | dtype : dtype object\n", " | Data-type of the field in which to place `val`.\n", " | offset : int, optional\n", " | The number of bytes into the field at which to place `val`.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | See Also\n", " | --------\n", " | getfield\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.eye(3)\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | >>> x.setfield(3, np.int32)\n", " | >>> x.getfield(np.int32)\n", " | array([[3, 3, 3],\n", " | [3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x\n", " | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]])\n", " | >>> x.setfield(np.eye(3), np.int32)\n", " | >>> x\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | \n", " | setflags(...)\n", " | a.setflags(write=None, align=None, uic=None)\n", " | \n", " | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.\n", " | \n", " | These Boolean-valued flags affect how numpy interprets the memory\n", " | area used by `a` (see Notes below). The ALIGNED flag can only\n", " | be set to True if the data is actually aligned according to the type.\n", " | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE\n", " | can only be set to True if the array owns its own memory, or the\n", " | ultimate owner of the memory exposes a writeable buffer interface,\n", " | or is a string. (The exception for string is made so that unpickling\n", " | can be done without copying memory.)\n", " | \n", " | Parameters\n", " | ----------\n", " | write : bool, optional\n", " | Describes whether or not `a` can be written to.\n", " | align : bool, optional\n", " | Describes whether or not `a` is aligned properly for its type.\n", " | uic : bool, optional\n", " | Describes whether or not `a` is a copy of another \"base\" array.\n", " | \n", " | Notes\n", " | -----\n", " | Array flags provide information about how the memory area used\n", " | for the array is to be interpreted. There are 6 Boolean flags\n", " | in use, only three of which can be changed by the user:\n", " | UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n", " | \n", " | WRITEABLE (W) the data area can be written to;\n", " | \n", " | ALIGNED (A) the data and strides are aligned appropriately for the hardware\n", " | (as determined by the compiler);\n", " | \n", " | UPDATEIFCOPY (U) this array is a copy of some other array (referenced\n", " | by .base). When this array is deallocated, the base array will be\n", " | updated with the contents of this array.\n", " | \n", " | All flags can be accessed using their first (upper case) letter as well\n", " | as the full name.\n", " | \n", " | Examples\n", " | --------\n", " | >>> y\n", " | array([[3, 1, 7],\n", " | [2, 0, 0],\n", " | [8, 5, 9]])\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : True\n", " | ALIGNED : True\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(write=0, align=0)\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : False\n", " | ALIGNED : False\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(uic=1)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: cannot set UPDATEIFCOPY flag to True\n", " | \n", " | sort(...)\n", " | a.sort(axis=-1, kind='quicksort', order=None)\n", " | \n", " | Sort an array, in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | axis : int, optional\n", " | Axis along which to sort. Default is -1, which means sort along the\n", " | last axis.\n", " | kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n", " | Sorting algorithm. Default is 'quicksort'.\n", " | order : list, optional\n", " | When `a` is an array with fields defined, this argument specifies\n", " | which fields to compare first, second, etc. Not all fields need be\n", " | specified.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.sort : Return a sorted copy of an array.\n", " | argsort : Indirect sort.\n", " | lexsort : Indirect stable sort on multiple keys.\n", " | searchsorted : Find elements in sorted array.\n", " | partition: Partial sort.\n", " | \n", " | Notes\n", " | -----\n", " | See ``sort`` for notes on the different sorting algorithms.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,4], [3,1]])\n", " | >>> a.sort(axis=1)\n", " | >>> a\n", " | array([[1, 4],\n", " | [1, 3]])\n", " | >>> a.sort(axis=0)\n", " | >>> a\n", " | array([[1, 3],\n", " | [1, 4]])\n", " | \n", " | Use the `order` keyword to specify a field to use when sorting a\n", " | structured array:\n", " | \n", " | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n", " | >>> a.sort(order='y')\n", " | >>> a\n", " | array([('c', 1), ('a', 2)],\n", " | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | tofile(...)\n", " | a.tofile(fid, sep=\"\", format=\"%s\")\n", " | \n", " | Write array to a file as text or binary (default).\n", " | \n", " | Data is always written in 'C' order, independent of the order of `a`.\n", " | The data produced by this method can be recovered using the function\n", " | fromfile().\n", " | \n", " | Parameters\n", " | ----------\n", " | fid : file or str\n", " | An open file object, or a string containing a filename.\n", " | sep : str\n", " | Separator between array items for text output.\n", " | If \"\" (empty), a binary file is written, equivalent to\n", " | ``file.write(a.tobytes())``.\n", " | format : str\n", " | Format string for text file output.\n", " | Each entry in the array is formatted to text by first converting\n", " | it to the closest Python type, and then using \"format\" % item.\n", " | \n", " | Notes\n", " | -----\n", " | This is a convenience function for quick storage of array data.\n", " | Information on endianness and precision is lost, so this method is not a\n", " | good choice for files intended to archive data or transport data between\n", " | machines with different endianness. Some of these problems can be overcome\n", " | by outputting the data as text files, at the expense of speed and file\n", " | size.\n", " | \n", " | tostring(...)\n", " | a.tostring(order='C')\n", " | \n", " | Construct Python bytes containing the raw data bytes in the array.\n", " | \n", " | Constructs Python bytes showing a copy of the raw contents of\n", " | data memory. The bytes object can be produced in either 'C' or 'Fortran',\n", " | or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n", " | unless the F_CONTIGUOUS flag in the array is set, in which case it\n", " | means 'Fortran' order.\n", " | \n", " | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', None}, optional\n", " | Order of the data for multidimensional arrays:\n", " | C, Fortran, or the same as for the original array.\n", " | \n", " | Returns\n", " | -------\n", " | s : bytes\n", " | Python bytes exhibiting a copy of `a`'s raw data.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | trace(...)\n", " | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n", " | \n", " | Return the sum along diagonals of the array.\n", " | \n", " | Refer to `numpy.trace` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.trace : equivalent function\n", " | \n", " | transpose(...)\n", " | a.transpose(*axes)\n", " | \n", " | Returns a view of the array with axes transposed.\n", " | \n", " | For a 1-D array, this has no effect. (To change between column and\n", " | row vectors, first cast the 1-D array into a matrix object.)\n", " | For a 2-D array, this is the usual matrix transpose.\n", " | For an n-D array, if axes are given, their order indicates how the\n", " | axes are permuted (see Examples). If axes are not provided and\n", " | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n", " | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n", " | \n", " | Parameters\n", " | ----------\n", " | axes : None, tuple of ints, or `n` ints\n", " | \n", " | * None or no argument: reverses the order of the axes.\n", " | \n", " | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n", " | `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n", " | \n", " | * `n` ints: same as an n-tuple of the same ints (this form is\n", " | intended simply as a \"convenience\" alternative to the tuple form)\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | View of `a`, with axes suitably permuted.\n", " | \n", " | See Also\n", " | --------\n", " | ndarray.T : Array property returning the array transposed.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> a\n", " | array([[1, 2],\n", " | [3, 4]])\n", " | >>> a.transpose()\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose((1, 0))\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose(1, 0)\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | \n", " | view(...)\n", " | a.view(dtype=None, type=None)\n", " | \n", " | New view of array with the same data.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : data-type or ndarray sub-class, optional\n", " | Data-type descriptor of the returned view, e.g., float32 or int16. The\n", " | default, None, results in the view having the same data-type as `a`.\n", " | This argument can also be specified as an ndarray sub-class, which\n", " | then specifies the type of the returned object (this is equivalent to\n", " | setting the ``type`` parameter).\n", " | type : Python type, optional\n", " | Type of the returned view, e.g., ndarray or matrix. Again, the\n", " | default None results in type preservation.\n", " | \n", " | Notes\n", " | -----\n", " | ``a.view()`` is used two different ways:\n", " | \n", " | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n", " | of the array's memory with a different data-type. This can cause a\n", " | reinterpretation of the bytes of memory.\n", " | \n", " | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n", " | returns an instance of `ndarray_subclass` that looks at the same array\n", " | (same shape, dtype, etc.) This does not cause a reinterpretation of the\n", " | memory.\n", " | \n", " | For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n", " | bytes per entry than the previous dtype (for example, converting a\n", " | regular array to a structured array), then the behavior of the view\n", " | cannot be predicted just from the superficial appearance of ``a`` (shown\n", " | by ``print(a)``). It also depends on exactly how ``a`` is stored in\n", " | memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n", " | defined as a slice or transpose, etc., the view may give different\n", " | results.\n", " | \n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n", " | \n", " | Viewing array data using a different type and dtype:\n", " | \n", " | >>> y = x.view(dtype=np.int16, type=np.matrix)\n", " | >>> y\n", " | matrix([[513]], dtype=int16)\n", " | >>> print type(y)\n", " | \n", " | \n", " | Creating a view on a structured array so it can be used in calculations\n", " | \n", " | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n", " | >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n", " | >>> xv\n", " | array([[1, 2],\n", " | [3, 4]], dtype=int8)\n", " | >>> xv.mean(0)\n", " | array([ 2., 3.])\n", " | \n", " | Making changes to the view changes the underlying array\n", " | \n", " | >>> xv[0,1] = 20\n", " | >>> print x\n", " | [(1, 20) (3, 4)]\n", " | \n", " | Using a view to convert an array to a record array:\n", " | \n", " | >>> z = x.view(np.recarray)\n", " | >>> z.a\n", " | array([1], dtype=int8)\n", " | \n", " | Views share data:\n", " | \n", " | >>> x[0] = (9, 10)\n", " | >>> z[0]\n", " | (9, 10)\n", " | \n", " | Views that change the dtype size (bytes per entry) should normally be\n", " | avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n", " | \n", " | >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n", " | >>> y = x[:, 0:2]\n", " | >>> y\n", " | array([[1, 2],\n", " | [4, 5]], dtype=int16)\n", " | >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: new type not compatible with array.\n", " | >>> z = y.copy()\n", " | >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n", " | array([[(1, 2)],\n", " | [(4, 5)]], dtype=[('width', '>> x = np.array([1,2,3,4])\n", " | >>> x.base is None\n", " | True\n", " | \n", " | Slicing creates a view, whose memory is shared with x:\n", " | \n", " | >>> y = x[2:]\n", " | >>> y.base is x\n", " | True\n", " | \n", " | ctypes\n", " | An object to simplify the interaction of the array with the ctypes\n", " | module.\n", " | \n", " | This attribute creates an object that makes it easier to use arrays\n", " | when calling shared libraries with the ctypes module. The returned\n", " | object has, among others, data, shape, and strides attributes (see\n", " | Notes below) which themselves return ctypes objects that can be used\n", " | as arguments to a shared library.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | c : Python object\n", " | Possessing attributes data, shape, strides, etc.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ctypeslib\n", " | \n", " | Notes\n", " | -----\n", " | Below are the public attributes of this object which were documented\n", " | in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n", " | as well as documented private attributes):\n", " | \n", " | * data: A pointer to the memory area of the array as a Python integer.\n", " | This memory area may contain data that is not aligned, or not in correct\n", " | byte-order. The memory area may not even be writeable. The array\n", " | flags and data-type of this array should be respected when passing this\n", " | attribute to arbitrary C-code to avoid trouble that can include Python\n", " | crashing. User Beware! The value of this attribute is exactly the same\n", " | as self._array_interface_['data'][0].\n", " | \n", " | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the C-integer corresponding to dtype('p') on this\n", " | platform. This base-type could be c_int, c_long, or c_longlong\n", " | depending on the platform. The c_intp type is defined accordingly in\n", " | numpy.ctypeslib. The ctypes array contains the shape of the underlying\n", " | array.\n", " | \n", " | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the same as for the shape attribute. This ctypes array\n", " | contains the strides information from the underlying array. This strides\n", " | information is important for showing how many bytes must be jumped to\n", " | get to the next element in the array.\n", " | \n", " | * data_as(obj): Return the data pointer cast to a particular c-types object.\n", " | For example, calling self._as_parameter_ is equivalent to\n", " | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a\n", " | pointer to a ctypes array of floating-point data:\n", " | self.data_as(ctypes.POINTER(ctypes.c_double)).\n", " | \n", " | * shape_as(obj): Return the shape tuple as an array of some other c-types\n", " | type. For example: self.shape_as(ctypes.c_short).\n", " | \n", " | * strides_as(obj): Return the strides tuple as an array of some other\n", " | c-types type. For example: self.strides_as(ctypes.c_longlong).\n", " | \n", " | Be careful using the ctypes attribute - especially on temporary\n", " | arrays or arrays constructed on the fly. For example, calling\n", " | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory\n", " | that is invalid because the array created as (a+b) is deallocated\n", " | before the next Python statement. You can avoid this problem using\n", " | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will\n", " | hold a reference to the array until ct is deleted or re-assigned.\n", " | \n", " | If the ctypes module is not available, then the ctypes attribute\n", " | of array objects still returns something useful, but ctypes objects\n", " | are not returned and errors may be raised instead. In particular,\n", " | the object will still have the as parameter attribute which will\n", " | return an integer equal to the data attribute.\n", " | \n", " | Examples\n", " | --------\n", " | >>> import ctypes\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.ctypes.data\n", " | 30439712\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))\n", " | \n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents\n", " | c_long(0)\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents\n", " | c_longlong(4294967296L)\n", " | >>> x.ctypes.shape\n", " | \n", " | >>> x.ctypes.shape_as(ctypes.c_long)\n", " | \n", " | >>> x.ctypes.strides\n", " | \n", " | >>> x.ctypes.strides_as(ctypes.c_longlong)\n", " | \n", " | \n", " | data\n", " | Python buffer object pointing to the start of the array's data.\n", " | \n", " | dtype\n", " | Data-type of the array's elements.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | d : numpy dtype object\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dtype\n", " | \n", " | Examples\n", " | --------\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.dtype\n", " | dtype('int32')\n", " | >>> type(x.dtype)\n", " | \n", " | \n", " | flags\n", " | Information about the memory layout of the array.\n", " | \n", " | Attributes\n", " | ----------\n", " | C_CONTIGUOUS (C)\n", " | The data is in a single, C-style contiguous segment.\n", " | F_CONTIGUOUS (F)\n", " | The data is in a single, Fortran-style contiguous segment.\n", " | OWNDATA (O)\n", " | The array owns the memory it uses or borrows it from another object.\n", " | WRITEABLE (W)\n", " | The data area can be written to. Setting this to False locks\n", " | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE\n", " | from its base array at creation time, but a view of a writeable\n", " | array may be subsequently locked while the base array remains writeable.\n", " | (The opposite is not true, in that a view of a locked array may not\n", " | be made writeable. However, currently, locking a base object does not\n", " | lock any views that already reference it, so under that circumstance it\n", " | is possible to alter the contents of a locked array via a previously\n", " | created writeable view onto it.) Attempting to change a non-writeable\n", " | array raises a RuntimeError exception.\n", " | ALIGNED (A)\n", " | The data and all elements are aligned appropriately for the hardware.\n", " | UPDATEIFCOPY (U)\n", " | This array is a copy of some other array. When this array is\n", " | deallocated, the base array will be updated with the contents of\n", " | this array.\n", " | FNC\n", " | F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | FORC\n", " | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n", " | BEHAVED (B)\n", " | ALIGNED and WRITEABLE.\n", " | CARRAY (CA)\n", " | BEHAVED and C_CONTIGUOUS.\n", " | FARRAY (FA)\n", " | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | \n", " | Notes\n", " | -----\n", " | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n", " | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n", " | names are only supported in dictionary access.\n", " | \n", " | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by\n", " | the user, via direct assignment to the attribute or dictionary entry,\n", " | or by calling `ndarray.setflags`.\n", " | \n", " | The array flags cannot be set arbitrarily:\n", " | \n", " | - UPDATEIFCOPY can only be set ``False``.\n", " | - ALIGNED can only be set ``True`` if the data is truly aligned.\n", " | - WRITEABLE can only be set ``True`` if the array owns its own memory\n", " | or the ultimate owner of the memory exposes a writeable buffer\n", " | interface or is a string.\n", " | \n", " | Arrays can be both C-style and Fortran-style contiguous simultaneously.\n", " | This is clear for 1-dimensional arrays, but can also be true for higher\n", " | dimensional arrays.\n", " | \n", " | Even for contiguous arrays a stride for a given dimension\n", " | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n", " | or the array has no elements.\n", " | It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n", " | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n", " | Fortran-style contiguous arrays is true.\n", " | \n", " | flat\n", " | A 1-D iterator over the array.\n", " | \n", " | This is a `numpy.flatiter` instance, which acts similarly to, but is not\n", " | a subclass of, Python's built-in iterator object.\n", " | \n", " | See Also\n", " | --------\n", " | flatten : Return a copy of the array collapsed into one dimension.\n", " | \n", " | flatiter\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(1, 7).reshape(2, 3)\n", " | >>> x\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | >>> x.flat[3]\n", " | 4\n", " | >>> x.T\n", " | array([[1, 4],\n", " | [2, 5],\n", " | [3, 6]])\n", " | >>> x.T.flat[3]\n", " | 5\n", " | >>> type(x.flat)\n", " | \n", " | \n", " | An assignment example:\n", " | \n", " | >>> x.flat = 3; x\n", " | array([[3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x.flat[[1,4]] = 1; x\n", " | array([[3, 1, 3],\n", " | [3, 1, 3]])\n", " | \n", " | imag\n", " | The imaginary part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.imag\n", " | array([ 0. , 0.70710678])\n", " | >>> x.imag.dtype\n", " | dtype('float64')\n", " | \n", " | itemsize\n", " | Length of one array element in bytes.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1,2,3], dtype=np.float64)\n", " | >>> x.itemsize\n", " | 8\n", " | >>> x = np.array([1,2,3], dtype=np.complex128)\n", " | >>> x.itemsize\n", " | 16\n", " | \n", " | nbytes\n", " | Total bytes consumed by the elements of the array.\n", " | \n", " | Notes\n", " | -----\n", " | Does not include memory consumed by non-element attributes of the\n", " | array object.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3,5,2), dtype=np.complex128)\n", " | >>> x.nbytes\n", " | 480\n", " | >>> np.prod(x.shape) * x.itemsize\n", " | 480\n", " | \n", " | ndim\n", " | Number of array dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> x.ndim\n", " | 1\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.ndim\n", " | 3\n", " | \n", " | real\n", " | The real part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.real\n", " | array([ 1. , 0.70710678])\n", " | >>> x.real.dtype\n", " | dtype('float64')\n", " | \n", " | See Also\n", " | --------\n", " | numpy.real : equivalent function\n", " | \n", " | shape\n", " | Tuple of array dimensions.\n", " | \n", " | Notes\n", " | -----\n", " | May be used to \"reshape\" the array, as long as this would not\n", " | require a change in the total number of elements\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3, 4])\n", " | >>> x.shape\n", " | (4,)\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.shape\n", " | (2, 3, 4)\n", " | >>> y.shape = (3, 8)\n", " | >>> y\n", " | array([[ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.]])\n", " | >>> y.shape = (3, 6)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: total size of new array must be unchanged\n", " | \n", " | size\n", " | Number of elements in the array.\n", " | \n", " | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's\n", " | dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n", " | >>> x.size\n", " | 30\n", " | >>> np.prod(x.shape)\n", " | 30\n", " | \n", " | strides\n", " | Tuple of bytes to step in each dimension when traversing an array.\n", " | \n", " | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n", " | is::\n", " | \n", " | offset = sum(np.array(i) * a.strides)\n", " | \n", " | A more detailed explanation of strides can be found in the\n", " | \"ndarray.rst\" file in the NumPy reference guide.\n", " | \n", " | Notes\n", " | -----\n", " | Imagine an array of 32-bit integers (each 4 bytes)::\n", " | \n", " | x = np.array([[0, 1, 2, 3, 4],\n", " | [5, 6, 7, 8, 9]], dtype=np.int32)\n", " | \n", " | This array is stored in memory as 40 bytes, one after the other\n", " | (known as a contiguous block of memory). The strides of an array tell\n", " | us how many bytes we have to skip in memory to move to the next position\n", " | along a certain axis. For example, we have to skip 4 bytes (1 value) to\n", " | move to the next column, but 20 bytes (5 values) to get to the same\n", " | position in the next row. As such, the strides for the array `x` will be\n", " | ``(20, 4)``.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.lib.stride_tricks.as_strided\n", " | \n", " | Examples\n", " | --------\n", " | >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n", " | >>> y\n", " | array([[[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]],\n", " | [[12, 13, 14, 15],\n", " | [16, 17, 18, 19],\n", " | [20, 21, 22, 23]]])\n", " | >>> y.strides\n", " | (48, 16, 4)\n", " | >>> y[1,1,1]\n", " | 17\n", " | >>> offset=sum(y.strides * np.array((1,1,1)))\n", " | >>> offset/y.itemsize\n", " | 17\n", " | \n", " | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n", " | >>> x.strides\n", " | (32, 4, 224, 1344)\n", " | >>> i = np.array([3,5,2,2])\n", " | >>> offset = sum(i * x.strides)\n", " | >>> x[3,5,2,2]\n", " | 813\n", " | >>> offset / x.itemsize\n", " | 813\n", " \n", " class memmap(numpy.ndarray)\n", " | Create a memory-map to an array stored in a *binary* file on disk.\n", " | \n", " | Memory-mapped files are used for accessing small segments of large files\n", " | on disk, without reading the entire file into memory. Numpy's\n", " | memmap's are array-like objects. This differs from Python's ``mmap``\n", " | module, which uses file-like objects.\n", " | \n", " | This subclass of ndarray has some unpleasant interactions with\n", " | some operations, because it doesn't quite fit properly as a subclass.\n", " | An alternative to using this subclass is to create the ``mmap``\n", " | object yourself, then create an ndarray with ndarray.__new__ directly,\n", " | passing the object created in its 'buffer=' parameter.\n", " | \n", " | This class may at some point be turned into a factory function\n", " | which returns a view into an mmap buffer.\n", " | \n", " | Delete the memmap instance to close.\n", " | \n", " | \n", " | Parameters\n", " | ----------\n", " | filename : str or file-like object\n", " | The file name or file object to be used as the array data buffer.\n", " | dtype : data-type, optional\n", " | The data-type used to interpret the file contents.\n", " | Default is `uint8`.\n", " | mode : {'r+', 'r', 'w+', 'c'}, optional\n", " | The file is opened in this mode:\n", " | \n", " | +------+-------------------------------------------------------------+\n", " | | 'r' | Open existing file for reading only. |\n", " | +------+-------------------------------------------------------------+\n", " | | 'r+' | Open existing file for reading and writing. |\n", " | +------+-------------------------------------------------------------+\n", " | | 'w+' | Create or overwrite existing file for reading and writing. |\n", " | +------+-------------------------------------------------------------+\n", " | | 'c' | Copy-on-write: assignments affect data in memory, but |\n", " | | | changes are not saved to disk. The file on disk is |\n", " | | | read-only. |\n", " | +------+-------------------------------------------------------------+\n", " | \n", " | Default is 'r+'.\n", " | offset : int, optional\n", " | In the file, array data starts at this offset. Since `offset` is\n", " | measured in bytes, it should normally be a multiple of the byte-size\n", " | of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of\n", " | file are valid; The file will be extended to accommodate the\n", " | additional data. By default, ``memmap`` will start at the beginning of\n", " | the file, even if ``filename`` is a file pointer ``fp`` and\n", " | ``fp.tell() != 0``.\n", " | shape : tuple, optional\n", " | The desired shape of the array. If ``mode == 'r'`` and the number\n", " | of remaining bytes after `offset` is not a multiple of the byte-size\n", " | of `dtype`, you must specify `shape`. By default, the returned array\n", " | will be 1-D with the number of elements determined by file size\n", " | and data-type.\n", " | order : {'C', 'F'}, optional\n", " | Specify the order of the ndarray memory layout: C (row-major) or\n", " | Fortran (column-major). This only has an effect if the shape is\n", " | greater than 1-D. The default order is 'C'.\n", " | \n", " | Attributes\n", " | ----------\n", " | filename : str\n", " | Path to the mapped file.\n", " | offset : int\n", " | Offset position in the file.\n", " | mode : str\n", " | File mode.\n", " | \n", " | Methods\n", " | -------\n", " | flush\n", " | Flush any changes in memory to file on disk.\n", " | When you delete a memmap object, flush is called first to write\n", " | changes to disk before removing the object.\n", " | \n", " | \n", " | Notes\n", " | -----\n", " | The memmap object can be used anywhere an ndarray is accepted.\n", " | Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns\n", " | ``True``.\n", " | \n", " | Memory-mapped arrays use the Python memory-map object which\n", " | (prior to Python 2.5) does not allow files to be larger than a\n", " | certain size depending on the platform. This size is always < 2GB\n", " | even on 64-bit systems.\n", " | \n", " | Examples\n", " | --------\n", " | >>> data = np.arange(12, dtype='float32')\n", " | >>> data.resize((3,4))\n", " | \n", " | This example uses a temporary file so that doctest doesn't write\n", " | files to your directory. You would use a 'normal' filename.\n", " | \n", " | >>> from tempfile import mkdtemp\n", " | >>> import os.path as path\n", " | >>> filename = path.join(mkdtemp(), 'newfile.dat')\n", " | \n", " | Create a memmap with dtype and shape that matches our data:\n", " | \n", " | >>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))\n", " | >>> fp\n", " | memmap([[ 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0.]], dtype=float32)\n", " | \n", " | Write data to memmap array:\n", " | \n", " | >>> fp[:] = data[:]\n", " | >>> fp\n", " | memmap([[ 0., 1., 2., 3.],\n", " | [ 4., 5., 6., 7.],\n", " | [ 8., 9., 10., 11.]], dtype=float32)\n", " | \n", " | >>> fp.filename == path.abspath(filename)\n", " | True\n", " | \n", " | Deletion flushes memory changes to disk before removing the object:\n", " | \n", " | >>> del fp\n", " | \n", " | Load the memmap and verify data was stored:\n", " | \n", " | >>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))\n", " | >>> newfp\n", " | memmap([[ 0., 1., 2., 3.],\n", " | [ 4., 5., 6., 7.],\n", " | [ 8., 9., 10., 11.]], dtype=float32)\n", " | \n", " | Read-only memmap:\n", " | \n", " | >>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))\n", " | >>> fpr.flags.writeable\n", " | False\n", " | \n", " | Copy-on-write memmap:\n", " | \n", " | >>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))\n", " | >>> fpc.flags.writeable\n", " | True\n", " | \n", " | It's possible to assign to copy-on-write array, but values are only\n", " | written into the memory copy of the array, and not written to disk:\n", " | \n", " | >>> fpc\n", " | memmap([[ 0., 1., 2., 3.],\n", " | [ 4., 5., 6., 7.],\n", " | [ 8., 9., 10., 11.]], dtype=float32)\n", " | >>> fpc[0,:] = 0\n", " | >>> fpc\n", " | memmap([[ 0., 0., 0., 0.],\n", " | [ 4., 5., 6., 7.],\n", " | [ 8., 9., 10., 11.]], dtype=float32)\n", " | \n", " | File on disk is unchanged:\n", " | \n", " | >>> fpr\n", " | memmap([[ 0., 1., 2., 3.],\n", " | [ 4., 5., 6., 7.],\n", " | [ 8., 9., 10., 11.]], dtype=float32)\n", " | \n", " | Offset into a memmap:\n", " | \n", " | >>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)\n", " | >>> fpo\n", " | memmap([ 4., 5., 6., 7., 8., 9., 10., 11.], dtype=float32)\n", " | \n", " | Method resolution order:\n", " | memmap\n", " | numpy.ndarray\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __array_finalize__(self, obj)\n", " | \n", " | flush(self)\n", " | Write any changes in the array to the file on disk.\n", " | \n", " | For further information, see `memmap`.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | See Also\n", " | --------\n", " | memmap\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(subtype, filename, dtype=, mode='r+', offset=0, shape=None, order='C')\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __array_priority__ = -100.0\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from numpy.ndarray:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | a.__array__(|dtype) -> reference if type unchanged, copy otherwise.\n", " | \n", " | Returns either a new reference to self if dtype is not given or a new array\n", " | of provided data type if dtype is different from the current dtype of the\n", " | array.\n", " | \n", " | __array_prepare__(...)\n", " | a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n", " | \n", " | __array_wrap__(...)\n", " | a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __copy__(...)\n", " | a.__copy__([order])\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | If order is 'C' (False) then the result is contiguous (default).\n", " | If order is 'Fortran' (True) then the result has fortran order.\n", " | If order is 'Any' (None) then the result has fortran order\n", " | only if the array already is in fortran order.\n", " | \n", " | __deepcopy__(...)\n", " | a.__deepcopy__() -> Deep copy of array.\n", " | \n", " | Used if copy.deepcopy is called on an array.\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __delslice__(...)\n", " | x.__delslice__(i, j) <==> del x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __iadd__(...)\n", " | x.__iadd__(y) <==> x+=y\n", " | \n", " | __iand__(...)\n", " | x.__iand__(y) <==> x&=y\n", " | \n", " | __idiv__(...)\n", " | x.__idiv__(y) <==> x/=y\n", " | \n", " | __ifloordiv__(...)\n", " | x.__ifloordiv__(y) <==> x//y\n", " | \n", " | __ilshift__(...)\n", " | x.__ilshift__(y) <==> x<<=y\n", " | \n", " | __imod__(...)\n", " | x.__imod__(y) <==> x%=y\n", " | \n", " | __imul__(...)\n", " | x.__imul__(y) <==> x*=y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __ior__(...)\n", " | x.__ior__(y) <==> x|=y\n", " | \n", " | __ipow__(...)\n", " | x.__ipow__(y) <==> x**=y\n", " | \n", " | __irshift__(...)\n", " | x.__irshift__(y) <==> x>>=y\n", " | \n", " | __isub__(...)\n", " | x.__isub__(y) <==> x-=y\n", " | \n", " | __iter__(...)\n", " | x.__iter__() <==> iter(x)\n", " | \n", " | __itruediv__(...)\n", " | x.__itruediv__(y) <==> x/y\n", " | \n", " | __ixor__(...)\n", " | x.__ixor__(y) <==> x^=y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | a.__reduce__()\n", " | \n", " | For pickling.\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | __setslice__(...)\n", " | x.__setslice__(i, j, y) <==> x[i:j]=y\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __setstate__(...)\n", " | a.__setstate__(version, shape, dtype, isfortran, rawdata)\n", " | \n", " | For unpickling.\n", " | \n", " | Parameters\n", " | ----------\n", " | version : int\n", " | optional pickle version. If omitted defaults to 0.\n", " | shape : tuple\n", " | dtype : data-type\n", " | isFortran : bool\n", " | rawdata : string or list\n", " | a binary string with the data (or a list if 'a' is an object array)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | a.all(axis=None, out=None)\n", " | \n", " | Returns True if all elements evaluate to True.\n", " | \n", " | Refer to `numpy.all` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.all : equivalent function\n", " | \n", " | any(...)\n", " | a.any(axis=None, out=None)\n", " | \n", " | Returns True if any of the elements of `a` evaluate to True.\n", " | \n", " | Refer to `numpy.any` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.any : equivalent function\n", " | \n", " | argmax(...)\n", " | a.argmax(axis=None, out=None)\n", " | \n", " | Return indices of the maximum values along the given axis.\n", " | \n", " | Refer to `numpy.argmax` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmax : equivalent function\n", " | \n", " | argmin(...)\n", " | a.argmin(axis=None, out=None)\n", " | \n", " | Return indices of the minimum values along the given axis of `a`.\n", " | \n", " | Refer to `numpy.argmin` for detailed documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmin : equivalent function\n", " | \n", " | argpartition(...)\n", " | a.argpartition(kth, axis=-1, kind='introselect', order=None)\n", " | \n", " | Returns the indices that would partition this array.\n", " | \n", " | Refer to `numpy.argpartition` for full documentation.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argpartition : equivalent function\n", " | \n", " | argsort(...)\n", " | a.argsort(axis=-1, kind='quicksort', order=None)\n", " | \n", " | Returns the indices that would sort this array.\n", " | \n", " | Refer to `numpy.argsort` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argsort : equivalent function\n", " | \n", " | astype(...)\n", " | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n", " | \n", " | Copy of the array, cast to a specified type.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | Typecode or data-type to which the array is cast.\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout order of the result.\n", " | 'C' means C order, 'F' means Fortran order, 'A'\n", " | means 'F' order if all the arrays are Fortran contiguous,\n", " | 'C' order otherwise, and 'K' means as close to the\n", " | order the array elements appear in memory as possible.\n", " | Default is 'K'.\n", " | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n", " | Controls what kind of data casting may occur. Defaults to 'unsafe'\n", " | for backwards compatibility.\n", " | \n", " | * 'no' means the data types should not be cast at all.\n", " | * 'equiv' means only byte-order changes are allowed.\n", " | * 'safe' means only casts which can preserve values are allowed.\n", " | * 'same_kind' means only safe casts or casts within a kind,\n", " | like float64 to float32, are allowed.\n", " | * 'unsafe' means any data conversions may be done.\n", " | subok : bool, optional\n", " | If True, then sub-classes will be passed-through (default), otherwise\n", " | the returned array will be forced to be a base-class array.\n", " | copy : bool, optional\n", " | By default, astype always returns a newly allocated array. If this\n", " | is set to false, and the `dtype`, `order`, and `subok`\n", " | requirements are satisfied, the input array is returned instead\n", " | of a copy.\n", " | \n", " | Returns\n", " | -------\n", " | arr_t : ndarray\n", " | Unless `copy` is False and the other conditions for returning the input\n", " | array are satisfied (see description for `copy` input paramter), `arr_t`\n", " | is a new array of the same shape as the input array, with dtype, order\n", " | given by `dtype`, `order`.\n", " | \n", " | Notes\n", " | -----\n", " | Starting in NumPy 1.9, astype method now returns an error if the string\n", " | dtype to cast to is not long enough in 'safe' casting mode to hold the max\n", " | value of integer/float array that is being casted. Previously the casting\n", " | was allowed even if the result was truncated.\n", " | \n", " | Raises\n", " | ------\n", " | ComplexWarning\n", " | When casting from complex to float or int. To avoid this,\n", " | one should use ``a.real.astype(t)``.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 2.5])\n", " | >>> x\n", " | array([ 1. , 2. , 2.5])\n", " | \n", " | >>> x.astype(int)\n", " | array([1, 2, 2])\n", " | \n", " | byteswap(...)\n", " | a.byteswap(inplace)\n", " | \n", " | Swap the bytes of the array elements\n", " | \n", " | Toggle between low-endian and big-endian data representation by\n", " | returning a byteswapped array, optionally swapped in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | inplace : bool, optional\n", " | If ``True``, swap bytes in-place, default is ``False``.\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | The byteswapped array. If `inplace` is ``True``, this is\n", " | a view to self.\n", " | \n", " | Examples\n", " | --------\n", " | >>> A = np.array([1, 256, 8755], dtype=np.int16)\n", " | >>> map(hex, A)\n", " | ['0x1', '0x100', '0x2233']\n", " | >>> A.byteswap(True)\n", " | array([ 256, 1, 13090], dtype=int16)\n", " | >>> map(hex, A)\n", " | ['0x100', '0x1', '0x3322']\n", " | \n", " | Arrays of strings are not swapped\n", " | \n", " | >>> A = np.array(['ceg', 'fac'])\n", " | >>> A.byteswap()\n", " | array(['ceg', 'fac'],\n", " | dtype='|S3')\n", " | \n", " | choose(...)\n", " | a.choose(choices, out=None, mode='raise')\n", " | \n", " | Use an index array to construct a new array from a set of choices.\n", " | \n", " | Refer to `numpy.choose` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.choose : equivalent function\n", " | \n", " | clip(...)\n", " | a.clip(a_min, a_max, out=None)\n", " | \n", " | Return an array whose values are limited to ``[a_min, a_max]``.\n", " | \n", " | Refer to `numpy.clip` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.clip : equivalent function\n", " | \n", " | compress(...)\n", " | a.compress(condition, axis=None, out=None)\n", " | \n", " | Return selected slices of this array along given axis.\n", " | \n", " | Refer to `numpy.compress` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.compress : equivalent function\n", " | \n", " | conj(...)\n", " | a.conj()\n", " | \n", " | Complex-conjugate all elements.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | conjugate(...)\n", " | a.conjugate()\n", " | \n", " | Return the complex conjugate, element-wise.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | copy(...)\n", " | a.copy(order='C')\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout of the copy. 'C' means C-order,\n", " | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n", " | 'C' otherwise. 'K' means match the layout of `a` as closely\n", " | as possible. (Note that this function and :func:numpy.copy are very\n", " | similar, but have different default values for their order=\n", " | arguments.)\n", " | \n", " | See also\n", " | --------\n", " | numpy.copy\n", " | numpy.copyto\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n", " | \n", " | >>> y = x.copy()\n", " | \n", " | >>> x.fill(0)\n", " | \n", " | >>> x\n", " | array([[0, 0, 0],\n", " | [0, 0, 0]])\n", " | \n", " | >>> y\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | \n", " | >>> y.flags['C_CONTIGUOUS']\n", " | True\n", " | \n", " | cumprod(...)\n", " | a.cumprod(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative product of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumprod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumprod : equivalent function\n", " | \n", " | cumsum(...)\n", " | a.cumsum(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative sum of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumsum` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumsum : equivalent function\n", " | \n", " | diagonal(...)\n", " | a.diagonal(offset=0, axis1=0, axis2=1)\n", " | \n", " | Return specified diagonals. In NumPy 1.9 the returned array is a\n", " | read-only view instead of a copy as in previous NumPy versions. In\n", " | NumPy 1.10 the read-only restriction will be removed.\n", " | \n", " | Refer to :func:`numpy.diagonal` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.diagonal : equivalent function\n", " | \n", " | dot(...)\n", " | a.dot(b, out=None)\n", " | \n", " | Dot product of two arrays.\n", " | \n", " | Refer to `numpy.dot` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dot : equivalent function\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.eye(2)\n", " | >>> b = np.ones((2, 2)) * 2\n", " | >>> a.dot(b)\n", " | array([[ 2., 2.],\n", " | [ 2., 2.]])\n", " | \n", " | This array method can be conveniently chained:\n", " | \n", " | >>> a.dot(b).dot(b)\n", " | array([[ 8., 8.],\n", " | [ 8., 8.]])\n", " | \n", " | dump(...)\n", " | a.dump(file)\n", " | \n", " | Dump a pickle of the array to the specified file.\n", " | The array can be read back with pickle.load or numpy.load.\n", " | \n", " | Parameters\n", " | ----------\n", " | file : str\n", " | A string naming the dump file.\n", " | \n", " | dumps(...)\n", " | a.dumps()\n", " | \n", " | Returns the pickle of the array as a string.\n", " | pickle.loads or numpy.loads will convert the string back to an array.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | fill(...)\n", " | a.fill(value)\n", " | \n", " | Fill the array with a scalar value.\n", " | \n", " | Parameters\n", " | ----------\n", " | value : scalar\n", " | All elements of `a` will be assigned this value.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([1, 2])\n", " | >>> a.fill(0)\n", " | >>> a\n", " | array([0, 0])\n", " | >>> a = np.empty(2)\n", " | >>> a.fill(1)\n", " | >>> a\n", " | array([ 1., 1.])\n", " | \n", " | flatten(...)\n", " | a.flatten(order='C')\n", " | \n", " | Return a copy of the array collapsed into one dimension.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | Whether to flatten in C (row-major), Fortran (column-major) order,\n", " | or preserve the C/Fortran ordering from `a`.\n", " | The default is 'C'.\n", " | \n", " | Returns\n", " | -------\n", " | y : ndarray\n", " | A copy of the input array, flattened to one dimension.\n", " | \n", " | See Also\n", " | --------\n", " | ravel : Return a flattened array.\n", " | flat : A 1-D flat iterator over the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,2], [3,4]])\n", " | >>> a.flatten()\n", " | array([1, 2, 3, 4])\n", " | >>> a.flatten('F')\n", " | array([1, 3, 2, 4])\n", " | \n", " | getfield(...)\n", " | a.getfield(dtype, offset=0)\n", " | \n", " | Returns a field of the given array as a certain type.\n", " | \n", " | A field is a view of the array data with a given data-type. The values in\n", " | the view are determined by the given type and the offset into the current\n", " | array in bytes. The offset needs to be such that the view dtype fits in the\n", " | array dtype; for example an array of dtype complex128 has 16-byte elements.\n", " | If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n", " | between 0 and 12 bytes.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | The data type of the view. The dtype size of the view can not be larger\n", " | than that of the array itself.\n", " | offset : int\n", " | Number of bytes to skip before beginning the element view.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.diag([1.+1.j]*2)\n", " | >>> x[1, 1] = 2 + 4.j\n", " | >>> x\n", " | array([[ 1.+1.j, 0.+0.j],\n", " | [ 0.+0.j, 2.+4.j]])\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0.],\n", " | [ 0., 2.]])\n", " | \n", " | By choosing an offset of 8 bytes we can select the complex part of the\n", " | array for our view:\n", " | \n", " | >>> x.getfield(np.float64, offset=8)\n", " | array([[ 1., 0.],\n", " | [ 0., 4.]])\n", " | \n", " | item(...)\n", " | a.item(*args)\n", " | \n", " | Copy an element of an array to a standard Python scalar and return it.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments (variable number and type)\n", " | \n", " | * none: in this case, the method only works for arrays\n", " | with one element (`a.size == 1`), which element is\n", " | copied into a standard Python scalar object and returned.\n", " | \n", " | * int_type: this argument is interpreted as a flat index into\n", " | the array, specifying which element to copy and return.\n", " | \n", " | * tuple of int_types: functions as does a single int_type argument,\n", " | except that the argument is interpreted as an nd-index into the\n", " | array.\n", " | \n", " | Returns\n", " | -------\n", " | z : Standard Python scalar object\n", " | A copy of the specified element of the array as a suitable\n", " | Python scalar\n", " | \n", " | Notes\n", " | -----\n", " | When the data type of `a` is longdouble or clongdouble, item() returns\n", " | a scalar array object because there is no available Python scalar that\n", " | would not lose information. Void arrays return a buffer object for item(),\n", " | unless fields are defined, in which case a tuple is returned.\n", " | \n", " | `item` is very similar to a[args], except, instead of an array scalar,\n", " | a standard Python scalar is returned. This can be useful for speeding up\n", " | access to elements of the array and doing arithmetic on elements of the\n", " | array using Python's optimized math.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.item(3)\n", " | 2\n", " | >>> x.item(7)\n", " | 5\n", " | >>> x.item((0, 1))\n", " | 1\n", " | >>> x.item((2, 2))\n", " | 3\n", " | \n", " | itemset(...)\n", " | a.itemset(*args)\n", " | \n", " | Insert scalar into an array (scalar is cast to array's dtype, if possible)\n", " | \n", " | There must be at least 1 argument, and define the last argument\n", " | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster\n", " | than ``a[args] = item``. The item should be a scalar value and `args`\n", " | must select a single item in the array `a`.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments\n", " | If one argument: a scalar, only used in case `a` is of size 1.\n", " | If two arguments: the last argument is the value to be set\n", " | and must be a scalar, the first argument specifies a single array\n", " | element location. It is either an int or a tuple.\n", " | \n", " | Notes\n", " | -----\n", " | Compared to indexing syntax, `itemset` provides some speed increase\n", " | for placing a scalar into a particular location in an `ndarray`,\n", " | if you must do this. However, generally this is discouraged:\n", " | among other problems, it complicates the appearance of the code.\n", " | Also, when using `itemset` (and `item`) inside a loop, be sure\n", " | to assign the methods to a local variable to avoid the attribute\n", " | look-up at each loop iteration.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.itemset(4, 0)\n", " | >>> x.itemset((2, 2), 9)\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 0, 3],\n", " | [8, 5, 9]])\n", " | \n", " | max(...)\n", " | a.max(axis=None, out=None)\n", " | \n", " | Return the maximum along a given axis.\n", " | \n", " | Refer to `numpy.amax` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.amax : equivalent function\n", " | \n", " | mean(...)\n", " | a.mean(axis=None, dtype=None, out=None)\n", " | \n", " | Returns the average of the array elements along given axis.\n", " | \n", " | Refer to `numpy.mean` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.mean : equivalent function\n", " | \n", " | min(...)\n", " | a.min(axis=None, out=None)\n", " | \n", " | Return the minimum along a given axis.\n", " | \n", " | Refer to `numpy.amin` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.amin : equivalent function\n", " | \n", " | newbyteorder(...)\n", " | arr.newbyteorder(new_order='S')\n", " | \n", " | Return the array with the same data viewed with a different byte order.\n", " | \n", " | Equivalent to::\n", " | \n", " | arr.view(arr.dtype.newbytorder(new_order))\n", " | \n", " | Changes are also made in all fields and sub-arrays of the array data\n", " | type.\n", " | \n", " | \n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : string, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. `new_order` codes can be any of::\n", " | \n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_arr : array\n", " | New array object with the dtype reflecting given change to the\n", " | byte order.\n", " | \n", " | nonzero(...)\n", " | a.nonzero()\n", " | \n", " | Return the indices of the elements that are non-zero.\n", " | \n", " | Refer to `numpy.nonzero` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.nonzero : equivalent function\n", " | \n", " | partition(...)\n", " | a.partition(kth, axis=-1, kind='introselect', order=None)\n", " | \n", " | Rearranges the elements in the array in such a way that value of the\n", " | element in kth position is in the position it would be in a sorted array.\n", " | All elements smaller than the kth element are moved before this element and\n", " | all equal or greater are moved behind it. The ordering of the elements in\n", " | the two partitions is undefined.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | Parameters\n", " | ----------\n", " | kth : int or sequence of ints\n", " | Element index to partition by. The kth element value will be in its\n", " | final sorted position and all smaller elements will be moved before it\n", " | and all equal or greater elements behind it.\n", " | The order all elements in the partitions is undefined.\n", " | If provided with a sequence of kth it will partition all elements\n", " | indexed by kth of them into their sorted position at once.\n", " | axis : int, optional\n", " | Axis along which to sort. Default is -1, which means sort along the\n", " | last axis.\n", " | kind : {'introselect'}, optional\n", " | Selection algorithm. Default is 'introselect'.\n", " | order : list, optional\n", " | When `a` is an array with fields defined, this argument specifies\n", " | which fields to compare first, second, etc. Not all fields need be\n", " | specified.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.partition : Return a parititioned copy of an array.\n", " | argpartition : Indirect partition.\n", " | sort : Full sort.\n", " | \n", " | Notes\n", " | -----\n", " | See ``np.partition`` for notes on the different algorithms.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([3, 4, 2, 1])\n", " | >>> a.partition(a, 3)\n", " | >>> a\n", " | array([2, 1, 3, 4])\n", " | \n", " | >>> a.partition((1, 3))\n", " | array([1, 2, 3, 4])\n", " | \n", " | prod(...)\n", " | a.prod(axis=None, dtype=None, out=None)\n", " | \n", " | Return the product of the array elements over the given axis\n", " | \n", " | Refer to `numpy.prod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.prod : equivalent function\n", " | \n", " | ptp(...)\n", " | a.ptp(axis=None, out=None)\n", " | \n", " | Peak to peak (maximum - minimum) value along a given axis.\n", " | \n", " | Refer to `numpy.ptp` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ptp : equivalent function\n", " | \n", " | put(...)\n", " | a.put(indices, values, mode='raise')\n", " | \n", " | Set ``a.flat[n] = values[n]`` for all `n` in indices.\n", " | \n", " | Refer to `numpy.put` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.put : equivalent function\n", " | \n", " | ravel(...)\n", " | a.ravel([order])\n", " | \n", " | Return a flattened array.\n", " | \n", " | Refer to `numpy.ravel` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ravel : equivalent function\n", " | \n", " | ndarray.flat : a flat iterator on the array.\n", " | \n", " | repeat(...)\n", " | a.repeat(repeats, axis=None)\n", " | \n", " | Repeat elements of an array.\n", " | \n", " | Refer to `numpy.repeat` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.repeat : equivalent function\n", " | \n", " | reshape(...)\n", " | a.reshape(shape, order='C')\n", " | \n", " | Returns an array containing the same data with a new shape.\n", " | \n", " | Refer to `numpy.reshape` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.reshape : equivalent function\n", " | \n", " | resize(...)\n", " | a.resize(new_shape, refcheck=True)\n", " | \n", " | Change shape and size of array in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | new_shape : tuple of ints, or `n` ints\n", " | Shape of resized array.\n", " | refcheck : bool, optional\n", " | If False, reference count will not be checked. Default is True.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | Raises\n", " | ------\n", " | ValueError\n", " | If `a` does not own its own data or references or views to it exist,\n", " | and the data memory must be changed.\n", " | \n", " | SystemError\n", " | If the `order` keyword argument is specified. This behaviour is a\n", " | bug in NumPy.\n", " | \n", " | See Also\n", " | --------\n", " | resize : Return a new array with the specified shape.\n", " | \n", " | Notes\n", " | -----\n", " | This reallocates space for the data area if necessary.\n", " | \n", " | Only contiguous arrays (data elements consecutive in memory) can be\n", " | resized.\n", " | \n", " | The purpose of the reference count check is to make sure you\n", " | do not use this array as a buffer for another Python object and then\n", " | reallocate the memory. However, reference counts can increase in\n", " | other ways so if you are sure that you have not shared the memory\n", " | for this array with another Python object, then you may safely set\n", " | `refcheck` to False.\n", " | \n", " | Examples\n", " | --------\n", " | Shrinking an array: array is flattened (in the order that the data are\n", " | stored in memory), resized, and reshaped:\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='C')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [1]])\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='F')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [2]])\n", " | \n", " | Enlarging an array: as above, but missing entries are filled with zeros:\n", " | \n", " | >>> b = np.array([[0, 1], [2, 3]])\n", " | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n", " | >>> b\n", " | array([[0, 1, 2],\n", " | [3, 0, 0]])\n", " | \n", " | Referencing an array prevents resizing...\n", " | \n", " | >>> c = a\n", " | >>> a.resize((1, 1))\n", " | Traceback (most recent call last):\n", " | ...\n", " | ValueError: cannot resize an array that has been referenced ...\n", " | \n", " | Unless `refcheck` is False:\n", " | \n", " | >>> a.resize((1, 1), refcheck=False)\n", " | >>> a\n", " | array([[0]])\n", " | >>> c\n", " | array([[0]])\n", " | \n", " | round(...)\n", " | a.round(decimals=0, out=None)\n", " | \n", " | Return `a` with each element rounded to the given number of decimals.\n", " | \n", " | Refer to `numpy.around` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.around : equivalent function\n", " | \n", " | searchsorted(...)\n", " | a.searchsorted(v, side='left', sorter=None)\n", " | \n", " | Find indices where elements of v should be inserted in a to maintain order.\n", " | \n", " | For full documentation, see `numpy.searchsorted`\n", " | \n", " | See Also\n", " | --------\n", " | numpy.searchsorted : equivalent function\n", " | \n", " | setfield(...)\n", " | a.setfield(val, dtype, offset=0)\n", " | \n", " | Put a value into a specified place in a field defined by a data-type.\n", " | \n", " | Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n", " | bytes into the field.\n", " | \n", " | Parameters\n", " | ----------\n", " | val : object\n", " | Value to be placed in field.\n", " | dtype : dtype object\n", " | Data-type of the field in which to place `val`.\n", " | offset : int, optional\n", " | The number of bytes into the field at which to place `val`.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | See Also\n", " | --------\n", " | getfield\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.eye(3)\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | >>> x.setfield(3, np.int32)\n", " | >>> x.getfield(np.int32)\n", " | array([[3, 3, 3],\n", " | [3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x\n", " | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]])\n", " | >>> x.setfield(np.eye(3), np.int32)\n", " | >>> x\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | \n", " | setflags(...)\n", " | a.setflags(write=None, align=None, uic=None)\n", " | \n", " | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.\n", " | \n", " | These Boolean-valued flags affect how numpy interprets the memory\n", " | area used by `a` (see Notes below). The ALIGNED flag can only\n", " | be set to True if the data is actually aligned according to the type.\n", " | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE\n", " | can only be set to True if the array owns its own memory, or the\n", " | ultimate owner of the memory exposes a writeable buffer interface,\n", " | or is a string. (The exception for string is made so that unpickling\n", " | can be done without copying memory.)\n", " | \n", " | Parameters\n", " | ----------\n", " | write : bool, optional\n", " | Describes whether or not `a` can be written to.\n", " | align : bool, optional\n", " | Describes whether or not `a` is aligned properly for its type.\n", " | uic : bool, optional\n", " | Describes whether or not `a` is a copy of another \"base\" array.\n", " | \n", " | Notes\n", " | -----\n", " | Array flags provide information about how the memory area used\n", " | for the array is to be interpreted. There are 6 Boolean flags\n", " | in use, only three of which can be changed by the user:\n", " | UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n", " | \n", " | WRITEABLE (W) the data area can be written to;\n", " | \n", " | ALIGNED (A) the data and strides are aligned appropriately for the hardware\n", " | (as determined by the compiler);\n", " | \n", " | UPDATEIFCOPY (U) this array is a copy of some other array (referenced\n", " | by .base). When this array is deallocated, the base array will be\n", " | updated with the contents of this array.\n", " | \n", " | All flags can be accessed using their first (upper case) letter as well\n", " | as the full name.\n", " | \n", " | Examples\n", " | --------\n", " | >>> y\n", " | array([[3, 1, 7],\n", " | [2, 0, 0],\n", " | [8, 5, 9]])\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : True\n", " | ALIGNED : True\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(write=0, align=0)\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : False\n", " | ALIGNED : False\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(uic=1)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: cannot set UPDATEIFCOPY flag to True\n", " | \n", " | sort(...)\n", " | a.sort(axis=-1, kind='quicksort', order=None)\n", " | \n", " | Sort an array, in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | axis : int, optional\n", " | Axis along which to sort. Default is -1, which means sort along the\n", " | last axis.\n", " | kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n", " | Sorting algorithm. Default is 'quicksort'.\n", " | order : list, optional\n", " | When `a` is an array with fields defined, this argument specifies\n", " | which fields to compare first, second, etc. Not all fields need be\n", " | specified.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.sort : Return a sorted copy of an array.\n", " | argsort : Indirect sort.\n", " | lexsort : Indirect stable sort on multiple keys.\n", " | searchsorted : Find elements in sorted array.\n", " | partition: Partial sort.\n", " | \n", " | Notes\n", " | -----\n", " | See ``sort`` for notes on the different sorting algorithms.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,4], [3,1]])\n", " | >>> a.sort(axis=1)\n", " | >>> a\n", " | array([[1, 4],\n", " | [1, 3]])\n", " | >>> a.sort(axis=0)\n", " | >>> a\n", " | array([[1, 3],\n", " | [1, 4]])\n", " | \n", " | Use the `order` keyword to specify a field to use when sorting a\n", " | structured array:\n", " | \n", " | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n", " | >>> a.sort(order='y')\n", " | >>> a\n", " | array([('c', 1), ('a', 2)],\n", " | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | tofile(...)\n", " | a.tofile(fid, sep=\"\", format=\"%s\")\n", " | \n", " | Write array to a file as text or binary (default).\n", " | \n", " | Data is always written in 'C' order, independent of the order of `a`.\n", " | The data produced by this method can be recovered using the function\n", " | fromfile().\n", " | \n", " | Parameters\n", " | ----------\n", " | fid : file or str\n", " | An open file object, or a string containing a filename.\n", " | sep : str\n", " | Separator between array items for text output.\n", " | If \"\" (empty), a binary file is written, equivalent to\n", " | ``file.write(a.tobytes())``.\n", " | format : str\n", " | Format string for text file output.\n", " | Each entry in the array is formatted to text by first converting\n", " | it to the closest Python type, and then using \"format\" % item.\n", " | \n", " | Notes\n", " | -----\n", " | This is a convenience function for quick storage of array data.\n", " | Information on endianness and precision is lost, so this method is not a\n", " | good choice for files intended to archive data or transport data between\n", " | machines with different endianness. Some of these problems can be overcome\n", " | by outputting the data as text files, at the expense of speed and file\n", " | size.\n", " | \n", " | tolist(...)\n", " | a.tolist()\n", " | \n", " | Return the array as a (possibly nested) list.\n", " | \n", " | Return a copy of the array data as a (nested) Python list.\n", " | Data items are converted to the nearest compatible Python type.\n", " | \n", " | Parameters\n", " | ----------\n", " | none\n", " | \n", " | Returns\n", " | -------\n", " | y : list\n", " | The possibly nested list of array elements.\n", " | \n", " | Notes\n", " | -----\n", " | The array may be recreated, ``a = np.array(a.tolist())``.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([1, 2])\n", " | >>> a.tolist()\n", " | [1, 2]\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> list(a)\n", " | [array([1, 2]), array([3, 4])]\n", " | >>> a.tolist()\n", " | [[1, 2], [3, 4]]\n", " | \n", " | tostring(...)\n", " | a.tostring(order='C')\n", " | \n", " | Construct Python bytes containing the raw data bytes in the array.\n", " | \n", " | Constructs Python bytes showing a copy of the raw contents of\n", " | data memory. The bytes object can be produced in either 'C' or 'Fortran',\n", " | or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n", " | unless the F_CONTIGUOUS flag in the array is set, in which case it\n", " | means 'Fortran' order.\n", " | \n", " | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', None}, optional\n", " | Order of the data for multidimensional arrays:\n", " | C, Fortran, or the same as for the original array.\n", " | \n", " | Returns\n", " | -------\n", " | s : bytes\n", " | Python bytes exhibiting a copy of `a`'s raw data.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | trace(...)\n", " | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n", " | \n", " | Return the sum along diagonals of the array.\n", " | \n", " | Refer to `numpy.trace` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.trace : equivalent function\n", " | \n", " | transpose(...)\n", " | a.transpose(*axes)\n", " | \n", " | Returns a view of the array with axes transposed.\n", " | \n", " | For a 1-D array, this has no effect. (To change between column and\n", " | row vectors, first cast the 1-D array into a matrix object.)\n", " | For a 2-D array, this is the usual matrix transpose.\n", " | For an n-D array, if axes are given, their order indicates how the\n", " | axes are permuted (see Examples). If axes are not provided and\n", " | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n", " | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n", " | \n", " | Parameters\n", " | ----------\n", " | axes : None, tuple of ints, or `n` ints\n", " | \n", " | * None or no argument: reverses the order of the axes.\n", " | \n", " | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n", " | `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n", " | \n", " | * `n` ints: same as an n-tuple of the same ints (this form is\n", " | intended simply as a \"convenience\" alternative to the tuple form)\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | View of `a`, with axes suitably permuted.\n", " | \n", " | See Also\n", " | --------\n", " | ndarray.T : Array property returning the array transposed.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> a\n", " | array([[1, 2],\n", " | [3, 4]])\n", " | >>> a.transpose()\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose((1, 0))\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose(1, 0)\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | \n", " | var(...)\n", " | a.var(axis=None, dtype=None, out=None, ddof=0)\n", " | \n", " | Returns the variance of the array elements, along given axis.\n", " | \n", " | Refer to `numpy.var` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.var : equivalent function\n", " | \n", " | view(...)\n", " | a.view(dtype=None, type=None)\n", " | \n", " | New view of array with the same data.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : data-type or ndarray sub-class, optional\n", " | Data-type descriptor of the returned view, e.g., float32 or int16. The\n", " | default, None, results in the view having the same data-type as `a`.\n", " | This argument can also be specified as an ndarray sub-class, which\n", " | then specifies the type of the returned object (this is equivalent to\n", " | setting the ``type`` parameter).\n", " | type : Python type, optional\n", " | Type of the returned view, e.g., ndarray or matrix. Again, the\n", " | default None results in type preservation.\n", " | \n", " | Notes\n", " | -----\n", " | ``a.view()`` is used two different ways:\n", " | \n", " | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n", " | of the array's memory with a different data-type. This can cause a\n", " | reinterpretation of the bytes of memory.\n", " | \n", " | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n", " | returns an instance of `ndarray_subclass` that looks at the same array\n", " | (same shape, dtype, etc.) This does not cause a reinterpretation of the\n", " | memory.\n", " | \n", " | For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n", " | bytes per entry than the previous dtype (for example, converting a\n", " | regular array to a structured array), then the behavior of the view\n", " | cannot be predicted just from the superficial appearance of ``a`` (shown\n", " | by ``print(a)``). It also depends on exactly how ``a`` is stored in\n", " | memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n", " | defined as a slice or transpose, etc., the view may give different\n", " | results.\n", " | \n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n", " | \n", " | Viewing array data using a different type and dtype:\n", " | \n", " | >>> y = x.view(dtype=np.int16, type=np.matrix)\n", " | >>> y\n", " | matrix([[513]], dtype=int16)\n", " | >>> print type(y)\n", " | \n", " | \n", " | Creating a view on a structured array so it can be used in calculations\n", " | \n", " | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n", " | >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n", " | >>> xv\n", " | array([[1, 2],\n", " | [3, 4]], dtype=int8)\n", " | >>> xv.mean(0)\n", " | array([ 2., 3.])\n", " | \n", " | Making changes to the view changes the underlying array\n", " | \n", " | >>> xv[0,1] = 20\n", " | >>> print x\n", " | [(1, 20) (3, 4)]\n", " | \n", " | Using a view to convert an array to a record array:\n", " | \n", " | >>> z = x.view(np.recarray)\n", " | >>> z.a\n", " | array([1], dtype=int8)\n", " | \n", " | Views share data:\n", " | \n", " | >>> x[0] = (9, 10)\n", " | >>> z[0]\n", " | (9, 10)\n", " | \n", " | Views that change the dtype size (bytes per entry) should normally be\n", " | avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n", " | \n", " | >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n", " | >>> y = x[:, 0:2]\n", " | >>> y\n", " | array([[1, 2],\n", " | [4, 5]], dtype=int16)\n", " | >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: new type not compatible with array.\n", " | >>> z = y.copy()\n", " | >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n", " | array([[(1, 2)],\n", " | [(4, 5)]], dtype=[('width', '>> x = np.array([[1.,2.],[3.,4.]])\n", " | >>> x\n", " | array([[ 1., 2.],\n", " | [ 3., 4.]])\n", " | >>> x.T\n", " | array([[ 1., 3.],\n", " | [ 2., 4.]])\n", " | >>> x = np.array([1.,2.,3.,4.])\n", " | >>> x\n", " | array([ 1., 2., 3., 4.])\n", " | >>> x.T\n", " | array([ 1., 2., 3., 4.])\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side.\n", " | \n", " | __array_struct__\n", " | Array protocol: C-struct side.\n", " | \n", " | base\n", " | Base object if memory is from some other object.\n", " | \n", " | Examples\n", " | --------\n", " | The base of an array that owns its memory is None:\n", " | \n", " | >>> x = np.array([1,2,3,4])\n", " | >>> x.base is None\n", " | True\n", " | \n", " | Slicing creates a view, whose memory is shared with x:\n", " | \n", " | >>> y = x[2:]\n", " | >>> y.base is x\n", " | True\n", " | \n", " | ctypes\n", " | An object to simplify the interaction of the array with the ctypes\n", " | module.\n", " | \n", " | This attribute creates an object that makes it easier to use arrays\n", " | when calling shared libraries with the ctypes module. The returned\n", " | object has, among others, data, shape, and strides attributes (see\n", " | Notes below) which themselves return ctypes objects that can be used\n", " | as arguments to a shared library.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | c : Python object\n", " | Possessing attributes data, shape, strides, etc.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ctypeslib\n", " | \n", " | Notes\n", " | -----\n", " | Below are the public attributes of this object which were documented\n", " | in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n", " | as well as documented private attributes):\n", " | \n", " | * data: A pointer to the memory area of the array as a Python integer.\n", " | This memory area may contain data that is not aligned, or not in correct\n", " | byte-order. The memory area may not even be writeable. The array\n", " | flags and data-type of this array should be respected when passing this\n", " | attribute to arbitrary C-code to avoid trouble that can include Python\n", " | crashing. User Beware! The value of this attribute is exactly the same\n", " | as self._array_interface_['data'][0].\n", " | \n", " | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the C-integer corresponding to dtype('p') on this\n", " | platform. This base-type could be c_int, c_long, or c_longlong\n", " | depending on the platform. The c_intp type is defined accordingly in\n", " | numpy.ctypeslib. The ctypes array contains the shape of the underlying\n", " | array.\n", " | \n", " | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the same as for the shape attribute. This ctypes array\n", " | contains the strides information from the underlying array. This strides\n", " | information is important for showing how many bytes must be jumped to\n", " | get to the next element in the array.\n", " | \n", " | * data_as(obj): Return the data pointer cast to a particular c-types object.\n", " | For example, calling self._as_parameter_ is equivalent to\n", " | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a\n", " | pointer to a ctypes array of floating-point data:\n", " | self.data_as(ctypes.POINTER(ctypes.c_double)).\n", " | \n", " | * shape_as(obj): Return the shape tuple as an array of some other c-types\n", " | type. For example: self.shape_as(ctypes.c_short).\n", " | \n", " | * strides_as(obj): Return the strides tuple as an array of some other\n", " | c-types type. For example: self.strides_as(ctypes.c_longlong).\n", " | \n", " | Be careful using the ctypes attribute - especially on temporary\n", " | arrays or arrays constructed on the fly. For example, calling\n", " | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory\n", " | that is invalid because the array created as (a+b) is deallocated\n", " | before the next Python statement. You can avoid this problem using\n", " | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will\n", " | hold a reference to the array until ct is deleted or re-assigned.\n", " | \n", " | If the ctypes module is not available, then the ctypes attribute\n", " | of array objects still returns something useful, but ctypes objects\n", " | are not returned and errors may be raised instead. In particular,\n", " | the object will still have the as parameter attribute which will\n", " | return an integer equal to the data attribute.\n", " | \n", " | Examples\n", " | --------\n", " | >>> import ctypes\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.ctypes.data\n", " | 30439712\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))\n", " | \n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents\n", " | c_long(0)\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents\n", " | c_longlong(4294967296L)\n", " | >>> x.ctypes.shape\n", " | \n", " | >>> x.ctypes.shape_as(ctypes.c_long)\n", " | \n", " | >>> x.ctypes.strides\n", " | \n", " | >>> x.ctypes.strides_as(ctypes.c_longlong)\n", " | \n", " | \n", " | data\n", " | Python buffer object pointing to the start of the array's data.\n", " | \n", " | dtype\n", " | Data-type of the array's elements.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | d : numpy dtype object\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dtype\n", " | \n", " | Examples\n", " | --------\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.dtype\n", " | dtype('int32')\n", " | >>> type(x.dtype)\n", " | \n", " | \n", " | flags\n", " | Information about the memory layout of the array.\n", " | \n", " | Attributes\n", " | ----------\n", " | C_CONTIGUOUS (C)\n", " | The data is in a single, C-style contiguous segment.\n", " | F_CONTIGUOUS (F)\n", " | The data is in a single, Fortran-style contiguous segment.\n", " | OWNDATA (O)\n", " | The array owns the memory it uses or borrows it from another object.\n", " | WRITEABLE (W)\n", " | The data area can be written to. Setting this to False locks\n", " | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE\n", " | from its base array at creation time, but a view of a writeable\n", " | array may be subsequently locked while the base array remains writeable.\n", " | (The opposite is not true, in that a view of a locked array may not\n", " | be made writeable. However, currently, locking a base object does not\n", " | lock any views that already reference it, so under that circumstance it\n", " | is possible to alter the contents of a locked array via a previously\n", " | created writeable view onto it.) Attempting to change a non-writeable\n", " | array raises a RuntimeError exception.\n", " | ALIGNED (A)\n", " | The data and all elements are aligned appropriately for the hardware.\n", " | UPDATEIFCOPY (U)\n", " | This array is a copy of some other array. When this array is\n", " | deallocated, the base array will be updated with the contents of\n", " | this array.\n", " | FNC\n", " | F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | FORC\n", " | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n", " | BEHAVED (B)\n", " | ALIGNED and WRITEABLE.\n", " | CARRAY (CA)\n", " | BEHAVED and C_CONTIGUOUS.\n", " | FARRAY (FA)\n", " | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | \n", " | Notes\n", " | -----\n", " | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n", " | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n", " | names are only supported in dictionary access.\n", " | \n", " | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by\n", " | the user, via direct assignment to the attribute or dictionary entry,\n", " | or by calling `ndarray.setflags`.\n", " | \n", " | The array flags cannot be set arbitrarily:\n", " | \n", " | - UPDATEIFCOPY can only be set ``False``.\n", " | - ALIGNED can only be set ``True`` if the data is truly aligned.\n", " | - WRITEABLE can only be set ``True`` if the array owns its own memory\n", " | or the ultimate owner of the memory exposes a writeable buffer\n", " | interface or is a string.\n", " | \n", " | Arrays can be both C-style and Fortran-style contiguous simultaneously.\n", " | This is clear for 1-dimensional arrays, but can also be true for higher\n", " | dimensional arrays.\n", " | \n", " | Even for contiguous arrays a stride for a given dimension\n", " | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n", " | or the array has no elements.\n", " | It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n", " | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n", " | Fortran-style contiguous arrays is true.\n", " | \n", " | flat\n", " | A 1-D iterator over the array.\n", " | \n", " | This is a `numpy.flatiter` instance, which acts similarly to, but is not\n", " | a subclass of, Python's built-in iterator object.\n", " | \n", " | See Also\n", " | --------\n", " | flatten : Return a copy of the array collapsed into one dimension.\n", " | \n", " | flatiter\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(1, 7).reshape(2, 3)\n", " | >>> x\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | >>> x.flat[3]\n", " | 4\n", " | >>> x.T\n", " | array([[1, 4],\n", " | [2, 5],\n", " | [3, 6]])\n", " | >>> x.T.flat[3]\n", " | 5\n", " | >>> type(x.flat)\n", " | \n", " | \n", " | An assignment example:\n", " | \n", " | >>> x.flat = 3; x\n", " | array([[3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x.flat[[1,4]] = 1; x\n", " | array([[3, 1, 3],\n", " | [3, 1, 3]])\n", " | \n", " | imag\n", " | The imaginary part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.imag\n", " | array([ 0. , 0.70710678])\n", " | >>> x.imag.dtype\n", " | dtype('float64')\n", " | \n", " | itemsize\n", " | Length of one array element in bytes.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1,2,3], dtype=np.float64)\n", " | >>> x.itemsize\n", " | 8\n", " | >>> x = np.array([1,2,3], dtype=np.complex128)\n", " | >>> x.itemsize\n", " | 16\n", " | \n", " | nbytes\n", " | Total bytes consumed by the elements of the array.\n", " | \n", " | Notes\n", " | -----\n", " | Does not include memory consumed by non-element attributes of the\n", " | array object.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3,5,2), dtype=np.complex128)\n", " | >>> x.nbytes\n", " | 480\n", " | >>> np.prod(x.shape) * x.itemsize\n", " | 480\n", " | \n", " | ndim\n", " | Number of array dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> x.ndim\n", " | 1\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.ndim\n", " | 3\n", " | \n", " | real\n", " | The real part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.real\n", " | array([ 1. , 0.70710678])\n", " | >>> x.real.dtype\n", " | dtype('float64')\n", " | \n", " | See Also\n", " | --------\n", " | numpy.real : equivalent function\n", " | \n", " | shape\n", " | Tuple of array dimensions.\n", " | \n", " | Notes\n", " | -----\n", " | May be used to \"reshape\" the array, as long as this would not\n", " | require a change in the total number of elements\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3, 4])\n", " | >>> x.shape\n", " | (4,)\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.shape\n", " | (2, 3, 4)\n", " | >>> y.shape = (3, 8)\n", " | >>> y\n", " | array([[ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.]])\n", " | >>> y.shape = (3, 6)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: total size of new array must be unchanged\n", " | \n", " | size\n", " | Number of elements in the array.\n", " | \n", " | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's\n", " | dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n", " | >>> x.size\n", " | 30\n", " | >>> np.prod(x.shape)\n", " | 30\n", " | \n", " | strides\n", " | Tuple of bytes to step in each dimension when traversing an array.\n", " | \n", " | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n", " | is::\n", " | \n", " | offset = sum(np.array(i) * a.strides)\n", " | \n", " | A more detailed explanation of strides can be found in the\n", " | \"ndarray.rst\" file in the NumPy reference guide.\n", " | \n", " | Notes\n", " | -----\n", " | Imagine an array of 32-bit integers (each 4 bytes)::\n", " | \n", " | x = np.array([[0, 1, 2, 3, 4],\n", " | [5, 6, 7, 8, 9]], dtype=np.int32)\n", " | \n", " | This array is stored in memory as 40 bytes, one after the other\n", " | (known as a contiguous block of memory). The strides of an array tell\n", " | us how many bytes we have to skip in memory to move to the next position\n", " | along a certain axis. For example, we have to skip 4 bytes (1 value) to\n", " | move to the next column, but 20 bytes (5 values) to get to the same\n", " | position in the next row. As such, the strides for the array `x` will be\n", " | ``(20, 4)``.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.lib.stride_tricks.as_strided\n", " | \n", " | Examples\n", " | --------\n", " | >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n", " | >>> y\n", " | array([[[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]],\n", " | [[12, 13, 14, 15],\n", " | [16, 17, 18, 19],\n", " | [20, 21, 22, 23]]])\n", " | >>> y.strides\n", " | (48, 16, 4)\n", " | >>> y[1,1,1]\n", " | 17\n", " | >>> offset=sum(y.strides * np.array((1,1,1)))\n", " | >>> offset/y.itemsize\n", " | 17\n", " | \n", " | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n", " | >>> x.strides\n", " | (32, 4, 224, 1344)\n", " | >>> i = np.array([3,5,2,2])\n", " | >>> offset = sum(i * x.strides)\n", " | >>> x[3,5,2,2]\n", " | 813\n", " | >>> offset / x.itemsize\n", " | 813\n", " \n", " class ndarray(__builtin__.object)\n", " | ndarray(shape, dtype=float, buffer=None, offset=0,\n", " | strides=None, order=None)\n", " | \n", " | An array object represents a multidimensional, homogeneous array\n", " | of fixed-size items. An associated data-type object describes the\n", " | format of each element in the array (its byte-order, how many bytes it\n", " | occupies in memory, whether it is an integer, a floating point number,\n", " | or something else, etc.)\n", " | \n", " | Arrays should be constructed using `array`, `zeros` or `empty` (refer\n", " | to the See Also section below). The parameters given here refer to\n", " | a low-level method (`ndarray(...)`) for instantiating an array.\n", " | \n", " | For more information, refer to the `numpy` module and examine the\n", " | the methods and attributes of an array.\n", " | \n", " | Parameters\n", " | ----------\n", " | (for the __new__ method; see Notes below)\n", " | \n", " | shape : tuple of ints\n", " | Shape of created array.\n", " | dtype : data-type, optional\n", " | Any object that can be interpreted as a numpy data type.\n", " | buffer : object exposing buffer interface, optional\n", " | Used to fill the array with data.\n", " | offset : int, optional\n", " | Offset of array data in buffer.\n", " | strides : tuple of ints, optional\n", " | Strides of data in memory.\n", " | order : {'C', 'F'}, optional\n", " | Row-major or column-major order.\n", " | \n", " | Attributes\n", " | ----------\n", " | T : ndarray\n", " | Transpose of the array.\n", " | data : buffer\n", " | The array's elements, in memory.\n", " | dtype : dtype object\n", " | Describes the format of the elements in the array.\n", " | flags : dict\n", " | Dictionary containing information related to memory use, e.g.,\n", " | 'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.\n", " | flat : numpy.flatiter object\n", " | Flattened version of the array as an iterator. The iterator\n", " | allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for\n", " | assignment examples; TODO).\n", " | imag : ndarray\n", " | Imaginary part of the array.\n", " | real : ndarray\n", " | Real part of the array.\n", " | size : int\n", " | Number of elements in the array.\n", " | itemsize : int\n", " | The memory use of each array element in bytes.\n", " | nbytes : int\n", " | The total number of bytes required to store the array data,\n", " | i.e., ``itemsize * size``.\n", " | ndim : int\n", " | The array's number of dimensions.\n", " | shape : tuple of ints\n", " | Shape of the array.\n", " | strides : tuple of ints\n", " | The step-size required to move from one element to the next in\n", " | memory. For example, a contiguous ``(3, 4)`` array of type\n", " | ``int16`` in C-order has strides ``(8, 2)``. This implies that\n", " | to move from element to element in memory requires jumps of 2 bytes.\n", " | To move from row-to-row, one needs to jump 8 bytes at a time\n", " | (``2 * 4``).\n", " | ctypes : ctypes object\n", " | Class containing properties of the array needed for interaction\n", " | with ctypes.\n", " | base : ndarray\n", " | If the array is a view into another array, that array is its `base`\n", " | (unless that array is also a view). The `base` array is where the\n", " | array data is actually stored.\n", " | \n", " | See Also\n", " | --------\n", " | array : Construct an array.\n", " | zeros : Create an array, each element of which is zero.\n", " | empty : Create an array, but leave its allocated memory unchanged (i.e.,\n", " | it contains \"garbage\").\n", " | dtype : Create a data-type.\n", " | \n", " | Notes\n", " | -----\n", " | There are two modes of creating an array using ``__new__``:\n", " | \n", " | 1. If `buffer` is None, then only `shape`, `dtype`, and `order`\n", " | are used.\n", " | 2. If `buffer` is an object exposing the buffer interface, then\n", " | all keywords are interpreted.\n", " | \n", " | No ``__init__`` method is needed because the array is fully initialized\n", " | after the ``__new__`` method.\n", " | \n", " | Examples\n", " | --------\n", " | These examples illustrate the low-level `ndarray` constructor. Refer\n", " | to the `See Also` section above for easier ways of constructing an\n", " | ndarray.\n", " | \n", " | First mode, `buffer` is None:\n", " | \n", " | >>> np.ndarray(shape=(2,2), dtype=float, order='F')\n", " | array([[ -1.13698227e+002, 4.25087011e-303],\n", " | [ 2.88528414e-306, 3.27025015e-309]]) #random\n", " | \n", " | Second mode:\n", " | \n", " | >>> np.ndarray((2,), buffer=np.array([1,2,3]),\n", " | ... offset=np.int_().itemsize,\n", " | ... dtype=int) # offset = 1*itemsize, i.e. skip first element\n", " | array([2, 3])\n", " | \n", " | Methods defined here:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | a.__array__(|dtype) -> reference if type unchanged, copy otherwise.\n", " | \n", " | Returns either a new reference to self if dtype is not given or a new array\n", " | of provided data type if dtype is different from the current dtype of the\n", " | array.\n", " | \n", " | __array_prepare__(...)\n", " | a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n", " | \n", " | __array_wrap__(...)\n", " | a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __copy__(...)\n", " | a.__copy__([order])\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | If order is 'C' (False) then the result is contiguous (default).\n", " | If order is 'Fortran' (True) then the result has fortran order.\n", " | If order is 'Any' (None) then the result has fortran order\n", " | only if the array already is in fortran order.\n", " | \n", " | __deepcopy__(...)\n", " | a.__deepcopy__() -> Deep copy of array.\n", " | \n", " | Used if copy.deepcopy is called on an array.\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __delslice__(...)\n", " | x.__delslice__(i, j) <==> del x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __iadd__(...)\n", " | x.__iadd__(y) <==> x+=y\n", " | \n", " | __iand__(...)\n", " | x.__iand__(y) <==> x&=y\n", " | \n", " | __idiv__(...)\n", " | x.__idiv__(y) <==> x/=y\n", " | \n", " | __ifloordiv__(...)\n", " | x.__ifloordiv__(y) <==> x//y\n", " | \n", " | __ilshift__(...)\n", " | x.__ilshift__(y) <==> x<<=y\n", " | \n", " | __imod__(...)\n", " | x.__imod__(y) <==> x%=y\n", " | \n", " | __imul__(...)\n", " | x.__imul__(y) <==> x*=y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __ior__(...)\n", " | x.__ior__(y) <==> x|=y\n", " | \n", " | __ipow__(...)\n", " | x.__ipow__(y) <==> x**=y\n", " | \n", " | __irshift__(...)\n", " | x.__irshift__(y) <==> x>>=y\n", " | \n", " | __isub__(...)\n", " | x.__isub__(y) <==> x-=y\n", " | \n", " | __iter__(...)\n", " | x.__iter__() <==> iter(x)\n", " | \n", " | __itruediv__(...)\n", " | x.__itruediv__(y) <==> x/y\n", " | \n", " | __ixor__(...)\n", " | x.__ixor__(y) <==> x^=y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | a.__reduce__()\n", " | \n", " | For pickling.\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | __setslice__(...)\n", " | x.__setslice__(i, j, y) <==> x[i:j]=y\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __setstate__(...)\n", " | a.__setstate__(version, shape, dtype, isfortran, rawdata)\n", " | \n", " | For unpickling.\n", " | \n", " | Parameters\n", " | ----------\n", " | version : int\n", " | optional pickle version. If omitted defaults to 0.\n", " | shape : tuple\n", " | dtype : data-type\n", " | isFortran : bool\n", " | rawdata : string or list\n", " | a binary string with the data (or a list if 'a' is an object array)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | a.all(axis=None, out=None)\n", " | \n", " | Returns True if all elements evaluate to True.\n", " | \n", " | Refer to `numpy.all` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.all : equivalent function\n", " | \n", " | any(...)\n", " | a.any(axis=None, out=None)\n", " | \n", " | Returns True if any of the elements of `a` evaluate to True.\n", " | \n", " | Refer to `numpy.any` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.any : equivalent function\n", " | \n", " | argmax(...)\n", " | a.argmax(axis=None, out=None)\n", " | \n", " | Return indices of the maximum values along the given axis.\n", " | \n", " | Refer to `numpy.argmax` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmax : equivalent function\n", " | \n", " | argmin(...)\n", " | a.argmin(axis=None, out=None)\n", " | \n", " | Return indices of the minimum values along the given axis of `a`.\n", " | \n", " | Refer to `numpy.argmin` for detailed documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmin : equivalent function\n", " | \n", " | argpartition(...)\n", " | a.argpartition(kth, axis=-1, kind='introselect', order=None)\n", " | \n", " | Returns the indices that would partition this array.\n", " | \n", " | Refer to `numpy.argpartition` for full documentation.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argpartition : equivalent function\n", " | \n", " | argsort(...)\n", " | a.argsort(axis=-1, kind='quicksort', order=None)\n", " | \n", " | Returns the indices that would sort this array.\n", " | \n", " | Refer to `numpy.argsort` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argsort : equivalent function\n", " | \n", " | astype(...)\n", " | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n", " | \n", " | Copy of the array, cast to a specified type.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | Typecode or data-type to which the array is cast.\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout order of the result.\n", " | 'C' means C order, 'F' means Fortran order, 'A'\n", " | means 'F' order if all the arrays are Fortran contiguous,\n", " | 'C' order otherwise, and 'K' means as close to the\n", " | order the array elements appear in memory as possible.\n", " | Default is 'K'.\n", " | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n", " | Controls what kind of data casting may occur. Defaults to 'unsafe'\n", " | for backwards compatibility.\n", " | \n", " | * 'no' means the data types should not be cast at all.\n", " | * 'equiv' means only byte-order changes are allowed.\n", " | * 'safe' means only casts which can preserve values are allowed.\n", " | * 'same_kind' means only safe casts or casts within a kind,\n", " | like float64 to float32, are allowed.\n", " | * 'unsafe' means any data conversions may be done.\n", " | subok : bool, optional\n", " | If True, then sub-classes will be passed-through (default), otherwise\n", " | the returned array will be forced to be a base-class array.\n", " | copy : bool, optional\n", " | By default, astype always returns a newly allocated array. If this\n", " | is set to false, and the `dtype`, `order`, and `subok`\n", " | requirements are satisfied, the input array is returned instead\n", " | of a copy.\n", " | \n", " | Returns\n", " | -------\n", " | arr_t : ndarray\n", " | Unless `copy` is False and the other conditions for returning the input\n", " | array are satisfied (see description for `copy` input paramter), `arr_t`\n", " | is a new array of the same shape as the input array, with dtype, order\n", " | given by `dtype`, `order`.\n", " | \n", " | Notes\n", " | -----\n", " | Starting in NumPy 1.9, astype method now returns an error if the string\n", " | dtype to cast to is not long enough in 'safe' casting mode to hold the max\n", " | value of integer/float array that is being casted. Previously the casting\n", " | was allowed even if the result was truncated.\n", " | \n", " | Raises\n", " | ------\n", " | ComplexWarning\n", " | When casting from complex to float or int. To avoid this,\n", " | one should use ``a.real.astype(t)``.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 2.5])\n", " | >>> x\n", " | array([ 1. , 2. , 2.5])\n", " | \n", " | >>> x.astype(int)\n", " | array([1, 2, 2])\n", " | \n", " | byteswap(...)\n", " | a.byteswap(inplace)\n", " | \n", " | Swap the bytes of the array elements\n", " | \n", " | Toggle between low-endian and big-endian data representation by\n", " | returning a byteswapped array, optionally swapped in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | inplace : bool, optional\n", " | If ``True``, swap bytes in-place, default is ``False``.\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | The byteswapped array. If `inplace` is ``True``, this is\n", " | a view to self.\n", " | \n", " | Examples\n", " | --------\n", " | >>> A = np.array([1, 256, 8755], dtype=np.int16)\n", " | >>> map(hex, A)\n", " | ['0x1', '0x100', '0x2233']\n", " | >>> A.byteswap(True)\n", " | array([ 256, 1, 13090], dtype=int16)\n", " | >>> map(hex, A)\n", " | ['0x100', '0x1', '0x3322']\n", " | \n", " | Arrays of strings are not swapped\n", " | \n", " | >>> A = np.array(['ceg', 'fac'])\n", " | >>> A.byteswap()\n", " | array(['ceg', 'fac'],\n", " | dtype='|S3')\n", " | \n", " | choose(...)\n", " | a.choose(choices, out=None, mode='raise')\n", " | \n", " | Use an index array to construct a new array from a set of choices.\n", " | \n", " | Refer to `numpy.choose` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.choose : equivalent function\n", " | \n", " | clip(...)\n", " | a.clip(a_min, a_max, out=None)\n", " | \n", " | Return an array whose values are limited to ``[a_min, a_max]``.\n", " | \n", " | Refer to `numpy.clip` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.clip : equivalent function\n", " | \n", " | compress(...)\n", " | a.compress(condition, axis=None, out=None)\n", " | \n", " | Return selected slices of this array along given axis.\n", " | \n", " | Refer to `numpy.compress` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.compress : equivalent function\n", " | \n", " | conj(...)\n", " | a.conj()\n", " | \n", " | Complex-conjugate all elements.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | conjugate(...)\n", " | a.conjugate()\n", " | \n", " | Return the complex conjugate, element-wise.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | copy(...)\n", " | a.copy(order='C')\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout of the copy. 'C' means C-order,\n", " | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n", " | 'C' otherwise. 'K' means match the layout of `a` as closely\n", " | as possible. (Note that this function and :func:numpy.copy are very\n", " | similar, but have different default values for their order=\n", " | arguments.)\n", " | \n", " | See also\n", " | --------\n", " | numpy.copy\n", " | numpy.copyto\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n", " | \n", " | >>> y = x.copy()\n", " | \n", " | >>> x.fill(0)\n", " | \n", " | >>> x\n", " | array([[0, 0, 0],\n", " | [0, 0, 0]])\n", " | \n", " | >>> y\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | \n", " | >>> y.flags['C_CONTIGUOUS']\n", " | True\n", " | \n", " | cumprod(...)\n", " | a.cumprod(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative product of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumprod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumprod : equivalent function\n", " | \n", " | cumsum(...)\n", " | a.cumsum(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative sum of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumsum` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumsum : equivalent function\n", " | \n", " | diagonal(...)\n", " | a.diagonal(offset=0, axis1=0, axis2=1)\n", " | \n", " | Return specified diagonals. In NumPy 1.9 the returned array is a\n", " | read-only view instead of a copy as in previous NumPy versions. In\n", " | NumPy 1.10 the read-only restriction will be removed.\n", " | \n", " | Refer to :func:`numpy.diagonal` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.diagonal : equivalent function\n", " | \n", " | dot(...)\n", " | a.dot(b, out=None)\n", " | \n", " | Dot product of two arrays.\n", " | \n", " | Refer to `numpy.dot` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dot : equivalent function\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.eye(2)\n", " | >>> b = np.ones((2, 2)) * 2\n", " | >>> a.dot(b)\n", " | array([[ 2., 2.],\n", " | [ 2., 2.]])\n", " | \n", " | This array method can be conveniently chained:\n", " | \n", " | >>> a.dot(b).dot(b)\n", " | array([[ 8., 8.],\n", " | [ 8., 8.]])\n", " | \n", " | dump(...)\n", " | a.dump(file)\n", " | \n", " | Dump a pickle of the array to the specified file.\n", " | The array can be read back with pickle.load or numpy.load.\n", " | \n", " | Parameters\n", " | ----------\n", " | file : str\n", " | A string naming the dump file.\n", " | \n", " | dumps(...)\n", " | a.dumps()\n", " | \n", " | Returns the pickle of the array as a string.\n", " | pickle.loads or numpy.loads will convert the string back to an array.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | fill(...)\n", " | a.fill(value)\n", " | \n", " | Fill the array with a scalar value.\n", " | \n", " | Parameters\n", " | ----------\n", " | value : scalar\n", " | All elements of `a` will be assigned this value.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([1, 2])\n", " | >>> a.fill(0)\n", " | >>> a\n", " | array([0, 0])\n", " | >>> a = np.empty(2)\n", " | >>> a.fill(1)\n", " | >>> a\n", " | array([ 1., 1.])\n", " | \n", " | flatten(...)\n", " | a.flatten(order='C')\n", " | \n", " | Return a copy of the array collapsed into one dimension.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | Whether to flatten in C (row-major), Fortran (column-major) order,\n", " | or preserve the C/Fortran ordering from `a`.\n", " | The default is 'C'.\n", " | \n", " | Returns\n", " | -------\n", " | y : ndarray\n", " | A copy of the input array, flattened to one dimension.\n", " | \n", " | See Also\n", " | --------\n", " | ravel : Return a flattened array.\n", " | flat : A 1-D flat iterator over the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,2], [3,4]])\n", " | >>> a.flatten()\n", " | array([1, 2, 3, 4])\n", " | >>> a.flatten('F')\n", " | array([1, 3, 2, 4])\n", " | \n", " | getfield(...)\n", " | a.getfield(dtype, offset=0)\n", " | \n", " | Returns a field of the given array as a certain type.\n", " | \n", " | A field is a view of the array data with a given data-type. The values in\n", " | the view are determined by the given type and the offset into the current\n", " | array in bytes. The offset needs to be such that the view dtype fits in the\n", " | array dtype; for example an array of dtype complex128 has 16-byte elements.\n", " | If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n", " | between 0 and 12 bytes.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | The data type of the view. The dtype size of the view can not be larger\n", " | than that of the array itself.\n", " | offset : int\n", " | Number of bytes to skip before beginning the element view.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.diag([1.+1.j]*2)\n", " | >>> x[1, 1] = 2 + 4.j\n", " | >>> x\n", " | array([[ 1.+1.j, 0.+0.j],\n", " | [ 0.+0.j, 2.+4.j]])\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0.],\n", " | [ 0., 2.]])\n", " | \n", " | By choosing an offset of 8 bytes we can select the complex part of the\n", " | array for our view:\n", " | \n", " | >>> x.getfield(np.float64, offset=8)\n", " | array([[ 1., 0.],\n", " | [ 0., 4.]])\n", " | \n", " | item(...)\n", " | a.item(*args)\n", " | \n", " | Copy an element of an array to a standard Python scalar and return it.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments (variable number and type)\n", " | \n", " | * none: in this case, the method only works for arrays\n", " | with one element (`a.size == 1`), which element is\n", " | copied into a standard Python scalar object and returned.\n", " | \n", " | * int_type: this argument is interpreted as a flat index into\n", " | the array, specifying which element to copy and return.\n", " | \n", " | * tuple of int_types: functions as does a single int_type argument,\n", " | except that the argument is interpreted as an nd-index into the\n", " | array.\n", " | \n", " | Returns\n", " | -------\n", " | z : Standard Python scalar object\n", " | A copy of the specified element of the array as a suitable\n", " | Python scalar\n", " | \n", " | Notes\n", " | -----\n", " | When the data type of `a` is longdouble or clongdouble, item() returns\n", " | a scalar array object because there is no available Python scalar that\n", " | would not lose information. Void arrays return a buffer object for item(),\n", " | unless fields are defined, in which case a tuple is returned.\n", " | \n", " | `item` is very similar to a[args], except, instead of an array scalar,\n", " | a standard Python scalar is returned. This can be useful for speeding up\n", " | access to elements of the array and doing arithmetic on elements of the\n", " | array using Python's optimized math.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.item(3)\n", " | 2\n", " | >>> x.item(7)\n", " | 5\n", " | >>> x.item((0, 1))\n", " | 1\n", " | >>> x.item((2, 2))\n", " | 3\n", " | \n", " | itemset(...)\n", " | a.itemset(*args)\n", " | \n", " | Insert scalar into an array (scalar is cast to array's dtype, if possible)\n", " | \n", " | There must be at least 1 argument, and define the last argument\n", " | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster\n", " | than ``a[args] = item``. The item should be a scalar value and `args`\n", " | must select a single item in the array `a`.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments\n", " | If one argument: a scalar, only used in case `a` is of size 1.\n", " | If two arguments: the last argument is the value to be set\n", " | and must be a scalar, the first argument specifies a single array\n", " | element location. It is either an int or a tuple.\n", " | \n", " | Notes\n", " | -----\n", " | Compared to indexing syntax, `itemset` provides some speed increase\n", " | for placing a scalar into a particular location in an `ndarray`,\n", " | if you must do this. However, generally this is discouraged:\n", " | among other problems, it complicates the appearance of the code.\n", " | Also, when using `itemset` (and `item`) inside a loop, be sure\n", " | to assign the methods to a local variable to avoid the attribute\n", " | look-up at each loop iteration.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.itemset(4, 0)\n", " | >>> x.itemset((2, 2), 9)\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 0, 3],\n", " | [8, 5, 9]])\n", " | \n", " | max(...)\n", " | a.max(axis=None, out=None)\n", " | \n", " | Return the maximum along a given axis.\n", " | \n", " | Refer to `numpy.amax` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.amax : equivalent function\n", " | \n", " | mean(...)\n", " | a.mean(axis=None, dtype=None, out=None)\n", " | \n", " | Returns the average of the array elements along given axis.\n", " | \n", " | Refer to `numpy.mean` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.mean : equivalent function\n", " | \n", " | min(...)\n", " | a.min(axis=None, out=None)\n", " | \n", " | Return the minimum along a given axis.\n", " | \n", " | Refer to `numpy.amin` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.amin : equivalent function\n", " | \n", " | newbyteorder(...)\n", " | arr.newbyteorder(new_order='S')\n", " | \n", " | Return the array with the same data viewed with a different byte order.\n", " | \n", " | Equivalent to::\n", " | \n", " | arr.view(arr.dtype.newbytorder(new_order))\n", " | \n", " | Changes are also made in all fields and sub-arrays of the array data\n", " | type.\n", " | \n", " | \n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : string, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. `new_order` codes can be any of::\n", " | \n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_arr : array\n", " | New array object with the dtype reflecting given change to the\n", " | byte order.\n", " | \n", " | nonzero(...)\n", " | a.nonzero()\n", " | \n", " | Return the indices of the elements that are non-zero.\n", " | \n", " | Refer to `numpy.nonzero` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.nonzero : equivalent function\n", " | \n", " | partition(...)\n", " | a.partition(kth, axis=-1, kind='introselect', order=None)\n", " | \n", " | Rearranges the elements in the array in such a way that value of the\n", " | element in kth position is in the position it would be in a sorted array.\n", " | All elements smaller than the kth element are moved before this element and\n", " | all equal or greater are moved behind it. The ordering of the elements in\n", " | the two partitions is undefined.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | Parameters\n", " | ----------\n", " | kth : int or sequence of ints\n", " | Element index to partition by. The kth element value will be in its\n", " | final sorted position and all smaller elements will be moved before it\n", " | and all equal or greater elements behind it.\n", " | The order all elements in the partitions is undefined.\n", " | If provided with a sequence of kth it will partition all elements\n", " | indexed by kth of them into their sorted position at once.\n", " | axis : int, optional\n", " | Axis along which to sort. Default is -1, which means sort along the\n", " | last axis.\n", " | kind : {'introselect'}, optional\n", " | Selection algorithm. Default is 'introselect'.\n", " | order : list, optional\n", " | When `a` is an array with fields defined, this argument specifies\n", " | which fields to compare first, second, etc. Not all fields need be\n", " | specified.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.partition : Return a parititioned copy of an array.\n", " | argpartition : Indirect partition.\n", " | sort : Full sort.\n", " | \n", " | Notes\n", " | -----\n", " | See ``np.partition`` for notes on the different algorithms.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([3, 4, 2, 1])\n", " | >>> a.partition(a, 3)\n", " | >>> a\n", " | array([2, 1, 3, 4])\n", " | \n", " | >>> a.partition((1, 3))\n", " | array([1, 2, 3, 4])\n", " | \n", " | prod(...)\n", " | a.prod(axis=None, dtype=None, out=None)\n", " | \n", " | Return the product of the array elements over the given axis\n", " | \n", " | Refer to `numpy.prod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.prod : equivalent function\n", " | \n", " | ptp(...)\n", " | a.ptp(axis=None, out=None)\n", " | \n", " | Peak to peak (maximum - minimum) value along a given axis.\n", " | \n", " | Refer to `numpy.ptp` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ptp : equivalent function\n", " | \n", " | put(...)\n", " | a.put(indices, values, mode='raise')\n", " | \n", " | Set ``a.flat[n] = values[n]`` for all `n` in indices.\n", " | \n", " | Refer to `numpy.put` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.put : equivalent function\n", " | \n", " | ravel(...)\n", " | a.ravel([order])\n", " | \n", " | Return a flattened array.\n", " | \n", " | Refer to `numpy.ravel` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ravel : equivalent function\n", " | \n", " | ndarray.flat : a flat iterator on the array.\n", " | \n", " | repeat(...)\n", " | a.repeat(repeats, axis=None)\n", " | \n", " | Repeat elements of an array.\n", " | \n", " | Refer to `numpy.repeat` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.repeat : equivalent function\n", " | \n", " | reshape(...)\n", " | a.reshape(shape, order='C')\n", " | \n", " | Returns an array containing the same data with a new shape.\n", " | \n", " | Refer to `numpy.reshape` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.reshape : equivalent function\n", " | \n", " | resize(...)\n", " | a.resize(new_shape, refcheck=True)\n", " | \n", " | Change shape and size of array in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | new_shape : tuple of ints, or `n` ints\n", " | Shape of resized array.\n", " | refcheck : bool, optional\n", " | If False, reference count will not be checked. Default is True.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | Raises\n", " | ------\n", " | ValueError\n", " | If `a` does not own its own data or references or views to it exist,\n", " | and the data memory must be changed.\n", " | \n", " | SystemError\n", " | If the `order` keyword argument is specified. This behaviour is a\n", " | bug in NumPy.\n", " | \n", " | See Also\n", " | --------\n", " | resize : Return a new array with the specified shape.\n", " | \n", " | Notes\n", " | -----\n", " | This reallocates space for the data area if necessary.\n", " | \n", " | Only contiguous arrays (data elements consecutive in memory) can be\n", " | resized.\n", " | \n", " | The purpose of the reference count check is to make sure you\n", " | do not use this array as a buffer for another Python object and then\n", " | reallocate the memory. However, reference counts can increase in\n", " | other ways so if you are sure that you have not shared the memory\n", " | for this array with another Python object, then you may safely set\n", " | `refcheck` to False.\n", " | \n", " | Examples\n", " | --------\n", " | Shrinking an array: array is flattened (in the order that the data are\n", " | stored in memory), resized, and reshaped:\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='C')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [1]])\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='F')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [2]])\n", " | \n", " | Enlarging an array: as above, but missing entries are filled with zeros:\n", " | \n", " | >>> b = np.array([[0, 1], [2, 3]])\n", " | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n", " | >>> b\n", " | array([[0, 1, 2],\n", " | [3, 0, 0]])\n", " | \n", " | Referencing an array prevents resizing...\n", " | \n", " | >>> c = a\n", " | >>> a.resize((1, 1))\n", " | Traceback (most recent call last):\n", " | ...\n", " | ValueError: cannot resize an array that has been referenced ...\n", " | \n", " | Unless `refcheck` is False:\n", " | \n", " | >>> a.resize((1, 1), refcheck=False)\n", " | >>> a\n", " | array([[0]])\n", " | >>> c\n", " | array([[0]])\n", " | \n", " | round(...)\n", " | a.round(decimals=0, out=None)\n", " | \n", " | Return `a` with each element rounded to the given number of decimals.\n", " | \n", " | Refer to `numpy.around` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.around : equivalent function\n", " | \n", " | searchsorted(...)\n", " | a.searchsorted(v, side='left', sorter=None)\n", " | \n", " | Find indices where elements of v should be inserted in a to maintain order.\n", " | \n", " | For full documentation, see `numpy.searchsorted`\n", " | \n", " | See Also\n", " | --------\n", " | numpy.searchsorted : equivalent function\n", " | \n", " | setfield(...)\n", " | a.setfield(val, dtype, offset=0)\n", " | \n", " | Put a value into a specified place in a field defined by a data-type.\n", " | \n", " | Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n", " | bytes into the field.\n", " | \n", " | Parameters\n", " | ----------\n", " | val : object\n", " | Value to be placed in field.\n", " | dtype : dtype object\n", " | Data-type of the field in which to place `val`.\n", " | offset : int, optional\n", " | The number of bytes into the field at which to place `val`.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | See Also\n", " | --------\n", " | getfield\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.eye(3)\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | >>> x.setfield(3, np.int32)\n", " | >>> x.getfield(np.int32)\n", " | array([[3, 3, 3],\n", " | [3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x\n", " | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]])\n", " | >>> x.setfield(np.eye(3), np.int32)\n", " | >>> x\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | \n", " | setflags(...)\n", " | a.setflags(write=None, align=None, uic=None)\n", " | \n", " | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.\n", " | \n", " | These Boolean-valued flags affect how numpy interprets the memory\n", " | area used by `a` (see Notes below). The ALIGNED flag can only\n", " | be set to True if the data is actually aligned according to the type.\n", " | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE\n", " | can only be set to True if the array owns its own memory, or the\n", " | ultimate owner of the memory exposes a writeable buffer interface,\n", " | or is a string. (The exception for string is made so that unpickling\n", " | can be done without copying memory.)\n", " | \n", " | Parameters\n", " | ----------\n", " | write : bool, optional\n", " | Describes whether or not `a` can be written to.\n", " | align : bool, optional\n", " | Describes whether or not `a` is aligned properly for its type.\n", " | uic : bool, optional\n", " | Describes whether or not `a` is a copy of another \"base\" array.\n", " | \n", " | Notes\n", " | -----\n", " | Array flags provide information about how the memory area used\n", " | for the array is to be interpreted. There are 6 Boolean flags\n", " | in use, only three of which can be changed by the user:\n", " | UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n", " | \n", " | WRITEABLE (W) the data area can be written to;\n", " | \n", " | ALIGNED (A) the data and strides are aligned appropriately for the hardware\n", " | (as determined by the compiler);\n", " | \n", " | UPDATEIFCOPY (U) this array is a copy of some other array (referenced\n", " | by .base). When this array is deallocated, the base array will be\n", " | updated with the contents of this array.\n", " | \n", " | All flags can be accessed using their first (upper case) letter as well\n", " | as the full name.\n", " | \n", " | Examples\n", " | --------\n", " | >>> y\n", " | array([[3, 1, 7],\n", " | [2, 0, 0],\n", " | [8, 5, 9]])\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : True\n", " | ALIGNED : True\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(write=0, align=0)\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : False\n", " | ALIGNED : False\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(uic=1)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: cannot set UPDATEIFCOPY flag to True\n", " | \n", " | sort(...)\n", " | a.sort(axis=-1, kind='quicksort', order=None)\n", " | \n", " | Sort an array, in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | axis : int, optional\n", " | Axis along which to sort. Default is -1, which means sort along the\n", " | last axis.\n", " | kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n", " | Sorting algorithm. Default is 'quicksort'.\n", " | order : list, optional\n", " | When `a` is an array with fields defined, this argument specifies\n", " | which fields to compare first, second, etc. Not all fields need be\n", " | specified.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.sort : Return a sorted copy of an array.\n", " | argsort : Indirect sort.\n", " | lexsort : Indirect stable sort on multiple keys.\n", " | searchsorted : Find elements in sorted array.\n", " | partition: Partial sort.\n", " | \n", " | Notes\n", " | -----\n", " | See ``sort`` for notes on the different sorting algorithms.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,4], [3,1]])\n", " | >>> a.sort(axis=1)\n", " | >>> a\n", " | array([[1, 4],\n", " | [1, 3]])\n", " | >>> a.sort(axis=0)\n", " | >>> a\n", " | array([[1, 3],\n", " | [1, 4]])\n", " | \n", " | Use the `order` keyword to specify a field to use when sorting a\n", " | structured array:\n", " | \n", " | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n", " | >>> a.sort(order='y')\n", " | >>> a\n", " | array([('c', 1), ('a', 2)],\n", " | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | tofile(...)\n", " | a.tofile(fid, sep=\"\", format=\"%s\")\n", " | \n", " | Write array to a file as text or binary (default).\n", " | \n", " | Data is always written in 'C' order, independent of the order of `a`.\n", " | The data produced by this method can be recovered using the function\n", " | fromfile().\n", " | \n", " | Parameters\n", " | ----------\n", " | fid : file or str\n", " | An open file object, or a string containing a filename.\n", " | sep : str\n", " | Separator between array items for text output.\n", " | If \"\" (empty), a binary file is written, equivalent to\n", " | ``file.write(a.tobytes())``.\n", " | format : str\n", " | Format string for text file output.\n", " | Each entry in the array is formatted to text by first converting\n", " | it to the closest Python type, and then using \"format\" % item.\n", " | \n", " | Notes\n", " | -----\n", " | This is a convenience function for quick storage of array data.\n", " | Information on endianness and precision is lost, so this method is not a\n", " | good choice for files intended to archive data or transport data between\n", " | machines with different endianness. Some of these problems can be overcome\n", " | by outputting the data as text files, at the expense of speed and file\n", " | size.\n", " | \n", " | tolist(...)\n", " | a.tolist()\n", " | \n", " | Return the array as a (possibly nested) list.\n", " | \n", " | Return a copy of the array data as a (nested) Python list.\n", " | Data items are converted to the nearest compatible Python type.\n", " | \n", " | Parameters\n", " | ----------\n", " | none\n", " | \n", " | Returns\n", " | -------\n", " | y : list\n", " | The possibly nested list of array elements.\n", " | \n", " | Notes\n", " | -----\n", " | The array may be recreated, ``a = np.array(a.tolist())``.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([1, 2])\n", " | >>> a.tolist()\n", " | [1, 2]\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> list(a)\n", " | [array([1, 2]), array([3, 4])]\n", " | >>> a.tolist()\n", " | [[1, 2], [3, 4]]\n", " | \n", " | tostring(...)\n", " | a.tostring(order='C')\n", " | \n", " | Construct Python bytes containing the raw data bytes in the array.\n", " | \n", " | Constructs Python bytes showing a copy of the raw contents of\n", " | data memory. The bytes object can be produced in either 'C' or 'Fortran',\n", " | or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n", " | unless the F_CONTIGUOUS flag in the array is set, in which case it\n", " | means 'Fortran' order.\n", " | \n", " | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', None}, optional\n", " | Order of the data for multidimensional arrays:\n", " | C, Fortran, or the same as for the original array.\n", " | \n", " | Returns\n", " | -------\n", " | s : bytes\n", " | Python bytes exhibiting a copy of `a`'s raw data.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | trace(...)\n", " | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n", " | \n", " | Return the sum along diagonals of the array.\n", " | \n", " | Refer to `numpy.trace` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.trace : equivalent function\n", " | \n", " | transpose(...)\n", " | a.transpose(*axes)\n", " | \n", " | Returns a view of the array with axes transposed.\n", " | \n", " | For a 1-D array, this has no effect. (To change between column and\n", " | row vectors, first cast the 1-D array into a matrix object.)\n", " | For a 2-D array, this is the usual matrix transpose.\n", " | For an n-D array, if axes are given, their order indicates how the\n", " | axes are permuted (see Examples). If axes are not provided and\n", " | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n", " | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n", " | \n", " | Parameters\n", " | ----------\n", " | axes : None, tuple of ints, or `n` ints\n", " | \n", " | * None or no argument: reverses the order of the axes.\n", " | \n", " | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n", " | `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n", " | \n", " | * `n` ints: same as an n-tuple of the same ints (this form is\n", " | intended simply as a \"convenience\" alternative to the tuple form)\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | View of `a`, with axes suitably permuted.\n", " | \n", " | See Also\n", " | --------\n", " | ndarray.T : Array property returning the array transposed.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> a\n", " | array([[1, 2],\n", " | [3, 4]])\n", " | >>> a.transpose()\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose((1, 0))\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose(1, 0)\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | \n", " | var(...)\n", " | a.var(axis=None, dtype=None, out=None, ddof=0)\n", " | \n", " | Returns the variance of the array elements, along given axis.\n", " | \n", " | Refer to `numpy.var` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.var : equivalent function\n", " | \n", " | view(...)\n", " | a.view(dtype=None, type=None)\n", " | \n", " | New view of array with the same data.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : data-type or ndarray sub-class, optional\n", " | Data-type descriptor of the returned view, e.g., float32 or int16. The\n", " | default, None, results in the view having the same data-type as `a`.\n", " | This argument can also be specified as an ndarray sub-class, which\n", " | then specifies the type of the returned object (this is equivalent to\n", " | setting the ``type`` parameter).\n", " | type : Python type, optional\n", " | Type of the returned view, e.g., ndarray or matrix. Again, the\n", " | default None results in type preservation.\n", " | \n", " | Notes\n", " | -----\n", " | ``a.view()`` is used two different ways:\n", " | \n", " | ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n", " | of the array's memory with a different data-type. This can cause a\n", " | reinterpretation of the bytes of memory.\n", " | \n", " | ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n", " | returns an instance of `ndarray_subclass` that looks at the same array\n", " | (same shape, dtype, etc.) This does not cause a reinterpretation of the\n", " | memory.\n", " | \n", " | For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n", " | bytes per entry than the previous dtype (for example, converting a\n", " | regular array to a structured array), then the behavior of the view\n", " | cannot be predicted just from the superficial appearance of ``a`` (shown\n", " | by ``print(a)``). It also depends on exactly how ``a`` is stored in\n", " | memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus\n", " | defined as a slice or transpose, etc., the view may give different\n", " | results.\n", " | \n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])\n", " | \n", " | Viewing array data using a different type and dtype:\n", " | \n", " | >>> y = x.view(dtype=np.int16, type=np.matrix)\n", " | >>> y\n", " | matrix([[513]], dtype=int16)\n", " | >>> print type(y)\n", " | \n", " | \n", " | Creating a view on a structured array so it can be used in calculations\n", " | \n", " | >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])\n", " | >>> xv = x.view(dtype=np.int8).reshape(-1,2)\n", " | >>> xv\n", " | array([[1, 2],\n", " | [3, 4]], dtype=int8)\n", " | >>> xv.mean(0)\n", " | array([ 2., 3.])\n", " | \n", " | Making changes to the view changes the underlying array\n", " | \n", " | >>> xv[0,1] = 20\n", " | >>> print x\n", " | [(1, 20) (3, 4)]\n", " | \n", " | Using a view to convert an array to a record array:\n", " | \n", " | >>> z = x.view(np.recarray)\n", " | >>> z.a\n", " | array([1], dtype=int8)\n", " | \n", " | Views share data:\n", " | \n", " | >>> x[0] = (9, 10)\n", " | >>> z[0]\n", " | (9, 10)\n", " | \n", " | Views that change the dtype size (bytes per entry) should normally be\n", " | avoided on arrays defined by slices, transposes, fortran-ordering, etc.:\n", " | \n", " | >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)\n", " | >>> y = x[:, 0:2]\n", " | >>> y\n", " | array([[1, 2],\n", " | [4, 5]], dtype=int16)\n", " | >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: new type not compatible with array.\n", " | >>> z = y.copy()\n", " | >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])\n", " | array([[(1, 2)],\n", " | [(4, 5)]], dtype=[('width', '>> x = np.array([[1.,2.],[3.,4.]])\n", " | >>> x\n", " | array([[ 1., 2.],\n", " | [ 3., 4.]])\n", " | >>> x.T\n", " | array([[ 1., 3.],\n", " | [ 2., 4.]])\n", " | >>> x = np.array([1.,2.,3.,4.])\n", " | >>> x\n", " | array([ 1., 2., 3., 4.])\n", " | >>> x.T\n", " | array([ 1., 2., 3., 4.])\n", " | \n", " | __array_finalize__\n", " | None.\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side.\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: C-struct side.\n", " | \n", " | base\n", " | Base object if memory is from some other object.\n", " | \n", " | Examples\n", " | --------\n", " | The base of an array that owns its memory is None:\n", " | \n", " | >>> x = np.array([1,2,3,4])\n", " | >>> x.base is None\n", " | True\n", " | \n", " | Slicing creates a view, whose memory is shared with x:\n", " | \n", " | >>> y = x[2:]\n", " | >>> y.base is x\n", " | True\n", " | \n", " | ctypes\n", " | An object to simplify the interaction of the array with the ctypes\n", " | module.\n", " | \n", " | This attribute creates an object that makes it easier to use arrays\n", " | when calling shared libraries with the ctypes module. The returned\n", " | object has, among others, data, shape, and strides attributes (see\n", " | Notes below) which themselves return ctypes objects that can be used\n", " | as arguments to a shared library.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | c : Python object\n", " | Possessing attributes data, shape, strides, etc.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ctypeslib\n", " | \n", " | Notes\n", " | -----\n", " | Below are the public attributes of this object which were documented\n", " | in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n", " | as well as documented private attributes):\n", " | \n", " | * data: A pointer to the memory area of the array as a Python integer.\n", " | This memory area may contain data that is not aligned, or not in correct\n", " | byte-order. The memory area may not even be writeable. The array\n", " | flags and data-type of this array should be respected when passing this\n", " | attribute to arbitrary C-code to avoid trouble that can include Python\n", " | crashing. User Beware! The value of this attribute is exactly the same\n", " | as self._array_interface_['data'][0].\n", " | \n", " | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the C-integer corresponding to dtype('p') on this\n", " | platform. This base-type could be c_int, c_long, or c_longlong\n", " | depending on the platform. The c_intp type is defined accordingly in\n", " | numpy.ctypeslib. The ctypes array contains the shape of the underlying\n", " | array.\n", " | \n", " | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the same as for the shape attribute. This ctypes array\n", " | contains the strides information from the underlying array. This strides\n", " | information is important for showing how many bytes must be jumped to\n", " | get to the next element in the array.\n", " | \n", " | * data_as(obj): Return the data pointer cast to a particular c-types object.\n", " | For example, calling self._as_parameter_ is equivalent to\n", " | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a\n", " | pointer to a ctypes array of floating-point data:\n", " | self.data_as(ctypes.POINTER(ctypes.c_double)).\n", " | \n", " | * shape_as(obj): Return the shape tuple as an array of some other c-types\n", " | type. For example: self.shape_as(ctypes.c_short).\n", " | \n", " | * strides_as(obj): Return the strides tuple as an array of some other\n", " | c-types type. For example: self.strides_as(ctypes.c_longlong).\n", " | \n", " | Be careful using the ctypes attribute - especially on temporary\n", " | arrays or arrays constructed on the fly. For example, calling\n", " | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory\n", " | that is invalid because the array created as (a+b) is deallocated\n", " | before the next Python statement. You can avoid this problem using\n", " | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will\n", " | hold a reference to the array until ct is deleted or re-assigned.\n", " | \n", " | If the ctypes module is not available, then the ctypes attribute\n", " | of array objects still returns something useful, but ctypes objects\n", " | are not returned and errors may be raised instead. In particular,\n", " | the object will still have the as parameter attribute which will\n", " | return an integer equal to the data attribute.\n", " | \n", " | Examples\n", " | --------\n", " | >>> import ctypes\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.ctypes.data\n", " | 30439712\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))\n", " | \n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents\n", " | c_long(0)\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents\n", " | c_longlong(4294967296L)\n", " | >>> x.ctypes.shape\n", " | \n", " | >>> x.ctypes.shape_as(ctypes.c_long)\n", " | \n", " | >>> x.ctypes.strides\n", " | \n", " | >>> x.ctypes.strides_as(ctypes.c_longlong)\n", " | \n", " | \n", " | data\n", " | Python buffer object pointing to the start of the array's data.\n", " | \n", " | dtype\n", " | Data-type of the array's elements.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | d : numpy dtype object\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dtype\n", " | \n", " | Examples\n", " | --------\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.dtype\n", " | dtype('int32')\n", " | >>> type(x.dtype)\n", " | \n", " | \n", " | flags\n", " | Information about the memory layout of the array.\n", " | \n", " | Attributes\n", " | ----------\n", " | C_CONTIGUOUS (C)\n", " | The data is in a single, C-style contiguous segment.\n", " | F_CONTIGUOUS (F)\n", " | The data is in a single, Fortran-style contiguous segment.\n", " | OWNDATA (O)\n", " | The array owns the memory it uses or borrows it from another object.\n", " | WRITEABLE (W)\n", " | The data area can be written to. Setting this to False locks\n", " | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE\n", " | from its base array at creation time, but a view of a writeable\n", " | array may be subsequently locked while the base array remains writeable.\n", " | (The opposite is not true, in that a view of a locked array may not\n", " | be made writeable. However, currently, locking a base object does not\n", " | lock any views that already reference it, so under that circumstance it\n", " | is possible to alter the contents of a locked array via a previously\n", " | created writeable view onto it.) Attempting to change a non-writeable\n", " | array raises a RuntimeError exception.\n", " | ALIGNED (A)\n", " | The data and all elements are aligned appropriately for the hardware.\n", " | UPDATEIFCOPY (U)\n", " | This array is a copy of some other array. When this array is\n", " | deallocated, the base array will be updated with the contents of\n", " | this array.\n", " | FNC\n", " | F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | FORC\n", " | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n", " | BEHAVED (B)\n", " | ALIGNED and WRITEABLE.\n", " | CARRAY (CA)\n", " | BEHAVED and C_CONTIGUOUS.\n", " | FARRAY (FA)\n", " | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | \n", " | Notes\n", " | -----\n", " | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n", " | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n", " | names are only supported in dictionary access.\n", " | \n", " | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by\n", " | the user, via direct assignment to the attribute or dictionary entry,\n", " | or by calling `ndarray.setflags`.\n", " | \n", " | The array flags cannot be set arbitrarily:\n", " | \n", " | - UPDATEIFCOPY can only be set ``False``.\n", " | - ALIGNED can only be set ``True`` if the data is truly aligned.\n", " | - WRITEABLE can only be set ``True`` if the array owns its own memory\n", " | or the ultimate owner of the memory exposes a writeable buffer\n", " | interface or is a string.\n", " | \n", " | Arrays can be both C-style and Fortran-style contiguous simultaneously.\n", " | This is clear for 1-dimensional arrays, but can also be true for higher\n", " | dimensional arrays.\n", " | \n", " | Even for contiguous arrays a stride for a given dimension\n", " | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n", " | or the array has no elements.\n", " | It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n", " | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n", " | Fortran-style contiguous arrays is true.\n", " | \n", " | flat\n", " | A 1-D iterator over the array.\n", " | \n", " | This is a `numpy.flatiter` instance, which acts similarly to, but is not\n", " | a subclass of, Python's built-in iterator object.\n", " | \n", " | See Also\n", " | --------\n", " | flatten : Return a copy of the array collapsed into one dimension.\n", " | \n", " | flatiter\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(1, 7).reshape(2, 3)\n", " | >>> x\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | >>> x.flat[3]\n", " | 4\n", " | >>> x.T\n", " | array([[1, 4],\n", " | [2, 5],\n", " | [3, 6]])\n", " | >>> x.T.flat[3]\n", " | 5\n", " | >>> type(x.flat)\n", " | \n", " | \n", " | An assignment example:\n", " | \n", " | >>> x.flat = 3; x\n", " | array([[3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x.flat[[1,4]] = 1; x\n", " | array([[3, 1, 3],\n", " | [3, 1, 3]])\n", " | \n", " | imag\n", " | The imaginary part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.imag\n", " | array([ 0. , 0.70710678])\n", " | >>> x.imag.dtype\n", " | dtype('float64')\n", " | \n", " | itemsize\n", " | Length of one array element in bytes.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1,2,3], dtype=np.float64)\n", " | >>> x.itemsize\n", " | 8\n", " | >>> x = np.array([1,2,3], dtype=np.complex128)\n", " | >>> x.itemsize\n", " | 16\n", " | \n", " | nbytes\n", " | Total bytes consumed by the elements of the array.\n", " | \n", " | Notes\n", " | -----\n", " | Does not include memory consumed by non-element attributes of the\n", " | array object.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3,5,2), dtype=np.complex128)\n", " | >>> x.nbytes\n", " | 480\n", " | >>> np.prod(x.shape) * x.itemsize\n", " | 480\n", " | \n", " | ndim\n", " | Number of array dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> x.ndim\n", " | 1\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.ndim\n", " | 3\n", " | \n", " | real\n", " | The real part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.real\n", " | array([ 1. , 0.70710678])\n", " | >>> x.real.dtype\n", " | dtype('float64')\n", " | \n", " | See Also\n", " | --------\n", " | numpy.real : equivalent function\n", " | \n", " | shape\n", " | Tuple of array dimensions.\n", " | \n", " | Notes\n", " | -----\n", " | May be used to \"reshape\" the array, as long as this would not\n", " | require a change in the total number of elements\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3, 4])\n", " | >>> x.shape\n", " | (4,)\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.shape\n", " | (2, 3, 4)\n", " | >>> y.shape = (3, 8)\n", " | >>> y\n", " | array([[ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.]])\n", " | >>> y.shape = (3, 6)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: total size of new array must be unchanged\n", " | \n", " | size\n", " | Number of elements in the array.\n", " | \n", " | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's\n", " | dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n", " | >>> x.size\n", " | 30\n", " | >>> np.prod(x.shape)\n", " | 30\n", " | \n", " | strides\n", " | Tuple of bytes to step in each dimension when traversing an array.\n", " | \n", " | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n", " | is::\n", " | \n", " | offset = sum(np.array(i) * a.strides)\n", " | \n", " | A more detailed explanation of strides can be found in the\n", " | \"ndarray.rst\" file in the NumPy reference guide.\n", " | \n", " | Notes\n", " | -----\n", " | Imagine an array of 32-bit integers (each 4 bytes)::\n", " | \n", " | x = np.array([[0, 1, 2, 3, 4],\n", " | [5, 6, 7, 8, 9]], dtype=np.int32)\n", " | \n", " | This array is stored in memory as 40 bytes, one after the other\n", " | (known as a contiguous block of memory). The strides of an array tell\n", " | us how many bytes we have to skip in memory to move to the next position\n", " | along a certain axis. For example, we have to skip 4 bytes (1 value) to\n", " | move to the next column, but 20 bytes (5 values) to get to the same\n", " | position in the next row. As such, the strides for the array `x` will be\n", " | ``(20, 4)``.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.lib.stride_tricks.as_strided\n", " | \n", " | Examples\n", " | --------\n", " | >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n", " | >>> y\n", " | array([[[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]],\n", " | [[12, 13, 14, 15],\n", " | [16, 17, 18, 19],\n", " | [20, 21, 22, 23]]])\n", " | >>> y.strides\n", " | (48, 16, 4)\n", " | >>> y[1,1,1]\n", " | 17\n", " | >>> offset=sum(y.strides * np.array((1,1,1)))\n", " | >>> offset/y.itemsize\n", " | 17\n", " | \n", " | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n", " | >>> x.strides\n", " | (32, 4, 224, 1344)\n", " | >>> i = np.array([3,5,2,2])\n", " | >>> offset = sum(i * x.strides)\n", " | >>> x[3,5,2,2]\n", " | 813\n", " | >>> offset / x.itemsize\n", " | 813\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " \n", " class ndenumerate(__builtin__.object)\n", " | Multidimensional index iterator.\n", " | \n", " | Return an iterator yielding pairs of array coordinates and values.\n", " | \n", " | Parameters\n", " | ----------\n", " | a : ndarray\n", " | Input array.\n", " | \n", " | See Also\n", " | --------\n", " | ndindex, flatiter\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> for index, x in np.ndenumerate(a):\n", " | ... print index, x\n", " | (0, 0) 1\n", " | (0, 1) 2\n", " | (1, 0) 3\n", " | (1, 1) 4\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, arr)\n", " | \n", " | __iter__(self)\n", " | \n", " | __next__(self)\n", " | Standard iterator method, returns the index tuple and array value.\n", " | \n", " | Returns\n", " | -------\n", " | coords : tuple of ints\n", " | The indices of the current iteration.\n", " | val : scalar\n", " | The array element of the current iteration.\n", " | \n", " | next = __next__(self)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " \n", " class ndindex(__builtin__.object)\n", " | An N-dimensional iterator object to index arrays.\n", " | \n", " | Given the shape of an array, an `ndindex` instance iterates over\n", " | the N-dimensional index of the array. At each iteration a tuple\n", " | of indices is returned, the last dimension is iterated over first.\n", " | \n", " | Parameters\n", " | ----------\n", " | `*args` : ints\n", " | The size of each dimension of the array.\n", " | \n", " | See Also\n", " | --------\n", " | ndenumerate, flatiter\n", " | \n", " | Examples\n", " | --------\n", " | >>> for index in np.ndindex(3, 2, 1):\n", " | ... print index\n", " | (0, 0, 0)\n", " | (0, 1, 0)\n", " | (1, 0, 0)\n", " | (1, 1, 0)\n", " | (2, 0, 0)\n", " | (2, 1, 0)\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, *shape)\n", " | \n", " | __iter__(self)\n", " | \n", " | __next__(self)\n", " | Standard iterator method, updates the index and returns the index\n", " | tuple.\n", " | \n", " | Returns\n", " | -------\n", " | val : tuple of ints\n", " | Returns a tuple containing the indices of the current\n", " | iteration.\n", " | \n", " | ndincr(self)\n", " | Increment the multi-dimensional index by one.\n", " | \n", " | This method is for backward compatibility only: do not use.\n", " | \n", " | next = __next__(self)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " \n", " class nditer(__builtin__.object)\n", " | Efficient multi-dimensional iterator object to iterate over arrays.\n", " | To get started using this object, see the\n", " | :ref:`introductory guide to array iteration `.\n", " | \n", " | Parameters\n", " | ----------\n", " | op : ndarray or sequence of array_like\n", " | The array(s) to iterate over.\n", " | flags : sequence of str, optional\n", " | Flags to control the behavior of the iterator.\n", " | \n", " | * \"buffered\" enables buffering when required.\n", " | * \"c_index\" causes a C-order index to be tracked.\n", " | * \"f_index\" causes a Fortran-order index to be tracked.\n", " | * \"multi_index\" causes a multi-index, or a tuple of indices\n", " | with one per iteration dimension, to be tracked.\n", " | * \"common_dtype\" causes all the operands to be converted to\n", " | a common data type, with copying or buffering as necessary.\n", " | * \"delay_bufalloc\" delays allocation of the buffers until\n", " | a reset() call is made. Allows \"allocate\" operands to\n", " | be initialized before their values are copied into the buffers.\n", " | * \"external_loop\" causes the `values` given to be\n", " | one-dimensional arrays with multiple values instead of\n", " | zero-dimensional arrays.\n", " | * \"grow_inner\" allows the `value` array sizes to be made\n", " | larger than the buffer size when both \"buffered\" and\n", " | \"external_loop\" is used.\n", " | * \"ranged\" allows the iterator to be restricted to a sub-range\n", " | of the iterindex values.\n", " | * \"refs_ok\" enables iteration of reference types, such as\n", " | object arrays.\n", " | * \"reduce_ok\" enables iteration of \"readwrite\" operands\n", " | which are broadcasted, also known as reduction operands.\n", " | * \"zerosize_ok\" allows `itersize` to be zero.\n", " | op_flags : list of list of str, optional\n", " | This is a list of flags for each operand. At minimum, one of\n", " | \"readonly\", \"readwrite\", or \"writeonly\" must be specified.\n", " | \n", " | * \"readonly\" indicates the operand will only be read from.\n", " | * \"readwrite\" indicates the operand will be read from and written to.\n", " | * \"writeonly\" indicates the operand will only be written to.\n", " | * \"no_broadcast\" prevents the operand from being broadcasted.\n", " | * \"contig\" forces the operand data to be contiguous.\n", " | * \"aligned\" forces the operand data to be aligned.\n", " | * \"nbo\" forces the operand data to be in native byte order.\n", " | * \"copy\" allows a temporary read-only copy if required.\n", " | * \"updateifcopy\" allows a temporary read-write copy if required.\n", " | * \"allocate\" causes the array to be allocated if it is None\n", " | in the `op` parameter.\n", " | * \"no_subtype\" prevents an \"allocate\" operand from using a subtype.\n", " | * \"arraymask\" indicates that this operand is the mask to use\n", " | for selecting elements when writing to operands with the\n", " | 'writemasked' flag set. The iterator does not enforce this,\n", " | but when writing from a buffer back to the array, it only\n", " | copies those elements indicated by this mask.\n", " | * 'writemasked' indicates that only elements where the chosen\n", " | 'arraymask' operand is True will be written to.\n", " | op_dtypes : dtype or tuple of dtype(s), optional\n", " | The required data type(s) of the operands. If copying or buffering\n", " | is enabled, the data will be converted to/from their original types.\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the iteration order. 'C' means C order, 'F' means\n", " | Fortran order, 'A' means 'F' order if all the arrays are Fortran\n", " | contiguous, 'C' order otherwise, and 'K' means as close to the\n", " | order the array elements appear in memory as possible. This also\n", " | affects the element memory order of \"allocate\" operands, as they\n", " | are allocated to be compatible with iteration order.\n", " | Default is 'K'.\n", " | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n", " | Controls what kind of data casting may occur when making a copy\n", " | or buffering. Setting this to 'unsafe' is not recommended,\n", " | as it can adversely affect accumulations.\n", " | \n", " | * 'no' means the data types should not be cast at all.\n", " | * 'equiv' means only byte-order changes are allowed.\n", " | * 'safe' means only casts which can preserve values are allowed.\n", " | * 'same_kind' means only safe casts or casts within a kind,\n", " | like float64 to float32, are allowed.\n", " | * 'unsafe' means any data conversions may be done.\n", " | op_axes : list of list of ints, optional\n", " | If provided, is a list of ints or None for each operands.\n", " | The list of axes for an operand is a mapping from the dimensions\n", " | of the iterator to the dimensions of the operand. A value of\n", " | -1 can be placed for entries, causing that dimension to be\n", " | treated as \"newaxis\".\n", " | itershape : tuple of ints, optional\n", " | The desired shape of the iterator. This allows \"allocate\" operands\n", " | with a dimension mapped by op_axes not corresponding to a dimension\n", " | of a different operand to get a value not equal to 1 for that\n", " | dimension.\n", " | buffersize : int, optional\n", " | When buffering is enabled, controls the size of the temporary\n", " | buffers. Set to 0 for the default value.\n", " | \n", " | Attributes\n", " | ----------\n", " | dtypes : tuple of dtype(s)\n", " | The data types of the values provided in `value`. This may be\n", " | different from the operand data types if buffering is enabled.\n", " | finished : bool\n", " | Whether the iteration over the operands is finished or not.\n", " | has_delayed_bufalloc : bool\n", " | If True, the iterator was created with the \"delay_bufalloc\" flag,\n", " | and no reset() function was called on it yet.\n", " | has_index : bool\n", " | If True, the iterator was created with either the \"c_index\" or\n", " | the \"f_index\" flag, and the property `index` can be used to\n", " | retrieve it.\n", " | has_multi_index : bool\n", " | If True, the iterator was created with the \"multi_index\" flag,\n", " | and the property `multi_index` can be used to retrieve it.\n", " | index :\n", " | When the \"c_index\" or \"f_index\" flag was used, this property\n", " | provides access to the index. Raises a ValueError if accessed\n", " | and `has_index` is False.\n", " | iterationneedsapi : bool\n", " | Whether iteration requires access to the Python API, for example\n", " | if one of the operands is an object array.\n", " | iterindex : int\n", " | An index which matches the order of iteration.\n", " | itersize : int\n", " | Size of the iterator.\n", " | itviews :\n", " | Structured view(s) of `operands` in memory, matching the reordered\n", " | and optimized iterator access pattern.\n", " | multi_index :\n", " | When the \"multi_index\" flag was used, this property\n", " | provides access to the index. Raises a ValueError if accessed\n", " | accessed and `has_multi_index` is False.\n", " | ndim : int\n", " | The iterator's dimension.\n", " | nop : int\n", " | The number of iterator operands.\n", " | operands : tuple of operand(s)\n", " | The array(s) to be iterated over.\n", " | shape : tuple of ints\n", " | Shape tuple, the shape of the iterator.\n", " | value :\n", " | Value of `operands` at current iteration. Normally, this is a\n", " | tuple of array scalars, but if the flag \"external_loop\" is used,\n", " | it is a tuple of one dimensional arrays.\n", " | \n", " | Notes\n", " | -----\n", " | `nditer` supersedes `flatiter`. The iterator implementation behind\n", " | `nditer` is also exposed by the Numpy C API.\n", " | \n", " | The Python exposure supplies two iteration interfaces, one which follows\n", " | the Python iterator protocol, and another which mirrors the C-style\n", " | do-while pattern. The native Python approach is better in most cases, but\n", " | if you need the iterator's coordinates or index, use the C-style pattern.\n", " | \n", " | Examples\n", " | --------\n", " | Here is how we might write an ``iter_add`` function, using the\n", " | Python iterator protocol::\n", " | \n", " | def iter_add_py(x, y, out=None):\n", " | addop = np.add\n", " | it = np.nditer([x, y, out], [],\n", " | [['readonly'], ['readonly'], ['writeonly','allocate']])\n", " | for (a, b, c) in it:\n", " | addop(a, b, out=c)\n", " | return it.operands[2]\n", " | \n", " | Here is the same function, but following the C-style pattern::\n", " | \n", " | def iter_add(x, y, out=None):\n", " | addop = np.add\n", " | \n", " | it = np.nditer([x, y, out], [],\n", " | [['readonly'], ['readonly'], ['writeonly','allocate']])\n", " | \n", " | while not it.finished:\n", " | addop(it[0], it[1], out=it[2])\n", " | it.iternext()\n", " | \n", " | return it.operands[2]\n", " | \n", " | Here is an example outer product function::\n", " | \n", " | def outer_it(x, y, out=None):\n", " | mulop = np.multiply\n", " | \n", " | it = np.nditer([x, y, out], ['external_loop'],\n", " | [['readonly'], ['readonly'], ['writeonly', 'allocate']],\n", " | op_axes=[range(x.ndim)+[-1]*y.ndim,\n", " | [-1]*x.ndim+range(y.ndim),\n", " | None])\n", " | \n", " | for (a, b, c) in it:\n", " | mulop(a, b, out=c)\n", " | \n", " | return it.operands[2]\n", " | \n", " | >>> a = np.arange(2)+1\n", " | >>> b = np.arange(3)+1\n", " | >>> outer_it(a,b)\n", " | array([[1, 2, 3],\n", " | [2, 4, 6]])\n", " | \n", " | Here is an example function which operates like a \"lambda\" ufunc::\n", " | \n", " | def luf(lamdaexpr, *args, **kwargs):\n", " | \"luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)\"\n", " | nargs = len(args)\n", " | op = (kwargs.get('out',None),) + args\n", " | it = np.nditer(op, ['buffered','external_loop'],\n", " | [['writeonly','allocate','no_broadcast']] +\n", " | [['readonly','nbo','aligned']]*nargs,\n", " | order=kwargs.get('order','K'),\n", " | casting=kwargs.get('casting','safe'),\n", " | buffersize=kwargs.get('buffersize',0))\n", " | while not it.finished:\n", " | it[0] = lamdaexpr(*it[1:])\n", " | it.iternext()\n", " | return it.operands[0]\n", " | \n", " | >>> a = np.arange(5)\n", " | >>> b = np.ones(5)\n", " | >>> luf(lambda i,j:i*i + j/2, a, b)\n", " | array([ 0.5, 1.5, 4.5, 9.5, 16.5])\n", " | \n", " | Methods defined here:\n", " | \n", " | __copy__(...)\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __delslice__(...)\n", " | x.__delslice__(i, j) <==> del x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __init__(...)\n", " | x.__init__(...) initializes x; see help(type(x)) for signature\n", " | \n", " | __iter__(...)\n", " | x.__iter__() <==> iter(x)\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | __setslice__(...)\n", " | x.__setslice__(i, j, y) <==> x[i:j]=y\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | copy(...)\n", " | copy()\n", " | \n", " | Get a copy of the iterator in its current state.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(10)\n", " | >>> y = x + 1\n", " | >>> it = np.nditer([x, y])\n", " | >>> it.next()\n", " | (array(0), array(1))\n", " | >>> it2 = it.copy()\n", " | >>> it2.next()\n", " | (array(1), array(2))\n", " | \n", " | debug_print(...)\n", " | debug_print()\n", " | \n", " | Print the current state of the `nditer` instance and debug info to stdout.\n", " | \n", " | enable_external_loop(...)\n", " | enable_external_loop()\n", " | \n", " | When the \"external_loop\" was not used during construction, but\n", " | is desired, this modifies the iterator to behave as if the flag\n", " | was specified.\n", " | \n", " | iternext(...)\n", " | iternext()\n", " | \n", " | Check whether iterations are left, and perform a single internal iteration\n", " | without returning the result. Used in the C-style pattern do-while\n", " | pattern. For an example, see `nditer`.\n", " | \n", " | Returns\n", " | -------\n", " | iternext : bool\n", " | Whether or not there are iterations left.\n", " | \n", " | next(...)\n", " | x.next() -> the next value, or raise StopIteration\n", " | \n", " | remove_axis(...)\n", " | remove_axis(i)\n", " | \n", " | Removes axis `i` from the iterator. Requires that the flag \"multi_index\"\n", " | be enabled.\n", " | \n", " | remove_multi_index(...)\n", " | remove_multi_index()\n", " | \n", " | When the \"multi_index\" flag was specified, this removes it, allowing\n", " | the internal iteration structure to be optimized further.\n", " | \n", " | reset(...)\n", " | reset()\n", " | \n", " | Reset the iterator to its initial state.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | dtypes\n", " | \n", " | finished\n", " | \n", " | has_delayed_bufalloc\n", " | \n", " | has_index\n", " | \n", " | has_multi_index\n", " | \n", " | index\n", " | \n", " | iterationneedsapi\n", " | \n", " | iterindex\n", " | \n", " | iterrange\n", " | \n", " | itersize\n", " | \n", " | itviews\n", " | \n", " | multi_index\n", " | \n", " | ndim\n", " | \n", " | nop\n", " | \n", " | operands\n", " | \n", " | shape\n", " | \n", " | value\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " \n", " class number(generic)\n", " | Method resolution order:\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " object0 = class object_(generic)\n", " | Any Python object. Character code: 'O'.\n", " | \n", " | Method resolution order:\n", " | object_\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __call__(...)\n", " | x.__call__(...) <==> x(...)\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __delattr__(...)\n", " | x.__delattr__('name') <==> del x.name\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __iadd__(...)\n", " | x.__iadd__(y) <==> x+=y\n", " | \n", " | __imul__(...)\n", " | x.__imul__(y) <==> x*=y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x*n\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(n) <==> n*x\n", " | \n", " | __setattr__(...)\n", " | x.__setattr__('name', value) <==> x.name = value\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class object_(generic)\n", " | Any Python object. Character code: 'O'.\n", " | \n", " | Method resolution order:\n", " | object_\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __call__(...)\n", " | x.__call__(...) <==> x(...)\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __delattr__(...)\n", " | x.__delattr__('name') <==> del x.name\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __iadd__(...)\n", " | x.__iadd__(y) <==> x+=y\n", " | \n", " | __imul__(...)\n", " | x.__imul__(y) <==> x*=y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x*n\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(n) <==> n*x\n", " | \n", " | __setattr__(...)\n", " | x.__setattr__('name', value) <==> x.name = value\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class poly1d(__builtin__.object)\n", " | A one-dimensional polynomial class.\n", " | \n", " | A convenience class, used to encapsulate \"natural\" operations on\n", " | polynomials so that said operations may take on their customary\n", " | form in code (see Examples).\n", " | \n", " | Parameters\n", " | ----------\n", " | c_or_r : array_like\n", " | The polynomial's coefficients, in decreasing powers, or if\n", " | the value of the second parameter is True, the polynomial's\n", " | roots (values where the polynomial evaluates to 0). For example,\n", " | ``poly1d([1, 2, 3])`` returns an object that represents\n", " | :math:`x^2 + 2x + 3`, whereas ``poly1d([1, 2, 3], True)`` returns\n", " | one that represents :math:`(x-1)(x-2)(x-3) = x^3 - 6x^2 + 11x -6`.\n", " | r : bool, optional\n", " | If True, `c_or_r` specifies the polynomial's roots; the default\n", " | is False.\n", " | variable : str, optional\n", " | Changes the variable used when printing `p` from `x` to `variable`\n", " | (see Examples).\n", " | \n", " | Examples\n", " | --------\n", " | Construct the polynomial :math:`x^2 + 2x + 3`:\n", " | \n", " | >>> p = np.poly1d([1, 2, 3])\n", " | >>> print np.poly1d(p)\n", " | 2\n", " | 1 x + 2 x + 3\n", " | \n", " | Evaluate the polynomial at :math:`x = 0.5`:\n", " | \n", " | >>> p(0.5)\n", " | 4.25\n", " | \n", " | Find the roots:\n", " | \n", " | >>> p.r\n", " | array([-1.+1.41421356j, -1.-1.41421356j])\n", " | >>> p(p.r)\n", " | array([ -4.44089210e-16+0.j, -4.44089210e-16+0.j])\n", " | \n", " | These numbers in the previous line represent (0, 0) to machine precision\n", " | \n", " | Show the coefficients:\n", " | \n", " | >>> p.c\n", " | array([1, 2, 3])\n", " | \n", " | Display the order (the leading zero-coefficients are removed):\n", " | \n", " | >>> p.order\n", " | 2\n", " | \n", " | Show the coefficient of the k-th power in the polynomial\n", " | (which is equivalent to ``p.c[-(i+1)]``):\n", " | \n", " | >>> p[1]\n", " | 2\n", " | \n", " | Polynomials can be added, subtracted, multiplied, and divided\n", " | (returns quotient and remainder):\n", " | \n", " | >>> p * p\n", " | poly1d([ 1, 4, 10, 12, 9])\n", " | \n", " | >>> (p**3 + 4) / p\n", " | (poly1d([ 1., 4., 10., 12., 9.]), poly1d([ 4.]))\n", " | \n", " | ``asarray(p)`` gives the coefficient array, so polynomials can be\n", " | used in all functions that accept arrays:\n", " | \n", " | >>> p**2 # square of polynomial\n", " | poly1d([ 1, 4, 10, 12, 9])\n", " | \n", " | >>> np.square(p) # square of individual coefficients\n", " | array([1, 4, 9])\n", " | \n", " | The variable used in the string representation of `p` can be modified,\n", " | using the `variable` parameter:\n", " | \n", " | >>> p = np.poly1d([1,2,3], variable='z')\n", " | >>> print p\n", " | 2\n", " | 1 z + 2 z + 3\n", " | \n", " | Construct a polynomial from its roots:\n", " | \n", " | >>> np.poly1d([1, 2], True)\n", " | poly1d([ 1, -3, 2])\n", " | \n", " | This is the same polynomial as obtained by:\n", " | \n", " | >>> np.poly1d([1, -1]) * np.poly1d([1, -2])\n", " | poly1d([ 1, -3, 2])\n", " | \n", " | Methods defined here:\n", " | \n", " | __add__(self, other)\n", " | \n", " | __array__(self, t=None)\n", " | \n", " | __call__(self, val)\n", " | \n", " | __div__(self, other)\n", " | \n", " | __eq__(self, other)\n", " | \n", " | __getattr__(self, key)\n", " | \n", " | __getitem__(self, val)\n", " | \n", " | __init__(self, c_or_r, r=0, variable=None)\n", " | \n", " | __iter__(self)\n", " | \n", " | __len__(self)\n", " | \n", " | __mul__(self, other)\n", " | \n", " | __ne__(self, other)\n", " | \n", " | __neg__(self)\n", " | \n", " | __pos__(self)\n", " | \n", " | __pow__(self, val)\n", " | \n", " | __radd__(self, other)\n", " | \n", " | __rdiv__(self, other)\n", " | \n", " | __repr__(self)\n", " | \n", " | __rmul__(self, other)\n", " | \n", " | __rsub__(self, other)\n", " | \n", " | __rtruediv__ = __rdiv__(self, other)\n", " | \n", " | __setattr__(self, key, val)\n", " | \n", " | __setitem__(self, key, val)\n", " | \n", " | __str__(self)\n", " | \n", " | __sub__(self, other)\n", " | \n", " | __truediv__ = __div__(self, other)\n", " | \n", " | deriv(self, m=1)\n", " | Return a derivative of this polynomial.\n", " | \n", " | Refer to `polyder` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | polyder : equivalent function\n", " | \n", " | integ(self, m=1, k=0)\n", " | Return an antiderivative (indefinite integral) of this polynomial.\n", " | \n", " | Refer to `polyint` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | polyint : equivalent function\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __hash__ = None\n", " | \n", " | coeffs = None\n", " | \n", " | order = None\n", " | \n", " | variable = None\n", " \n", " class recarray(numpy.ndarray)\n", " | Construct an ndarray that allows field access using attributes.\n", " | \n", " | Arrays may have a data-types containing fields, analogous\n", " | to columns in a spread sheet. An example is ``[(x, int), (y, float)]``,\n", " | where each entry in the array is a pair of ``(int, float)``. Normally,\n", " | these attributes are accessed using dictionary lookups such as ``arr['x']``\n", " | and ``arr['y']``. Record arrays allow the fields to be accessed as members\n", " | of the array, using ``arr.x`` and ``arr.y``.\n", " | \n", " | Parameters\n", " | ----------\n", " | shape : tuple\n", " | Shape of output array.\n", " | dtype : data-type, optional\n", " | The desired data-type. By default, the data-type is determined\n", " | from `formats`, `names`, `titles`, `aligned` and `byteorder`.\n", " | formats : list of data-types, optional\n", " | A list containing the data-types for the different columns, e.g.\n", " | ``['i4', 'f8', 'i4']``. `formats` does *not* support the new\n", " | convention of using types directly, i.e. ``(int, float, int)``.\n", " | Note that `formats` must be a list, not a tuple.\n", " | Given that `formats` is somewhat limited, we recommend specifying\n", " | `dtype` instead.\n", " | names : tuple of str, optional\n", " | The name of each column, e.g. ``('x', 'y', 'z')``.\n", " | buf : buffer, optional\n", " | By default, a new array is created of the given shape and data-type.\n", " | If `buf` is specified and is an object exposing the buffer interface,\n", " | the array will use the memory from the existing buffer. In this case,\n", " | the `offset` and `strides` keywords are available.\n", " | \n", " | Other Parameters\n", " | ----------------\n", " | titles : tuple of str, optional\n", " | Aliases for column names. For example, if `names` were\n", " | ``('x', 'y', 'z')`` and `titles` is\n", " | ``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then\n", " | ``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``.\n", " | byteorder : {'<', '>', '='}, optional\n", " | Byte-order for all fields.\n", " | aligned : bool, optional\n", " | Align the fields in memory as the C-compiler would.\n", " | strides : tuple of ints, optional\n", " | Buffer (`buf`) is interpreted according to these strides (strides\n", " | define how many bytes each array element, row, column, etc.\n", " | occupy in memory).\n", " | offset : int, optional\n", " | Start reading buffer (`buf`) from this offset onwards.\n", " | order : {'C', 'F'}, optional\n", " | Row-major or column-major order.\n", " | \n", " | Returns\n", " | -------\n", " | rec : recarray\n", " | Empty array of the given shape and type.\n", " | \n", " | See Also\n", " | --------\n", " | rec.fromrecords : Construct a record array from data.\n", " | record : fundamental data-type for `recarray`.\n", " | format_parser : determine a data-type from formats, names, titles.\n", " | \n", " | Notes\n", " | -----\n", " | This constructor can be compared to ``empty``: it creates a new record\n", " | array but does not fill it with data. To create a record array from data,\n", " | use one of the following methods:\n", " | \n", " | 1. Create a standard ndarray and convert it to a record array,\n", " | using ``arr.view(np.recarray)``\n", " | 2. Use the `buf` keyword.\n", " | 3. Use `np.rec.fromrecords`.\n", " | \n", " | Examples\n", " | --------\n", " | Create an array with two fields, ``x`` and ``y``:\n", " | \n", " | >>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)])\n", " | >>> x\n", " | array([(1.0, 2), (3.0, 4)],\n", " | dtype=[('x', '>> x['x']\n", " | array([ 1., 3.])\n", " | \n", " | View the array as a record array:\n", " | \n", " | >>> x = x.view(np.recarray)\n", " | \n", " | >>> x.x\n", " | array([ 1., 3.])\n", " | \n", " | >>> x.y\n", " | array([2, 4])\n", " | \n", " | Create a new, empty record array:\n", " | \n", " | >>> np.recarray((2,),\n", " | ... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP\n", " | rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),\n", " | (3471280, 1.2134086255804012e-316, 0)],\n", " | dtype=[('x', ' abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | a.__array__(|dtype) -> reference if type unchanged, copy otherwise.\n", " | \n", " | Returns either a new reference to self if dtype is not given or a new array\n", " | of provided data type if dtype is different from the current dtype of the\n", " | array.\n", " | \n", " | __array_prepare__(...)\n", " | a.__array_prepare__(obj) -> Object of same type as ndarray object obj.\n", " | \n", " | __array_wrap__(...)\n", " | a.__array_wrap__(obj) -> Object of same type as ndarray object a.\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __copy__(...)\n", " | a.__copy__([order])\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | If order is 'C' (False) then the result is contiguous (default).\n", " | If order is 'Fortran' (True) then the result has fortran order.\n", " | If order is 'Any' (None) then the result has fortran order\n", " | only if the array already is in fortran order.\n", " | \n", " | __deepcopy__(...)\n", " | a.__deepcopy__() -> Deep copy of array.\n", " | \n", " | Used if copy.deepcopy is called on an array.\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __delslice__(...)\n", " | x.__delslice__(i, j) <==> del x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __iadd__(...)\n", " | x.__iadd__(y) <==> x+=y\n", " | \n", " | __iand__(...)\n", " | x.__iand__(y) <==> x&=y\n", " | \n", " | __idiv__(...)\n", " | x.__idiv__(y) <==> x/=y\n", " | \n", " | __ifloordiv__(...)\n", " | x.__ifloordiv__(y) <==> x//y\n", " | \n", " | __ilshift__(...)\n", " | x.__ilshift__(y) <==> x<<=y\n", " | \n", " | __imod__(...)\n", " | x.__imod__(y) <==> x%=y\n", " | \n", " | __imul__(...)\n", " | x.__imul__(y) <==> x*=y\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __ior__(...)\n", " | x.__ior__(y) <==> x|=y\n", " | \n", " | __ipow__(...)\n", " | x.__ipow__(y) <==> x**=y\n", " | \n", " | __irshift__(...)\n", " | x.__irshift__(y) <==> x>>=y\n", " | \n", " | __isub__(...)\n", " | x.__isub__(y) <==> x-=y\n", " | \n", " | __iter__(...)\n", " | x.__iter__() <==> iter(x)\n", " | \n", " | __itruediv__(...)\n", " | x.__itruediv__(y) <==> x/y\n", " | \n", " | __ixor__(...)\n", " | x.__ixor__(y) <==> x^=y\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | a.__reduce__()\n", " | \n", " | For pickling.\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | __setslice__(...)\n", " | x.__setslice__(i, j, y) <==> x[i:j]=y\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __setstate__(...)\n", " | a.__setstate__(version, shape, dtype, isfortran, rawdata)\n", " | \n", " | For unpickling.\n", " | \n", " | Parameters\n", " | ----------\n", " | version : int\n", " | optional pickle version. If omitted defaults to 0.\n", " | shape : tuple\n", " | dtype : data-type\n", " | isFortran : bool\n", " | rawdata : string or list\n", " | a binary string with the data (or a list if 'a' is an object array)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | a.all(axis=None, out=None)\n", " | \n", " | Returns True if all elements evaluate to True.\n", " | \n", " | Refer to `numpy.all` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.all : equivalent function\n", " | \n", " | any(...)\n", " | a.any(axis=None, out=None)\n", " | \n", " | Returns True if any of the elements of `a` evaluate to True.\n", " | \n", " | Refer to `numpy.any` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.any : equivalent function\n", " | \n", " | argmax(...)\n", " | a.argmax(axis=None, out=None)\n", " | \n", " | Return indices of the maximum values along the given axis.\n", " | \n", " | Refer to `numpy.argmax` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmax : equivalent function\n", " | \n", " | argmin(...)\n", " | a.argmin(axis=None, out=None)\n", " | \n", " | Return indices of the minimum values along the given axis of `a`.\n", " | \n", " | Refer to `numpy.argmin` for detailed documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argmin : equivalent function\n", " | \n", " | argpartition(...)\n", " | a.argpartition(kth, axis=-1, kind='introselect', order=None)\n", " | \n", " | Returns the indices that would partition this array.\n", " | \n", " | Refer to `numpy.argpartition` for full documentation.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argpartition : equivalent function\n", " | \n", " | argsort(...)\n", " | a.argsort(axis=-1, kind='quicksort', order=None)\n", " | \n", " | Returns the indices that would sort this array.\n", " | \n", " | Refer to `numpy.argsort` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.argsort : equivalent function\n", " | \n", " | astype(...)\n", " | a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)\n", " | \n", " | Copy of the array, cast to a specified type.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | Typecode or data-type to which the array is cast.\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout order of the result.\n", " | 'C' means C order, 'F' means Fortran order, 'A'\n", " | means 'F' order if all the arrays are Fortran contiguous,\n", " | 'C' order otherwise, and 'K' means as close to the\n", " | order the array elements appear in memory as possible.\n", " | Default is 'K'.\n", " | casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n", " | Controls what kind of data casting may occur. Defaults to 'unsafe'\n", " | for backwards compatibility.\n", " | \n", " | * 'no' means the data types should not be cast at all.\n", " | * 'equiv' means only byte-order changes are allowed.\n", " | * 'safe' means only casts which can preserve values are allowed.\n", " | * 'same_kind' means only safe casts or casts within a kind,\n", " | like float64 to float32, are allowed.\n", " | * 'unsafe' means any data conversions may be done.\n", " | subok : bool, optional\n", " | If True, then sub-classes will be passed-through (default), otherwise\n", " | the returned array will be forced to be a base-class array.\n", " | copy : bool, optional\n", " | By default, astype always returns a newly allocated array. If this\n", " | is set to false, and the `dtype`, `order`, and `subok`\n", " | requirements are satisfied, the input array is returned instead\n", " | of a copy.\n", " | \n", " | Returns\n", " | -------\n", " | arr_t : ndarray\n", " | Unless `copy` is False and the other conditions for returning the input\n", " | array are satisfied (see description for `copy` input paramter), `arr_t`\n", " | is a new array of the same shape as the input array, with dtype, order\n", " | given by `dtype`, `order`.\n", " | \n", " | Notes\n", " | -----\n", " | Starting in NumPy 1.9, astype method now returns an error if the string\n", " | dtype to cast to is not long enough in 'safe' casting mode to hold the max\n", " | value of integer/float array that is being casted. Previously the casting\n", " | was allowed even if the result was truncated.\n", " | \n", " | Raises\n", " | ------\n", " | ComplexWarning\n", " | When casting from complex to float or int. To avoid this,\n", " | one should use ``a.real.astype(t)``.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 2.5])\n", " | >>> x\n", " | array([ 1. , 2. , 2.5])\n", " | \n", " | >>> x.astype(int)\n", " | array([1, 2, 2])\n", " | \n", " | byteswap(...)\n", " | a.byteswap(inplace)\n", " | \n", " | Swap the bytes of the array elements\n", " | \n", " | Toggle between low-endian and big-endian data representation by\n", " | returning a byteswapped array, optionally swapped in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | inplace : bool, optional\n", " | If ``True``, swap bytes in-place, default is ``False``.\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | The byteswapped array. If `inplace` is ``True``, this is\n", " | a view to self.\n", " | \n", " | Examples\n", " | --------\n", " | >>> A = np.array([1, 256, 8755], dtype=np.int16)\n", " | >>> map(hex, A)\n", " | ['0x1', '0x100', '0x2233']\n", " | >>> A.byteswap(True)\n", " | array([ 256, 1, 13090], dtype=int16)\n", " | >>> map(hex, A)\n", " | ['0x100', '0x1', '0x3322']\n", " | \n", " | Arrays of strings are not swapped\n", " | \n", " | >>> A = np.array(['ceg', 'fac'])\n", " | >>> A.byteswap()\n", " | array(['ceg', 'fac'],\n", " | dtype='|S3')\n", " | \n", " | choose(...)\n", " | a.choose(choices, out=None, mode='raise')\n", " | \n", " | Use an index array to construct a new array from a set of choices.\n", " | \n", " | Refer to `numpy.choose` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.choose : equivalent function\n", " | \n", " | clip(...)\n", " | a.clip(a_min, a_max, out=None)\n", " | \n", " | Return an array whose values are limited to ``[a_min, a_max]``.\n", " | \n", " | Refer to `numpy.clip` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.clip : equivalent function\n", " | \n", " | compress(...)\n", " | a.compress(condition, axis=None, out=None)\n", " | \n", " | Return selected slices of this array along given axis.\n", " | \n", " | Refer to `numpy.compress` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.compress : equivalent function\n", " | \n", " | conj(...)\n", " | a.conj()\n", " | \n", " | Complex-conjugate all elements.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | conjugate(...)\n", " | a.conjugate()\n", " | \n", " | Return the complex conjugate, element-wise.\n", " | \n", " | Refer to `numpy.conjugate` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.conjugate : equivalent function\n", " | \n", " | copy(...)\n", " | a.copy(order='C')\n", " | \n", " | Return a copy of the array.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A', 'K'}, optional\n", " | Controls the memory layout of the copy. 'C' means C-order,\n", " | 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n", " | 'C' otherwise. 'K' means match the layout of `a` as closely\n", " | as possible. (Note that this function and :func:numpy.copy are very\n", " | similar, but have different default values for their order=\n", " | arguments.)\n", " | \n", " | See also\n", " | --------\n", " | numpy.copy\n", " | numpy.copyto\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[1,2,3],[4,5,6]], order='F')\n", " | \n", " | >>> y = x.copy()\n", " | \n", " | >>> x.fill(0)\n", " | \n", " | >>> x\n", " | array([[0, 0, 0],\n", " | [0, 0, 0]])\n", " | \n", " | >>> y\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | \n", " | >>> y.flags['C_CONTIGUOUS']\n", " | True\n", " | \n", " | cumprod(...)\n", " | a.cumprod(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative product of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumprod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumprod : equivalent function\n", " | \n", " | cumsum(...)\n", " | a.cumsum(axis=None, dtype=None, out=None)\n", " | \n", " | Return the cumulative sum of the elements along the given axis.\n", " | \n", " | Refer to `numpy.cumsum` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.cumsum : equivalent function\n", " | \n", " | diagonal(...)\n", " | a.diagonal(offset=0, axis1=0, axis2=1)\n", " | \n", " | Return specified diagonals. In NumPy 1.9 the returned array is a\n", " | read-only view instead of a copy as in previous NumPy versions. In\n", " | NumPy 1.10 the read-only restriction will be removed.\n", " | \n", " | Refer to :func:`numpy.diagonal` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.diagonal : equivalent function\n", " | \n", " | dot(...)\n", " | a.dot(b, out=None)\n", " | \n", " | Dot product of two arrays.\n", " | \n", " | Refer to `numpy.dot` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dot : equivalent function\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.eye(2)\n", " | >>> b = np.ones((2, 2)) * 2\n", " | >>> a.dot(b)\n", " | array([[ 2., 2.],\n", " | [ 2., 2.]])\n", " | \n", " | This array method can be conveniently chained:\n", " | \n", " | >>> a.dot(b).dot(b)\n", " | array([[ 8., 8.],\n", " | [ 8., 8.]])\n", " | \n", " | dump(...)\n", " | a.dump(file)\n", " | \n", " | Dump a pickle of the array to the specified file.\n", " | The array can be read back with pickle.load or numpy.load.\n", " | \n", " | Parameters\n", " | ----------\n", " | file : str\n", " | A string naming the dump file.\n", " | \n", " | dumps(...)\n", " | a.dumps()\n", " | \n", " | Returns the pickle of the array as a string.\n", " | pickle.loads or numpy.loads will convert the string back to an array.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | fill(...)\n", " | a.fill(value)\n", " | \n", " | Fill the array with a scalar value.\n", " | \n", " | Parameters\n", " | ----------\n", " | value : scalar\n", " | All elements of `a` will be assigned this value.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([1, 2])\n", " | >>> a.fill(0)\n", " | >>> a\n", " | array([0, 0])\n", " | >>> a = np.empty(2)\n", " | >>> a.fill(1)\n", " | >>> a\n", " | array([ 1., 1.])\n", " | \n", " | flatten(...)\n", " | a.flatten(order='C')\n", " | \n", " | Return a copy of the array collapsed into one dimension.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', 'A'}, optional\n", " | Whether to flatten in C (row-major), Fortran (column-major) order,\n", " | or preserve the C/Fortran ordering from `a`.\n", " | The default is 'C'.\n", " | \n", " | Returns\n", " | -------\n", " | y : ndarray\n", " | A copy of the input array, flattened to one dimension.\n", " | \n", " | See Also\n", " | --------\n", " | ravel : Return a flattened array.\n", " | flat : A 1-D flat iterator over the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,2], [3,4]])\n", " | >>> a.flatten()\n", " | array([1, 2, 3, 4])\n", " | >>> a.flatten('F')\n", " | array([1, 3, 2, 4])\n", " | \n", " | getfield(...)\n", " | a.getfield(dtype, offset=0)\n", " | \n", " | Returns a field of the given array as a certain type.\n", " | \n", " | A field is a view of the array data with a given data-type. The values in\n", " | the view are determined by the given type and the offset into the current\n", " | array in bytes. The offset needs to be such that the view dtype fits in the\n", " | array dtype; for example an array of dtype complex128 has 16-byte elements.\n", " | If taking a view with a 32-bit integer (4 bytes), the offset needs to be\n", " | between 0 and 12 bytes.\n", " | \n", " | Parameters\n", " | ----------\n", " | dtype : str or dtype\n", " | The data type of the view. The dtype size of the view can not be larger\n", " | than that of the array itself.\n", " | offset : int\n", " | Number of bytes to skip before beginning the element view.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.diag([1.+1.j]*2)\n", " | >>> x[1, 1] = 2 + 4.j\n", " | >>> x\n", " | array([[ 1.+1.j, 0.+0.j],\n", " | [ 0.+0.j, 2.+4.j]])\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0.],\n", " | [ 0., 2.]])\n", " | \n", " | By choosing an offset of 8 bytes we can select the complex part of the\n", " | array for our view:\n", " | \n", " | >>> x.getfield(np.float64, offset=8)\n", " | array([[ 1., 0.],\n", " | [ 0., 4.]])\n", " | \n", " | item(...)\n", " | a.item(*args)\n", " | \n", " | Copy an element of an array to a standard Python scalar and return it.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments (variable number and type)\n", " | \n", " | * none: in this case, the method only works for arrays\n", " | with one element (`a.size == 1`), which element is\n", " | copied into a standard Python scalar object and returned.\n", " | \n", " | * int_type: this argument is interpreted as a flat index into\n", " | the array, specifying which element to copy and return.\n", " | \n", " | * tuple of int_types: functions as does a single int_type argument,\n", " | except that the argument is interpreted as an nd-index into the\n", " | array.\n", " | \n", " | Returns\n", " | -------\n", " | z : Standard Python scalar object\n", " | A copy of the specified element of the array as a suitable\n", " | Python scalar\n", " | \n", " | Notes\n", " | -----\n", " | When the data type of `a` is longdouble or clongdouble, item() returns\n", " | a scalar array object because there is no available Python scalar that\n", " | would not lose information. Void arrays return a buffer object for item(),\n", " | unless fields are defined, in which case a tuple is returned.\n", " | \n", " | `item` is very similar to a[args], except, instead of an array scalar,\n", " | a standard Python scalar is returned. This can be useful for speeding up\n", " | access to elements of the array and doing arithmetic on elements of the\n", " | array using Python's optimized math.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.item(3)\n", " | 2\n", " | >>> x.item(7)\n", " | 5\n", " | >>> x.item((0, 1))\n", " | 1\n", " | >>> x.item((2, 2))\n", " | 3\n", " | \n", " | itemset(...)\n", " | a.itemset(*args)\n", " | \n", " | Insert scalar into an array (scalar is cast to array's dtype, if possible)\n", " | \n", " | There must be at least 1 argument, and define the last argument\n", " | as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster\n", " | than ``a[args] = item``. The item should be a scalar value and `args`\n", " | must select a single item in the array `a`.\n", " | \n", " | Parameters\n", " | ----------\n", " | \\*args : Arguments\n", " | If one argument: a scalar, only used in case `a` is of size 1.\n", " | If two arguments: the last argument is the value to be set\n", " | and must be a scalar, the first argument specifies a single array\n", " | element location. It is either an int or a tuple.\n", " | \n", " | Notes\n", " | -----\n", " | Compared to indexing syntax, `itemset` provides some speed increase\n", " | for placing a scalar into a particular location in an `ndarray`,\n", " | if you must do this. However, generally this is discouraged:\n", " | among other problems, it complicates the appearance of the code.\n", " | Also, when using `itemset` (and `item`) inside a loop, be sure\n", " | to assign the methods to a local variable to avoid the attribute\n", " | look-up at each loop iteration.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.random.randint(9, size=(3, 3))\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 8, 3],\n", " | [8, 5, 3]])\n", " | >>> x.itemset(4, 0)\n", " | >>> x.itemset((2, 2), 9)\n", " | >>> x\n", " | array([[3, 1, 7],\n", " | [2, 0, 3],\n", " | [8, 5, 9]])\n", " | \n", " | max(...)\n", " | a.max(axis=None, out=None)\n", " | \n", " | Return the maximum along a given axis.\n", " | \n", " | Refer to `numpy.amax` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.amax : equivalent function\n", " | \n", " | mean(...)\n", " | a.mean(axis=None, dtype=None, out=None)\n", " | \n", " | Returns the average of the array elements along given axis.\n", " | \n", " | Refer to `numpy.mean` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.mean : equivalent function\n", " | \n", " | min(...)\n", " | a.min(axis=None, out=None)\n", " | \n", " | Return the minimum along a given axis.\n", " | \n", " | Refer to `numpy.amin` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.amin : equivalent function\n", " | \n", " | newbyteorder(...)\n", " | arr.newbyteorder(new_order='S')\n", " | \n", " | Return the array with the same data viewed with a different byte order.\n", " | \n", " | Equivalent to::\n", " | \n", " | arr.view(arr.dtype.newbytorder(new_order))\n", " | \n", " | Changes are also made in all fields and sub-arrays of the array data\n", " | type.\n", " | \n", " | \n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : string, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. `new_order` codes can be any of::\n", " | \n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_arr : array\n", " | New array object with the dtype reflecting given change to the\n", " | byte order.\n", " | \n", " | nonzero(...)\n", " | a.nonzero()\n", " | \n", " | Return the indices of the elements that are non-zero.\n", " | \n", " | Refer to `numpy.nonzero` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.nonzero : equivalent function\n", " | \n", " | partition(...)\n", " | a.partition(kth, axis=-1, kind='introselect', order=None)\n", " | \n", " | Rearranges the elements in the array in such a way that value of the\n", " | element in kth position is in the position it would be in a sorted array.\n", " | All elements smaller than the kth element are moved before this element and\n", " | all equal or greater are moved behind it. The ordering of the elements in\n", " | the two partitions is undefined.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | Parameters\n", " | ----------\n", " | kth : int or sequence of ints\n", " | Element index to partition by. The kth element value will be in its\n", " | final sorted position and all smaller elements will be moved before it\n", " | and all equal or greater elements behind it.\n", " | The order all elements in the partitions is undefined.\n", " | If provided with a sequence of kth it will partition all elements\n", " | indexed by kth of them into their sorted position at once.\n", " | axis : int, optional\n", " | Axis along which to sort. Default is -1, which means sort along the\n", " | last axis.\n", " | kind : {'introselect'}, optional\n", " | Selection algorithm. Default is 'introselect'.\n", " | order : list, optional\n", " | When `a` is an array with fields defined, this argument specifies\n", " | which fields to compare first, second, etc. Not all fields need be\n", " | specified.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.partition : Return a parititioned copy of an array.\n", " | argpartition : Indirect partition.\n", " | sort : Full sort.\n", " | \n", " | Notes\n", " | -----\n", " | See ``np.partition`` for notes on the different algorithms.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([3, 4, 2, 1])\n", " | >>> a.partition(a, 3)\n", " | >>> a\n", " | array([2, 1, 3, 4])\n", " | \n", " | >>> a.partition((1, 3))\n", " | array([1, 2, 3, 4])\n", " | \n", " | prod(...)\n", " | a.prod(axis=None, dtype=None, out=None)\n", " | \n", " | Return the product of the array elements over the given axis\n", " | \n", " | Refer to `numpy.prod` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.prod : equivalent function\n", " | \n", " | ptp(...)\n", " | a.ptp(axis=None, out=None)\n", " | \n", " | Peak to peak (maximum - minimum) value along a given axis.\n", " | \n", " | Refer to `numpy.ptp` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ptp : equivalent function\n", " | \n", " | put(...)\n", " | a.put(indices, values, mode='raise')\n", " | \n", " | Set ``a.flat[n] = values[n]`` for all `n` in indices.\n", " | \n", " | Refer to `numpy.put` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.put : equivalent function\n", " | \n", " | ravel(...)\n", " | a.ravel([order])\n", " | \n", " | Return a flattened array.\n", " | \n", " | Refer to `numpy.ravel` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ravel : equivalent function\n", " | \n", " | ndarray.flat : a flat iterator on the array.\n", " | \n", " | repeat(...)\n", " | a.repeat(repeats, axis=None)\n", " | \n", " | Repeat elements of an array.\n", " | \n", " | Refer to `numpy.repeat` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.repeat : equivalent function\n", " | \n", " | reshape(...)\n", " | a.reshape(shape, order='C')\n", " | \n", " | Returns an array containing the same data with a new shape.\n", " | \n", " | Refer to `numpy.reshape` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.reshape : equivalent function\n", " | \n", " | resize(...)\n", " | a.resize(new_shape, refcheck=True)\n", " | \n", " | Change shape and size of array in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | new_shape : tuple of ints, or `n` ints\n", " | Shape of resized array.\n", " | refcheck : bool, optional\n", " | If False, reference count will not be checked. Default is True.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | Raises\n", " | ------\n", " | ValueError\n", " | If `a` does not own its own data or references or views to it exist,\n", " | and the data memory must be changed.\n", " | \n", " | SystemError\n", " | If the `order` keyword argument is specified. This behaviour is a\n", " | bug in NumPy.\n", " | \n", " | See Also\n", " | --------\n", " | resize : Return a new array with the specified shape.\n", " | \n", " | Notes\n", " | -----\n", " | This reallocates space for the data area if necessary.\n", " | \n", " | Only contiguous arrays (data elements consecutive in memory) can be\n", " | resized.\n", " | \n", " | The purpose of the reference count check is to make sure you\n", " | do not use this array as a buffer for another Python object and then\n", " | reallocate the memory. However, reference counts can increase in\n", " | other ways so if you are sure that you have not shared the memory\n", " | for this array with another Python object, then you may safely set\n", " | `refcheck` to False.\n", " | \n", " | Examples\n", " | --------\n", " | Shrinking an array: array is flattened (in the order that the data are\n", " | stored in memory), resized, and reshaped:\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='C')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [1]])\n", " | \n", " | >>> a = np.array([[0, 1], [2, 3]], order='F')\n", " | >>> a.resize((2, 1))\n", " | >>> a\n", " | array([[0],\n", " | [2]])\n", " | \n", " | Enlarging an array: as above, but missing entries are filled with zeros:\n", " | \n", " | >>> b = np.array([[0, 1], [2, 3]])\n", " | >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple\n", " | >>> b\n", " | array([[0, 1, 2],\n", " | [3, 0, 0]])\n", " | \n", " | Referencing an array prevents resizing...\n", " | \n", " | >>> c = a\n", " | >>> a.resize((1, 1))\n", " | Traceback (most recent call last):\n", " | ...\n", " | ValueError: cannot resize an array that has been referenced ...\n", " | \n", " | Unless `refcheck` is False:\n", " | \n", " | >>> a.resize((1, 1), refcheck=False)\n", " | >>> a\n", " | array([[0]])\n", " | >>> c\n", " | array([[0]])\n", " | \n", " | round(...)\n", " | a.round(decimals=0, out=None)\n", " | \n", " | Return `a` with each element rounded to the given number of decimals.\n", " | \n", " | Refer to `numpy.around` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.around : equivalent function\n", " | \n", " | searchsorted(...)\n", " | a.searchsorted(v, side='left', sorter=None)\n", " | \n", " | Find indices where elements of v should be inserted in a to maintain order.\n", " | \n", " | For full documentation, see `numpy.searchsorted`\n", " | \n", " | See Also\n", " | --------\n", " | numpy.searchsorted : equivalent function\n", " | \n", " | setfield(...)\n", " | a.setfield(val, dtype, offset=0)\n", " | \n", " | Put a value into a specified place in a field defined by a data-type.\n", " | \n", " | Place `val` into `a`'s field defined by `dtype` and beginning `offset`\n", " | bytes into the field.\n", " | \n", " | Parameters\n", " | ----------\n", " | val : object\n", " | Value to be placed in field.\n", " | dtype : dtype object\n", " | Data-type of the field in which to place `val`.\n", " | offset : int, optional\n", " | The number of bytes into the field at which to place `val`.\n", " | \n", " | Returns\n", " | -------\n", " | None\n", " | \n", " | See Also\n", " | --------\n", " | getfield\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.eye(3)\n", " | >>> x.getfield(np.float64)\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | >>> x.setfield(3, np.int32)\n", " | >>> x.getfield(np.int32)\n", " | array([[3, 3, 3],\n", " | [3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x\n", " | array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323],\n", " | [ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]])\n", " | >>> x.setfield(np.eye(3), np.int32)\n", " | >>> x\n", " | array([[ 1., 0., 0.],\n", " | [ 0., 1., 0.],\n", " | [ 0., 0., 1.]])\n", " | \n", " | setflags(...)\n", " | a.setflags(write=None, align=None, uic=None)\n", " | \n", " | Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.\n", " | \n", " | These Boolean-valued flags affect how numpy interprets the memory\n", " | area used by `a` (see Notes below). The ALIGNED flag can only\n", " | be set to True if the data is actually aligned according to the type.\n", " | The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE\n", " | can only be set to True if the array owns its own memory, or the\n", " | ultimate owner of the memory exposes a writeable buffer interface,\n", " | or is a string. (The exception for string is made so that unpickling\n", " | can be done without copying memory.)\n", " | \n", " | Parameters\n", " | ----------\n", " | write : bool, optional\n", " | Describes whether or not `a` can be written to.\n", " | align : bool, optional\n", " | Describes whether or not `a` is aligned properly for its type.\n", " | uic : bool, optional\n", " | Describes whether or not `a` is a copy of another \"base\" array.\n", " | \n", " | Notes\n", " | -----\n", " | Array flags provide information about how the memory area used\n", " | for the array is to be interpreted. There are 6 Boolean flags\n", " | in use, only three of which can be changed by the user:\n", " | UPDATEIFCOPY, WRITEABLE, and ALIGNED.\n", " | \n", " | WRITEABLE (W) the data area can be written to;\n", " | \n", " | ALIGNED (A) the data and strides are aligned appropriately for the hardware\n", " | (as determined by the compiler);\n", " | \n", " | UPDATEIFCOPY (U) this array is a copy of some other array (referenced\n", " | by .base). When this array is deallocated, the base array will be\n", " | updated with the contents of this array.\n", " | \n", " | All flags can be accessed using their first (upper case) letter as well\n", " | as the full name.\n", " | \n", " | Examples\n", " | --------\n", " | >>> y\n", " | array([[3, 1, 7],\n", " | [2, 0, 0],\n", " | [8, 5, 9]])\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : True\n", " | ALIGNED : True\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(write=0, align=0)\n", " | >>> y.flags\n", " | C_CONTIGUOUS : True\n", " | F_CONTIGUOUS : False\n", " | OWNDATA : True\n", " | WRITEABLE : False\n", " | ALIGNED : False\n", " | UPDATEIFCOPY : False\n", " | >>> y.setflags(uic=1)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: cannot set UPDATEIFCOPY flag to True\n", " | \n", " | sort(...)\n", " | a.sort(axis=-1, kind='quicksort', order=None)\n", " | \n", " | Sort an array, in-place.\n", " | \n", " | Parameters\n", " | ----------\n", " | axis : int, optional\n", " | Axis along which to sort. Default is -1, which means sort along the\n", " | last axis.\n", " | kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n", " | Sorting algorithm. Default is 'quicksort'.\n", " | order : list, optional\n", " | When `a` is an array with fields defined, this argument specifies\n", " | which fields to compare first, second, etc. Not all fields need be\n", " | specified.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.sort : Return a sorted copy of an array.\n", " | argsort : Indirect sort.\n", " | lexsort : Indirect stable sort on multiple keys.\n", " | searchsorted : Find elements in sorted array.\n", " | partition: Partial sort.\n", " | \n", " | Notes\n", " | -----\n", " | See ``sort`` for notes on the different sorting algorithms.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1,4], [3,1]])\n", " | >>> a.sort(axis=1)\n", " | >>> a\n", " | array([[1, 4],\n", " | [1, 3]])\n", " | >>> a.sort(axis=0)\n", " | >>> a\n", " | array([[1, 3],\n", " | [1, 4]])\n", " | \n", " | Use the `order` keyword to specify a field to use when sorting a\n", " | structured array:\n", " | \n", " | >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])\n", " | >>> a.sort(order='y')\n", " | >>> a\n", " | array([('c', 1), ('a', 2)],\n", " | dtype=[('x', '|S1'), ('y', '>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | tofile(...)\n", " | a.tofile(fid, sep=\"\", format=\"%s\")\n", " | \n", " | Write array to a file as text or binary (default).\n", " | \n", " | Data is always written in 'C' order, independent of the order of `a`.\n", " | The data produced by this method can be recovered using the function\n", " | fromfile().\n", " | \n", " | Parameters\n", " | ----------\n", " | fid : file or str\n", " | An open file object, or a string containing a filename.\n", " | sep : str\n", " | Separator between array items for text output.\n", " | If \"\" (empty), a binary file is written, equivalent to\n", " | ``file.write(a.tobytes())``.\n", " | format : str\n", " | Format string for text file output.\n", " | Each entry in the array is formatted to text by first converting\n", " | it to the closest Python type, and then using \"format\" % item.\n", " | \n", " | Notes\n", " | -----\n", " | This is a convenience function for quick storage of array data.\n", " | Information on endianness and precision is lost, so this method is not a\n", " | good choice for files intended to archive data or transport data between\n", " | machines with different endianness. Some of these problems can be overcome\n", " | by outputting the data as text files, at the expense of speed and file\n", " | size.\n", " | \n", " | tolist(...)\n", " | a.tolist()\n", " | \n", " | Return the array as a (possibly nested) list.\n", " | \n", " | Return a copy of the array data as a (nested) Python list.\n", " | Data items are converted to the nearest compatible Python type.\n", " | \n", " | Parameters\n", " | ----------\n", " | none\n", " | \n", " | Returns\n", " | -------\n", " | y : list\n", " | The possibly nested list of array elements.\n", " | \n", " | Notes\n", " | -----\n", " | The array may be recreated, ``a = np.array(a.tolist())``.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([1, 2])\n", " | >>> a.tolist()\n", " | [1, 2]\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> list(a)\n", " | [array([1, 2]), array([3, 4])]\n", " | >>> a.tolist()\n", " | [[1, 2], [3, 4]]\n", " | \n", " | tostring(...)\n", " | a.tostring(order='C')\n", " | \n", " | Construct Python bytes containing the raw data bytes in the array.\n", " | \n", " | Constructs Python bytes showing a copy of the raw contents of\n", " | data memory. The bytes object can be produced in either 'C' or 'Fortran',\n", " | or 'Any' order (the default is 'C'-order). 'Any' order means C-order\n", " | unless the F_CONTIGUOUS flag in the array is set, in which case it\n", " | means 'Fortran' order.\n", " | \n", " | This function is a compatibility alias for tobytes. Despite its name it returns bytes not strings.\n", " | \n", " | Parameters\n", " | ----------\n", " | order : {'C', 'F', None}, optional\n", " | Order of the data for multidimensional arrays:\n", " | C, Fortran, or the same as for the original array.\n", " | \n", " | Returns\n", " | -------\n", " | s : bytes\n", " | Python bytes exhibiting a copy of `a`'s raw data.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[0, 1], [2, 3]])\n", " | >>> x.tobytes()\n", " | b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | >>> x.tobytes('C') == x.tobytes()\n", " | True\n", " | >>> x.tobytes('F')\n", " | b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'\n", " | \n", " | trace(...)\n", " | a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)\n", " | \n", " | Return the sum along diagonals of the array.\n", " | \n", " | Refer to `numpy.trace` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.trace : equivalent function\n", " | \n", " | transpose(...)\n", " | a.transpose(*axes)\n", " | \n", " | Returns a view of the array with axes transposed.\n", " | \n", " | For a 1-D array, this has no effect. (To change between column and\n", " | row vectors, first cast the 1-D array into a matrix object.)\n", " | For a 2-D array, this is the usual matrix transpose.\n", " | For an n-D array, if axes are given, their order indicates how the\n", " | axes are permuted (see Examples). If axes are not provided and\n", " | ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then\n", " | ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.\n", " | \n", " | Parameters\n", " | ----------\n", " | axes : None, tuple of ints, or `n` ints\n", " | \n", " | * None or no argument: reverses the order of the axes.\n", " | \n", " | * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s\n", " | `i`-th axis becomes `a.transpose()`'s `j`-th axis.\n", " | \n", " | * `n` ints: same as an n-tuple of the same ints (this form is\n", " | intended simply as a \"convenience\" alternative to the tuple form)\n", " | \n", " | Returns\n", " | -------\n", " | out : ndarray\n", " | View of `a`, with axes suitably permuted.\n", " | \n", " | See Also\n", " | --------\n", " | ndarray.T : Array property returning the array transposed.\n", " | \n", " | Examples\n", " | --------\n", " | >>> a = np.array([[1, 2], [3, 4]])\n", " | >>> a\n", " | array([[1, 2],\n", " | [3, 4]])\n", " | >>> a.transpose()\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose((1, 0))\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | >>> a.transpose(1, 0)\n", " | array([[1, 3],\n", " | [2, 4]])\n", " | \n", " | var(...)\n", " | a.var(axis=None, dtype=None, out=None, ddof=0)\n", " | \n", " | Returns the variance of the array elements, along given axis.\n", " | \n", " | Refer to `numpy.var` for full documentation.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.var : equivalent function\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from numpy.ndarray:\n", " | \n", " | T\n", " | Same as self.transpose(), except that self is returned if\n", " | self.ndim < 2.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([[1.,2.],[3.,4.]])\n", " | >>> x\n", " | array([[ 1., 2.],\n", " | [ 3., 4.]])\n", " | >>> x.T\n", " | array([[ 1., 3.],\n", " | [ 2., 4.]])\n", " | >>> x = np.array([1.,2.,3.,4.])\n", " | >>> x\n", " | array([ 1., 2., 3., 4.])\n", " | >>> x.T\n", " | array([ 1., 2., 3., 4.])\n", " | \n", " | __array_finalize__\n", " | None.\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side.\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: C-struct side.\n", " | \n", " | base\n", " | Base object if memory is from some other object.\n", " | \n", " | Examples\n", " | --------\n", " | The base of an array that owns its memory is None:\n", " | \n", " | >>> x = np.array([1,2,3,4])\n", " | >>> x.base is None\n", " | True\n", " | \n", " | Slicing creates a view, whose memory is shared with x:\n", " | \n", " | >>> y = x[2:]\n", " | >>> y.base is x\n", " | True\n", " | \n", " | ctypes\n", " | An object to simplify the interaction of the array with the ctypes\n", " | module.\n", " | \n", " | This attribute creates an object that makes it easier to use arrays\n", " | when calling shared libraries with the ctypes module. The returned\n", " | object has, among others, data, shape, and strides attributes (see\n", " | Notes below) which themselves return ctypes objects that can be used\n", " | as arguments to a shared library.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | c : Python object\n", " | Possessing attributes data, shape, strides, etc.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ctypeslib\n", " | \n", " | Notes\n", " | -----\n", " | Below are the public attributes of this object which were documented\n", " | in \"Guide to NumPy\" (we have omitted undocumented public attributes,\n", " | as well as documented private attributes):\n", " | \n", " | * data: A pointer to the memory area of the array as a Python integer.\n", " | This memory area may contain data that is not aligned, or not in correct\n", " | byte-order. The memory area may not even be writeable. The array\n", " | flags and data-type of this array should be respected when passing this\n", " | attribute to arbitrary C-code to avoid trouble that can include Python\n", " | crashing. User Beware! The value of this attribute is exactly the same\n", " | as self._array_interface_['data'][0].\n", " | \n", " | * shape (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the C-integer corresponding to dtype('p') on this\n", " | platform. This base-type could be c_int, c_long, or c_longlong\n", " | depending on the platform. The c_intp type is defined accordingly in\n", " | numpy.ctypeslib. The ctypes array contains the shape of the underlying\n", " | array.\n", " | \n", " | * strides (c_intp*self.ndim): A ctypes array of length self.ndim where\n", " | the basetype is the same as for the shape attribute. This ctypes array\n", " | contains the strides information from the underlying array. This strides\n", " | information is important for showing how many bytes must be jumped to\n", " | get to the next element in the array.\n", " | \n", " | * data_as(obj): Return the data pointer cast to a particular c-types object.\n", " | For example, calling self._as_parameter_ is equivalent to\n", " | self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a\n", " | pointer to a ctypes array of floating-point data:\n", " | self.data_as(ctypes.POINTER(ctypes.c_double)).\n", " | \n", " | * shape_as(obj): Return the shape tuple as an array of some other c-types\n", " | type. For example: self.shape_as(ctypes.c_short).\n", " | \n", " | * strides_as(obj): Return the strides tuple as an array of some other\n", " | c-types type. For example: self.strides_as(ctypes.c_longlong).\n", " | \n", " | Be careful using the ctypes attribute - especially on temporary\n", " | arrays or arrays constructed on the fly. For example, calling\n", " | ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory\n", " | that is invalid because the array created as (a+b) is deallocated\n", " | before the next Python statement. You can avoid this problem using\n", " | either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will\n", " | hold a reference to the array until ct is deleted or re-assigned.\n", " | \n", " | If the ctypes module is not available, then the ctypes attribute\n", " | of array objects still returns something useful, but ctypes objects\n", " | are not returned and errors may be raised instead. In particular,\n", " | the object will still have the as parameter attribute which will\n", " | return an integer equal to the data attribute.\n", " | \n", " | Examples\n", " | --------\n", " | >>> import ctypes\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.ctypes.data\n", " | 30439712\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))\n", " | \n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents\n", " | c_long(0)\n", " | >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents\n", " | c_longlong(4294967296L)\n", " | >>> x.ctypes.shape\n", " | \n", " | >>> x.ctypes.shape_as(ctypes.c_long)\n", " | \n", " | >>> x.ctypes.strides\n", " | \n", " | >>> x.ctypes.strides_as(ctypes.c_longlong)\n", " | \n", " | \n", " | data\n", " | Python buffer object pointing to the start of the array's data.\n", " | \n", " | dtype\n", " | Data-type of the array's elements.\n", " | \n", " | Parameters\n", " | ----------\n", " | None\n", " | \n", " | Returns\n", " | -------\n", " | d : numpy dtype object\n", " | \n", " | See Also\n", " | --------\n", " | numpy.dtype\n", " | \n", " | Examples\n", " | --------\n", " | >>> x\n", " | array([[0, 1],\n", " | [2, 3]])\n", " | >>> x.dtype\n", " | dtype('int32')\n", " | >>> type(x.dtype)\n", " | \n", " | \n", " | flags\n", " | Information about the memory layout of the array.\n", " | \n", " | Attributes\n", " | ----------\n", " | C_CONTIGUOUS (C)\n", " | The data is in a single, C-style contiguous segment.\n", " | F_CONTIGUOUS (F)\n", " | The data is in a single, Fortran-style contiguous segment.\n", " | OWNDATA (O)\n", " | The array owns the memory it uses or borrows it from another object.\n", " | WRITEABLE (W)\n", " | The data area can be written to. Setting this to False locks\n", " | the data, making it read-only. A view (slice, etc.) inherits WRITEABLE\n", " | from its base array at creation time, but a view of a writeable\n", " | array may be subsequently locked while the base array remains writeable.\n", " | (The opposite is not true, in that a view of a locked array may not\n", " | be made writeable. However, currently, locking a base object does not\n", " | lock any views that already reference it, so under that circumstance it\n", " | is possible to alter the contents of a locked array via a previously\n", " | created writeable view onto it.) Attempting to change a non-writeable\n", " | array raises a RuntimeError exception.\n", " | ALIGNED (A)\n", " | The data and all elements are aligned appropriately for the hardware.\n", " | UPDATEIFCOPY (U)\n", " | This array is a copy of some other array. When this array is\n", " | deallocated, the base array will be updated with the contents of\n", " | this array.\n", " | FNC\n", " | F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | FORC\n", " | F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).\n", " | BEHAVED (B)\n", " | ALIGNED and WRITEABLE.\n", " | CARRAY (CA)\n", " | BEHAVED and C_CONTIGUOUS.\n", " | FARRAY (FA)\n", " | BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.\n", " | \n", " | Notes\n", " | -----\n", " | The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),\n", " | or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag\n", " | names are only supported in dictionary access.\n", " | \n", " | Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by\n", " | the user, via direct assignment to the attribute or dictionary entry,\n", " | or by calling `ndarray.setflags`.\n", " | \n", " | The array flags cannot be set arbitrarily:\n", " | \n", " | - UPDATEIFCOPY can only be set ``False``.\n", " | - ALIGNED can only be set ``True`` if the data is truly aligned.\n", " | - WRITEABLE can only be set ``True`` if the array owns its own memory\n", " | or the ultimate owner of the memory exposes a writeable buffer\n", " | interface or is a string.\n", " | \n", " | Arrays can be both C-style and Fortran-style contiguous simultaneously.\n", " | This is clear for 1-dimensional arrays, but can also be true for higher\n", " | dimensional arrays.\n", " | \n", " | Even for contiguous arrays a stride for a given dimension\n", " | ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``\n", " | or the array has no elements.\n", " | It does *not* generally hold that ``self.strides[-1] == self.itemsize``\n", " | for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for\n", " | Fortran-style contiguous arrays is true.\n", " | \n", " | flat\n", " | A 1-D iterator over the array.\n", " | \n", " | This is a `numpy.flatiter` instance, which acts similarly to, but is not\n", " | a subclass of, Python's built-in iterator object.\n", " | \n", " | See Also\n", " | --------\n", " | flatten : Return a copy of the array collapsed into one dimension.\n", " | \n", " | flatiter\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.arange(1, 7).reshape(2, 3)\n", " | >>> x\n", " | array([[1, 2, 3],\n", " | [4, 5, 6]])\n", " | >>> x.flat[3]\n", " | 4\n", " | >>> x.T\n", " | array([[1, 4],\n", " | [2, 5],\n", " | [3, 6]])\n", " | >>> x.T.flat[3]\n", " | 5\n", " | >>> type(x.flat)\n", " | \n", " | \n", " | An assignment example:\n", " | \n", " | >>> x.flat = 3; x\n", " | array([[3, 3, 3],\n", " | [3, 3, 3]])\n", " | >>> x.flat[[1,4]] = 1; x\n", " | array([[3, 1, 3],\n", " | [3, 1, 3]])\n", " | \n", " | imag\n", " | The imaginary part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.imag\n", " | array([ 0. , 0.70710678])\n", " | >>> x.imag.dtype\n", " | dtype('float64')\n", " | \n", " | itemsize\n", " | Length of one array element in bytes.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1,2,3], dtype=np.float64)\n", " | >>> x.itemsize\n", " | 8\n", " | >>> x = np.array([1,2,3], dtype=np.complex128)\n", " | >>> x.itemsize\n", " | 16\n", " | \n", " | nbytes\n", " | Total bytes consumed by the elements of the array.\n", " | \n", " | Notes\n", " | -----\n", " | Does not include memory consumed by non-element attributes of the\n", " | array object.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3,5,2), dtype=np.complex128)\n", " | >>> x.nbytes\n", " | 480\n", " | >>> np.prod(x.shape) * x.itemsize\n", " | 480\n", " | \n", " | ndim\n", " | Number of array dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3])\n", " | >>> x.ndim\n", " | 1\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.ndim\n", " | 3\n", " | \n", " | real\n", " | The real part of the array.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.sqrt([1+0j, 0+1j])\n", " | >>> x.real\n", " | array([ 1. , 0.70710678])\n", " | >>> x.real.dtype\n", " | dtype('float64')\n", " | \n", " | See Also\n", " | --------\n", " | numpy.real : equivalent function\n", " | \n", " | shape\n", " | Tuple of array dimensions.\n", " | \n", " | Notes\n", " | -----\n", " | May be used to \"reshape\" the array, as long as this would not\n", " | require a change in the total number of elements\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.array([1, 2, 3, 4])\n", " | >>> x.shape\n", " | (4,)\n", " | >>> y = np.zeros((2, 3, 4))\n", " | >>> y.shape\n", " | (2, 3, 4)\n", " | >>> y.shape = (3, 8)\n", " | >>> y\n", " | array([[ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.],\n", " | [ 0., 0., 0., 0., 0., 0., 0., 0.]])\n", " | >>> y.shape = (3, 6)\n", " | Traceback (most recent call last):\n", " | File \"\", line 1, in \n", " | ValueError: total size of new array must be unchanged\n", " | \n", " | size\n", " | Number of elements in the array.\n", " | \n", " | Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's\n", " | dimensions.\n", " | \n", " | Examples\n", " | --------\n", " | >>> x = np.zeros((3, 5, 2), dtype=np.complex128)\n", " | >>> x.size\n", " | 30\n", " | >>> np.prod(x.shape)\n", " | 30\n", " | \n", " | strides\n", " | Tuple of bytes to step in each dimension when traversing an array.\n", " | \n", " | The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`\n", " | is::\n", " | \n", " | offset = sum(np.array(i) * a.strides)\n", " | \n", " | A more detailed explanation of strides can be found in the\n", " | \"ndarray.rst\" file in the NumPy reference guide.\n", " | \n", " | Notes\n", " | -----\n", " | Imagine an array of 32-bit integers (each 4 bytes)::\n", " | \n", " | x = np.array([[0, 1, 2, 3, 4],\n", " | [5, 6, 7, 8, 9]], dtype=np.int32)\n", " | \n", " | This array is stored in memory as 40 bytes, one after the other\n", " | (known as a contiguous block of memory). The strides of an array tell\n", " | us how many bytes we have to skip in memory to move to the next position\n", " | along a certain axis. For example, we have to skip 4 bytes (1 value) to\n", " | move to the next column, but 20 bytes (5 values) to get to the same\n", " | position in the next row. As such, the strides for the array `x` will be\n", " | ``(20, 4)``.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.lib.stride_tricks.as_strided\n", " | \n", " | Examples\n", " | --------\n", " | >>> y = np.reshape(np.arange(2*3*4), (2,3,4))\n", " | >>> y\n", " | array([[[ 0, 1, 2, 3],\n", " | [ 4, 5, 6, 7],\n", " | [ 8, 9, 10, 11]],\n", " | [[12, 13, 14, 15],\n", " | [16, 17, 18, 19],\n", " | [20, 21, 22, 23]]])\n", " | >>> y.strides\n", " | (48, 16, 4)\n", " | >>> y[1,1,1]\n", " | 17\n", " | >>> offset=sum(y.strides * np.array((1,1,1)))\n", " | >>> offset/y.itemsize\n", " | 17\n", " | \n", " | >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)\n", " | >>> x.strides\n", " | (32, 4, 224, 1344)\n", " | >>> i = np.array([3,5,2,2])\n", " | >>> offset = sum(i * x.strides)\n", " | >>> x[3,5,2,2]\n", " | 813\n", " | >>> offset / x.itemsize\n", " | 813\n", " \n", " class record(numpy.void)\n", " | A data-type scalar that allows field access as attribute lookup.\n", " | \n", " | Method resolution order:\n", " | record\n", " | numpy.void\n", " | numpy.flexible\n", " | numpy.generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __getattribute__(self, attr)\n", " | \n", " | __repr__(self)\n", " | \n", " | __setattr__(self, attr, val)\n", " | \n", " | __str__(self)\n", " | \n", " | pprint(self)\n", " | Pretty-print all fields.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from numpy.void:\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | getfield(...)\n", " | \n", " | setfield(...)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from numpy.void:\n", " | \n", " | dtype\n", " | dtype object\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes inherited from numpy.void:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from numpy.generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from numpy.generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " short = class int16(signedinteger)\n", " | 16-bit integer. Character code ``h``. C short compatible.\n", " | \n", " | Method resolution order:\n", " | int16\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class signedinteger(integer)\n", " | Method resolution order:\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " single = class float32(floating)\n", " | 32-bit floating-point number. Character code 'f'. C float compatible.\n", " | \n", " | Method resolution order:\n", " | float32\n", " | floating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " singlecomplex = class complex64(complexfloating)\n", " | Composed of two 32 bit floats\n", " | \n", " | Method resolution order:\n", " | complex64\n", " | complexfloating\n", " | inexact\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __complex__(...)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " str_ = class string_(__builtin__.str, character)\n", " | Method resolution order:\n", " | string_\n", " | __builtin__.str\n", " | __builtin__.basestring\n", " | character\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.str:\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __format__(...)\n", " | S.__format__(format_spec) -> string\n", " | \n", " | Return a formatted version of S as described by format_spec.\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __mod__(...)\n", " | x.__mod__(y) <==> x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(n) <==> x*n\n", " | \n", " | __rmod__(...)\n", " | x.__rmod__(y) <==> y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(n) <==> n*x\n", " | \n", " | __sizeof__(...)\n", " | S.__sizeof__() -> size of S in memory, in bytes\n", " | \n", " | capitalize(...)\n", " | S.capitalize() -> string\n", " | \n", " | Return a copy of the string S with only its first character\n", " | capitalized.\n", " | \n", " | center(...)\n", " | S.center(width[, fillchar]) -> string\n", " | \n", " | Return S centered in a string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | count(...)\n", " | S.count(sub[, start[, end]]) -> int\n", " | \n", " | Return the number of non-overlapping occurrences of substring sub in\n", " | string S[start:end]. Optional arguments start and end are interpreted\n", " | as in slice notation.\n", " | \n", " | decode(...)\n", " | S.decode([encoding[,errors]]) -> object\n", " | \n", " | Decodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n", " | as well as any other name registered with codecs.register_error that is\n", " | able to handle UnicodeDecodeErrors.\n", " | \n", " | encode(...)\n", " | S.encode([encoding[,errors]]) -> object\n", " | \n", " | Encodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n", " | 'xmlcharrefreplace' as well as any other name registered with\n", " | codecs.register_error that is able to handle UnicodeEncodeErrors.\n", " | \n", " | endswith(...)\n", " | S.endswith(suffix[, start[, end]]) -> bool\n", " | \n", " | Return True if S ends with the specified suffix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | suffix can also be a tuple of strings to try.\n", " | \n", " | expandtabs(...)\n", " | S.expandtabs([tabsize]) -> string\n", " | \n", " | Return a copy of S where all tab characters are expanded using spaces.\n", " | If tabsize is not given, a tab size of 8 characters is assumed.\n", " | \n", " | find(...)\n", " | S.find(sub [,start [,end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | format(...)\n", " | S.format(*args, **kwargs) -> string\n", " | \n", " | Return a formatted version of S, using substitutions from args and kwargs.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | index(...)\n", " | S.index(sub [,start [,end]]) -> int\n", " | \n", " | Like S.find() but raise ValueError when the substring is not found.\n", " | \n", " | isalnum(...)\n", " | S.isalnum() -> bool\n", " | \n", " | Return True if all characters in S are alphanumeric\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isalpha(...)\n", " | S.isalpha() -> bool\n", " | \n", " | Return True if all characters in S are alphabetic\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isdigit(...)\n", " | S.isdigit() -> bool\n", " | \n", " | Return True if all characters in S are digits\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | islower(...)\n", " | S.islower() -> bool\n", " | \n", " | Return True if all cased characters in S are lowercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | isspace(...)\n", " | S.isspace() -> bool\n", " | \n", " | Return True if all characters in S are whitespace\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | istitle(...)\n", " | S.istitle() -> bool\n", " | \n", " | Return True if S is a titlecased string and there is at least one\n", " | character in S, i.e. uppercase characters may only follow uncased\n", " | characters and lowercase characters only cased ones. Return False\n", " | otherwise.\n", " | \n", " | isupper(...)\n", " | S.isupper() -> bool\n", " | \n", " | Return True if all cased characters in S are uppercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | join(...)\n", " | S.join(iterable) -> string\n", " | \n", " | Return a string which is the concatenation of the strings in the\n", " | iterable. The separator between elements is S.\n", " | \n", " | ljust(...)\n", " | S.ljust(width[, fillchar]) -> string\n", " | \n", " | Return S left-justified in a string of length width. Padding is\n", " | done using the specified fill character (default is a space).\n", " | \n", " | lower(...)\n", " | S.lower() -> string\n", " | \n", " | Return a copy of the string S converted to lowercase.\n", " | \n", " | lstrip(...)\n", " | S.lstrip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with leading whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | partition(...)\n", " | S.partition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, and return the part before it,\n", " | the separator itself, and the part after it. If the separator is not\n", " | found, return S and two empty strings.\n", " | \n", " | replace(...)\n", " | S.replace(old, new[, count]) -> string\n", " | \n", " | Return a copy of string S with all occurrences of substring\n", " | old replaced by new. If the optional argument count is\n", " | given, only the first count occurrences are replaced.\n", " | \n", " | rfind(...)\n", " | S.rfind(sub [,start [,end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | rindex(...)\n", " | S.rindex(sub [,start [,end]]) -> int\n", " | \n", " | Like S.rfind() but raise ValueError when the substring is not found.\n", " | \n", " | rjust(...)\n", " | S.rjust(width[, fillchar]) -> string\n", " | \n", " | Return S right-justified in a string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | rpartition(...)\n", " | S.rpartition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, starting at the end of S, and return\n", " | the part before it, the separator itself, and the part after it. If the\n", " | separator is not found, return two empty strings and S.\n", " | \n", " | rsplit(...)\n", " | S.rsplit([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in the string S, using sep as the\n", " | delimiter string, starting at the end of the string and working\n", " | to the front. If maxsplit is given, at most maxsplit splits are\n", " | done. If sep is not specified or is None, any whitespace string\n", " | is a separator.\n", " | \n", " | rstrip(...)\n", " | S.rstrip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with trailing whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | split(...)\n", " | S.split([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in the string S, using sep as the\n", " | delimiter string. If maxsplit is given, at most maxsplit\n", " | splits are done. If sep is not specified or is None, any\n", " | whitespace string is a separator and empty strings are removed\n", " | from the result.\n", " | \n", " | splitlines(...)\n", " | S.splitlines(keepends=False) -> list of strings\n", " | \n", " | Return a list of the lines in S, breaking at line boundaries.\n", " | Line breaks are not included in the resulting list unless keepends\n", " | is given and true.\n", " | \n", " | startswith(...)\n", " | S.startswith(prefix[, start[, end]]) -> bool\n", " | \n", " | Return True if S starts with the specified prefix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | prefix can also be a tuple of strings to try.\n", " | \n", " | strip(...)\n", " | S.strip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with leading and trailing\n", " | whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | swapcase(...)\n", " | S.swapcase() -> string\n", " | \n", " | Return a copy of the string S with uppercase characters\n", " | converted to lowercase and vice versa.\n", " | \n", " | title(...)\n", " | S.title() -> string\n", " | \n", " | Return a titlecased version of S, i.e. words start with uppercase\n", " | characters, all remaining cased characters have lowercase.\n", " | \n", " | translate(...)\n", " | S.translate(table [,deletechars]) -> string\n", " | \n", " | Return a copy of the string S, where all characters occurring\n", " | in the optional argument deletechars are removed, and the\n", " | remaining characters have been mapped through the given\n", " | translation table, which must be a string of length 256 or None.\n", " | If the table argument is None, no translation is applied and\n", " | the operation simply removes the characters in deletechars.\n", " | \n", " | upper(...)\n", " | S.upper() -> string\n", " | \n", " | Return a copy of the string S converted to uppercase.\n", " | \n", " | zfill(...)\n", " | S.zfill(width) -> string\n", " | \n", " | Pad a numeric string S with zeros on the left, to fill a field\n", " | of the specified width. The string S is never truncated.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " string0 = class string_(__builtin__.str, character)\n", " | Method resolution order:\n", " | string_\n", " | __builtin__.str\n", " | __builtin__.basestring\n", " | character\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.str:\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __format__(...)\n", " | S.__format__(format_spec) -> string\n", " | \n", " | Return a formatted version of S as described by format_spec.\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __mod__(...)\n", " | x.__mod__(y) <==> x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(n) <==> x*n\n", " | \n", " | __rmod__(...)\n", " | x.__rmod__(y) <==> y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(n) <==> n*x\n", " | \n", " | __sizeof__(...)\n", " | S.__sizeof__() -> size of S in memory, in bytes\n", " | \n", " | capitalize(...)\n", " | S.capitalize() -> string\n", " | \n", " | Return a copy of the string S with only its first character\n", " | capitalized.\n", " | \n", " | center(...)\n", " | S.center(width[, fillchar]) -> string\n", " | \n", " | Return S centered in a string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | count(...)\n", " | S.count(sub[, start[, end]]) -> int\n", " | \n", " | Return the number of non-overlapping occurrences of substring sub in\n", " | string S[start:end]. Optional arguments start and end are interpreted\n", " | as in slice notation.\n", " | \n", " | decode(...)\n", " | S.decode([encoding[,errors]]) -> object\n", " | \n", " | Decodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n", " | as well as any other name registered with codecs.register_error that is\n", " | able to handle UnicodeDecodeErrors.\n", " | \n", " | encode(...)\n", " | S.encode([encoding[,errors]]) -> object\n", " | \n", " | Encodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n", " | 'xmlcharrefreplace' as well as any other name registered with\n", " | codecs.register_error that is able to handle UnicodeEncodeErrors.\n", " | \n", " | endswith(...)\n", " | S.endswith(suffix[, start[, end]]) -> bool\n", " | \n", " | Return True if S ends with the specified suffix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | suffix can also be a tuple of strings to try.\n", " | \n", " | expandtabs(...)\n", " | S.expandtabs([tabsize]) -> string\n", " | \n", " | Return a copy of S where all tab characters are expanded using spaces.\n", " | If tabsize is not given, a tab size of 8 characters is assumed.\n", " | \n", " | find(...)\n", " | S.find(sub [,start [,end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | format(...)\n", " | S.format(*args, **kwargs) -> string\n", " | \n", " | Return a formatted version of S, using substitutions from args and kwargs.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | index(...)\n", " | S.index(sub [,start [,end]]) -> int\n", " | \n", " | Like S.find() but raise ValueError when the substring is not found.\n", " | \n", " | isalnum(...)\n", " | S.isalnum() -> bool\n", " | \n", " | Return True if all characters in S are alphanumeric\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isalpha(...)\n", " | S.isalpha() -> bool\n", " | \n", " | Return True if all characters in S are alphabetic\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isdigit(...)\n", " | S.isdigit() -> bool\n", " | \n", " | Return True if all characters in S are digits\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | islower(...)\n", " | S.islower() -> bool\n", " | \n", " | Return True if all cased characters in S are lowercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | isspace(...)\n", " | S.isspace() -> bool\n", " | \n", " | Return True if all characters in S are whitespace\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | istitle(...)\n", " | S.istitle() -> bool\n", " | \n", " | Return True if S is a titlecased string and there is at least one\n", " | character in S, i.e. uppercase characters may only follow uncased\n", " | characters and lowercase characters only cased ones. Return False\n", " | otherwise.\n", " | \n", " | isupper(...)\n", " | S.isupper() -> bool\n", " | \n", " | Return True if all cased characters in S are uppercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | join(...)\n", " | S.join(iterable) -> string\n", " | \n", " | Return a string which is the concatenation of the strings in the\n", " | iterable. The separator between elements is S.\n", " | \n", " | ljust(...)\n", " | S.ljust(width[, fillchar]) -> string\n", " | \n", " | Return S left-justified in a string of length width. Padding is\n", " | done using the specified fill character (default is a space).\n", " | \n", " | lower(...)\n", " | S.lower() -> string\n", " | \n", " | Return a copy of the string S converted to lowercase.\n", " | \n", " | lstrip(...)\n", " | S.lstrip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with leading whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | partition(...)\n", " | S.partition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, and return the part before it,\n", " | the separator itself, and the part after it. If the separator is not\n", " | found, return S and two empty strings.\n", " | \n", " | replace(...)\n", " | S.replace(old, new[, count]) -> string\n", " | \n", " | Return a copy of string S with all occurrences of substring\n", " | old replaced by new. If the optional argument count is\n", " | given, only the first count occurrences are replaced.\n", " | \n", " | rfind(...)\n", " | S.rfind(sub [,start [,end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | rindex(...)\n", " | S.rindex(sub [,start [,end]]) -> int\n", " | \n", " | Like S.rfind() but raise ValueError when the substring is not found.\n", " | \n", " | rjust(...)\n", " | S.rjust(width[, fillchar]) -> string\n", " | \n", " | Return S right-justified in a string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | rpartition(...)\n", " | S.rpartition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, starting at the end of S, and return\n", " | the part before it, the separator itself, and the part after it. If the\n", " | separator is not found, return two empty strings and S.\n", " | \n", " | rsplit(...)\n", " | S.rsplit([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in the string S, using sep as the\n", " | delimiter string, starting at the end of the string and working\n", " | to the front. If maxsplit is given, at most maxsplit splits are\n", " | done. If sep is not specified or is None, any whitespace string\n", " | is a separator.\n", " | \n", " | rstrip(...)\n", " | S.rstrip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with trailing whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | split(...)\n", " | S.split([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in the string S, using sep as the\n", " | delimiter string. If maxsplit is given, at most maxsplit\n", " | splits are done. If sep is not specified or is None, any\n", " | whitespace string is a separator and empty strings are removed\n", " | from the result.\n", " | \n", " | splitlines(...)\n", " | S.splitlines(keepends=False) -> list of strings\n", " | \n", " | Return a list of the lines in S, breaking at line boundaries.\n", " | Line breaks are not included in the resulting list unless keepends\n", " | is given and true.\n", " | \n", " | startswith(...)\n", " | S.startswith(prefix[, start[, end]]) -> bool\n", " | \n", " | Return True if S starts with the specified prefix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | prefix can also be a tuple of strings to try.\n", " | \n", " | strip(...)\n", " | S.strip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with leading and trailing\n", " | whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | swapcase(...)\n", " | S.swapcase() -> string\n", " | \n", " | Return a copy of the string S with uppercase characters\n", " | converted to lowercase and vice versa.\n", " | \n", " | title(...)\n", " | S.title() -> string\n", " | \n", " | Return a titlecased version of S, i.e. words start with uppercase\n", " | characters, all remaining cased characters have lowercase.\n", " | \n", " | translate(...)\n", " | S.translate(table [,deletechars]) -> string\n", " | \n", " | Return a copy of the string S, where all characters occurring\n", " | in the optional argument deletechars are removed, and the\n", " | remaining characters have been mapped through the given\n", " | translation table, which must be a string of length 256 or None.\n", " | If the table argument is None, no translation is applied and\n", " | the operation simply removes the characters in deletechars.\n", " | \n", " | upper(...)\n", " | S.upper() -> string\n", " | \n", " | Return a copy of the string S converted to uppercase.\n", " | \n", " | zfill(...)\n", " | S.zfill(width) -> string\n", " | \n", " | Pad a numeric string S with zeros on the left, to fill a field\n", " | of the specified width. The string S is never truncated.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class string_(__builtin__.str, character)\n", " | Method resolution order:\n", " | string_\n", " | __builtin__.str\n", " | __builtin__.basestring\n", " | character\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.str:\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __format__(...)\n", " | S.__format__(format_spec) -> string\n", " | \n", " | Return a formatted version of S as described by format_spec.\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __mod__(...)\n", " | x.__mod__(y) <==> x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(n) <==> x*n\n", " | \n", " | __rmod__(...)\n", " | x.__rmod__(y) <==> y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(n) <==> n*x\n", " | \n", " | __sizeof__(...)\n", " | S.__sizeof__() -> size of S in memory, in bytes\n", " | \n", " | capitalize(...)\n", " | S.capitalize() -> string\n", " | \n", " | Return a copy of the string S with only its first character\n", " | capitalized.\n", " | \n", " | center(...)\n", " | S.center(width[, fillchar]) -> string\n", " | \n", " | Return S centered in a string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | count(...)\n", " | S.count(sub[, start[, end]]) -> int\n", " | \n", " | Return the number of non-overlapping occurrences of substring sub in\n", " | string S[start:end]. Optional arguments start and end are interpreted\n", " | as in slice notation.\n", " | \n", " | decode(...)\n", " | S.decode([encoding[,errors]]) -> object\n", " | \n", " | Decodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n", " | as well as any other name registered with codecs.register_error that is\n", " | able to handle UnicodeDecodeErrors.\n", " | \n", " | encode(...)\n", " | S.encode([encoding[,errors]]) -> object\n", " | \n", " | Encodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n", " | 'xmlcharrefreplace' as well as any other name registered with\n", " | codecs.register_error that is able to handle UnicodeEncodeErrors.\n", " | \n", " | endswith(...)\n", " | S.endswith(suffix[, start[, end]]) -> bool\n", " | \n", " | Return True if S ends with the specified suffix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | suffix can also be a tuple of strings to try.\n", " | \n", " | expandtabs(...)\n", " | S.expandtabs([tabsize]) -> string\n", " | \n", " | Return a copy of S where all tab characters are expanded using spaces.\n", " | If tabsize is not given, a tab size of 8 characters is assumed.\n", " | \n", " | find(...)\n", " | S.find(sub [,start [,end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | format(...)\n", " | S.format(*args, **kwargs) -> string\n", " | \n", " | Return a formatted version of S, using substitutions from args and kwargs.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | index(...)\n", " | S.index(sub [,start [,end]]) -> int\n", " | \n", " | Like S.find() but raise ValueError when the substring is not found.\n", " | \n", " | isalnum(...)\n", " | S.isalnum() -> bool\n", " | \n", " | Return True if all characters in S are alphanumeric\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isalpha(...)\n", " | S.isalpha() -> bool\n", " | \n", " | Return True if all characters in S are alphabetic\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isdigit(...)\n", " | S.isdigit() -> bool\n", " | \n", " | Return True if all characters in S are digits\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | islower(...)\n", " | S.islower() -> bool\n", " | \n", " | Return True if all cased characters in S are lowercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | isspace(...)\n", " | S.isspace() -> bool\n", " | \n", " | Return True if all characters in S are whitespace\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | istitle(...)\n", " | S.istitle() -> bool\n", " | \n", " | Return True if S is a titlecased string and there is at least one\n", " | character in S, i.e. uppercase characters may only follow uncased\n", " | characters and lowercase characters only cased ones. Return False\n", " | otherwise.\n", " | \n", " | isupper(...)\n", " | S.isupper() -> bool\n", " | \n", " | Return True if all cased characters in S are uppercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | join(...)\n", " | S.join(iterable) -> string\n", " | \n", " | Return a string which is the concatenation of the strings in the\n", " | iterable. The separator between elements is S.\n", " | \n", " | ljust(...)\n", " | S.ljust(width[, fillchar]) -> string\n", " | \n", " | Return S left-justified in a string of length width. Padding is\n", " | done using the specified fill character (default is a space).\n", " | \n", " | lower(...)\n", " | S.lower() -> string\n", " | \n", " | Return a copy of the string S converted to lowercase.\n", " | \n", " | lstrip(...)\n", " | S.lstrip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with leading whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | partition(...)\n", " | S.partition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, and return the part before it,\n", " | the separator itself, and the part after it. If the separator is not\n", " | found, return S and two empty strings.\n", " | \n", " | replace(...)\n", " | S.replace(old, new[, count]) -> string\n", " | \n", " | Return a copy of string S with all occurrences of substring\n", " | old replaced by new. If the optional argument count is\n", " | given, only the first count occurrences are replaced.\n", " | \n", " | rfind(...)\n", " | S.rfind(sub [,start [,end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | rindex(...)\n", " | S.rindex(sub [,start [,end]]) -> int\n", " | \n", " | Like S.rfind() but raise ValueError when the substring is not found.\n", " | \n", " | rjust(...)\n", " | S.rjust(width[, fillchar]) -> string\n", " | \n", " | Return S right-justified in a string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | rpartition(...)\n", " | S.rpartition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, starting at the end of S, and return\n", " | the part before it, the separator itself, and the part after it. If the\n", " | separator is not found, return two empty strings and S.\n", " | \n", " | rsplit(...)\n", " | S.rsplit([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in the string S, using sep as the\n", " | delimiter string, starting at the end of the string and working\n", " | to the front. If maxsplit is given, at most maxsplit splits are\n", " | done. If sep is not specified or is None, any whitespace string\n", " | is a separator.\n", " | \n", " | rstrip(...)\n", " | S.rstrip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with trailing whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | split(...)\n", " | S.split([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in the string S, using sep as the\n", " | delimiter string. If maxsplit is given, at most maxsplit\n", " | splits are done. If sep is not specified or is None, any\n", " | whitespace string is a separator and empty strings are removed\n", " | from the result.\n", " | \n", " | splitlines(...)\n", " | S.splitlines(keepends=False) -> list of strings\n", " | \n", " | Return a list of the lines in S, breaking at line boundaries.\n", " | Line breaks are not included in the resulting list unless keepends\n", " | is given and true.\n", " | \n", " | startswith(...)\n", " | S.startswith(prefix[, start[, end]]) -> bool\n", " | \n", " | Return True if S starts with the specified prefix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | prefix can also be a tuple of strings to try.\n", " | \n", " | strip(...)\n", " | S.strip([chars]) -> string or unicode\n", " | \n", " | Return a copy of the string S with leading and trailing\n", " | whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is unicode, S will be converted to unicode before stripping\n", " | \n", " | swapcase(...)\n", " | S.swapcase() -> string\n", " | \n", " | Return a copy of the string S with uppercase characters\n", " | converted to lowercase and vice versa.\n", " | \n", " | title(...)\n", " | S.title() -> string\n", " | \n", " | Return a titlecased version of S, i.e. words start with uppercase\n", " | characters, all remaining cased characters have lowercase.\n", " | \n", " | translate(...)\n", " | S.translate(table [,deletechars]) -> string\n", " | \n", " | Return a copy of the string S, where all characters occurring\n", " | in the optional argument deletechars are removed, and the\n", " | remaining characters have been mapped through the given\n", " | translation table, which must be a string of length 256 or None.\n", " | If the table argument is None, no translation is applied and\n", " | the operation simply removes the characters in deletechars.\n", " | \n", " | upper(...)\n", " | S.upper() -> string\n", " | \n", " | Return a copy of the string S converted to uppercase.\n", " | \n", " | zfill(...)\n", " | S.zfill(width) -> string\n", " | \n", " | Pad a numeric string S with zeros on the left, to fill a field\n", " | of the specified width. The string S is never truncated.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class timedelta64(signedinteger)\n", " | Method resolution order:\n", " | timedelta64\n", " | signedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " ubyte = class uint8(unsignedinteger)\n", " | Method resolution order:\n", " | uint8\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class ufunc(__builtin__.object)\n", " | Functions that operate element by element on whole arrays.\n", " | \n", " | To see the documentation for a specific ufunc, use np.info(). For\n", " | example, np.info(np.sin). Because ufuncs are written in C\n", " | (for speed) and linked into Python with NumPy's ufunc facility,\n", " | Python's help() function finds this page whenever help() is called\n", " | on a ufunc.\n", " | \n", " | A detailed explanation of ufuncs can be found in the \"ufuncs.rst\"\n", " | file in the NumPy reference guide.\n", " | \n", " | Unary ufuncs:\n", " | =============\n", " | \n", " | op(X, out=None)\n", " | Apply op to X elementwise\n", " | \n", " | Parameters\n", " | ----------\n", " | X : array_like\n", " | Input array.\n", " | out : array_like\n", " | An array to store the output. Must be the same shape as `X`.\n", " | \n", " | Returns\n", " | -------\n", " | r : array_like\n", " | `r` will have the same shape as `X`; if out is provided, `r`\n", " | will be equal to out.\n", " | \n", " | Binary ufuncs:\n", " | ==============\n", " | \n", " | op(X, Y, out=None)\n", " | Apply `op` to `X` and `Y` elementwise. May \"broadcast\" to make\n", " | the shapes of `X` and `Y` congruent.\n", " | \n", " | The broadcasting rules are:\n", " | \n", " | * Dimensions of length 1 may be prepended to either array.\n", " | * Arrays may be repeated along dimensions of length 1.\n", " | \n", " | Parameters\n", " | ----------\n", " | X : array_like\n", " | First input array.\n", " | Y : array_like\n", " | Second input array.\n", " | out : array_like\n", " | An array to store the output. Must be the same shape as the\n", " | output would have.\n", " | \n", " | Returns\n", " | -------\n", " | r : array_like\n", " | The return value; if out is provided, `r` will be equal to out.\n", " | \n", " | Methods defined here:\n", " | \n", " | __call__(...)\n", " | x.__call__(...) <==> x(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | accumulate(...)\n", " | accumulate(array, axis=0, dtype=None, out=None)\n", " | \n", " | Accumulate the result of applying the operator to all elements.\n", " | \n", " | For a one-dimensional array, accumulate produces results equivalent to::\n", " | \n", " | r = np.empty(len(A))\n", " | t = op.identity # op = the ufunc being applied to A's elements\n", " | for i in range(len(A)):\n", " | t = op(t, A[i])\n", " | r[i] = t\n", " | return r\n", " | \n", " | For example, add.accumulate() is equivalent to np.cumsum().\n", " | \n", " | For a multi-dimensional array, accumulate is applied along only one\n", " | axis (axis zero by default; see Examples below) so repeated use is\n", " | necessary if one wants to accumulate over multiple axes.\n", " | \n", " | Parameters\n", " | ----------\n", " | array : array_like\n", " | The array to act on.\n", " | axis : int, optional\n", " | The axis along which to apply the accumulation; default is zero.\n", " | dtype : data-type code, optional\n", " | The data-type used to represent the intermediate results. Defaults\n", " | to the data-type of the output array if such is provided, or the\n", " | the data-type of the input array if no output array is provided.\n", " | out : ndarray, optional\n", " | A location into which the result is stored. If not provided a\n", " | freshly-allocated array is returned.\n", " | \n", " | Returns\n", " | -------\n", " | r : ndarray\n", " | The accumulated values. If `out` was supplied, `r` is a reference to\n", " | `out`.\n", " | \n", " | Examples\n", " | --------\n", " | 1-D array examples:\n", " | \n", " | >>> np.add.accumulate([2, 3, 5])\n", " | array([ 2, 5, 10])\n", " | >>> np.multiply.accumulate([2, 3, 5])\n", " | array([ 2, 6, 30])\n", " | \n", " | 2-D array examples:\n", " | \n", " | >>> I = np.eye(2)\n", " | >>> I\n", " | array([[ 1., 0.],\n", " | [ 0., 1.]])\n", " | \n", " | Accumulate along axis 0 (rows), down columns:\n", " | \n", " | >>> np.add.accumulate(I, 0)\n", " | array([[ 1., 0.],\n", " | [ 1., 1.]])\n", " | >>> np.add.accumulate(I) # no axis specified = axis zero\n", " | array([[ 1., 0.],\n", " | [ 1., 1.]])\n", " | \n", " | Accumulate along axis 1 (columns), through rows:\n", " | \n", " | >>> np.add.accumulate(I, 1)\n", " | array([[ 1., 1.],\n", " | [ 0., 1.]])\n", " | \n", " | at(...)\n", " | at(a, indices, b=None)\n", " | \n", " | Performs unbuffered in place operation on operand 'a' for elements\n", " | specified by 'indices'. For addition ufunc, this method is equivalent to\n", " | `a[indices] += b`, except that results are accumulated for elements that\n", " | are indexed more than once. For example, `a[[0,0]] += 1` will only\n", " | increment the first element once because of buffering, whereas\n", " | `add.at(a, [0,0], 1)` will increment the first element twice.\n", " | \n", " | .. versionadded:: 1.8.0\n", " | \n", " | Parameters\n", " | ----------\n", " | a : array_like\n", " | The array to perform in place operation on.\n", " | indices : array_like or tuple\n", " | Array like index object or slice object for indexing into first\n", " | operand. If first operand has multiple dimensions, indices can be a\n", " | tuple of array like index objects or slice objects.\n", " | b : array_like\n", " | Second operand for ufuncs requiring two operands. Operand must be\n", " | broadcastable over first operand after indexing or slicing.\n", " | \n", " | Examples\n", " | --------\n", " | Set items 0 and 1 to their negative values:\n", " | \n", " | >>> a = np.array([1, 2, 3, 4])\n", " | >>> np.negative.at(a, [0, 1])\n", " | >>> print(a)\n", " | array([-1, -2, 3, 4])\n", " | \n", " | ::\n", " | \n", " | Increment items 0 and 1, and increment item 2 twice:\n", " | \n", " | >>> a = np.array([1, 2, 3, 4])\n", " | >>> np.add.at(a, [0, 1, 2, 2], 1)\n", " | >>> print(a)\n", " | array([2, 3, 5, 4])\n", " | \n", " | ::\n", " | \n", " | Add items 0 and 1 in first array to second array,\n", " | and store results in first array:\n", " | \n", " | >>> a = np.array([1, 2, 3, 4])\n", " | >>> b = np.array([1, 2])\n", " | >>> np.add.at(a, [0, 1], b)\n", " | >>> print(a)\n", " | array([2, 4, 3, 4])\n", " | \n", " | outer(...)\n", " | outer(A, B)\n", " | \n", " | Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.\n", " | \n", " | Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of\n", " | ``op.outer(A, B)`` is an array of dimension M + N such that:\n", " | \n", " | .. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =\n", " | op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])\n", " | \n", " | For `A` and `B` one-dimensional, this is equivalent to::\n", " | \n", " | r = empty(len(A),len(B))\n", " | for i in range(len(A)):\n", " | for j in range(len(B)):\n", " | r[i,j] = op(A[i], B[j]) # op = ufunc in question\n", " | \n", " | Parameters\n", " | ----------\n", " | A : array_like\n", " | First array\n", " | B : array_like\n", " | Second array\n", " | \n", " | Returns\n", " | -------\n", " | r : ndarray\n", " | Output array\n", " | \n", " | See Also\n", " | --------\n", " | numpy.outer\n", " | \n", " | Examples\n", " | --------\n", " | >>> np.multiply.outer([1, 2, 3], [4, 5, 6])\n", " | array([[ 4, 5, 6],\n", " | [ 8, 10, 12],\n", " | [12, 15, 18]])\n", " | \n", " | A multi-dimensional example:\n", " | \n", " | >>> A = np.array([[1, 2, 3], [4, 5, 6]])\n", " | >>> A.shape\n", " | (2, 3)\n", " | >>> B = np.array([[1, 2, 3, 4]])\n", " | >>> B.shape\n", " | (1, 4)\n", " | >>> C = np.multiply.outer(A, B)\n", " | >>> C.shape; C\n", " | (2, 3, 1, 4)\n", " | array([[[[ 1, 2, 3, 4]],\n", " | [[ 2, 4, 6, 8]],\n", " | [[ 3, 6, 9, 12]]],\n", " | [[[ 4, 8, 12, 16]],\n", " | [[ 5, 10, 15, 20]],\n", " | [[ 6, 12, 18, 24]]]])\n", " | \n", " | reduce(...)\n", " | reduce(a, axis=0, dtype=None, out=None, keepdims=False)\n", " | \n", " | Reduces `a`'s dimension by one, by applying ufunc along one axis.\n", " | \n", " | Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`. Then\n", " | :math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =\n", " | the result of iterating `j` over :math:`range(N_i)`, cumulatively applying\n", " | ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.\n", " | For a one-dimensional array, reduce produces results equivalent to:\n", " | ::\n", " | \n", " | r = op.identity # op = ufunc\n", " | for i in range(len(A)):\n", " | r = op(r, A[i])\n", " | return r\n", " | \n", " | For example, add.reduce() is equivalent to sum().\n", " | \n", " | Parameters\n", " | ----------\n", " | a : array_like\n", " | The array to act on.\n", " | axis : None or int or tuple of ints, optional\n", " | Axis or axes along which a reduction is performed.\n", " | The default (`axis` = 0) is perform a reduction over the first\n", " | dimension of the input array. `axis` may be negative, in\n", " | which case it counts from the last to the first axis.\n", " | \n", " | .. versionadded:: 1.7.0\n", " | \n", " | If this is `None`, a reduction is performed over all the axes.\n", " | If this is a tuple of ints, a reduction is performed on multiple\n", " | axes, instead of a single axis or all the axes as before.\n", " | \n", " | For operations which are either not commutative or not associative,\n", " | doing a reduction over multiple axes is not well-defined. The\n", " | ufuncs do not currently raise an exception in this case, but will\n", " | likely do so in the future.\n", " | dtype : data-type code, optional\n", " | The type used to represent the intermediate results. Defaults\n", " | to the data-type of the output array if this is provided, or\n", " | the data-type of the input array if no output array is provided.\n", " | out : ndarray, optional\n", " | A location into which the result is stored. If not provided, a\n", " | freshly-allocated array is returned.\n", " | keepdims : bool, optional\n", " | If this is set to True, the axes which are reduced are left\n", " | in the result as dimensions with size one. With this option,\n", " | the result will broadcast correctly against the original `arr`.\n", " | \n", " | .. versionadded:: 1.7.0\n", " | \n", " | Returns\n", " | -------\n", " | r : ndarray\n", " | The reduced array. If `out` was supplied, `r` is a reference to it.\n", " | \n", " | Examples\n", " | --------\n", " | >>> np.multiply.reduce([2,3,5])\n", " | 30\n", " | \n", " | A multi-dimensional array example:\n", " | \n", " | >>> X = np.arange(8).reshape((2,2,2))\n", " | >>> X\n", " | array([[[0, 1],\n", " | [2, 3]],\n", " | [[4, 5],\n", " | [6, 7]]])\n", " | >>> np.add.reduce(X, 0)\n", " | array([[ 4, 6],\n", " | [ 8, 10]])\n", " | >>> np.add.reduce(X) # confirm: default axis value is 0\n", " | array([[ 4, 6],\n", " | [ 8, 10]])\n", " | >>> np.add.reduce(X, 1)\n", " | array([[ 2, 4],\n", " | [10, 12]])\n", " | >>> np.add.reduce(X, 2)\n", " | array([[ 1, 5],\n", " | [ 9, 13]])\n", " | \n", " | reduceat(...)\n", " | reduceat(a, indices, axis=0, dtype=None, out=None)\n", " | \n", " | Performs a (local) reduce with specified slices over a single axis.\n", " | \n", " | For i in ``range(len(indices))``, `reduceat` computes\n", " | ``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th\n", " | generalized \"row\" parallel to `axis` in the final result (i.e., in a\n", " | 2-D array, for example, if `axis = 0`, it becomes the i-th row, but if\n", " | `axis = 1`, it becomes the i-th column). There are three exceptions to this:\n", " | \n", " | * when ``i = len(indices) - 1`` (so for the last index),\n", " | ``indices[i+1] = a.shape[axis]``.\n", " | * if ``indices[i] >= indices[i + 1]``, the i-th generalized \"row\" is\n", " | simply ``a[indices[i]]``.\n", " | * if ``indices[i] >= len(a)`` or ``indices[i] < 0``, an error is raised.\n", " | \n", " | The shape of the output depends on the size of `indices`, and may be\n", " | larger than `a` (this happens if ``len(indices) > a.shape[axis]``).\n", " | \n", " | Parameters\n", " | ----------\n", " | a : array_like\n", " | The array to act on.\n", " | indices : array_like\n", " | Paired indices, comma separated (not colon), specifying slices to\n", " | reduce.\n", " | axis : int, optional\n", " | The axis along which to apply the reduceat.\n", " | dtype : data-type code, optional\n", " | The type used to represent the intermediate results. Defaults\n", " | to the data type of the output array if this is provided, or\n", " | the data type of the input array if no output array is provided.\n", " | out : ndarray, optional\n", " | A location into which the result is stored. If not provided a\n", " | freshly-allocated array is returned.\n", " | \n", " | Returns\n", " | -------\n", " | r : ndarray\n", " | The reduced values. If `out` was supplied, `r` is a reference to\n", " | `out`.\n", " | \n", " | Notes\n", " | -----\n", " | A descriptive example:\n", " | \n", " | If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as\n", " | ``ufunc.reduceat(a, indices)[::2]`` where `indices` is\n", " | ``range(len(array) - 1)`` with a zero placed\n", " | in every other element:\n", " | ``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``.\n", " | \n", " | Don't be fooled by this attribute's name: `reduceat(a)` is not\n", " | necessarily smaller than `a`.\n", " | \n", " | Examples\n", " | --------\n", " | To take the running sum of four successive values:\n", " | \n", " | >>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]\n", " | array([ 6, 10, 14, 18])\n", " | \n", " | A 2-D example:\n", " | \n", " | >>> x = np.linspace(0, 15, 16).reshape(4,4)\n", " | >>> x\n", " | array([[ 0., 1., 2., 3.],\n", " | [ 4., 5., 6., 7.],\n", " | [ 8., 9., 10., 11.],\n", " | [ 12., 13., 14., 15.]])\n", " | \n", " | ::\n", " | \n", " | # reduce such that the result has the following five rows:\n", " | # [row1 + row2 + row3]\n", " | # [row4]\n", " | # [row2]\n", " | # [row3]\n", " | # [row1 + row2 + row3 + row4]\n", " | \n", " | >>> np.add.reduceat(x, [0, 3, 1, 2, 0])\n", " | array([[ 12., 15., 18., 21.],\n", " | [ 12., 13., 14., 15.],\n", " | [ 4., 5., 6., 7.],\n", " | [ 8., 9., 10., 11.],\n", " | [ 24., 28., 32., 36.]])\n", " | \n", " | ::\n", " | \n", " | # reduce such that result has the following two columns:\n", " | # [col1 * col2 * col3, col4]\n", " | \n", " | >>> np.multiply.reduceat(x, [0, 3], 1)\n", " | array([[ 0., 3.],\n", " | [ 120., 7.],\n", " | [ 720., 11.],\n", " | [ 2184., 15.]])\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | identity\n", " | The identity value.\n", " | \n", " | Data attribute containing the identity element for the ufunc, if it has one.\n", " | If it does not, the attribute value is None.\n", " | \n", " | Examples\n", " | --------\n", " | >>> np.add.identity\n", " | 0\n", " | >>> np.multiply.identity\n", " | 1\n", " | >>> np.power.identity\n", " | 1\n", " | >>> print np.exp.identity\n", " | None\n", " | \n", " | nargs\n", " | The number of arguments.\n", " | \n", " | Data attribute containing the number of arguments the ufunc takes, including\n", " | optional ones.\n", " | \n", " | Notes\n", " | -----\n", " | Typically this value will be one more than what you might expect because all\n", " | ufuncs take the optional \"out\" argument.\n", " | \n", " | Examples\n", " | --------\n", " | >>> np.add.nargs\n", " | 3\n", " | >>> np.multiply.nargs\n", " | 3\n", " | >>> np.power.nargs\n", " | 3\n", " | >>> np.exp.nargs\n", " | 2\n", " | \n", " | nin\n", " | The number of inputs.\n", " | \n", " | Data attribute containing the number of arguments the ufunc treats as input.\n", " | \n", " | Examples\n", " | --------\n", " | >>> np.add.nin\n", " | 2\n", " | >>> np.multiply.nin\n", " | 2\n", " | >>> np.power.nin\n", " | 2\n", " | >>> np.exp.nin\n", " | 1\n", " | \n", " | nout\n", " | The number of outputs.\n", " | \n", " | Data attribute containing the number of arguments the ufunc treats as output.\n", " | \n", " | Notes\n", " | -----\n", " | Since all ufuncs can take output arguments, this will always be (at least) 1.\n", " | \n", " | Examples\n", " | --------\n", " | >>> np.add.nout\n", " | 1\n", " | >>> np.multiply.nout\n", " | 1\n", " | >>> np.power.nout\n", " | 1\n", " | >>> np.exp.nout\n", " | 1\n", " | \n", " | ntypes\n", " | The number of types.\n", " | \n", " | The number of numerical NumPy types - of which there are 18 total - on which\n", " | the ufunc can operate.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ufunc.types\n", " | \n", " | Examples\n", " | --------\n", " | >>> np.add.ntypes\n", " | 18\n", " | >>> np.multiply.ntypes\n", " | 18\n", " | >>> np.power.ntypes\n", " | 17\n", " | >>> np.exp.ntypes\n", " | 7\n", " | >>> np.remainder.ntypes\n", " | 14\n", " | \n", " | signature\n", " | \n", " | types\n", " | Returns a list with types grouped input->output.\n", " | \n", " | Data attribute listing the data-type \"Domain-Range\" groupings the ufunc can\n", " | deliver. The data-types are given using the character codes.\n", " | \n", " | See Also\n", " | --------\n", " | numpy.ufunc.ntypes\n", " | \n", " | Examples\n", " | --------\n", " | >>> np.add.types\n", " | ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',\n", " | 'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',\n", " | 'GG->G', 'OO->O']\n", " | \n", " | >>> np.multiply.types\n", " | ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',\n", " | 'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',\n", " | 'GG->G', 'OO->O']\n", " | \n", " | >>> np.power.types\n", " | ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',\n", " | 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',\n", " | 'OO->O']\n", " | \n", " | >>> np.exp.types\n", " | ['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']\n", " | \n", " | >>> np.remainder.types\n", " | ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',\n", " | 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']\n", " \n", " uint = class uint64(unsignedinteger)\n", " | Method resolution order:\n", " | uint64\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " uint0 = class uint64(unsignedinteger)\n", " | Method resolution order:\n", " | uint64\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class uint16(unsignedinteger)\n", " | Method resolution order:\n", " | uint16\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class uint32(unsignedinteger)\n", " | Method resolution order:\n", " | uint32\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class uint64(unsignedinteger)\n", " | Method resolution order:\n", " | uint64\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class uint8(unsignedinteger)\n", " | Method resolution order:\n", " | uint8\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " uintc = class uint32(unsignedinteger)\n", " | Method resolution order:\n", " | uint32\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " uintp = class uint64(unsignedinteger)\n", " | Method resolution order:\n", " | uint64\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " ulonglong = class uint64(unsignedinteger)\n", " | Method resolution order:\n", " | uint64\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " unicode0 = class unicode_(__builtin__.unicode, character)\n", " | Method resolution order:\n", " | unicode_\n", " | __builtin__.unicode\n", " | __builtin__.basestring\n", " | character\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.unicode:\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __format__(...)\n", " | S.__format__(format_spec) -> unicode\n", " | \n", " | Return a formatted version of S as described by format_spec.\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __mod__(...)\n", " | x.__mod__(y) <==> x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(n) <==> x*n\n", " | \n", " | __rmod__(...)\n", " | x.__rmod__(y) <==> y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(n) <==> n*x\n", " | \n", " | __sizeof__(...)\n", " | S.__sizeof__() -> size of S in memory, in bytes\n", " | \n", " | capitalize(...)\n", " | S.capitalize() -> unicode\n", " | \n", " | Return a capitalized version of S, i.e. make the first character\n", " | have upper case and the rest lower case.\n", " | \n", " | center(...)\n", " | S.center(width[, fillchar]) -> unicode\n", " | \n", " | Return S centered in a Unicode string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | count(...)\n", " | S.count(sub[, start[, end]]) -> int\n", " | \n", " | Return the number of non-overlapping occurrences of substring sub in\n", " | Unicode string S[start:end]. Optional arguments start and end are\n", " | interpreted as in slice notation.\n", " | \n", " | decode(...)\n", " | S.decode([encoding[,errors]]) -> string or unicode\n", " | \n", " | Decodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n", " | as well as any other name registered with codecs.register_error that is\n", " | able to handle UnicodeDecodeErrors.\n", " | \n", " | encode(...)\n", " | S.encode([encoding[,errors]]) -> string or unicode\n", " | \n", " | Encodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n", " | 'xmlcharrefreplace' as well as any other name registered with\n", " | codecs.register_error that can handle UnicodeEncodeErrors.\n", " | \n", " | endswith(...)\n", " | S.endswith(suffix[, start[, end]]) -> bool\n", " | \n", " | Return True if S ends with the specified suffix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | suffix can also be a tuple of strings to try.\n", " | \n", " | expandtabs(...)\n", " | S.expandtabs([tabsize]) -> unicode\n", " | \n", " | Return a copy of S where all tab characters are expanded using spaces.\n", " | If tabsize is not given, a tab size of 8 characters is assumed.\n", " | \n", " | find(...)\n", " | S.find(sub [,start [,end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | format(...)\n", " | S.format(*args, **kwargs) -> unicode\n", " | \n", " | Return a formatted version of S, using substitutions from args and kwargs.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | index(...)\n", " | S.index(sub [,start [,end]]) -> int\n", " | \n", " | Like S.find() but raise ValueError when the substring is not found.\n", " | \n", " | isalnum(...)\n", " | S.isalnum() -> bool\n", " | \n", " | Return True if all characters in S are alphanumeric\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isalpha(...)\n", " | S.isalpha() -> bool\n", " | \n", " | Return True if all characters in S are alphabetic\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isdecimal(...)\n", " | S.isdecimal() -> bool\n", " | \n", " | Return True if there are only decimal characters in S,\n", " | False otherwise.\n", " | \n", " | isdigit(...)\n", " | S.isdigit() -> bool\n", " | \n", " | Return True if all characters in S are digits\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | islower(...)\n", " | S.islower() -> bool\n", " | \n", " | Return True if all cased characters in S are lowercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | isnumeric(...)\n", " | S.isnumeric() -> bool\n", " | \n", " | Return True if there are only numeric characters in S,\n", " | False otherwise.\n", " | \n", " | isspace(...)\n", " | S.isspace() -> bool\n", " | \n", " | Return True if all characters in S are whitespace\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | istitle(...)\n", " | S.istitle() -> bool\n", " | \n", " | Return True if S is a titlecased string and there is at least one\n", " | character in S, i.e. upper- and titlecase characters may only\n", " | follow uncased characters and lowercase characters only cased ones.\n", " | Return False otherwise.\n", " | \n", " | isupper(...)\n", " | S.isupper() -> bool\n", " | \n", " | Return True if all cased characters in S are uppercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | join(...)\n", " | S.join(iterable) -> unicode\n", " | \n", " | Return a string which is the concatenation of the strings in the\n", " | iterable. The separator between elements is S.\n", " | \n", " | ljust(...)\n", " | S.ljust(width[, fillchar]) -> int\n", " | \n", " | Return S left-justified in a Unicode string of length width. Padding is\n", " | done using the specified fill character (default is a space).\n", " | \n", " | lower(...)\n", " | S.lower() -> unicode\n", " | \n", " | Return a copy of the string S converted to lowercase.\n", " | \n", " | lstrip(...)\n", " | S.lstrip([chars]) -> unicode\n", " | \n", " | Return a copy of the string S with leading whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is a str, it will be converted to unicode before stripping\n", " | \n", " | partition(...)\n", " | S.partition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, and return the part before it,\n", " | the separator itself, and the part after it. If the separator is not\n", " | found, return S and two empty strings.\n", " | \n", " | replace(...)\n", " | S.replace(old, new[, count]) -> unicode\n", " | \n", " | Return a copy of S with all occurrences of substring\n", " | old replaced by new. If the optional argument count is\n", " | given, only the first count occurrences are replaced.\n", " | \n", " | rfind(...)\n", " | S.rfind(sub [,start [,end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | rindex(...)\n", " | S.rindex(sub [,start [,end]]) -> int\n", " | \n", " | Like S.rfind() but raise ValueError when the substring is not found.\n", " | \n", " | rjust(...)\n", " | S.rjust(width[, fillchar]) -> unicode\n", " | \n", " | Return S right-justified in a Unicode string of length width. Padding is\n", " | done using the specified fill character (default is a space).\n", " | \n", " | rpartition(...)\n", " | S.rpartition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, starting at the end of S, and return\n", " | the part before it, the separator itself, and the part after it. If the\n", " | separator is not found, return two empty strings and S.\n", " | \n", " | rsplit(...)\n", " | S.rsplit([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in S, using sep as the\n", " | delimiter string, starting at the end of the string and\n", " | working to the front. If maxsplit is given, at most maxsplit\n", " | splits are done. If sep is not specified, any whitespace string\n", " | is a separator.\n", " | \n", " | rstrip(...)\n", " | S.rstrip([chars]) -> unicode\n", " | \n", " | Return a copy of the string S with trailing whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is a str, it will be converted to unicode before stripping\n", " | \n", " | split(...)\n", " | S.split([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in S, using sep as the\n", " | delimiter string. If maxsplit is given, at most maxsplit\n", " | splits are done. If sep is not specified or is None, any\n", " | whitespace string is a separator and empty strings are\n", " | removed from the result.\n", " | \n", " | splitlines(...)\n", " | S.splitlines(keepends=False) -> list of strings\n", " | \n", " | Return a list of the lines in S, breaking at line boundaries.\n", " | Line breaks are not included in the resulting list unless keepends\n", " | is given and true.\n", " | \n", " | startswith(...)\n", " | S.startswith(prefix[, start[, end]]) -> bool\n", " | \n", " | Return True if S starts with the specified prefix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | prefix can also be a tuple of strings to try.\n", " | \n", " | strip(...)\n", " | S.strip([chars]) -> unicode\n", " | \n", " | Return a copy of the string S with leading and trailing\n", " | whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is a str, it will be converted to unicode before stripping\n", " | \n", " | swapcase(...)\n", " | S.swapcase() -> unicode\n", " | \n", " | Return a copy of S with uppercase characters converted to lowercase\n", " | and vice versa.\n", " | \n", " | title(...)\n", " | S.title() -> unicode\n", " | \n", " | Return a titlecased version of S, i.e. words start with title case\n", " | characters, all remaining cased characters have lower case.\n", " | \n", " | translate(...)\n", " | S.translate(table) -> unicode\n", " | \n", " | Return a copy of the string S, where all characters have been mapped\n", " | through the given translation table, which must be a mapping of\n", " | Unicode ordinals to Unicode ordinals, Unicode strings or None.\n", " | Unmapped characters are left untouched. Characters mapped to None\n", " | are deleted.\n", " | \n", " | upper(...)\n", " | S.upper() -> unicode\n", " | \n", " | Return a copy of S converted to uppercase.\n", " | \n", " | zfill(...)\n", " | S.zfill(width) -> unicode\n", " | \n", " | Pad a numeric string S with zeros on the left, to fill a field\n", " | of the specified width. The string S is never truncated.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class unicode_(__builtin__.unicode, character)\n", " | Method resolution order:\n", " | unicode_\n", " | __builtin__.unicode\n", " | __builtin__.basestring\n", " | character\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from __builtin__.unicode:\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __contains__(...)\n", " | x.__contains__(y) <==> y in x\n", " | \n", " | __format__(...)\n", " | S.__format__(format_spec) -> unicode\n", " | \n", " | Return a formatted version of S as described by format_spec.\n", " | \n", " | __getattribute__(...)\n", " | x.__getattribute__('name') <==> x.name\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __getslice__(...)\n", " | x.__getslice__(i, j) <==> x[i:j]\n", " | \n", " | Use of negative indices is not supported.\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __mod__(...)\n", " | x.__mod__(y) <==> x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(n) <==> x*n\n", " | \n", " | __rmod__(...)\n", " | x.__rmod__(y) <==> y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(n) <==> n*x\n", " | \n", " | __sizeof__(...)\n", " | S.__sizeof__() -> size of S in memory, in bytes\n", " | \n", " | capitalize(...)\n", " | S.capitalize() -> unicode\n", " | \n", " | Return a capitalized version of S, i.e. make the first character\n", " | have upper case and the rest lower case.\n", " | \n", " | center(...)\n", " | S.center(width[, fillchar]) -> unicode\n", " | \n", " | Return S centered in a Unicode string of length width. Padding is\n", " | done using the specified fill character (default is a space)\n", " | \n", " | count(...)\n", " | S.count(sub[, start[, end]]) -> int\n", " | \n", " | Return the number of non-overlapping occurrences of substring sub in\n", " | Unicode string S[start:end]. Optional arguments start and end are\n", " | interpreted as in slice notation.\n", " | \n", " | decode(...)\n", " | S.decode([encoding[,errors]]) -> string or unicode\n", " | \n", " | Decodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'\n", " | as well as any other name registered with codecs.register_error that is\n", " | able to handle UnicodeDecodeErrors.\n", " | \n", " | encode(...)\n", " | S.encode([encoding[,errors]]) -> string or unicode\n", " | \n", " | Encodes S using the codec registered for encoding. encoding defaults\n", " | to the default encoding. errors may be given to set a different error\n", " | handling scheme. Default is 'strict' meaning that encoding errors raise\n", " | a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n", " | 'xmlcharrefreplace' as well as any other name registered with\n", " | codecs.register_error that can handle UnicodeEncodeErrors.\n", " | \n", " | endswith(...)\n", " | S.endswith(suffix[, start[, end]]) -> bool\n", " | \n", " | Return True if S ends with the specified suffix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | suffix can also be a tuple of strings to try.\n", " | \n", " | expandtabs(...)\n", " | S.expandtabs([tabsize]) -> unicode\n", " | \n", " | Return a copy of S where all tab characters are expanded using spaces.\n", " | If tabsize is not given, a tab size of 8 characters is assumed.\n", " | \n", " | find(...)\n", " | S.find(sub [,start [,end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | format(...)\n", " | S.format(*args, **kwargs) -> unicode\n", " | \n", " | Return a formatted version of S, using substitutions from args and kwargs.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | index(...)\n", " | S.index(sub [,start [,end]]) -> int\n", " | \n", " | Like S.find() but raise ValueError when the substring is not found.\n", " | \n", " | isalnum(...)\n", " | S.isalnum() -> bool\n", " | \n", " | Return True if all characters in S are alphanumeric\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isalpha(...)\n", " | S.isalpha() -> bool\n", " | \n", " | Return True if all characters in S are alphabetic\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | isdecimal(...)\n", " | S.isdecimal() -> bool\n", " | \n", " | Return True if there are only decimal characters in S,\n", " | False otherwise.\n", " | \n", " | isdigit(...)\n", " | S.isdigit() -> bool\n", " | \n", " | Return True if all characters in S are digits\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | islower(...)\n", " | S.islower() -> bool\n", " | \n", " | Return True if all cased characters in S are lowercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | isnumeric(...)\n", " | S.isnumeric() -> bool\n", " | \n", " | Return True if there are only numeric characters in S,\n", " | False otherwise.\n", " | \n", " | isspace(...)\n", " | S.isspace() -> bool\n", " | \n", " | Return True if all characters in S are whitespace\n", " | and there is at least one character in S, False otherwise.\n", " | \n", " | istitle(...)\n", " | S.istitle() -> bool\n", " | \n", " | Return True if S is a titlecased string and there is at least one\n", " | character in S, i.e. upper- and titlecase characters may only\n", " | follow uncased characters and lowercase characters only cased ones.\n", " | Return False otherwise.\n", " | \n", " | isupper(...)\n", " | S.isupper() -> bool\n", " | \n", " | Return True if all cased characters in S are uppercase and there is\n", " | at least one cased character in S, False otherwise.\n", " | \n", " | join(...)\n", " | S.join(iterable) -> unicode\n", " | \n", " | Return a string which is the concatenation of the strings in the\n", " | iterable. The separator between elements is S.\n", " | \n", " | ljust(...)\n", " | S.ljust(width[, fillchar]) -> int\n", " | \n", " | Return S left-justified in a Unicode string of length width. Padding is\n", " | done using the specified fill character (default is a space).\n", " | \n", " | lower(...)\n", " | S.lower() -> unicode\n", " | \n", " | Return a copy of the string S converted to lowercase.\n", " | \n", " | lstrip(...)\n", " | S.lstrip([chars]) -> unicode\n", " | \n", " | Return a copy of the string S with leading whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is a str, it will be converted to unicode before stripping\n", " | \n", " | partition(...)\n", " | S.partition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, and return the part before it,\n", " | the separator itself, and the part after it. If the separator is not\n", " | found, return S and two empty strings.\n", " | \n", " | replace(...)\n", " | S.replace(old, new[, count]) -> unicode\n", " | \n", " | Return a copy of S with all occurrences of substring\n", " | old replaced by new. If the optional argument count is\n", " | given, only the first count occurrences are replaced.\n", " | \n", " | rfind(...)\n", " | S.rfind(sub [,start [,end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | rindex(...)\n", " | S.rindex(sub [,start [,end]]) -> int\n", " | \n", " | Like S.rfind() but raise ValueError when the substring is not found.\n", " | \n", " | rjust(...)\n", " | S.rjust(width[, fillchar]) -> unicode\n", " | \n", " | Return S right-justified in a Unicode string of length width. Padding is\n", " | done using the specified fill character (default is a space).\n", " | \n", " | rpartition(...)\n", " | S.rpartition(sep) -> (head, sep, tail)\n", " | \n", " | Search for the separator sep in S, starting at the end of S, and return\n", " | the part before it, the separator itself, and the part after it. If the\n", " | separator is not found, return two empty strings and S.\n", " | \n", " | rsplit(...)\n", " | S.rsplit([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in S, using sep as the\n", " | delimiter string, starting at the end of the string and\n", " | working to the front. If maxsplit is given, at most maxsplit\n", " | splits are done. If sep is not specified, any whitespace string\n", " | is a separator.\n", " | \n", " | rstrip(...)\n", " | S.rstrip([chars]) -> unicode\n", " | \n", " | Return a copy of the string S with trailing whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is a str, it will be converted to unicode before stripping\n", " | \n", " | split(...)\n", " | S.split([sep [,maxsplit]]) -> list of strings\n", " | \n", " | Return a list of the words in S, using sep as the\n", " | delimiter string. If maxsplit is given, at most maxsplit\n", " | splits are done. If sep is not specified or is None, any\n", " | whitespace string is a separator and empty strings are\n", " | removed from the result.\n", " | \n", " | splitlines(...)\n", " | S.splitlines(keepends=False) -> list of strings\n", " | \n", " | Return a list of the lines in S, breaking at line boundaries.\n", " | Line breaks are not included in the resulting list unless keepends\n", " | is given and true.\n", " | \n", " | startswith(...)\n", " | S.startswith(prefix[, start[, end]]) -> bool\n", " | \n", " | Return True if S starts with the specified prefix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | prefix can also be a tuple of strings to try.\n", " | \n", " | strip(...)\n", " | S.strip([chars]) -> unicode\n", " | \n", " | Return a copy of the string S with leading and trailing\n", " | whitespace removed.\n", " | If chars is given and not None, remove characters in chars instead.\n", " | If chars is a str, it will be converted to unicode before stripping\n", " | \n", " | swapcase(...)\n", " | S.swapcase() -> unicode\n", " | \n", " | Return a copy of S with uppercase characters converted to lowercase\n", " | and vice versa.\n", " | \n", " | title(...)\n", " | S.title() -> unicode\n", " | \n", " | Return a titlecased version of S, i.e. words start with title case\n", " | characters, all remaining cased characters have lower case.\n", " | \n", " | translate(...)\n", " | S.translate(table) -> unicode\n", " | \n", " | Return a copy of the string S, where all characters have been mapped\n", " | through the given translation table, which must be a mapping of\n", " | Unicode ordinals to Unicode ordinals, Unicode strings or None.\n", " | Unmapped characters are left untouched. Characters mapped to None\n", " | are deleted.\n", " | \n", " | upper(...)\n", " | S.upper() -> unicode\n", " | \n", " | Return a copy of S converted to uppercase.\n", " | \n", " | zfill(...)\n", " | S.zfill(width) -> unicode\n", " | \n", " | Pad a numeric string S with zeros on the left, to fill a field\n", " | of the specified width. The string S is never truncated.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class unsignedinteger(integer)\n", " | Method resolution order:\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __ne__(...)\n", " | x.__ne__(y) <==> x!=y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " ushort = class uint16(unsignedinteger)\n", " | Method resolution order:\n", " | uint16\n", " | unsignedinteger\n", " | integer\n", " | number\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __index__(...)\n", " | x[y:z] <==> x[y.__index__():z.__index__()]\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from integer:\n", " | \n", " | denominator\n", " | denominator of value (1)\n", " | \n", " | numerator\n", " | numerator of value (the value itself)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | getfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setfield(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | dtype\n", " | get array data-descriptor\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " class vectorize(__builtin__.object)\n", " | vectorize(pyfunc, otypes='', doc=None, excluded=None, cache=False)\n", " | \n", " | Generalized function class.\n", " | \n", " | Define a vectorized function which takes a nested sequence\n", " | of objects or numpy arrays as inputs and returns a\n", " | numpy array as output. The vectorized function evaluates `pyfunc` over\n", " | successive tuples of the input arrays like the python map function,\n", " | except it uses the broadcasting rules of numpy.\n", " | \n", " | The data type of the output of `vectorized` is determined by calling\n", " | the function with the first element of the input. This can be avoided\n", " | by specifying the `otypes` argument.\n", " | \n", " | Parameters\n", " | ----------\n", " | pyfunc : callable\n", " | A python function or method.\n", " | otypes : str or list of dtypes, optional\n", " | The output data type. It must be specified as either a string of\n", " | typecode characters or a list of data type specifiers. There should\n", " | be one data type specifier for each output.\n", " | doc : str, optional\n", " | The docstring for the function. If `None`, the docstring will be the\n", " | ``pyfunc.__doc__``.\n", " | excluded : set, optional\n", " | Set of strings or integers representing the positional or keyword\n", " | arguments for which the function will not be vectorized. These will be\n", " | passed directly to `pyfunc` unmodified.\n", " | \n", " | .. versionadded:: 1.7.0\n", " | \n", " | cache : bool, optional\n", " | If `True`, then cache the first function call that determines the number\n", " | of outputs if `otypes` is not provided.\n", " | \n", " | .. versionadded:: 1.7.0\n", " | \n", " | Returns\n", " | -------\n", " | vectorized : callable\n", " | Vectorized function.\n", " | \n", " | Examples\n", " | --------\n", " | >>> def myfunc(a, b):\n", " | ... \"Return a-b if a>b, otherwise return a+b\"\n", " | ... if a > b:\n", " | ... return a - b\n", " | ... else:\n", " | ... return a + b\n", " | \n", " | >>> vfunc = np.vectorize(myfunc)\n", " | >>> vfunc([1, 2, 3, 4], 2)\n", " | array([3, 4, 1, 2])\n", " | \n", " | The docstring is taken from the input function to `vectorize` unless it\n", " | is specified\n", " | \n", " | >>> vfunc.__doc__\n", " | 'Return a-b if a>b, otherwise return a+b'\n", " | >>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`')\n", " | >>> vfunc.__doc__\n", " | 'Vectorized `myfunc`'\n", " | \n", " | The output type is determined by evaluating the first element of the input,\n", " | unless it is specified\n", " | \n", " | >>> out = vfunc([1, 2, 3, 4], 2)\n", " | >>> type(out[0])\n", " | \n", " | >>> vfunc = np.vectorize(myfunc, otypes=[np.float])\n", " | >>> out = vfunc([1, 2, 3, 4], 2)\n", " | >>> type(out[0])\n", " | \n", " | \n", " | The `excluded` argument can be used to prevent vectorizing over certain\n", " | arguments. This can be useful for array-like arguments of a fixed length\n", " | such as the coefficients for a polynomial as in `polyval`:\n", " | \n", " | >>> def mypolyval(p, x):\n", " | ... _p = list(p)\n", " | ... res = _p.pop(0)\n", " | ... while _p:\n", " | ... res = res*x + _p.pop(0)\n", " | ... return res\n", " | >>> vpolyval = np.vectorize(mypolyval, excluded=['p'])\n", " | >>> vpolyval(p=[1, 2, 3], x=[0, 1])\n", " | array([3, 6])\n", " | \n", " | Positional arguments may also be excluded by specifying their position:\n", " | \n", " | >>> vpolyval.excluded.add(0)\n", " | >>> vpolyval([1, 2, 3], x=[0, 1])\n", " | array([3, 6])\n", " | \n", " | Notes\n", " | -----\n", " | The `vectorize` function is provided primarily for convenience, not for\n", " | performance. The implementation is essentially a for loop.\n", " | \n", " | If `otypes` is not specified, then a call to the function with the\n", " | first argument will be used to determine the number of outputs. The\n", " | results of this call will be cached if `cache` is `True` to prevent\n", " | calling the function twice. However, to implement the cache, the\n", " | original function must be wrapped which will slow down subsequent\n", " | calls, so only do this if your function is expensive.\n", " | \n", " | The new keyword argument interface and `excluded` argument support\n", " | further degrades performance.\n", " | \n", " | Methods defined here:\n", " | \n", " | __call__(self, *args, **kwargs)\n", " | Return arrays with the results of `pyfunc` broadcast (vectorized) over\n", " | `args` and `kwargs` not in `excluded`.\n", " | \n", " | __init__(self, pyfunc, otypes='', doc=None, excluded=None, cache=False)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", " \n", " class void(flexible)\n", " | Method resolution order:\n", " | void\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | getfield(...)\n", " | \n", " | setfield(...)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | dtype\n", " | dtype object\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", " \n", " void0 = class void(flexible)\n", " | Method resolution order:\n", " | void\n", " | flexible\n", " | generic\n", " | __builtin__.object\n", " | \n", " | Methods defined here:\n", " | \n", " | __delitem__(...)\n", " | x.__delitem__(y) <==> del x[y]\n", " | \n", " | __eq__(...)\n", " | x.__eq__(y) <==> x==y\n", " | \n", " | __ge__(...)\n", " | x.__ge__(y) <==> x>=y\n", " | \n", " | __getitem__(...)\n", " | x.__getitem__(y) <==> x[y]\n", " | \n", " | __gt__(...)\n", " | x.__gt__(y) <==> x>y\n", " | \n", " | __hash__(...)\n", " | x.__hash__() <==> hash(x)\n", " | \n", " | __le__(...)\n", " | x.__le__(y) <==> x<=y\n", " | \n", " | __len__(...)\n", " | x.__len__() <==> len(x)\n", " | \n", " | __lt__(...)\n", " | x.__lt__(y) <==> x x!=y\n", " | \n", " | __setitem__(...)\n", " | x.__setitem__(i, y) <==> x[i]=y\n", " | \n", " | getfield(...)\n", " | \n", " | setfield(...)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | dtype\n", " | dtype object\n", " | \n", " | flags\n", " | integer value of flags\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data and other attributes defined here:\n", " | \n", " | __new__ = \n", " | T.__new__(S, ...) -> a new object with type S, a subtype of T\n", " | \n", " | ----------------------------------------------------------------------\n", " | Methods inherited from generic:\n", " | \n", " | __abs__(...)\n", " | x.__abs__() <==> abs(x)\n", " | \n", " | __add__(...)\n", " | x.__add__(y) <==> x+y\n", " | \n", " | __and__(...)\n", " | x.__and__(y) <==> x&y\n", " | \n", " | __array__(...)\n", " | sc.__array__(|type) return 0-dim array\n", " | \n", " | __array_wrap__(...)\n", " | sc.__array_wrap__(obj) return scalar from array\n", " | \n", " | __copy__(...)\n", " | \n", " | __deepcopy__(...)\n", " | \n", " | __div__(...)\n", " | x.__div__(y) <==> x/y\n", " | \n", " | __divmod__(...)\n", " | x.__divmod__(y) <==> divmod(x, y)\n", " | \n", " | __float__(...)\n", " | x.__float__() <==> float(x)\n", " | \n", " | __floordiv__(...)\n", " | x.__floordiv__(y) <==> x//y\n", " | \n", " | __format__(...)\n", " | NumPy array scalar formatter\n", " | \n", " | __hex__(...)\n", " | x.__hex__() <==> hex(x)\n", " | \n", " | __int__(...)\n", " | x.__int__() <==> int(x)\n", " | \n", " | __invert__(...)\n", " | x.__invert__() <==> ~x\n", " | \n", " | __long__(...)\n", " | x.__long__() <==> long(x)\n", " | \n", " | __lshift__(...)\n", " | x.__lshift__(y) <==> x< x%y\n", " | \n", " | __mul__(...)\n", " | x.__mul__(y) <==> x*y\n", " | \n", " | __neg__(...)\n", " | x.__neg__() <==> -x\n", " | \n", " | __nonzero__(...)\n", " | x.__nonzero__() <==> x != 0\n", " | \n", " | __oct__(...)\n", " | x.__oct__() <==> oct(x)\n", " | \n", " | __or__(...)\n", " | x.__or__(y) <==> x|y\n", " | \n", " | __pos__(...)\n", " | x.__pos__() <==> +x\n", " | \n", " | __pow__(...)\n", " | x.__pow__(y[, z]) <==> pow(x, y[, z])\n", " | \n", " | __radd__(...)\n", " | x.__radd__(y) <==> y+x\n", " | \n", " | __rand__(...)\n", " | x.__rand__(y) <==> y&x\n", " | \n", " | __rdiv__(...)\n", " | x.__rdiv__(y) <==> y/x\n", " | \n", " | __rdivmod__(...)\n", " | x.__rdivmod__(y) <==> divmod(y, x)\n", " | \n", " | __reduce__(...)\n", " | \n", " | __repr__(...)\n", " | x.__repr__() <==> repr(x)\n", " | \n", " | __rfloordiv__(...)\n", " | x.__rfloordiv__(y) <==> y//x\n", " | \n", " | __rlshift__(...)\n", " | x.__rlshift__(y) <==> y< y%x\n", " | \n", " | __rmul__(...)\n", " | x.__rmul__(y) <==> y*x\n", " | \n", " | __ror__(...)\n", " | x.__ror__(y) <==> y|x\n", " | \n", " | __rpow__(...)\n", " | y.__rpow__(x[, z]) <==> pow(x, y[, z])\n", " | \n", " | __rrshift__(...)\n", " | x.__rrshift__(y) <==> y>>x\n", " | \n", " | __rshift__(...)\n", " | x.__rshift__(y) <==> x>>y\n", " | \n", " | __rsub__(...)\n", " | x.__rsub__(y) <==> y-x\n", " | \n", " | __rtruediv__(...)\n", " | x.__rtruediv__(y) <==> y/x\n", " | \n", " | __rxor__(...)\n", " | x.__rxor__(y) <==> y^x\n", " | \n", " | __setstate__(...)\n", " | \n", " | __str__(...)\n", " | x.__str__() <==> str(x)\n", " | \n", " | __sub__(...)\n", " | x.__sub__(y) <==> x-y\n", " | \n", " | __truediv__(...)\n", " | x.__truediv__(y) <==> x/y\n", " | \n", " | __xor__(...)\n", " | x.__xor__(y) <==> x^y\n", " | \n", " | all(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | any(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmax(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argmin(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | argsort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | astype(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | byteswap(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | choose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | clip(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | compress(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | conj(...)\n", " | \n", " | conjugate(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | copy(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumprod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | cumsum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | diagonal(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dump(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | dumps(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | fill(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | flatten(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | item(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | itemset(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | max(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | mean(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | min(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | newbyteorder(...)\n", " | newbyteorder(new_order='S')\n", " | \n", " | Return a new `dtype` with a different byte order.\n", " | \n", " | Changes are also made in all fields and sub-arrays of the data type.\n", " | \n", " | The `new_order` code can be any from the following:\n", " | \n", " | * {'<', 'L'} - little endian\n", " | * {'>', 'B'} - big endian\n", " | * {'=', 'N'} - native order\n", " | * 'S' - swap dtype from current to opposite endian\n", " | * {'|', 'I'} - ignore (no change to byte order)\n", " | \n", " | Parameters\n", " | ----------\n", " | new_order : str, optional\n", " | Byte order to force; a value from the byte order specifications\n", " | above. The default value ('S') results in swapping the current\n", " | byte order. The code does a case-insensitive check on the first\n", " | letter of `new_order` for the alternatives above. For example,\n", " | any of 'B' or 'b' or 'biggish' are valid to specify big-endian.\n", " | \n", " | \n", " | Returns\n", " | -------\n", " | new_dtype : dtype\n", " | New `dtype` object with the given change to the byte order.\n", " | \n", " | nonzero(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | prod(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ptp(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | put(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ravel(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | repeat(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | reshape(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | resize(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | round(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | searchsorted(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | setflags(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class so as to\n", " | provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sort(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | squeeze(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | std(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | sum(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | swapaxes(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | take(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tobytes(...)\n", " | \n", " | tofile(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tolist(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | tostring(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | trace(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | transpose(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | var(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | view(...)\n", " | Not implemented (virtual attribute)\n", " | \n", " | Class generic exists solely to derive numpy scalars from, and possesses,\n", " | albeit unimplemented, all the attributes of the ndarray class\n", " | so as to provide a uniform API.\n", " | \n", " | See Also\n", " | --------\n", " | The corresponding attribute of the derived class of interest.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors inherited from generic:\n", " | \n", " | T\n", " | transpose\n", " | \n", " | __array_interface__\n", " | Array protocol: Python side\n", " | \n", " | __array_priority__\n", " | Array priority.\n", " | \n", " | __array_struct__\n", " | Array protocol: struct\n", " | \n", " | base\n", " | base object\n", " | \n", " | data\n", " | pointer to start of data\n", " | \n", " | flat\n", " | a 1-d view of scalar\n", " | \n", " | imag\n", " | imaginary part of scalar\n", " | \n", " | itemsize\n", " | length of one element in bytes\n", " | \n", " | nbytes\n", " | length of item in bytes\n", " | \n", " | ndim\n", " | number of array dimensions\n", " | \n", " | real\n", " | real part of scalar\n", " | \n", " | shape\n", " | tuple of array dimensions\n", " | \n", " | size\n", " | number of elements in the gentype\n", " | \n", " | strides\n", " | tuple of bytes steps in each dimension\n", "\n", "FUNCTIONS\n", " add_docstring(...)\n", " add_docstring(obj, docstring)\n", " \n", " Add a docstring to a built-in obj if possible.\n", " If the obj already has a docstring raise a RuntimeError\n", " If this routine does not know how to add a docstring to the object\n", " raise a TypeError\n", " \n", " add_newdoc(place, obj, doc)\n", " Adds documentation to obj which is in module place.\n", " \n", " If doc is a string add it to obj as a docstring\n", " \n", " If doc is a tuple, then the first element is interpreted as\n", " an attribute of obj and the second as the docstring\n", " (method, docstring)\n", " \n", " If doc is a list, then each element of the list should be a\n", " sequence of length two --> [(method1, docstring1),\n", " (method2, docstring2), ...]\n", " \n", " This routine never raises an error.\n", " \n", " This routine cannot modify read-only docstrings, as appear\n", " in new-style classes or built-in functions. Because this\n", " routine never raises an error the caller must check manually\n", " that the docstrings were changed.\n", " \n", " add_newdoc_ufunc(...)\n", " add_ufunc_docstring(ufunc, new_docstring)\n", " \n", " Replace the docstring for a ufunc with new_docstring.\n", " This method will only work if the current docstring for\n", " the ufunc is NULL. (At the C level, i.e. when ufunc->doc is NULL.)\n", " \n", " Parameters\n", " ----------\n", " ufunc : numpy.ufunc\n", " A ufunc whose current doc is NULL.\n", " new_docstring : string\n", " The new docstring for the ufunc.\n", " \n", " Notes\n", " -----\n", " This method allocates memory for new_docstring on\n", " the heap. Technically this creates a mempory leak, since this\n", " memory will not be reclaimed until the end of the program\n", " even if the ufunc itself is removed. However this will only\n", " be a problem if the user is repeatedly creating ufuncs with\n", " no documentation, adding documentation via add_newdoc_ufunc,\n", " and then throwing away the ufunc.\n", " \n", " alen(a)\n", " Return the length of the first dimension of the input array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " \n", " Returns\n", " -------\n", " alen : int\n", " Length of the first dimension of `a`.\n", " \n", " See Also\n", " --------\n", " shape, size\n", " \n", " Examples\n", " --------\n", " >>> a = np.zeros((7,4,5))\n", " >>> a.shape[0]\n", " 7\n", " >>> np.alen(a)\n", " 7\n", " \n", " all(a, axis=None, out=None, keepdims=False)\n", " Test whether all array elements along a given axis evaluate to True.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array or object that can be converted to an array.\n", " axis : None or int or tuple of ints, optional\n", " Axis or axes along which a logical AND reduction is performed.\n", " The default (`axis` = `None`) is to perform a logical AND over all\n", " the dimensions of the input array. `axis` may be negative, in\n", " which case it counts from the last to the first axis.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " If this is a tuple of ints, a reduction is performed on multiple\n", " axes, instead of a single axis or all the axes as before.\n", " out : ndarray, optional\n", " Alternate output array in which to place the result.\n", " It must have the same shape as the expected output and its\n", " type is preserved (e.g., if ``dtype(out)`` is float, the result\n", " will consist of 0.0's and 1.0's). See `doc.ufuncs` (Section\n", " \"Output arguments\") for more details.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " all : ndarray, bool\n", " A new boolean or array is returned unless `out` is specified,\n", " in which case a reference to `out` is returned.\n", " \n", " See Also\n", " --------\n", " ndarray.all : equivalent method\n", " \n", " any : Test whether any element along a given axis evaluates to True.\n", " \n", " Notes\n", " -----\n", " Not a Number (NaN), positive infinity and negative infinity\n", " evaluate to `True` because these are not equal to zero.\n", " \n", " Examples\n", " --------\n", " >>> np.all([[True,False],[True,True]])\n", " False\n", " \n", " >>> np.all([[True,False],[True,True]], axis=0)\n", " array([ True, False], dtype=bool)\n", " \n", " >>> np.all([-1, 4, 5])\n", " True\n", " \n", " >>> np.all([1.0, np.nan])\n", " True\n", " \n", " >>> o=np.array([False])\n", " >>> z=np.all([-1, 4, 5], out=o)\n", " >>> id(z), id(o), z # doctest: +SKIP\n", " (28293632, 28293632, array([ True], dtype=bool))\n", " \n", " allclose(a, b, rtol=1e-05, atol=1e-08)\n", " Returns True if two arrays are element-wise equal within a tolerance.\n", " \n", " The tolerance values are positive, typically very small numbers. The\n", " relative difference (`rtol` * abs(`b`)) and the absolute difference\n", " `atol` are added together to compare against the absolute difference\n", " between `a` and `b`.\n", " \n", " If either array contains one or more NaNs, False is returned.\n", " Infs are treated as equal if they are in the same place and of the same\n", " sign in both arrays.\n", " \n", " Parameters\n", " ----------\n", " a, b : array_like\n", " Input arrays to compare.\n", " rtol : float\n", " The relative tolerance parameter (see Notes).\n", " atol : float\n", " The absolute tolerance parameter (see Notes).\n", " \n", " Returns\n", " -------\n", " allclose : bool\n", " Returns True if the two arrays are equal within the given\n", " tolerance; False otherwise.\n", " \n", " See Also\n", " --------\n", " isclose, all, any\n", " \n", " Notes\n", " -----\n", " If the following equation is element-wise True, then allclose returns\n", " True.\n", " \n", " absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))\n", " \n", " The above equation is not symmetric in `a` and `b`, so that\n", " `allclose(a, b)` might be different from `allclose(b, a)` in\n", " some rare cases.\n", " \n", " Examples\n", " --------\n", " >>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])\n", " False\n", " >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9])\n", " True\n", " >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9])\n", " False\n", " >>> np.allclose([1.0, np.nan], [1.0, np.nan])\n", " False\n", " \n", " alltrue(a, axis=None, out=None, keepdims=False)\n", " Check if all elements of input array are true.\n", " \n", " See Also\n", " --------\n", " numpy.all : Equivalent function; see for details.\n", " \n", " alterdot(...)\n", " Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions.\n", " \n", " Typically, as a user of Numpy, you do not explicitly call this function. If\n", " Numpy is built with an accelerated BLAS, this function is automatically\n", " called when Numpy is imported.\n", " \n", " When Numpy is built with an accelerated BLAS like ATLAS, these functions\n", " are replaced to make use of the faster implementations. The faster\n", " implementations only affect float32, float64, complex64, and complex128\n", " arrays. Furthermore, the BLAS API only includes matrix-matrix,\n", " matrix-vector, and vector-vector products. Products of arrays with larger\n", " dimensionalities use the built in functions and are not accelerated.\n", " \n", " See Also\n", " --------\n", " restoredot : `restoredot` undoes the effects of `alterdot`.\n", " \n", " amax(a, axis=None, out=None, keepdims=False)\n", " Return the maximum of an array or maximum along an axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " axis : int, optional\n", " Axis along which to operate. By default, flattened input is used.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. Must\n", " be of the same shape and buffer length as the expected output.\n", " See `doc.ufuncs` (Section \"Output arguments\") for more details.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " amax : ndarray or scalar\n", " Maximum of `a`. If `axis` is None, the result is a scalar value.\n", " If `axis` is given, the result is an array of dimension\n", " ``a.ndim - 1``.\n", " \n", " See Also\n", " --------\n", " amin :\n", " The minimum value of an array along a given axis, propagating any NaNs.\n", " nanmax :\n", " The maximum value of an array along a given axis, ignoring any NaNs.\n", " maximum :\n", " Element-wise maximum of two arrays, propagating any NaNs.\n", " fmax :\n", " Element-wise maximum of two arrays, ignoring any NaNs.\n", " argmax :\n", " Return the indices of the maximum values.\n", " \n", " nanmin, minimum, fmin\n", " \n", " Notes\n", " -----\n", " NaN values are propagated, that is if at least one item is NaN, the\n", " corresponding max value will be NaN as well. To ignore NaN values\n", " (MATLAB behavior), please use nanmax.\n", " \n", " Don't use `amax` for element-wise comparison of 2 arrays; when\n", " ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than\n", " ``amax(a, axis=0)``.\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(4).reshape((2,2))\n", " >>> a\n", " array([[0, 1],\n", " [2, 3]])\n", " >>> np.amax(a) # Maximum of the flattened array\n", " 3\n", " >>> np.amax(a, axis=0) # Maxima along the first axis\n", " array([2, 3])\n", " >>> np.amax(a, axis=1) # Maxima along the second axis\n", " array([1, 3])\n", " \n", " >>> b = np.arange(5, dtype=np.float)\n", " >>> b[2] = np.NaN\n", " >>> np.amax(b)\n", " nan\n", " >>> np.nanmax(b)\n", " 4.0\n", " \n", " amin(a, axis=None, out=None, keepdims=False)\n", " Return the minimum of an array or minimum along an axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " axis : int, optional\n", " Axis along which to operate. By default, flattened input is used.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. Must\n", " be of the same shape and buffer length as the expected output.\n", " See `doc.ufuncs` (Section \"Output arguments\") for more details.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " amin : ndarray or scalar\n", " Minimum of `a`. If `axis` is None, the result is a scalar value.\n", " If `axis` is given, the result is an array of dimension\n", " ``a.ndim - 1``.\n", " \n", " See Also\n", " --------\n", " amax :\n", " The maximum value of an array along a given axis, propagating any NaNs.\n", " nanmin :\n", " The minimum value of an array along a given axis, ignoring any NaNs.\n", " minimum :\n", " Element-wise minimum of two arrays, propagating any NaNs.\n", " fmin :\n", " Element-wise minimum of two arrays, ignoring any NaNs.\n", " argmin :\n", " Return the indices of the minimum values.\n", " \n", " nanmax, maximum, fmax\n", " \n", " Notes\n", " -----\n", " NaN values are propagated, that is if at least one item is NaN, the\n", " corresponding min value will be NaN as well. To ignore NaN values\n", " (MATLAB behavior), please use nanmin.\n", " \n", " Don't use `amin` for element-wise comparison of 2 arrays; when\n", " ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than\n", " ``amin(a, axis=0)``.\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(4).reshape((2,2))\n", " >>> a\n", " array([[0, 1],\n", " [2, 3]])\n", " >>> np.amin(a) # Minimum of the flattened array\n", " 0\n", " >>> np.amin(a, axis=0) # Minima along the first axis\n", " array([0, 1])\n", " >>> np.amin(a, axis=1) # Minima along the second axis\n", " array([0, 2])\n", " \n", " >>> b = np.arange(5, dtype=np.float)\n", " >>> b[2] = np.NaN\n", " >>> np.amin(b)\n", " nan\n", " >>> np.nanmin(b)\n", " 0.0\n", " \n", " angle(z, deg=0)\n", " Return the angle of the complex argument.\n", " \n", " Parameters\n", " ----------\n", " z : array_like\n", " A complex number or sequence of complex numbers.\n", " deg : bool, optional\n", " Return angle in degrees if True, radians if False (default).\n", " \n", " Returns\n", " -------\n", " angle : {ndarray, scalar}\n", " The counterclockwise angle from the positive real axis on\n", " the complex plane, with dtype as numpy.float64.\n", " \n", " See Also\n", " --------\n", " arctan2\n", " absolute\n", " \n", " \n", " \n", " Examples\n", " --------\n", " >>> np.angle([1.0, 1.0j, 1+1j]) # in radians\n", " array([ 0. , 1.57079633, 0.78539816])\n", " >>> np.angle(1+1j, deg=True) # in degrees\n", " 45.0\n", " \n", " any(a, axis=None, out=None, keepdims=False)\n", " Test whether any array element along a given axis evaluates to True.\n", " \n", " Returns single boolean unless `axis` is not ``None``\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array or object that can be converted to an array.\n", " axis : None or int or tuple of ints, optional\n", " Axis or axes along which a logical OR reduction is performed.\n", " The default (`axis` = `None`) is to perform a logical OR over all\n", " the dimensions of the input array. `axis` may be negative, in\n", " which case it counts from the last to the first axis.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " If this is a tuple of ints, a reduction is performed on multiple\n", " axes, instead of a single axis or all the axes as before.\n", " out : ndarray, optional\n", " Alternate output array in which to place the result. It must have\n", " the same shape as the expected output and its type is preserved\n", " (e.g., if it is of type float, then it will remain so, returning\n", " 1.0 for True and 0.0 for False, regardless of the type of `a`).\n", " See `doc.ufuncs` (Section \"Output arguments\") for details.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " any : bool or ndarray\n", " A new boolean or `ndarray` is returned unless `out` is specified,\n", " in which case a reference to `out` is returned.\n", " \n", " See Also\n", " --------\n", " ndarray.any : equivalent method\n", " \n", " all : Test whether all elements along a given axis evaluate to True.\n", " \n", " Notes\n", " -----\n", " Not a Number (NaN), positive infinity and negative infinity evaluate\n", " to `True` because these are not equal to zero.\n", " \n", " Examples\n", " --------\n", " >>> np.any([[True, False], [True, True]])\n", " True\n", " \n", " >>> np.any([[True, False], [False, False]], axis=0)\n", " array([ True, False], dtype=bool)\n", " \n", " >>> np.any([-1, 0, 5])\n", " True\n", " \n", " >>> np.any(np.nan)\n", " True\n", " \n", " >>> o=np.array([False])\n", " >>> z=np.any([-1, 4, 5], out=o)\n", " >>> z, o\n", " (array([ True], dtype=bool), array([ True], dtype=bool))\n", " >>> # Check now that z is a reference to o\n", " >>> z is o\n", " True\n", " >>> id(z), id(o) # identity of z and o # doctest: +SKIP\n", " (191614240, 191614240)\n", " \n", " append(arr, values, axis=None)\n", " Append values to the end of an array.\n", " \n", " Parameters\n", " ----------\n", " arr : array_like\n", " Values are appended to a copy of this array.\n", " values : array_like\n", " These values are appended to a copy of `arr`. It must be of the\n", " correct shape (the same shape as `arr`, excluding `axis`). If\n", " `axis` is not specified, `values` can be any shape and will be\n", " flattened before use.\n", " axis : int, optional\n", " The axis along which `values` are appended. If `axis` is not\n", " given, both `arr` and `values` are flattened before use.\n", " \n", " Returns\n", " -------\n", " append : ndarray\n", " A copy of `arr` with `values` appended to `axis`. Note that\n", " `append` does not occur in-place: a new array is allocated and\n", " filled. If `axis` is None, `out` is a flattened array.\n", " \n", " See Also\n", " --------\n", " insert : Insert elements into an array.\n", " delete : Delete elements from an array.\n", " \n", " Examples\n", " --------\n", " >>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])\n", " array([1, 2, 3, 4, 5, 6, 7, 8, 9])\n", " \n", " When `axis` is specified, `values` must have the correct shape.\n", " \n", " >>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)\n", " array([[1, 2, 3],\n", " [4, 5, 6],\n", " [7, 8, 9]])\n", " >>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)\n", " Traceback (most recent call last):\n", " ...\n", " ValueError: arrays must have same number of dimensions\n", " \n", " apply_along_axis(func1d, axis, arr, *args, **kwargs)\n", " Apply a function to 1-D slices along the given axis.\n", " \n", " Execute `func1d(a, *args)` where `func1d` operates on 1-D arrays and `a`\n", " is a 1-D slice of `arr` along `axis`.\n", " \n", " Parameters\n", " ----------\n", " func1d : function\n", " This function should accept 1-D arrays. It is applied to 1-D\n", " slices of `arr` along the specified axis.\n", " axis : integer\n", " Axis along which `arr` is sliced.\n", " arr : ndarray\n", " Input array.\n", " args : any\n", " Additional arguments to `func1d`.\n", " kwargs: any\n", " Additional named arguments to `func1d`.\n", " \n", " .. versionadded:: 1.9.0\n", " \n", " \n", " Returns\n", " -------\n", " apply_along_axis : ndarray\n", " The output array. The shape of `outarr` is identical to the shape of\n", " `arr`, except along the `axis` dimension, where the length of `outarr`\n", " is equal to the size of the return value of `func1d`. If `func1d`\n", " returns a scalar `outarr` will have one fewer dimensions than `arr`.\n", " \n", " See Also\n", " --------\n", " apply_over_axes : Apply a function repeatedly over multiple axes.\n", " \n", " Examples\n", " --------\n", " >>> def my_func(a):\n", " ... \"\"\"Average first and last element of a 1-D array\"\"\"\n", " ... return (a[0] + a[-1]) * 0.5\n", " >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", " >>> np.apply_along_axis(my_func, 0, b)\n", " array([ 4., 5., 6.])\n", " >>> np.apply_along_axis(my_func, 1, b)\n", " array([ 2., 5., 8.])\n", " \n", " For a function that doesn't return a scalar, the number of dimensions in\n", " `outarr` is the same as `arr`.\n", " \n", " >>> b = np.array([[8,1,7], [4,3,9], [5,2,6]])\n", " >>> np.apply_along_axis(sorted, 1, b)\n", " array([[1, 7, 8],\n", " [3, 4, 9],\n", " [2, 5, 6]])\n", " \n", " apply_over_axes(func, a, axes)\n", " Apply a function repeatedly over multiple axes.\n", " \n", " `func` is called as `res = func(a, axis)`, where `axis` is the first\n", " element of `axes`. The result `res` of the function call must have\n", " either the same dimensions as `a` or one less dimension. If `res`\n", " has one less dimension than `a`, a dimension is inserted before\n", " `axis`. The call to `func` is then repeated for each axis in `axes`,\n", " with `res` as the first argument.\n", " \n", " Parameters\n", " ----------\n", " func : function\n", " This function must take two arguments, `func(a, axis)`.\n", " a : array_like\n", " Input array.\n", " axes : array_like\n", " Axes over which `func` is applied; the elements must be integers.\n", " \n", " Returns\n", " -------\n", " apply_over_axis : ndarray\n", " The output array. The number of dimensions is the same as `a`,\n", " but the shape can be different. This depends on whether `func`\n", " changes the shape of its output with respect to its input.\n", " \n", " See Also\n", " --------\n", " apply_along_axis :\n", " Apply a function to 1-D slices of an array along the given axis.\n", " \n", " Notes\n", " ------\n", " This function is equivalent to tuple axis arguments to reorderable ufuncs\n", " with keepdims=True. Tuple axis arguments to ufuncs have been availabe since\n", " version 1.7.0.\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(24).reshape(2,3,4)\n", " >>> a\n", " array([[[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11]],\n", " [[12, 13, 14, 15],\n", " [16, 17, 18, 19],\n", " [20, 21, 22, 23]]])\n", " \n", " Sum over axes 0 and 2. The result has same number of dimensions\n", " as the original array:\n", " \n", " >>> np.apply_over_axes(np.sum, a, [0,2])\n", " array([[[ 60],\n", " [ 92],\n", " [124]]])\n", " \n", " Tuple axis arguments to ufuncs are equivalent:\n", " \n", " >>> np.sum(a, axis=(0,2), keepdims=True)\n", " array([[[ 60],\n", " [ 92],\n", " [124]]])\n", " \n", " arange(...)\n", " arange([start,] stop[, step,], dtype=None)\n", " \n", " Return evenly spaced values within a given interval.\n", " \n", " Values are generated within the half-open interval ``[start, stop)``\n", " (in other words, the interval including `start` but excluding `stop`).\n", " For integer arguments the function is equivalent to the Python built-in\n", " `range `_ function,\n", " but returns an ndarray rather than a list.\n", " \n", " When using a non-integer step, such as 0.1, the results will often not\n", " be consistent. It is better to use ``linspace`` for these cases.\n", " \n", " Parameters\n", " ----------\n", " start : number, optional\n", " Start of interval. The interval includes this value. The default\n", " start value is 0.\n", " stop : number\n", " End of interval. The interval does not include this value, except\n", " in some cases where `step` is not an integer and floating point\n", " round-off affects the length of `out`.\n", " step : number, optional\n", " Spacing between values. For any output `out`, this is the distance\n", " between two adjacent values, ``out[i+1] - out[i]``. The default\n", " step size is 1. If `step` is specified, `start` must also be given.\n", " dtype : dtype\n", " The type of the output array. If `dtype` is not given, infer the data\n", " type from the other input arguments.\n", " \n", " Returns\n", " -------\n", " arange : ndarray\n", " Array of evenly spaced values.\n", " \n", " For floating point arguments, the length of the result is\n", " ``ceil((stop - start)/step)``. Because of floating point overflow,\n", " this rule may result in the last element of `out` being greater\n", " than `stop`.\n", " \n", " See Also\n", " --------\n", " linspace : Evenly spaced numbers with careful handling of endpoints.\n", " ogrid: Arrays of evenly spaced numbers in N-dimensions.\n", " mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.\n", " \n", " Examples\n", " --------\n", " >>> np.arange(3)\n", " array([0, 1, 2])\n", " >>> np.arange(3.0)\n", " array([ 0., 1., 2.])\n", " >>> np.arange(3,7)\n", " array([3, 4, 5, 6])\n", " >>> np.arange(3,7,2)\n", " array([3, 5])\n", " \n", " arccos(x)\n", " Compute the inverse cosine of x.\n", " \n", " Return the \"principal value\" (for a description of this, see\n", " `numpy.arccos`) of the inverse cosine of `x`. For real `x` such that\n", " `abs(x) <= 1`, this is a real number in the closed interval\n", " :math:`[0, \\pi]`. Otherwise, the complex principle value is returned.\n", " \n", " Parameters\n", " ----------\n", " x : array_like or scalar\n", " The value(s) whose arccos is (are) required.\n", " \n", " Returns\n", " -------\n", " out : ndarray or scalar\n", " The inverse cosine(s) of the `x` value(s). If `x` was a scalar, so\n", " is `out`, otherwise an array object is returned.\n", " \n", " See Also\n", " --------\n", " numpy.arccos\n", " \n", " Notes\n", " -----\n", " For an arccos() that returns ``NAN`` when real `x` is not in the\n", " interval ``[-1,1]``, use `numpy.arccos`.\n", " \n", " Examples\n", " --------\n", " >>> np.set_printoptions(precision=4)\n", " \n", " >>> np.emath.arccos(1) # a scalar is returned\n", " 0.0\n", " \n", " >>> np.emath.arccos([1,2])\n", " array([ 0.-0.j , 0.+1.317j])\n", " \n", " arcsin(x)\n", " Compute the inverse sine of x.\n", " \n", " Return the \"principal value\" (for a description of this, see\n", " `numpy.arcsin`) of the inverse sine of `x`. For real `x` such that\n", " `abs(x) <= 1`, this is a real number in the closed interval\n", " :math:`[-\\pi/2, \\pi/2]`. Otherwise, the complex principle value is\n", " returned.\n", " \n", " Parameters\n", " ----------\n", " x : array_like or scalar\n", " The value(s) whose arcsin is (are) required.\n", " \n", " Returns\n", " -------\n", " out : ndarray or scalar\n", " The inverse sine(s) of the `x` value(s). If `x` was a scalar, so\n", " is `out`, otherwise an array object is returned.\n", " \n", " See Also\n", " --------\n", " numpy.arcsin\n", " \n", " Notes\n", " -----\n", " For an arcsin() that returns ``NAN`` when real `x` is not in the\n", " interval ``[-1,1]``, use `numpy.arcsin`.\n", " \n", " Examples\n", " --------\n", " >>> np.set_printoptions(precision=4)\n", " \n", " >>> np.emath.arcsin(0)\n", " 0.0\n", " \n", " >>> np.emath.arcsin([0,1])\n", " array([ 0. , 1.5708])\n", " \n", " arctanh(x)\n", " Compute the inverse hyperbolic tangent of `x`.\n", " \n", " Return the \"principal value\" (for a description of this, see\n", " `numpy.arctanh`) of `arctanh(x)`. For real `x` such that\n", " `abs(x) < 1`, this is a real number. If `abs(x) > 1`, or if `x` is\n", " complex, the result is complex. Finally, `x = 1` returns``inf`` and\n", " `x=-1` returns ``-inf``.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " The value(s) whose arctanh is (are) required.\n", " \n", " Returns\n", " -------\n", " out : ndarray or scalar\n", " The inverse hyperbolic tangent(s) of the `x` value(s). If `x` was\n", " a scalar so is `out`, otherwise an array is returned.\n", " \n", " \n", " See Also\n", " --------\n", " numpy.arctanh\n", " \n", " Notes\n", " -----\n", " For an arctanh() that returns ``NAN`` when real `x` is not in the\n", " interval ``(-1,1)``, use `numpy.arctanh` (this latter, however, does\n", " return +/-inf for `x = +/-1`).\n", " \n", " Examples\n", " --------\n", " >>> np.set_printoptions(precision=4)\n", " \n", " >>> np.emath.arctanh(np.matrix(np.eye(2)))\n", " array([[ Inf, 0.],\n", " [ 0., Inf]])\n", " >>> np.emath.arctanh([1j])\n", " array([ 0.+0.7854j])\n", " \n", " argmax(a, axis=None)\n", " Indices of the maximum values along an axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " axis : int, optional\n", " By default, the index is into the flattened array, otherwise\n", " along the specified axis.\n", " \n", " Returns\n", " -------\n", " index_array : ndarray of ints\n", " Array of indices into the array. It has the same shape as `a.shape`\n", " with the dimension along `axis` removed.\n", " \n", " See Also\n", " --------\n", " ndarray.argmax, argmin\n", " amax : The maximum value along a given axis.\n", " unravel_index : Convert a flat index into an index tuple.\n", " \n", " Notes\n", " -----\n", " In case of multiple occurrences of the maximum values, the indices\n", " corresponding to the first occurrence are returned.\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(6).reshape(2,3)\n", " >>> a\n", " array([[0, 1, 2],\n", " [3, 4, 5]])\n", " >>> np.argmax(a)\n", " 5\n", " >>> np.argmax(a, axis=0)\n", " array([1, 1, 1])\n", " >>> np.argmax(a, axis=1)\n", " array([2, 2])\n", " \n", " >>> b = np.arange(6)\n", " >>> b[1] = 5\n", " >>> b\n", " array([0, 5, 2, 3, 4, 5])\n", " >>> np.argmax(b) # Only the first occurrence is returned.\n", " 1\n", " \n", " argmin(a, axis=None)\n", " Return the indices of the minimum values along an axis.\n", " \n", " See Also\n", " --------\n", " argmax : Similar function. Please refer to `numpy.argmax` for detailed\n", " documentation.\n", " \n", " argpartition(a, kth, axis=-1, kind='introselect', order=None)\n", " Perform an indirect partition along the given axis using the algorithm\n", " specified by the `kind` keyword. It returns an array of indices of the\n", " same shape as `a` that index data along the given axis in partitioned\n", " order.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array to sort.\n", " kth : int or sequence of ints\n", " Element index to partition by. The kth element will be in its final\n", " sorted position and all smaller elements will be moved before it and\n", " all larger elements behind it.\n", " The order all elements in the partitions is undefined.\n", " If provided with a sequence of kth it will partition all of them into\n", " their sorted position at once.\n", " axis : int or None, optional\n", " Axis along which to sort. The default is -1 (the last axis). If None,\n", " the flattened array is used.\n", " kind : {'introselect'}, optional\n", " Selection algorithm. Default is 'introselect'\n", " order : list, optional\n", " When `a` is an array with fields defined, this argument specifies\n", " which fields to compare first, second, etc. Not all fields need be\n", " specified.\n", " \n", " Returns\n", " -------\n", " index_array : ndarray, int\n", " Array of indices that partition `a` along the specified axis.\n", " In other words, ``a[index_array]`` yields a sorted `a`.\n", " \n", " See Also\n", " --------\n", " partition : Describes partition algorithms used.\n", " ndarray.partition : Inplace partition.\n", " argsort : Full indirect sort\n", " \n", " Notes\n", " -----\n", " See `partition` for notes on the different selection algorithms.\n", " \n", " Examples\n", " --------\n", " One dimensional array:\n", " \n", " >>> x = np.array([3, 4, 2, 1])\n", " >>> x[np.argpartition(x, 3)]\n", " array([2, 1, 3, 4])\n", " >>> x[np.argpartition(x, (1, 3))]\n", " array([1, 2, 3, 4])\n", " \n", " argsort(a, axis=-1, kind='quicksort', order=None)\n", " Returns the indices that would sort an array.\n", " \n", " Perform an indirect sort along the given axis using the algorithm specified\n", " by the `kind` keyword. It returns an array of indices of the same shape as\n", " `a` that index data along the given axis in sorted order.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array to sort.\n", " axis : int or None, optional\n", " Axis along which to sort. The default is -1 (the last axis). If None,\n", " the flattened array is used.\n", " kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n", " Sorting algorithm.\n", " order : list, optional\n", " When `a` is an array with fields defined, this argument specifies\n", " which fields to compare first, second, etc. Not all fields need be\n", " specified.\n", " \n", " Returns\n", " -------\n", " index_array : ndarray, int\n", " Array of indices that sort `a` along the specified axis.\n", " In other words, ``a[index_array]`` yields a sorted `a`.\n", " \n", " See Also\n", " --------\n", " sort : Describes sorting algorithms used.\n", " lexsort : Indirect stable sort with multiple keys.\n", " ndarray.sort : Inplace sort.\n", " argpartition : Indirect partial sort.\n", " \n", " Notes\n", " -----\n", " See `sort` for notes on the different sorting algorithms.\n", " \n", " As of NumPy 1.4.0 `argsort` works with real/complex arrays containing\n", " nan values. The enhanced sort order is documented in `sort`.\n", " \n", " Examples\n", " --------\n", " One dimensional array:\n", " \n", " >>> x = np.array([3, 1, 2])\n", " >>> np.argsort(x)\n", " array([1, 2, 0])\n", " \n", " Two-dimensional array:\n", " \n", " >>> x = np.array([[0, 3], [2, 2]])\n", " >>> x\n", " array([[0, 3],\n", " [2, 2]])\n", " \n", " >>> np.argsort(x, axis=0)\n", " array([[0, 1],\n", " [1, 0]])\n", " \n", " >>> np.argsort(x, axis=1)\n", " array([[0, 1],\n", " [0, 1]])\n", " \n", " Sorting with keys:\n", " \n", " >>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '>> x\n", " array([(1, 0), (0, 1)],\n", " dtype=[('x', '>> np.argsort(x, order=('x','y'))\n", " array([1, 0])\n", " \n", " >>> np.argsort(x, order=('y','x'))\n", " array([0, 1])\n", " \n", " argwhere(a)\n", " Find the indices of array elements that are non-zero, grouped by element.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " \n", " Returns\n", " -------\n", " index_array : ndarray\n", " Indices of elements that are non-zero. Indices are grouped by element.\n", " \n", " See Also\n", " --------\n", " where, nonzero\n", " \n", " Notes\n", " -----\n", " ``np.argwhere(a)`` is the same as ``np.transpose(np.nonzero(a))``.\n", " \n", " The output of ``argwhere`` is not suitable for indexing arrays.\n", " For this purpose use ``where(a)`` instead.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(6).reshape(2,3)\n", " >>> x\n", " array([[0, 1, 2],\n", " [3, 4, 5]])\n", " >>> np.argwhere(x>1)\n", " array([[0, 2],\n", " [1, 0],\n", " [1, 1],\n", " [1, 2]])\n", " \n", " around(a, decimals=0, out=None)\n", " Evenly round to the given number of decimals.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " decimals : int, optional\n", " Number of decimal places to round to (default: 0). If\n", " decimals is negative, it specifies the number of positions to\n", " the left of the decimal point.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must have\n", " the same shape as the expected output, but the type of the output\n", " values will be cast if necessary. See `doc.ufuncs` (Section\n", " \"Output arguments\") for details.\n", " \n", " Returns\n", " -------\n", " rounded_array : ndarray\n", " An array of the same type as `a`, containing the rounded values.\n", " Unless `out` was specified, a new array is created. A reference to\n", " the result is returned.\n", " \n", " The real and imaginary parts of complex numbers are rounded\n", " separately. The result of rounding a float is a float.\n", " \n", " See Also\n", " --------\n", " ndarray.round : equivalent method\n", " \n", " ceil, fix, floor, rint, trunc\n", " \n", " \n", " Notes\n", " -----\n", " For values exactly halfway between rounded decimal values, Numpy\n", " rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,\n", " -0.5 and 0.5 round to 0.0, etc. Results may also be surprising due\n", " to the inexact representation of decimal fractions in the IEEE\n", " floating point standard [1]_ and errors introduced when scaling\n", " by powers of ten.\n", " \n", " References\n", " ----------\n", " .. [1] \"Lecture Notes on the Status of IEEE 754\", William Kahan,\n", " http://www.cs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF\n", " .. [2] \"How Futile are Mindless Assessments of\n", " Roundoff in Floating-Point Computation?\", William Kahan,\n", " http://www.cs.berkeley.edu/~wkahan/Mindless.pdf\n", " \n", " Examples\n", " --------\n", " >>> np.around([0.37, 1.64])\n", " array([ 0., 2.])\n", " >>> np.around([0.37, 1.64], decimals=1)\n", " array([ 0.4, 1.6])\n", " >>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value\n", " array([ 0., 2., 2., 4., 4.])\n", " >>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned\n", " array([ 1, 2, 3, 11])\n", " >>> np.around([1,2,3,11], decimals=-1)\n", " array([ 0, 0, 0, 10])\n", " \n", " array(...)\n", " array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)\n", " \n", " Create an array.\n", " \n", " Parameters\n", " ----------\n", " object : array_like\n", " An array, any object exposing the array interface, an\n", " object whose __array__ method returns an array, or any\n", " (nested) sequence.\n", " dtype : data-type, optional\n", " The desired data-type for the array. If not given, then\n", " the type will be determined as the minimum type required\n", " to hold the objects in the sequence. This argument can only\n", " be used to 'upcast' the array. For downcasting, use the\n", " .astype(t) method.\n", " copy : bool, optional\n", " If true (default), then the object is copied. Otherwise, a copy\n", " will only be made if __array__ returns a copy, if obj is a\n", " nested sequence, or if a copy is needed to satisfy any of the other\n", " requirements (`dtype`, `order`, etc.).\n", " order : {'C', 'F', 'A'}, optional\n", " Specify the order of the array. If order is 'C' (default), then the\n", " array will be in C-contiguous order (last-index varies the\n", " fastest). If order is 'F', then the returned array\n", " will be in Fortran-contiguous order (first-index varies the\n", " fastest). If order is 'A', then the returned array may\n", " be in any order (either C-, Fortran-contiguous, or even\n", " discontiguous).\n", " subok : bool, optional\n", " If True, then sub-classes will be passed-through, otherwise\n", " the returned array will be forced to be a base-class array (default).\n", " ndmin : int, optional\n", " Specifies the minimum number of dimensions that the resulting\n", " array should have. Ones will be pre-pended to the shape as\n", " needed to meet this requirement.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " An array object satisfying the specified requirements.\n", " \n", " See Also\n", " --------\n", " empty, empty_like, zeros, zeros_like, ones, ones_like, fill\n", " \n", " Examples\n", " --------\n", " >>> np.array([1, 2, 3])\n", " array([1, 2, 3])\n", " \n", " Upcasting:\n", " \n", " >>> np.array([1, 2, 3.0])\n", " array([ 1., 2., 3.])\n", " \n", " More than one dimension:\n", " \n", " >>> np.array([[1, 2], [3, 4]])\n", " array([[1, 2],\n", " [3, 4]])\n", " \n", " Minimum dimensions 2:\n", " \n", " >>> np.array([1, 2, 3], ndmin=2)\n", " array([[1, 2, 3]])\n", " \n", " Type provided:\n", " \n", " >>> np.array([1, 2, 3], dtype=complex)\n", " array([ 1.+0.j, 2.+0.j, 3.+0.j])\n", " \n", " Data-type consisting of more than one element:\n", " \n", " >>> x = np.array([(1,2),(3,4)],dtype=[('a','>> x['a']\n", " array([1, 3])\n", " \n", " Creating an array from sub-classes:\n", " \n", " >>> np.array(np.mat('1 2; 3 4'))\n", " array([[1, 2],\n", " [3, 4]])\n", " \n", " >>> np.array(np.mat('1 2; 3 4'), subok=True)\n", " matrix([[1, 2],\n", " [3, 4]])\n", " \n", " array2string(a, max_line_width=None, precision=None, suppress_small=None, separator=' ', prefix='', style=, formatter=None)\n", " Return a string representation of an array.\n", " \n", " Parameters\n", " ----------\n", " a : ndarray\n", " Input array.\n", " max_line_width : int, optional\n", " The maximum number of columns the string should span. Newline\n", " characters splits the string appropriately after array elements.\n", " precision : int, optional\n", " Floating point precision. Default is the current printing\n", " precision (usually 8), which can be altered using `set_printoptions`.\n", " suppress_small : bool, optional\n", " Represent very small numbers as zero. A number is \"very small\" if it\n", " is smaller than the current printing precision.\n", " separator : str, optional\n", " Inserted between elements.\n", " prefix : str, optional\n", " An array is typically printed as::\n", " \n", " 'prefix(' + array2string(a) + ')'\n", " \n", " The length of the prefix string is used to align the\n", " output correctly.\n", " style : function, optional\n", " A function that accepts an ndarray and returns a string. Used only\n", " when the shape of `a` is equal to ``()``, i.e. for 0-D arrays.\n", " formatter : dict of callables, optional\n", " If not None, the keys should indicate the type(s) that the respective\n", " formatting function applies to. Callables should return a string.\n", " Types that are not specified (by their corresponding keys) are handled\n", " by the default formatters. Individual types for which a formatter\n", " can be set are::\n", " \n", " - 'bool'\n", " - 'int'\n", " - 'timedelta' : a `numpy.timedelta64`\n", " - 'datetime' : a `numpy.datetime64`\n", " - 'float'\n", " - 'longfloat' : 128-bit floats\n", " - 'complexfloat'\n", " - 'longcomplexfloat' : composed of two 128-bit floats\n", " - 'numpy_str' : types `numpy.string_` and `numpy.unicode_`\n", " - 'str' : all other strings\n", " \n", " Other keys that can be used to set a group of types at once are::\n", " \n", " - 'all' : sets all types\n", " - 'int_kind' : sets 'int'\n", " - 'float_kind' : sets 'float' and 'longfloat'\n", " - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'\n", " - 'str_kind' : sets 'str' and 'numpystr'\n", " \n", " Returns\n", " -------\n", " array_str : str\n", " String representation of the array.\n", " \n", " Raises\n", " ------\n", " TypeError\n", " if a callable in `formatter` does not return a string.\n", " \n", " See Also\n", " --------\n", " array_str, array_repr, set_printoptions, get_printoptions\n", " \n", " Notes\n", " -----\n", " If a formatter is specified for a certain type, the `precision` keyword is\n", " ignored for that type.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([1e-16,1,2,3])\n", " >>> print np.array2string(x, precision=2, separator=',',\n", " ... suppress_small=True)\n", " [ 0., 1., 2., 3.]\n", " \n", " >>> x = np.arange(3.)\n", " >>> np.array2string(x, formatter={'float_kind':lambda x: \"%.2f\" % x})\n", " '[0.00 1.00 2.00]'\n", " \n", " >>> x = np.arange(3)\n", " >>> np.array2string(x, formatter={'int':lambda x: hex(x)})\n", " '[0x0L 0x1L 0x2L]'\n", " \n", " array_equal(a1, a2)\n", " True if two arrays have the same shape and elements, False otherwise.\n", " \n", " Parameters\n", " ----------\n", " a1, a2 : array_like\n", " Input arrays.\n", " \n", " Returns\n", " -------\n", " b : bool\n", " Returns True if the arrays are equal.\n", " \n", " See Also\n", " --------\n", " allclose: Returns True if two arrays are element-wise equal within a\n", " tolerance.\n", " array_equiv: Returns True if input arrays are shape consistent and all\n", " elements equal.\n", " \n", " Examples\n", " --------\n", " >>> np.array_equal([1, 2], [1, 2])\n", " True\n", " >>> np.array_equal(np.array([1, 2]), np.array([1, 2]))\n", " True\n", " >>> np.array_equal([1, 2], [1, 2, 3])\n", " False\n", " >>> np.array_equal([1, 2], [1, 4])\n", " False\n", " \n", " array_equiv(a1, a2)\n", " Returns True if input arrays are shape consistent and all elements equal.\n", " \n", " Shape consistent means they are either the same shape, or one input array\n", " can be broadcasted to create the same shape as the other one.\n", " \n", " Parameters\n", " ----------\n", " a1, a2 : array_like\n", " Input arrays.\n", " \n", " Returns\n", " -------\n", " out : bool\n", " True if equivalent, False otherwise.\n", " \n", " Examples\n", " --------\n", " >>> np.array_equiv([1, 2], [1, 2])\n", " True\n", " >>> np.array_equiv([1, 2], [1, 3])\n", " False\n", " \n", " Showing the shape equivalence:\n", " \n", " >>> np.array_equiv([1, 2], [[1, 2], [1, 2]])\n", " True\n", " >>> np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])\n", " False\n", " \n", " >>> np.array_equiv([1, 2], [[1, 2], [1, 3]])\n", " False\n", " \n", " array_repr(arr, max_line_width=None, precision=None, suppress_small=None)\n", " Return the string representation of an array.\n", " \n", " Parameters\n", " ----------\n", " arr : ndarray\n", " Input array.\n", " max_line_width : int, optional\n", " The maximum number of columns the string should span. Newline\n", " characters split the string appropriately after array elements.\n", " precision : int, optional\n", " Floating point precision. Default is the current printing precision\n", " (usually 8), which can be altered using `set_printoptions`.\n", " suppress_small : bool, optional\n", " Represent very small numbers as zero, default is False. Very small\n", " is defined by `precision`, if the precision is 8 then\n", " numbers smaller than 5e-9 are represented as zero.\n", " \n", " Returns\n", " -------\n", " string : str\n", " The string representation of an array.\n", " \n", " See Also\n", " --------\n", " array_str, array2string, set_printoptions\n", " \n", " Examples\n", " --------\n", " >>> np.array_repr(np.array([1,2]))\n", " 'array([1, 2])'\n", " >>> np.array_repr(np.ma.array([0.]))\n", " 'MaskedArray([ 0.])'\n", " >>> np.array_repr(np.array([], np.int32))\n", " 'array([], dtype=int32)'\n", " \n", " >>> x = np.array([1e-6, 4e-7, 2, 3])\n", " >>> np.array_repr(x, precision=6, suppress_small=True)\n", " 'array([ 0.000001, 0. , 2. , 3. ])'\n", " \n", " array_split(ary, indices_or_sections, axis=0)\n", " Split an array into multiple sub-arrays.\n", " \n", " Please refer to the ``split`` documentation. The only difference\n", " between these functions is that ``array_split`` allows\n", " `indices_or_sections` to be an integer that does *not* equally\n", " divide the axis.\n", " \n", " See Also\n", " --------\n", " split : Split array into multiple sub-arrays of equal size.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(8.0)\n", " >>> np.array_split(x, 3)\n", " [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7.])]\n", " \n", " array_str(a, max_line_width=None, precision=None, suppress_small=None)\n", " Return a string representation of the data in an array.\n", " \n", " The data in the array is returned as a single string. This function is\n", " similar to `array_repr`, the difference being that `array_repr` also\n", " returns information on the kind of array and its data type.\n", " \n", " Parameters\n", " ----------\n", " a : ndarray\n", " Input array.\n", " max_line_width : int, optional\n", " Inserts newlines if text is longer than `max_line_width`. The\n", " default is, indirectly, 75.\n", " precision : int, optional\n", " Floating point precision. Default is the current printing precision\n", " (usually 8), which can be altered using `set_printoptions`.\n", " suppress_small : bool, optional\n", " Represent numbers \"very close\" to zero as zero; default is False.\n", " Very close is defined by precision: if the precision is 8, e.g.,\n", " numbers smaller (in absolute value) than 5e-9 are represented as\n", " zero.\n", " \n", " See Also\n", " --------\n", " array2string, array_repr, set_printoptions\n", " \n", " Examples\n", " --------\n", " >>> np.array_str(np.arange(3))\n", " '[0 1 2]'\n", " \n", " asanyarray(a, dtype=None, order=None)\n", " Convert the input to an ndarray, but pass ndarray subclasses through.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data, in any form that can be converted to an array. This\n", " includes scalars, lists, lists of tuples, tuples, tuples of tuples,\n", " tuples of lists, and ndarrays.\n", " dtype : data-type, optional\n", " By default, the data-type is inferred from the input data.\n", " order : {'C', 'F'}, optional\n", " Whether to use row-major ('C') or column-major ('F') memory\n", " representation. Defaults to 'C'.\n", " \n", " Returns\n", " -------\n", " out : ndarray or an ndarray subclass\n", " Array interpretation of `a`. If `a` is an ndarray or a subclass\n", " of ndarray, it is returned as-is and no copy is performed.\n", " \n", " See Also\n", " --------\n", " asarray : Similar function which always returns ndarrays.\n", " ascontiguousarray : Convert input to a contiguous array.\n", " asfarray : Convert input to a floating point ndarray.\n", " asfortranarray : Convert input to an ndarray with column-major\n", " memory order.\n", " asarray_chkfinite : Similar function which checks input for NaNs and\n", " Infs.\n", " fromiter : Create an array from an iterator.\n", " fromfunction : Construct an array by executing a function on grid\n", " positions.\n", " \n", " Examples\n", " --------\n", " Convert a list into an array:\n", " \n", " >>> a = [1, 2]\n", " >>> np.asanyarray(a)\n", " array([1, 2])\n", " \n", " Instances of `ndarray` subclasses are passed through as-is:\n", " \n", " >>> a = np.matrix([1, 2])\n", " >>> np.asanyarray(a) is a\n", " True\n", " \n", " asarray(a, dtype=None, order=None)\n", " Convert the input to an array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data, in any form that can be converted to an array. This\n", " includes lists, lists of tuples, tuples, tuples of tuples, tuples\n", " of lists and ndarrays.\n", " dtype : data-type, optional\n", " By default, the data-type is inferred from the input data.\n", " order : {'C', 'F'}, optional\n", " Whether to use row-major ('C') or column-major ('F' for FORTRAN)\n", " memory representation. Defaults to 'C'.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Array interpretation of `a`. No copy is performed if the input\n", " is already an ndarray. If `a` is a subclass of ndarray, a base\n", " class ndarray is returned.\n", " \n", " See Also\n", " --------\n", " asanyarray : Similar function which passes through subclasses.\n", " ascontiguousarray : Convert input to a contiguous array.\n", " asfarray : Convert input to a floating point ndarray.\n", " asfortranarray : Convert input to an ndarray with column-major\n", " memory order.\n", " asarray_chkfinite : Similar function which checks input for NaNs and Infs.\n", " fromiter : Create an array from an iterator.\n", " fromfunction : Construct an array by executing a function on grid\n", " positions.\n", " \n", " Examples\n", " --------\n", " Convert a list into an array:\n", " \n", " >>> a = [1, 2]\n", " >>> np.asarray(a)\n", " array([1, 2])\n", " \n", " Existing arrays are not copied:\n", " \n", " >>> a = np.array([1, 2])\n", " >>> np.asarray(a) is a\n", " True\n", " \n", " If `dtype` is set, array is copied only if dtype does not match:\n", " \n", " >>> a = np.array([1, 2], dtype=np.float32)\n", " >>> np.asarray(a, dtype=np.float32) is a\n", " True\n", " >>> np.asarray(a, dtype=np.float64) is a\n", " False\n", " \n", " Contrary to `asanyarray`, ndarray subclasses are not passed through:\n", " \n", " >>> issubclass(np.matrix, np.ndarray)\n", " True\n", " >>> a = np.matrix([[1, 2]])\n", " >>> np.asarray(a) is a\n", " False\n", " >>> np.asanyarray(a) is a\n", " True\n", " \n", " asarray_chkfinite(a, dtype=None, order=None)\n", " Convert the input to an array, checking for NaNs or Infs.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data, in any form that can be converted to an array. This\n", " includes lists, lists of tuples, tuples, tuples of tuples, tuples\n", " of lists and ndarrays. Success requires no NaNs or Infs.\n", " dtype : data-type, optional\n", " By default, the data-type is inferred from the input data.\n", " order : {'C', 'F'}, optional\n", " Whether to use row-major ('C') or column-major ('FORTRAN') memory\n", " representation. Defaults to 'C'.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Array interpretation of `a`. No copy is performed if the input\n", " is already an ndarray. If `a` is a subclass of ndarray, a base\n", " class ndarray is returned.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity).\n", " \n", " See Also\n", " --------\n", " asarray : Create and array.\n", " asanyarray : Similar function which passes through subclasses.\n", " ascontiguousarray : Convert input to a contiguous array.\n", " asfarray : Convert input to a floating point ndarray.\n", " asfortranarray : Convert input to an ndarray with column-major\n", " memory order.\n", " fromiter : Create an array from an iterator.\n", " fromfunction : Construct an array by executing a function on grid\n", " positions.\n", " \n", " Examples\n", " --------\n", " Convert a list into an array. If all elements are finite\n", " ``asarray_chkfinite`` is identical to ``asarray``.\n", " \n", " >>> a = [1, 2]\n", " >>> np.asarray_chkfinite(a, dtype=float)\n", " array([1., 2.])\n", " \n", " Raises ValueError if array_like contains Nans or Infs.\n", " \n", " >>> a = [1, 2, np.inf]\n", " >>> try:\n", " ... np.asarray_chkfinite(a)\n", " ... except ValueError:\n", " ... print 'ValueError'\n", " ...\n", " ValueError\n", " \n", " ascontiguousarray(a, dtype=None)\n", " Return a contiguous array in memory (C order).\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " dtype : str or dtype object, optional\n", " Data-type of returned array.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Contiguous array of same shape and content as `a`, with type `dtype`\n", " if specified.\n", " \n", " See Also\n", " --------\n", " asfortranarray : Convert input to an ndarray with column-major\n", " memory order.\n", " require : Return an ndarray that satisfies requirements.\n", " ndarray.flags : Information about the memory layout of the array.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(6).reshape(2,3)\n", " >>> np.ascontiguousarray(x, dtype=np.float32)\n", " array([[ 0., 1., 2.],\n", " [ 3., 4., 5.]], dtype=float32)\n", " >>> x.flags['C_CONTIGUOUS']\n", " True\n", " \n", " asfarray(a, dtype=)\n", " Return an array converted to a float type.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " The input array.\n", " dtype : str or dtype object, optional\n", " Float type code to coerce input array `a`. If `dtype` is one of the\n", " 'int' dtypes, it is replaced with float64.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The input `a` as a float ndarray.\n", " \n", " Examples\n", " --------\n", " >>> np.asfarray([2, 3])\n", " array([ 2., 3.])\n", " >>> np.asfarray([2, 3], dtype='float')\n", " array([ 2., 3.])\n", " >>> np.asfarray([2, 3], dtype='int8')\n", " array([ 2., 3.])\n", " \n", " asfortranarray(a, dtype=None)\n", " Return an array laid out in Fortran order in memory.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " dtype : str or dtype object, optional\n", " By default, the data-type is inferred from the input data.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The input `a` in Fortran, or column-major, order.\n", " \n", " See Also\n", " --------\n", " ascontiguousarray : Convert input to a contiguous (C order) array.\n", " asanyarray : Convert input to an ndarray with either row or\n", " column-major memory order.\n", " require : Return an ndarray that satisfies requirements.\n", " ndarray.flags : Information about the memory layout of the array.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(6).reshape(2,3)\n", " >>> y = np.asfortranarray(x)\n", " >>> x.flags['F_CONTIGUOUS']\n", " False\n", " >>> y.flags['F_CONTIGUOUS']\n", " True\n", " \n", " asmatrix(data, dtype=None)\n", " Interpret the input as a matrix.\n", " \n", " Unlike `matrix`, `asmatrix` does not make a copy if the input is already\n", " a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``.\n", " \n", " Parameters\n", " ----------\n", " data : array_like\n", " Input data.\n", " \n", " Returns\n", " -------\n", " mat : matrix\n", " `data` interpreted as a matrix.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([[1, 2], [3, 4]])\n", " \n", " >>> m = np.asmatrix(x)\n", " \n", " >>> x[0,0] = 5\n", " \n", " >>> m\n", " matrix([[5, 2],\n", " [3, 4]])\n", " \n", " asscalar(a)\n", " Convert an array of size 1 to its scalar equivalent.\n", " \n", " Parameters\n", " ----------\n", " a : ndarray\n", " Input array of size 1.\n", " \n", " Returns\n", " -------\n", " out : scalar\n", " Scalar representation of `a`. The output data type is the same type\n", " returned by the input's `item` method.\n", " \n", " Examples\n", " --------\n", " >>> np.asscalar(np.array([24]))\n", " 24\n", " \n", " atleast_1d(*arys)\n", " Convert inputs to arrays with at least one dimension.\n", " \n", " Scalar inputs are converted to 1-dimensional arrays, whilst\n", " higher-dimensional inputs are preserved.\n", " \n", " Parameters\n", " ----------\n", " arys1, arys2, ... : array_like\n", " One or more input arrays.\n", " \n", " Returns\n", " -------\n", " ret : ndarray\n", " An array, or sequence of arrays, each with ``a.ndim >= 1``.\n", " Copies are made only if necessary.\n", " \n", " See Also\n", " --------\n", " atleast_2d, atleast_3d\n", " \n", " Examples\n", " --------\n", " >>> np.atleast_1d(1.0)\n", " array([ 1.])\n", " \n", " >>> x = np.arange(9.0).reshape(3,3)\n", " >>> np.atleast_1d(x)\n", " array([[ 0., 1., 2.],\n", " [ 3., 4., 5.],\n", " [ 6., 7., 8.]])\n", " >>> np.atleast_1d(x) is x\n", " True\n", " \n", " >>> np.atleast_1d(1, [3, 4])\n", " [array([1]), array([3, 4])]\n", " \n", " atleast_2d(*arys)\n", " View inputs as arrays with at least two dimensions.\n", " \n", " Parameters\n", " ----------\n", " arys1, arys2, ... : array_like\n", " One or more array-like sequences. Non-array inputs are converted\n", " to arrays. Arrays that already have two or more dimensions are\n", " preserved.\n", " \n", " Returns\n", " -------\n", " res, res2, ... : ndarray\n", " An array, or tuple of arrays, each with ``a.ndim >= 2``.\n", " Copies are avoided where possible, and views with two or more\n", " dimensions are returned.\n", " \n", " See Also\n", " --------\n", " atleast_1d, atleast_3d\n", " \n", " Examples\n", " --------\n", " >>> np.atleast_2d(3.0)\n", " array([[ 3.]])\n", " \n", " >>> x = np.arange(3.0)\n", " >>> np.atleast_2d(x)\n", " array([[ 0., 1., 2.]])\n", " >>> np.atleast_2d(x).base is x\n", " True\n", " \n", " >>> np.atleast_2d(1, [1, 2], [[1, 2]])\n", " [array([[1]]), array([[1, 2]]), array([[1, 2]])]\n", " \n", " atleast_3d(*arys)\n", " View inputs as arrays with at least three dimensions.\n", " \n", " Parameters\n", " ----------\n", " arys1, arys2, ... : array_like\n", " One or more array-like sequences. Non-array inputs are converted to\n", " arrays. Arrays that already have three or more dimensions are\n", " preserved.\n", " \n", " Returns\n", " -------\n", " res1, res2, ... : ndarray\n", " An array, or tuple of arrays, each with ``a.ndim >= 3``. Copies are\n", " avoided where possible, and views with three or more dimensions are\n", " returned. For example, a 1-D array of shape ``(N,)`` becomes a view\n", " of shape ``(1, N, 1)``, and a 2-D array of shape ``(M, N)`` becomes a\n", " view of shape ``(M, N, 1)``.\n", " \n", " See Also\n", " --------\n", " atleast_1d, atleast_2d\n", " \n", " Examples\n", " --------\n", " >>> np.atleast_3d(3.0)\n", " array([[[ 3.]]])\n", " \n", " >>> x = np.arange(3.0)\n", " >>> np.atleast_3d(x).shape\n", " (1, 3, 1)\n", " \n", " >>> x = np.arange(12.0).reshape(4,3)\n", " >>> np.atleast_3d(x).shape\n", " (4, 3, 1)\n", " >>> np.atleast_3d(x).base is x\n", " True\n", " \n", " >>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]):\n", " ... print arr, arr.shape\n", " ...\n", " [[[1]\n", " [2]]] (1, 2, 1)\n", " [[[1]\n", " [2]]] (1, 2, 1)\n", " [[[1 2]]] (1, 1, 2)\n", " \n", " average(a, axis=None, weights=None, returned=False)\n", " Compute the weighted average along the specified axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array containing data to be averaged. If `a` is not an array, a\n", " conversion is attempted.\n", " axis : int, optional\n", " Axis along which to average `a`. If `None`, averaging is done over\n", " the flattened array.\n", " weights : array_like, optional\n", " An array of weights associated with the values in `a`. Each value in\n", " `a` contributes to the average according to its associated weight.\n", " The weights array can either be 1-D (in which case its length must be\n", " the size of `a` along the given axis) or of the same shape as `a`.\n", " If `weights=None`, then all data in `a` are assumed to have a\n", " weight equal to one.\n", " returned : bool, optional\n", " Default is `False`. If `True`, the tuple (`average`, `sum_of_weights`)\n", " is returned, otherwise only the average is returned.\n", " If `weights=None`, `sum_of_weights` is equivalent to the number of\n", " elements over which the average is taken.\n", " \n", " \n", " Returns\n", " -------\n", " average, [sum_of_weights] : {array_type, double}\n", " Return the average along the specified axis. When returned is `True`,\n", " return a tuple with the average as the first element and the sum\n", " of the weights as the second element. The return type is `Float`\n", " if `a` is of integer type, otherwise it is of the same type as `a`.\n", " `sum_of_weights` is of the same type as `average`.\n", " \n", " Raises\n", " ------\n", " ZeroDivisionError\n", " When all weights along axis are zero. See `numpy.ma.average` for a\n", " version robust to this type of error.\n", " TypeError\n", " When the length of 1D `weights` is not the same as the shape of `a`\n", " along axis.\n", " \n", " See Also\n", " --------\n", " mean\n", " \n", " ma.average : average for masked arrays -- useful if your data contains\n", " \"missing\" values\n", " \n", " Examples\n", " --------\n", " >>> data = range(1,5)\n", " >>> data\n", " [1, 2, 3, 4]\n", " >>> np.average(data)\n", " 2.5\n", " >>> np.average(range(1,11), weights=range(10,0,-1))\n", " 4.0\n", " \n", " >>> data = np.arange(6).reshape((3,2))\n", " >>> data\n", " array([[0, 1],\n", " [2, 3],\n", " [4, 5]])\n", " >>> np.average(data, axis=1, weights=[1./4, 3./4])\n", " array([ 0.75, 2.75, 4.75])\n", " >>> np.average(data, weights=[1./4, 3./4])\n", " Traceback (most recent call last):\n", " ...\n", " TypeError: Axis must be specified when shapes of a and weights differ.\n", " \n", " bartlett(M)\n", " Return the Bartlett window.\n", " \n", " The Bartlett window is very similar to a triangular window, except\n", " that the end points are at zero. It is often used in signal\n", " processing for tapering a signal, without generating too much\n", " ripple in the frequency domain.\n", " \n", " Parameters\n", " ----------\n", " M : int\n", " Number of points in the output window. If zero or less, an\n", " empty array is returned.\n", " \n", " Returns\n", " -------\n", " out : array\n", " The triangular window, with the maximum value normalized to one\n", " (the value one appears only if the number of samples is odd), with\n", " the first and last samples equal to zero.\n", " \n", " See Also\n", " --------\n", " blackman, hamming, hanning, kaiser\n", " \n", " Notes\n", " -----\n", " The Bartlett window is defined as\n", " \n", " .. math:: w(n) = \\frac{2}{M-1} \\left(\n", " \\frac{M-1}{2} - \\left|n - \\frac{M-1}{2}\\right|\n", " \\right)\n", " \n", " Most references to the Bartlett window come from the signal\n", " processing literature, where it is used as one of many windowing\n", " functions for smoothing values. Note that convolution with this\n", " window produces linear interpolation. It is also known as an\n", " apodization (which means\"removing the foot\", i.e. smoothing\n", " discontinuities at the beginning and end of the sampled signal) or\n", " tapering function. The fourier transform of the Bartlett is the product\n", " of two sinc functions.\n", " Note the excellent discussion in Kanasewich.\n", " \n", " References\n", " ----------\n", " .. [1] M.S. Bartlett, \"Periodogram Analysis and Continuous Spectra\",\n", " Biometrika 37, 1-16, 1950.\n", " .. [2] E.R. Kanasewich, \"Time Sequence Analysis in Geophysics\",\n", " The University of Alberta Press, 1975, pp. 109-110.\n", " .. [3] A.V. Oppenheim and R.W. Schafer, \"Discrete-Time Signal\n", " Processing\", Prentice-Hall, 1999, pp. 468-471.\n", " .. [4] Wikipedia, \"Window function\",\n", " http://en.wikipedia.org/wiki/Window_function\n", " .. [5] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,\n", " \"Numerical Recipes\", Cambridge University Press, 1986, page 429.\n", " \n", " \n", " Examples\n", " --------\n", " >>> np.bartlett(12)\n", " array([ 0. , 0.18181818, 0.36363636, 0.54545455, 0.72727273,\n", " 0.90909091, 0.90909091, 0.72727273, 0.54545455, 0.36363636,\n", " 0.18181818, 0. ])\n", " \n", " Plot the window and its frequency response (requires SciPy and matplotlib):\n", " \n", " >>> from numpy.fft import fft, fftshift\n", " >>> window = np.bartlett(51)\n", " >>> plt.plot(window)\n", " []\n", " >>> plt.title(\"Bartlett window\")\n", " \n", " >>> plt.ylabel(\"Amplitude\")\n", " \n", " >>> plt.xlabel(\"Sample\")\n", " \n", " >>> plt.show()\n", " \n", " >>> plt.figure()\n", " \n", " >>> A = fft(window, 2048) / 25.5\n", " >>> mag = np.abs(fftshift(A))\n", " >>> freq = np.linspace(-0.5, 0.5, len(A))\n", " >>> response = 20 * np.log10(mag)\n", " >>> response = np.clip(response, -100, 100)\n", " >>> plt.plot(freq, response)\n", " []\n", " >>> plt.title(\"Frequency response of Bartlett window\")\n", " \n", " >>> plt.ylabel(\"Magnitude [dB]\")\n", " \n", " >>> plt.xlabel(\"Normalized frequency [cycles per sample]\")\n", " \n", " >>> plt.axis('tight')\n", " (-0.5, 0.5, -100.0, ...)\n", " >>> plt.show()\n", " \n", " base_repr(number, base=2, padding=0)\n", " Return a string representation of a number in the given base system.\n", " \n", " Parameters\n", " ----------\n", " number : int\n", " The value to convert. Only positive values are handled.\n", " base : int, optional\n", " Convert `number` to the `base` number system. The valid range is 2-36,\n", " the default value is 2.\n", " padding : int, optional\n", " Number of zeros padded on the left. Default is 0 (no padding).\n", " \n", " Returns\n", " -------\n", " out : str\n", " String representation of `number` in `base` system.\n", " \n", " See Also\n", " --------\n", " binary_repr : Faster version of `base_repr` for base 2.\n", " \n", " Examples\n", " --------\n", " >>> np.base_repr(5)\n", " '101'\n", " >>> np.base_repr(6, 5)\n", " '11'\n", " >>> np.base_repr(7, base=5, padding=3)\n", " '00012'\n", " \n", " >>> np.base_repr(10, base=16)\n", " 'A'\n", " >>> np.base_repr(32, base=16)\n", " '20'\n", " \n", " binary_repr(num, width=None)\n", " Return the binary representation of the input number as a string.\n", " \n", " For negative numbers, if width is not given, a minus sign is added to the\n", " front. If width is given, the two's complement of the number is\n", " returned, with respect to that width.\n", " \n", " In a two's-complement system negative numbers are represented by the two's\n", " complement of the absolute value. This is the most common method of\n", " representing signed integers on computers [1]_. A N-bit two's-complement\n", " system can represent every integer in the range\n", " :math:`-2^{N-1}` to :math:`+2^{N-1}-1`.\n", " \n", " Parameters\n", " ----------\n", " num : int\n", " Only an integer decimal number can be used.\n", " width : int, optional\n", " The length of the returned string if `num` is positive, the length of\n", " the two's complement if `num` is negative.\n", " \n", " Returns\n", " -------\n", " bin : str\n", " Binary representation of `num` or two's complement of `num`.\n", " \n", " See Also\n", " --------\n", " base_repr: Return a string representation of a number in the given base\n", " system.\n", " \n", " Notes\n", " -----\n", " `binary_repr` is equivalent to using `base_repr` with base 2, but about 25x\n", " faster.\n", " \n", " References\n", " ----------\n", " .. [1] Wikipedia, \"Two's complement\",\n", " http://en.wikipedia.org/wiki/Two's_complement\n", " \n", " Examples\n", " --------\n", " >>> np.binary_repr(3)\n", " '11'\n", " >>> np.binary_repr(-3)\n", " '-11'\n", " >>> np.binary_repr(3, width=4)\n", " '0011'\n", " \n", " The two's complement is returned when the input number is negative and\n", " width is specified:\n", " \n", " >>> np.binary_repr(-3, width=4)\n", " '1101'\n", " \n", " bincount(...)\n", " bincount(x, weights=None, minlength=None)\n", " \n", " Count number of occurrences of each value in array of non-negative ints.\n", " \n", " The number of bins (of size 1) is one larger than the largest value in\n", " `x`. If `minlength` is specified, there will be at least this number\n", " of bins in the output array (though it will be longer if necessary,\n", " depending on the contents of `x`).\n", " Each bin gives the number of occurrences of its index value in `x`.\n", " If `weights` is specified the input array is weighted by it, i.e. if a\n", " value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead\n", " of ``out[n] += 1``.\n", " \n", " Parameters\n", " ----------\n", " x : array_like, 1 dimension, nonnegative ints\n", " Input array.\n", " weights : array_like, optional\n", " Weights, array of the same shape as `x`.\n", " minlength : int, optional\n", " .. versionadded:: 1.6.0\n", " \n", " A minimum number of bins for the output array.\n", " \n", " Returns\n", " -------\n", " out : ndarray of ints\n", " The result of binning the input array.\n", " The length of `out` is equal to ``np.amax(x)+1``.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If the input is not 1-dimensional, or contains elements with negative\n", " values, or if `minlength` is non-positive.\n", " TypeError\n", " If the type of the input is float or complex.\n", " \n", " See Also\n", " --------\n", " histogram, digitize, unique\n", " \n", " Examples\n", " --------\n", " >>> np.bincount(np.arange(5))\n", " array([1, 1, 1, 1, 1])\n", " >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))\n", " array([1, 3, 1, 1, 0, 0, 0, 1])\n", " \n", " >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])\n", " >>> np.bincount(x).size == np.amax(x)+1\n", " True\n", " \n", " The input array needs to be of integer dtype, otherwise a\n", " TypeError is raised:\n", " \n", " >>> np.bincount(np.arange(5, dtype=np.float))\n", " Traceback (most recent call last):\n", " File \"\", line 1, in \n", " TypeError: array cannot be safely cast to required type\n", " \n", " A possible use of ``bincount`` is to perform sums over\n", " variable-size chunks of an array, using the ``weights`` keyword.\n", " \n", " >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights\n", " >>> x = np.array([0, 1, 1, 2, 2, 2])\n", " >>> np.bincount(x, weights=w)\n", " array([ 0.3, 0.7, 1.1])\n", " \n", " blackman(M)\n", " Return the Blackman window.\n", " \n", " The Blackman window is a taper formed by using the first three\n", " terms of a summation of cosines. It was designed to have close to the\n", " minimal leakage possible. It is close to optimal, only slightly worse\n", " than a Kaiser window.\n", " \n", " Parameters\n", " ----------\n", " M : int\n", " Number of points in the output window. If zero or less, an empty\n", " array is returned.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The window, with the maximum value normalized to one (the value one\n", " appears only if the number of samples is odd).\n", " \n", " See Also\n", " --------\n", " bartlett, hamming, hanning, kaiser\n", " \n", " Notes\n", " -----\n", " The Blackman window is defined as\n", " \n", " .. math:: w(n) = 0.42 - 0.5 \\cos(2\\pi n/M) + 0.08 \\cos(4\\pi n/M)\n", " \n", " Most references to the Blackman window come from the signal processing\n", " literature, where it is used as one of many windowing functions for\n", " smoothing values. It is also known as an apodization (which means\n", " \"removing the foot\", i.e. smoothing discontinuities at the beginning\n", " and end of the sampled signal) or tapering function. It is known as a\n", " \"near optimal\" tapering function, almost as good (by some measures)\n", " as the kaiser window.\n", " \n", " References\n", " ----------\n", " Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,\n", " Dover Publications, New York.\n", " \n", " Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.\n", " Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.\n", " \n", " Examples\n", " --------\n", " >>> np.blackman(12)\n", " array([ -1.38777878e-17, 3.26064346e-02, 1.59903635e-01,\n", " 4.14397981e-01, 7.36045180e-01, 9.67046769e-01,\n", " 9.67046769e-01, 7.36045180e-01, 4.14397981e-01,\n", " 1.59903635e-01, 3.26064346e-02, -1.38777878e-17])\n", " \n", " \n", " Plot the window and the frequency response:\n", " \n", " >>> from numpy.fft import fft, fftshift\n", " >>> window = np.blackman(51)\n", " >>> plt.plot(window)\n", " []\n", " >>> plt.title(\"Blackman window\")\n", " \n", " >>> plt.ylabel(\"Amplitude\")\n", " \n", " >>> plt.xlabel(\"Sample\")\n", " \n", " >>> plt.show()\n", " \n", " >>> plt.figure()\n", " \n", " >>> A = fft(window, 2048) / 25.5\n", " >>> mag = np.abs(fftshift(A))\n", " >>> freq = np.linspace(-0.5, 0.5, len(A))\n", " >>> response = 20 * np.log10(mag)\n", " >>> response = np.clip(response, -100, 100)\n", " >>> plt.plot(freq, response)\n", " []\n", " >>> plt.title(\"Frequency response of Blackman window\")\n", " \n", " >>> plt.ylabel(\"Magnitude [dB]\")\n", " \n", " >>> plt.xlabel(\"Normalized frequency [cycles per sample]\")\n", " \n", " >>> plt.axis('tight')\n", " (-0.5, 0.5, -100.0, ...)\n", " >>> plt.show()\n", " \n", " bmat(obj, ldict=None, gdict=None)\n", " Build a matrix object from a string, nested sequence, or array.\n", " \n", " Parameters\n", " ----------\n", " obj : str or array_like\n", " Input data. Names of variables in the current scope may be\n", " referenced, even if `obj` is a string.\n", " \n", " Returns\n", " -------\n", " out : matrix\n", " Returns a matrix object, which is a specialized 2-D array.\n", " \n", " See Also\n", " --------\n", " matrix\n", " \n", " Examples\n", " --------\n", " >>> A = np.mat('1 1; 1 1')\n", " >>> B = np.mat('2 2; 2 2')\n", " >>> C = np.mat('3 4; 5 6')\n", " >>> D = np.mat('7 8; 9 0')\n", " \n", " All the following expressions construct the same block matrix:\n", " \n", " >>> np.bmat([[A, B], [C, D]])\n", " matrix([[1, 1, 2, 2],\n", " [1, 1, 2, 2],\n", " [3, 4, 7, 8],\n", " [5, 6, 9, 0]])\n", " >>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]])\n", " matrix([[1, 1, 2, 2],\n", " [1, 1, 2, 2],\n", " [3, 4, 7, 8],\n", " [5, 6, 9, 0]])\n", " >>> np.bmat('A,B; C,D')\n", " matrix([[1, 1, 2, 2],\n", " [1, 1, 2, 2],\n", " [3, 4, 7, 8],\n", " [5, 6, 9, 0]])\n", " \n", " broadcast_arrays(*args)\n", " Broadcast any number of arrays against each other.\n", " \n", " Parameters\n", " ----------\n", " `*args` : array_likes\n", " The arrays to broadcast.\n", " \n", " Returns\n", " -------\n", " broadcasted : list of arrays\n", " These arrays are views on the original arrays. They are typically\n", " not contiguous. Furthermore, more than one element of a\n", " broadcasted array may refer to a single memory location. If you\n", " need to write to the arrays, make copies first.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([[1,2,3]])\n", " >>> y = np.array([[1],[2],[3]])\n", " >>> np.broadcast_arrays(x, y)\n", " [array([[1, 2, 3],\n", " [1, 2, 3],\n", " [1, 2, 3]]), array([[1, 1, 1],\n", " [2, 2, 2],\n", " [3, 3, 3]])]\n", " \n", " Here is a useful idiom for getting contiguous copies instead of\n", " non-contiguous views.\n", " \n", " >>> [np.array(a) for a in np.broadcast_arrays(x, y)]\n", " [array([[1, 2, 3],\n", " [1, 2, 3],\n", " [1, 2, 3]]), array([[1, 1, 1],\n", " [2, 2, 2],\n", " [3, 3, 3]])]\n", " \n", " busday_count(...)\n", " busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None)\n", " \n", " Counts the number of valid days between `begindates` and\n", " `enddates`, not including the day of `enddates`.\n", " \n", " If ``enddates`` specifies a date value that is earlier than the\n", " corresponding ``begindates`` date value, the count will be negative.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " Parameters\n", " ----------\n", " begindates : array_like of datetime64[D]\n", " The array of the first dates for counting.\n", " enddates : array_like of datetime64[D]\n", " The array of the end dates for counting, which are excluded\n", " from the count themselves.\n", " weekmask : str or array_like of bool, optional\n", " A seven-element array indicating which of Monday through Sunday are\n", " valid days. May be specified as a length-seven list or array, like\n", " [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n", " like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n", " weekdays, optionally separated by white space. Valid abbreviations\n", " are: Mon Tue Wed Thu Fri Sat Sun\n", " holidays : array_like of datetime64[D], optional\n", " An array of dates to consider as invalid dates. They may be\n", " specified in any order, and NaT (not-a-time) dates are ignored.\n", " This list is saved in a normalized form that is suited for\n", " fast calculations of valid days.\n", " busdaycal : busdaycalendar, optional\n", " A `busdaycalendar` object which specifies the valid days. If this\n", " parameter is provided, neither weekmask nor holidays may be\n", " provided.\n", " out : array of int, optional\n", " If provided, this array is filled with the result.\n", " \n", " Returns\n", " -------\n", " out : array of int\n", " An array with a shape from broadcasting ``begindates`` and ``enddates``\n", " together, containing the number of valid days between\n", " the begin and end dates.\n", " \n", " See Also\n", " --------\n", " busdaycalendar: An object that specifies a custom set of valid days.\n", " is_busday : Returns a boolean array indicating valid days.\n", " busday_offset : Applies an offset counted in valid days.\n", " \n", " Examples\n", " --------\n", " >>> # Number of weekdays in January 2011\n", " ... np.busday_count('2011-01', '2011-02')\n", " 21\n", " >>> # Number of weekdays in 2011\n", " ... np.busday_count('2011', '2012')\n", " 260\n", " >>> # Number of Saturdays in 2011\n", " ... np.busday_count('2011', '2012', weekmask='Sat')\n", " 53\n", " \n", " busday_offset(...)\n", " busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None)\n", " \n", " First adjusts the date to fall on a valid day according to\n", " the ``roll`` rule, then applies offsets to the given dates\n", " counted in valid days.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " Parameters\n", " ----------\n", " dates : array_like of datetime64[D]\n", " The array of dates to process.\n", " offsets : array_like of int\n", " The array of offsets, which is broadcast with ``dates``.\n", " roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}, optional\n", " How to treat dates that do not fall on a valid day. The default\n", " is 'raise'.\n", " \n", " * 'raise' means to raise an exception for an invalid day.\n", " * 'nat' means to return a NaT (not-a-time) for an invalid day.\n", " * 'forward' and 'following' mean to take the first valid day\n", " later in time.\n", " * 'backward' and 'preceding' mean to take the first valid day\n", " earlier in time.\n", " * 'modifiedfollowing' means to take the first valid day\n", " later in time unless it is across a Month boundary, in which\n", " case to take the first valid day earlier in time.\n", " * 'modifiedpreceding' means to take the first valid day\n", " earlier in time unless it is across a Month boundary, in which\n", " case to take the first valid day later in time.\n", " weekmask : str or array_like of bool, optional\n", " A seven-element array indicating which of Monday through Sunday are\n", " valid days. May be specified as a length-seven list or array, like\n", " [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n", " like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n", " weekdays, optionally separated by white space. Valid abbreviations\n", " are: Mon Tue Wed Thu Fri Sat Sun\n", " holidays : array_like of datetime64[D], optional\n", " An array of dates to consider as invalid dates. They may be\n", " specified in any order, and NaT (not-a-time) dates are ignored.\n", " This list is saved in a normalized form that is suited for\n", " fast calculations of valid days.\n", " busdaycal : busdaycalendar, optional\n", " A `busdaycalendar` object which specifies the valid days. If this\n", " parameter is provided, neither weekmask nor holidays may be\n", " provided.\n", " out : array of datetime64[D], optional\n", " If provided, this array is filled with the result.\n", " \n", " Returns\n", " -------\n", " out : array of datetime64[D]\n", " An array with a shape from broadcasting ``dates`` and ``offsets``\n", " together, containing the dates with offsets applied.\n", " \n", " See Also\n", " --------\n", " busdaycalendar: An object that specifies a custom set of valid days.\n", " is_busday : Returns a boolean array indicating valid days.\n", " busday_count : Counts how many valid days are in a half-open date range.\n", " \n", " Examples\n", " --------\n", " >>> # First business day in October 2011 (not accounting for holidays)\n", " ... np.busday_offset('2011-10', 0, roll='forward')\n", " numpy.datetime64('2011-10-03','D')\n", " >>> # Last business day in February 2012 (not accounting for holidays)\n", " ... np.busday_offset('2012-03', -1, roll='forward')\n", " numpy.datetime64('2012-02-29','D')\n", " >>> # Third Wednesday in January 2011\n", " ... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')\n", " numpy.datetime64('2011-01-19','D')\n", " >>> # 2012 Mother's Day in Canada and the U.S.\n", " ... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')\n", " numpy.datetime64('2012-05-13','D')\n", " \n", " >>> # First business day on or after a date\n", " ... np.busday_offset('2011-03-20', 0, roll='forward')\n", " numpy.datetime64('2011-03-21','D')\n", " >>> np.busday_offset('2011-03-22', 0, roll='forward')\n", " numpy.datetime64('2011-03-22','D')\n", " >>> # First business day after a date\n", " ... np.busday_offset('2011-03-20', 1, roll='backward')\n", " numpy.datetime64('2011-03-21','D')\n", " >>> np.busday_offset('2011-03-22', 1, roll='backward')\n", " numpy.datetime64('2011-03-23','D')\n", " \n", " byte_bounds(a)\n", " Returns pointers to the end-points of an array.\n", " \n", " Parameters\n", " ----------\n", " a : ndarray\n", " Input array. It must conform to the Python-side of the array\n", " interface.\n", " \n", " Returns\n", " -------\n", " (low, high) : tuple of 2 integers\n", " The first integer is the first byte of the array, the second\n", " integer is just past the last byte of the array. If `a` is not\n", " contiguous it will not use every byte between the (`low`, `high`)\n", " values.\n", " \n", " Examples\n", " --------\n", " >>> I = np.eye(2, dtype='f'); I.dtype\n", " dtype('float32')\n", " >>> low, high = np.byte_bounds(I)\n", " >>> high - low == I.size*I.itemsize\n", " True\n", " >>> I = np.eye(2, dtype='G'); I.dtype\n", " dtype('complex192')\n", " >>> low, high = np.byte_bounds(I)\n", " >>> high - low == I.size*I.itemsize\n", " True\n", " \n", " can_cast(...)\n", " can_cast(from, totype, casting = 'safe')\n", " \n", " Returns True if cast between data types can occur according to the\n", " casting rule. If from is a scalar or array scalar, also returns\n", " True if the scalar value can be cast without overflow or truncation\n", " to an integer.\n", " \n", " Parameters\n", " ----------\n", " from : dtype, dtype specifier, scalar, or array\n", " Data type, scalar, or array to cast from.\n", " totype : dtype or dtype specifier\n", " Data type to cast to.\n", " casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n", " Controls what kind of data casting may occur.\n", " \n", " * 'no' means the data types should not be cast at all.\n", " * 'equiv' means only byte-order changes are allowed.\n", " * 'safe' means only casts which can preserve values are allowed.\n", " * 'same_kind' means only safe casts or casts within a kind,\n", " like float64 to float32, are allowed.\n", " * 'unsafe' means any data conversions may be done.\n", " \n", " Returns\n", " -------\n", " out : bool\n", " True if cast can occur according to the casting rule.\n", " \n", " Notes\n", " -----\n", " Starting in NumPy 1.9, can_cast function now returns False in 'safe'\n", " casting mode for integer/float dtype and string dtype if the string dtype\n", " length is not long enough to store the max integer/float value converted\n", " to a string. Previously can_cast in 'safe' mode returned True for\n", " integer/float dtype and a string dtype of any length.\n", " \n", " See also\n", " --------\n", " dtype, result_type\n", " \n", " Examples\n", " --------\n", " Basic examples\n", " \n", " >>> np.can_cast(np.int32, np.int64)\n", " True\n", " >>> np.can_cast(np.float64, np.complex)\n", " True\n", " >>> np.can_cast(np.complex, np.float)\n", " False\n", " \n", " >>> np.can_cast('i8', 'f8')\n", " True\n", " >>> np.can_cast('i8', 'f4')\n", " False\n", " >>> np.can_cast('i4', 'S4')\n", " False\n", " \n", " Casting scalars\n", " \n", " >>> np.can_cast(100, 'i1')\n", " True\n", " >>> np.can_cast(150, 'i1')\n", " False\n", " >>> np.can_cast(150, 'u1')\n", " True\n", " \n", " >>> np.can_cast(3.5e100, np.float32)\n", " False\n", " >>> np.can_cast(1000.0, np.float32)\n", " True\n", " \n", " Array scalar checks the value, array does not\n", " \n", " >>> np.can_cast(np.array(1000.0), np.float32)\n", " True\n", " >>> np.can_cast(np.array([1000.0]), np.float32)\n", " False\n", " \n", " Using the casting rules\n", " \n", " >>> np.can_cast('i8', 'i8', 'no')\n", " True\n", " >>> np.can_cast('i8', 'no')\n", " False\n", " \n", " >>> np.can_cast('i8', 'equiv')\n", " True\n", " >>> np.can_cast('i8', 'equiv')\n", " False\n", " \n", " >>> np.can_cast('i8', 'safe')\n", " True\n", " >>> np.can_cast('i4', 'safe')\n", " False\n", " \n", " >>> np.can_cast('i4', 'same_kind')\n", " True\n", " >>> np.can_cast('u4', 'same_kind')\n", " False\n", " \n", " >>> np.can_cast('u4', 'unsafe')\n", " True\n", " \n", " choose(a, choices, out=None, mode='raise')\n", " Construct an array from an index array and a set of arrays to choose from.\n", " \n", " First of all, if confused or uncertain, definitely look at the Examples -\n", " in its full generality, this function is less simple than it might\n", " seem from the following code description (below ndi =\n", " `numpy.lib.index_tricks`):\n", " \n", " ``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.\n", " \n", " But this omits some subtleties. Here is a fully general summary:\n", " \n", " Given an \"index\" array (`a`) of integers and a sequence of `n` arrays\n", " (`choices`), `a` and each choice array are first broadcast, as necessary,\n", " to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =\n", " 0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``\n", " for each `i`. Then, a new array with shape ``Ba.shape`` is created as\n", " follows:\n", " \n", " * if ``mode=raise`` (the default), then, first of all, each element of\n", " `a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that\n", " `i` (in that range) is the value at the `(j0, j1, ..., jm)` position\n", " in `Ba` - then the value at the same position in the new array is the\n", " value in `Bchoices[i]` at that same position;\n", " \n", " * if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed)\n", " integer; modular arithmetic is used to map integers outside the range\n", " `[0, n-1]` back into that range; and then the new array is constructed\n", " as above;\n", " \n", " * if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed)\n", " integer; negative integers are mapped to 0; values greater than `n-1`\n", " are mapped to `n-1`; and then the new array is constructed as above.\n", " \n", " Parameters\n", " ----------\n", " a : int array\n", " This array must contain integers in `[0, n-1]`, where `n` is the number\n", " of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any\n", " integers are permissible.\n", " choices : sequence of arrays\n", " Choice arrays. `a` and all of the choices must be broadcastable to the\n", " same shape. If `choices` is itself an array (not recommended), then\n", " its outermost dimension (i.e., the one corresponding to\n", " ``choices.shape[0]``) is taken as defining the \"sequence\".\n", " out : array, optional\n", " If provided, the result will be inserted into this array. It should\n", " be of the appropriate shape and dtype.\n", " mode : {'raise' (default), 'wrap', 'clip'}, optional\n", " Specifies how indices outside `[0, n-1]` will be treated:\n", " \n", " * 'raise' : an exception is raised\n", " * 'wrap' : value becomes value mod `n`\n", " * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1\n", " \n", " Returns\n", " -------\n", " merged_array : array\n", " The merged result.\n", " \n", " Raises\n", " ------\n", " ValueError: shape mismatch\n", " If `a` and each choice array are not all broadcastable to the same\n", " shape.\n", " \n", " See Also\n", " --------\n", " ndarray.choose : equivalent method\n", " \n", " Notes\n", " -----\n", " To reduce the chance of misinterpretation, even though the following\n", " \"abuse\" is nominally supported, `choices` should neither be, nor be\n", " thought of as, a single array, i.e., the outermost sequence-like container\n", " should be either a list or a tuple.\n", " \n", " Examples\n", " --------\n", " \n", " >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],\n", " ... [20, 21, 22, 23], [30, 31, 32, 33]]\n", " >>> np.choose([2, 3, 1, 0], choices\n", " ... # the first element of the result will be the first element of the\n", " ... # third (2+1) \"array\" in choices, namely, 20; the second element\n", " ... # will be the second element of the fourth (3+1) choice array, i.e.,\n", " ... # 31, etc.\n", " ... )\n", " array([20, 31, 12, 3])\n", " >>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)\n", " array([20, 31, 12, 3])\n", " >>> # because there are 4 choice arrays\n", " >>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)\n", " array([20, 1, 12, 3])\n", " >>> # i.e., 0\n", " \n", " A couple examples illustrating how choose broadcasts:\n", " \n", " >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]\n", " >>> choices = [-10, 10]\n", " >>> np.choose(a, choices)\n", " array([[ 10, -10, 10],\n", " [-10, 10, -10],\n", " [ 10, -10, 10]])\n", " \n", " >>> # With thanks to Anne Archibald\n", " >>> a = np.array([0, 1]).reshape((2,1,1))\n", " >>> c1 = np.array([1, 2, 3]).reshape((1,3,1))\n", " >>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))\n", " >>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2\n", " array([[[ 1, 1, 1, 1, 1],\n", " [ 2, 2, 2, 2, 2],\n", " [ 3, 3, 3, 3, 3]],\n", " [[-1, -2, -3, -4, -5],\n", " [-1, -2, -3, -4, -5],\n", " [-1, -2, -3, -4, -5]]])\n", " \n", " clip(a, a_min, a_max, out=None)\n", " Clip (limit) the values in an array.\n", " \n", " Given an interval, values outside the interval are clipped to\n", " the interval edges. For example, if an interval of ``[0, 1]``\n", " is specified, values smaller than 0 become 0, and values larger\n", " than 1 become 1.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array containing elements to clip.\n", " a_min : scalar or array_like\n", " Minimum value.\n", " a_max : scalar or array_like\n", " Maximum value. If `a_min` or `a_max` are array_like, then they will\n", " be broadcasted to the shape of `a`.\n", " out : ndarray, optional\n", " The results will be placed in this array. It may be the input\n", " array for in-place clipping. `out` must be of the right shape\n", " to hold the output. Its type is preserved.\n", " \n", " Returns\n", " -------\n", " clipped_array : ndarray\n", " An array with the elements of `a`, but where values\n", " < `a_min` are replaced with `a_min`, and those > `a_max`\n", " with `a_max`.\n", " \n", " See Also\n", " --------\n", " numpy.doc.ufuncs : Section \"Output arguments\"\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(10)\n", " >>> np.clip(a, 1, 8)\n", " array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])\n", " >>> a\n", " array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", " >>> np.clip(a, 3, 6, out=a)\n", " array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])\n", " >>> a = np.arange(10)\n", " >>> a\n", " array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", " >>> np.clip(a, [3,4,1,1,1,4,4,4,4,4], 8)\n", " array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])\n", " \n", " column_stack(tup)\n", " Stack 1-D arrays as columns into a 2-D array.\n", " \n", " Take a sequence of 1-D arrays and stack them as columns\n", " to make a single 2-D array. 2-D arrays are stacked as-is,\n", " just like with `hstack`. 1-D arrays are turned into 2-D columns\n", " first.\n", " \n", " Parameters\n", " ----------\n", " tup : sequence of 1-D or 2-D arrays.\n", " Arrays to stack. All of them must have the same first dimension.\n", " \n", " Returns\n", " -------\n", " stacked : 2-D array\n", " The array formed by stacking the given arrays.\n", " \n", " See Also\n", " --------\n", " hstack, vstack, concatenate\n", " \n", " Examples\n", " --------\n", " >>> a = np.array((1,2,3))\n", " >>> b = np.array((2,3,4))\n", " >>> np.column_stack((a,b))\n", " array([[1, 2],\n", " [2, 3],\n", " [3, 4]])\n", " \n", " common_type(*arrays)\n", " Return a scalar type which is common to the input arrays.\n", " \n", " The return type will always be an inexact (i.e. floating point) scalar\n", " type, even if all the arrays are integer arrays. If one of the inputs is\n", " an integer array, the minimum precision type that is returned is a\n", " 64-bit floating point dtype.\n", " \n", " All input arrays can be safely cast to the returned dtype without loss\n", " of information.\n", " \n", " Parameters\n", " ----------\n", " array1, array2, ... : ndarrays\n", " Input arrays.\n", " \n", " Returns\n", " -------\n", " out : data type code\n", " Data type code.\n", " \n", " See Also\n", " --------\n", " dtype, mintypecode\n", " \n", " Examples\n", " --------\n", " >>> np.common_type(np.arange(2, dtype=np.float32))\n", " \n", " >>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))\n", " \n", " >>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))\n", " \n", " \n", " compare_chararrays(...)\n", " \n", " compress(condition, a, axis=None, out=None)\n", " Return selected slices of an array along given axis.\n", " \n", " When working along a given axis, a slice along that axis is returned in\n", " `output` for each index where `condition` evaluates to True. When\n", " working on a 1-D array, `compress` is equivalent to `extract`.\n", " \n", " Parameters\n", " ----------\n", " condition : 1-D array of bools\n", " Array that selects which entries to return. If len(condition)\n", " is less than the size of `a` along the given axis, then output is\n", " truncated to the length of the condition array.\n", " a : array_like\n", " Array from which to extract a part.\n", " axis : int, optional\n", " Axis along which to take slices. If None (default), work on the\n", " flattened array.\n", " out : ndarray, optional\n", " Output array. Its type is preserved and it must be of the right\n", " shape to hold the output.\n", " \n", " Returns\n", " -------\n", " compressed_array : ndarray\n", " A copy of `a` without the slices along axis for which `condition`\n", " is false.\n", " \n", " See Also\n", " --------\n", " take, choose, diag, diagonal, select\n", " ndarray.compress : Equivalent method in ndarray\n", " np.extract: Equivalent method when working on 1-D arrays\n", " numpy.doc.ufuncs : Section \"Output arguments\"\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, 2], [3, 4], [5, 6]])\n", " >>> a\n", " array([[1, 2],\n", " [3, 4],\n", " [5, 6]])\n", " >>> np.compress([0, 1], a, axis=0)\n", " array([[3, 4]])\n", " >>> np.compress([False, True, True], a, axis=0)\n", " array([[3, 4],\n", " [5, 6]])\n", " >>> np.compress([False, True], a, axis=1)\n", " array([[2],\n", " [4],\n", " [6]])\n", " \n", " Working on the flattened array does not return slices along an axis but\n", " selects elements.\n", " \n", " >>> np.compress([False, True], a)\n", " array([2])\n", " \n", " concatenate(...)\n", " concatenate((a1, a2, ...), axis=0)\n", " \n", " Join a sequence of arrays together.\n", " \n", " Parameters\n", " ----------\n", " a1, a2, ... : sequence of array_like\n", " The arrays must have the same shape, except in the dimension\n", " corresponding to `axis` (the first, by default).\n", " axis : int, optional\n", " The axis along which the arrays will be joined. Default is 0.\n", " \n", " Returns\n", " -------\n", " res : ndarray\n", " The concatenated array.\n", " \n", " See Also\n", " --------\n", " ma.concatenate : Concatenate function that preserves input masks.\n", " array_split : Split an array into multiple sub-arrays of equal or\n", " near-equal size.\n", " split : Split array into a list of multiple sub-arrays of equal size.\n", " hsplit : Split array into multiple sub-arrays horizontally (column wise)\n", " vsplit : Split array into multiple sub-arrays vertically (row wise)\n", " dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).\n", " hstack : Stack arrays in sequence horizontally (column wise)\n", " vstack : Stack arrays in sequence vertically (row wise)\n", " dstack : Stack arrays in sequence depth wise (along third dimension)\n", " \n", " Notes\n", " -----\n", " When one or more of the arrays to be concatenated is a MaskedArray,\n", " this function will return a MaskedArray object instead of an ndarray,\n", " but the input masks are *not* preserved. In cases where a MaskedArray\n", " is expected as input, use the ma.concatenate function from the masked\n", " array module instead.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, 2], [3, 4]])\n", " >>> b = np.array([[5, 6]])\n", " >>> np.concatenate((a, b), axis=0)\n", " array([[1, 2],\n", " [3, 4],\n", " [5, 6]])\n", " >>> np.concatenate((a, b.T), axis=1)\n", " array([[1, 2, 5],\n", " [3, 4, 6]])\n", " \n", " This function will not preserve masking of MaskedArray inputs.\n", " \n", " >>> a = np.ma.arange(3)\n", " >>> a[1] = np.ma.masked\n", " >>> b = np.arange(2, 5)\n", " >>> a\n", " masked_array(data = [0 -- 2],\n", " mask = [False True False],\n", " fill_value = 999999)\n", " >>> b\n", " array([2, 3, 4])\n", " >>> np.concatenate([a, b])\n", " masked_array(data = [0 1 2 2 3 4],\n", " mask = False,\n", " fill_value = 999999)\n", " >>> np.ma.concatenate([a, b])\n", " masked_array(data = [0 -- 2 2 3 4],\n", " mask = [False True False False False False],\n", " fill_value = 999999)\n", " \n", " convolve(a, v, mode='full')\n", " Returns the discrete, linear convolution of two one-dimensional sequences.\n", " \n", " The convolution operator is often seen in signal processing, where it\n", " models the effect of a linear time-invariant system on a signal [1]_. In\n", " probability theory, the sum of two independent random variables is\n", " distributed according to the convolution of their individual\n", " distributions.\n", " \n", " If `v` is longer than `a`, the arrays are swapped before computation.\n", " \n", " Parameters\n", " ----------\n", " a : (N,) array_like\n", " First one-dimensional input array.\n", " v : (M,) array_like\n", " Second one-dimensional input array.\n", " mode : {'full', 'valid', 'same'}, optional\n", " 'full':\n", " By default, mode is 'full'. This returns the convolution\n", " at each point of overlap, with an output shape of (N+M-1,). At\n", " the end-points of the convolution, the signals do not overlap\n", " completely, and boundary effects may be seen.\n", " \n", " 'same':\n", " Mode `same` returns output of length ``max(M, N)``. Boundary\n", " effects are still visible.\n", " \n", " 'valid':\n", " Mode `valid` returns output of length\n", " ``max(M, N) - min(M, N) + 1``. The convolution product is only given\n", " for points where the signals overlap completely. Values outside\n", " the signal boundary have no effect.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Discrete, linear convolution of `a` and `v`.\n", " \n", " See Also\n", " --------\n", " scipy.signal.fftconvolve : Convolve two arrays using the Fast Fourier\n", " Transform.\n", " scipy.linalg.toeplitz : Used to construct the convolution operator.\n", " polymul : Polynomial multiplication. Same output as convolve, but also\n", " accepts poly1d objects as input.\n", " \n", " Notes\n", " -----\n", " The discrete convolution operation is defined as\n", " \n", " .. math:: (a * v)[n] = \\sum_{m = -\\infty}^{\\infty} a[m] v[n - m]\n", " \n", " It can be shown that a convolution :math:`x(t) * y(t)` in time/space\n", " is equivalent to the multiplication :math:`X(f) Y(f)` in the Fourier\n", " domain, after appropriate padding (padding is necessary to prevent\n", " circular convolution). Since multiplication is more efficient (faster)\n", " than convolution, the function `scipy.signal.fftconvolve` exploits the\n", " FFT to calculate the convolution of large data-sets.\n", " \n", " References\n", " ----------\n", " .. [1] Wikipedia, \"Convolution\", http://en.wikipedia.org/wiki/Convolution.\n", " \n", " Examples\n", " --------\n", " Note how the convolution operator flips the second array\n", " before \"sliding\" the two across one another:\n", " \n", " >>> np.convolve([1, 2, 3], [0, 1, 0.5])\n", " array([ 0. , 1. , 2.5, 4. , 1.5])\n", " \n", " Only return the middle values of the convolution.\n", " Contains boundary effects, where zeros are taken\n", " into account:\n", " \n", " >>> np.convolve([1,2,3],[0,1,0.5], 'same')\n", " array([ 1. , 2.5, 4. ])\n", " \n", " The two arrays are of the same length, so there\n", " is only one position where they completely overlap:\n", " \n", " >>> np.convolve([1,2,3],[0,1,0.5], 'valid')\n", " array([ 2.5])\n", " \n", " copy(a, order='K')\n", " Return an array copy of the given object.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " order : {'C', 'F', 'A', 'K'}, optional\n", " Controls the memory layout of the copy. 'C' means C-order,\n", " 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n", " 'C' otherwise. 'K' means match the layout of `a` as closely\n", " as possible. (Note that this function and :meth:ndarray.copy are very\n", " similar, but have different default values for their order=\n", " arguments.)\n", " \n", " Returns\n", " -------\n", " arr : ndarray\n", " Array interpretation of `a`.\n", " \n", " Notes\n", " -----\n", " This is equivalent to\n", " \n", " >>> np.array(a, copy=True) #doctest: +SKIP\n", " \n", " Examples\n", " --------\n", " Create an array x, with a reference y and a copy z:\n", " \n", " >>> x = np.array([1, 2, 3])\n", " >>> y = x\n", " >>> z = np.copy(x)\n", " \n", " Note that, when we modify x, y changes, but not z:\n", " \n", " >>> x[0] = 10\n", " >>> x[0] == y[0]\n", " True\n", " >>> x[0] == z[0]\n", " False\n", " \n", " copyto(...)\n", " copyto(dst, src, casting='same_kind', where=None)\n", " \n", " Copies values from one array to another, broadcasting as necessary.\n", " \n", " Raises a TypeError if the `casting` rule is violated, and if\n", " `where` is provided, it selects which elements to copy.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " Parameters\n", " ----------\n", " dst : ndarray\n", " The array into which values are copied.\n", " src : array_like\n", " The array from which values are copied.\n", " casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n", " Controls what kind of data casting may occur when copying.\n", " \n", " * 'no' means the data types should not be cast at all.\n", " * 'equiv' means only byte-order changes are allowed.\n", " * 'safe' means only casts which can preserve values are allowed.\n", " * 'same_kind' means only safe casts or casts within a kind,\n", " like float64 to float32, are allowed.\n", " * 'unsafe' means any data conversions may be done.\n", " where : array_like of bool, optional\n", " A boolean array which is broadcasted to match the dimensions\n", " of `dst`, and selects elements to copy from `src` to `dst`\n", " wherever it contains the value True.\n", " \n", " corrcoef(x, y=None, rowvar=1, bias=0, ddof=None)\n", " Return correlation coefficients.\n", " \n", " Please refer to the documentation for `cov` for more detail. The\n", " relationship between the correlation coefficient matrix, `P`, and the\n", " covariance matrix, `C`, is\n", " \n", " .. math:: P_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} * C_{jj} } }\n", " \n", " The values of `P` are between -1 and 1, inclusive.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " A 1-D or 2-D array containing multiple variables and observations.\n", " Each row of `m` represents a variable, and each column a single\n", " observation of all those variables. Also see `rowvar` below.\n", " y : array_like, optional\n", " An additional set of variables and observations. `y` has the same\n", " shape as `m`.\n", " rowvar : int, optional\n", " If `rowvar` is non-zero (default), then each row represents a\n", " variable, with observations in the columns. Otherwise, the relationship\n", " is transposed: each column represents a variable, while the rows\n", " contain observations.\n", " bias : int, optional\n", " Default normalization is by ``(N - 1)``, where ``N`` is the number of\n", " observations (unbiased estimate). If `bias` is 1, then\n", " normalization is by ``N``. These values can be overridden by using\n", " the keyword ``ddof`` in numpy versions >= 1.5.\n", " ddof : {None, int}, optional\n", " .. versionadded:: 1.5\n", " If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is\n", " the number of observations; this overrides the value implied by\n", " ``bias``. The default value is ``None``.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The correlation coefficient matrix of the variables.\n", " \n", " See Also\n", " --------\n", " cov : Covariance matrix\n", " \n", " correlate(a, v, mode='valid', old_behavior=False)\n", " Cross-correlation of two 1-dimensional sequences.\n", " \n", " This function computes the correlation as generally defined in signal\n", " processing texts::\n", " \n", " c_{av}[k] = sum_n a[n+k] * conj(v[n])\n", " \n", " with a and v sequences being zero-padded where necessary and conj being\n", " the conjugate.\n", " \n", " Parameters\n", " ----------\n", " a, v : array_like\n", " Input sequences.\n", " mode : {'valid', 'same', 'full'}, optional\n", " Refer to the `convolve` docstring. Note that the default\n", " is `valid`, unlike `convolve`, which uses `full`.\n", " old_behavior : bool\n", " If True, uses the old behavior from Numeric,\n", " (correlate(a,v) == correlate(v,a), and the conjugate is not taken\n", " for complex arrays). If False, uses the conventional signal\n", " processing definition.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Discrete cross-correlation of `a` and `v`.\n", " \n", " See Also\n", " --------\n", " convolve : Discrete, linear convolution of two one-dimensional sequences.\n", " \n", " Notes\n", " -----\n", " The definition of correlation above is not unique and sometimes correlation\n", " may be defined differently. Another common definition is::\n", " \n", " c'_{av}[k] = sum_n a[n] conj(v[n+k])\n", " \n", " which is related to ``c_{av}[k]`` by ``c'_{av}[k] = c_{av}[-k]``.\n", " \n", " Examples\n", " --------\n", " >>> np.correlate([1, 2, 3], [0, 1, 0.5])\n", " array([ 3.5])\n", " >>> np.correlate([1, 2, 3], [0, 1, 0.5], \"same\")\n", " array([ 2. , 3.5, 3. ])\n", " >>> np.correlate([1, 2, 3], [0, 1, 0.5], \"full\")\n", " array([ 0.5, 2. , 3.5, 3. , 0. ])\n", " \n", " Using complex sequences:\n", " \n", " >>> np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full')\n", " array([ 0.5-0.5j, 1.0+0.j , 1.5-1.5j, 3.0-1.j , 0.0+0.j ])\n", " \n", " Note that you get the time reversed, complex conjugated result\n", " when the two input sequences change places, i.e.,\n", " ``c_{va}[k] = c^{*}_{av}[-k]``:\n", " \n", " >>> np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full')\n", " array([ 0.0+0.j , 3.0+1.j , 1.5+1.5j, 1.0+0.j , 0.5+0.5j])\n", " \n", " count_nonzero(...)\n", " count_nonzero(a)\n", " \n", " Counts the number of non-zero values in the array ``a``.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " The array for which to count non-zeros.\n", " \n", " Returns\n", " -------\n", " count : int or array of int\n", " Number of non-zero values in the array.\n", " \n", " See Also\n", " --------\n", " nonzero : Return the coordinates of all the non-zero values.\n", " \n", " Examples\n", " --------\n", " >>> np.count_nonzero(np.eye(4))\n", " 4\n", " >>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]])\n", " 5\n", " \n", " cov(m, y=None, rowvar=1, bias=0, ddof=None)\n", " Estimate a covariance matrix, given data.\n", " \n", " Covariance indicates the level to which two variables vary together.\n", " If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`,\n", " then the covariance matrix element :math:`C_{ij}` is the covariance of\n", " :math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance\n", " of :math:`x_i`.\n", " \n", " Parameters\n", " ----------\n", " m : array_like\n", " A 1-D or 2-D array containing multiple variables and observations.\n", " Each row of `m` represents a variable, and each column a single\n", " observation of all those variables. Also see `rowvar` below.\n", " y : array_like, optional\n", " An additional set of variables and observations. `y` has the same\n", " form as that of `m`.\n", " rowvar : int, optional\n", " If `rowvar` is non-zero (default), then each row represents a\n", " variable, with observations in the columns. Otherwise, the relationship\n", " is transposed: each column represents a variable, while the rows\n", " contain observations.\n", " bias : int, optional\n", " Default normalization is by ``(N - 1)``, where ``N`` is the number of\n", " observations given (unbiased estimate). If `bias` is 1, then\n", " normalization is by ``N``. These values can be overridden by using\n", " the keyword ``ddof`` in numpy versions >= 1.5.\n", " ddof : int, optional\n", " .. versionadded:: 1.5\n", " If not ``None`` normalization is by ``(N - ddof)``, where ``N`` is\n", " the number of observations; this overrides the value implied by\n", " ``bias``. The default value is ``None``.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The covariance matrix of the variables.\n", " \n", " See Also\n", " --------\n", " corrcoef : Normalized covariance matrix\n", " \n", " Examples\n", " --------\n", " Consider two variables, :math:`x_0` and :math:`x_1`, which\n", " correlate perfectly, but in opposite directions:\n", " \n", " >>> x = np.array([[0, 2], [1, 1], [2, 0]]).T\n", " >>> x\n", " array([[0, 1, 2],\n", " [2, 1, 0]])\n", " \n", " Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance\n", " matrix shows this clearly:\n", " \n", " >>> np.cov(x)\n", " array([[ 1., -1.],\n", " [-1., 1.]])\n", " \n", " Note that element :math:`C_{0,1}`, which shows the correlation between\n", " :math:`x_0` and :math:`x_1`, is negative.\n", " \n", " Further, note how `x` and `y` are combined:\n", " \n", " >>> x = [-2.1, -1, 4.3]\n", " >>> y = [3, 1.1, 0.12]\n", " >>> X = np.vstack((x,y))\n", " >>> print np.cov(X)\n", " [[ 11.71 -4.286 ]\n", " [ -4.286 2.14413333]]\n", " >>> print np.cov(x, y)\n", " [[ 11.71 -4.286 ]\n", " [ -4.286 2.14413333]]\n", " >>> print np.cov(x)\n", " 11.71\n", " \n", " cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None)\n", " Return the cross product of two (arrays of) vectors.\n", " \n", " The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular\n", " to both `a` and `b`. If `a` and `b` are arrays of vectors, the vectors\n", " are defined by the last axis of `a` and `b` by default, and these axes\n", " can have dimensions 2 or 3. Where the dimension of either `a` or `b` is\n", " 2, the third component of the input vector is assumed to be zero and the\n", " cross product calculated accordingly. In cases where both input vectors\n", " have dimension 2, the z-component of the cross product is returned.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Components of the first vector(s).\n", " b : array_like\n", " Components of the second vector(s).\n", " axisa : int, optional\n", " Axis of `a` that defines the vector(s). By default, the last axis.\n", " axisb : int, optional\n", " Axis of `b` that defines the vector(s). By default, the last axis.\n", " axisc : int, optional\n", " Axis of `c` containing the cross product vector(s). By default, the\n", " last axis.\n", " axis : int, optional\n", " If defined, the axis of `a`, `b` and `c` that defines the vector(s)\n", " and cross product(s). Overrides `axisa`, `axisb` and `axisc`.\n", " \n", " Returns\n", " -------\n", " c : ndarray\n", " Vector cross product(s).\n", " \n", " Raises\n", " ------\n", " ValueError\n", " When the dimension of the vector(s) in `a` and/or `b` does not\n", " equal 2 or 3.\n", " \n", " See Also\n", " --------\n", " inner : Inner product\n", " outer : Outer product.\n", " ix_ : Construct index arrays.\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.9.0\n", " Supports full broadcasting of the inputs.\n", " \n", " Examples\n", " --------\n", " Vector cross-product.\n", " \n", " >>> x = [1, 2, 3]\n", " >>> y = [4, 5, 6]\n", " >>> np.cross(x, y)\n", " array([-3, 6, -3])\n", " \n", " One vector with dimension 2.\n", " \n", " >>> x = [1, 2]\n", " >>> y = [4, 5, 6]\n", " >>> np.cross(x, y)\n", " array([12, -6, -3])\n", " \n", " Equivalently:\n", " \n", " >>> x = [1, 2, 0]\n", " >>> y = [4, 5, 6]\n", " >>> np.cross(x, y)\n", " array([12, -6, -3])\n", " \n", " Both vectors with dimension 2.\n", " \n", " >>> x = [1,2]\n", " >>> y = [4,5]\n", " >>> np.cross(x, y)\n", " -3\n", " \n", " Multiple vector cross-products. Note that the direction of the cross\n", " product vector is defined by the `right-hand rule`.\n", " \n", " >>> x = np.array([[1,2,3], [4,5,6]])\n", " >>> y = np.array([[4,5,6], [1,2,3]])\n", " >>> np.cross(x, y)\n", " array([[-3, 6, -3],\n", " [ 3, -6, 3]])\n", " \n", " The orientation of `c` can be changed using the `axisc` keyword.\n", " \n", " >>> np.cross(x, y, axisc=0)\n", " array([[-3, 3],\n", " [ 6, -6],\n", " [-3, 3]])\n", " \n", " Change the vector definition of `x` and `y` using `axisa` and `axisb`.\n", " \n", " >>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]])\n", " >>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]])\n", " >>> np.cross(x, y)\n", " array([[ -6, 12, -6],\n", " [ 0, 0, 0],\n", " [ 6, -12, 6]])\n", " >>> np.cross(x, y, axisa=0, axisb=0)\n", " array([[-24, 48, -24],\n", " [-30, 60, -30],\n", " [-36, 72, -36]])\n", " \n", " cumprod(a, axis=None, dtype=None, out=None)\n", " Return the cumulative product of elements along a given axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " axis : int, optional\n", " Axis along which the cumulative product is computed. By default\n", " the input is flattened.\n", " dtype : dtype, optional\n", " Type of the returned array, as well as of the accumulator in which\n", " the elements are multiplied. If *dtype* is not specified, it\n", " defaults to the dtype of `a`, unless `a` has an integer dtype with\n", " a precision less than that of the default platform integer. In\n", " that case, the default platform integer is used instead.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must\n", " have the same shape and buffer length as the expected output\n", " but the type of the resulting values will be cast if necessary.\n", " \n", " Returns\n", " -------\n", " cumprod : ndarray\n", " A new array holding the result is returned unless `out` is\n", " specified, in which case a reference to out is returned.\n", " \n", " See Also\n", " --------\n", " numpy.doc.ufuncs : Section \"Output arguments\"\n", " \n", " Notes\n", " -----\n", " Arithmetic is modular when using integer types, and no error is\n", " raised on overflow.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([1,2,3])\n", " >>> np.cumprod(a) # intermediate results 1, 1*2\n", " ... # total product 1*2*3 = 6\n", " array([1, 2, 6])\n", " >>> a = np.array([[1, 2, 3], [4, 5, 6]])\n", " >>> np.cumprod(a, dtype=float) # specify type of output\n", " array([ 1., 2., 6., 24., 120., 720.])\n", " \n", " The cumulative product for each column (i.e., over the rows) of `a`:\n", " \n", " >>> np.cumprod(a, axis=0)\n", " array([[ 1, 2, 3],\n", " [ 4, 10, 18]])\n", " \n", " The cumulative product for each row (i.e. over the columns) of `a`:\n", " \n", " >>> np.cumprod(a,axis=1)\n", " array([[ 1, 2, 6],\n", " [ 4, 20, 120]])\n", " \n", " cumproduct(a, axis=None, dtype=None, out=None)\n", " Return the cumulative product over the given axis.\n", " \n", " \n", " See Also\n", " --------\n", " cumprod : equivalent function; see for details.\n", " \n", " cumsum(a, axis=None, dtype=None, out=None)\n", " Return the cumulative sum of the elements along a given axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " axis : int, optional\n", " Axis along which the cumulative sum is computed. The default\n", " (None) is to compute the cumsum over the flattened array.\n", " dtype : dtype, optional\n", " Type of the returned array and of the accumulator in which the\n", " elements are summed. If `dtype` is not specified, it defaults\n", " to the dtype of `a`, unless `a` has an integer dtype with a\n", " precision less than that of the default platform integer. In\n", " that case, the default platform integer is used.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must\n", " have the same shape and buffer length as the expected output\n", " but the type will be cast if necessary. See `doc.ufuncs`\n", " (Section \"Output arguments\") for more details.\n", " \n", " Returns\n", " -------\n", " cumsum_along_axis : ndarray.\n", " A new array holding the result is returned unless `out` is\n", " specified, in which case a reference to `out` is returned. The\n", " result has the same size as `a`, and the same shape as `a` if\n", " `axis` is not None or `a` is a 1-d array.\n", " \n", " \n", " See Also\n", " --------\n", " sum : Sum array elements.\n", " \n", " trapz : Integration of array values using the composite trapezoidal rule.\n", " \n", " diff : Calculate the n-th order discrete difference along given axis.\n", " \n", " Notes\n", " -----\n", " Arithmetic is modular when using integer types, and no error is\n", " raised on overflow.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1,2,3], [4,5,6]])\n", " >>> a\n", " array([[1, 2, 3],\n", " [4, 5, 6]])\n", " >>> np.cumsum(a)\n", " array([ 1, 3, 6, 10, 15, 21])\n", " >>> np.cumsum(a, dtype=float) # specifies type of output value(s)\n", " array([ 1., 3., 6., 10., 15., 21.])\n", " \n", " >>> np.cumsum(a,axis=0) # sum over rows for each of the 3 columns\n", " array([[1, 2, 3],\n", " [5, 7, 9]])\n", " >>> np.cumsum(a,axis=1) # sum over columns for each of the 2 rows\n", " array([[ 1, 3, 6],\n", " [ 4, 9, 15]])\n", " \n", " datetime_as_string(...)\n", " \n", " datetime_data(...)\n", " \n", " delete(arr, obj, axis=None)\n", " Return a new array with sub-arrays along an axis deleted. For a one\n", " dimensional array, this returns those entries not returned by\n", " `arr[obj]`.\n", " \n", " Parameters\n", " ----------\n", " arr : array_like\n", " Input array.\n", " obj : slice, int or array of ints\n", " Indicate which sub-arrays to remove.\n", " axis : int, optional\n", " The axis along which to delete the subarray defined by `obj`.\n", " If `axis` is None, `obj` is applied to the flattened array.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " A copy of `arr` with the elements specified by `obj` removed. Note\n", " that `delete` does not occur in-place. If `axis` is None, `out` is\n", " a flattened array.\n", " \n", " See Also\n", " --------\n", " insert : Insert elements into an array.\n", " append : Append elements at the end of an array.\n", " \n", " Notes\n", " -----\n", " Often it is preferable to use a boolean mask. For example:\n", " \n", " >>> mask = np.ones(len(arr), dtype=bool)\n", " >>> mask[[0,2,4]] = False\n", " >>> result = arr[mask,...]\n", " \n", " Is equivalent to `np.delete(arr, [0,2,4], axis=0)`, but allows further\n", " use of `mask`.\n", " \n", " Examples\n", " --------\n", " >>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n", " >>> arr\n", " array([[ 1, 2, 3, 4],\n", " [ 5, 6, 7, 8],\n", " [ 9, 10, 11, 12]])\n", " >>> np.delete(arr, 1, 0)\n", " array([[ 1, 2, 3, 4],\n", " [ 9, 10, 11, 12]])\n", " \n", " >>> np.delete(arr, np.s_[::2], 1)\n", " array([[ 2, 4],\n", " [ 6, 8],\n", " [10, 12]])\n", " >>> np.delete(arr, [1,3,5], None)\n", " array([ 1, 3, 5, 7, 8, 9, 10, 11, 12])\n", " \n", " deprecate(*args, **kwargs)\n", " Issues a DeprecationWarning, adds warning to `old_name`'s\n", " docstring, rebinds ``old_name.__name__`` and returns the new\n", " function object.\n", " \n", " This function may also be used as a decorator.\n", " \n", " Parameters\n", " ----------\n", " func : function\n", " The function to be deprecated.\n", " old_name : str, optional\n", " The name of the function to be deprecated. Default is None, in\n", " which case the name of `func` is used.\n", " new_name : str, optional\n", " The new name for the function. Default is None, in which case the\n", " deprecation message is that `old_name` is deprecated. If given, the\n", " deprecation message is that `old_name` is deprecated and `new_name`\n", " should be used instead.\n", " message : str, optional\n", " Additional explanation of the deprecation. Displayed in the\n", " docstring after the warning.\n", " \n", " Returns\n", " -------\n", " old_func : function\n", " The deprecated function.\n", " \n", " Examples\n", " --------\n", " Note that ``olduint`` returns a value after printing Deprecation\n", " Warning:\n", " \n", " >>> olduint = np.deprecate(np.uint)\n", " >>> olduint(6)\n", " /usr/lib/python2.5/site-packages/numpy/lib/utils.py:114:\n", " DeprecationWarning: uint32 is deprecated\n", " warnings.warn(str1, DeprecationWarning)\n", " 6\n", " \n", " deprecate_with_doc lambda msg\n", " \n", " diag(v, k=0)\n", " Extract a diagonal or construct a diagonal array.\n", " \n", " See the more detailed documentation for ``numpy.diagonal`` if you use this\n", " function to extract a diagonal and wish to write to the resulting array;\n", " whether it returns a copy or a view depends on what version of numpy you\n", " are using.\n", " \n", " Parameters\n", " ----------\n", " v : array_like\n", " If `v` is a 2-D array, return a copy of its `k`-th diagonal.\n", " If `v` is a 1-D array, return a 2-D array with `v` on the `k`-th\n", " diagonal.\n", " k : int, optional\n", " Diagonal in question. The default is 0. Use `k>0` for diagonals\n", " above the main diagonal, and `k<0` for diagonals below the main\n", " diagonal.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The extracted diagonal or constructed diagonal array.\n", " \n", " See Also\n", " --------\n", " diagonal : Return specified diagonals.\n", " diagflat : Create a 2-D array with the flattened input as a diagonal.\n", " trace : Sum along diagonals.\n", " triu : Upper triangle of an array.\n", " tril : Lower triangle of an array.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(9).reshape((3,3))\n", " >>> x\n", " array([[0, 1, 2],\n", " [3, 4, 5],\n", " [6, 7, 8]])\n", " \n", " >>> np.diag(x)\n", " array([0, 4, 8])\n", " >>> np.diag(x, k=1)\n", " array([1, 5])\n", " >>> np.diag(x, k=-1)\n", " array([3, 7])\n", " \n", " >>> np.diag(np.diag(x))\n", " array([[0, 0, 0],\n", " [0, 4, 0],\n", " [0, 0, 8]])\n", " \n", " diag_indices(n, ndim=2)\n", " Return the indices to access the main diagonal of an array.\n", " \n", " This returns a tuple of indices that can be used to access the main\n", " diagonal of an array `a` with ``a.ndim >= 2`` dimensions and shape\n", " (n, n, ..., n). For ``a.ndim = 2`` this is the usual diagonal, for\n", " ``a.ndim > 2`` this is the set of indices to access ``a[i, i, ..., i]``\n", " for ``i = [0..n-1]``.\n", " \n", " Parameters\n", " ----------\n", " n : int\n", " The size, along each dimension, of the arrays for which the returned\n", " indices can be used.\n", " \n", " ndim : int, optional\n", " The number of dimensions.\n", " \n", " See also\n", " --------\n", " diag_indices_from\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.4.0\n", " \n", " Examples\n", " --------\n", " Create a set of indices to access the diagonal of a (4, 4) array:\n", " \n", " >>> di = np.diag_indices(4)\n", " >>> di\n", " (array([0, 1, 2, 3]), array([0, 1, 2, 3]))\n", " >>> a = np.arange(16).reshape(4, 4)\n", " >>> a\n", " array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11],\n", " [12, 13, 14, 15]])\n", " >>> a[di] = 100\n", " >>> a\n", " array([[100, 1, 2, 3],\n", " [ 4, 100, 6, 7],\n", " [ 8, 9, 100, 11],\n", " [ 12, 13, 14, 100]])\n", " \n", " Now, we create indices to manipulate a 3-D array:\n", " \n", " >>> d3 = np.diag_indices(2, 3)\n", " >>> d3\n", " (array([0, 1]), array([0, 1]), array([0, 1]))\n", " \n", " And use it to set the diagonal of an array of zeros to 1:\n", " \n", " >>> a = np.zeros((2, 2, 2), dtype=np.int)\n", " >>> a[d3] = 1\n", " >>> a\n", " array([[[1, 0],\n", " [0, 0]],\n", " [[0, 0],\n", " [0, 1]]])\n", " \n", " diag_indices_from(arr)\n", " Return the indices to access the main diagonal of an n-dimensional array.\n", " \n", " See `diag_indices` for full details.\n", " \n", " Parameters\n", " ----------\n", " arr : array, at least 2-D\n", " \n", " See Also\n", " --------\n", " diag_indices\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.4.0\n", " \n", " diagflat(v, k=0)\n", " Create a two-dimensional array with the flattened input as a diagonal.\n", " \n", " Parameters\n", " ----------\n", " v : array_like\n", " Input data, which is flattened and set as the `k`-th\n", " diagonal of the output.\n", " k : int, optional\n", " Diagonal to set; 0, the default, corresponds to the \"main\" diagonal,\n", " a positive (negative) `k` giving the number of the diagonal above\n", " (below) the main.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The 2-D output array.\n", " \n", " See Also\n", " --------\n", " diag : MATLAB work-alike for 1-D and 2-D arrays.\n", " diagonal : Return specified diagonals.\n", " trace : Sum along diagonals.\n", " \n", " Examples\n", " --------\n", " >>> np.diagflat([[1,2], [3,4]])\n", " array([[1, 0, 0, 0],\n", " [0, 2, 0, 0],\n", " [0, 0, 3, 0],\n", " [0, 0, 0, 4]])\n", " \n", " >>> np.diagflat([1,2], 1)\n", " array([[0, 1, 0],\n", " [0, 0, 2],\n", " [0, 0, 0]])\n", " \n", " diagonal(a, offset=0, axis1=0, axis2=1)\n", " Return specified diagonals.\n", " \n", " If `a` is 2-D, returns the diagonal of `a` with the given offset,\n", " i.e., the collection of elements of the form ``a[i, i+offset]``. If\n", " `a` has more than two dimensions, then the axes specified by `axis1`\n", " and `axis2` are used to determine the 2-D sub-array whose diagonal is\n", " returned. The shape of the resulting array can be determined by\n", " removing `axis1` and `axis2` and appending an index to the right equal\n", " to the size of the resulting diagonals.\n", " \n", " In versions of NumPy prior to 1.7, this function always returned a new,\n", " independent array containing a copy of the values in the diagonal.\n", " \n", " In NumPy 1.7 and 1.8, it continues to return a copy of the diagonal,\n", " but depending on this fact is deprecated. Writing to the resulting\n", " array continues to work as it used to, but a FutureWarning is issued.\n", " \n", " In NumPy 1.9 it returns a read-only view on the original array.\n", " Attempting to write to the resulting array will produce an error.\n", " \n", " In NumPy 1.10, it will return a read/write view, Writing to the returned\n", " array will alter your original array.\n", " \n", " If you don't write to the array returned by this function, then you can\n", " just ignore all of the above.\n", " \n", " If you depend on the current behavior, then we suggest copying the\n", " returned array explicitly, i.e., use ``np.diagonal(a).copy()`` instead of\n", " just ``np.diagonal(a)``. This will work with both past and future versions\n", " of NumPy.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array from which the diagonals are taken.\n", " offset : int, optional\n", " Offset of the diagonal from the main diagonal. Can be positive or\n", " negative. Defaults to main diagonal (0).\n", " axis1 : int, optional\n", " Axis to be used as the first axis of the 2-D sub-arrays from which\n", " the diagonals should be taken. Defaults to first axis (0).\n", " axis2 : int, optional\n", " Axis to be used as the second axis of the 2-D sub-arrays from\n", " which the diagonals should be taken. Defaults to second axis (1).\n", " \n", " Returns\n", " -------\n", " array_of_diagonals : ndarray\n", " If `a` is 2-D, a 1-D array containing the diagonal is returned.\n", " If the dimension of `a` is larger, then an array of diagonals is\n", " returned, \"packed\" from left-most dimension to right-most (e.g.,\n", " if `a` is 3-D, then the diagonals are \"packed\" along rows).\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If the dimension of `a` is less than 2.\n", " \n", " See Also\n", " --------\n", " diag : MATLAB work-a-like for 1-D and 2-D arrays.\n", " diagflat : Create diagonal arrays.\n", " trace : Sum along diagonals.\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(4).reshape(2,2)\n", " >>> a\n", " array([[0, 1],\n", " [2, 3]])\n", " >>> a.diagonal()\n", " array([0, 3])\n", " >>> a.diagonal(1)\n", " array([1])\n", " \n", " A 3-D example:\n", " \n", " >>> a = np.arange(8).reshape(2,2,2); a\n", " array([[[0, 1],\n", " [2, 3]],\n", " [[4, 5],\n", " [6, 7]]])\n", " >>> a.diagonal(0, # Main diagonals of two arrays created by skipping\n", " ... 0, # across the outer(left)-most axis last and\n", " ... 1) # the \"middle\" (row) axis first.\n", " array([[0, 6],\n", " [1, 7]])\n", " \n", " The sub-arrays whose main diagonals we just obtained; note that each\n", " corresponds to fixing the right-most (column) axis, and that the\n", " diagonals are \"packed\" in rows.\n", " \n", " >>> a[:,:,0] # main diagonal is [0 6]\n", " array([[0, 2],\n", " [4, 6]])\n", " >>> a[:,:,1] # main diagonal is [1 7]\n", " array([[1, 3],\n", " [5, 7]])\n", " \n", " diff(a, n=1, axis=-1)\n", " Calculate the n-th order discrete difference along given axis.\n", " \n", " The first order difference is given by ``out[n] = a[n+1] - a[n]`` along\n", " the given axis, higher order differences are calculated by using `diff`\n", " recursively.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array\n", " n : int, optional\n", " The number of times values are differenced.\n", " axis : int, optional\n", " The axis along which the difference is taken, default is the last axis.\n", " \n", " Returns\n", " -------\n", " diff : ndarray\n", " The `n` order differences. The shape of the output is the same as `a`\n", " except along `axis` where the dimension is smaller by `n`.\n", " \n", " See Also\n", " --------\n", " gradient, ediff1d, cumsum\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([1, 2, 4, 7, 0])\n", " >>> np.diff(x)\n", " array([ 1, 2, 3, -7])\n", " >>> np.diff(x, n=2)\n", " array([ 1, 1, -10])\n", " \n", " >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])\n", " >>> np.diff(x)\n", " array([[2, 3, 4],\n", " [5, 1, 2]])\n", " >>> np.diff(x, axis=0)\n", " array([[-1, 2, 0, -2]])\n", " \n", " digitize(...)\n", " digitize(x, bins, right=False)\n", " \n", " Return the indices of the bins to which each value in input array belongs.\n", " \n", " Each index ``i`` returned is such that ``bins[i-1] <= x < bins[i]`` if\n", " `bins` is monotonically increasing, or ``bins[i-1] > x >= bins[i]`` if\n", " `bins` is monotonically decreasing. If values in `x` are beyond the\n", " bounds of `bins`, 0 or ``len(bins)`` is returned as appropriate. If right\n", " is True, then the right bin is closed so that the index ``i`` is such\n", " that ``bins[i-1] < x <= bins[i]`` or bins[i-1] >= x > bins[i]`` if `bins`\n", " is monotonically increasing or decreasing, respectively.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " Input array to be binned. It has to be 1-dimensional.\n", " bins : array_like\n", " Array of bins. It has to be 1-dimensional and monotonic.\n", " right : bool, optional\n", " Indicating whether the intervals include the right or the left bin\n", " edge. Default behavior is (right==False) indicating that the interval\n", " does not include the right edge. The left bin and is open in this\n", " case. Ie., bins[i-1] <= x < bins[i] is the default behavior for\n", " monotonically increasing bins.\n", " \n", " Returns\n", " -------\n", " out : ndarray of ints\n", " Output array of indices, of same shape as `x`.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If the input is not 1-dimensional, or if `bins` is not monotonic.\n", " TypeError\n", " If the type of the input is complex.\n", " \n", " See Also\n", " --------\n", " bincount, histogram, unique\n", " \n", " Notes\n", " -----\n", " If values in `x` are such that they fall outside the bin range,\n", " attempting to index `bins` with the indices that `digitize` returns\n", " will result in an IndexError.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([0.2, 6.4, 3.0, 1.6])\n", " >>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])\n", " >>> inds = np.digitize(x, bins)\n", " >>> inds\n", " array([1, 4, 3, 2])\n", " >>> for n in range(x.size):\n", " ... print bins[inds[n]-1], \"<=\", x[n], \"<\", bins[inds[n]]\n", " ...\n", " 0.0 <= 0.2 < 1.0\n", " 4.0 <= 6.4 < 10.0\n", " 2.5 <= 3.0 < 4.0\n", " 1.0 <= 1.6 < 2.5\n", " \n", " >>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.])\n", " >>> bins = np.array([0,5,10,15,20])\n", " >>> np.digitize(x,bins,right=True)\n", " array([1, 2, 3, 4, 4])\n", " >>> np.digitize(x,bins,right=False)\n", " array([1, 3, 3, 4, 5])\n", " \n", " disp(mesg, device=None, linefeed=True)\n", " Display a message on a device.\n", " \n", " Parameters\n", " ----------\n", " mesg : str\n", " Message to display.\n", " device : object\n", " Device to write message. If None, defaults to ``sys.stdout`` which is\n", " very similar to ``print``. `device` needs to have ``write()`` and\n", " ``flush()`` methods.\n", " linefeed : bool, optional\n", " Option whether to print a line feed or not. Defaults to True.\n", " \n", " Raises\n", " ------\n", " AttributeError\n", " If `device` does not have a ``write()`` or ``flush()`` method.\n", " \n", " Examples\n", " --------\n", " Besides ``sys.stdout``, a file-like object can also be used as it has\n", " both required methods:\n", " \n", " >>> from StringIO import StringIO\n", " >>> buf = StringIO()\n", " >>> np.disp('\"Display\" in a file', device=buf)\n", " >>> buf.getvalue()\n", " '\"Display\" in a file\\n'\n", " \n", " dot(...)\n", " dot(a, b, out=None)\n", " \n", " Dot product of two arrays.\n", " \n", " For 2-D arrays it is equivalent to matrix multiplication, and for 1-D\n", " arrays to inner product of vectors (without complex conjugation). For\n", " N dimensions it is a sum product over the last axis of `a` and\n", " the second-to-last of `b`::\n", " \n", " dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " First argument.\n", " b : array_like\n", " Second argument.\n", " out : ndarray, optional\n", " Output argument. This must have the exact kind that would be returned\n", " if it was not used. In particular, it must have the right type, must be\n", " C-contiguous, and its dtype must be the dtype that would be returned\n", " for `dot(a,b)`. This is a performance feature. Therefore, if these\n", " conditions are not met, an exception is raised, instead of attempting\n", " to be flexible.\n", " \n", " Returns\n", " -------\n", " output : ndarray\n", " Returns the dot product of `a` and `b`. If `a` and `b` are both\n", " scalars or both 1-D arrays then a scalar is returned; otherwise\n", " an array is returned.\n", " If `out` is given, then it is returned.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If the last dimension of `a` is not the same size as\n", " the second-to-last dimension of `b`.\n", " \n", " See Also\n", " --------\n", " vdot : Complex-conjugating dot product.\n", " tensordot : Sum products over arbitrary axes.\n", " einsum : Einstein summation convention.\n", " \n", " Examples\n", " --------\n", " >>> np.dot(3, 4)\n", " 12\n", " \n", " Neither argument is complex-conjugated:\n", " \n", " >>> np.dot([2j, 3j], [2j, 3j])\n", " (-13+0j)\n", " \n", " For 2-D arrays it's the matrix product:\n", " \n", " >>> a = [[1, 0], [0, 1]]\n", " >>> b = [[4, 1], [2, 2]]\n", " >>> np.dot(a, b)\n", " array([[4, 1],\n", " [2, 2]])\n", " \n", " >>> a = np.arange(3*4*5*6).reshape((3,4,5,6))\n", " >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))\n", " >>> np.dot(a, b)[2,3,2,1,2,2]\n", " 499128\n", " >>> sum(a[2,3,2,:] * b[1,2,:,2])\n", " 499128\n", " \n", " dsplit(ary, indices_or_sections)\n", " Split array into multiple sub-arrays along the 3rd axis (depth).\n", " \n", " Please refer to the `split` documentation. `dsplit` is equivalent\n", " to `split` with ``axis=2``, the array is always split along the third\n", " axis provided the array dimension is greater than or equal to 3.\n", " \n", " See Also\n", " --------\n", " split : Split an array into multiple sub-arrays of equal size.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(16.0).reshape(2, 2, 4)\n", " >>> x\n", " array([[[ 0., 1., 2., 3.],\n", " [ 4., 5., 6., 7.]],\n", " [[ 8., 9., 10., 11.],\n", " [ 12., 13., 14., 15.]]])\n", " >>> np.dsplit(x, 2)\n", " [array([[[ 0., 1.],\n", " [ 4., 5.]],\n", " [[ 8., 9.],\n", " [ 12., 13.]]]),\n", " array([[[ 2., 3.],\n", " [ 6., 7.]],\n", " [[ 10., 11.],\n", " [ 14., 15.]]])]\n", " >>> np.dsplit(x, np.array([3, 6]))\n", " [array([[[ 0., 1., 2.],\n", " [ 4., 5., 6.]],\n", " [[ 8., 9., 10.],\n", " [ 12., 13., 14.]]]),\n", " array([[[ 3.],\n", " [ 7.]],\n", " [[ 11.],\n", " [ 15.]]]),\n", " array([], dtype=float64)]\n", " \n", " dstack(tup)\n", " Stack arrays in sequence depth wise (along third axis).\n", " \n", " Takes a sequence of arrays and stack them along the third axis\n", " to make a single array. Rebuilds arrays divided by `dsplit`.\n", " This is a simple way to stack 2D arrays (images) into a single\n", " 3D array for processing.\n", " \n", " Parameters\n", " ----------\n", " tup : sequence of arrays\n", " Arrays to stack. All of them must have the same shape along all\n", " but the third axis.\n", " \n", " Returns\n", " -------\n", " stacked : ndarray\n", " The array formed by stacking the given arrays.\n", " \n", " See Also\n", " --------\n", " vstack : Stack along first axis.\n", " hstack : Stack along second axis.\n", " concatenate : Join arrays.\n", " dsplit : Split array along third axis.\n", " \n", " Notes\n", " -----\n", " Equivalent to ``np.concatenate(tup, axis=2)``.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array((1,2,3))\n", " >>> b = np.array((2,3,4))\n", " >>> np.dstack((a,b))\n", " array([[[1, 2],\n", " [2, 3],\n", " [3, 4]]])\n", " \n", " >>> a = np.array([[1],[2],[3]])\n", " >>> b = np.array([[2],[3],[4]])\n", " >>> np.dstack((a,b))\n", " array([[[1, 2]],\n", " [[2, 3]],\n", " [[3, 4]]])\n", " \n", " ediff1d(ary, to_end=None, to_begin=None)\n", " The differences between consecutive elements of an array.\n", " \n", " Parameters\n", " ----------\n", " ary : array_like\n", " If necessary, will be flattened before the differences are taken.\n", " to_end : array_like, optional\n", " Number(s) to append at the end of the returned differences.\n", " to_begin : array_like, optional\n", " Number(s) to prepend at the beginning of the returned differences.\n", " \n", " Returns\n", " -------\n", " ediff1d : ndarray\n", " The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.\n", " \n", " See Also\n", " --------\n", " diff, gradient\n", " \n", " Notes\n", " -----\n", " When applied to masked arrays, this function drops the mask information\n", " if the `to_begin` and/or `to_end` parameters are used.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([1, 2, 4, 7, 0])\n", " >>> np.ediff1d(x)\n", " array([ 1, 2, 3, -7])\n", " \n", " >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))\n", " array([-99, 1, 2, 3, -7, 88, 99])\n", " \n", " The returned array is always 1D.\n", " \n", " >>> y = [[1, 2, 4], [1, 6, 24]]\n", " >>> np.ediff1d(y)\n", " array([ 1, 2, -3, 5, 18])\n", " \n", " einsum(...)\n", " einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe')\n", " \n", " Evaluates the Einstein summation convention on the operands.\n", " \n", " Using the Einstein summation convention, many common multi-dimensional\n", " array operations can be represented in a simple fashion. This function\n", " provides a way compute such summations. The best way to understand this\n", " function is to try the examples below, which show how many common NumPy\n", " functions can be implemented as calls to `einsum`.\n", " \n", " Parameters\n", " ----------\n", " subscripts : str\n", " Specifies the subscripts for summation.\n", " operands : list of array_like\n", " These are the arrays for the operation.\n", " out : ndarray, optional\n", " If provided, the calculation is done into this array.\n", " dtype : data-type, optional\n", " If provided, forces the calculation to use the data type specified.\n", " Note that you may have to also give a more liberal `casting`\n", " parameter to allow the conversions.\n", " order : {'C', 'F', 'A', 'K'}, optional\n", " Controls the memory layout of the output. 'C' means it should\n", " be C contiguous. 'F' means it should be Fortran contiguous,\n", " 'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.\n", " 'K' means it should be as close to the layout as the inputs as\n", " is possible, including arbitrarily permuted axes.\n", " Default is 'K'.\n", " casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional\n", " Controls what kind of data casting may occur. Setting this to\n", " 'unsafe' is not recommended, as it can adversely affect accumulations.\n", " \n", " * 'no' means the data types should not be cast at all.\n", " * 'equiv' means only byte-order changes are allowed.\n", " * 'safe' means only casts which can preserve values are allowed.\n", " * 'same_kind' means only safe casts or casts within a kind,\n", " like float64 to float32, are allowed.\n", " * 'unsafe' means any data conversions may be done.\n", " \n", " Returns\n", " -------\n", " output : ndarray\n", " The calculation based on the Einstein summation convention.\n", " \n", " See Also\n", " --------\n", " dot, inner, outer, tensordot\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.6.0\n", " \n", " The subscripts string is a comma-separated list of subscript labels,\n", " where each label refers to a dimension of the corresponding operand.\n", " Repeated subscripts labels in one operand take the diagonal. For example,\n", " ``np.einsum('ii', a)`` is equivalent to ``np.trace(a)``.\n", " \n", " Whenever a label is repeated, it is summed, so ``np.einsum('i,i', a, b)``\n", " is equivalent to ``np.inner(a,b)``. If a label appears only once,\n", " it is not summed, so ``np.einsum('i', a)`` produces a view of ``a``\n", " with no changes.\n", " \n", " The order of labels in the output is by default alphabetical. This\n", " means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while\n", " ``np.einsum('ji', a)`` takes its transpose.\n", " \n", " The output can be controlled by specifying output subscript labels\n", " as well. This specifies the label order, and allows summing to\n", " be disallowed or forced when desired. The call ``np.einsum('i->', a)``\n", " is like ``np.sum(a, axis=-1)``, and ``np.einsum('ii->i', a)``\n", " is like ``np.diag(a)``. The difference is that `einsum` does not\n", " allow broadcasting by default.\n", " \n", " To enable and control broadcasting, use an ellipsis. Default\n", " NumPy-style broadcasting is done by adding an ellipsis\n", " to the left of each term, like ``np.einsum('...ii->...i', a)``.\n", " To take the trace along the first and last axes,\n", " you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix\n", " product with the left-most indices instead of rightmost, you can do\n", " ``np.einsum('ij...,jk...->ik...', a, b)``.\n", " \n", " When there is only one operand, no axes are summed, and no output\n", " parameter is provided, a view into the operand is returned instead\n", " of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)``\n", " produces a view.\n", " \n", " An alternative way to provide the subscripts and operands is as\n", " ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``. The examples\n", " below have corresponding `einsum` calls with the two parameter methods.\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(25).reshape(5,5)\n", " >>> b = np.arange(5)\n", " >>> c = np.arange(6).reshape(2,3)\n", " \n", " >>> np.einsum('ii', a)\n", " 60\n", " >>> np.einsum(a, [0,0])\n", " 60\n", " >>> np.trace(a)\n", " 60\n", " \n", " >>> np.einsum('ii->i', a)\n", " array([ 0, 6, 12, 18, 24])\n", " >>> np.einsum(a, [0,0], [0])\n", " array([ 0, 6, 12, 18, 24])\n", " >>> np.diag(a)\n", " array([ 0, 6, 12, 18, 24])\n", " \n", " >>> np.einsum('ij,j', a, b)\n", " array([ 30, 80, 130, 180, 230])\n", " >>> np.einsum(a, [0,1], b, [1])\n", " array([ 30, 80, 130, 180, 230])\n", " >>> np.dot(a, b)\n", " array([ 30, 80, 130, 180, 230])\n", " >>> np.einsum('...j,j', a, b)\n", " array([ 30, 80, 130, 180, 230])\n", " \n", " >>> np.einsum('ji', c)\n", " array([[0, 3],\n", " [1, 4],\n", " [2, 5]])\n", " >>> np.einsum(c, [1,0])\n", " array([[0, 3],\n", " [1, 4],\n", " [2, 5]])\n", " >>> c.T\n", " array([[0, 3],\n", " [1, 4],\n", " [2, 5]])\n", " \n", " >>> np.einsum('..., ...', 3, c)\n", " array([[ 0, 3, 6],\n", " [ 9, 12, 15]])\n", " >>> np.einsum(3, [Ellipsis], c, [Ellipsis])\n", " array([[ 0, 3, 6],\n", " [ 9, 12, 15]])\n", " >>> np.multiply(3, c)\n", " array([[ 0, 3, 6],\n", " [ 9, 12, 15]])\n", " \n", " >>> np.einsum('i,i', b, b)\n", " 30\n", " >>> np.einsum(b, [0], b, [0])\n", " 30\n", " >>> np.inner(b,b)\n", " 30\n", " \n", " >>> np.einsum('i,j', np.arange(2)+1, b)\n", " array([[0, 1, 2, 3, 4],\n", " [0, 2, 4, 6, 8]])\n", " >>> np.einsum(np.arange(2)+1, [0], b, [1])\n", " array([[0, 1, 2, 3, 4],\n", " [0, 2, 4, 6, 8]])\n", " >>> np.outer(np.arange(2)+1, b)\n", " array([[0, 1, 2, 3, 4],\n", " [0, 2, 4, 6, 8]])\n", " \n", " >>> np.einsum('i...->...', a)\n", " array([50, 55, 60, 65, 70])\n", " >>> np.einsum(a, [0,Ellipsis], [Ellipsis])\n", " array([50, 55, 60, 65, 70])\n", " >>> np.sum(a, axis=0)\n", " array([50, 55, 60, 65, 70])\n", " \n", " >>> a = np.arange(60.).reshape(3,4,5)\n", " >>> b = np.arange(24.).reshape(4,3,2)\n", " >>> np.einsum('ijk,jil->kl', a, b)\n", " array([[ 4400., 4730.],\n", " [ 4532., 4874.],\n", " [ 4664., 5018.],\n", " [ 4796., 5162.],\n", " [ 4928., 5306.]])\n", " >>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])\n", " array([[ 4400., 4730.],\n", " [ 4532., 4874.],\n", " [ 4664., 5018.],\n", " [ 4796., 5162.],\n", " [ 4928., 5306.]])\n", " >>> np.tensordot(a,b, axes=([1,0],[0,1]))\n", " array([[ 4400., 4730.],\n", " [ 4532., 4874.],\n", " [ 4664., 5018.],\n", " [ 4796., 5162.],\n", " [ 4928., 5306.]])\n", " \n", " >>> a = np.arange(6).reshape((3,2))\n", " >>> b = np.arange(12).reshape((4,3))\n", " >>> np.einsum('ki,jk->ij', a, b)\n", " array([[10, 28, 46, 64],\n", " [13, 40, 67, 94]])\n", " >>> np.einsum('ki,...k->i...', a, b)\n", " array([[10, 28, 46, 64],\n", " [13, 40, 67, 94]])\n", " >>> np.einsum('k...,jk', a, b)\n", " array([[10, 28, 46, 64],\n", " [13, 40, 67, 94]])\n", " \n", " empty(...)\n", " empty(shape, dtype=float, order='C')\n", " \n", " Return a new array of given shape and type, without initializing entries.\n", " \n", " Parameters\n", " ----------\n", " shape : int or tuple of int\n", " Shape of the empty array\n", " dtype : data-type, optional\n", " Desired output data-type.\n", " order : {'C', 'F'}, optional\n", " Whether to store multi-dimensional data in C (row-major) or\n", " Fortran (column-major) order in memory.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Array of uninitialized (arbitrary) data with the given\n", " shape, dtype, and order.\n", " \n", " See Also\n", " --------\n", " empty_like, zeros, ones\n", " \n", " Notes\n", " -----\n", " `empty`, unlike `zeros`, does not set the array values to zero,\n", " and may therefore be marginally faster. On the other hand, it requires\n", " the user to manually set all the values in the array, and should be\n", " used with caution.\n", " \n", " Examples\n", " --------\n", " >>> np.empty([2, 2])\n", " array([[ -9.74499359e+001, 6.69583040e-309],\n", " [ 2.13182611e-314, 3.06959433e-309]]) #random\n", " \n", " >>> np.empty([2, 2], dtype=int)\n", " array([[-1073741821, -1067949133],\n", " [ 496041986, 19249760]]) #random\n", " \n", " empty_like(...)\n", " empty_like(a, dtype=None, order='K', subok=True)\n", " \n", " Return a new array with the same shape and type as a given array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " The shape and data-type of `a` define these same attributes of the\n", " returned array.\n", " dtype : data-type, optional\n", " .. versionadded:: 1.6.0\n", " Overrides the data type of the result.\n", " order : {'C', 'F', 'A', or 'K'}, optional\n", " .. versionadded:: 1.6.0\n", " Overrides the memory layout of the result. 'C' means C-order,\n", " 'F' means F-order, 'A' means 'F' if ``a`` is Fortran contiguous,\n", " 'C' otherwise. 'K' means match the layout of ``a`` as closely\n", " as possible.\n", " subok : bool, optional.\n", " If True, then the newly created array will use the sub-class\n", " type of 'a', otherwise it will be a base-class array. Defaults\n", " to True.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Array of uninitialized (arbitrary) data with the same\n", " shape and type as `a`.\n", " \n", " See Also\n", " --------\n", " ones_like : Return an array of ones with shape and type of input.\n", " zeros_like : Return an array of zeros with shape and type of input.\n", " empty : Return a new uninitialized array.\n", " ones : Return a new array setting values to one.\n", " zeros : Return a new array setting values to zero.\n", " \n", " Notes\n", " -----\n", " This function does *not* initialize the returned array; to do that use\n", " `zeros_like` or `ones_like` instead. It may be marginally faster than\n", " the functions that do set the array values.\n", " \n", " Examples\n", " --------\n", " >>> a = ([1,2,3], [4,5,6]) # a is array-like\n", " >>> np.empty_like(a)\n", " array([[-1073741821, -1073741821, 3], #random\n", " [ 0, 0, -1073741821]])\n", " >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])\n", " >>> np.empty_like(a)\n", " array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000],#random\n", " [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])\n", " \n", " expand_dims(a, axis)\n", " Expand the shape of an array.\n", " \n", " Insert a new axis, corresponding to a given position in the array shape.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " axis : int\n", " Position (amongst axes) where new axis is to be inserted.\n", " \n", " Returns\n", " -------\n", " res : ndarray\n", " Output array. The number of dimensions is one greater than that of\n", " the input array.\n", " \n", " See Also\n", " --------\n", " doc.indexing, atleast_1d, atleast_2d, atleast_3d\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([1,2])\n", " >>> x.shape\n", " (2,)\n", " \n", " The following is equivalent to ``x[np.newaxis,:]`` or ``x[np.newaxis]``:\n", " \n", " >>> y = np.expand_dims(x, axis=0)\n", " >>> y\n", " array([[1, 2]])\n", " >>> y.shape\n", " (1, 2)\n", " \n", " >>> y = np.expand_dims(x, axis=1) # Equivalent to x[:,newaxis]\n", " >>> y\n", " array([[1],\n", " [2]])\n", " >>> y.shape\n", " (2, 1)\n", " \n", " Note that some examples may use ``None`` instead of ``np.newaxis``. These\n", " are the same objects:\n", " \n", " >>> np.newaxis is None\n", " True\n", " \n", " extract(condition, arr)\n", " Return the elements of an array that satisfy some condition.\n", " \n", " This is equivalent to ``np.compress(ravel(condition), ravel(arr))``. If\n", " `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.\n", " \n", " Parameters\n", " ----------\n", " condition : array_like\n", " An array whose nonzero or True entries indicate the elements of `arr`\n", " to extract.\n", " arr : array_like\n", " Input array of the same size as `condition`.\n", " \n", " Returns\n", " -------\n", " extract : ndarray\n", " Rank 1 array of values from `arr` where `condition` is True.\n", " \n", " See Also\n", " --------\n", " take, put, copyto, compress\n", " \n", " Examples\n", " --------\n", " >>> arr = np.arange(12).reshape((3, 4))\n", " >>> arr\n", " array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11]])\n", " >>> condition = np.mod(arr, 3)==0\n", " >>> condition\n", " array([[ True, False, False, True],\n", " [False, False, True, False],\n", " [False, True, False, False]], dtype=bool)\n", " >>> np.extract(condition, arr)\n", " array([0, 3, 6, 9])\n", " \n", " \n", " If `condition` is boolean:\n", " \n", " >>> arr[condition]\n", " array([0, 3, 6, 9])\n", " \n", " eye(N, M=None, k=0, dtype=)\n", " Return a 2-D array with ones on the diagonal and zeros elsewhere.\n", " \n", " Parameters\n", " ----------\n", " N : int\n", " Number of rows in the output.\n", " M : int, optional\n", " Number of columns in the output. If None, defaults to `N`.\n", " k : int, optional\n", " Index of the diagonal: 0 (the default) refers to the main diagonal,\n", " a positive value refers to an upper diagonal, and a negative value\n", " to a lower diagonal.\n", " dtype : data-type, optional\n", " Data-type of the returned array.\n", " \n", " Returns\n", " -------\n", " I : ndarray of shape (N,M)\n", " An array where all elements are equal to zero, except for the `k`-th\n", " diagonal, whose values are equal to one.\n", " \n", " See Also\n", " --------\n", " identity : (almost) equivalent function\n", " diag : diagonal 2-D array from a 1-D array specified by the user.\n", " \n", " Examples\n", " --------\n", " >>> np.eye(2, dtype=int)\n", " array([[1, 0],\n", " [0, 1]])\n", " >>> np.eye(3, k=1)\n", " array([[ 0., 1., 0.],\n", " [ 0., 0., 1.],\n", " [ 0., 0., 0.]])\n", " \n", " fastCopyAndTranspose = _fastCopyAndTranspose(...)\n", " _fastCopyAndTranspose(a)\n", " \n", " fft(a, n=None, axis=-1)\n", " Compute the one-dimensional discrete Fourier Transform.\n", " \n", " This function computes the one-dimensional *n*-point discrete Fourier\n", " Transform (DFT) with the efficient Fast Fourier Transform (FFT)\n", " algorithm [CT].\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array, can be complex.\n", " n : int, optional\n", " Length of the transformed axis of the output.\n", " If `n` is smaller than the length of the input, the input is cropped.\n", " If it is larger, the input is padded with zeros. If `n` is not given,\n", " the length of the input along the axis specified by `axis` is used.\n", " axis : int, optional\n", " Axis over which to compute the FFT. If not given, the last axis is\n", " used.\n", " \n", " Returns\n", " -------\n", " out : complex ndarray\n", " The truncated or zero-padded input, transformed along the axis\n", " indicated by `axis`, or the last one if `axis` is not specified.\n", " \n", " Raises\n", " ------\n", " IndexError\n", " if `axes` is larger than the last axis of `a`.\n", " \n", " See Also\n", " --------\n", " numpy.fft : for definition of the DFT and conventions used.\n", " ifft : The inverse of `fft`.\n", " fft2 : The two-dimensional FFT.\n", " fftn : The *n*-dimensional FFT.\n", " rfftn : The *n*-dimensional FFT of real input.\n", " fftfreq : Frequency bins for given FFT parameters.\n", " \n", " Notes\n", " -----\n", " FFT (Fast Fourier Transform) refers to a way the discrete Fourier\n", " Transform (DFT) can be calculated efficiently, by using symmetries in the\n", " calculated terms. The symmetry is highest when `n` is a power of 2, and\n", " the transform is therefore most efficient for these sizes.\n", " \n", " The DFT is defined, with the conventions used in this implementation, in\n", " the documentation for the `numpy.fft` module.\n", " \n", " References\n", " ----------\n", " .. [CT] Cooley, James W., and John W. Tukey, 1965, \"An algorithm for the\n", " machine calculation of complex Fourier series,\" *Math. Comput.*\n", " 19: 297-301.\n", " \n", " Examples\n", " --------\n", " >>> np.fft.fft(np.exp(2j * np.pi * np.arange(8) / 8))\n", " array([ -3.44505240e-16 +1.14383329e-17j,\n", " 8.00000000e+00 -5.71092652e-15j,\n", " 2.33482938e-16 +1.22460635e-16j,\n", " 1.64863782e-15 +1.77635684e-15j,\n", " 9.95839695e-17 +2.33482938e-16j,\n", " 0.00000000e+00 +1.66837030e-15j,\n", " 1.14383329e-17 +1.22460635e-16j,\n", " -1.64863782e-15 +1.77635684e-15j])\n", " \n", " >>> import matplotlib.pyplot as plt\n", " >>> t = np.arange(256)\n", " >>> sp = np.fft.fft(np.sin(t))\n", " >>> freq = np.fft.fftfreq(t.shape[-1])\n", " >>> plt.plot(freq, sp.real, freq, sp.imag)\n", " [, ]\n", " >>> plt.show()\n", " \n", " In this example, real input has an FFT which is Hermitian, i.e., symmetric\n", " in the real part and anti-symmetric in the imaginary part, as described in\n", " the `numpy.fft` documentation.\n", " \n", " fill_diagonal(a, val, wrap=False)\n", " Fill the main diagonal of the given array of any dimensionality.\n", " \n", " For an array `a` with ``a.ndim > 2``, the diagonal is the list of\n", " locations with indices ``a[i, i, ..., i]`` all identical. This function\n", " modifies the input array in-place, it does not return a value.\n", " \n", " Parameters\n", " ----------\n", " a : array, at least 2-D.\n", " Array whose diagonal is to be filled, it gets modified in-place.\n", " \n", " val : scalar\n", " Value to be written on the diagonal, its type must be compatible with\n", " that of the array a.\n", " \n", " wrap : bool\n", " For tall matrices in NumPy version up to 1.6.2, the\n", " diagonal \"wrapped\" after N columns. You can have this behavior\n", " with this option. This affect only tall matrices.\n", " \n", " See also\n", " --------\n", " diag_indices, diag_indices_from\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.4.0\n", " \n", " This functionality can be obtained via `diag_indices`, but internally\n", " this version uses a much faster implementation that never constructs the\n", " indices and uses simple slicing.\n", " \n", " Examples\n", " --------\n", " >>> a = np.zeros((3, 3), int)\n", " >>> np.fill_diagonal(a, 5)\n", " >>> a\n", " array([[5, 0, 0],\n", " [0, 5, 0],\n", " [0, 0, 5]])\n", " \n", " The same function can operate on a 4-D array:\n", " \n", " >>> a = np.zeros((3, 3, 3, 3), int)\n", " >>> np.fill_diagonal(a, 4)\n", " \n", " We only show a few blocks for clarity:\n", " \n", " >>> a[0, 0]\n", " array([[4, 0, 0],\n", " [0, 0, 0],\n", " [0, 0, 0]])\n", " >>> a[1, 1]\n", " array([[0, 0, 0],\n", " [0, 4, 0],\n", " [0, 0, 0]])\n", " >>> a[2, 2]\n", " array([[0, 0, 0],\n", " [0, 0, 0],\n", " [0, 0, 4]])\n", " \n", " # tall matrices no wrap\n", " >>> a = np.zeros((5, 3),int)\n", " >>> fill_diagonal(a, 4)\n", " array([[4, 0, 0],\n", " [0, 4, 0],\n", " [0, 0, 4],\n", " [0, 0, 0],\n", " [0, 0, 0]])\n", " \n", " # tall matrices wrap\n", " >>> a = np.zeros((5, 3),int)\n", " >>> fill_diagonal(a, 4)\n", " array([[4, 0, 0],\n", " [0, 4, 0],\n", " [0, 0, 4],\n", " [0, 0, 0],\n", " [4, 0, 0]])\n", " \n", " # wide matrices\n", " >>> a = np.zeros((3, 5),int)\n", " >>> fill_diagonal(a, 4)\n", " array([[4, 0, 0, 0, 0],\n", " [0, 4, 0, 0, 0],\n", " [0, 0, 4, 0, 0]])\n", " \n", " find_common_type(array_types, scalar_types)\n", " Determine common type following standard coercion rules.\n", " \n", " Parameters\n", " ----------\n", " array_types : sequence\n", " A list of dtypes or dtype convertible objects representing arrays.\n", " scalar_types : sequence\n", " A list of dtypes or dtype convertible objects representing scalars.\n", " \n", " Returns\n", " -------\n", " datatype : dtype\n", " The common data type, which is the maximum of `array_types` ignoring\n", " `scalar_types`, unless the maximum of `scalar_types` is of a\n", " different kind (`dtype.kind`). If the kind is not understood, then\n", " None is returned.\n", " \n", " See Also\n", " --------\n", " dtype, common_type, can_cast, mintypecode\n", " \n", " Examples\n", " --------\n", " >>> np.find_common_type([], [np.int64, np.float32, np.complex])\n", " dtype('complex128')\n", " >>> np.find_common_type([np.int64, np.float32], [])\n", " dtype('float64')\n", " \n", " The standard casting rules ensure that a scalar cannot up-cast an\n", " array unless the scalar is of a fundamentally different kind of data\n", " (i.e. under a different hierarchy in the data type hierarchy) then\n", " the array:\n", " \n", " >>> np.find_common_type([np.float32], [np.int64, np.float64])\n", " dtype('float32')\n", " \n", " Complex is of a different type, so it up-casts the float in the\n", " `array_types` argument:\n", " \n", " >>> np.find_common_type([np.float32], [np.complex])\n", " dtype('complex128')\n", " \n", " Type specifier strings are convertible to dtypes and can therefore\n", " be used instead of dtypes:\n", " \n", " >>> np.find_common_type(['f4', 'f4', 'i4'], ['c8'])\n", " dtype('complex128')\n", " \n", " fix(x, y=None)\n", " Round to nearest integer towards zero.\n", " \n", " Round an array of floats element-wise to nearest integer towards zero.\n", " The rounded values are returned as floats.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " An array of floats to be rounded\n", " y : ndarray, optional\n", " Output array\n", " \n", " Returns\n", " -------\n", " out : ndarray of floats\n", " The array of rounded numbers\n", " \n", " See Also\n", " --------\n", " trunc, floor, ceil\n", " around : Round to given number of decimals\n", " \n", " Examples\n", " --------\n", " >>> np.fix(3.14)\n", " 3.0\n", " >>> np.fix(3)\n", " 3.0\n", " >>> np.fix([2.1, 2.9, -2.1, -2.9])\n", " array([ 2., 2., -2., -2.])\n", " \n", " flatnonzero(a)\n", " Return indices that are non-zero in the flattened version of a.\n", " \n", " This is equivalent to a.ravel().nonzero()[0].\n", " \n", " Parameters\n", " ----------\n", " a : ndarray\n", " Input array.\n", " \n", " Returns\n", " -------\n", " res : ndarray\n", " Output array, containing the indices of the elements of `a.ravel()`\n", " that are non-zero.\n", " \n", " See Also\n", " --------\n", " nonzero : Return the indices of the non-zero elements of the input array.\n", " ravel : Return a 1-D array containing the elements of the input array.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(-2, 3)\n", " >>> x\n", " array([-2, -1, 0, 1, 2])\n", " >>> np.flatnonzero(x)\n", " array([0, 1, 3, 4])\n", " \n", " Use the indices of the non-zero elements as an index array to extract\n", " these elements:\n", " \n", " >>> x.ravel()[np.flatnonzero(x)]\n", " array([-2, -1, 1, 2])\n", " \n", " fliplr(m)\n", " Flip array in the left/right direction.\n", " \n", " Flip the entries in each row in the left/right direction.\n", " Columns are preserved, but appear in a different order than before.\n", " \n", " Parameters\n", " ----------\n", " m : array_like\n", " Input array, must be at least 2-D.\n", " \n", " Returns\n", " -------\n", " f : ndarray\n", " A view of `m` with the columns reversed. Since a view\n", " is returned, this operation is :math:`\\mathcal O(1)`.\n", " \n", " See Also\n", " --------\n", " flipud : Flip array in the up/down direction.\n", " rot90 : Rotate array counterclockwise.\n", " \n", " Notes\n", " -----\n", " Equivalent to A[:,::-1]. Requires the array to be at least 2-D.\n", " \n", " Examples\n", " --------\n", " >>> A = np.diag([1.,2.,3.])\n", " >>> A\n", " array([[ 1., 0., 0.],\n", " [ 0., 2., 0.],\n", " [ 0., 0., 3.]])\n", " >>> np.fliplr(A)\n", " array([[ 0., 0., 1.],\n", " [ 0., 2., 0.],\n", " [ 3., 0., 0.]])\n", " \n", " >>> A = np.random.randn(2,3,5)\n", " >>> np.all(np.fliplr(A)==A[:,::-1,...])\n", " True\n", " \n", " flipud(m)\n", " Flip array in the up/down direction.\n", " \n", " Flip the entries in each column in the up/down direction.\n", " Rows are preserved, but appear in a different order than before.\n", " \n", " Parameters\n", " ----------\n", " m : array_like\n", " Input array.\n", " \n", " Returns\n", " -------\n", " out : array_like\n", " A view of `m` with the rows reversed. Since a view is\n", " returned, this operation is :math:`\\mathcal O(1)`.\n", " \n", " See Also\n", " --------\n", " fliplr : Flip array in the left/right direction.\n", " rot90 : Rotate array counterclockwise.\n", " \n", " Notes\n", " -----\n", " Equivalent to ``A[::-1,...]``.\n", " Does not require the array to be two-dimensional.\n", " \n", " Examples\n", " --------\n", " >>> A = np.diag([1.0, 2, 3])\n", " >>> A\n", " array([[ 1., 0., 0.],\n", " [ 0., 2., 0.],\n", " [ 0., 0., 3.]])\n", " >>> np.flipud(A)\n", " array([[ 0., 0., 3.],\n", " [ 0., 2., 0.],\n", " [ 1., 0., 0.]])\n", " \n", " >>> A = np.random.randn(2,3,5)\n", " >>> np.all(np.flipud(A)==A[::-1,...])\n", " True\n", " \n", " >>> np.flipud([1,2])\n", " array([2, 1])\n", " \n", " frombuffer(...)\n", " frombuffer(buffer, dtype=float, count=-1, offset=0)\n", " \n", " Interpret a buffer as a 1-dimensional array.\n", " \n", " Parameters\n", " ----------\n", " buffer : buffer_like\n", " An object that exposes the buffer interface.\n", " dtype : data-type, optional\n", " Data-type of the returned array; default: float.\n", " count : int, optional\n", " Number of items to read. ``-1`` means all data in the buffer.\n", " offset : int, optional\n", " Start reading the buffer from this offset; default: 0.\n", " \n", " Notes\n", " -----\n", " If the buffer has data that is not in machine byte-order, this should\n", " be specified as part of the data-type, e.g.::\n", " \n", " >>> dt = np.dtype(int)\n", " >>> dt = dt.newbyteorder('>')\n", " >>> np.frombuffer(buf, dtype=dt)\n", " \n", " The data of the resulting array will not be byteswapped, but will be\n", " interpreted correctly.\n", " \n", " Examples\n", " --------\n", " >>> s = 'hello world'\n", " >>> np.frombuffer(s, dtype='S1', count=5, offset=6)\n", " array(['w', 'o', 'r', 'l', 'd'],\n", " dtype='|S1')\n", " \n", " fromfile(...)\n", " fromfile(file, dtype=float, count=-1, sep='')\n", " \n", " Construct an array from data in a text or binary file.\n", " \n", " A highly efficient way of reading binary data with a known data-type,\n", " as well as parsing simply formatted text files. Data written using the\n", " `tofile` method can be read using this function.\n", " \n", " Parameters\n", " ----------\n", " file : file or str\n", " Open file object or filename.\n", " dtype : data-type\n", " Data type of the returned array.\n", " For binary files, it is used to determine the size and byte-order\n", " of the items in the file.\n", " count : int\n", " Number of items to read. ``-1`` means all items (i.e., the complete\n", " file).\n", " sep : str\n", " Separator between items if file is a text file.\n", " Empty (\"\") separator means the file should be treated as binary.\n", " Spaces (\" \") in the separator match zero or more whitespace characters.\n", " A separator consisting only of spaces must match at least one\n", " whitespace.\n", " \n", " See also\n", " --------\n", " load, save\n", " ndarray.tofile\n", " loadtxt : More flexible way of loading data from a text file.\n", " \n", " Notes\n", " -----\n", " Do not rely on the combination of `tofile` and `fromfile` for\n", " data storage, as the binary files generated are are not platform\n", " independent. In particular, no byte-order or data-type information is\n", " saved. Data can be stored in the platform independent ``.npy`` format\n", " using `save` and `load` instead.\n", " \n", " Examples\n", " --------\n", " Construct an ndarray:\n", " \n", " >>> dt = np.dtype([('time', [('min', int), ('sec', int)]),\n", " ... ('temp', float)])\n", " >>> x = np.zeros((1,), dtype=dt)\n", " >>> x['time']['min'] = 10; x['temp'] = 98.25\n", " >>> x\n", " array([((10, 0), 98.25)],\n", " dtype=[('time', [('min', '>> import os\n", " >>> fname = os.tmpnam()\n", " >>> x.tofile(fname)\n", " \n", " Read the raw data from disk:\n", " \n", " >>> np.fromfile(fname, dtype=dt)\n", " array([((10, 0), 98.25)],\n", " dtype=[('time', [('min', '>> np.save(fname, x)\n", " >>> np.load(fname + '.npy')\n", " array([((10, 0), 98.25)],\n", " dtype=[('time', [('min', '>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)\n", " array([[ True, False, False],\n", " [False, True, False],\n", " [False, False, True]], dtype=bool)\n", " \n", " >>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)\n", " array([[0, 1, 2],\n", " [1, 2, 3],\n", " [2, 3, 4]])\n", " \n", " fromiter(...)\n", " fromiter(iterable, dtype, count=-1)\n", " \n", " Create a new 1-dimensional array from an iterable object.\n", " \n", " Parameters\n", " ----------\n", " iterable : iterable object\n", " An iterable object providing data for the array.\n", " dtype : data-type\n", " The data-type of the returned array.\n", " count : int, optional\n", " The number of items to read from *iterable*. The default is -1,\n", " which means all data is read.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The output array.\n", " \n", " Notes\n", " -----\n", " Specify `count` to improve performance. It allows ``fromiter`` to\n", " pre-allocate the output array, instead of resizing it on demand.\n", " \n", " Examples\n", " --------\n", " >>> iterable = (x*x for x in range(5))\n", " >>> np.fromiter(iterable, np.float)\n", " array([ 0., 1., 4., 9., 16.])\n", " \n", " frompyfunc(...)\n", " frompyfunc(func, nin, nout)\n", " \n", " Takes an arbitrary Python function and returns a Numpy ufunc.\n", " \n", " Can be used, for example, to add broadcasting to a built-in Python\n", " function (see Examples section).\n", " \n", " Parameters\n", " ----------\n", " func : Python function object\n", " An arbitrary Python function.\n", " nin : int\n", " The number of input arguments.\n", " nout : int\n", " The number of objects returned by `func`.\n", " \n", " Returns\n", " -------\n", " out : ufunc\n", " Returns a Numpy universal function (``ufunc``) object.\n", " \n", " Notes\n", " -----\n", " The returned ufunc always returns PyObject arrays.\n", " \n", " Examples\n", " --------\n", " Use frompyfunc to add broadcasting to the Python function ``oct``:\n", " \n", " >>> oct_array = np.frompyfunc(oct, 1, 1)\n", " >>> oct_array(np.array((10, 30, 100)))\n", " array([012, 036, 0144], dtype=object)\n", " >>> np.array((oct(10), oct(30), oct(100))) # for comparison\n", " array(['012', '036', '0144'],\n", " dtype='|S4')\n", " \n", " fromregex(file, regexp, dtype)\n", " Construct an array from a text file, using regular expression parsing.\n", " \n", " The returned array is always a structured array, and is constructed from\n", " all matches of the regular expression in the file. Groups in the regular\n", " expression are converted to fields of the structured array.\n", " \n", " Parameters\n", " ----------\n", " file : str or file\n", " File name or file object to read.\n", " regexp : str or regexp\n", " Regular expression used to parse the file.\n", " Groups in the regular expression correspond to fields in the dtype.\n", " dtype : dtype or list of dtypes\n", " Dtype for the structured array.\n", " \n", " Returns\n", " -------\n", " output : ndarray\n", " The output array, containing the part of the content of `file` that\n", " was matched by `regexp`. `output` is always a structured array.\n", " \n", " Raises\n", " ------\n", " TypeError\n", " When `dtype` is not a valid dtype for a structured array.\n", " \n", " See Also\n", " --------\n", " fromstring, loadtxt\n", " \n", " Notes\n", " -----\n", " Dtypes for structured arrays can be specified in several forms, but all\n", " forms specify at least the data type and field name. For details see\n", " `doc.structured_arrays`.\n", " \n", " Examples\n", " --------\n", " >>> f = open('test.dat', 'w')\n", " >>> f.write(\"1312 foo\\n1534 bar\\n444 qux\")\n", " >>> f.close()\n", " \n", " >>> regexp = r\"(\\d+)\\s+(...)\" # match [digits, whitespace, anything]\n", " >>> output = np.fromregex('test.dat', regexp,\n", " ... [('num', np.int64), ('key', 'S3')])\n", " >>> output\n", " array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],\n", " dtype=[('num', '>> output['num']\n", " array([1312, 1534, 444], dtype=int64)\n", " \n", " fromstring(...)\n", " fromstring(string, dtype=float, count=-1, sep='')\n", " \n", " A new 1-D array initialized from raw binary or text data in a string.\n", " \n", " Parameters\n", " ----------\n", " string : str\n", " A string containing the data.\n", " dtype : data-type, optional\n", " The data type of the array; default: float. For binary input data,\n", " the data must be in exactly this format.\n", " count : int, optional\n", " Read this number of `dtype` elements from the data. If this is\n", " negative (the default), the count will be determined from the\n", " length of the data.\n", " sep : str, optional\n", " If not provided or, equivalently, the empty string, the data will\n", " be interpreted as binary data; otherwise, as ASCII text with\n", " decimal numbers. Also in this latter case, this argument is\n", " interpreted as the string separating numbers in the data; extra\n", " whitespace between elements is also ignored.\n", " \n", " Returns\n", " -------\n", " arr : ndarray\n", " The constructed array.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If the string is not the correct size to satisfy the requested\n", " `dtype` and `count`.\n", " \n", " See Also\n", " --------\n", " frombuffer, fromfile, fromiter\n", " \n", " Examples\n", " --------\n", " >>> np.fromstring('\\x01\\x02', dtype=np.uint8)\n", " array([1, 2], dtype=uint8)\n", " >>> np.fromstring('1 2', dtype=int, sep=' ')\n", " array([1, 2])\n", " >>> np.fromstring('1, 2', dtype=int, sep=',')\n", " array([1, 2])\n", " >>> np.fromstring('\\x01\\x02\\x03\\x04\\x05', dtype=np.uint8, count=3)\n", " array([1, 2, 3], dtype=uint8)\n", " \n", " full(shape, fill_value, dtype=None, order='C')\n", " Return a new array of given shape and type, filled with `fill_value`.\n", " \n", " Parameters\n", " ----------\n", " shape : int or sequence of ints\n", " Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n", " fill_value : scalar\n", " Fill value.\n", " dtype : data-type, optional\n", " The desired data-type for the array, e.g., `numpy.int8`. Default is\n", " is chosen as `np.array(fill_value).dtype`.\n", " order : {'C', 'F'}, optional\n", " Whether to store multidimensional data in C- or Fortran-contiguous\n", " (row- or column-wise) order in memory.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Array of `fill_value` with the given shape, dtype, and order.\n", " \n", " See Also\n", " --------\n", " zeros_like : Return an array of zeros with shape and type of input.\n", " ones_like : Return an array of ones with shape and type of input.\n", " empty_like : Return an empty array with shape and type of input.\n", " full_like : Fill an array with shape and type of input.\n", " zeros : Return a new array setting values to zero.\n", " ones : Return a new array setting values to one.\n", " empty : Return a new uninitialized array.\n", " \n", " Examples\n", " --------\n", " >>> np.full((2, 2), np.inf)\n", " array([[ inf, inf],\n", " [ inf, inf]])\n", " >>> np.full((2, 2), 10, dtype=np.int)\n", " array([[10, 10],\n", " [10, 10]])\n", " \n", " full_like(a, fill_value, dtype=None, order='K', subok=True)\n", " Return a full array with the same shape and type as a given array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " The shape and data-type of `a` define these same attributes of\n", " the returned array.\n", " fill_value : scalar\n", " Fill value.\n", " dtype : data-type, optional\n", " Overrides the data type of the result.\n", " order : {'C', 'F', 'A', or 'K'}, optional\n", " Overrides the memory layout of the result. 'C' means C-order,\n", " 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n", " 'C' otherwise. 'K' means match the layout of `a` as closely\n", " as possible.\n", " subok : bool, optional.\n", " If True, then the newly created array will use the sub-class\n", " type of 'a', otherwise it will be a base-class array. Defaults\n", " to True.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Array of `fill_value` with the same shape and type as `a`.\n", " \n", " See Also\n", " --------\n", " zeros_like : Return an array of zeros with shape and type of input.\n", " ones_like : Return an array of ones with shape and type of input.\n", " empty_like : Return an empty array with shape and type of input.\n", " zeros : Return a new array setting values to zero.\n", " ones : Return a new array setting values to one.\n", " empty : Return a new uninitialized array.\n", " full : Fill a new array.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(6, dtype=np.int)\n", " >>> np.full_like(x, 1)\n", " array([1, 1, 1, 1, 1, 1])\n", " >>> np.full_like(x, 0.1)\n", " array([0, 0, 0, 0, 0, 0])\n", " >>> np.full_like(x, 0.1, dtype=np.double)\n", " array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])\n", " >>> np.full_like(x, np.nan, dtype=np.double)\n", " array([ nan, nan, nan, nan, nan, nan])\n", " \n", " >>> y = np.arange(6, dtype=np.double)\n", " >>> np.full_like(y, 0.1)\n", " array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])\n", " \n", " fv(rate, nper, pmt, pv, when='end')\n", " Compute the future value.\n", " \n", " Given:\n", " * a present value, `pv`\n", " * an interest `rate` compounded once per period, of which\n", " there are\n", " * `nper` total\n", " * a (fixed) payment, `pmt`, paid either\n", " * at the beginning (`when` = {'begin', 1}) or the end\n", " (`when` = {'end', 0}) of each period\n", " \n", " Return:\n", " the value at the end of the `nper` periods\n", " \n", " Parameters\n", " ----------\n", " rate : scalar or array_like of shape(M, )\n", " Rate of interest as decimal (not per cent) per period\n", " nper : scalar or array_like of shape(M, )\n", " Number of compounding periods\n", " pmt : scalar or array_like of shape(M, )\n", " Payment\n", " pv : scalar or array_like of shape(M, )\n", " Present value\n", " when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n", " When payments are due ('begin' (1) or 'end' (0)).\n", " Defaults to {'end', 0}.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Future values. If all input is scalar, returns a scalar float. If\n", " any input is array_like, returns future values for each input element.\n", " If multiple inputs are array_like, they all must have the same shape.\n", " \n", " Notes\n", " -----\n", " The future value is computed by solving the equation::\n", " \n", " fv +\n", " pv*(1+rate)**nper +\n", " pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) == 0\n", " \n", " or, when ``rate == 0``::\n", " \n", " fv + pv + pmt * nper == 0\n", " \n", " References\n", " ----------\n", " .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).\n", " Open Document Format for Office Applications (OpenDocument)v1.2,\n", " Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,\n", " Pre-Draft 12. Organization for the Advancement of Structured Information\n", " Standards (OASIS). Billerica, MA, USA. [ODT Document].\n", " Available:\n", " http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula\n", " OpenDocument-formula-20090508.odt\n", " \n", " Examples\n", " --------\n", " What is the future value after 10 years of saving $100 now, with\n", " an additional monthly savings of $100. Assume the interest rate is\n", " 5% (annually) compounded monthly?\n", " \n", " >>> np.fv(0.05/12, 10*12, -100, -100)\n", " 15692.928894335748\n", " \n", " By convention, the negative sign represents cash flow out (i.e. money not\n", " available today). Thus, saving $100 a month at 5% annual interest leads\n", " to $15,692.93 available to spend in 10 years.\n", " \n", " If any input is array_like, returns an array of equal shape. Let's\n", " compare different interest rates from the example above.\n", " \n", " >>> a = np.array((0.05, 0.06, 0.07))/12\n", " >>> np.fv(a, 10*12, -100, -100)\n", " array([ 15692.92889434, 16569.87435405, 17509.44688102])\n", " \n", " genfromtxt(fname, dtype=, comments='#', delimiter=None, skiprows=0, skip_header=0, skip_footer=0, converters=None, missing='', missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True)\n", " Load data from a text file, with missing values handled as specified.\n", " \n", " Each line past the first `skip_header` lines is split at the `delimiter`\n", " character, and characters following the `comments` character are discarded.\n", " \n", " Parameters\n", " ----------\n", " fname : file or str\n", " File, filename, or generator to read. If the filename extension is\n", " `.gz` or `.bz2`, the file is first decompressed. Note that\n", " generators must return byte strings in Python 3k.\n", " dtype : dtype, optional\n", " Data type of the resulting array.\n", " If None, the dtypes will be determined by the contents of each\n", " column, individually.\n", " comments : str, optional\n", " The character used to indicate the start of a comment.\n", " All the characters occurring on a line after a comment are discarded\n", " delimiter : str, int, or sequence, optional\n", " The string used to separate values. By default, any consecutive\n", " whitespaces act as delimiter. An integer or sequence of integers\n", " can also be provided as width(s) of each field.\n", " skip_rows : int, optional\n", " `skip_rows` was deprecated in numpy 1.5, and will be removed in\n", " numpy 2.0. Please use `skip_header` instead.\n", " skip_header : int, optional\n", " The number of lines to skip at the beginning of the file.\n", " skip_footer : int, optional\n", " The number of lines to skip at the end of the file.\n", " converters : variable, optional\n", " The set of functions that convert the data of a column to a value.\n", " The converters can also be used to provide a default value\n", " for missing data: ``converters = {3: lambda s: float(s or 0)}``.\n", " missing : variable, optional\n", " `missing` was deprecated in numpy 1.5, and will be removed in\n", " numpy 2.0. Please use `missing_values` instead.\n", " missing_values : variable, optional\n", " The set of strings corresponding to missing data.\n", " filling_values : variable, optional\n", " The set of values to be used as default when the data are missing.\n", " usecols : sequence, optional\n", " Which columns to read, with 0 being the first. For example,\n", " ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.\n", " names : {None, True, str, sequence}, optional\n", " If `names` is True, the field names are read from the first valid line\n", " after the first `skip_header` lines.\n", " If `names` is a sequence or a single-string of comma-separated names,\n", " the names will be used to define the field names in a structured dtype.\n", " If `names` is None, the names of the dtype fields will be used, if any.\n", " excludelist : sequence, optional\n", " A list of names to exclude. This list is appended to the default list\n", " ['return','file','print']. Excluded names are appended an underscore:\n", " for example, `file` would become `file_`.\n", " deletechars : str, optional\n", " A string combining invalid characters that must be deleted from the\n", " names.\n", " defaultfmt : str, optional\n", " A format used to define default field names, such as \"f%i\" or \"f_%02i\".\n", " autostrip : bool, optional\n", " Whether to automatically strip white spaces from the variables.\n", " replace_space : char, optional\n", " Character(s) used in replacement of white spaces in the variables\n", " names. By default, use a '_'.\n", " case_sensitive : {True, False, 'upper', 'lower'}, optional\n", " If True, field names are case sensitive.\n", " If False or 'upper', field names are converted to upper case.\n", " If 'lower', field names are converted to lower case.\n", " unpack : bool, optional\n", " If True, the returned array is transposed, so that arguments may be\n", " unpacked using ``x, y, z = loadtxt(...)``\n", " usemask : bool, optional\n", " If True, return a masked array.\n", " If False, return a regular array.\n", " loose : bool, optional\n", " If True, do not raise errors for invalid values.\n", " invalid_raise : bool, optional\n", " If True, an exception is raised if an inconsistency is detected in the\n", " number of columns.\n", " If False, a warning is emitted and the offending lines are skipped.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Data read from the text file. If `usemask` is True, this is a\n", " masked array.\n", " \n", " See Also\n", " --------\n", " numpy.loadtxt : equivalent function when no data is missing.\n", " \n", " Notes\n", " -----\n", " * When spaces are used as delimiters, or when no delimiter has been given\n", " as input, there should not be any missing data between two fields.\n", " * When the variables are named (either by a flexible dtype or with `names`,\n", " there must not be any header in the file (else a ValueError\n", " exception is raised).\n", " * Individual values are not stripped of spaces by default.\n", " When using a custom converter, make sure the function does remove spaces.\n", " \n", " References\n", " ----------\n", " .. [1] Numpy User Guide, section `I/O with Numpy\n", " `_.\n", " \n", " Examples\n", " ---------\n", " >>> from StringIO import StringIO\n", " >>> import numpy as np\n", " \n", " Comma delimited file with mixed dtype\n", " \n", " >>> s = StringIO(\"1,1.3,abcde\")\n", " >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),\n", " ... ('mystring','S5')], delimiter=\",\")\n", " >>> data\n", " array((1, 1.3, 'abcde'),\n", " dtype=[('myint', '>> s.seek(0) # needed for StringIO example only\n", " >>> data = np.genfromtxt(s, dtype=None,\n", " ... names = ['myint','myfloat','mystring'], delimiter=\",\")\n", " >>> data\n", " array((1, 1.3, 'abcde'),\n", " dtype=[('myint', '>> s.seek(0)\n", " >>> data = np.genfromtxt(s, dtype=\"i8,f8,S5\",\n", " ... names=['myint','myfloat','mystring'], delimiter=\",\")\n", " >>> data\n", " array((1, 1.3, 'abcde'),\n", " dtype=[('myint', '>> s = StringIO(\"11.3abcde\")\n", " >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],\n", " ... delimiter=[1,3,5])\n", " >>> data\n", " array((1, 1.3, 'abcde'),\n", " dtype=[('intvar', '>> buf = np.getbuffer(np.ones(5), 1, 3)\n", " >>> len(buf)\n", " 3\n", " >>> buf[0]\n", " '\\x00'\n", " >>> buf\n", " \n", " \n", " getbufsize()\n", " Return the size of the buffer used in ufuncs.\n", " \n", " Returns\n", " -------\n", " getbufsize : int\n", " Size of ufunc buffer in bytes.\n", " \n", " geterr()\n", " Get the current way of handling floating-point errors.\n", " \n", " Returns\n", " -------\n", " res : dict\n", " A dictionary with keys \"divide\", \"over\", \"under\", and \"invalid\",\n", " whose values are from the strings \"ignore\", \"print\", \"log\", \"warn\",\n", " \"raise\", and \"call\". The keys represent possible floating-point\n", " exceptions, and the values define how these exceptions are handled.\n", " \n", " See Also\n", " --------\n", " geterrcall, seterr, seterrcall\n", " \n", " Notes\n", " -----\n", " For complete documentation of the types of floating-point exceptions and\n", " treatment options, see `seterr`.\n", " \n", " Examples\n", " --------\n", " >>> np.geterr()\n", " {'over': 'warn', 'divide': 'warn', 'invalid': 'warn',\n", " 'under': 'ignore'}\n", " >>> np.arange(3.) / np.arange(3.)\n", " array([ NaN, 1., 1.])\n", " \n", " >>> oldsettings = np.seterr(all='warn', over='raise')\n", " >>> np.geterr()\n", " {'over': 'raise', 'divide': 'warn', 'invalid': 'warn', 'under': 'warn'}\n", " >>> np.arange(3.) / np.arange(3.)\n", " __main__:1: RuntimeWarning: invalid value encountered in divide\n", " array([ NaN, 1., 1.])\n", " \n", " geterrcall()\n", " Return the current callback function used on floating-point errors.\n", " \n", " When the error handling for a floating-point error (one of \"divide\",\n", " \"over\", \"under\", or \"invalid\") is set to 'call' or 'log', the function\n", " that is called or the log instance that is written to is returned by\n", " `geterrcall`. This function or log instance has been set with\n", " `seterrcall`.\n", " \n", " Returns\n", " -------\n", " errobj : callable, log instance or None\n", " The current error handler. If no handler was set through `seterrcall`,\n", " ``None`` is returned.\n", " \n", " See Also\n", " --------\n", " seterrcall, seterr, geterr\n", " \n", " Notes\n", " -----\n", " For complete documentation of the types of floating-point exceptions and\n", " treatment options, see `seterr`.\n", " \n", " Examples\n", " --------\n", " >>> np.geterrcall() # we did not yet set a handler, returns None\n", " \n", " >>> oldsettings = np.seterr(all='call')\n", " >>> def err_handler(type, flag):\n", " ... print \"Floating point error (%s), with flag %s\" % (type, flag)\n", " >>> oldhandler = np.seterrcall(err_handler)\n", " >>> np.array([1, 2, 3]) / 0.0\n", " Floating point error (divide by zero), with flag 1\n", " array([ Inf, Inf, Inf])\n", " \n", " >>> cur_handler = np.geterrcall()\n", " >>> cur_handler is err_handler\n", " True\n", " \n", " geterrobj(...)\n", " geterrobj()\n", " \n", " Return the current object that defines floating-point error handling.\n", " \n", " The error object contains all information that defines the error handling\n", " behavior in Numpy. `geterrobj` is used internally by the other\n", " functions that get and set error handling behavior (`geterr`, `seterr`,\n", " `geterrcall`, `seterrcall`).\n", " \n", " Returns\n", " -------\n", " errobj : list\n", " The error object, a list containing three elements:\n", " [internal numpy buffer size, error mask, error callback function].\n", " \n", " The error mask is a single integer that holds the treatment information\n", " on all four floating point errors. The information for each error type\n", " is contained in three bits of the integer. If we print it in base 8, we\n", " can see what treatment is set for \"invalid\", \"under\", \"over\", and\n", " \"divide\" (in that order). The printed string can be interpreted with\n", " \n", " * 0 : 'ignore'\n", " * 1 : 'warn'\n", " * 2 : 'raise'\n", " * 3 : 'call'\n", " * 4 : 'print'\n", " * 5 : 'log'\n", " \n", " See Also\n", " --------\n", " seterrobj, seterr, geterr, seterrcall, geterrcall\n", " getbufsize, setbufsize\n", " \n", " Notes\n", " -----\n", " For complete documentation of the types of floating-point exceptions and\n", " treatment options, see `seterr`.\n", " \n", " Examples\n", " --------\n", " >>> np.geterrobj() # first get the defaults\n", " [10000, 0, None]\n", " \n", " >>> def err_handler(type, flag):\n", " ... print \"Floating point error (%s), with flag %s\" % (type, flag)\n", " ...\n", " >>> old_bufsize = np.setbufsize(20000)\n", " >>> old_err = np.seterr(divide='raise')\n", " >>> old_handler = np.seterrcall(err_handler)\n", " >>> np.geterrobj()\n", " [20000, 2, ]\n", " \n", " >>> old_err = np.seterr(all='ignore')\n", " >>> np.base_repr(np.geterrobj()[1], 8)\n", " '0'\n", " >>> old_err = np.seterr(divide='warn', over='log', under='call',\n", " invalid='print')\n", " >>> np.base_repr(np.geterrobj()[1], 8)\n", " '4351'\n", " \n", " gradient(f, *varargs, **kwargs)\n", " Return the gradient of an N-dimensional array.\n", " \n", " The gradient is computed using second order accurate central differences\n", " in the interior and either first differences or second order accurate \n", " one-sides (forward or backwards) differences at the boundaries. The\n", " returned gradient hence has the same shape as the input array.\n", " \n", " Parameters\n", " ----------\n", " f : array_like\n", " An N-dimensional array containing samples of a scalar function.\n", " varargs : list of scalar, optional\n", " N scalars specifying the sample distances for each dimension,\n", " i.e. `dx`, `dy`, `dz`, ... Default distance: 1.\n", " edge_order : {1, 2}, optional\n", " Gradient is calculated using N\\ :sup:`th` order accurate differences\n", " at the boundaries. Default: 1.\n", " \n", " .. versionadded:: 1.9.1\n", " \n", " Returns\n", " -------\n", " gradient : ndarray\n", " N arrays of the same shape as `f` giving the derivative of `f` with\n", " respect to each dimension.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)\n", " >>> np.gradient(x)\n", " array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ])\n", " >>> np.gradient(x, 2)\n", " array([ 0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ])\n", " \n", " >>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))\n", " [array([[ 2., 2., -1.],\n", " [ 2., 2., -1.]]), array([[ 1. , 2.5, 4. ],\n", " [ 1. , 1. , 1. ]])]\n", " \n", " >>> x = np.array([0, 1, 2, 3, 4])\n", " >>> dx = np.gradient(x)\n", " >>> y = x**2\n", " >>> np.gradient(y, dx, edge_order=2)\n", " array([-0., 2., 4., 6., 8.])\n", " \n", " hamming(M)\n", " Return the Hamming window.\n", " \n", " The Hamming window is a taper formed by using a weighted cosine.\n", " \n", " Parameters\n", " ----------\n", " M : int\n", " Number of points in the output window. If zero or less, an\n", " empty array is returned.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The window, with the maximum value normalized to one (the value\n", " one appears only if the number of samples is odd).\n", " \n", " See Also\n", " --------\n", " bartlett, blackman, hanning, kaiser\n", " \n", " Notes\n", " -----\n", " The Hamming window is defined as\n", " \n", " .. math:: w(n) = 0.54 - 0.46cos\\left(\\frac{2\\pi{n}}{M-1}\\right)\n", " \\qquad 0 \\leq n \\leq M-1\n", " \n", " The Hamming was named for R. W. Hamming, an associate of J. W. Tukey\n", " and is described in Blackman and Tukey. It was recommended for\n", " smoothing the truncated autocovariance function in the time domain.\n", " Most references to the Hamming window come from the signal processing\n", " literature, where it is used as one of many windowing functions for\n", " smoothing values. It is also known as an apodization (which means\n", " \"removing the foot\", i.e. smoothing discontinuities at the beginning\n", " and end of the sampled signal) or tapering function.\n", " \n", " References\n", " ----------\n", " .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power\n", " spectra, Dover Publications, New York.\n", " .. [2] E.R. Kanasewich, \"Time Sequence Analysis in Geophysics\", The\n", " University of Alberta Press, 1975, pp. 109-110.\n", " .. [3] Wikipedia, \"Window function\",\n", " http://en.wikipedia.org/wiki/Window_function\n", " .. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,\n", " \"Numerical Recipes\", Cambridge University Press, 1986, page 425.\n", " \n", " Examples\n", " --------\n", " >>> np.hamming(12)\n", " array([ 0.08 , 0.15302337, 0.34890909, 0.60546483, 0.84123594,\n", " 0.98136677, 0.98136677, 0.84123594, 0.60546483, 0.34890909,\n", " 0.15302337, 0.08 ])\n", " \n", " Plot the window and the frequency response:\n", " \n", " >>> from numpy.fft import fft, fftshift\n", " >>> window = np.hamming(51)\n", " >>> plt.plot(window)\n", " []\n", " >>> plt.title(\"Hamming window\")\n", " \n", " >>> plt.ylabel(\"Amplitude\")\n", " \n", " >>> plt.xlabel(\"Sample\")\n", " \n", " >>> plt.show()\n", " \n", " >>> plt.figure()\n", " \n", " >>> A = fft(window, 2048) / 25.5\n", " >>> mag = np.abs(fftshift(A))\n", " >>> freq = np.linspace(-0.5, 0.5, len(A))\n", " >>> response = 20 * np.log10(mag)\n", " >>> response = np.clip(response, -100, 100)\n", " >>> plt.plot(freq, response)\n", " []\n", " >>> plt.title(\"Frequency response of Hamming window\")\n", " \n", " >>> plt.ylabel(\"Magnitude [dB]\")\n", " \n", " >>> plt.xlabel(\"Normalized frequency [cycles per sample]\")\n", " \n", " >>> plt.axis('tight')\n", " (-0.5, 0.5, -100.0, ...)\n", " >>> plt.show()\n", " \n", " hanning(M)\n", " Return the Hanning window.\n", " \n", " The Hanning window is a taper formed by using a weighted cosine.\n", " \n", " Parameters\n", " ----------\n", " M : int\n", " Number of points in the output window. If zero or less, an\n", " empty array is returned.\n", " \n", " Returns\n", " -------\n", " out : ndarray, shape(M,)\n", " The window, with the maximum value normalized to one (the value\n", " one appears only if `M` is odd).\n", " \n", " See Also\n", " --------\n", " bartlett, blackman, hamming, kaiser\n", " \n", " Notes\n", " -----\n", " The Hanning window is defined as\n", " \n", " .. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right)\n", " \\qquad 0 \\leq n \\leq M-1\n", " \n", " The Hanning was named for Julius van Hann, an Austrian meteorologist.\n", " It is also known as the Cosine Bell. Some authors prefer that it be\n", " called a Hann window, to help avoid confusion with the very similar\n", " Hamming window.\n", " \n", " Most references to the Hanning window come from the signal processing\n", " literature, where it is used as one of many windowing functions for\n", " smoothing values. It is also known as an apodization (which means\n", " \"removing the foot\", i.e. smoothing discontinuities at the beginning\n", " and end of the sampled signal) or tapering function.\n", " \n", " References\n", " ----------\n", " .. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power\n", " spectra, Dover Publications, New York.\n", " .. [2] E.R. Kanasewich, \"Time Sequence Analysis in Geophysics\",\n", " The University of Alberta Press, 1975, pp. 106-108.\n", " .. [3] Wikipedia, \"Window function\",\n", " http://en.wikipedia.org/wiki/Window_function\n", " .. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,\n", " \"Numerical Recipes\", Cambridge University Press, 1986, page 425.\n", " \n", " Examples\n", " --------\n", " >>> np.hanning(12)\n", " array([ 0. , 0.07937323, 0.29229249, 0.57115742, 0.82743037,\n", " 0.97974649, 0.97974649, 0.82743037, 0.57115742, 0.29229249,\n", " 0.07937323, 0. ])\n", " \n", " Plot the window and its frequency response:\n", " \n", " >>> from numpy.fft import fft, fftshift\n", " >>> window = np.hanning(51)\n", " >>> plt.plot(window)\n", " []\n", " >>> plt.title(\"Hann window\")\n", " \n", " >>> plt.ylabel(\"Amplitude\")\n", " \n", " >>> plt.xlabel(\"Sample\")\n", " \n", " >>> plt.show()\n", " \n", " >>> plt.figure()\n", " \n", " >>> A = fft(window, 2048) / 25.5\n", " >>> mag = np.abs(fftshift(A))\n", " >>> freq = np.linspace(-0.5, 0.5, len(A))\n", " >>> response = 20 * np.log10(mag)\n", " >>> response = np.clip(response, -100, 100)\n", " >>> plt.plot(freq, response)\n", " []\n", " >>> plt.title(\"Frequency response of the Hann window\")\n", " \n", " >>> plt.ylabel(\"Magnitude [dB]\")\n", " \n", " >>> plt.xlabel(\"Normalized frequency [cycles per sample]\")\n", " \n", " >>> plt.axis('tight')\n", " (-0.5, 0.5, -100.0, ...)\n", " >>> plt.show()\n", " \n", " histogram(a, bins=10, range=None, normed=False, weights=None, density=None)\n", " Compute the histogram of a set of data.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data. The histogram is computed over the flattened array.\n", " bins : int or sequence of scalars, optional\n", " If `bins` is an int, it defines the number of equal-width\n", " bins in the given range (10, by default). If `bins` is a sequence,\n", " it defines the bin edges, including the rightmost edge, allowing\n", " for non-uniform bin widths.\n", " range : (float, float), optional\n", " The lower and upper range of the bins. If not provided, range\n", " is simply ``(a.min(), a.max())``. Values outside the range are\n", " ignored.\n", " normed : bool, optional\n", " This keyword is deprecated in Numpy 1.6 due to confusing/buggy\n", " behavior. It will be removed in Numpy 2.0. Use the density keyword\n", " instead.\n", " If False, the result will contain the number of samples\n", " in each bin. If True, the result is the value of the\n", " probability *density* function at the bin, normalized such that\n", " the *integral* over the range is 1. Note that this latter behavior is\n", " known to be buggy with unequal bin widths; use `density` instead.\n", " weights : array_like, optional\n", " An array of weights, of the same shape as `a`. Each value in `a`\n", " only contributes its associated weight towards the bin count\n", " (instead of 1). If `normed` is True, the weights are normalized,\n", " so that the integral of the density over the range remains 1\n", " density : bool, optional\n", " If False, the result will contain the number of samples\n", " in each bin. If True, the result is the value of the\n", " probability *density* function at the bin, normalized such that\n", " the *integral* over the range is 1. Note that the sum of the\n", " histogram values will not be equal to 1 unless bins of unity\n", " width are chosen; it is not a probability *mass* function.\n", " Overrides the `normed` keyword if given.\n", " \n", " Returns\n", " -------\n", " hist : array\n", " The values of the histogram. See `normed` and `weights` for a\n", " description of the possible semantics.\n", " bin_edges : array of dtype float\n", " Return the bin edges ``(length(hist)+1)``.\n", " \n", " \n", " See Also\n", " --------\n", " histogramdd, bincount, searchsorted, digitize\n", " \n", " Notes\n", " -----\n", " All but the last (righthand-most) bin is half-open. In other words, if\n", " `bins` is::\n", " \n", " [1, 2, 3, 4]\n", " \n", " then the first bin is ``[1, 2)`` (including 1, but excluding 2) and the\n", " second ``[2, 3)``. The last bin, however, is ``[3, 4]``, which *includes*\n", " 4.\n", " \n", " Examples\n", " --------\n", " >>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])\n", " (array([0, 2, 1]), array([0, 1, 2, 3]))\n", " >>> np.histogram(np.arange(4), bins=np.arange(5), density=True)\n", " (array([ 0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))\n", " >>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])\n", " (array([1, 4, 1]), array([0, 1, 2, 3]))\n", " \n", " >>> a = np.arange(5)\n", " >>> hist, bin_edges = np.histogram(a, density=True)\n", " >>> hist\n", " array([ 0.5, 0. , 0.5, 0. , 0. , 0.5, 0. , 0.5, 0. , 0.5])\n", " >>> hist.sum()\n", " 2.4999999999999996\n", " >>> np.sum(hist*np.diff(bin_edges))\n", " 1.0\n", " \n", " histogram2d(x, y, bins=10, range=None, normed=False, weights=None)\n", " Compute the bi-dimensional histogram of two data samples.\n", " \n", " Parameters\n", " ----------\n", " x : array_like, shape (N,)\n", " An array containing the x coordinates of the points to be\n", " histogrammed.\n", " y : array_like, shape (N,)\n", " An array containing the y coordinates of the points to be\n", " histogrammed.\n", " bins : int or [int, int] or array_like or [array, array], optional\n", " The bin specification:\n", " \n", " * If int, the number of bins for the two dimensions (nx=ny=bins).\n", " * If [int, int], the number of bins in each dimension\n", " (nx, ny = bins).\n", " * If array_like, the bin edges for the two dimensions\n", " (x_edges=y_edges=bins).\n", " * If [array, array], the bin edges in each dimension\n", " (x_edges, y_edges = bins).\n", " \n", " range : array_like, shape(2,2), optional\n", " The leftmost and rightmost edges of the bins along each dimension\n", " (if not specified explicitly in the `bins` parameters):\n", " ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range\n", " will be considered outliers and not tallied in the histogram.\n", " normed : bool, optional\n", " If False, returns the number of samples in each bin. If True,\n", " returns the bin density ``bin_count / sample_count / bin_area``.\n", " weights : array_like, shape(N,), optional\n", " An array of values ``w_i`` weighing each sample ``(x_i, y_i)``.\n", " Weights are normalized to 1 if `normed` is True. If `normed` is\n", " False, the values of the returned histogram are equal to the sum of\n", " the weights belonging to the samples falling into each bin.\n", " \n", " Returns\n", " -------\n", " H : ndarray, shape(nx, ny)\n", " The bi-dimensional histogram of samples `x` and `y`. Values in `x`\n", " are histogrammed along the first dimension and values in `y` are\n", " histogrammed along the second dimension.\n", " xedges : ndarray, shape(nx,)\n", " The bin edges along the first dimension.\n", " yedges : ndarray, shape(ny,)\n", " The bin edges along the second dimension.\n", " \n", " See Also\n", " --------\n", " histogram : 1D histogram\n", " histogramdd : Multidimensional histogram\n", " \n", " Notes\n", " -----\n", " When `normed` is True, then the returned histogram is the sample\n", " density, defined such that the sum over bins of the product\n", " ``bin_value * bin_area`` is 1.\n", " \n", " Please note that the histogram does not follow the Cartesian convention\n", " where `x` values are on the abscissa and `y` values on the ordinate\n", " axis. Rather, `x` is histogrammed along the first dimension of the\n", " array (vertical), and `y` along the second dimension of the array\n", " (horizontal). This ensures compatibility with `histogramdd`.\n", " \n", " Examples\n", " --------\n", " >>> import matplotlib as mpl\n", " >>> import matplotlib.pyplot as plt\n", " \n", " Construct a 2D-histogram with variable bin width. First define the bin\n", " edges:\n", " \n", " >>> xedges = [0, 1, 1.5, 3, 5]\n", " >>> yedges = [0, 2, 3, 4, 6]\n", " \n", " Next we create a histogram H with random bin content:\n", " \n", " >>> x = np.random.normal(3, 1, 100)\n", " >>> y = np.random.normal(1, 1, 100)\n", " >>> H, xedges, yedges = np.histogram2d(y, x, bins=(xedges, yedges))\n", " \n", " Or we fill the histogram H with a determined bin content:\n", " \n", " >>> H = np.ones((4, 4)).cumsum().reshape(4, 4)\n", " >>> print H[::-1] # This shows the bin content in the order as plotted\n", " [[ 13. 14. 15. 16.]\n", " [ 9. 10. 11. 12.]\n", " [ 5. 6. 7. 8.]\n", " [ 1. 2. 3. 4.]]\n", " \n", " Imshow can only do an equidistant representation of bins:\n", " \n", " >>> fig = plt.figure(figsize=(7, 3))\n", " >>> ax = fig.add_subplot(131)\n", " >>> ax.set_title('imshow: equidistant')\n", " >>> im = plt.imshow(H, interpolation='nearest', origin='low',\n", " extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])\n", " \n", " pcolormesh can display exact bin edges:\n", " \n", " >>> ax = fig.add_subplot(132)\n", " >>> ax.set_title('pcolormesh: exact bin edges')\n", " >>> X, Y = np.meshgrid(xedges, yedges)\n", " >>> ax.pcolormesh(X, Y, H)\n", " >>> ax.set_aspect('equal')\n", " \n", " NonUniformImage displays exact bin edges with interpolation:\n", " \n", " >>> ax = fig.add_subplot(133)\n", " >>> ax.set_title('NonUniformImage: interpolated')\n", " >>> im = mpl.image.NonUniformImage(ax, interpolation='bilinear')\n", " >>> xcenters = xedges[:-1] + 0.5 * (xedges[1:] - xedges[:-1])\n", " >>> ycenters = yedges[:-1] + 0.5 * (yedges[1:] - yedges[:-1])\n", " >>> im.set_data(xcenters, ycenters, H)\n", " >>> ax.images.append(im)\n", " >>> ax.set_xlim(xedges[0], xedges[-1])\n", " >>> ax.set_ylim(yedges[0], yedges[-1])\n", " >>> ax.set_aspect('equal')\n", " >>> plt.show()\n", " \n", " histogramdd(sample, bins=10, range=None, normed=False, weights=None)\n", " Compute the multidimensional histogram of some data.\n", " \n", " Parameters\n", " ----------\n", " sample : array_like\n", " The data to be histogrammed. It must be an (N,D) array or data\n", " that can be converted to such. The rows of the resulting array\n", " are the coordinates of points in a D dimensional polytope.\n", " bins : sequence or int, optional\n", " The bin specification:\n", " \n", " * A sequence of arrays describing the bin edges along each dimension.\n", " * The number of bins for each dimension (nx, ny, ... =bins)\n", " * The number of bins for all dimensions (nx=ny=...=bins).\n", " \n", " range : sequence, optional\n", " A sequence of lower and upper bin edges to be used if the edges are\n", " not given explicitly in `bins`. Defaults to the minimum and maximum\n", " values along each dimension.\n", " normed : bool, optional\n", " If False, returns the number of samples in each bin. If True,\n", " returns the bin density ``bin_count / sample_count / bin_volume``.\n", " weights : array_like (N,), optional\n", " An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.\n", " Weights are normalized to 1 if normed is True. If normed is False,\n", " the values of the returned histogram are equal to the sum of the\n", " weights belonging to the samples falling into each bin.\n", " \n", " Returns\n", " -------\n", " H : ndarray\n", " The multidimensional histogram of sample x. See normed and weights\n", " for the different possible semantics.\n", " edges : list\n", " A list of D arrays describing the bin edges for each dimension.\n", " \n", " See Also\n", " --------\n", " histogram: 1-D histogram\n", " histogram2d: 2-D histogram\n", " \n", " Examples\n", " --------\n", " >>> r = np.random.randn(100,3)\n", " >>> H, edges = np.histogramdd(r, bins = (5, 8, 4))\n", " >>> H.shape, edges[0].size, edges[1].size, edges[2].size\n", " ((5, 8, 4), 6, 9, 5)\n", " \n", " hsplit(ary, indices_or_sections)\n", " Split an array into multiple sub-arrays horizontally (column-wise).\n", " \n", " Please refer to the `split` documentation. `hsplit` is equivalent\n", " to `split` with ``axis=1``, the array is always split along the second\n", " axis regardless of the array dimension.\n", " \n", " See Also\n", " --------\n", " split : Split an array into multiple sub-arrays of equal size.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(16.0).reshape(4, 4)\n", " >>> x\n", " array([[ 0., 1., 2., 3.],\n", " [ 4., 5., 6., 7.],\n", " [ 8., 9., 10., 11.],\n", " [ 12., 13., 14., 15.]])\n", " >>> np.hsplit(x, 2)\n", " [array([[ 0., 1.],\n", " [ 4., 5.],\n", " [ 8., 9.],\n", " [ 12., 13.]]),\n", " array([[ 2., 3.],\n", " [ 6., 7.],\n", " [ 10., 11.],\n", " [ 14., 15.]])]\n", " >>> np.hsplit(x, np.array([3, 6]))\n", " [array([[ 0., 1., 2.],\n", " [ 4., 5., 6.],\n", " [ 8., 9., 10.],\n", " [ 12., 13., 14.]]),\n", " array([[ 3.],\n", " [ 7.],\n", " [ 11.],\n", " [ 15.]]),\n", " array([], dtype=float64)]\n", " \n", " With a higher dimensional array the split is still along the second axis.\n", " \n", " >>> x = np.arange(8.0).reshape(2, 2, 2)\n", " >>> x\n", " array([[[ 0., 1.],\n", " [ 2., 3.]],\n", " [[ 4., 5.],\n", " [ 6., 7.]]])\n", " >>> np.hsplit(x, 2)\n", " [array([[[ 0., 1.]],\n", " [[ 4., 5.]]]),\n", " array([[[ 2., 3.]],\n", " [[ 6., 7.]]])]\n", " \n", " hstack(tup)\n", " Stack arrays in sequence horizontally (column wise).\n", " \n", " Take a sequence of arrays and stack them horizontally to make\n", " a single array. Rebuild arrays divided by `hsplit`.\n", " \n", " Parameters\n", " ----------\n", " tup : sequence of ndarrays\n", " All arrays must have the same shape along all but the second axis.\n", " \n", " Returns\n", " -------\n", " stacked : ndarray\n", " The array formed by stacking the given arrays.\n", " \n", " See Also\n", " --------\n", " vstack : Stack arrays in sequence vertically (row wise).\n", " dstack : Stack arrays in sequence depth wise (along third axis).\n", " concatenate : Join a sequence of arrays together.\n", " hsplit : Split array along second axis.\n", " \n", " Notes\n", " -----\n", " Equivalent to ``np.concatenate(tup, axis=1)``\n", " \n", " Examples\n", " --------\n", " >>> a = np.array((1,2,3))\n", " >>> b = np.array((2,3,4))\n", " >>> np.hstack((a,b))\n", " array([1, 2, 3, 2, 3, 4])\n", " >>> a = np.array([[1],[2],[3]])\n", " >>> b = np.array([[2],[3],[4]])\n", " >>> np.hstack((a,b))\n", " array([[1, 2],\n", " [2, 3],\n", " [3, 4]])\n", " \n", " i0(x)\n", " Modified Bessel function of the first kind, order 0.\n", " \n", " Usually denoted :math:`I_0`. This function does broadcast, but will *not*\n", " \"up-cast\" int dtype arguments unless accompanied by at least one float or\n", " complex dtype argument (see Raises below).\n", " \n", " Parameters\n", " ----------\n", " x : array_like, dtype float or complex\n", " Argument of the Bessel function.\n", " \n", " Returns\n", " -------\n", " out : ndarray, shape = x.shape, dtype = x.dtype\n", " The modified Bessel function evaluated at each of the elements of `x`.\n", " \n", " Raises\n", " ------\n", " TypeError: array cannot be safely cast to required type\n", " If argument consists exclusively of int dtypes.\n", " \n", " See Also\n", " --------\n", " scipy.special.iv, scipy.special.ive\n", " \n", " Notes\n", " -----\n", " We use the algorithm published by Clenshaw [1]_ and referenced by\n", " Abramowitz and Stegun [2]_, for which the function domain is\n", " partitioned into the two intervals [0,8] and (8,inf), and Chebyshev\n", " polynomial expansions are employed in each interval. Relative error on\n", " the domain [0,30] using IEEE arithmetic is documented [3]_ as having a\n", " peak of 5.8e-16 with an rms of 1.4e-16 (n = 30000).\n", " \n", " References\n", " ----------\n", " .. [1] C. W. Clenshaw, \"Chebyshev series for mathematical functions\", in\n", " *National Physical Laboratory Mathematical Tables*, vol. 5, London:\n", " Her Majesty's Stationery Office, 1962.\n", " .. [2] M. Abramowitz and I. A. Stegun, *Handbook of Mathematical\n", " Functions*, 10th printing, New York: Dover, 1964, pp. 379.\n", " http://www.math.sfu.ca/~cbm/aands/page_379.htm\n", " .. [3] http://kobesearch.cpan.org/htdocs/Math-Cephes/Math/Cephes.html\n", " \n", " Examples\n", " --------\n", " >>> np.i0([0.])\n", " array(1.0)\n", " >>> np.i0([0., 1. + 2j])\n", " array([ 1.00000000+0.j , 0.18785373+0.64616944j])\n", " \n", " identity(n, dtype=None)\n", " Return the identity array.\n", " \n", " The identity array is a square array with ones on\n", " the main diagonal.\n", " \n", " Parameters\n", " ----------\n", " n : int\n", " Number of rows (and columns) in `n` x `n` output.\n", " dtype : data-type, optional\n", " Data-type of the output. Defaults to ``float``.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " `n` x `n` array with its main diagonal set to one,\n", " and all other elements 0.\n", " \n", " Examples\n", " --------\n", " >>> np.identity(3)\n", " array([[ 1., 0., 0.],\n", " [ 0., 1., 0.],\n", " [ 0., 0., 1.]])\n", " \n", " ifft(a, n=None, axis=-1)\n", " Compute the one-dimensional inverse discrete Fourier Transform.\n", " \n", " This function computes the inverse of the one-dimensional *n*-point\n", " discrete Fourier transform computed by `fft`. In other words,\n", " ``ifft(fft(a)) == a`` to within numerical accuracy.\n", " For a general description of the algorithm and definitions,\n", " see `numpy.fft`.\n", " \n", " The input should be ordered in the same way as is returned by `fft`,\n", " i.e., ``a[0]`` should contain the zero frequency term,\n", " ``a[1:n/2+1]`` should contain the positive-frequency terms, and\n", " ``a[n/2+1:]`` should contain the negative-frequency terms, in order of\n", " decreasingly negative frequency. See `numpy.fft` for details.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array, can be complex.\n", " n : int, optional\n", " Length of the transformed axis of the output.\n", " If `n` is smaller than the length of the input, the input is cropped.\n", " If it is larger, the input is padded with zeros. If `n` is not given,\n", " the length of the input along the axis specified by `axis` is used.\n", " See notes about padding issues.\n", " axis : int, optional\n", " Axis over which to compute the inverse DFT. If not given, the last\n", " axis is used.\n", " \n", " Returns\n", " -------\n", " out : complex ndarray\n", " The truncated or zero-padded input, transformed along the axis\n", " indicated by `axis`, or the last one if `axis` is not specified.\n", " \n", " Raises\n", " ------\n", " IndexError\n", " If `axes` is larger than the last axis of `a`.\n", " \n", " See Also\n", " --------\n", " numpy.fft : An introduction, with definitions and general explanations.\n", " fft : The one-dimensional (forward) FFT, of which `ifft` is the inverse\n", " ifft2 : The two-dimensional inverse FFT.\n", " ifftn : The n-dimensional inverse FFT.\n", " \n", " Notes\n", " -----\n", " If the input parameter `n` is larger than the size of the input, the input\n", " is padded by appending zeros at the end. Even though this is the common\n", " approach, it might lead to surprising results. If a different padding is\n", " desired, it must be performed before calling `ifft`.\n", " \n", " Examples\n", " --------\n", " >>> np.fft.ifft([0, 4, 0, 0])\n", " array([ 1.+0.j, 0.+1.j, -1.+0.j, 0.-1.j])\n", " \n", " Create and plot a band-limited signal with random phases:\n", " \n", " >>> import matplotlib.pyplot as plt\n", " >>> t = np.arange(400)\n", " >>> n = np.zeros((400,), dtype=complex)\n", " >>> n[40:60] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20,)))\n", " >>> s = np.fft.ifft(n)\n", " >>> plt.plot(t, s.real, 'b-', t, s.imag, 'r--')\n", " [, ]\n", " >>> plt.legend(('real', 'imaginary'))\n", " \n", " >>> plt.show()\n", " \n", " imag(val)\n", " Return the imaginary part of the elements of the array.\n", " \n", " Parameters\n", " ----------\n", " val : array_like\n", " Input array.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Output array. If `val` is real, the type of `val` is used for the\n", " output. If `val` has complex elements, the returned type is float.\n", " \n", " See Also\n", " --------\n", " real, angle, real_if_close\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([1+2j, 3+4j, 5+6j])\n", " >>> a.imag\n", " array([ 2., 4., 6.])\n", " >>> a.imag = np.array([8, 10, 12])\n", " >>> a\n", " array([ 1. +8.j, 3.+10.j, 5.+12.j])\n", " \n", " in1d(ar1, ar2, assume_unique=False, invert=False)\n", " Test whether each element of a 1-D array is also present in a second array.\n", " \n", " Returns a boolean array the same length as `ar1` that is True\n", " where an element of `ar1` is in `ar2` and False otherwise.\n", " \n", " Parameters\n", " ----------\n", " ar1 : (M,) array_like\n", " Input array.\n", " ar2 : array_like\n", " The values against which to test each value of `ar1`.\n", " assume_unique : bool, optional\n", " If True, the input arrays are both assumed to be unique, which\n", " can speed up the calculation. Default is False.\n", " invert : bool, optional\n", " If True, the values in the returned array are inverted (that is,\n", " False where an element of `ar1` is in `ar2` and True otherwise).\n", " Default is False. ``np.in1d(a, b, invert=True)`` is equivalent\n", " to (but is faster than) ``np.invert(in1d(a, b))``.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Returns\n", " -------\n", " in1d : (M,) ndarray, bool\n", " The values `ar1[in1d]` are in `ar2`.\n", " \n", " See Also\n", " --------\n", " numpy.lib.arraysetops : Module with a number of other functions for\n", " performing set operations on arrays.\n", " \n", " Notes\n", " -----\n", " `in1d` can be considered as an element-wise function version of the\n", " python keyword `in`, for 1-D sequences. ``in1d(a, b)`` is roughly\n", " equivalent to ``np.array([item in b for item in a])``.\n", " \n", " .. versionadded:: 1.4.0\n", " \n", " Examples\n", " --------\n", " >>> test = np.array([0, 1, 2, 5, 0])\n", " >>> states = [0, 2]\n", " >>> mask = np.in1d(test, states)\n", " >>> mask\n", " array([ True, False, True, False, True], dtype=bool)\n", " >>> test[mask]\n", " array([0, 2, 0])\n", " >>> mask = np.in1d(test, states, invert=True)\n", " >>> mask\n", " array([False, True, False, True, False], dtype=bool)\n", " >>> test[mask]\n", " array([1, 5])\n", " \n", " indices(dimensions, dtype=)\n", " Return an array representing the indices of a grid.\n", " \n", " Compute an array where the subarrays contain index values 0,1,...\n", " varying only along the corresponding axis.\n", " \n", " Parameters\n", " ----------\n", " dimensions : sequence of ints\n", " The shape of the grid.\n", " dtype : dtype, optional\n", " Data type of the result.\n", " \n", " Returns\n", " -------\n", " grid : ndarray\n", " The array of grid indices,\n", " ``grid.shape = (len(dimensions),) + tuple(dimensions)``.\n", " \n", " See Also\n", " --------\n", " mgrid, meshgrid\n", " \n", " Notes\n", " -----\n", " The output shape is obtained by prepending the number of dimensions\n", " in front of the tuple of dimensions, i.e. if `dimensions` is a tuple\n", " ``(r0, ..., rN-1)`` of length ``N``, the output shape is\n", " ``(N,r0,...,rN-1)``.\n", " \n", " The subarrays ``grid[k]`` contains the N-D array of indices along the\n", " ``k-th`` axis. Explicitly::\n", " \n", " grid[k,i0,i1,...,iN-1] = ik\n", " \n", " Examples\n", " --------\n", " >>> grid = np.indices((2, 3))\n", " >>> grid.shape\n", " (2, 2, 3)\n", " >>> grid[0] # row indices\n", " array([[0, 0, 0],\n", " [1, 1, 1]])\n", " >>> grid[1] # column indices\n", " array([[0, 1, 2],\n", " [0, 1, 2]])\n", " \n", " The indices can be used as an index into an array.\n", " \n", " >>> x = np.arange(20).reshape(5, 4)\n", " >>> row, col = np.indices((2, 3))\n", " >>> x[row, col]\n", " array([[0, 1, 2],\n", " [4, 5, 6]])\n", " \n", " Note that it would be more straightforward in the above example to\n", " extract the required elements directly with ``x[:2, :3]``.\n", " \n", " info(object=None, maxwidth=76, output=, toplevel='numpy')\n", " Get help information for a function, class, or module.\n", " \n", " Parameters\n", " ----------\n", " object : object or str, optional\n", " Input object or name to get information about. If `object` is a\n", " numpy object, its docstring is given. If it is a string, available\n", " modules are searched for matching objects. If None, information\n", " about `info` itself is returned.\n", " maxwidth : int, optional\n", " Printing width.\n", " output : file like object, optional\n", " File like object that the output is written to, default is\n", " ``stdout``. The object has to be opened in 'w' or 'a' mode.\n", " toplevel : str, optional\n", " Start search at this level.\n", " \n", " See Also\n", " --------\n", " source, lookfor\n", " \n", " Notes\n", " -----\n", " When used interactively with an object, ``np.info(obj)`` is equivalent\n", " to ``help(obj)`` on the Python prompt or ``obj?`` on the IPython\n", " prompt.\n", " \n", " Examples\n", " --------\n", " >>> np.info(np.polyval) # doctest: +SKIP\n", " polyval(p, x)\n", " Evaluate the polynomial p at x.\n", " ...\n", " \n", " When using a string for `object` it is possible to get multiple results.\n", " \n", " >>> np.info('fft') # doctest: +SKIP\n", " *** Found in numpy ***\n", " Core FFT routines\n", " ...\n", " *** Found in numpy.fft ***\n", " fft(a, n=None, axis=-1)\n", " ...\n", " *** Repeat reference found in numpy.fft.fftpack ***\n", " *** Total of 3 references found. ***\n", " \n", " inner(...)\n", " inner(a, b)\n", " \n", " Inner product of two arrays.\n", " \n", " Ordinary inner product of vectors for 1-D arrays (without complex\n", " conjugation), in higher dimensions a sum product over the last axes.\n", " \n", " Parameters\n", " ----------\n", " a, b : array_like\n", " If `a` and `b` are nonscalar, their last dimensions of must match.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " `out.shape = a.shape[:-1] + b.shape[:-1]`\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If the last dimension of `a` and `b` has different size.\n", " \n", " See Also\n", " --------\n", " tensordot : Sum products over arbitrary axes.\n", " dot : Generalised matrix product, using second last dimension of `b`.\n", " einsum : Einstein summation convention.\n", " \n", " Notes\n", " -----\n", " For vectors (1-D arrays) it computes the ordinary inner-product::\n", " \n", " np.inner(a, b) = sum(a[:]*b[:])\n", " \n", " More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::\n", " \n", " np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))\n", " \n", " or explicitly::\n", " \n", " np.inner(a, b)[i0,...,ir-1,j0,...,js-1]\n", " = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])\n", " \n", " In addition `a` or `b` may be scalars, in which case::\n", " \n", " np.inner(a,b) = a*b\n", " \n", " Examples\n", " --------\n", " Ordinary inner product for vectors:\n", " \n", " >>> a = np.array([1,2,3])\n", " >>> b = np.array([0,1,0])\n", " >>> np.inner(a, b)\n", " 2\n", " \n", " A multidimensional example:\n", " \n", " >>> a = np.arange(24).reshape((2,3,4))\n", " >>> b = np.arange(4)\n", " >>> np.inner(a, b)\n", " array([[ 14, 38, 62],\n", " [ 86, 110, 134]])\n", " \n", " An example where `b` is a scalar:\n", " \n", " >>> np.inner(np.eye(2), 7)\n", " array([[ 7., 0.],\n", " [ 0., 7.]])\n", " \n", " insert(arr, obj, values, axis=None)\n", " Insert values along the given axis before the given indices.\n", " \n", " Parameters\n", " ----------\n", " arr : array_like\n", " Input array.\n", " obj : int, slice or sequence of ints\n", " Object that defines the index or indices before which `values` is\n", " inserted.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Support for multiple insertions when `obj` is a single scalar or a\n", " sequence with one element (similar to calling insert multiple\n", " times).\n", " values : array_like\n", " Values to insert into `arr`. If the type of `values` is different\n", " from that of `arr`, `values` is converted to the type of `arr`.\n", " `values` should be shaped so that ``arr[...,obj,...] = values``\n", " is legal.\n", " axis : int, optional\n", " Axis along which to insert `values`. If `axis` is None then `arr`\n", " is flattened first.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " A copy of `arr` with `values` inserted. Note that `insert`\n", " does not occur in-place: a new array is returned. If\n", " `axis` is None, `out` is a flattened array.\n", " \n", " See Also\n", " --------\n", " append : Append elements at the end of an array.\n", " concatenate : Join a sequence of arrays together.\n", " delete : Delete elements from an array.\n", " \n", " Notes\n", " -----\n", " Note that for higher dimensional inserts `obj=0` behaves very different\n", " from `obj=[0]` just like `arr[:,0,:] = values` is different from\n", " `arr[:,[0],:] = values`.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, 1], [2, 2], [3, 3]])\n", " >>> a\n", " array([[1, 1],\n", " [2, 2],\n", " [3, 3]])\n", " >>> np.insert(a, 1, 5)\n", " array([1, 5, 1, 2, 2, 3, 3])\n", " >>> np.insert(a, 1, 5, axis=1)\n", " array([[1, 5, 1],\n", " [2, 5, 2],\n", " [3, 5, 3]])\n", " \n", " Difference between sequence and scalars:\n", " >>> np.insert(a, [1], [[1],[2],[3]], axis=1)\n", " array([[1, 1, 1],\n", " [2, 2, 2],\n", " [3, 3, 3]])\n", " >>> np.array_equal(np.insert(a, 1, [1, 2, 3], axis=1),\n", " ... np.insert(a, [1], [[1],[2],[3]], axis=1))\n", " True\n", " \n", " >>> b = a.flatten()\n", " >>> b\n", " array([1, 1, 2, 2, 3, 3])\n", " >>> np.insert(b, [2, 2], [5, 6])\n", " array([1, 1, 5, 6, 2, 2, 3, 3])\n", " \n", " >>> np.insert(b, slice(2, 4), [5, 6])\n", " array([1, 1, 5, 2, 6, 2, 3, 3])\n", " \n", " >>> np.insert(b, [2, 2], [7.13, False]) # type casting\n", " array([1, 1, 7, 0, 2, 2, 3, 3])\n", " \n", " >>> x = np.arange(8).reshape(2, 4)\n", " >>> idx = (1, 3)\n", " >>> np.insert(x, idx, 999, axis=1)\n", " array([[ 0, 999, 1, 2, 999, 3],\n", " [ 4, 999, 5, 6, 999, 7]])\n", " \n", " int_asbuffer(...)\n", " \n", " interp(x, xp, fp, left=None, right=None)\n", " One-dimensional linear interpolation.\n", " \n", " Returns the one-dimensional piecewise linear interpolant to a function\n", " with given values at discrete data-points.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " The x-coordinates of the interpolated values.\n", " \n", " xp : 1-D sequence of floats\n", " The x-coordinates of the data points, must be increasing.\n", " \n", " fp : 1-D sequence of floats\n", " The y-coordinates of the data points, same length as `xp`.\n", " \n", " left : float, optional\n", " Value to return for `x < xp[0]`, default is `fp[0]`.\n", " \n", " right : float, optional\n", " Value to return for `x > xp[-1]`, default is `fp[-1]`.\n", " \n", " Returns\n", " -------\n", " y : {float, ndarray}\n", " The interpolated values, same shape as `x`.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If `xp` and `fp` have different length\n", " \n", " Notes\n", " -----\n", " Does not check that the x-coordinate sequence `xp` is increasing.\n", " If `xp` is not increasing, the results are nonsense.\n", " A simple check for increasing is::\n", " \n", " np.all(np.diff(xp) > 0)\n", " \n", " \n", " Examples\n", " --------\n", " >>> xp = [1, 2, 3]\n", " >>> fp = [3, 2, 0]\n", " >>> np.interp(2.5, xp, fp)\n", " 1.0\n", " >>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)\n", " array([ 3. , 3. , 2.5 , 0.56, 0. ])\n", " >>> UNDEF = -99.0\n", " >>> np.interp(3.14, xp, fp, right=UNDEF)\n", " -99.0\n", " \n", " Plot an interpolant to the sine function:\n", " \n", " >>> x = np.linspace(0, 2*np.pi, 10)\n", " >>> y = np.sin(x)\n", " >>> xvals = np.linspace(0, 2*np.pi, 50)\n", " >>> yinterp = np.interp(xvals, x, y)\n", " >>> import matplotlib.pyplot as plt\n", " >>> plt.plot(x, y, 'o')\n", " []\n", " >>> plt.plot(xvals, yinterp, '-x')\n", " []\n", " >>> plt.show()\n", " \n", " intersect1d(ar1, ar2, assume_unique=False)\n", " Find the intersection of two arrays.\n", " \n", " Return the sorted, unique values that are in both of the input arrays.\n", " \n", " Parameters\n", " ----------\n", " ar1, ar2 : array_like\n", " Input arrays.\n", " assume_unique : bool\n", " If True, the input arrays are both assumed to be unique, which\n", " can speed up the calculation. Default is False.\n", " \n", " Returns\n", " -------\n", " intersect1d : ndarray\n", " Sorted 1D array of common and unique elements.\n", " \n", " See Also\n", " --------\n", " numpy.lib.arraysetops : Module with a number of other functions for\n", " performing set operations on arrays.\n", " \n", " Examples\n", " --------\n", " >>> np.intersect1d([1, 3, 4, 3], [3, 1, 2, 1])\n", " array([1, 3])\n", " \n", " ipmt(rate, per, nper, pv, fv=0.0, when='end')\n", " Compute the interest portion of a payment.\n", " \n", " Parameters\n", " ----------\n", " rate : scalar or array_like of shape(M, )\n", " Rate of interest as decimal (not per cent) per period\n", " per : scalar or array_like of shape(M, )\n", " Interest paid against the loan changes during the life or the loan.\n", " The `per` is the payment period to calculate the interest amount.\n", " nper : scalar or array_like of shape(M, )\n", " Number of compounding periods\n", " pv : scalar or array_like of shape(M, )\n", " Present value\n", " fv : scalar or array_like of shape(M, ), optional\n", " Future value\n", " when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n", " When payments are due ('begin' (1) or 'end' (0)).\n", " Defaults to {'end', 0}.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Interest portion of payment. If all input is scalar, returns a scalar\n", " float. If any input is array_like, returns interest payment for each\n", " input element. If multiple inputs are array_like, they all must have\n", " the same shape.\n", " \n", " See Also\n", " --------\n", " ppmt, pmt, pv\n", " \n", " Notes\n", " -----\n", " The total payment is made up of payment against principal plus interest.\n", " \n", " ``pmt = ppmt + ipmt``\n", " \n", " Examples\n", " --------\n", " What is the amortization schedule for a 1 year loan of $2500 at\n", " 8.24% interest per year compounded monthly?\n", " \n", " >>> principal = 2500.00\n", " \n", " The 'per' variable represents the periods of the loan. Remember that\n", " financial equations start the period count at 1!\n", " \n", " >>> per = np.arange(1*12) + 1\n", " >>> ipmt = np.ipmt(0.0824/12, per, 1*12, principal)\n", " >>> ppmt = np.ppmt(0.0824/12, per, 1*12, principal)\n", " \n", " Each element of the sum of the 'ipmt' and 'ppmt' arrays should equal\n", " 'pmt'.\n", " \n", " >>> pmt = np.pmt(0.0824/12, 1*12, principal)\n", " >>> np.allclose(ipmt + ppmt, pmt)\n", " True\n", " \n", " >>> fmt = '{0:2d} {1:8.2f} {2:8.2f} {3:8.2f}'\n", " >>> for payment in per:\n", " ... index = payment - 1\n", " ... principal = principal + ppmt[index]\n", " ... print fmt.format(payment, ppmt[index], ipmt[index], principal)\n", " 1 -200.58 -17.17 2299.42\n", " 2 -201.96 -15.79 2097.46\n", " 3 -203.35 -14.40 1894.11\n", " 4 -204.74 -13.01 1689.37\n", " 5 -206.15 -11.60 1483.22\n", " 6 -207.56 -10.18 1275.66\n", " 7 -208.99 -8.76 1066.67\n", " 8 -210.42 -7.32 856.25\n", " 9 -211.87 -5.88 644.38\n", " 10 -213.32 -4.42 431.05\n", " 11 -214.79 -2.96 216.26\n", " 12 -216.26 -1.49 -0.00\n", " \n", " >>> interestpd = np.sum(ipmt)\n", " >>> np.round(interestpd, 2)\n", " -112.98\n", " \n", " irr(values)\n", " Return the Internal Rate of Return (IRR).\n", " \n", " This is the \"average\" periodically compounded rate of return\n", " that gives a net present value of 0.0; for a more complete explanation,\n", " see Notes below.\n", " \n", " Parameters\n", " ----------\n", " values : array_like, shape(N,)\n", " Input cash flows per time period. By convention, net \"deposits\"\n", " are negative and net \"withdrawals\" are positive. Thus, for\n", " example, at least the first element of `values`, which represents\n", " the initial investment, will typically be negative.\n", " \n", " Returns\n", " -------\n", " out : float\n", " Internal Rate of Return for periodic input values.\n", " \n", " Notes\n", " -----\n", " The IRR is perhaps best understood through an example (illustrated\n", " using np.irr in the Examples section below). Suppose one invests 100\n", " units and then makes the following withdrawals at regular (fixed)\n", " intervals: 39, 59, 55, 20. Assuming the ending value is 0, one's 100\n", " unit investment yields 173 units; however, due to the combination of\n", " compounding and the periodic withdrawals, the \"average\" rate of return\n", " is neither simply 0.73/4 nor (1.73)^0.25-1. Rather, it is the solution\n", " (for :math:`r`) of the equation:\n", " \n", " .. math:: -100 + \\frac{39}{1+r} + \\frac{59}{(1+r)^2}\n", " + \\frac{55}{(1+r)^3} + \\frac{20}{(1+r)^4} = 0\n", " \n", " In general, for `values` :math:`= [v_0, v_1, ... v_M]`,\n", " irr is the solution of the equation: [G]_\n", " \n", " .. math:: \\sum_{t=0}^M{\\frac{v_t}{(1+irr)^{t}}} = 0\n", " \n", " References\n", " ----------\n", " .. [G] L. J. Gitman, \"Principles of Managerial Finance, Brief,\" 3rd ed.,\n", " Addison-Wesley, 2003, pg. 348.\n", " \n", " Examples\n", " --------\n", " >>> round(irr([-100, 39, 59, 55, 20]), 5)\n", " 0.28095\n", " >>> round(irr([-100, 0, 0, 74]), 5)\n", " -0.0955\n", " >>> round(irr([-100, 100, 0, -7]), 5)\n", " -0.0833\n", " >>> round(irr([-100, 100, 0, 7]), 5)\n", " 0.06206\n", " >>> round(irr([-5, 10.5, 1, -8, 1]), 5)\n", " 0.0886\n", " \n", " (Compare with the Example given for numpy.lib.financial.npv)\n", " \n", " is_busday(...)\n", " is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)\n", " \n", " Calculates which of the given dates are valid days, and which are not.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " Parameters\n", " ----------\n", " dates : array_like of datetime64[D]\n", " The array of dates to process.\n", " weekmask : str or array_like of bool, optional\n", " A seven-element array indicating which of Monday through Sunday are\n", " valid days. May be specified as a length-seven list or array, like\n", " [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string\n", " like \"Mon Tue Wed Thu Fri\", made up of 3-character abbreviations for\n", " weekdays, optionally separated by white space. Valid abbreviations\n", " are: Mon Tue Wed Thu Fri Sat Sun\n", " holidays : array_like of datetime64[D], optional\n", " An array of dates to consider as invalid dates. They may be\n", " specified in any order, and NaT (not-a-time) dates are ignored.\n", " This list is saved in a normalized form that is suited for\n", " fast calculations of valid days.\n", " busdaycal : busdaycalendar, optional\n", " A `busdaycalendar` object which specifies the valid days. If this\n", " parameter is provided, neither weekmask nor holidays may be\n", " provided.\n", " out : array of bool, optional\n", " If provided, this array is filled with the result.\n", " \n", " Returns\n", " -------\n", " out : array of bool\n", " An array with the same shape as ``dates``, containing True for\n", " each valid day, and False for each invalid day.\n", " \n", " See Also\n", " --------\n", " busdaycalendar: An object that specifies a custom set of valid days.\n", " busday_offset : Applies an offset counted in valid days.\n", " busday_count : Counts how many valid days are in a half-open date range.\n", " \n", " Examples\n", " --------\n", " >>> # The weekdays are Friday, Saturday, and Monday\n", " ... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'],\n", " ... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])\n", " array([False, False, True], dtype='bool')\n", " \n", " isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)\n", " Returns a boolean array where two arrays are element-wise equal within a\n", " tolerance.\n", " \n", " The tolerance values are positive, typically very small numbers. The\n", " relative difference (`rtol` * abs(`b`)) and the absolute difference\n", " `atol` are added together to compare against the absolute difference\n", " between `a` and `b`.\n", " \n", " Parameters\n", " ----------\n", " a, b : array_like\n", " Input arrays to compare.\n", " rtol : float\n", " The relative tolerance parameter (see Notes).\n", " atol : float\n", " The absolute tolerance parameter (see Notes).\n", " equal_nan : bool\n", " Whether to compare NaN's as equal. If True, NaN's in `a` will be\n", " considered equal to NaN's in `b` in the output array.\n", " \n", " Returns\n", " -------\n", " y : array_like\n", " Returns a boolean array of where `a` and `b` are equal within the\n", " given tolerance. If both `a` and `b` are scalars, returns a single\n", " boolean value.\n", " \n", " See Also\n", " --------\n", " allclose\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.7.0\n", " \n", " For finite values, isclose uses the following equation to test whether\n", " two floating point values are equivalent.\n", " \n", " absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))\n", " \n", " The above equation is not symmetric in `a` and `b`, so that\n", " `isclose(a, b)` might be different from `isclose(b, a)` in\n", " some rare cases.\n", " \n", " Examples\n", " --------\n", " >>> np.isclose([1e10,1e-7], [1.00001e10,1e-8])\n", " array([True, False])\n", " >>> np.isclose([1e10,1e-8], [1.00001e10,1e-9])\n", " array([True, True])\n", " >>> np.isclose([1e10,1e-8], [1.0001e10,1e-9])\n", " array([False, True])\n", " >>> np.isclose([1.0, np.nan], [1.0, np.nan])\n", " array([True, False])\n", " >>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)\n", " array([True, True])\n", " \n", " iscomplex(x)\n", " Returns a bool array, where True if input element is complex.\n", " \n", " What is tested is whether the input has a non-zero imaginary part, not if\n", " the input type is complex.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " Input array.\n", " \n", " Returns\n", " -------\n", " out : ndarray of bools\n", " Output array.\n", " \n", " See Also\n", " --------\n", " isreal\n", " iscomplexobj : Return True if x is a complex type or an array of complex\n", " numbers.\n", " \n", " Examples\n", " --------\n", " >>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])\n", " array([ True, False, False, False, False, True], dtype=bool)\n", " \n", " iscomplexobj(x)\n", " Check for a complex type or an array of complex numbers.\n", " \n", " The type of the input is checked, not the value. Even if the input\n", " has an imaginary part equal to zero, `iscomplexobj` evaluates to True.\n", " \n", " Parameters\n", " ----------\n", " x : any\n", " The input can be of any type and shape.\n", " \n", " Returns\n", " -------\n", " iscomplexobj : bool\n", " The return value, True if `x` is of a complex type or has at least\n", " one complex element.\n", " \n", " See Also\n", " --------\n", " isrealobj, iscomplex\n", " \n", " Examples\n", " --------\n", " >>> np.iscomplexobj(1)\n", " False\n", " >>> np.iscomplexobj(1+0j)\n", " True\n", " >>> np.iscomplexobj([3, 1+0j, True])\n", " True\n", " \n", " isfortran(a)\n", " Returns True if array is arranged in Fortran-order in memory\n", " and not C-order.\n", " \n", " Parameters\n", " ----------\n", " a : ndarray\n", " Input array.\n", " \n", " \n", " Examples\n", " --------\n", " \n", " np.array allows to specify whether the array is written in C-contiguous\n", " order (last index varies the fastest), or FORTRAN-contiguous order in\n", " memory (first index varies the fastest).\n", " \n", " >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')\n", " >>> a\n", " array([[1, 2, 3],\n", " [4, 5, 6]])\n", " >>> np.isfortran(a)\n", " False\n", " \n", " >>> b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN')\n", " >>> b\n", " array([[1, 2, 3],\n", " [4, 5, 6]])\n", " >>> np.isfortran(b)\n", " True\n", " \n", " \n", " The transpose of a C-ordered array is a FORTRAN-ordered array.\n", " \n", " >>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')\n", " >>> a\n", " array([[1, 2, 3],\n", " [4, 5, 6]])\n", " >>> np.isfortran(a)\n", " False\n", " >>> b = a.T\n", " >>> b\n", " array([[1, 4],\n", " [2, 5],\n", " [3, 6]])\n", " >>> np.isfortran(b)\n", " True\n", " \n", " C-ordered arrays evaluate as False even if they are also FORTRAN-ordered.\n", " \n", " >>> np.isfortran(np.array([1, 2], order='FORTRAN'))\n", " False\n", " \n", " isneginf(x, y=None)\n", " Test element-wise for negative infinity, return result as bool array.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " The input array.\n", " y : array_like, optional\n", " A boolean array with the same shape and type as `x` to store the\n", " result.\n", " \n", " Returns\n", " -------\n", " y : ndarray\n", " A boolean array with the same dimensions as the input.\n", " If second argument is not supplied then a numpy boolean array is\n", " returned with values True where the corresponding element of the\n", " input is negative infinity and values False where the element of\n", " the input is not negative infinity.\n", " \n", " If a second argument is supplied the result is stored there. If the\n", " type of that array is a numeric type the result is represented as\n", " zeros and ones, if the type is boolean then as False and True. The\n", " return value `y` is then a reference to that array.\n", " \n", " See Also\n", " --------\n", " isinf, isposinf, isnan, isfinite\n", " \n", " Notes\n", " -----\n", " Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic\n", " (IEEE 754).\n", " \n", " Errors result if the second argument is also supplied when x is a scalar\n", " input, or if first and second arguments have different shapes.\n", " \n", " Examples\n", " --------\n", " >>> np.isneginf(np.NINF)\n", " array(True, dtype=bool)\n", " >>> np.isneginf(np.inf)\n", " array(False, dtype=bool)\n", " >>> np.isneginf(np.PINF)\n", " array(False, dtype=bool)\n", " >>> np.isneginf([-np.inf, 0., np.inf])\n", " array([ True, False, False], dtype=bool)\n", " \n", " >>> x = np.array([-np.inf, 0., np.inf])\n", " >>> y = np.array([2, 2, 2])\n", " >>> np.isneginf(x, y)\n", " array([1, 0, 0])\n", " >>> y\n", " array([1, 0, 0])\n", " \n", " isposinf(x, y=None)\n", " Test element-wise for positive infinity, return result as bool array.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " The input array.\n", " y : array_like, optional\n", " A boolean array with the same shape as `x` to store the result.\n", " \n", " Returns\n", " -------\n", " y : ndarray\n", " A boolean array with the same dimensions as the input.\n", " If second argument is not supplied then a boolean array is returned\n", " with values True where the corresponding element of the input is\n", " positive infinity and values False where the element of the input is\n", " not positive infinity.\n", " \n", " If a second argument is supplied the result is stored there. If the\n", " type of that array is a numeric type the result is represented as zeros\n", " and ones, if the type is boolean then as False and True.\n", " The return value `y` is then a reference to that array.\n", " \n", " See Also\n", " --------\n", " isinf, isneginf, isfinite, isnan\n", " \n", " Notes\n", " -----\n", " Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic\n", " (IEEE 754).\n", " \n", " Errors result if the second argument is also supplied when `x` is a\n", " scalar input, or if first and second arguments have different shapes.\n", " \n", " Examples\n", " --------\n", " >>> np.isposinf(np.PINF)\n", " array(True, dtype=bool)\n", " >>> np.isposinf(np.inf)\n", " array(True, dtype=bool)\n", " >>> np.isposinf(np.NINF)\n", " array(False, dtype=bool)\n", " >>> np.isposinf([-np.inf, 0., np.inf])\n", " array([False, False, True], dtype=bool)\n", " \n", " >>> x = np.array([-np.inf, 0., np.inf])\n", " >>> y = np.array([2, 2, 2])\n", " >>> np.isposinf(x, y)\n", " array([0, 0, 1])\n", " >>> y\n", " array([0, 0, 1])\n", " \n", " isreal(x)\n", " Returns a bool array, where True if input element is real.\n", " \n", " If element has complex type with zero complex part, the return value\n", " for that element is True.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " Input array.\n", " \n", " Returns\n", " -------\n", " out : ndarray, bool\n", " Boolean array of same shape as `x`.\n", " \n", " See Also\n", " --------\n", " iscomplex\n", " isrealobj : Return True if x is not a complex type.\n", " \n", " Examples\n", " --------\n", " >>> np.isreal([1+1j, 1+0j, 4.5, 3, 2, 2j])\n", " array([False, True, True, True, True, False], dtype=bool)\n", " \n", " isrealobj(x)\n", " Return True if x is a not complex type or an array of complex numbers.\n", " \n", " The type of the input is checked, not the value. So even if the input\n", " has an imaginary part equal to zero, `isrealobj` evaluates to False\n", " if the data type is complex.\n", " \n", " Parameters\n", " ----------\n", " x : any\n", " The input can be of any type and shape.\n", " \n", " Returns\n", " -------\n", " y : bool\n", " The return value, False if `x` is of a complex type.\n", " \n", " See Also\n", " --------\n", " iscomplexobj, isreal\n", " \n", " Examples\n", " --------\n", " >>> np.isrealobj(1)\n", " True\n", " >>> np.isrealobj(1+0j)\n", " False\n", " >>> np.isrealobj([3, 1+0j, True])\n", " False\n", " \n", " isscalar(num)\n", " Returns True if the type of `num` is a scalar type.\n", " \n", " Parameters\n", " ----------\n", " num : any\n", " Input argument, can be of any type and shape.\n", " \n", " Returns\n", " -------\n", " val : bool\n", " True if `num` is a scalar type, False if it is not.\n", " \n", " Examples\n", " --------\n", " >>> np.isscalar(3.1)\n", " True\n", " >>> np.isscalar([3.1])\n", " False\n", " >>> np.isscalar(False)\n", " True\n", " \n", " issctype(rep)\n", " Determines whether the given object represents a scalar data-type.\n", " \n", " Parameters\n", " ----------\n", " rep : any\n", " If `rep` is an instance of a scalar dtype, True is returned. If not,\n", " False is returned.\n", " \n", " Returns\n", " -------\n", " out : bool\n", " Boolean result of check whether `rep` is a scalar dtype.\n", " \n", " See Also\n", " --------\n", " issubsctype, issubdtype, obj2sctype, sctype2char\n", " \n", " Examples\n", " --------\n", " >>> np.issctype(np.int32)\n", " True\n", " >>> np.issctype(list)\n", " False\n", " >>> np.issctype(1.1)\n", " False\n", " \n", " Strings are also a scalar type:\n", " \n", " >>> np.issctype(np.dtype('str'))\n", " True\n", " \n", " issubclass_(arg1, arg2)\n", " Determine if a class is a subclass of a second class.\n", " \n", " `issubclass_` is equivalent to the Python built-in ``issubclass``,\n", " except that it returns False instead of raising a TypeError is one\n", " of the arguments is not a class.\n", " \n", " Parameters\n", " ----------\n", " arg1 : class\n", " Input class. True is returned if `arg1` is a subclass of `arg2`.\n", " arg2 : class or tuple of classes.\n", " Input class. If a tuple of classes, True is returned if `arg1` is a\n", " subclass of any of the tuple elements.\n", " \n", " Returns\n", " -------\n", " out : bool\n", " Whether `arg1` is a subclass of `arg2` or not.\n", " \n", " See Also\n", " --------\n", " issubsctype, issubdtype, issctype\n", " \n", " Examples\n", " --------\n", " >>> np.issubclass_(np.int32, np.int)\n", " True\n", " >>> np.issubclass_(np.int32, np.float)\n", " False\n", " \n", " issubdtype(arg1, arg2)\n", " Returns True if first argument is a typecode lower/equal in type hierarchy.\n", " \n", " Parameters\n", " ----------\n", " arg1, arg2 : dtype_like\n", " dtype or string representing a typecode.\n", " \n", " Returns\n", " -------\n", " out : bool\n", " \n", " See Also\n", " --------\n", " issubsctype, issubclass_\n", " numpy.core.numerictypes : Overview of numpy type hierarchy.\n", " \n", " Examples\n", " --------\n", " >>> np.issubdtype('S1', str)\n", " True\n", " >>> np.issubdtype(np.float64, np.float32)\n", " False\n", " \n", " issubsctype(arg1, arg2)\n", " Determine if the first argument is a subclass of the second argument.\n", " \n", " Parameters\n", " ----------\n", " arg1, arg2 : dtype or dtype specifier\n", " Data-types.\n", " \n", " Returns\n", " -------\n", " out : bool\n", " The result.\n", " \n", " See Also\n", " --------\n", " issctype, issubdtype,obj2sctype\n", " \n", " Examples\n", " --------\n", " >>> np.issubsctype('S8', str)\n", " True\n", " >>> np.issubsctype(np.array([1]), np.int)\n", " True\n", " >>> np.issubsctype(np.array([1]), np.float)\n", " False\n", " \n", " iterable(y)\n", " Check whether or not an object can be iterated over.\n", " \n", " Parameters\n", " ----------\n", " y : object\n", " Input object.\n", " \n", " Returns\n", " -------\n", " b : {0, 1}\n", " Return 1 if the object has an iterator method or is a sequence,\n", " and 0 otherwise.\n", " \n", " \n", " Examples\n", " --------\n", " >>> np.iterable([1, 2, 3])\n", " 1\n", " >>> np.iterable(2)\n", " 0\n", " \n", " ix_(*args)\n", " Construct an open mesh from multiple sequences.\n", " \n", " This function takes N 1-D sequences and returns N outputs with N\n", " dimensions each, such that the shape is 1 in all but one dimension\n", " and the dimension with the non-unit shape value cycles through all\n", " N dimensions.\n", " \n", " Using `ix_` one can quickly construct index arrays that will index\n", " the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array\n", " ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``.\n", " \n", " Parameters\n", " ----------\n", " args : 1-D sequences\n", " \n", " Returns\n", " -------\n", " out : tuple of ndarrays\n", " N arrays with N dimensions each, with N the number of input\n", " sequences. Together these arrays form an open mesh.\n", " \n", " See Also\n", " --------\n", " ogrid, mgrid, meshgrid\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(10).reshape(2, 5)\n", " >>> a\n", " array([[0, 1, 2, 3, 4],\n", " [5, 6, 7, 8, 9]])\n", " >>> ixgrid = np.ix_([0,1], [2,4])\n", " >>> ixgrid\n", " (array([[0],\n", " [1]]), array([[2, 4]]))\n", " >>> ixgrid[0].shape, ixgrid[1].shape\n", " ((2, 1), (1, 2))\n", " >>> a[ixgrid]\n", " array([[2, 4],\n", " [7, 9]])\n", " \n", " kaiser(M, beta)\n", " Return the Kaiser window.\n", " \n", " The Kaiser window is a taper formed by using a Bessel function.\n", " \n", " Parameters\n", " ----------\n", " M : int\n", " Number of points in the output window. If zero or less, an\n", " empty array is returned.\n", " beta : float\n", " Shape parameter for window.\n", " \n", " Returns\n", " -------\n", " out : array\n", " The window, with the maximum value normalized to one (the value\n", " one appears only if the number of samples is odd).\n", " \n", " See Also\n", " --------\n", " bartlett, blackman, hamming, hanning\n", " \n", " Notes\n", " -----\n", " The Kaiser window is defined as\n", " \n", " .. math:: w(n) = I_0\\left( \\beta \\sqrt{1-\\frac{4n^2}{(M-1)^2}}\n", " \\right)/I_0(\\beta)\n", " \n", " with\n", " \n", " .. math:: \\quad -\\frac{M-1}{2} \\leq n \\leq \\frac{M-1}{2},\n", " \n", " where :math:`I_0` is the modified zeroth-order Bessel function.\n", " \n", " The Kaiser was named for Jim Kaiser, who discovered a simple\n", " approximation to the DPSS window based on Bessel functions. The Kaiser\n", " window is a very good approximation to the Digital Prolate Spheroidal\n", " Sequence, or Slepian window, which is the transform which maximizes the\n", " energy in the main lobe of the window relative to total energy.\n", " \n", " The Kaiser can approximate many other windows by varying the beta\n", " parameter.\n", " \n", " ==== =======================\n", " beta Window shape\n", " ==== =======================\n", " 0 Rectangular\n", " 5 Similar to a Hamming\n", " 6 Similar to a Hanning\n", " 8.6 Similar to a Blackman\n", " ==== =======================\n", " \n", " A beta value of 14 is probably a good starting point. Note that as beta\n", " gets large, the window narrows, and so the number of samples needs to be\n", " large enough to sample the increasingly narrow spike, otherwise NaNs will\n", " get returned.\n", " \n", " Most references to the Kaiser window come from the signal processing\n", " literature, where it is used as one of many windowing functions for\n", " smoothing values. It is also known as an apodization (which means\n", " \"removing the foot\", i.e. smoothing discontinuities at the beginning\n", " and end of the sampled signal) or tapering function.\n", " \n", " References\n", " ----------\n", " .. [1] J. F. Kaiser, \"Digital Filters\" - Ch 7 in \"Systems analysis by\n", " digital computer\", Editors: F.F. Kuo and J.F. Kaiser, p 218-285.\n", " John Wiley and Sons, New York, (1966).\n", " .. [2] E.R. Kanasewich, \"Time Sequence Analysis in Geophysics\", The\n", " University of Alberta Press, 1975, pp. 177-178.\n", " .. [3] Wikipedia, \"Window function\",\n", " http://en.wikipedia.org/wiki/Window_function\n", " \n", " Examples\n", " --------\n", " >>> np.kaiser(12, 14)\n", " array([ 7.72686684e-06, 3.46009194e-03, 4.65200189e-02,\n", " 2.29737120e-01, 5.99885316e-01, 9.45674898e-01,\n", " 9.45674898e-01, 5.99885316e-01, 2.29737120e-01,\n", " 4.65200189e-02, 3.46009194e-03, 7.72686684e-06])\n", " \n", " \n", " Plot the window and the frequency response:\n", " \n", " >>> from numpy.fft import fft, fftshift\n", " >>> window = np.kaiser(51, 14)\n", " >>> plt.plot(window)\n", " []\n", " >>> plt.title(\"Kaiser window\")\n", " \n", " >>> plt.ylabel(\"Amplitude\")\n", " \n", " >>> plt.xlabel(\"Sample\")\n", " \n", " >>> plt.show()\n", " \n", " >>> plt.figure()\n", " \n", " >>> A = fft(window, 2048) / 25.5\n", " >>> mag = np.abs(fftshift(A))\n", " >>> freq = np.linspace(-0.5, 0.5, len(A))\n", " >>> response = 20 * np.log10(mag)\n", " >>> response = np.clip(response, -100, 100)\n", " >>> plt.plot(freq, response)\n", " []\n", " >>> plt.title(\"Frequency response of Kaiser window\")\n", " \n", " >>> plt.ylabel(\"Magnitude [dB]\")\n", " \n", " >>> plt.xlabel(\"Normalized frequency [cycles per sample]\")\n", " \n", " >>> plt.axis('tight')\n", " (-0.5, 0.5, -100.0, ...)\n", " >>> plt.show()\n", " \n", " kron(a, b)\n", " Kronecker product of two arrays.\n", " \n", " Computes the Kronecker product, a composite array made of blocks of the\n", " second array scaled by the first.\n", " \n", " Parameters\n", " ----------\n", " a, b : array_like\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " \n", " See Also\n", " --------\n", " outer : The outer product\n", " \n", " Notes\n", " -----\n", " The function assumes that the number of dimenensions of `a` and `b`\n", " are the same, if necessary prepending the smallest with ones.\n", " If `a.shape = (r0,r1,..,rN)` and `b.shape = (s0,s1,...,sN)`,\n", " the Kronecker product has shape `(r0*s0, r1*s1, ..., rN*SN)`.\n", " The elements are products of elements from `a` and `b`, organized\n", " explicitly by::\n", " \n", " kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN]\n", " \n", " where::\n", " \n", " kt = it * st + jt, t = 0,...,N\n", " \n", " In the common 2-D case (N=1), the block structure can be visualized::\n", " \n", " [[ a[0,0]*b, a[0,1]*b, ... , a[0,-1]*b ],\n", " [ ... ... ],\n", " [ a[-1,0]*b, a[-1,1]*b, ... , a[-1,-1]*b ]]\n", " \n", " \n", " Examples\n", " --------\n", " >>> np.kron([1,10,100], [5,6,7])\n", " array([ 5, 6, 7, 50, 60, 70, 500, 600, 700])\n", " >>> np.kron([5,6,7], [1,10,100])\n", " array([ 5, 50, 500, 6, 60, 600, 7, 70, 700])\n", " \n", " >>> np.kron(np.eye(2), np.ones((2,2)))\n", " array([[ 1., 1., 0., 0.],\n", " [ 1., 1., 0., 0.],\n", " [ 0., 0., 1., 1.],\n", " [ 0., 0., 1., 1.]])\n", " \n", " >>> a = np.arange(100).reshape((2,5,2,5))\n", " >>> b = np.arange(24).reshape((2,3,4))\n", " >>> c = np.kron(a,b)\n", " >>> c.shape\n", " (2, 10, 6, 20)\n", " >>> I = (1,3,0,2)\n", " >>> J = (0,2,1)\n", " >>> J1 = (0,) + J # extend to ndim=4\n", " >>> S1 = (1,) + b.shape\n", " >>> K = tuple(np.array(I) * np.array(S1) + np.array(J1))\n", " >>> c[K] == a[I]*b[J]\n", " True\n", " \n", " lexsort(...)\n", " lexsort(keys, axis=-1)\n", " \n", " Perform an indirect sort using a sequence of keys.\n", " \n", " Given multiple sorting keys, which can be interpreted as columns in a\n", " spreadsheet, lexsort returns an array of integer indices that describes\n", " the sort order by multiple columns. The last key in the sequence is used\n", " for the primary sort order, the second-to-last key for the secondary sort\n", " order, and so on. The keys argument must be a sequence of objects that\n", " can be converted to arrays of the same shape. If a 2D array is provided\n", " for the keys argument, it's rows are interpreted as the sorting keys and\n", " sorting is according to the last row, second last row etc.\n", " \n", " Parameters\n", " ----------\n", " keys : (k, N) array or tuple containing k (N,)-shaped sequences\n", " The `k` different \"columns\" to be sorted. The last column (or row if\n", " `keys` is a 2D array) is the primary sort key.\n", " axis : int, optional\n", " Axis to be indirectly sorted. By default, sort over the last axis.\n", " \n", " Returns\n", " -------\n", " indices : (N,) ndarray of ints\n", " Array of indices that sort the keys along the specified axis.\n", " \n", " See Also\n", " --------\n", " argsort : Indirect sort.\n", " ndarray.sort : In-place sort.\n", " sort : Return a sorted copy of an array.\n", " \n", " Examples\n", " --------\n", " Sort names: first by surname, then by name.\n", " \n", " >>> surnames = ('Hertz', 'Galilei', 'Hertz')\n", " >>> first_names = ('Heinrich', 'Galileo', 'Gustav')\n", " >>> ind = np.lexsort((first_names, surnames))\n", " >>> ind\n", " array([1, 2, 0])\n", " \n", " >>> [surnames[i] + \", \" + first_names[i] for i in ind]\n", " ['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']\n", " \n", " Sort two columns of numbers:\n", " \n", " >>> a = [1,5,1,4,3,4,4] # First column\n", " >>> b = [9,4,0,4,0,2,1] # Second column\n", " >>> ind = np.lexsort((b,a)) # Sort by a, then by b\n", " >>> print ind\n", " [2 0 4 6 5 3 1]\n", " \n", " >>> [(a[i],b[i]) for i in ind]\n", " [(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]\n", " \n", " Note that sorting is first according to the elements of ``a``.\n", " Secondary sorting is according to the elements of ``b``.\n", " \n", " A normal ``argsort`` would have yielded:\n", " \n", " >>> [(a[i],b[i]) for i in np.argsort(a)]\n", " [(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]\n", " \n", " Structured arrays are sorted lexically by ``argsort``:\n", " \n", " >>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],\n", " ... dtype=np.dtype([('x', int), ('y', int)]))\n", " \n", " >>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))\n", " array([2, 0, 4, 6, 5, 3, 1])\n", " \n", " linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None)\n", " Return evenly spaced numbers over a specified interval.\n", " \n", " Returns `num` evenly spaced samples, calculated over the\n", " interval [`start`, `stop` ].\n", " \n", " The endpoint of the interval can optionally be excluded.\n", " \n", " Parameters\n", " ----------\n", " start : scalar\n", " The starting value of the sequence.\n", " stop : scalar\n", " The end value of the sequence, unless `endpoint` is set to False.\n", " In that case, the sequence consists of all but the last of ``num + 1``\n", " evenly spaced samples, so that `stop` is excluded. Note that the step\n", " size changes when `endpoint` is False.\n", " num : int, optional\n", " Number of samples to generate. Default is 50.\n", " endpoint : bool, optional\n", " If True, `stop` is the last sample. Otherwise, it is not included.\n", " Default is True.\n", " retstep : bool, optional\n", " If True, return (`samples`, `step`), where `step` is the spacing\n", " between samples.\n", " dtype : dtype\n", " The type of the output array. If `dtype` is not given, infer the data\n", " type from the other input arguments.\n", " \n", " .. versionadded:: 1.9.0\n", " \n", " Returns\n", " -------\n", " samples : ndarray\n", " There are `num` equally spaced samples in the closed interval\n", " ``[start, stop]`` or the half-open interval ``[start, stop)``\n", " (depending on whether `endpoint` is True or False).\n", " step : float (only if `retstep` is True)\n", " Size of spacing between samples.\n", " \n", " \n", " See Also\n", " --------\n", " arange : Similar to `linspace`, but uses a step size (instead of the\n", " number of samples).\n", " logspace : Samples uniformly distributed in log space.\n", " \n", " Examples\n", " --------\n", " >>> np.linspace(2.0, 3.0, num=5)\n", " array([ 2. , 2.25, 2.5 , 2.75, 3. ])\n", " >>> np.linspace(2.0, 3.0, num=5, endpoint=False)\n", " array([ 2. , 2.2, 2.4, 2.6, 2.8])\n", " >>> np.linspace(2.0, 3.0, num=5, retstep=True)\n", " (array([ 2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)\n", " \n", " Graphical illustration:\n", " \n", " >>> import matplotlib.pyplot as plt\n", " >>> N = 8\n", " >>> y = np.zeros(N)\n", " >>> x1 = np.linspace(0, 10, N, endpoint=True)\n", " >>> x2 = np.linspace(0, 10, N, endpoint=False)\n", " >>> plt.plot(x1, y, 'o')\n", " []\n", " >>> plt.plot(x2, y + 0.5, 'o')\n", " []\n", " >>> plt.ylim([-0.5, 1])\n", " (-0.5, 1)\n", " >>> plt.show()\n", " \n", " load(file, mmap_mode=None)\n", " Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.\n", " \n", " Parameters\n", " ----------\n", " file : file-like object or string\n", " The file to read. File-like objects must support the\n", " ``seek()`` and ``read()`` methods. Pickled files require that the\n", " file-like object support the ``readline()`` method as well.\n", " mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional\n", " If not None, then memory-map the file, using the given mode (see\n", " `numpy.memmap` for a detailed description of the modes). A\n", " memory-mapped array is kept on disk. However, it can be accessed\n", " and sliced like any ndarray. Memory mapping is especially useful\n", " for accessing small fragments of large files without reading the\n", " entire file into memory.\n", " \n", " Returns\n", " -------\n", " result : array, tuple, dict, etc.\n", " Data stored in the file. For ``.npz`` files, the returned instance\n", " of NpzFile class must be closed to avoid leaking file descriptors.\n", " \n", " Raises\n", " ------\n", " IOError\n", " If the input file does not exist or cannot be read.\n", " \n", " See Also\n", " --------\n", " save, savez, savez_compressed, loadtxt\n", " memmap : Create a memory-map to an array stored in a file on disk.\n", " \n", " Notes\n", " -----\n", " - If the file contains pickle data, then whatever object is stored\n", " in the pickle is returned.\n", " - If the file is a ``.npy`` file, then a single array is returned.\n", " - If the file is a ``.npz`` file, then a dictionary-like object is\n", " returned, containing ``{filename: array}`` key-value pairs, one for\n", " each file in the archive.\n", " - If the file is a ``.npz`` file, the returned value supports the\n", " context manager protocol in a similar fashion to the open function::\n", " \n", " with load('foo.npz') as data:\n", " a = data['a']\n", " \n", " The underlying file descriptor is closed when exiting the 'with'\n", " block.\n", " \n", " Examples\n", " --------\n", " Store data to disk, and load it again:\n", " \n", " >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))\n", " >>> np.load('/tmp/123.npy')\n", " array([[1, 2, 3],\n", " [4, 5, 6]])\n", " \n", " Store compressed data to disk, and load it again:\n", " \n", " >>> a=np.array([[1, 2, 3], [4, 5, 6]])\n", " >>> b=np.array([1, 2])\n", " >>> np.savez('/tmp/123.npz', a=a, b=b)\n", " >>> data = np.load('/tmp/123.npz')\n", " >>> data['a']\n", " array([[1, 2, 3],\n", " [4, 5, 6]])\n", " >>> data['b']\n", " array([1, 2])\n", " >>> data.close()\n", " \n", " Mem-map the stored array, and then access the second row\n", " directly from disk:\n", " \n", " >>> X = np.load('/tmp/123.npy', mmap_mode='r')\n", " >>> X[1, :]\n", " memmap([4, 5, 6])\n", " \n", " loads(...)\n", " loads(string) -- Load a pickle from the given string\n", " \n", " loadtxt(fname, dtype=, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0)\n", " Load data from a text file.\n", " \n", " Each row in the text file must have the same number of values.\n", " \n", " Parameters\n", " ----------\n", " fname : file or str\n", " File, filename, or generator to read. If the filename extension is\n", " ``.gz`` or ``.bz2``, the file is first decompressed. Note that\n", " generators should return byte strings for Python 3k.\n", " dtype : data-type, optional\n", " Data-type of the resulting array; default: float. If this is a\n", " record data-type, the resulting array will be 1-dimensional, and\n", " each row will be interpreted as an element of the array. In this\n", " case, the number of columns used must match the number of fields in\n", " the data-type.\n", " comments : str, optional\n", " The character used to indicate the start of a comment;\n", " default: '#'.\n", " delimiter : str, optional\n", " The string used to separate values. By default, this is any\n", " whitespace.\n", " converters : dict, optional\n", " A dictionary mapping column number to a function that will convert\n", " that column to a float. E.g., if column 0 is a date string:\n", " ``converters = {0: datestr2num}``. Converters can also be used to\n", " provide a default value for missing data (but see also `genfromtxt`):\n", " ``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.\n", " skiprows : int, optional\n", " Skip the first `skiprows` lines; default: 0.\n", " usecols : sequence, optional\n", " Which columns to read, with 0 being the first. For example,\n", " ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\n", " The default, None, results in all columns being read.\n", " unpack : bool, optional\n", " If True, the returned array is transposed, so that arguments may be\n", " unpacked using ``x, y, z = loadtxt(...)``. When used with a record\n", " data-type, arrays are returned for each field. Default is False.\n", " ndmin : int, optional\n", " The returned array will have at least `ndmin` dimensions.\n", " Otherwise mono-dimensional axes will be squeezed.\n", " Legal values: 0 (default), 1 or 2.\n", " \n", " .. versionadded:: 1.6.0\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Data read from the text file.\n", " \n", " See Also\n", " --------\n", " load, fromstring, fromregex\n", " genfromtxt : Load data with missing values handled as specified.\n", " scipy.io.loadmat : reads MATLAB data files\n", " \n", " Notes\n", " -----\n", " This function aims to be a fast reader for simply formatted files. The\n", " `genfromtxt` function provides more sophisticated handling of, e.g.,\n", " lines with missing values.\n", " \n", " Examples\n", " --------\n", " >>> from StringIO import StringIO # StringIO behaves like a file object\n", " >>> c = StringIO(\"0 1\\n2 3\")\n", " >>> np.loadtxt(c)\n", " array([[ 0., 1.],\n", " [ 2., 3.]])\n", " \n", " >>> d = StringIO(\"M 21 72\\nF 35 58\")\n", " >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),\n", " ... 'formats': ('S1', 'i4', 'f4')})\n", " array([('M', 21, 72.0), ('F', 35, 58.0)],\n", " dtype=[('gender', '|S1'), ('age', '>> c = StringIO(\"1,0,2\\n3,0,4\")\n", " >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)\n", " >>> x\n", " array([ 1., 3.])\n", " >>> y\n", " array([ 2., 4.])\n", " \n", " log(x)\n", " Compute the natural logarithm of `x`.\n", " \n", " Return the \"principal value\" (for a description of this, see `numpy.log`)\n", " of :math:`log_e(x)`. For real `x > 0`, this is a real number (``log(0)``\n", " returns ``-inf`` and ``log(np.inf)`` returns ``inf``). Otherwise, the\n", " complex principle value is returned.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " The value(s) whose log is (are) required.\n", " \n", " Returns\n", " -------\n", " out : ndarray or scalar\n", " The log of the `x` value(s). If `x` was a scalar, so is `out`,\n", " otherwise an array is returned.\n", " \n", " See Also\n", " --------\n", " numpy.log\n", " \n", " Notes\n", " -----\n", " For a log() that returns ``NAN`` when real `x < 0`, use `numpy.log`\n", " (note, however, that otherwise `numpy.log` and this `log` are identical,\n", " i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`, and,\n", " notably, the complex principle value if ``x.imag != 0``).\n", " \n", " Examples\n", " --------\n", " >>> np.emath.log(np.exp(1))\n", " 1.0\n", " \n", " Negative arguments are handled \"correctly\" (recall that\n", " ``exp(log(x)) == x`` does *not* hold for real ``x < 0``):\n", " \n", " >>> np.emath.log(-np.exp(1)) == (1 + np.pi * 1j)\n", " True\n", " \n", " log10(x)\n", " Compute the logarithm base 10 of `x`.\n", " \n", " Return the \"principal value\" (for a description of this, see\n", " `numpy.log10`) of :math:`log_{10}(x)`. For real `x > 0`, this\n", " is a real number (``log10(0)`` returns ``-inf`` and ``log10(np.inf)``\n", " returns ``inf``). Otherwise, the complex principle value is returned.\n", " \n", " Parameters\n", " ----------\n", " x : array_like or scalar\n", " The value(s) whose log base 10 is (are) required.\n", " \n", " Returns\n", " -------\n", " out : ndarray or scalar\n", " The log base 10 of the `x` value(s). If `x` was a scalar, so is `out`,\n", " otherwise an array object is returned.\n", " \n", " See Also\n", " --------\n", " numpy.log10\n", " \n", " Notes\n", " -----\n", " For a log10() that returns ``NAN`` when real `x < 0`, use `numpy.log10`\n", " (note, however, that otherwise `numpy.log10` and this `log10` are\n", " identical, i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`,\n", " and, notably, the complex principle value if ``x.imag != 0``).\n", " \n", " Examples\n", " --------\n", " \n", " (We set the printing precision so the example can be auto-tested)\n", " \n", " >>> np.set_printoptions(precision=4)\n", " \n", " >>> np.emath.log10(10**1)\n", " 1.0\n", " \n", " >>> np.emath.log10([-10**1, -10**2, 10**2])\n", " array([ 1.+1.3644j, 2.+1.3644j, 2.+0.j ])\n", " \n", " log2(x)\n", " Compute the logarithm base 2 of `x`.\n", " \n", " Return the \"principal value\" (for a description of this, see\n", " `numpy.log2`) of :math:`log_2(x)`. For real `x > 0`, this is\n", " a real number (``log2(0)`` returns ``-inf`` and ``log2(np.inf)`` returns\n", " ``inf``). Otherwise, the complex principle value is returned.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " The value(s) whose log base 2 is (are) required.\n", " \n", " Returns\n", " -------\n", " out : ndarray or scalar\n", " The log base 2 of the `x` value(s). If `x` was a scalar, so is `out`,\n", " otherwise an array is returned.\n", " \n", " See Also\n", " --------\n", " numpy.log2\n", " \n", " Notes\n", " -----\n", " For a log2() that returns ``NAN`` when real `x < 0`, use `numpy.log2`\n", " (note, however, that otherwise `numpy.log2` and this `log2` are\n", " identical, i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`,\n", " and, notably, the complex principle value if ``x.imag != 0``).\n", " \n", " Examples\n", " --------\n", " We set the printing precision so the example can be auto-tested:\n", " \n", " >>> np.set_printoptions(precision=4)\n", " \n", " >>> np.emath.log2(8)\n", " 3.0\n", " >>> np.emath.log2([-4, -8, 8])\n", " array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])\n", " \n", " logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None)\n", " Return numbers spaced evenly on a log scale.\n", " \n", " In linear space, the sequence starts at ``base ** start``\n", " (`base` to the power of `start`) and ends with ``base ** stop``\n", " (see `endpoint` below).\n", " \n", " Parameters\n", " ----------\n", " start : float\n", " ``base ** start`` is the starting value of the sequence.\n", " stop : float\n", " ``base ** stop`` is the final value of the sequence, unless `endpoint`\n", " is False. In that case, ``num + 1`` values are spaced over the\n", " interval in log-space, of which all but the last (a sequence of\n", " length ``num``) are returned.\n", " num : integer, optional\n", " Number of samples to generate. Default is 50.\n", " endpoint : boolean, optional\n", " If true, `stop` is the last sample. Otherwise, it is not included.\n", " Default is True.\n", " base : float, optional\n", " The base of the log space. The step size between the elements in\n", " ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.\n", " Default is 10.0.\n", " dtype : dtype\n", " The type of the output array. If `dtype` is not given, infer the data\n", " type from the other input arguments.\n", " \n", " Returns\n", " -------\n", " samples : ndarray\n", " `num` samples, equally spaced on a log scale.\n", " \n", " See Also\n", " --------\n", " arange : Similar to linspace, with the step size specified instead of the\n", " number of samples. Note that, when used with a float endpoint, the\n", " endpoint may or may not be included.\n", " linspace : Similar to logspace, but with the samples uniformly distributed\n", " in linear space, instead of log space.\n", " \n", " Notes\n", " -----\n", " Logspace is equivalent to the code\n", " \n", " >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)\n", " ... # doctest: +SKIP\n", " >>> power(base, y).astype(dtype)\n", " ... # doctest: +SKIP\n", " \n", " Examples\n", " --------\n", " >>> np.logspace(2.0, 3.0, num=4)\n", " array([ 100. , 215.443469 , 464.15888336, 1000. ])\n", " >>> np.logspace(2.0, 3.0, num=4, endpoint=False)\n", " array([ 100. , 177.827941 , 316.22776602, 562.34132519])\n", " >>> np.logspace(2.0, 3.0, num=4, base=2.0)\n", " array([ 4. , 5.0396842 , 6.34960421, 8. ])\n", " \n", " Graphical illustration:\n", " \n", " >>> import matplotlib.pyplot as plt\n", " >>> N = 10\n", " >>> x1 = np.logspace(0.1, 1, N, endpoint=True)\n", " >>> x2 = np.logspace(0.1, 1, N, endpoint=False)\n", " >>> y = np.zeros(N)\n", " >>> plt.plot(x1, y, 'o')\n", " []\n", " >>> plt.plot(x2, y + 0.5, 'o')\n", " []\n", " >>> plt.ylim([-0.5, 1])\n", " (-0.5, 1)\n", " >>> plt.show()\n", " \n", " lookfor(what, module=None, import_modules=True, regenerate=False, output=None)\n", " Do a keyword search on docstrings.\n", " \n", " A list of of objects that matched the search is displayed,\n", " sorted by relevance. All given keywords need to be found in the\n", " docstring for it to be returned as a result, but the order does\n", " not matter.\n", " \n", " Parameters\n", " ----------\n", " what : str\n", " String containing words to look for.\n", " module : str or list, optional\n", " Name of module(s) whose docstrings to go through.\n", " import_modules : bool, optional\n", " Whether to import sub-modules in packages. Default is True.\n", " regenerate : bool, optional\n", " Whether to re-generate the docstring cache. Default is False.\n", " output : file-like, optional\n", " File-like object to write the output to. If omitted, use a pager.\n", " \n", " See Also\n", " --------\n", " source, info\n", " \n", " Notes\n", " -----\n", " Relevance is determined only roughly, by checking if the keywords occur\n", " in the function name, at the start of a docstring, etc.\n", " \n", " Examples\n", " --------\n", " >>> np.lookfor('binary representation')\n", " Search results for 'binary representation'\n", " ------------------------------------------\n", " numpy.binary_repr\n", " Return the binary representation of the input number as a string.\n", " numpy.core.setup_common.long_double_representation\n", " Given a binary dump as given by GNU od -b, look for long double\n", " numpy.base_repr\n", " Return a string representation of a number in the given base system.\n", " ...\n", " \n", " mafromtxt(fname, **kwargs)\n", " Load ASCII data stored in a text file and return a masked array.\n", " \n", " Parameters\n", " ----------\n", " fname, kwargs : For a description of input parameters, see `genfromtxt`.\n", " \n", " See Also\n", " --------\n", " numpy.genfromtxt : generic function to load ASCII data.\n", " \n", " mask_indices(n, mask_func, k=0)\n", " Return the indices to access (n, n) arrays, given a masking function.\n", " \n", " Assume `mask_func` is a function that, for a square array a of size\n", " ``(n, n)`` with a possible offset argument `k`, when called as\n", " ``mask_func(a, k)`` returns a new array with zeros in certain locations\n", " (functions like `triu` or `tril` do precisely this). Then this function\n", " returns the indices where the non-zero values would be located.\n", " \n", " Parameters\n", " ----------\n", " n : int\n", " The returned indices will be valid to access arrays of shape (n, n).\n", " mask_func : callable\n", " A function whose call signature is similar to that of `triu`, `tril`.\n", " That is, ``mask_func(x, k)`` returns a boolean array, shaped like `x`.\n", " `k` is an optional argument to the function.\n", " k : scalar\n", " An optional argument which is passed through to `mask_func`. Functions\n", " like `triu`, `tril` take a second argument that is interpreted as an\n", " offset.\n", " \n", " Returns\n", " -------\n", " indices : tuple of arrays.\n", " The `n` arrays of indices corresponding to the locations where\n", " ``mask_func(np.ones((n, n)), k)`` is True.\n", " \n", " See Also\n", " --------\n", " triu, tril, triu_indices, tril_indices\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.4.0\n", " \n", " Examples\n", " --------\n", " These are the indices that would allow you to access the upper triangular\n", " part of any 3x3 array:\n", " \n", " >>> iu = np.mask_indices(3, np.triu)\n", " \n", " For example, if `a` is a 3x3 array:\n", " \n", " >>> a = np.arange(9).reshape(3, 3)\n", " >>> a\n", " array([[0, 1, 2],\n", " [3, 4, 5],\n", " [6, 7, 8]])\n", " >>> a[iu]\n", " array([0, 1, 2, 4, 5, 8])\n", " \n", " An offset can be passed also to the masking function. This gets us the\n", " indices starting on the first diagonal right of the main one:\n", " \n", " >>> iu1 = np.mask_indices(3, np.triu, 1)\n", " \n", " with which we now extract only three elements:\n", " \n", " >>> a[iu1]\n", " array([1, 2, 5])\n", " \n", " mat = asmatrix(data, dtype=None)\n", " Interpret the input as a matrix.\n", " \n", " Unlike `matrix`, `asmatrix` does not make a copy if the input is already\n", " a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``.\n", " \n", " Parameters\n", " ----------\n", " data : array_like\n", " Input data.\n", " \n", " Returns\n", " -------\n", " mat : matrix\n", " `data` interpreted as a matrix.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([[1, 2], [3, 4]])\n", " \n", " >>> m = np.asmatrix(x)\n", " \n", " >>> x[0,0] = 5\n", " \n", " >>> m\n", " matrix([[5, 2],\n", " [3, 4]])\n", " \n", " maximum_sctype(t)\n", " Return the scalar type of highest precision of the same kind as the input.\n", " \n", " Parameters\n", " ----------\n", " t : dtype or dtype specifier\n", " The input data type. This can be a `dtype` object or an object that\n", " is convertible to a `dtype`.\n", " \n", " Returns\n", " -------\n", " out : dtype\n", " The highest precision data type of the same kind (`dtype.kind`) as `t`.\n", " \n", " See Also\n", " --------\n", " obj2sctype, mintypecode, sctype2char\n", " dtype\n", " \n", " Examples\n", " --------\n", " >>> np.maximum_sctype(np.int)\n", " \n", " >>> np.maximum_sctype(np.uint8)\n", " \n", " >>> np.maximum_sctype(np.complex)\n", " \n", " \n", " >>> np.maximum_sctype(str)\n", " \n", " \n", " >>> np.maximum_sctype('i2')\n", " \n", " >>> np.maximum_sctype('f4')\n", " \n", " \n", " may_share_memory(...)\n", " Determine if two arrays can share memory\n", " \n", " The memory-bounds of a and b are computed. If they overlap then\n", " this function returns True. Otherwise, it returns False.\n", " \n", " A return of True does not necessarily mean that the two arrays\n", " share any element. It just means that they *might*.\n", " \n", " Parameters\n", " ----------\n", " a, b : ndarray\n", " \n", " Returns\n", " -------\n", " out : bool\n", " \n", " Examples\n", " --------\n", " >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))\n", " False\n", " \n", " mean(a, axis=None, dtype=None, out=None, keepdims=False)\n", " Compute the arithmetic mean along the specified axis.\n", " \n", " Returns the average of the array elements. The average is taken over\n", " the flattened array by default, otherwise over the specified axis.\n", " `float64` intermediate and return values are used for integer inputs.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array containing numbers whose mean is desired. If `a` is not an\n", " array, a conversion is attempted.\n", " axis : int, optional\n", " Axis along which the means are computed. The default is to compute\n", " the mean of the flattened array.\n", " dtype : data-type, optional\n", " Type to use in computing the mean. For integer inputs, the default\n", " is `float64`; for floating point inputs, it is the same as the\n", " input dtype.\n", " out : ndarray, optional\n", " Alternate output array in which to place the result. The default\n", " is ``None``; if provided, it must have the same shape as the\n", " expected output, but the type will be cast if necessary.\n", " See `doc.ufuncs` for details.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " m : ndarray, see dtype parameter above\n", " If `out=None`, returns a new array containing the mean values,\n", " otherwise a reference to the output array is returned.\n", " \n", " See Also\n", " --------\n", " average : Weighted average\n", " std, var, nanmean, nanstd, nanvar\n", " \n", " Notes\n", " -----\n", " The arithmetic mean is the sum of the elements along the axis divided\n", " by the number of elements.\n", " \n", " Note that for floating-point input, the mean is computed using the\n", " same precision the input has. Depending on the input data, this can\n", " cause the results to be inaccurate, especially for `float32` (see\n", " example below). Specifying a higher-precision accumulator using the\n", " `dtype` keyword can alleviate this issue.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, 2], [3, 4]])\n", " >>> np.mean(a)\n", " 2.5\n", " >>> np.mean(a, axis=0)\n", " array([ 2., 3.])\n", " >>> np.mean(a, axis=1)\n", " array([ 1.5, 3.5])\n", " \n", " In single precision, `mean` can be inaccurate:\n", " \n", " >>> a = np.zeros((2, 512*512), dtype=np.float32)\n", " >>> a[0, :] = 1.0\n", " >>> a[1, :] = 0.1\n", " >>> np.mean(a)\n", " 0.546875\n", " \n", " Computing the mean in float64 is more accurate:\n", " \n", " >>> np.mean(a, dtype=np.float64)\n", " 0.55000000074505806\n", " \n", " median(a, axis=None, out=None, overwrite_input=False, keepdims=False)\n", " Compute the median along the specified axis.\n", " \n", " Returns the median of the array elements.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array or object that can be converted to an array.\n", " axis : int or sequence of int, optional\n", " Axis along which the medians are computed. The default (axis=None)\n", " is to compute the median along a flattened version of the array.\n", " A sequence of axes is supported since version 1.9.0.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must have\n", " the same shape and buffer length as the expected output, but the\n", " type (of the output) will be cast if necessary.\n", " overwrite_input : bool, optional\n", " If True, then allow use of memory of input array (a) for\n", " calculations. The input array will be modified by the call to\n", " median. This will save memory when you do not need to preserve the\n", " contents of the input array. Treat the input as undefined, but it\n", " will probably be fully or partially sorted. Default is False. Note\n", " that, if `overwrite_input` is True and the input is not already an\n", " ndarray, an error will be raised.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " .. versionadded:: 1.9.0\n", " \n", " \n", " Returns\n", " -------\n", " median : ndarray\n", " A new array holding the result (unless `out` is specified, in which\n", " case that array is returned instead). If the input contains\n", " integers, or floats of smaller precision than 64, then the output\n", " data-type is float64. Otherwise, the output data-type is the same\n", " as that of the input.\n", " \n", " See Also\n", " --------\n", " mean, percentile\n", " \n", " Notes\n", " -----\n", " Given a vector V of length N, the median of V is the middle value of\n", " a sorted copy of V, ``V_sorted`` - i.e., ``V_sorted[(N-1)/2]``, when N is\n", " odd. When N is even, it is the average of the two middle values of\n", " ``V_sorted``.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[10, 7, 4], [3, 2, 1]])\n", " >>> a\n", " array([[10, 7, 4],\n", " [ 3, 2, 1]])\n", " >>> np.median(a)\n", " 3.5\n", " >>> np.median(a, axis=0)\n", " array([ 6.5, 4.5, 2.5])\n", " >>> np.median(a, axis=1)\n", " array([ 7., 2.])\n", " >>> m = np.median(a, axis=0)\n", " >>> out = np.zeros_like(m)\n", " >>> np.median(a, axis=0, out=m)\n", " array([ 6.5, 4.5, 2.5])\n", " >>> m\n", " array([ 6.5, 4.5, 2.5])\n", " >>> b = a.copy()\n", " >>> np.median(b, axis=1, overwrite_input=True)\n", " array([ 7., 2.])\n", " >>> assert not np.all(a==b)\n", " >>> b = a.copy()\n", " >>> np.median(b, axis=None, overwrite_input=True)\n", " 3.5\n", " >>> assert not np.all(a==b)\n", " \n", " meshgrid(*xi, **kwargs)\n", " Return coordinate matrices from coordinate vectors.\n", " \n", " Make N-D coordinate arrays for vectorized evaluations of\n", " N-D scalar/vector fields over N-D grids, given\n", " one-dimensional coordinate arrays x1, x2,..., xn.\n", " \n", " .. versionchanged:: 1.9\n", " 1-D and 0-D cases are allowed.\n", " \n", " Parameters\n", " ----------\n", " x1, x2,..., xn : array_like\n", " 1-D arrays representing the coordinates of a grid.\n", " indexing : {'xy', 'ij'}, optional\n", " Cartesian ('xy', default) or matrix ('ij') indexing of output.\n", " See Notes for more details.\n", " \n", " .. versionadded:: 1.7.0\n", " sparse : bool, optional\n", " If True a sparse grid is returned in order to conserve memory.\n", " Default is False.\n", " \n", " .. versionadded:: 1.7.0\n", " copy : bool, optional\n", " If False, a view into the original arrays are returned in order to\n", " conserve memory. Default is True. Please note that\n", " ``sparse=False, copy=False`` will likely return non-contiguous\n", " arrays. Furthermore, more than one element of a broadcast array\n", " may refer to a single memory location. If you need to write to the\n", " arrays, make copies first.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " Returns\n", " -------\n", " X1, X2,..., XN : ndarray\n", " For vectors `x1`, `x2`,..., 'xn' with lengths ``Ni=len(xi)`` ,\n", " return ``(N1, N2, N3,...Nn)`` shaped arrays if indexing='ij'\n", " or ``(N2, N1, N3,...Nn)`` shaped arrays if indexing='xy'\n", " with the elements of `xi` repeated to fill the matrix along\n", " the first dimension for `x1`, the second for `x2` and so on.\n", " \n", " Notes\n", " -----\n", " This function supports both indexing conventions through the indexing\n", " keyword argument. Giving the string 'ij' returns a meshgrid with\n", " matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing.\n", " In the 2-D case with inputs of length M and N, the outputs are of shape\n", " (N, M) for 'xy' indexing and (M, N) for 'ij' indexing. In the 3-D case\n", " with inputs of length M, N and P, outputs are of shape (N, M, P) for\n", " 'xy' indexing and (M, N, P) for 'ij' indexing. The difference is\n", " illustrated by the following code snippet::\n", " \n", " xv, yv = meshgrid(x, y, sparse=False, indexing='ij')\n", " for i in range(nx):\n", " for j in range(ny):\n", " # treat xv[i,j], yv[i,j]\n", " \n", " xv, yv = meshgrid(x, y, sparse=False, indexing='xy')\n", " for i in range(nx):\n", " for j in range(ny):\n", " # treat xv[j,i], yv[j,i]\n", " \n", " In the 1-D and 0-D case, the indexing and sparse keywords have no effect.\n", " \n", " See Also\n", " --------\n", " index_tricks.mgrid : Construct a multi-dimensional \"meshgrid\"\n", " using indexing notation.\n", " index_tricks.ogrid : Construct an open multi-dimensional \"meshgrid\"\n", " using indexing notation.\n", " \n", " Examples\n", " --------\n", " >>> nx, ny = (3, 2)\n", " >>> x = np.linspace(0, 1, nx)\n", " >>> y = np.linspace(0, 1, ny)\n", " >>> xv, yv = meshgrid(x, y)\n", " >>> xv\n", " array([[ 0. , 0.5, 1. ],\n", " [ 0. , 0.5, 1. ]])\n", " >>> yv\n", " array([[ 0., 0., 0.],\n", " [ 1., 1., 1.]])\n", " >>> xv, yv = meshgrid(x, y, sparse=True) # make sparse output arrays\n", " >>> xv\n", " array([[ 0. , 0.5, 1. ]])\n", " >>> yv\n", " array([[ 0.],\n", " [ 1.]])\n", " \n", " `meshgrid` is very useful to evaluate functions on a grid.\n", " \n", " >>> x = np.arange(-5, 5, 0.1)\n", " >>> y = np.arange(-5, 5, 0.1)\n", " >>> xx, yy = meshgrid(x, y, sparse=True)\n", " >>> z = np.sin(xx**2 + yy**2) / (xx**2 + yy**2)\n", " >>> h = plt.contourf(x,y,z)\n", " \n", " min_scalar_type(...)\n", " min_scalar_type(a)\n", " \n", " For scalar ``a``, returns the data type with the smallest size\n", " and smallest scalar kind which can hold its value. For non-scalar\n", " array ``a``, returns the vector's dtype unmodified.\n", " \n", " Floating point values are not demoted to integers,\n", " and complex values are not demoted to floats.\n", " \n", " Parameters\n", " ----------\n", " a : scalar or array_like\n", " The value whose minimal data type is to be found.\n", " \n", " Returns\n", " -------\n", " out : dtype\n", " The minimal data type.\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.6.0\n", " \n", " See Also\n", " --------\n", " result_type, promote_types, dtype, can_cast\n", " \n", " Examples\n", " --------\n", " >>> np.min_scalar_type(10)\n", " dtype('uint8')\n", " \n", " >>> np.min_scalar_type(-260)\n", " dtype('int16')\n", " \n", " >>> np.min_scalar_type(3.1)\n", " dtype('float16')\n", " \n", " >>> np.min_scalar_type(1e50)\n", " dtype('float64')\n", " \n", " >>> np.min_scalar_type(np.arange(4,dtype='f8'))\n", " dtype('float64')\n", " \n", " mintypecode(typechars, typeset='GDFgdf', default='d')\n", " Return the character for the minimum-size type to which given types can\n", " be safely cast.\n", " \n", " The returned type character must represent the smallest size dtype such\n", " that an array of the returned type can handle the data from an array of\n", " all types in `typechars` (or if `typechars` is an array, then its\n", " dtype.char).\n", " \n", " Parameters\n", " ----------\n", " typechars : list of str or array_like\n", " If a list of strings, each string should represent a dtype.\n", " If array_like, the character representation of the array dtype is used.\n", " typeset : str or list of str, optional\n", " The set of characters that the returned character is chosen from.\n", " The default set is 'GDFgdf'.\n", " default : str, optional\n", " The default character, this is returned if none of the characters in\n", " `typechars` matches a character in `typeset`.\n", " \n", " Returns\n", " -------\n", " typechar : str\n", " The character representing the minimum-size type that was found.\n", " \n", " See Also\n", " --------\n", " dtype, sctype2char, maximum_sctype\n", " \n", " Examples\n", " --------\n", " >>> np.mintypecode(['d', 'f', 'S'])\n", " 'd'\n", " >>> x = np.array([1.1, 2-3.j])\n", " >>> np.mintypecode(x)\n", " 'D'\n", " \n", " >>> np.mintypecode('abceh', default='G')\n", " 'G'\n", " \n", " mirr(values, finance_rate, reinvest_rate)\n", " Modified internal rate of return.\n", " \n", " Parameters\n", " ----------\n", " values : array_like\n", " Cash flows (must contain at least one positive and one negative\n", " value) or nan is returned. The first value is considered a sunk\n", " cost at time zero.\n", " finance_rate : scalar\n", " Interest rate paid on the cash flows\n", " reinvest_rate : scalar\n", " Interest rate received on the cash flows upon reinvestment\n", " \n", " Returns\n", " -------\n", " out : float\n", " Modified internal rate of return\n", " \n", " msort(a)\n", " Return a copy of an array sorted along the first axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array to be sorted.\n", " \n", " Returns\n", " -------\n", " sorted_array : ndarray\n", " Array of the same type and shape as `a`.\n", " \n", " See Also\n", " --------\n", " sort\n", " \n", " Notes\n", " -----\n", " ``np.msort(a)`` is equivalent to ``np.sort(a, axis=0)``.\n", " \n", " nan_to_num(x)\n", " Replace nan with zero and inf with finite numbers.\n", " \n", " Returns an array or scalar replacing Not a Number (NaN) with zero,\n", " (positive) infinity with a very large number and negative infinity\n", " with a very small (or negative) number.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " Input data.\n", " \n", " Returns\n", " -------\n", " out : ndarray, float\n", " Array with the same shape as `x` and dtype of the element in `x` with\n", " the greatest precision. NaN is replaced by zero, and infinity\n", " (-infinity) is replaced by the largest (smallest or most negative)\n", " floating point value that fits in the output dtype. All finite numbers\n", " are upcast to the output dtype (default float64).\n", " \n", " See Also\n", " --------\n", " isinf : Shows which elements are negative or negative infinity.\n", " isneginf : Shows which elements are negative infinity.\n", " isposinf : Shows which elements are positive infinity.\n", " isnan : Shows which elements are Not a Number (NaN).\n", " isfinite : Shows which elements are finite (not NaN, not infinity)\n", " \n", " Notes\n", " -----\n", " Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic\n", " (IEEE 754). This means that Not a Number is not equivalent to infinity.\n", " \n", " \n", " Examples\n", " --------\n", " >>> np.set_printoptions(precision=8)\n", " >>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])\n", " >>> np.nan_to_num(x)\n", " array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000,\n", " -1.28000000e+002, 1.28000000e+002])\n", " \n", " nanargmax(a, axis=None)\n", " Return the indices of the maximum values in the specified axis ignoring\n", " NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the\n", " results cannot be trusted if a slice contains only NaNs and -Infs.\n", " \n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " axis : int, optional\n", " Axis along which to operate. By default flattened input is used.\n", " \n", " Returns\n", " -------\n", " index_array : ndarray\n", " An array of indices or a single index value.\n", " \n", " See Also\n", " --------\n", " argmax, nanargmin\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[np.nan, 4], [2, 3]])\n", " >>> np.argmax(a)\n", " 0\n", " >>> np.nanargmax(a)\n", " 1\n", " >>> np.nanargmax(a, axis=0)\n", " array([1, 0])\n", " >>> np.nanargmax(a, axis=1)\n", " array([1, 1])\n", " \n", " nanargmin(a, axis=None)\n", " Return the indices of the minimum values in the specified axis ignoring\n", " NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results\n", " cannot be trusted if a slice contains only NaNs and Infs.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " axis : int, optional\n", " Axis along which to operate. By default flattened input is used.\n", " \n", " Returns\n", " -------\n", " index_array : ndarray\n", " An array of indices or a single index value.\n", " \n", " See Also\n", " --------\n", " argmin, nanargmax\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[np.nan, 4], [2, 3]])\n", " >>> np.argmin(a)\n", " 0\n", " >>> np.nanargmin(a)\n", " 2\n", " >>> np.nanargmin(a, axis=0)\n", " array([1, 1])\n", " >>> np.nanargmin(a, axis=1)\n", " array([1, 0])\n", " \n", " nanmax(a, axis=None, out=None, keepdims=False)\n", " Return the maximum of an array or maximum along an axis, ignoring any\n", " NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is\n", " raised and NaN is returned for that slice.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array containing numbers whose maximum is desired. If `a` is not an\n", " array, a conversion is attempted.\n", " axis : int, optional\n", " Axis along which the maximum is computed. The default is to compute\n", " the maximum of the flattened array.\n", " out : ndarray, optional\n", " Alternate output array in which to place the result. The default\n", " is ``None``; if provided, it must have the same shape as the\n", " expected output, but the type will be cast if necessary. See\n", " `doc.ufuncs` for details.\n", " \n", " .. versionadded:: 1.8.0\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left in the\n", " result as dimensions with size one. With this option, the result\n", " will broadcast correctly against the original `a`.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Returns\n", " -------\n", " nanmax : ndarray\n", " An array with the same shape as `a`, with the specified axis removed.\n", " If `a` is a 0-d array, or if axis is None, an ndarray scalar is\n", " returned. The same dtype as `a` is returned.\n", " \n", " See Also\n", " --------\n", " nanmin :\n", " The minimum value of an array along a given axis, ignoring any NaNs.\n", " amax :\n", " The maximum value of an array along a given axis, propagating any NaNs.\n", " fmax :\n", " Element-wise maximum of two arrays, ignoring any NaNs.\n", " maximum :\n", " Element-wise maximum of two arrays, propagating any NaNs.\n", " isnan :\n", " Shows which elements are Not a Number (NaN).\n", " isfinite:\n", " Shows which elements are neither NaN nor infinity.\n", " \n", " amin, fmin, minimum\n", " \n", " Notes\n", " -----\n", " Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic\n", " (IEEE 754). This means that Not a Number is not equivalent to infinity.\n", " Positive infinity is treated as a very large number and negative\n", " infinity is treated as a very small (i.e. negative) number.\n", " \n", " If the input has a integer type the function is equivalent to np.max.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, 2], [3, np.nan]])\n", " >>> np.nanmax(a)\n", " 3.0\n", " >>> np.nanmax(a, axis=0)\n", " array([ 3., 2.])\n", " >>> np.nanmax(a, axis=1)\n", " array([ 2., 3.])\n", " \n", " When positive infinity and negative infinity are present:\n", " \n", " >>> np.nanmax([1, 2, np.nan, np.NINF])\n", " 2.0\n", " >>> np.nanmax([1, 2, np.nan, np.inf])\n", " inf\n", " \n", " nanmean(a, axis=None, dtype=None, out=None, keepdims=False)\n", " Compute the arithmetic mean along the specified axis, ignoring NaNs.\n", " \n", " Returns the average of the array elements. The average is taken over\n", " the flattened array by default, otherwise over the specified axis.\n", " `float64` intermediate and return values are used for integer inputs.\n", " \n", " For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array containing numbers whose mean is desired. If `a` is not an\n", " array, a conversion is attempted.\n", " axis : int, optional\n", " Axis along which the means are computed. The default is to compute\n", " the mean of the flattened array.\n", " dtype : data-type, optional\n", " Type to use in computing the mean. For integer inputs, the default\n", " is `float64`; for inexact inputs, it is the same as the input\n", " dtype.\n", " out : ndarray, optional\n", " Alternate output array in which to place the result. The default\n", " is ``None``; if provided, it must have the same shape as the\n", " expected output, but the type will be cast if necessary. See\n", " `doc.ufuncs` for details.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left in the\n", " result as dimensions with size one. With this option, the result\n", " will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " m : ndarray, see dtype parameter above\n", " If `out=None`, returns a new array containing the mean values,\n", " otherwise a reference to the output array is returned. Nan is\n", " returned for slices that contain only NaNs.\n", " \n", " See Also\n", " --------\n", " average : Weighted average\n", " mean : Arithmetic mean taken while not ignoring NaNs\n", " var, nanvar\n", " \n", " Notes\n", " -----\n", " The arithmetic mean is the sum of the non-NaN elements along the axis\n", " divided by the number of non-NaN elements.\n", " \n", " Note that for floating-point input, the mean is computed using the same\n", " precision the input has. Depending on the input data, this can cause\n", " the results to be inaccurate, especially for `float32`. Specifying a\n", " higher-precision accumulator using the `dtype` keyword can alleviate\n", " this issue.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, np.nan], [3, 4]])\n", " >>> np.nanmean(a)\n", " 2.6666666666666665\n", " >>> np.nanmean(a, axis=0)\n", " array([ 2., 4.])\n", " >>> np.nanmean(a, axis=1)\n", " array([ 1., 3.5])\n", " \n", " nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False)\n", " Compute the median along the specified axis, while ignoring NaNs.\n", " \n", " Returns the median of the array elements.\n", " \n", " .. versionadded:: 1.9.0\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array or object that can be converted to an array.\n", " axis : int, optional\n", " Axis along which the medians are computed. The default (axis=None)\n", " is to compute the median along a flattened version of the array.\n", " A sequence of axes is supported since version 1.9.0.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must have\n", " the same shape and buffer length as the expected output, but the\n", " type (of the output) will be cast if necessary.\n", " overwrite_input : bool, optional\n", " If True, then allow use of memory of input array (a) for\n", " calculations. The input array will be modified by the call to\n", " median. This will save memory when you do not need to preserve\n", " the contents of the input array. Treat the input as undefined,\n", " but it will probably be fully or partially sorted. Default is\n", " False. Note that, if `overwrite_input` is True and the input\n", " is not already an ndarray, an error will be raised.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " \n", " \n", " Returns\n", " -------\n", " median : ndarray\n", " A new array holding the result. If the input contains integers, or\n", " floats of smaller precision than 64, then the output data-type is\n", " float64. Otherwise, the output data-type is the same as that of the\n", " input.\n", " \n", " See Also\n", " --------\n", " mean, median, percentile\n", " \n", " Notes\n", " -----\n", " Given a vector V of length N, the median of V is the middle value of\n", " a sorted copy of V, ``V_sorted`` - i.e., ``V_sorted[(N-1)/2]``, when N is\n", " odd. When N is even, it is the average of the two middle values of\n", " ``V_sorted``.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[10.0, 7, 4], [3, 2, 1]])\n", " >>> a[0, 1] = np.nan\n", " >>> a\n", " array([[ 10., nan, 4.],\n", " [ 3., 2., 1.]])\n", " >>> np.median(a)\n", " nan\n", " >>> np.nanmedian(a)\n", " 3.0\n", " >>> np.nanmedian(a, axis=0)\n", " array([ 6.5, 2., 2.5])\n", " >>> np.median(a, axis=1)\n", " array([ 7., 2.])\n", " >>> b = a.copy()\n", " >>> np.nanmedian(b, axis=1, overwrite_input=True)\n", " array([ 7., 2.])\n", " >>> assert not np.all(a==b)\n", " >>> b = a.copy()\n", " >>> np.nanmedian(b, axis=None, overwrite_input=True)\n", " 3.0\n", " >>> assert not np.all(a==b)\n", " \n", " nanmin(a, axis=None, out=None, keepdims=False)\n", " Return minimum of an array or minimum along an axis, ignoring any NaNs.\n", " When all-NaN slices are encountered a ``RuntimeWarning`` is raised and\n", " Nan is returned for that slice.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array containing numbers whose minimum is desired. If `a` is not an\n", " array, a conversion is attempted.\n", " axis : int, optional\n", " Axis along which the minimum is computed. The default is to compute\n", " the minimum of the flattened array.\n", " out : ndarray, optional\n", " Alternate output array in which to place the result. The default\n", " is ``None``; if provided, it must have the same shape as the\n", " expected output, but the type will be cast if necessary. See\n", " `doc.ufuncs` for details.\n", " \n", " .. versionadded:: 1.8.0\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left in the\n", " result as dimensions with size one. With this option, the result\n", " will broadcast correctly against the original `a`.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Returns\n", " -------\n", " nanmin : ndarray\n", " An array with the same shape as `a`, with the specified axis\n", " removed. If `a` is a 0-d array, or if axis is None, an ndarray\n", " scalar is returned. The same dtype as `a` is returned.\n", " \n", " See Also\n", " --------\n", " nanmax :\n", " The maximum value of an array along a given axis, ignoring any NaNs.\n", " amin :\n", " The minimum value of an array along a given axis, propagating any NaNs.\n", " fmin :\n", " Element-wise minimum of two arrays, ignoring any NaNs.\n", " minimum :\n", " Element-wise minimum of two arrays, propagating any NaNs.\n", " isnan :\n", " Shows which elements are Not a Number (NaN).\n", " isfinite:\n", " Shows which elements are neither NaN nor infinity.\n", " \n", " amax, fmax, maximum\n", " \n", " Notes\n", " -----\n", " Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic\n", " (IEEE 754). This means that Not a Number is not equivalent to infinity.\n", " Positive infinity is treated as a very large number and negative\n", " infinity is treated as a very small (i.e. negative) number.\n", " \n", " If the input has a integer type the function is equivalent to np.min.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, 2], [3, np.nan]])\n", " >>> np.nanmin(a)\n", " 1.0\n", " >>> np.nanmin(a, axis=0)\n", " array([ 1., 2.])\n", " >>> np.nanmin(a, axis=1)\n", " array([ 1., 3.])\n", " \n", " When positive infinity and negative infinity are present:\n", " \n", " >>> np.nanmin([1, 2, np.nan, np.inf])\n", " 1.0\n", " >>> np.nanmin([1, 2, np.nan, np.NINF])\n", " -inf\n", " \n", " nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n", " Compute the qth percentile of the data along the specified axis, while\n", " ignoring nan values.\n", " \n", " Returns the qth percentile of the array elements.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array or object that can be converted to an array.\n", " q : float in range of [0,100] (or sequence of floats)\n", " Percentile to compute which must be between 0 and 100 inclusive.\n", " axis : int or sequence of int, optional\n", " Axis along which the percentiles are computed. The default (None)\n", " is to compute the percentiles along a flattened version of the array.\n", " A sequence of axes is supported since version 1.9.0.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must\n", " have the same shape and buffer length as the expected output,\n", " but the type (of the output) will be cast if necessary.\n", " overwrite_input : bool, optional\n", " If True, then allow use of memory of input array `a` for\n", " calculations. The input array will be modified by the call to\n", " percentile. This will save memory when you do not need to preserve\n", " the contents of the input array. In this case you should not make\n", " any assumptions about the content of the passed in array `a` after\n", " this function completes -- treat it as undefined. Default is False.\n", " Note that, if the `a` input is not already an array this parameter\n", " will have no effect, `a` will be converted to an array internally\n", " regardless of the value of this parameter.\n", " interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}\n", " This optional parameter specifies the interpolation method to use,\n", " when the desired quantile lies between two data points `i` and `j`:\n", " * linear: `i + (j - i) * fraction`, where `fraction` is the\n", " fractional part of the index surrounded by `i` and `j`.\n", " * lower: `i`.\n", " * higher: `j`.\n", " * nearest: `i` or `j` whichever is nearest.\n", " * midpoint: (`i` + `j`) / 2.\n", " \n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " \n", " Returns\n", " -------\n", " nanpercentile : scalar or ndarray\n", " If a single percentile `q` is given and axis=None a scalar is\n", " returned. If multiple percentiles `q` are given an array holding\n", " the result is returned. The results are listed in the first axis.\n", " (If `out` is specified, in which case that array is returned\n", " instead). If the input contains integers, or floats of smaller\n", " precision than 64, then the output data-type is float64. Otherwise,\n", " the output data-type is the same as that of the input.\n", " \n", " See Also\n", " --------\n", " nanmean, nanmedian, percentile, median, mean\n", " \n", " Notes\n", " -----\n", " Given a vector V of length N, the q-th percentile of V is the q-th ranked\n", " value in a sorted copy of V. The values and distances of the two\n", " nearest neighbors as well as the `interpolation` parameter will\n", " determine the percentile if the normalized ranking does not match q\n", " exactly. This function is the same as the median if ``q=50``, the same\n", " as the minimum if ``q=0``and the same as the maximum if ``q=100``.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[10., 7., 4.], [3., 2., 1.]])\n", " >>> a[0][1] = np.nan\n", " >>> a\n", " array([[ 10., nan, 4.],\n", " [ 3., 2., 1.]])\n", " >>> np.percentile(a, 50)\n", " nan\n", " >>> np.nanpercentile(a, 50)\n", " 3.5\n", " >>> np.nanpercentile(a, 50, axis=0)\n", " array([[ 6.5, 4.5, 2.5]])\n", " >>> np.nanpercentile(a, 50, axis=1)\n", " array([[ 7.],\n", " [ 2.]])\n", " >>> m = np.nanpercentile(a, 50, axis=0)\n", " >>> out = np.zeros_like(m)\n", " >>> np.nanpercentile(a, 50, axis=0, out=m)\n", " array([[ 6.5, 4.5, 2.5]])\n", " >>> m\n", " array([[ 6.5, 4.5, 2.5]])\n", " >>> b = a.copy()\n", " >>> np.nanpercentile(b, 50, axis=1, overwrite_input=True)\n", " array([[ 7.],\n", " [ 2.]])\n", " >>> assert not np.all(a==b)\n", " >>> b = a.copy()\n", " >>> np.nanpercentile(b, 50, axis=None, overwrite_input=True)\n", " array([ 3.5])\n", " \n", " nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n", " Compute the standard deviation along the specified axis, while\n", " ignoring NaNs.\n", " \n", " Returns the standard deviation, a measure of the spread of a\n", " distribution, of the non-NaN array elements. The standard deviation is\n", " computed for the flattened array by default, otherwise over the\n", " specified axis.\n", " \n", " For all-NaN slices or slices with zero degrees of freedom, NaN is\n", " returned and a `RuntimeWarning` is raised.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Calculate the standard deviation of the non-NaN values.\n", " axis : int, optional\n", " Axis along which the standard deviation is computed. The default is\n", " to compute the standard deviation of the flattened array.\n", " dtype : dtype, optional\n", " Type to use in computing the standard deviation. For arrays of\n", " integer type the default is float64, for arrays of float types it\n", " is the same as the array type.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must have\n", " the same shape as the expected output but the type (of the\n", " calculated values) will be cast if necessary.\n", " ddof : int, optional\n", " Means Delta Degrees of Freedom. The divisor used in calculations\n", " is ``N - ddof``, where ``N`` represents the number of non-NaN\n", " elements. By default `ddof` is zero.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " standard_deviation : ndarray, see dtype parameter above.\n", " If `out` is None, return a new array containing the standard\n", " deviation, otherwise return a reference to the output array. If\n", " ddof is >= the number of non-NaN elements in a slice or the slice\n", " contains only NaNs, then the result for that slice is NaN.\n", " \n", " See Also\n", " --------\n", " var, mean, std\n", " nanvar, nanmean\n", " numpy.doc.ufuncs : Section \"Output arguments\"\n", " \n", " Notes\n", " -----\n", " The standard deviation is the square root of the average of the squared\n", " deviations from the mean: ``std = sqrt(mean(abs(x - x.mean())**2))``.\n", " \n", " The average squared deviation is normally calculated as\n", " ``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is\n", " specified, the divisor ``N - ddof`` is used instead. In standard\n", " statistical practice, ``ddof=1`` provides an unbiased estimator of the\n", " variance of the infinite population. ``ddof=0`` provides a maximum\n", " likelihood estimate of the variance for normally distributed variables.\n", " The standard deviation computed in this function is the square root of\n", " the estimated variance, so even with ``ddof=1``, it will not be an\n", " unbiased estimate of the standard deviation per se.\n", " \n", " Note that, for complex numbers, `std` takes the absolute value before\n", " squaring, so that the result is always real and nonnegative.\n", " \n", " For floating-point input, the *std* is computed using the same\n", " precision the input has. Depending on the input data, this can cause\n", " the results to be inaccurate, especially for float32 (see example\n", " below). Specifying a higher-accuracy accumulator using the `dtype`\n", " keyword can alleviate this issue.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, np.nan], [3, 4]])\n", " >>> np.nanstd(a)\n", " 1.247219128924647\n", " >>> np.nanstd(a, axis=0)\n", " array([ 1., 0.])\n", " >>> np.nanstd(a, axis=1)\n", " array([ 0., 0.5])\n", " \n", " nansum(a, axis=None, dtype=None, out=None, keepdims=0)\n", " Return the sum of array elements over a given axis treating Not a\n", " Numbers (NaNs) as zero.\n", " \n", " In Numpy versions <= 1.8 Nan is returned for slices that are all-NaN or\n", " empty. In later versions zero is returned.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array containing numbers whose sum is desired. If `a` is not an\n", " array, a conversion is attempted.\n", " axis : int, optional\n", " Axis along which the sum is computed. The default is to compute the\n", " sum of the flattened array.\n", " dtype : data-type, optional\n", " The type of the returned array and of the accumulator in which the\n", " elements are summed. By default, the dtype of `a` is used. An\n", " exception is when `a` has an integer type with less precision than\n", " the platform (u)intp. In that case, the default will be either\n", " (u)int32 or (u)int64 depending on whether the platform is 32 or 64\n", " bits. For inexact inputs, dtype must be inexact.\n", " \n", " .. versionadded:: 1.8.0\n", " out : ndarray, optional\n", " Alternate output array in which to place the result. The default\n", " is ``None``. If provided, it must have the same shape as the\n", " expected output, but the type will be cast if necessary. See\n", " `doc.ufuncs` for details. The casting of NaN to integer can yield\n", " unexpected results.\n", " \n", " .. versionadded:: 1.8.0\n", " keepdims : bool, optional\n", " If True, the axes which are reduced are left in the result as\n", " dimensions with size one. With this option, the result will\n", " broadcast correctly against the original `arr`.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Returns\n", " -------\n", " y : ndarray or numpy scalar\n", " \n", " See Also\n", " --------\n", " numpy.sum : Sum across array propagating NaNs.\n", " isnan : Show which elements are NaN.\n", " isfinite: Show which elements are not NaN or +/-inf.\n", " \n", " Notes\n", " -----\n", " If both positive and negative infinity are present, the sum will be Not\n", " A Number (NaN).\n", " \n", " Numpy integer arithmetic is modular. If the size of a sum exceeds the\n", " size of an integer accumulator, its value will wrap around and the\n", " result will be incorrect. Specifying ``dtype=double`` can alleviate\n", " that problem.\n", " \n", " Examples\n", " --------\n", " >>> np.nansum(1)\n", " 1\n", " >>> np.nansum([1])\n", " 1\n", " >>> np.nansum([1, np.nan])\n", " 1.0\n", " >>> a = np.array([[1, 1], [1, np.nan]])\n", " >>> np.nansum(a)\n", " 3.0\n", " >>> np.nansum(a, axis=0)\n", " array([ 2., 1.])\n", " >>> np.nansum([1, np.nan, np.inf])\n", " inf\n", " >>> np.nansum([1, np.nan, np.NINF])\n", " -inf\n", " >>> np.nansum([1, np.nan, np.inf, -np.inf]) # both +/- infinity present\n", " nan\n", " \n", " nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n", " Compute the variance along the specified axis, while ignoring NaNs.\n", " \n", " Returns the variance of the array elements, a measure of the spread of\n", " a distribution. The variance is computed for the flattened array by\n", " default, otherwise over the specified axis.\n", " \n", " For all-NaN slices or slices with zero degrees of freedom, NaN is\n", " returned and a `RuntimeWarning` is raised.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array containing numbers whose variance is desired. If `a` is not an\n", " array, a conversion is attempted.\n", " axis : int, optional\n", " Axis along which the variance is computed. The default is to compute\n", " the variance of the flattened array.\n", " dtype : data-type, optional\n", " Type to use in computing the variance. For arrays of integer type\n", " the default is `float32`; for arrays of float types it is the same as\n", " the array type.\n", " out : ndarray, optional\n", " Alternate output array in which to place the result. It must have\n", " the same shape as the expected output, but the type is cast if\n", " necessary.\n", " ddof : int, optional\n", " \"Delta Degrees of Freedom\": the divisor used in the calculation is\n", " ``N - ddof``, where ``N`` represents the number of non-NaN\n", " elements. By default `ddof` is zero.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " variance : ndarray, see dtype parameter above\n", " If `out` is None, return a new array containing the variance,\n", " otherwise return a reference to the output array. If ddof is >= the\n", " number of non-NaN elements in a slice or the slice contains only\n", " NaNs, then the result for that slice is NaN.\n", " \n", " See Also\n", " --------\n", " std : Standard deviation\n", " mean : Average\n", " var : Variance while not ignoring NaNs\n", " nanstd, nanmean\n", " numpy.doc.ufuncs : Section \"Output arguments\"\n", " \n", " Notes\n", " -----\n", " The variance is the average of the squared deviations from the mean,\n", " i.e., ``var = mean(abs(x - x.mean())**2)``.\n", " \n", " The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.\n", " If, however, `ddof` is specified, the divisor ``N - ddof`` is used\n", " instead. In standard statistical practice, ``ddof=1`` provides an\n", " unbiased estimator of the variance of a hypothetical infinite\n", " population. ``ddof=0`` provides a maximum likelihood estimate of the\n", " variance for normally distributed variables.\n", " \n", " Note that for complex numbers, the absolute value is taken before\n", " squaring, so that the result is always real and nonnegative.\n", " \n", " For floating-point input, the variance is computed using the same\n", " precision the input has. Depending on the input data, this can cause\n", " the results to be inaccurate, especially for `float32` (see example\n", " below). Specifying a higher-accuracy accumulator using the ``dtype``\n", " keyword can alleviate this issue.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, np.nan], [3, 4]])\n", " >>> np.var(a)\n", " 1.5555555555555554\n", " >>> np.nanvar(a, axis=0)\n", " array([ 1., 0.])\n", " >>> np.nanvar(a, axis=1)\n", " array([ 0., 0.25])\n", " \n", " ndfromtxt(fname, **kwargs)\n", " Load ASCII data stored in a file and return it as a single array.\n", " \n", " Parameters\n", " ----------\n", " fname, kwargs : For a description of input parameters, see `genfromtxt`.\n", " \n", " See Also\n", " --------\n", " numpy.genfromtxt : generic function.\n", " \n", " ndim(a)\n", " Return the number of dimensions of an array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array. If it is not already an ndarray, a conversion is\n", " attempted.\n", " \n", " Returns\n", " -------\n", " number_of_dimensions : int\n", " The number of dimensions in `a`. Scalars are zero-dimensional.\n", " \n", " See Also\n", " --------\n", " ndarray.ndim : equivalent method\n", " shape : dimensions of array\n", " ndarray.shape : dimensions of array\n", " \n", " Examples\n", " --------\n", " >>> np.ndim([[1,2,3],[4,5,6]])\n", " 2\n", " >>> np.ndim(np.array([[1,2,3],[4,5,6]]))\n", " 2\n", " >>> np.ndim(1)\n", " 0\n", " \n", " nested_iters(...)\n", " \n", " newbuffer(...)\n", " newbuffer(size)\n", " \n", " Return a new uninitialized buffer object.\n", " \n", " Parameters\n", " ----------\n", " size : int\n", " Size in bytes of returned buffer object.\n", " \n", " Returns\n", " -------\n", " newbuffer : buffer object\n", " Returned, uninitialized buffer object of `size` bytes.\n", " \n", " nonzero(a)\n", " Return the indices of the elements that are non-zero.\n", " \n", " Returns a tuple of arrays, one for each dimension of `a`, containing\n", " the indices of the non-zero elements in that dimension. The\n", " corresponding non-zero values can be obtained with::\n", " \n", " a[nonzero(a)]\n", " \n", " To group the indices by element, rather than dimension, use::\n", " \n", " transpose(nonzero(a))\n", " \n", " The result of this is always a 2-D array, with a row for\n", " each non-zero element.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " \n", " Returns\n", " -------\n", " tuple_of_arrays : tuple\n", " Indices of elements that are non-zero.\n", " \n", " See Also\n", " --------\n", " flatnonzero :\n", " Return indices that are non-zero in the flattened version of the input\n", " array.\n", " ndarray.nonzero :\n", " Equivalent ndarray method.\n", " count_nonzero :\n", " Counts the number of non-zero elements in the input array.\n", " \n", " Examples\n", " --------\n", " >>> x = np.eye(3)\n", " >>> x\n", " array([[ 1., 0., 0.],\n", " [ 0., 1., 0.],\n", " [ 0., 0., 1.]])\n", " >>> np.nonzero(x)\n", " (array([0, 1, 2]), array([0, 1, 2]))\n", " \n", " >>> x[np.nonzero(x)]\n", " array([ 1., 1., 1.])\n", " >>> np.transpose(np.nonzero(x))\n", " array([[0, 0],\n", " [1, 1],\n", " [2, 2]])\n", " \n", " A common use for ``nonzero`` is to find the indices of an array, where\n", " a condition is True. Given an array `a`, the condition `a` > 3 is a\n", " boolean array and since False is interpreted as 0, np.nonzero(a > 3)\n", " yields the indices of the `a` where the condition is true.\n", " \n", " >>> a = np.array([[1,2,3],[4,5,6],[7,8,9]])\n", " >>> a > 3\n", " array([[False, False, False],\n", " [ True, True, True],\n", " [ True, True, True]], dtype=bool)\n", " >>> np.nonzero(a > 3)\n", " (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))\n", " \n", " The ``nonzero`` method of the boolean array can also be called.\n", " \n", " >>> (a > 3).nonzero()\n", " (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))\n", " \n", " nper(rate, pmt, pv, fv=0, when='end')\n", " Compute the number of periodic payments.\n", " \n", " Parameters\n", " ----------\n", " rate : array_like\n", " Rate of interest (per period)\n", " pmt : array_like\n", " Payment\n", " pv : array_like\n", " Present value\n", " fv : array_like, optional\n", " Future value\n", " when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n", " When payments are due ('begin' (1) or 'end' (0))\n", " \n", " Notes\n", " -----\n", " The number of periods ``nper`` is computed by solving the equation::\n", " \n", " fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate*((1+rate)**nper-1) = 0\n", " \n", " but if ``rate = 0`` then::\n", " \n", " fv + pv + pmt*nper = 0\n", " \n", " Examples\n", " --------\n", " If you only had $150/month to pay towards the loan, how long would it take\n", " to pay-off a loan of $8,000 at 7% annual interest?\n", " \n", " >>> print round(np.nper(0.07/12, -150, 8000), 5)\n", " 64.07335\n", " \n", " So, over 64 months would be required to pay off the loan.\n", " \n", " The same analysis could be done with several different interest rates\n", " and/or payments and/or total amounts to produce an entire table.\n", " \n", " >>> np.nper(*(np.ogrid[0.07/12: 0.08/12: 0.01/12,\n", " ... -150 : -99 : 50 ,\n", " ... 8000 : 9001 : 1000]))\n", " array([[[ 64.07334877, 74.06368256],\n", " [ 108.07548412, 127.99022654]],\n", " [[ 66.12443902, 76.87897353],\n", " [ 114.70165583, 137.90124779]]])\n", " \n", " npv(rate, values)\n", " Returns the NPV (Net Present Value) of a cash flow series.\n", " \n", " Parameters\n", " ----------\n", " rate : scalar\n", " The discount rate.\n", " values : array_like, shape(M, )\n", " The values of the time series of cash flows. The (fixed) time\n", " interval between cash flow \"events\" must be the same as that for\n", " which `rate` is given (i.e., if `rate` is per year, then precisely\n", " a year is understood to elapse between each cash flow event). By\n", " convention, investments or \"deposits\" are negative, income or\n", " \"withdrawals\" are positive; `values` must begin with the initial\n", " investment, thus `values[0]` will typically be negative.\n", " \n", " Returns\n", " -------\n", " out : float\n", " The NPV of the input cash flow series `values` at the discount\n", " `rate`.\n", " \n", " Notes\n", " -----\n", " Returns the result of: [G]_\n", " \n", " .. math :: \\sum_{t=0}^{M-1}{\\frac{values_t}{(1+rate)^{t}}}\n", " \n", " References\n", " ----------\n", " .. [G] L. J. Gitman, \"Principles of Managerial Finance, Brief,\" 3rd ed.,\n", " Addison-Wesley, 2003, pg. 346.\n", " \n", " Examples\n", " --------\n", " >>> np.npv(0.281,[-100, 39, 59, 55, 20])\n", " -0.0084785916384548798\n", " \n", " (Compare with the Example given for numpy.lib.financial.irr)\n", " \n", " obj2sctype(rep, default=None)\n", " Return the scalar dtype or NumPy equivalent of Python type of an object.\n", " \n", " Parameters\n", " ----------\n", " rep : any\n", " The object of which the type is returned.\n", " default : any, optional\n", " If given, this is returned for objects whose types can not be\n", " determined. If not given, None is returned for those objects.\n", " \n", " Returns\n", " -------\n", " dtype : dtype or Python type\n", " The data type of `rep`.\n", " \n", " See Also\n", " --------\n", " sctype2char, issctype, issubsctype, issubdtype, maximum_sctype\n", " \n", " Examples\n", " --------\n", " >>> np.obj2sctype(np.int32)\n", " \n", " >>> np.obj2sctype(np.array([1., 2.]))\n", " \n", " >>> np.obj2sctype(np.array([1.j]))\n", " \n", " \n", " >>> np.obj2sctype(dict)\n", " \n", " >>> np.obj2sctype('string')\n", " \n", " \n", " >>> np.obj2sctype(1, default=list)\n", " \n", " \n", " ones(shape, dtype=None, order='C')\n", " Return a new array of given shape and type, filled with ones.\n", " \n", " Parameters\n", " ----------\n", " shape : int or sequence of ints\n", " Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n", " dtype : data-type, optional\n", " The desired data-type for the array, e.g., `numpy.int8`. Default is\n", " `numpy.float64`.\n", " order : {'C', 'F'}, optional\n", " Whether to store multidimensional data in C- or Fortran-contiguous\n", " (row- or column-wise) order in memory.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Array of ones with the given shape, dtype, and order.\n", " \n", " See Also\n", " --------\n", " zeros, ones_like\n", " \n", " Examples\n", " --------\n", " >>> np.ones(5)\n", " array([ 1., 1., 1., 1., 1.])\n", " \n", " >>> np.ones((5,), dtype=np.int)\n", " array([1, 1, 1, 1, 1])\n", " \n", " >>> np.ones((2, 1))\n", " array([[ 1.],\n", " [ 1.]])\n", " \n", " >>> s = (2,2)\n", " >>> np.ones(s)\n", " array([[ 1., 1.],\n", " [ 1., 1.]])\n", " \n", " ones_like(a, dtype=None, order='K', subok=True)\n", " Return an array of ones with the same shape and type as a given array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " The shape and data-type of `a` define these same attributes of\n", " the returned array.\n", " dtype : data-type, optional\n", " .. versionadded:: 1.6.0\n", " Overrides the data type of the result.\n", " order : {'C', 'F', 'A', or 'K'}, optional\n", " .. versionadded:: 1.6.0\n", " Overrides the memory layout of the result. 'C' means C-order,\n", " 'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,\n", " 'C' otherwise. 'K' means match the layout of `a` as closely\n", " as possible.\n", " subok : bool, optional.\n", " If True, then the newly created array will use the sub-class\n", " type of 'a', otherwise it will be a base-class array. Defaults\n", " to True.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Array of ones with the same shape and type as `a`.\n", " \n", " See Also\n", " --------\n", " zeros_like : Return an array of zeros with shape and type of input.\n", " empty_like : Return an empty array with shape and type of input.\n", " zeros : Return a new array setting values to zero.\n", " ones : Return a new array setting values to one.\n", " empty : Return a new uninitialized array.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(6)\n", " >>> x = x.reshape((2, 3))\n", " >>> x\n", " array([[0, 1, 2],\n", " [3, 4, 5]])\n", " >>> np.ones_like(x)\n", " array([[1, 1, 1],\n", " [1, 1, 1]])\n", " \n", " >>> y = np.arange(3, dtype=np.float)\n", " >>> y\n", " array([ 0., 1., 2.])\n", " >>> np.ones_like(y)\n", " array([ 1., 1., 1.])\n", " \n", " outer(a, b, out=None)\n", " Compute the outer product of two vectors.\n", " \n", " Given two vectors, ``a = [a0, a1, ..., aM]`` and\n", " ``b = [b0, b1, ..., bN]``,\n", " the outer product [1]_ is::\n", " \n", " [[a0*b0 a0*b1 ... a0*bN ]\n", " [a1*b0 .\n", " [ ... .\n", " [aM*b0 aM*bN ]]\n", " \n", " Parameters\n", " ----------\n", " a : (M,) array_like\n", " First input vector. Input is flattened if\n", " not already 1-dimensional.\n", " b : (N,) array_like\n", " Second input vector. Input is flattened if\n", " not already 1-dimensional.\n", " out : (M, N) ndarray, optional\n", " A location where the result is stored\n", " \n", " .. versionadded:: 1.9.0\n", " \n", " Returns\n", " -------\n", " out : (M, N) ndarray\n", " ``out[i, j] = a[i] * b[j]``\n", " \n", " See also\n", " --------\n", " inner, einsum\n", " \n", " References\n", " ----------\n", " .. [1] : G. H. Golub and C. F. van Loan, *Matrix Computations*, 3rd\n", " ed., Baltimore, MD, Johns Hopkins University Press, 1996,\n", " pg. 8.\n", " \n", " Examples\n", " --------\n", " Make a (*very* coarse) grid for computing a Mandelbrot set:\n", " \n", " >>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))\n", " >>> rl\n", " array([[-2., -1., 0., 1., 2.],\n", " [-2., -1., 0., 1., 2.],\n", " [-2., -1., 0., 1., 2.],\n", " [-2., -1., 0., 1., 2.],\n", " [-2., -1., 0., 1., 2.]])\n", " >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))\n", " >>> im\n", " array([[ 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j],\n", " [ 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j],\n", " [ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],\n", " [ 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j],\n", " [ 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]])\n", " >>> grid = rl + im\n", " >>> grid\n", " array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j],\n", " [-2.+1.j, -1.+1.j, 0.+1.j, 1.+1.j, 2.+1.j],\n", " [-2.+0.j, -1.+0.j, 0.+0.j, 1.+0.j, 2.+0.j],\n", " [-2.-1.j, -1.-1.j, 0.-1.j, 1.-1.j, 2.-1.j],\n", " [-2.-2.j, -1.-2.j, 0.-2.j, 1.-2.j, 2.-2.j]])\n", " \n", " An example using a \"vector\" of letters:\n", " \n", " >>> x = np.array(['a', 'b', 'c'], dtype=object)\n", " >>> np.outer(x, [1, 2, 3])\n", " array([[a, aa, aaa],\n", " [b, bb, bbb],\n", " [c, cc, ccc]], dtype=object)\n", " \n", " packbits(...)\n", " packbits(myarray, axis=None)\n", " \n", " Packs the elements of a binary-valued array into bits in a uint8 array.\n", " \n", " The result is padded to full bytes by inserting zero bits at the end.\n", " \n", " Parameters\n", " ----------\n", " myarray : array_like\n", " An integer type array whose elements should be packed to bits.\n", " axis : int, optional\n", " The dimension over which bit-packing is done.\n", " ``None`` implies packing the flattened array.\n", " \n", " Returns\n", " -------\n", " packed : ndarray\n", " Array of type uint8 whose elements represent bits corresponding to the\n", " logical (0 or nonzero) value of the input elements. The shape of\n", " `packed` has the same number of dimensions as the input (unless `axis`\n", " is None, in which case the output is 1-D).\n", " \n", " See Also\n", " --------\n", " unpackbits: Unpacks elements of a uint8 array into a binary-valued output\n", " array.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[[1,0,1],\n", " ... [0,1,0]],\n", " ... [[1,1,0],\n", " ... [0,0,1]]])\n", " >>> b = np.packbits(a, axis=-1)\n", " >>> b\n", " array([[[160],[64]],[[192],[32]]], dtype=uint8)\n", " \n", " Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000,\n", " and 32 = 0010 0000.\n", " \n", " pad(array, pad_width, mode=None, **kwargs)\n", " Pads an array.\n", " \n", " Parameters\n", " ----------\n", " array : array_like of rank N\n", " Input array\n", " pad_width : {sequence, int}\n", " Number of values padded to the edges of each axis.\n", " ((before_1, after_1), ... (before_N, after_N)) unique pad widths\n", " for each axis.\n", " ((before, after),) yields same before and after pad for each axis.\n", " (pad,) or int is a shortcut for before = after = pad width for all\n", " axes.\n", " mode : {str, function}\n", " One of the following string values or a user supplied function.\n", " \n", " 'constant'\n", " Pads with a constant value.\n", " 'edge'\n", " Pads with the edge values of array.\n", " 'linear_ramp'\n", " Pads with the linear ramp between end_value and the\n", " array edge value.\n", " 'maximum'\n", " Pads with the maximum value of all or part of the\n", " vector along each axis.\n", " 'mean'\n", " Pads with the mean value of all or part of the\n", " vector along each axis.\n", " 'median'\n", " Pads with the median value of all or part of the\n", " vector along each axis.\n", " 'minimum'\n", " Pads with the minimum value of all or part of the\n", " vector along each axis.\n", " 'reflect'\n", " Pads with the reflection of the vector mirrored on\n", " the first and last values of the vector along each\n", " axis.\n", " 'symmetric'\n", " Pads with the reflection of the vector mirrored\n", " along the edge of the array.\n", " 'wrap'\n", " Pads with the wrap of the vector along the axis.\n", " The first values are used to pad the end and the\n", " end values are used to pad the beginning.\n", " \n", " Padding function, see Notes.\n", " stat_length : {sequence, int}, optional\n", " Used in 'maximum', 'mean', 'median', and 'minimum'. Number of\n", " values at edge of each axis used to calculate the statistic value.\n", " \n", " ((before_1, after_1), ... (before_N, after_N)) unique statistic\n", " lengths for each axis.\n", " \n", " ((before, after),) yields same before and after statistic lengths\n", " for each axis.\n", " \n", " (stat_length,) or int is a shortcut for before = after = statistic\n", " length for all axes.\n", " \n", " Default is ``None``, to use the entire axis.\n", " constant_values : {sequence, int}, optional\n", " Used in 'constant'. The values to set the padded values for each\n", " axis.\n", " \n", " ((before_1, after_1), ... (before_N, after_N)) unique pad constants\n", " for each axis.\n", " \n", " ((before, after),) yields same before and after constants for each\n", " axis.\n", " \n", " (constant,) or int is a shortcut for before = after = constant for\n", " all axes.\n", " \n", " Default is 0.\n", " end_values : {sequence, int}, optional\n", " Used in 'linear_ramp'. The values used for the ending value of the\n", " linear_ramp and that will form the edge of the padded array.\n", " \n", " ((before_1, after_1), ... (before_N, after_N)) unique end values\n", " for each axis.\n", " \n", " ((before, after),) yields same before and after end values for each\n", " axis.\n", " \n", " (constant,) or int is a shortcut for before = after = end value for\n", " all axes.\n", " \n", " Default is 0.\n", " reflect_type : str {'even', 'odd'}, optional\n", " Used in 'reflect', and 'symmetric'. The 'even' style is the\n", " default with an unaltered reflection around the edge value. For\n", " the 'odd' style, the extented part of the array is created by\n", " subtracting the reflected values from two times the edge value.\n", " \n", " Returns\n", " -------\n", " pad : ndarray\n", " Padded array of rank equal to `array` with shape increased\n", " according to `pad_width`.\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.7.0\n", " \n", " For an array with rank greater than 1, some of the padding of later\n", " axes is calculated from padding of previous axes. This is easiest to\n", " think about with a rank 2 array where the corners of the padded array\n", " are calculated by using padded values from the first axis.\n", " \n", " The padding function, if used, should return a rank 1 array equal in\n", " length to the vector argument with padded values replaced. It has the\n", " following signature::\n", " \n", " padding_func(vector, iaxis_pad_width, iaxis, **kwargs)\n", " \n", " where\n", " \n", " vector : ndarray\n", " A rank 1 array already padded with zeros. Padded values are\n", " vector[:pad_tuple[0]] and vector[-pad_tuple[1]:].\n", " iaxis_pad_width : tuple\n", " A 2-tuple of ints, iaxis_pad_width[0] represents the number of\n", " values padded at the beginning of vector where\n", " iaxis_pad_width[1] represents the number of values padded at\n", " the end of vector.\n", " iaxis : int\n", " The axis currently being calculated.\n", " kwargs : misc\n", " Any keyword arguments the function requires.\n", " \n", " Examples\n", " --------\n", " >>> a = [1, 2, 3, 4, 5]\n", " >>> np.lib.pad(a, (2,3), 'constant', constant_values=(4,6))\n", " array([4, 4, 1, 2, 3, 4, 5, 6, 6, 6])\n", " \n", " >>> np.lib.pad(a, (2,3), 'edge')\n", " array([1, 1, 1, 2, 3, 4, 5, 5, 5, 5])\n", " \n", " >>> np.lib.pad(a, (2,3), 'linear_ramp', end_values=(5,-4))\n", " array([ 5, 3, 1, 2, 3, 4, 5, 2, -1, -4])\n", " \n", " >>> np.lib.pad(a, (2,), 'maximum')\n", " array([5, 5, 1, 2, 3, 4, 5, 5, 5])\n", " \n", " >>> np.lib.pad(a, (2,), 'mean')\n", " array([3, 3, 1, 2, 3, 4, 5, 3, 3])\n", " \n", " >>> np.lib.pad(a, (2,), 'median')\n", " array([3, 3, 1, 2, 3, 4, 5, 3, 3])\n", " \n", " >>> a = [[1,2], [3,4]]\n", " >>> np.lib.pad(a, ((3, 2), (2, 3)), 'minimum')\n", " array([[1, 1, 1, 2, 1, 1, 1],\n", " [1, 1, 1, 2, 1, 1, 1],\n", " [1, 1, 1, 2, 1, 1, 1],\n", " [1, 1, 1, 2, 1, 1, 1],\n", " [3, 3, 3, 4, 3, 3, 3],\n", " [1, 1, 1, 2, 1, 1, 1],\n", " [1, 1, 1, 2, 1, 1, 1]])\n", " \n", " >>> a = [1, 2, 3, 4, 5]\n", " >>> np.lib.pad(a, (2,3), 'reflect')\n", " array([3, 2, 1, 2, 3, 4, 5, 4, 3, 2])\n", " \n", " >>> np.lib.pad(a, (2,3), 'reflect', reflect_type='odd')\n", " array([-1, 0, 1, 2, 3, 4, 5, 6, 7, 8])\n", " \n", " >>> np.lib.pad(a, (2,3), 'symmetric')\n", " array([2, 1, 1, 2, 3, 4, 5, 5, 4, 3])\n", " \n", " >>> np.lib.pad(a, (2,3), 'symmetric', reflect_type='odd')\n", " array([0, 1, 1, 2, 3, 4, 5, 5, 6, 7])\n", " \n", " >>> np.lib.pad(a, (2,3), 'wrap')\n", " array([4, 5, 1, 2, 3, 4, 5, 1, 2, 3])\n", " \n", " >>> def padwithtens(vector, pad_width, iaxis, kwargs):\n", " ... vector[:pad_width[0]] = 10\n", " ... vector[-pad_width[1]:] = 10\n", " ... return vector\n", " \n", " >>> a = np.arange(6)\n", " >>> a = a.reshape((2,3))\n", " \n", " >>> np.lib.pad(a, 2, padwithtens)\n", " array([[10, 10, 10, 10, 10, 10, 10],\n", " [10, 10, 10, 10, 10, 10, 10],\n", " [10, 10, 0, 1, 2, 10, 10],\n", " [10, 10, 3, 4, 5, 10, 10],\n", " [10, 10, 10, 10, 10, 10, 10],\n", " [10, 10, 10, 10, 10, 10, 10]])\n", " \n", " partition(a, kth, axis=-1, kind='introselect', order=None)\n", " Return a partitioned copy of an array.\n", " \n", " Creates a copy of the array with its elements rearranged in such a way that\n", " the value of the element in kth position is in the position it would be in\n", " a sorted array. All elements smaller than the kth element are moved before\n", " this element and all equal or greater are moved behind it. The ordering of\n", " the elements in the two partitions is undefined.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array to be sorted.\n", " kth : int or sequence of ints\n", " Element index to partition by. The kth value of the element will be in\n", " its final sorted position and all smaller elements will be moved before\n", " it and all equal or greater elements behind it.\n", " The order all elements in the partitions is undefined.\n", " If provided with a sequence of kth it will partition all elements\n", " indexed by kth of them into their sorted position at once.\n", " axis : int or None, optional\n", " Axis along which to sort. If None, the array is flattened before\n", " sorting. The default is -1, which sorts along the last axis.\n", " kind : {'introselect'}, optional\n", " Selection algorithm. Default is 'introselect'.\n", " order : list, optional\n", " When `a` is a structured array, this argument specifies which fields\n", " to compare first, second, and so on. This list does not need to\n", " include all of the fields.\n", " \n", " Returns\n", " -------\n", " partitioned_array : ndarray\n", " Array of the same type and shape as `a`.\n", " \n", " See Also\n", " --------\n", " ndarray.partition : Method to sort an array in-place.\n", " argpartition : Indirect partition.\n", " sort : Full sorting\n", " \n", " Notes\n", " -----\n", " The various selection algorithms are characterized by their average speed,\n", " worst case performance, work space size, and whether they are stable. A\n", " stable sort keeps items with the same key in the same relative order. The\n", " available algorithms have the following properties:\n", " \n", " ================= ======= ============= ============ =======\n", " kind speed worst case work space stable\n", " ================= ======= ============= ============ =======\n", " 'introselect' 1 O(n) 0 no\n", " ================= ======= ============= ============ =======\n", " \n", " All the partition algorithms make temporary copies of the data when\n", " partitioning along any but the last axis. Consequently, partitioning\n", " along the last axis is faster and uses less space than partitioning\n", " along any other axis.\n", " \n", " The sort order for complex numbers is lexicographic. If both the real\n", " and imaginary parts are non-nan then the order is determined by the\n", " real parts except when they are equal, in which case the order is\n", " determined by the imaginary parts.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([3, 4, 2, 1])\n", " >>> np.partition(a, 3)\n", " array([2, 1, 3, 4])\n", " \n", " >>> np.partition(a, (1, 3))\n", " array([1, 2, 3, 4])\n", " \n", " percentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False)\n", " Compute the qth percentile of the data along the specified axis.\n", " \n", " Returns the qth percentile of the array elements.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array or object that can be converted to an array.\n", " q : float in range of [0,100] (or sequence of floats)\n", " Percentile to compute which must be between 0 and 100 inclusive.\n", " axis : int or sequence of int, optional\n", " Axis along which the percentiles are computed. The default (None)\n", " is to compute the percentiles along a flattened version of the array.\n", " A sequence of axes is supported since version 1.9.0.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must\n", " have the same shape and buffer length as the expected output,\n", " but the type (of the output) will be cast if necessary.\n", " overwrite_input : bool, optional\n", " If True, then allow use of memory of input array `a` for\n", " calculations. The input array will be modified by the call to\n", " percentile. This will save memory when you do not need to preserve\n", " the contents of the input array. In this case you should not make\n", " any assumptions about the content of the passed in array `a` after\n", " this function completes -- treat it as undefined. Default is False.\n", " Note that, if the `a` input is not already an array this parameter\n", " will have no effect, `a` will be converted to an array internally\n", " regardless of the value of this parameter.\n", " interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}\n", " This optional parameter specifies the interpolation method to use,\n", " when the desired quantile lies between two data points `i` and `j`:\n", " * linear: `i + (j - i) * fraction`, where `fraction` is the\n", " fractional part of the index surrounded by `i` and `j`.\n", " * lower: `i`.\n", " * higher: `j`.\n", " * nearest: `i` or `j` whichever is nearest.\n", " * midpoint: (`i` + `j`) / 2.\n", " \n", " .. versionadded:: 1.9.0\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " .. versionadded:: 1.9.0\n", " \n", " Returns\n", " -------\n", " percentile : scalar or ndarray\n", " If a single percentile `q` is given and axis=None a scalar is\n", " returned. If multiple percentiles `q` are given an array holding\n", " the result is returned. The results are listed in the first axis.\n", " (If `out` is specified, in which case that array is returned\n", " instead). If the input contains integers, or floats of smaller\n", " precision than 64, then the output data-type is float64. Otherwise,\n", " the output data-type is the same as that of the input.\n", " \n", " See Also\n", " --------\n", " mean, median\n", " \n", " Notes\n", " -----\n", " Given a vector V of length N, the q-th percentile of V is the q-th ranked\n", " value in a sorted copy of V. The values and distances of the two\n", " nearest neighbors as well as the `interpolation` parameter will\n", " determine the percentile if the normalized ranking does not match q\n", " exactly. This function is the same as the median if ``q=50``, the same\n", " as the minimum if ``q=0`` and the same as the maximum if ``q=100``.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[10, 7, 4], [3, 2, 1]])\n", " >>> a\n", " array([[10, 7, 4],\n", " [ 3, 2, 1]])\n", " >>> np.percentile(a, 50)\n", " array([ 3.5])\n", " >>> np.percentile(a, 50, axis=0)\n", " array([[ 6.5, 4.5, 2.5]])\n", " >>> np.percentile(a, 50, axis=1)\n", " array([[ 7.],\n", " [ 2.]])\n", " \n", " >>> m = np.percentile(a, 50, axis=0)\n", " >>> out = np.zeros_like(m)\n", " >>> np.percentile(a, 50, axis=0, out=m)\n", " array([[ 6.5, 4.5, 2.5]])\n", " >>> m\n", " array([[ 6.5, 4.5, 2.5]])\n", " \n", " >>> b = a.copy()\n", " >>> np.percentile(b, 50, axis=1, overwrite_input=True)\n", " array([[ 7.],\n", " [ 2.]])\n", " >>> assert not np.all(a==b)\n", " >>> b = a.copy()\n", " >>> np.percentile(b, 50, axis=None, overwrite_input=True)\n", " array([ 3.5])\n", " \n", " piecewise(x, condlist, funclist, *args, **kw)\n", " Evaluate a piecewise-defined function.\n", " \n", " Given a set of conditions and corresponding functions, evaluate each\n", " function on the input data wherever its condition is true.\n", " \n", " Parameters\n", " ----------\n", " x : ndarray\n", " The input domain.\n", " condlist : list of bool arrays\n", " Each boolean array corresponds to a function in `funclist`. Wherever\n", " `condlist[i]` is True, `funclist[i](x)` is used as the output value.\n", " \n", " Each boolean array in `condlist` selects a piece of `x`,\n", " and should therefore be of the same shape as `x`.\n", " \n", " The length of `condlist` must correspond to that of `funclist`.\n", " If one extra function is given, i.e. if\n", " ``len(funclist) - len(condlist) == 1``, then that extra function\n", " is the default value, used wherever all conditions are false.\n", " funclist : list of callables, f(x,*args,**kw), or scalars\n", " Each function is evaluated over `x` wherever its corresponding\n", " condition is True. It should take an array as input and give an array\n", " or a scalar value as output. If, instead of a callable,\n", " a scalar is provided then a constant function (``lambda x: scalar``) is\n", " assumed.\n", " args : tuple, optional\n", " Any further arguments given to `piecewise` are passed to the functions\n", " upon execution, i.e., if called ``piecewise(..., ..., 1, 'a')``, then\n", " each function is called as ``f(x, 1, 'a')``.\n", " kw : dict, optional\n", " Keyword arguments used in calling `piecewise` are passed to the\n", " functions upon execution, i.e., if called\n", " ``piecewise(..., ..., lambda=1)``, then each function is called as\n", " ``f(x, lambda=1)``.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " The output is the same shape and type as x and is found by\n", " calling the functions in `funclist` on the appropriate portions of `x`,\n", " as defined by the boolean arrays in `condlist`. Portions not covered\n", " by any condition have a default value of 0.\n", " \n", " \n", " See Also\n", " --------\n", " choose, select, where\n", " \n", " Notes\n", " -----\n", " This is similar to choose or select, except that functions are\n", " evaluated on elements of `x` that satisfy the corresponding condition from\n", " `condlist`.\n", " \n", " The result is::\n", " \n", " |--\n", " |funclist[0](x[condlist[0]])\n", " out = |funclist[1](x[condlist[1]])\n", " |...\n", " |funclist[n2](x[condlist[n2]])\n", " |--\n", " \n", " Examples\n", " --------\n", " Define the sigma function, which is -1 for ``x < 0`` and +1 for ``x >= 0``.\n", " \n", " >>> x = np.linspace(-2.5, 2.5, 6)\n", " >>> np.piecewise(x, [x < 0, x >= 0], [-1, 1])\n", " array([-1., -1., -1., 1., 1., 1.])\n", " \n", " Define the absolute value, which is ``-x`` for ``x <0`` and ``x`` for\n", " ``x >= 0``.\n", " \n", " >>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x])\n", " array([ 2.5, 1.5, 0.5, 0.5, 1.5, 2.5])\n", " \n", " pkgload(*packages, **options)\n", " Load one or more packages into parent package top-level namespace.\n", " \n", " This function is intended to shorten the need to import many\n", " subpackages, say of scipy, constantly with statements such as\n", " \n", " import scipy.linalg, scipy.fftpack, scipy.etc...\n", " \n", " Instead, you can say:\n", " \n", " import scipy\n", " scipy.pkgload('linalg','fftpack',...)\n", " \n", " or\n", " \n", " scipy.pkgload()\n", " \n", " to load all of them in one call.\n", " \n", " If a name which doesn't exist in scipy's namespace is\n", " given, a warning is shown.\n", " \n", " Parameters\n", " ----------\n", " *packages : arg-tuple\n", " the names (one or more strings) of all the modules one\n", " wishes to load into the top-level namespace.\n", " verbose= : integer\n", " verbosity level [default: -1].\n", " verbose=-1 will suspend also warnings.\n", " force= : bool\n", " when True, force reloading loaded packages [default: False].\n", " postpone= : bool\n", " when True, don't load packages [default: False]\n", " \n", " place(arr, mask, vals)\n", " Change elements of an array based on conditional and input values.\n", " \n", " Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that\n", " `place` uses the first N elements of `vals`, where N is the number of\n", " True values in `mask`, while `copyto` uses the elements where `mask`\n", " is True.\n", " \n", " Note that `extract` does the exact opposite of `place`.\n", " \n", " Parameters\n", " ----------\n", " arr : array_like\n", " Array to put data into.\n", " mask : array_like\n", " Boolean mask array. Must have the same size as `a`.\n", " vals : 1-D sequence\n", " Values to put into `a`. Only the first N elements are used, where\n", " N is the number of True values in `mask`. If `vals` is smaller\n", " than N it will be repeated.\n", " \n", " See Also\n", " --------\n", " copyto, put, take, extract\n", " \n", " Examples\n", " --------\n", " >>> arr = np.arange(6).reshape(2, 3)\n", " >>> np.place(arr, arr>2, [44, 55])\n", " >>> arr\n", " array([[ 0, 1, 2],\n", " [44, 55, 44]])\n", " \n", " pmt(rate, nper, pv, fv=0, when='end')\n", " Compute the payment against loan principal plus interest.\n", " \n", " Given:\n", " * a present value, `pv` (e.g., an amount borrowed)\n", " * a future value, `fv` (e.g., 0)\n", " * an interest `rate` compounded once per period, of which\n", " there are\n", " * `nper` total\n", " * and (optional) specification of whether payment is made\n", " at the beginning (`when` = {'begin', 1}) or the end\n", " (`when` = {'end', 0}) of each period\n", " \n", " Return:\n", " the (fixed) periodic payment.\n", " \n", " Parameters\n", " ----------\n", " rate : array_like\n", " Rate of interest (per period)\n", " nper : array_like\n", " Number of compounding periods\n", " pv : array_like\n", " Present value\n", " fv : array_like (optional)\n", " Future value (default = 0)\n", " when : {{'begin', 1}, {'end', 0}}, {string, int}\n", " When payments are due ('begin' (1) or 'end' (0))\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Payment against loan plus interest. If all input is scalar, returns a\n", " scalar float. If any input is array_like, returns payment for each\n", " input element. If multiple inputs are array_like, they all must have\n", " the same shape.\n", " \n", " Notes\n", " -----\n", " The payment is computed by solving the equation::\n", " \n", " fv +\n", " pv*(1 + rate)**nper +\n", " pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) == 0\n", " \n", " or, when ``rate == 0``::\n", " \n", " fv + pv + pmt * nper == 0\n", " \n", " for ``pmt``.\n", " \n", " Note that computing a monthly mortgage payment is only\n", " one use for this function. For example, pmt returns the\n", " periodic deposit one must make to achieve a specified\n", " future balance given an initial deposit, a fixed,\n", " periodically compounded interest rate, and the total\n", " number of periods.\n", " \n", " References\n", " ----------\n", " .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).\n", " Open Document Format for Office Applications (OpenDocument)v1.2,\n", " Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,\n", " Pre-Draft 12. Organization for the Advancement of Structured Information\n", " Standards (OASIS). Billerica, MA, USA. [ODT Document].\n", " Available:\n", " http://www.oasis-open.org/committees/documents.php\n", " ?wg_abbrev=office-formulaOpenDocument-formula-20090508.odt\n", " \n", " Examples\n", " --------\n", " What is the monthly payment needed to pay off a $200,000 loan in 15\n", " years at an annual interest rate of 7.5%?\n", " \n", " >>> np.pmt(0.075/12, 12*15, 200000)\n", " -1854.0247200054619\n", " \n", " In order to pay-off (i.e., have a future-value of 0) the $200,000 obtained\n", " today, a monthly payment of $1,854.02 would be required. Note that this\n", " example illustrates usage of `fv` having a default value of 0.\n", " \n", " poly(seq_of_zeros)\n", " Find the coefficients of a polynomial with the given sequence of roots.\n", " \n", " Returns the coefficients of the polynomial whose leading coefficient\n", " is one for the given sequence of zeros (multiple roots must be included\n", " in the sequence as many times as their multiplicity; see Examples).\n", " A square matrix (or array, which will be treated as a matrix) can also\n", " be given, in which case the coefficients of the characteristic polynomial\n", " of the matrix are returned.\n", " \n", " Parameters\n", " ----------\n", " seq_of_zeros : array_like, shape (N,) or (N, N)\n", " A sequence of polynomial roots, or a square array or matrix object.\n", " \n", " Returns\n", " -------\n", " c : ndarray\n", " 1D array of polynomial coefficients from highest to lowest degree:\n", " \n", " ``c[0] * x**(N) + c[1] * x**(N-1) + ... + c[N-1] * x + c[N]``\n", " where c[0] always equals 1.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If input is the wrong shape (the input must be a 1-D or square\n", " 2-D array).\n", " \n", " See Also\n", " --------\n", " polyval : Evaluate a polynomial at a point.\n", " roots : Return the roots of a polynomial.\n", " polyfit : Least squares polynomial fit.\n", " poly1d : A one-dimensional polynomial class.\n", " \n", " Notes\n", " -----\n", " Specifying the roots of a polynomial still leaves one degree of\n", " freedom, typically represented by an undetermined leading\n", " coefficient. [1]_ In the case of this function, that coefficient -\n", " the first one in the returned array - is always taken as one. (If\n", " for some reason you have one other point, the only automatic way\n", " presently to leverage that information is to use ``polyfit``.)\n", " \n", " The characteristic polynomial, :math:`p_a(t)`, of an `n`-by-`n`\n", " matrix **A** is given by\n", " \n", " :math:`p_a(t) = \\mathrm{det}(t\\, \\mathbf{I} - \\mathbf{A})`,\n", " \n", " where **I** is the `n`-by-`n` identity matrix. [2]_\n", " \n", " References\n", " ----------\n", " .. [1] M. Sullivan and M. Sullivan, III, \"Algebra and Trignometry,\n", " Enhanced With Graphing Utilities,\" Prentice-Hall, pg. 318, 1996.\n", " \n", " .. [2] G. Strang, \"Linear Algebra and Its Applications, 2nd Edition,\"\n", " Academic Press, pg. 182, 1980.\n", " \n", " Examples\n", " --------\n", " Given a sequence of a polynomial's zeros:\n", " \n", " >>> np.poly((0, 0, 0)) # Multiple root example\n", " array([1, 0, 0, 0])\n", " \n", " The line above represents z**3 + 0*z**2 + 0*z + 0.\n", " \n", " >>> np.poly((-1./2, 0, 1./2))\n", " array([ 1. , 0. , -0.25, 0. ])\n", " \n", " The line above represents z**3 - z/4\n", " \n", " >>> np.poly((np.random.random(1.)[0], 0, np.random.random(1.)[0]))\n", " array([ 1. , -0.77086955, 0.08618131, 0. ]) #random\n", " \n", " Given a square array object:\n", " \n", " >>> P = np.array([[0, 1./3], [-1./2, 0]])\n", " >>> np.poly(P)\n", " array([ 1. , 0. , 0.16666667])\n", " \n", " Or a square matrix object:\n", " \n", " >>> np.poly(np.matrix(P))\n", " array([ 1. , 0. , 0.16666667])\n", " \n", " Note how in all cases the leading coefficient is always 1.\n", " \n", " polyadd(a1, a2)\n", " Find the sum of two polynomials.\n", " \n", " Returns the polynomial resulting from the sum of two input polynomials.\n", " Each input must be either a poly1d object or a 1D sequence of polynomial\n", " coefficients, from highest to lowest degree.\n", " \n", " Parameters\n", " ----------\n", " a1, a2 : array_like or poly1d object\n", " Input polynomials.\n", " \n", " Returns\n", " -------\n", " out : ndarray or poly1d object\n", " The sum of the inputs. If either input is a poly1d object, then the\n", " output is also a poly1d object. Otherwise, it is a 1D array of\n", " polynomial coefficients from highest to lowest degree.\n", " \n", " See Also\n", " --------\n", " poly1d : A one-dimensional polynomial class.\n", " poly, polyadd, polyder, polydiv, polyfit, polyint, polysub, polyval\n", " \n", " Examples\n", " --------\n", " >>> np.polyadd([1, 2], [9, 5, 4])\n", " array([9, 6, 6])\n", " \n", " Using poly1d objects:\n", " \n", " >>> p1 = np.poly1d([1, 2])\n", " >>> p2 = np.poly1d([9, 5, 4])\n", " >>> print p1\n", " 1 x + 2\n", " >>> print p2\n", " 2\n", " 9 x + 5 x + 4\n", " >>> print np.polyadd(p1, p2)\n", " 2\n", " 9 x + 6 x + 6\n", " \n", " polyder(p, m=1)\n", " Return the derivative of the specified order of a polynomial.\n", " \n", " Parameters\n", " ----------\n", " p : poly1d or sequence\n", " Polynomial to differentiate.\n", " A sequence is interpreted as polynomial coefficients, see `poly1d`.\n", " m : int, optional\n", " Order of differentiation (default: 1)\n", " \n", " Returns\n", " -------\n", " der : poly1d\n", " A new polynomial representing the derivative.\n", " \n", " See Also\n", " --------\n", " polyint : Anti-derivative of a polynomial.\n", " poly1d : Class for one-dimensional polynomials.\n", " \n", " Examples\n", " --------\n", " The derivative of the polynomial :math:`x^3 + x^2 + x^1 + 1` is:\n", " \n", " >>> p = np.poly1d([1,1,1,1])\n", " >>> p2 = np.polyder(p)\n", " >>> p2\n", " poly1d([3, 2, 1])\n", " \n", " which evaluates to:\n", " \n", " >>> p2(2.)\n", " 17.0\n", " \n", " We can verify this, approximating the derivative with\n", " ``(f(x + h) - f(x))/h``:\n", " \n", " >>> (p(2. + 0.001) - p(2.)) / 0.001\n", " 17.007000999997857\n", " \n", " The fourth-order derivative of a 3rd-order polynomial is zero:\n", " \n", " >>> np.polyder(p, 2)\n", " poly1d([6, 2])\n", " >>> np.polyder(p, 3)\n", " poly1d([6])\n", " >>> np.polyder(p, 4)\n", " poly1d([ 0.])\n", " \n", " polydiv(u, v)\n", " Returns the quotient and remainder of polynomial division.\n", " \n", " The input arrays are the coefficients (including any coefficients\n", " equal to zero) of the \"numerator\" (dividend) and \"denominator\"\n", " (divisor) polynomials, respectively.\n", " \n", " Parameters\n", " ----------\n", " u : array_like or poly1d\n", " Dividend polynomial's coefficients.\n", " \n", " v : array_like or poly1d\n", " Divisor polynomial's coefficients.\n", " \n", " Returns\n", " -------\n", " q : ndarray\n", " Coefficients, including those equal to zero, of the quotient.\n", " r : ndarray\n", " Coefficients, including those equal to zero, of the remainder.\n", " \n", " See Also\n", " --------\n", " poly, polyadd, polyder, polydiv, polyfit, polyint, polymul, polysub,\n", " polyval\n", " \n", " Notes\n", " -----\n", " Both `u` and `v` must be 0-d or 1-d (ndim = 0 or 1), but `u.ndim` need\n", " not equal `v.ndim`. In other words, all four possible combinations -\n", " ``u.ndim = v.ndim = 0``, ``u.ndim = v.ndim = 1``,\n", " ``u.ndim = 1, v.ndim = 0``, and ``u.ndim = 0, v.ndim = 1`` - work.\n", " \n", " Examples\n", " --------\n", " .. math:: \\frac{3x^2 + 5x + 2}{2x + 1} = 1.5x + 1.75, remainder 0.25\n", " \n", " >>> x = np.array([3.0, 5.0, 2.0])\n", " >>> y = np.array([2.0, 1.0])\n", " >>> np.polydiv(x, y)\n", " (array([ 1.5 , 1.75]), array([ 0.25]))\n", " \n", " polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False)\n", " Least squares polynomial fit.\n", " \n", " Fit a polynomial ``p(x) = p[0] * x**deg + ... + p[deg]`` of degree `deg`\n", " to points `(x, y)`. Returns a vector of coefficients `p` that minimises\n", " the squared error.\n", " \n", " Parameters\n", " ----------\n", " x : array_like, shape (M,)\n", " x-coordinates of the M sample points ``(x[i], y[i])``.\n", " y : array_like, shape (M,) or (M, K)\n", " y-coordinates of the sample points. Several data sets of sample\n", " points sharing the same x-coordinates can be fitted at once by\n", " passing in a 2D-array that contains one dataset per column.\n", " deg : int\n", " Degree of the fitting polynomial\n", " rcond : float, optional\n", " Relative condition number of the fit. Singular values smaller than\n", " this relative to the largest singular value will be ignored. The\n", " default value is len(x)*eps, where eps is the relative precision of\n", " the float type, about 2e-16 in most cases.\n", " full : bool, optional\n", " Switch determining nature of return value. When it is False (the\n", " default) just the coefficients are returned, when True diagnostic\n", " information from the singular value decomposition is also returned.\n", " w : array_like, shape (M,), optional\n", " weights to apply to the y-coordinates of the sample points.\n", " cov : bool, optional\n", " Return the estimate and the covariance matrix of the estimate\n", " If full is True, then cov is not returned.\n", " \n", " Returns\n", " -------\n", " p : ndarray, shape (M,) or (M, K)\n", " Polynomial coefficients, highest power first. If `y` was 2-D, the\n", " coefficients for `k`-th data set are in ``p[:,k]``.\n", " \n", " residuals, rank, singular_values, rcond :\n", " Present only if `full` = True. Residuals of the least-squares fit,\n", " the effective rank of the scaled Vandermonde coefficient matrix,\n", " its singular values, and the specified value of `rcond`. For more\n", " details, see `linalg.lstsq`.\n", " \n", " V : ndarray, shape (M,M) or (M,M,K)\n", " Present only if `full` = False and `cov`=True. The covariance\n", " matrix of the polynomial coefficient estimates. The diagonal of\n", " this matrix are the variance estimates for each coefficient. If y\n", " is a 2-D array, then the covariance matrix for the `k`-th data set\n", " are in ``V[:,:,k]``\n", " \n", " \n", " Warns\n", " -----\n", " RankWarning\n", " The rank of the coefficient matrix in the least-squares fit is\n", " deficient. The warning is only raised if `full` = False.\n", " \n", " The warnings can be turned off by\n", " \n", " >>> import warnings\n", " >>> warnings.simplefilter('ignore', np.RankWarning)\n", " \n", " See Also\n", " --------\n", " polyval : Computes polynomial values.\n", " linalg.lstsq : Computes a least-squares fit.\n", " scipy.interpolate.UnivariateSpline : Computes spline fits.\n", " \n", " Notes\n", " -----\n", " The solution minimizes the squared error\n", " \n", " .. math ::\n", " E = \\sum_{j=0}^k |p(x_j) - y_j|^2\n", " \n", " in the equations::\n", " \n", " x[0]**n * p[0] + ... + x[0] * p[n-1] + p[n] = y[0]\n", " x[1]**n * p[0] + ... + x[1] * p[n-1] + p[n] = y[1]\n", " ...\n", " x[k]**n * p[0] + ... + x[k] * p[n-1] + p[n] = y[k]\n", " \n", " The coefficient matrix of the coefficients `p` is a Vandermonde matrix.\n", " \n", " `polyfit` issues a `RankWarning` when the least-squares fit is badly\n", " conditioned. This implies that the best fit is not well-defined due\n", " to numerical error. The results may be improved by lowering the polynomial\n", " degree or by replacing `x` by `x` - `x`.mean(). The `rcond` parameter\n", " can also be set to a value smaller than its default, but the resulting\n", " fit may be spurious: including contributions from the small singular\n", " values can add numerical noise to the result.\n", " \n", " Note that fitting polynomial coefficients is inherently badly conditioned\n", " when the degree of the polynomial is large or the interval of sample points\n", " is badly centered. The quality of the fit should always be checked in these\n", " cases. When polynomial fits are not satisfactory, splines may be a good\n", " alternative.\n", " \n", " References\n", " ----------\n", " .. [1] Wikipedia, \"Curve fitting\",\n", " http://en.wikipedia.org/wiki/Curve_fitting\n", " .. [2] Wikipedia, \"Polynomial interpolation\",\n", " http://en.wikipedia.org/wiki/Polynomial_interpolation\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])\n", " >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])\n", " >>> z = np.polyfit(x, y, 3)\n", " >>> z\n", " array([ 0.08703704, -0.81349206, 1.69312169, -0.03968254])\n", " \n", " It is convenient to use `poly1d` objects for dealing with polynomials:\n", " \n", " >>> p = np.poly1d(z)\n", " >>> p(0.5)\n", " 0.6143849206349179\n", " >>> p(3.5)\n", " -0.34732142857143039\n", " >>> p(10)\n", " 22.579365079365115\n", " \n", " High-order polynomials may oscillate wildly:\n", " \n", " >>> p30 = np.poly1d(np.polyfit(x, y, 30))\n", " /... RankWarning: Polyfit may be poorly conditioned...\n", " >>> p30(4)\n", " -0.80000000000000204\n", " >>> p30(5)\n", " -0.99999999999999445\n", " >>> p30(4.5)\n", " -0.10547061179440398\n", " \n", " Illustration:\n", " \n", " >>> import matplotlib.pyplot as plt\n", " >>> xp = np.linspace(-2, 6, 100)\n", " >>> _ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')\n", " >>> plt.ylim(-2,2)\n", " (-2, 2)\n", " >>> plt.show()\n", " \n", " polyint(p, m=1, k=None)\n", " Return an antiderivative (indefinite integral) of a polynomial.\n", " \n", " The returned order `m` antiderivative `P` of polynomial `p` satisfies\n", " :math:`\\frac{d^m}{dx^m}P(x) = p(x)` and is defined up to `m - 1`\n", " integration constants `k`. The constants determine the low-order\n", " polynomial part\n", " \n", " .. math:: \\frac{k_{m-1}}{0!} x^0 + \\ldots + \\frac{k_0}{(m-1)!}x^{m-1}\n", " \n", " of `P` so that :math:`P^{(j)}(0) = k_{m-j-1}`.\n", " \n", " Parameters\n", " ----------\n", " p : {array_like, poly1d}\n", " Polynomial to differentiate.\n", " A sequence is interpreted as polynomial coefficients, see `poly1d`.\n", " m : int, optional\n", " Order of the antiderivative. (Default: 1)\n", " k : {None, list of `m` scalars, scalar}, optional\n", " Integration constants. They are given in the order of integration:\n", " those corresponding to highest-order terms come first.\n", " \n", " If ``None`` (default), all constants are assumed to be zero.\n", " If `m = 1`, a single scalar can be given instead of a list.\n", " \n", " See Also\n", " --------\n", " polyder : derivative of a polynomial\n", " poly1d.integ : equivalent method\n", " \n", " Examples\n", " --------\n", " The defining property of the antiderivative:\n", " \n", " >>> p = np.poly1d([1,1,1])\n", " >>> P = np.polyint(p)\n", " >>> P\n", " poly1d([ 0.33333333, 0.5 , 1. , 0. ])\n", " >>> np.polyder(P) == p\n", " True\n", " \n", " The integration constants default to zero, but can be specified:\n", " \n", " >>> P = np.polyint(p, 3)\n", " >>> P(0)\n", " 0.0\n", " >>> np.polyder(P)(0)\n", " 0.0\n", " >>> np.polyder(P, 2)(0)\n", " 0.0\n", " >>> P = np.polyint(p, 3, k=[6,5,3])\n", " >>> P\n", " poly1d([ 0.01666667, 0.04166667, 0.16666667, 3. , 5. , 3. ])\n", " \n", " Note that 3 = 6 / 2!, and that the constants are given in the order of\n", " integrations. Constant of the highest-order polynomial term comes first:\n", " \n", " >>> np.polyder(P, 2)(0)\n", " 6.0\n", " >>> np.polyder(P, 1)(0)\n", " 5.0\n", " >>> P(0)\n", " 3.0\n", " \n", " polymul(a1, a2)\n", " Find the product of two polynomials.\n", " \n", " Finds the polynomial resulting from the multiplication of the two input\n", " polynomials. Each input must be either a poly1d object or a 1D sequence\n", " of polynomial coefficients, from highest to lowest degree.\n", " \n", " Parameters\n", " ----------\n", " a1, a2 : array_like or poly1d object\n", " Input polynomials.\n", " \n", " Returns\n", " -------\n", " out : ndarray or poly1d object\n", " The polynomial resulting from the multiplication of the inputs. If\n", " either inputs is a poly1d object, then the output is also a poly1d\n", " object. Otherwise, it is a 1D array of polynomial coefficients from\n", " highest to lowest degree.\n", " \n", " See Also\n", " --------\n", " poly1d : A one-dimensional polynomial class.\n", " poly, polyadd, polyder, polydiv, polyfit, polyint, polysub,\n", " polyval\n", " convolve : Array convolution. Same output as polymul, but has parameter\n", " for overlap mode.\n", " \n", " Examples\n", " --------\n", " >>> np.polymul([1, 2, 3], [9, 5, 1])\n", " array([ 9, 23, 38, 17, 3])\n", " \n", " Using poly1d objects:\n", " \n", " >>> p1 = np.poly1d([1, 2, 3])\n", " >>> p2 = np.poly1d([9, 5, 1])\n", " >>> print p1\n", " 2\n", " 1 x + 2 x + 3\n", " >>> print p2\n", " 2\n", " 9 x + 5 x + 1\n", " >>> print np.polymul(p1, p2)\n", " 4 3 2\n", " 9 x + 23 x + 38 x + 17 x + 3\n", " \n", " polysub(a1, a2)\n", " Difference (subtraction) of two polynomials.\n", " \n", " Given two polynomials `a1` and `a2`, returns ``a1 - a2``.\n", " `a1` and `a2` can be either array_like sequences of the polynomials'\n", " coefficients (including coefficients equal to zero), or `poly1d` objects.\n", " \n", " Parameters\n", " ----------\n", " a1, a2 : array_like or poly1d\n", " Minuend and subtrahend polynomials, respectively.\n", " \n", " Returns\n", " -------\n", " out : ndarray or poly1d\n", " Array or `poly1d` object of the difference polynomial's coefficients.\n", " \n", " See Also\n", " --------\n", " polyval, polydiv, polymul, polyadd\n", " \n", " Examples\n", " --------\n", " .. math:: (2 x^2 + 10 x - 2) - (3 x^2 + 10 x -4) = (-x^2 + 2)\n", " \n", " >>> np.polysub([2, 10, -2], [3, 10, -4])\n", " array([-1, 0, 2])\n", " \n", " polyval(p, x)\n", " Evaluate a polynomial at specific values.\n", " \n", " If `p` is of length N, this function returns the value:\n", " \n", " ``p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x + p[N-1]``\n", " \n", " If `x` is a sequence, then `p(x)` is returned for each element of `x`.\n", " If `x` is another polynomial then the composite polynomial `p(x(t))`\n", " is returned.\n", " \n", " Parameters\n", " ----------\n", " p : array_like or poly1d object\n", " 1D array of polynomial coefficients (including coefficients equal\n", " to zero) from highest degree to the constant term, or an\n", " instance of poly1d.\n", " x : array_like or poly1d object\n", " A number, a 1D array of numbers, or an instance of poly1d, \"at\"\n", " which to evaluate `p`.\n", " \n", " Returns\n", " -------\n", " values : ndarray or poly1d\n", " If `x` is a poly1d instance, the result is the composition of the two\n", " polynomials, i.e., `x` is \"substituted\" in `p` and the simplified\n", " result is returned. In addition, the type of `x` - array_like or\n", " poly1d - governs the type of the output: `x` array_like => `values`\n", " array_like, `x` a poly1d object => `values` is also.\n", " \n", " See Also\n", " --------\n", " poly1d: A polynomial class.\n", " \n", " Notes\n", " -----\n", " Horner's scheme [1]_ is used to evaluate the polynomial. Even so,\n", " for polynomials of high degree the values may be inaccurate due to\n", " rounding errors. Use carefully.\n", " \n", " References\n", " ----------\n", " .. [1] I. N. Bronshtein, K. A. Semendyayev, and K. A. Hirsch (Eng.\n", " trans. Ed.), *Handbook of Mathematics*, New York, Van Nostrand\n", " Reinhold Co., 1985, pg. 720.\n", " \n", " Examples\n", " --------\n", " >>> np.polyval([3,0,1], 5) # 3 * 5**2 + 0 * 5**1 + 1\n", " 76\n", " >>> np.polyval([3,0,1], np.poly1d(5))\n", " poly1d([ 76.])\n", " >>> np.polyval(np.poly1d([3,0,1]), 5)\n", " 76\n", " >>> np.polyval(np.poly1d([3,0,1]), np.poly1d(5))\n", " poly1d([ 76.])\n", " \n", " power(x, p)\n", " Return x to the power p, (x**p).\n", " \n", " If `x` contains negative values, the output is converted to the\n", " complex domain.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " The input value(s).\n", " p : array_like of ints\n", " The power(s) to which `x` is raised. If `x` contains multiple values,\n", " `p` has to either be a scalar, or contain the same number of values\n", " as `x`. In the latter case, the result is\n", " ``x[0]**p[0], x[1]**p[1], ...``.\n", " \n", " Returns\n", " -------\n", " out : ndarray or scalar\n", " The result of ``x**p``. If `x` and `p` are scalars, so is `out`,\n", " otherwise an array is returned.\n", " \n", " See Also\n", " --------\n", " numpy.power\n", " \n", " Examples\n", " --------\n", " >>> np.set_printoptions(precision=4)\n", " \n", " >>> np.lib.scimath.power([2, 4], 2)\n", " array([ 4, 16])\n", " >>> np.lib.scimath.power([2, 4], -2)\n", " array([ 0.25 , 0.0625])\n", " >>> np.lib.scimath.power([-2, 4], 2)\n", " array([ 4.+0.j, 16.+0.j])\n", " \n", " ppmt(rate, per, nper, pv, fv=0.0, when='end')\n", " Compute the payment against loan principal.\n", " \n", " Parameters\n", " ----------\n", " rate : array_like\n", " Rate of interest (per period)\n", " per : array_like, int\n", " Amount paid against the loan changes. The `per` is the period of\n", " interest.\n", " nper : array_like\n", " Number of compounding periods\n", " pv : array_like\n", " Present value\n", " fv : array_like, optional\n", " Future value\n", " when : {{'begin', 1}, {'end', 0}}, {string, int}\n", " When payments are due ('begin' (1) or 'end' (0))\n", " \n", " See Also\n", " --------\n", " pmt, pv, ipmt\n", " \n", " prod(a, axis=None, dtype=None, out=None, keepdims=False)\n", " Return the product of array elements over a given axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " axis : None or int or tuple of ints, optional\n", " Axis or axes along which a product is performed.\n", " The default (`axis` = `None`) is perform a product over all\n", " the dimensions of the input array. `axis` may be negative, in\n", " which case it counts from the last to the first axis.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " If this is a tuple of ints, a product is performed on multiple\n", " axes, instead of a single axis or all the axes as before.\n", " dtype : data-type, optional\n", " The data-type of the returned array, as well as of the accumulator\n", " in which the elements are multiplied. By default, if `a` is of\n", " integer type, `dtype` is the default platform integer. (Note: if\n", " the type of `a` is unsigned, then so is `dtype`.) Otherwise,\n", " the dtype is the same as that of `a`.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must have\n", " the same shape as the expected output, but the type of the\n", " output values will be cast if necessary.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " product_along_axis : ndarray, see `dtype` parameter above.\n", " An array shaped as `a` but with the specified axis removed.\n", " Returns a reference to `out` if specified.\n", " \n", " See Also\n", " --------\n", " ndarray.prod : equivalent method\n", " numpy.doc.ufuncs : Section \"Output arguments\"\n", " \n", " Notes\n", " -----\n", " Arithmetic is modular when using integer types, and no error is\n", " raised on overflow. That means that, on a 32-bit platform:\n", " \n", " >>> x = np.array([536870910, 536870910, 536870910, 536870910])\n", " >>> np.prod(x) #random\n", " 16\n", " \n", " Examples\n", " --------\n", " By default, calculate the product of all elements:\n", " \n", " >>> np.prod([1.,2.])\n", " 2.0\n", " \n", " Even when the input array is two-dimensional:\n", " \n", " >>> np.prod([[1.,2.],[3.,4.]])\n", " 24.0\n", " \n", " But we can also specify the axis over which to multiply:\n", " \n", " >>> np.prod([[1.,2.],[3.,4.]], axis=1)\n", " array([ 2., 12.])\n", " \n", " If the type of `x` is unsigned, then the output type is\n", " the unsigned platform integer:\n", " \n", " >>> x = np.array([1, 2, 3], dtype=np.uint8)\n", " >>> np.prod(x).dtype == np.uint\n", " True\n", " \n", " If `x` is of a signed integer type, then the output type\n", " is the default platform integer:\n", " \n", " >>> x = np.array([1, 2, 3], dtype=np.int8)\n", " >>> np.prod(x).dtype == np.int\n", " True\n", " \n", " product(a, axis=None, dtype=None, out=None, keepdims=False)\n", " Return the product of array elements over a given axis.\n", " \n", " See Also\n", " --------\n", " prod : equivalent function; see for details.\n", " \n", " promote_types(...)\n", " promote_types(type1, type2)\n", " \n", " Returns the data type with the smallest size and smallest scalar\n", " kind to which both ``type1`` and ``type2`` may be safely cast.\n", " The returned data type is always in native byte order.\n", " \n", " This function is symmetric and associative.\n", " \n", " Parameters\n", " ----------\n", " type1 : dtype or dtype specifier\n", " First data type.\n", " type2 : dtype or dtype specifier\n", " Second data type.\n", " \n", " Returns\n", " -------\n", " out : dtype\n", " The promoted data type.\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.6.0\n", " Starting in NumPy 1.9, promote_types function now returns a valid string\n", " length when given an integer or float dtype as one argument and a string\n", " dtype as another argument. Previously it always returned the input string\n", " dtype, even if it wasn't long enough to store the max integer/float value\n", " converted to a string.\n", " \n", " See Also\n", " --------\n", " result_type, dtype, can_cast\n", " \n", " Examples\n", " --------\n", " >>> np.promote_types('f4', 'f8')\n", " dtype('float64')\n", " \n", " >>> np.promote_types('i8', 'f4')\n", " dtype('float64')\n", " \n", " >>> np.promote_types('>i8', '>> np.promote_types('i4', 'S8')\n", " dtype('S11')\n", " \n", " ptp(a, axis=None, out=None)\n", " Range of values (maximum - minimum) along an axis.\n", " \n", " The name of the function comes from the acronym for 'peak to peak'.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input values.\n", " axis : int, optional\n", " Axis along which to find the peaks. By default, flatten the\n", " array.\n", " out : array_like\n", " Alternative output array in which to place the result. It must\n", " have the same shape and buffer length as the expected output,\n", " but the type of the output values will be cast if necessary.\n", " \n", " Returns\n", " -------\n", " ptp : ndarray\n", " A new array holding the result, unless `out` was\n", " specified, in which case a reference to `out` is returned.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(4).reshape((2,2))\n", " >>> x\n", " array([[0, 1],\n", " [2, 3]])\n", " \n", " >>> np.ptp(x, axis=0)\n", " array([2, 2])\n", " \n", " >>> np.ptp(x, axis=1)\n", " array([1, 1])\n", " \n", " put(a, ind, v, mode='raise')\n", " Replaces specified elements of an array with given values.\n", " \n", " The indexing works on the flattened target array. `put` is roughly\n", " equivalent to:\n", " \n", " ::\n", " \n", " a.flat[ind] = v\n", " \n", " Parameters\n", " ----------\n", " a : ndarray\n", " Target array.\n", " ind : array_like\n", " Target indices, interpreted as integers.\n", " v : array_like\n", " Values to place in `a` at target indices. If `v` is shorter than\n", " `ind` it will be repeated as necessary.\n", " mode : {'raise', 'wrap', 'clip'}, optional\n", " Specifies how out-of-bounds indices will behave.\n", " \n", " * 'raise' -- raise an error (default)\n", " * 'wrap' -- wrap around\n", " * 'clip' -- clip to the range\n", " \n", " 'clip' mode means that all indices that are too large are replaced\n", " by the index that addresses the last element along that axis. Note\n", " that this disables indexing with negative numbers.\n", " \n", " See Also\n", " --------\n", " putmask, place\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(5)\n", " >>> np.put(a, [0, 2], [-44, -55])\n", " >>> a\n", " array([-44, 1, -55, 3, 4])\n", " \n", " >>> a = np.arange(5)\n", " >>> np.put(a, 22, -5, mode='clip')\n", " >>> a\n", " array([ 0, 1, 2, 3, -5])\n", " \n", " putmask(...)\n", " putmask(a, mask, values)\n", " \n", " Changes elements of an array based on conditional and input values.\n", " \n", " Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.\n", " \n", " If `values` is not the same size as `a` and `mask` then it will repeat.\n", " This gives behavior different from ``a[mask] = values``.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Target array.\n", " mask : array_like\n", " Boolean mask array. It has to be the same shape as `a`.\n", " values : array_like\n", " Values to put into `a` where `mask` is True. If `values` is smaller\n", " than `a` it will be repeated.\n", " \n", " See Also\n", " --------\n", " place, put, take, copyto\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(6).reshape(2, 3)\n", " >>> np.putmask(x, x>2, x**2)\n", " >>> x\n", " array([[ 0, 1, 2],\n", " [ 9, 16, 25]])\n", " \n", " If `values` is smaller than `a` it is repeated:\n", " \n", " >>> x = np.arange(5)\n", " >>> np.putmask(x, x>1, [-33, -44])\n", " >>> x\n", " array([ 0, 1, -33, -44, -33])\n", " \n", " pv(rate, nper, pmt, fv=0.0, when='end')\n", " Compute the present value.\n", " \n", " Given:\n", " * a future value, `fv`\n", " * an interest `rate` compounded once per period, of which\n", " there are\n", " * `nper` total\n", " * a (fixed) payment, `pmt`, paid either\n", " * at the beginning (`when` = {'begin', 1}) or the end\n", " (`when` = {'end', 0}) of each period\n", " \n", " Return:\n", " the value now\n", " \n", " Parameters\n", " ----------\n", " rate : array_like\n", " Rate of interest (per period)\n", " nper : array_like\n", " Number of compounding periods\n", " pmt : array_like\n", " Payment\n", " fv : array_like, optional\n", " Future value\n", " when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n", " When payments are due ('begin' (1) or 'end' (0))\n", " \n", " Returns\n", " -------\n", " out : ndarray, float\n", " Present value of a series of payments or investments.\n", " \n", " Notes\n", " -----\n", " The present value is computed by solving the equation::\n", " \n", " fv +\n", " pv*(1 + rate)**nper +\n", " pmt*(1 + rate*when)/rate*((1 + rate)**nper - 1) = 0\n", " \n", " or, when ``rate = 0``::\n", " \n", " fv + pv + pmt * nper = 0\n", " \n", " for `pv`, which is then returned.\n", " \n", " References\n", " ----------\n", " .. [WRW] Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May).\n", " Open Document Format for Office Applications (OpenDocument)v1.2,\n", " Part 2: Recalculated Formula (OpenFormula) Format - Annotated Version,\n", " Pre-Draft 12. Organization for the Advancement of Structured Information\n", " Standards (OASIS). Billerica, MA, USA. [ODT Document].\n", " Available:\n", " http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula\n", " OpenDocument-formula-20090508.odt\n", " \n", " Examples\n", " --------\n", " What is the present value (e.g., the initial investment)\n", " of an investment that needs to total $15692.93\n", " after 10 years of saving $100 every month? Assume the\n", " interest rate is 5% (annually) compounded monthly.\n", " \n", " >>> np.pv(0.05/12, 10*12, -100, 15692.93)\n", " -100.00067131625819\n", " \n", " By convention, the negative sign represents cash flow out\n", " (i.e., money not available today). Thus, to end up with\n", " $15,692.93 in 10 years saving $100 a month at 5% annual\n", " interest, one's initial deposit should also be $100.\n", " \n", " If any input is array_like, ``pv`` returns an array of equal shape.\n", " Let's compare different interest rates in the example above:\n", " \n", " >>> a = np.array((0.05, 0.04, 0.03))/12\n", " >>> np.pv(a, 10*12, -100, 15692.93)\n", " array([ -100.00067132, -649.26771385, -1273.78633713])\n", " \n", " So, to end up with the same $15692.93 under the same $100 per month\n", " \"savings plan,\" for annual interest rates of 4% and 3%, one would\n", " need initial investments of $649.27 and $1273.79, respectively.\n", " \n", " rand(...)\n", " rand(d0, d1, ..., dn)\n", " \n", " Random values in a given shape.\n", " \n", " Create an array of the given shape and propagate it with\n", " random samples from a uniform distribution\n", " over ``[0, 1)``.\n", " \n", " Parameters\n", " ----------\n", " d0, d1, ..., dn : int, optional\n", " The dimensions of the returned array, should all be positive.\n", " If no argument is given a single Python float is returned.\n", " \n", " Returns\n", " -------\n", " out : ndarray, shape ``(d0, d1, ..., dn)``\n", " Random values.\n", " \n", " See Also\n", " --------\n", " random\n", " \n", " Notes\n", " -----\n", " This is a convenience function. If you want an interface that\n", " takes a shape-tuple as the first argument, refer to\n", " np.random.random_sample .\n", " \n", " Examples\n", " --------\n", " >>> np.random.rand(3,2)\n", " array([[ 0.14022471, 0.96360618], #random\n", " [ 0.37601032, 0.25528411], #random\n", " [ 0.49313049, 0.94909878]]) #random\n", " \n", " randn(...)\n", " randn(d0, d1, ..., dn)\n", " \n", " Return a sample (or samples) from the \"standard normal\" distribution.\n", " \n", " If positive, int_like or int-convertible arguments are provided,\n", " `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n", " with random floats sampled from a univariate \"normal\" (Gaussian)\n", " distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n", " floats, they are first converted to integers by truncation). A single\n", " float randomly sampled from the distribution is returned if no\n", " argument is provided.\n", " \n", " This is a convenience function. If you want an interface that takes a\n", " tuple as the first argument, use `numpy.random.standard_normal` instead.\n", " \n", " Parameters\n", " ----------\n", " d0, d1, ..., dn : int, optional\n", " The dimensions of the returned array, should be all positive.\n", " If no argument is given a single Python float is returned.\n", " \n", " Returns\n", " -------\n", " Z : ndarray or float\n", " A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n", " the standard normal distribution, or a single such float if\n", " no parameters were supplied.\n", " \n", " See Also\n", " --------\n", " random.standard_normal : Similar, but takes a tuple as its argument.\n", " \n", " Notes\n", " -----\n", " For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n", " \n", " ``sigma * np.random.randn(...) + mu``\n", " \n", " Examples\n", " --------\n", " >>> np.random.randn()\n", " 2.1923875335537315 #random\n", " \n", " Two-by-four array of samples from N(3, 6.25):\n", " \n", " >>> 2.5 * np.random.randn(2, 4) + 3\n", " array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random\n", " [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random\n", " \n", " rank(a)\n", " Return the number of dimensions of an array.\n", " \n", " If `a` is not already an array, a conversion is attempted.\n", " Scalars are zero dimensional.\n", " \n", " .. note::\n", " This function is deprecated in NumPy 1.9 to avoid confusion with\n", " `numpy.linalg.matrix_rank`. The ``ndim`` attribute or function\n", " should be used instead.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array whose number of dimensions is desired. If `a` is not an array,\n", " a conversion is attempted.\n", " \n", " Returns\n", " -------\n", " number_of_dimensions : int\n", " The number of dimensions in the array.\n", " \n", " See Also\n", " --------\n", " ndim : equivalent function\n", " ndarray.ndim : equivalent property\n", " shape : dimensions of array\n", " ndarray.shape : dimensions of array\n", " \n", " Notes\n", " -----\n", " In the old Numeric package, `rank` was the term used for the number of\n", " dimensions, but in Numpy `ndim` is used instead.\n", " \n", " Examples\n", " --------\n", " >>> np.rank([1,2,3])\n", " 1\n", " >>> np.rank(np.array([[1,2,3],[4,5,6]]))\n", " 2\n", " >>> np.rank(1)\n", " 0\n", " \n", " rate(nper, pmt, pv, fv, when='end', guess=0.1, tol=1e-06, maxiter=100)\n", " Compute the rate of interest per period.\n", " \n", " Parameters\n", " ----------\n", " nper : array_like\n", " Number of compounding periods\n", " pmt : array_like\n", " Payment\n", " pv : array_like\n", " Present value\n", " fv : array_like\n", " Future value\n", " when : {{'begin', 1}, {'end', 0}}, {string, int}, optional\n", " When payments are due ('begin' (1) or 'end' (0))\n", " guess : float, optional\n", " Starting guess for solving the rate of interest\n", " tol : float, optional\n", " Required tolerance for the solution\n", " maxiter : int, optional\n", " Maximum iterations in finding the solution\n", " \n", " Notes\n", " -----\n", " The rate of interest is computed by iteratively solving the\n", " (non-linear) equation::\n", " \n", " fv + pv*(1+rate)**nper + pmt*(1+rate*when)/rate * ((1+rate)**nper - 1) = 0\n", " \n", " for ``rate``.\n", " \n", " References\n", " ----------\n", " Wheeler, D. A., E. Rathke, and R. Weir (Eds.) (2009, May). Open Document\n", " Format for Office Applications (OpenDocument)v1.2, Part 2: Recalculated\n", " Formula (OpenFormula) Format - Annotated Version, Pre-Draft 12.\n", " Organization for the Advancement of Structured Information Standards\n", " (OASIS). Billerica, MA, USA. [ODT Document]. Available:\n", " http://www.oasis-open.org/committees/documents.php?wg_abbrev=office-formula\n", " OpenDocument-formula-20090508.odt\n", " \n", " ravel(a, order='C')\n", " Return a flattened array.\n", " \n", " A 1-D array, containing the elements of the input, is returned. A copy is\n", " made only if needed.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array. The elements in `a` are read in the order specified by\n", " `order`, and packed as a 1-D array.\n", " order : {'C','F', 'A', 'K'}, optional\n", " The elements of `a` are read using this index order. 'C' means to\n", " index the elements in C-like order, with the last axis index changing\n", " fastest, back to the first axis index changing slowest. 'F' means to\n", " index the elements in Fortran-like index order, with the first index\n", " changing fastest, and the last index changing slowest. Note that the 'C'\n", " and 'F' options take no account of the memory layout of the underlying\n", " array, and only refer to the order of axis indexing. 'A' means to read\n", " the elements in Fortran-like index order if `a` is Fortran *contiguous*\n", " in memory, C-like order otherwise. 'K' means to read the elements in\n", " the order they occur in memory, except for reversing the data when\n", " strides are negative. By default, 'C' index order is used.\n", " \n", " Returns\n", " -------\n", " 1d_array : ndarray\n", " Output of the same dtype as `a`, and of shape ``(a.size,)``.\n", " \n", " See Also\n", " --------\n", " ndarray.flat : 1-D iterator over an array.\n", " ndarray.flatten : 1-D array copy of the elements of an array\n", " in row-major order.\n", " \n", " Notes\n", " -----\n", " In C-like (row-major) order, in two dimensions, the row index varies the\n", " slowest, and the column index the quickest. This can be generalized to\n", " multiple dimensions, where row-major order implies that the index along the\n", " first axis varies slowest, and the index along the last quickest. The\n", " opposite holds for Fortran-like, or column-major, index ordering.\n", " \n", " Examples\n", " --------\n", " It is equivalent to ``reshape(-1, order=order)``.\n", " \n", " >>> x = np.array([[1, 2, 3], [4, 5, 6]])\n", " >>> print np.ravel(x)\n", " [1 2 3 4 5 6]\n", " \n", " >>> print x.reshape(-1)\n", " [1 2 3 4 5 6]\n", " \n", " >>> print np.ravel(x, order='F')\n", " [1 4 2 5 3 6]\n", " \n", " When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:\n", " \n", " >>> print np.ravel(x.T)\n", " [1 4 2 5 3 6]\n", " >>> print np.ravel(x.T, order='A')\n", " [1 2 3 4 5 6]\n", " \n", " When ``order`` is 'K', it will preserve orderings that are neither 'C'\n", " nor 'F', but won't reverse axes:\n", " \n", " >>> a = np.arange(3)[::-1]; a\n", " array([2, 1, 0])\n", " >>> a.ravel(order='C')\n", " array([2, 1, 0])\n", " >>> a.ravel(order='K')\n", " array([2, 1, 0])\n", " \n", " >>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a\n", " array([[[ 0, 2, 4],\n", " [ 1, 3, 5]],\n", " [[ 6, 8, 10],\n", " [ 7, 9, 11]]])\n", " >>> a.ravel(order='C')\n", " array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11])\n", " >>> a.ravel(order='K')\n", " array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n", " \n", " ravel_multi_index(...)\n", " ravel_multi_index(multi_index, dims, mode='raise', order='C')\n", " \n", " Converts a tuple of index arrays into an array of flat\n", " indices, applying boundary modes to the multi-index.\n", " \n", " Parameters\n", " ----------\n", " multi_index : tuple of array_like\n", " A tuple of integer arrays, one array for each dimension.\n", " dims : tuple of ints\n", " The shape of array into which the indices from ``multi_index`` apply.\n", " mode : {'raise', 'wrap', 'clip'}, optional\n", " Specifies how out-of-bounds indices are handled. Can specify\n", " either one mode or a tuple of modes, one mode per index.\n", " \n", " * 'raise' -- raise an error (default)\n", " * 'wrap' -- wrap around\n", " * 'clip' -- clip to the range\n", " \n", " In 'clip' mode, a negative index which would normally\n", " wrap will clip to 0 instead.\n", " order : {'C', 'F'}, optional\n", " Determines whether the multi-index should be viewed as indexing in\n", " C (row-major) order or FORTRAN (column-major) order.\n", " \n", " Returns\n", " -------\n", " raveled_indices : ndarray\n", " An array of indices into the flattened version of an array\n", " of dimensions ``dims``.\n", " \n", " See Also\n", " --------\n", " unravel_index\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.6.0\n", " \n", " Examples\n", " --------\n", " >>> arr = np.array([[3,6,6],[4,5,1]])\n", " >>> np.ravel_multi_index(arr, (7,6))\n", " array([22, 41, 37])\n", " >>> np.ravel_multi_index(arr, (7,6), order='F')\n", " array([31, 41, 13])\n", " >>> np.ravel_multi_index(arr, (4,6), mode='clip')\n", " array([22, 23, 19])\n", " >>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))\n", " array([12, 13, 13])\n", " \n", " >>> np.ravel_multi_index((3,1,4,1), (6,7,8,9))\n", " 1621\n", " \n", " real(val)\n", " Return the real part of the elements of the array.\n", " \n", " Parameters\n", " ----------\n", " val : array_like\n", " Input array.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Output array. If `val` is real, the type of `val` is used for the\n", " output. If `val` has complex elements, the returned type is float.\n", " \n", " See Also\n", " --------\n", " real_if_close, imag, angle\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([1+2j, 3+4j, 5+6j])\n", " >>> a.real\n", " array([ 1., 3., 5.])\n", " >>> a.real = 9\n", " >>> a\n", " array([ 9.+2.j, 9.+4.j, 9.+6.j])\n", " >>> a.real = np.array([9, 8, 7])\n", " >>> a\n", " array([ 9.+2.j, 8.+4.j, 7.+6.j])\n", " \n", " real_if_close(a, tol=100)\n", " If complex input returns a real array if complex parts are close to zero.\n", " \n", " \"Close to zero\" is defined as `tol` * (machine epsilon of the type for\n", " `a`).\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " tol : float\n", " Tolerance in machine epsilons for the complex part of the elements\n", " in the array.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " If `a` is real, the type of `a` is used for the output. If `a`\n", " has complex elements, the returned type is float.\n", " \n", " See Also\n", " --------\n", " real, imag, angle\n", " \n", " Notes\n", " -----\n", " Machine epsilon varies from machine to machine and between data types\n", " but Python floats on most platforms have a machine epsilon equal to\n", " 2.2204460492503131e-16. You can use 'np.finfo(np.float).eps' to print\n", " out the machine epsilon for floats.\n", " \n", " Examples\n", " --------\n", " >>> np.finfo(np.float).eps\n", " 2.2204460492503131e-16\n", " \n", " >>> np.real_if_close([2.1 + 4e-14j], tol=1000)\n", " array([ 2.1])\n", " >>> np.real_if_close([2.1 + 4e-13j], tol=1000)\n", " array([ 2.1 +4.00000000e-13j])\n", " \n", " recfromcsv(fname, **kwargs)\n", " Load ASCII data stored in a comma-separated file.\n", " \n", " The returned array is a record array (if ``usemask=False``, see\n", " `recarray`) or a masked record array (if ``usemask=True``,\n", " see `ma.mrecords.MaskedRecords`).\n", " \n", " Parameters\n", " ----------\n", " fname, kwargs : For a description of input parameters, see `genfromtxt`.\n", " \n", " See Also\n", " --------\n", " numpy.genfromtxt : generic function to load ASCII data.\n", " \n", " Notes\n", " -----\n", " By default, `dtype` is None, which means that the data-type of the output\n", " array will be determined from the data.\n", " \n", " recfromtxt(fname, **kwargs)\n", " Load ASCII data from a file and return it in a record array.\n", " \n", " If ``usemask=False`` a standard `recarray` is returned,\n", " if ``usemask=True`` a MaskedRecords array is returned.\n", " \n", " Parameters\n", " ----------\n", " fname, kwargs : For a description of input parameters, see `genfromtxt`.\n", " \n", " See Also\n", " --------\n", " numpy.genfromtxt : generic function\n", " \n", " Notes\n", " -----\n", " By default, `dtype` is None, which means that the data-type of the output\n", " array will be determined from the data.\n", " \n", " repeat(a, repeats, axis=None)\n", " Repeat elements of an array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " repeats : {int, array of ints}\n", " The number of repetitions for each element. `repeats` is broadcasted\n", " to fit the shape of the given axis.\n", " axis : int, optional\n", " The axis along which to repeat values. By default, use the\n", " flattened input array, and return a flat output array.\n", " \n", " Returns\n", " -------\n", " repeated_array : ndarray\n", " Output array which has the same shape as `a`, except along\n", " the given axis.\n", " \n", " See Also\n", " --------\n", " tile : Tile an array.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([[1,2],[3,4]])\n", " >>> np.repeat(x, 2)\n", " array([1, 1, 2, 2, 3, 3, 4, 4])\n", " >>> np.repeat(x, 3, axis=1)\n", " array([[1, 1, 1, 2, 2, 2],\n", " [3, 3, 3, 4, 4, 4]])\n", " >>> np.repeat(x, [1, 2], axis=0)\n", " array([[1, 2],\n", " [3, 4],\n", " [3, 4]])\n", " \n", " require(a, dtype=None, requirements=None)\n", " Return an ndarray of the provided type that satisfies requirements.\n", " \n", " This function is useful to be sure that an array with the correct flags\n", " is returned for passing to compiled code (perhaps through ctypes).\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " The object to be converted to a type-and-requirement-satisfying array.\n", " dtype : data-type\n", " The required data-type, the default data-type is float64).\n", " requirements : str or list of str\n", " The requirements list can be any of the following\n", " \n", " * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array\n", " * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array\n", " * 'ALIGNED' ('A') - ensure a data-type aligned array\n", " * 'WRITEABLE' ('W') - ensure a writable array\n", " * 'OWNDATA' ('O') - ensure an array that owns its own data\n", " \n", " See Also\n", " --------\n", " asarray : Convert input to an ndarray.\n", " asanyarray : Convert to an ndarray, but pass through ndarray subclasses.\n", " ascontiguousarray : Convert input to a contiguous array.\n", " asfortranarray : Convert input to an ndarray with column-major\n", " memory order.\n", " ndarray.flags : Information about the memory layout of the array.\n", " \n", " Notes\n", " -----\n", " The returned array will be guaranteed to have the listed requirements\n", " by making a copy if needed.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(6).reshape(2,3)\n", " >>> x.flags\n", " C_CONTIGUOUS : True\n", " F_CONTIGUOUS : False\n", " OWNDATA : False\n", " WRITEABLE : True\n", " ALIGNED : True\n", " UPDATEIFCOPY : False\n", " \n", " >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])\n", " >>> y.flags\n", " C_CONTIGUOUS : False\n", " F_CONTIGUOUS : True\n", " OWNDATA : True\n", " WRITEABLE : True\n", " ALIGNED : True\n", " UPDATEIFCOPY : False\n", " \n", " reshape(a, newshape, order='C')\n", " Gives a new shape to an array without changing its data.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array to be reshaped.\n", " newshape : int or tuple of ints\n", " The new shape should be compatible with the original shape. If\n", " an integer, then the result will be a 1-D array of that length.\n", " One shape dimension can be -1. In this case, the value is inferred\n", " from the length of the array and remaining dimensions.\n", " order : {'C', 'F', 'A'}, optional\n", " Read the elements of `a` using this index order, and place the elements\n", " into the reshaped array using this index order. 'C' means to\n", " read / write the elements using C-like index order, with the last axis index\n", " changing fastest, back to the first axis index changing slowest. 'F'\n", " means to read / write the elements using Fortran-like index order, with\n", " the first index changing fastest, and the last index changing slowest.\n", " Note that the 'C' and 'F' options take no account of the memory layout\n", " of the underlying array, and only refer to the order of indexing. 'A'\n", " means to read / write the elements in Fortran-like index order if `a` is\n", " Fortran *contiguous* in memory, C-like order otherwise.\n", " \n", " Returns\n", " -------\n", " reshaped_array : ndarray\n", " This will be a new view object if possible; otherwise, it will\n", " be a copy. Note there is no guarantee of the *memory layout* (C- or\n", " Fortran- contiguous) of the returned array.\n", " \n", " See Also\n", " --------\n", " ndarray.reshape : Equivalent method.\n", " \n", " Notes\n", " -----\n", " It is not always possible to change the shape of an array without\n", " copying the data. If you want an error to be raise if the data is copied,\n", " you should assign the new shape to the shape attribute of the array::\n", " \n", " >>> a = np.zeros((10, 2))\n", " # A transpose make the array non-contiguous\n", " >>> b = a.T\n", " # Taking a view makes it possible to modify the shape without modifying the\n", " # initial object.\n", " >>> c = b.view()\n", " >>> c.shape = (20)\n", " AttributeError: incompatible shape for a non-contiguous array\n", " \n", " The `order` keyword gives the index ordering both for *fetching* the values\n", " from `a`, and then *placing* the values into the output array. For example,\n", " let's say you have an array:\n", " \n", " >>> a = np.arange(6).reshape((3, 2))\n", " >>> a\n", " array([[0, 1],\n", " [2, 3],\n", " [4, 5]])\n", " \n", " You can think of reshaping as first raveling the array (using the given\n", " index order), then inserting the elements from the raveled array into the\n", " new array using the same kind of index ordering as was used for the\n", " raveling.\n", " \n", " >>> np.reshape(a, (2, 3)) # C-like index ordering\n", " array([[0, 1, 2],\n", " [3, 4, 5]])\n", " >>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape\n", " array([[0, 1, 2],\n", " [3, 4, 5]])\n", " >>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering\n", " array([[0, 4, 3],\n", " [2, 1, 5]])\n", " >>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')\n", " array([[0, 4, 3],\n", " [2, 1, 5]])\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1,2,3], [4,5,6]])\n", " >>> np.reshape(a, 6)\n", " array([1, 2, 3, 4, 5, 6])\n", " >>> np.reshape(a, 6, order='F')\n", " array([1, 4, 2, 5, 3, 6])\n", " \n", " >>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2\n", " array([[1, 2],\n", " [3, 4],\n", " [5, 6]])\n", " \n", " resize(a, new_shape)\n", " Return a new array with the specified shape.\n", " \n", " If the new array is larger than the original array, then the new\n", " array is filled with repeated copies of `a`. Note that this behavior\n", " is different from a.resize(new_shape) which fills with zeros instead\n", " of repeated copies of `a`.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array to be resized.\n", " \n", " new_shape : int or tuple of int\n", " Shape of resized array.\n", " \n", " Returns\n", " -------\n", " reshaped_array : ndarray\n", " The new array is formed from the data in the old array, repeated\n", " if necessary to fill out the required number of elements. The\n", " data are repeated in the order that they are stored in memory.\n", " \n", " See Also\n", " --------\n", " ndarray.resize : resize an array in-place.\n", " \n", " Examples\n", " --------\n", " >>> a=np.array([[0,1],[2,3]])\n", " >>> np.resize(a,(1,4))\n", " array([[0, 1, 2, 3]])\n", " >>> np.resize(a,(2,4))\n", " array([[0, 1, 2, 3],\n", " [0, 1, 2, 3]])\n", " \n", " restoredot(...)\n", " Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS\n", " implementations.\n", " \n", " Typically, the user will only need to call this when troubleshooting and\n", " installation problem, reproducing the conditions of a build without an\n", " accelerated BLAS, or when being very careful about benchmarking linear\n", " algebra operations.\n", " \n", " See Also\n", " --------\n", " alterdot : `restoredot` undoes the effects of `alterdot`.\n", " \n", " result_type(...)\n", " result_type(*arrays_and_dtypes)\n", " \n", " Returns the type that results from applying the NumPy\n", " type promotion rules to the arguments.\n", " \n", " Type promotion in NumPy works similarly to the rules in languages\n", " like C++, with some slight differences. When both scalars and\n", " arrays are used, the array's type takes precedence and the actual value\n", " of the scalar is taken into account.\n", " \n", " For example, calculating 3*a, where a is an array of 32-bit floats,\n", " intuitively should result in a 32-bit float output. If the 3 is a\n", " 32-bit integer, the NumPy rules indicate it can't convert losslessly\n", " into a 32-bit float, so a 64-bit float should be the result type.\n", " By examining the value of the constant, '3', we see that it fits in\n", " an 8-bit integer, which can be cast losslessly into the 32-bit float.\n", " \n", " Parameters\n", " ----------\n", " arrays_and_dtypes : list of arrays and dtypes\n", " The operands of some operation whose result type is needed.\n", " \n", " Returns\n", " -------\n", " out : dtype\n", " The result type.\n", " \n", " See also\n", " --------\n", " dtype, promote_types, min_scalar_type, can_cast\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.6.0\n", " \n", " The specific algorithm used is as follows.\n", " \n", " Categories are determined by first checking which of boolean,\n", " integer (int/uint), or floating point (float/complex) the maximum\n", " kind of all the arrays and the scalars are.\n", " \n", " If there are only scalars or the maximum category of the scalars\n", " is higher than the maximum category of the arrays,\n", " the data types are combined with :func:`promote_types`\n", " to produce the return value.\n", " \n", " Otherwise, `min_scalar_type` is called on each array, and\n", " the resulting data types are all combined with :func:`promote_types`\n", " to produce the return value.\n", " \n", " The set of int values is not a subset of the uint values for types\n", " with the same number of bits, something not reflected in\n", " :func:`min_scalar_type`, but handled as a special case in `result_type`.\n", " \n", " Examples\n", " --------\n", " >>> np.result_type(3, np.arange(7, dtype='i1'))\n", " dtype('int8')\n", " \n", " >>> np.result_type('i4', 'c8')\n", " dtype('complex128')\n", " \n", " >>> np.result_type(3.0, -2)\n", " dtype('float64')\n", " \n", " roll(a, shift, axis=None)\n", " Roll array elements along a given axis.\n", " \n", " Elements that roll beyond the last position are re-introduced at\n", " the first.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " shift : int\n", " The number of places by which elements are shifted.\n", " axis : int, optional\n", " The axis along which elements are shifted. By default, the array\n", " is flattened before shifting, after which the original\n", " shape is restored.\n", " \n", " Returns\n", " -------\n", " res : ndarray\n", " Output array, with the same shape as `a`.\n", " \n", " See Also\n", " --------\n", " rollaxis : Roll the specified axis backwards, until it lies in a\n", " given position.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(10)\n", " >>> np.roll(x, 2)\n", " array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])\n", " \n", " >>> x2 = np.reshape(x, (2,5))\n", " >>> x2\n", " array([[0, 1, 2, 3, 4],\n", " [5, 6, 7, 8, 9]])\n", " >>> np.roll(x2, 1)\n", " array([[9, 0, 1, 2, 3],\n", " [4, 5, 6, 7, 8]])\n", " >>> np.roll(x2, 1, axis=0)\n", " array([[5, 6, 7, 8, 9],\n", " [0, 1, 2, 3, 4]])\n", " >>> np.roll(x2, 1, axis=1)\n", " array([[4, 0, 1, 2, 3],\n", " [9, 5, 6, 7, 8]])\n", " \n", " rollaxis(a, axis, start=0)\n", " Roll the specified axis backwards, until it lies in a given position.\n", " \n", " Parameters\n", " ----------\n", " a : ndarray\n", " Input array.\n", " axis : int\n", " The axis to roll backwards. The positions of the other axes do not\n", " change relative to one another.\n", " start : int, optional\n", " The axis is rolled until it lies before this position. The default,\n", " 0, results in a \"complete\" roll.\n", " \n", " Returns\n", " -------\n", " res : ndarray\n", " Output array.\n", " \n", " See Also\n", " --------\n", " roll : Roll the elements of an array by a number of positions along a\n", " given axis.\n", " \n", " Examples\n", " --------\n", " >>> a = np.ones((3,4,5,6))\n", " >>> np.rollaxis(a, 3, 1).shape\n", " (3, 6, 4, 5)\n", " >>> np.rollaxis(a, 2).shape\n", " (5, 3, 4, 6)\n", " >>> np.rollaxis(a, 1, 4).shape\n", " (3, 5, 6, 4)\n", " \n", " roots(p)\n", " Return the roots of a polynomial with coefficients given in p.\n", " \n", " The values in the rank-1 array `p` are coefficients of a polynomial.\n", " If the length of `p` is n+1 then the polynomial is described by::\n", " \n", " p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]\n", " \n", " Parameters\n", " ----------\n", " p : array_like\n", " Rank-1 array of polynomial coefficients.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " An array containing the complex roots of the polynomial.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " When `p` cannot be converted to a rank-1 array.\n", " \n", " See also\n", " --------\n", " poly : Find the coefficients of a polynomial with a given sequence\n", " of roots.\n", " polyval : Evaluate a polynomial at a point.\n", " polyfit : Least squares polynomial fit.\n", " poly1d : A one-dimensional polynomial class.\n", " \n", " Notes\n", " -----\n", " The algorithm relies on computing the eigenvalues of the\n", " companion matrix [1]_.\n", " \n", " References\n", " ----------\n", " .. [1] R. A. Horn & C. R. Johnson, *Matrix Analysis*. Cambridge, UK:\n", " Cambridge University Press, 1999, pp. 146-7.\n", " \n", " Examples\n", " --------\n", " >>> coeff = [3.2, 2, 1]\n", " >>> np.roots(coeff)\n", " array([-0.3125+0.46351241j, -0.3125-0.46351241j])\n", " \n", " rot90(m, k=1)\n", " Rotate an array by 90 degrees in the counter-clockwise direction.\n", " \n", " The first two dimensions are rotated; therefore, the array must be at\n", " least 2-D.\n", " \n", " Parameters\n", " ----------\n", " m : array_like\n", " Array of two or more dimensions.\n", " k : integer\n", " Number of times the array is rotated by 90 degrees.\n", " \n", " Returns\n", " -------\n", " y : ndarray\n", " Rotated array.\n", " \n", " See Also\n", " --------\n", " fliplr : Flip an array horizontally.\n", " flipud : Flip an array vertically.\n", " \n", " Examples\n", " --------\n", " >>> m = np.array([[1,2],[3,4]], int)\n", " >>> m\n", " array([[1, 2],\n", " [3, 4]])\n", " >>> np.rot90(m)\n", " array([[2, 4],\n", " [1, 3]])\n", " >>> np.rot90(m, 2)\n", " array([[4, 3],\n", " [2, 1]])\n", " \n", " round_(a, decimals=0, out=None)\n", " Round an array to the given number of decimals.\n", " \n", " Refer to `around` for full documentation.\n", " \n", " See Also\n", " --------\n", " around : equivalent function\n", " \n", " row_stack = vstack(tup)\n", " Stack arrays in sequence vertically (row wise).\n", " \n", " Take a sequence of arrays and stack them vertically to make a single\n", " array. Rebuild arrays divided by `vsplit`.\n", " \n", " Parameters\n", " ----------\n", " tup : sequence of ndarrays\n", " Tuple containing arrays to be stacked. The arrays must have the same\n", " shape along all but the first axis.\n", " \n", " Returns\n", " -------\n", " stacked : ndarray\n", " The array formed by stacking the given arrays.\n", " \n", " See Also\n", " --------\n", " hstack : Stack arrays in sequence horizontally (column wise).\n", " dstack : Stack arrays in sequence depth wise (along third dimension).\n", " concatenate : Join a sequence of arrays together.\n", " vsplit : Split array into a list of multiple sub-arrays vertically.\n", " \n", " Notes\n", " -----\n", " Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that\n", " are at least 2-dimensional.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([1, 2, 3])\n", " >>> b = np.array([2, 3, 4])\n", " >>> np.vstack((a,b))\n", " array([[1, 2, 3],\n", " [2, 3, 4]])\n", " \n", " >>> a = np.array([[1], [2], [3]])\n", " >>> b = np.array([[2], [3], [4]])\n", " >>> np.vstack((a,b))\n", " array([[1],\n", " [2],\n", " [3],\n", " [2],\n", " [3],\n", " [4]])\n", " \n", " safe_eval(source)\n", " Protected string evaluation.\n", " \n", " Evaluate a string containing a Python literal expression without\n", " allowing the execution of arbitrary non-literal code.\n", " \n", " Parameters\n", " ----------\n", " source : str\n", " The string to evaluate.\n", " \n", " Returns\n", " -------\n", " obj : object\n", " The result of evaluating `source`.\n", " \n", " Raises\n", " ------\n", " SyntaxError\n", " If the code has invalid Python syntax, or if it contains\n", " non-literal code.\n", " \n", " Examples\n", " --------\n", " >>> np.safe_eval('1')\n", " 1\n", " >>> np.safe_eval('[1, 2, 3]')\n", " [1, 2, 3]\n", " >>> np.safe_eval('{\"foo\": (\"bar\", 10.0)}')\n", " {'foo': ('bar', 10.0)}\n", " \n", " >>> np.safe_eval('import os')\n", " Traceback (most recent call last):\n", " ...\n", " SyntaxError: invalid syntax\n", " \n", " >>> np.safe_eval('open(\"/home/user/.ssh/id_dsa\").read()')\n", " Traceback (most recent call last):\n", " ...\n", " SyntaxError: Unsupported source construct: compiler.ast.CallFunc\n", " \n", " save(file, arr)\n", " Save an array to a binary file in NumPy ``.npy`` format.\n", " \n", " Parameters\n", " ----------\n", " file : file or str\n", " File or filename to which the data is saved. If file is a file-object,\n", " then the filename is unchanged. If file is a string, a ``.npy``\n", " extension will be appended to the file name if it does not already\n", " have one.\n", " arr : array_like\n", " Array data to be saved.\n", " \n", " See Also\n", " --------\n", " savez : Save several arrays into a ``.npz`` archive\n", " savetxt, load\n", " \n", " Notes\n", " -----\n", " For a description of the ``.npy`` format, see `format`.\n", " \n", " Examples\n", " --------\n", " >>> from tempfile import TemporaryFile\n", " >>> outfile = TemporaryFile()\n", " \n", " >>> x = np.arange(10)\n", " >>> np.save(outfile, x)\n", " \n", " >>> outfile.seek(0) # Only needed here to simulate closing & reopening file\n", " >>> np.load(outfile)\n", " array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", " \n", " savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\n', header='', footer='', comments='# ')\n", " Save an array to a text file.\n", " \n", " Parameters\n", " ----------\n", " fname : filename or file handle\n", " If the filename ends in ``.gz``, the file is automatically saved in\n", " compressed gzip format. `loadtxt` understands gzipped files\n", " transparently.\n", " X : array_like\n", " Data to be saved to a text file.\n", " fmt : str or sequence of strs, optional\n", " A single format (%10.5f), a sequence of formats, or a\n", " multi-format string, e.g. 'Iteration %d -- %10.5f', in which\n", " case `delimiter` is ignored. For complex `X`, the legal options\n", " for `fmt` are:\n", " a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted\n", " like `' (%s+%sj)' % (fmt, fmt)`\n", " b) a full string specifying every real and imaginary part, e.g.\n", " `' %.4e %+.4j %.4e %+.4j %.4e %+.4j'` for 3 columns\n", " c) a list of specifiers, one per column - in this case, the real\n", " and imaginary part must have separate specifiers,\n", " e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns\n", " delimiter : str, optional\n", " String or character separating columns.\n", " newline : str, optional\n", " String or character separating lines.\n", " \n", " .. versionadded:: 1.5.0\n", " header : str, optional\n", " String that will be written at the beginning of the file.\n", " \n", " .. versionadded:: 1.7.0\n", " footer : str, optional\n", " String that will be written at the end of the file.\n", " \n", " .. versionadded:: 1.7.0\n", " comments : str, optional\n", " String that will be prepended to the ``header`` and ``footer`` strings,\n", " to mark them as comments. Default: '# ', as expected by e.g.\n", " ``numpy.loadtxt``.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " \n", " See Also\n", " --------\n", " save : Save an array to a binary file in NumPy ``.npy`` format\n", " savez : Save several arrays into an uncompressed ``.npz`` archive\n", " savez_compressed : Save several arrays into a compressed ``.npz`` archive\n", " \n", " Notes\n", " -----\n", " Further explanation of the `fmt` parameter\n", " (``%[flag]width[.precision]specifier``):\n", " \n", " flags:\n", " ``-`` : left justify\n", " \n", " ``+`` : Forces to precede result with + or -.\n", " \n", " ``0`` : Left pad the number with zeros instead of space (see width).\n", " \n", " width:\n", " Minimum number of characters to be printed. The value is not truncated\n", " if it has more characters.\n", " \n", " precision:\n", " - For integer specifiers (eg. ``d,i,o,x``), the minimum number of\n", " digits.\n", " - For ``e, E`` and ``f`` specifiers, the number of digits to print\n", " after the decimal point.\n", " - For ``g`` and ``G``, the maximum number of significant digits.\n", " - For ``s``, the maximum number of characters.\n", " \n", " specifiers:\n", " ``c`` : character\n", " \n", " ``d`` or ``i`` : signed decimal integer\n", " \n", " ``e`` or ``E`` : scientific notation with ``e`` or ``E``.\n", " \n", " ``f`` : decimal floating point\n", " \n", " ``g,G`` : use the shorter of ``e,E`` or ``f``\n", " \n", " ``o`` : signed octal\n", " \n", " ``s`` : string of characters\n", " \n", " ``u`` : unsigned decimal integer\n", " \n", " ``x,X`` : unsigned hexadecimal integer\n", " \n", " This explanation of ``fmt`` is not complete, for an exhaustive\n", " specification see [1]_.\n", " \n", " References\n", " ----------\n", " .. [1] `Format Specification Mini-Language\n", " `_, Python Documentation.\n", " \n", " Examples\n", " --------\n", " >>> x = y = z = np.arange(0.0,5.0,1.0)\n", " >>> np.savetxt('test.out', x, delimiter=',') # X is an array\n", " >>> np.savetxt('test.out', (x,y,z)) # x,y,z equal sized 1D arrays\n", " >>> np.savetxt('test.out', x, fmt='%1.4e') # use exponential notation\n", " \n", " savez(file, *args, **kwds)\n", " Save several arrays into a single file in uncompressed ``.npz`` format.\n", " \n", " If arguments are passed in with no keywords, the corresponding variable\n", " names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword\n", " arguments are given, the corresponding variable names, in the ``.npz``\n", " file will match the keyword names.\n", " \n", " Parameters\n", " ----------\n", " file : str or file\n", " Either the file name (string) or an open file (file-like object)\n", " where the data will be saved. If file is a string, the ``.npz``\n", " extension will be appended to the file name if it is not already there.\n", " args : Arguments, optional\n", " Arrays to save to the file. Since it is not possible for Python to\n", " know the names of the arrays outside `savez`, the arrays will be saved\n", " with names \"arr_0\", \"arr_1\", and so on. These arguments can be any\n", " expression.\n", " kwds : Keyword arguments, optional\n", " Arrays to save to the file. Arrays will be saved in the file with the\n", " keyword names.\n", " \n", " Returns\n", " -------\n", " None\n", " \n", " See Also\n", " --------\n", " save : Save a single array to a binary file in NumPy format.\n", " savetxt : Save an array to a file as plain text.\n", " savez_compressed : Save several arrays into a compressed ``.npz`` archive\n", " \n", " Notes\n", " -----\n", " The ``.npz`` file format is a zipped archive of files named after the\n", " variables they contain. The archive is not compressed and each file\n", " in the archive contains one variable in ``.npy`` format. For a\n", " description of the ``.npy`` format, see `format`.\n", " \n", " When opening the saved ``.npz`` file with `load` a `NpzFile` object is\n", " returned. This is a dictionary-like object which can be queried for\n", " its list of arrays (with the ``.files`` attribute), and for the arrays\n", " themselves.\n", " \n", " Examples\n", " --------\n", " >>> from tempfile import TemporaryFile\n", " >>> outfile = TemporaryFile()\n", " >>> x = np.arange(10)\n", " >>> y = np.sin(x)\n", " \n", " Using `savez` with \\*args, the arrays are saved with default names.\n", " \n", " >>> np.savez(outfile, x, y)\n", " >>> outfile.seek(0) # Only needed here to simulate closing & reopening file\n", " >>> npzfile = np.load(outfile)\n", " >>> npzfile.files\n", " ['arr_1', 'arr_0']\n", " >>> npzfile['arr_0']\n", " array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", " \n", " Using `savez` with \\**kwds, the arrays are saved with the keyword names.\n", " \n", " >>> outfile = TemporaryFile()\n", " >>> np.savez(outfile, x=x, y=y)\n", " >>> outfile.seek(0)\n", " >>> npzfile = np.load(outfile)\n", " >>> npzfile.files\n", " ['y', 'x']\n", " >>> npzfile['x']\n", " array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", " \n", " savez_compressed(file, *args, **kwds)\n", " Save several arrays into a single file in compressed ``.npz`` format.\n", " \n", " If keyword arguments are given, then filenames are taken from the keywords.\n", " If arguments are passed in with no keywords, then stored file names are\n", " arr_0, arr_1, etc.\n", " \n", " Parameters\n", " ----------\n", " file : str\n", " File name of ``.npz`` file.\n", " args : Arguments\n", " Function arguments.\n", " kwds : Keyword arguments\n", " Keywords.\n", " \n", " See Also\n", " --------\n", " numpy.savez : Save several arrays into an uncompressed ``.npz`` file format\n", " numpy.load : Load the files created by savez_compressed.\n", " \n", " sctype2char(sctype)\n", " Return the string representation of a scalar dtype.\n", " \n", " Parameters\n", " ----------\n", " sctype : scalar dtype or object\n", " If a scalar dtype, the corresponding string character is\n", " returned. If an object, `sctype2char` tries to infer its scalar type\n", " and then return the corresponding string character.\n", " \n", " Returns\n", " -------\n", " typechar : str\n", " The string character corresponding to the scalar type.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If `sctype` is an object for which the type can not be inferred.\n", " \n", " See Also\n", " --------\n", " obj2sctype, issctype, issubsctype, mintypecode\n", " \n", " Examples\n", " --------\n", " >>> for sctype in [np.int32, np.float, np.complex, np.string_, np.ndarray]:\n", " ... print np.sctype2char(sctype)\n", " l\n", " d\n", " D\n", " S\n", " O\n", " \n", " >>> x = np.array([1., 2-1.j])\n", " >>> np.sctype2char(x)\n", " 'D'\n", " >>> np.sctype2char(list)\n", " 'O'\n", " \n", " searchsorted(a, v, side='left', sorter=None)\n", " Find indices where elements should be inserted to maintain order.\n", " \n", " Find the indices into a sorted array `a` such that, if the\n", " corresponding elements in `v` were inserted before the indices, the\n", " order of `a` would be preserved.\n", " \n", " Parameters\n", " ----------\n", " a : 1-D array_like\n", " Input array. If `sorter` is None, then it must be sorted in\n", " ascending order, otherwise `sorter` must be an array of indices\n", " that sort it.\n", " v : array_like\n", " Values to insert into `a`.\n", " side : {'left', 'right'}, optional\n", " If 'left', the index of the first suitable location found is given.\n", " If 'right', return the last such index. If there is no suitable\n", " index, return either 0 or N (where N is the length of `a`).\n", " sorter : 1-D array_like, optional\n", " .. versionadded:: 1.7.0\n", " Optional array of integer indices that sort array a into ascending\n", " order. They are typically the result of argsort.\n", " \n", " Returns\n", " -------\n", " indices : array of ints\n", " Array of insertion points with the same shape as `v`.\n", " \n", " See Also\n", " --------\n", " sort : Return a sorted copy of an array.\n", " histogram : Produce histogram from 1-D data.\n", " \n", " Notes\n", " -----\n", " Binary search is used to find the required insertion points.\n", " \n", " As of Numpy 1.4.0 `searchsorted` works with real/complex arrays containing\n", " `nan` values. The enhanced sort order is documented in `sort`.\n", " \n", " Examples\n", " --------\n", " >>> np.searchsorted([1,2,3,4,5], 3)\n", " 2\n", " >>> np.searchsorted([1,2,3,4,5], 3, side='right')\n", " 3\n", " >>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])\n", " array([0, 5, 1, 2])\n", " \n", " select(condlist, choicelist, default=0)\n", " Return an array drawn from elements in choicelist, depending on conditions.\n", " \n", " Parameters\n", " ----------\n", " condlist : list of bool ndarrays\n", " The list of conditions which determine from which array in `choicelist`\n", " the output elements are taken. When multiple conditions are satisfied,\n", " the first one encountered in `condlist` is used.\n", " choicelist : list of ndarrays\n", " The list of arrays from which the output elements are taken. It has\n", " to be of the same length as `condlist`.\n", " default : scalar, optional\n", " The element inserted in `output` when all conditions evaluate to False.\n", " \n", " Returns\n", " -------\n", " output : ndarray\n", " The output at position m is the m-th element of the array in\n", " `choicelist` where the m-th element of the corresponding array in\n", " `condlist` is True.\n", " \n", " See Also\n", " --------\n", " where : Return elements from one of two arrays depending on condition.\n", " take, choose, compress, diag, diagonal\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(10)\n", " >>> condlist = [x<3, x>5]\n", " >>> choicelist = [x, x**2]\n", " >>> np.select(condlist, choicelist)\n", " array([ 0, 1, 2, 0, 0, 0, 36, 49, 64, 81])\n", " \n", " set_numeric_ops(...)\n", " set_numeric_ops(op1=func1, op2=func2, ...)\n", " \n", " Set numerical operators for array objects.\n", " \n", " Parameters\n", " ----------\n", " op1, op2, ... : callable\n", " Each ``op = func`` pair describes an operator to be replaced.\n", " For example, ``add = lambda x, y: np.add(x, y) % 5`` would replace\n", " addition by modulus 5 addition.\n", " \n", " Returns\n", " -------\n", " saved_ops : list of callables\n", " A list of all operators, stored before making replacements.\n", " \n", " Notes\n", " -----\n", " .. WARNING::\n", " Use with care! Incorrect usage may lead to memory errors.\n", " \n", " A function replacing an operator cannot make use of that operator.\n", " For example, when replacing add, you may not use ``+``. Instead,\n", " directly call ufuncs.\n", " \n", " Examples\n", " --------\n", " >>> def add_mod5(x, y):\n", " ... return np.add(x, y) % 5\n", " ...\n", " >>> old_funcs = np.set_numeric_ops(add=add_mod5)\n", " \n", " >>> x = np.arange(12).reshape((3, 4))\n", " >>> x + x\n", " array([[0, 2, 4, 1],\n", " [3, 0, 2, 4],\n", " [1, 3, 0, 2]])\n", " \n", " >>> ignore = np.set_numeric_ops(**old_funcs) # restore operators\n", " \n", " set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None, formatter=None)\n", " Set printing options.\n", " \n", " These options determine the way floating point numbers, arrays and\n", " other NumPy objects are displayed.\n", " \n", " Parameters\n", " ----------\n", " precision : int, optional\n", " Number of digits of precision for floating point output (default 8).\n", " threshold : int, optional\n", " Total number of array elements which trigger summarization\n", " rather than full repr (default 1000).\n", " edgeitems : int, optional\n", " Number of array items in summary at beginning and end of\n", " each dimension (default 3).\n", " linewidth : int, optional\n", " The number of characters per line for the purpose of inserting\n", " line breaks (default 75).\n", " suppress : bool, optional\n", " Whether or not suppress printing of small floating point values\n", " using scientific notation (default False).\n", " nanstr : str, optional\n", " String representation of floating point not-a-number (default nan).\n", " infstr : str, optional\n", " String representation of floating point infinity (default inf).\n", " formatter : dict of callables, optional\n", " If not None, the keys should indicate the type(s) that the respective\n", " formatting function applies to. Callables should return a string.\n", " Types that are not specified (by their corresponding keys) are handled\n", " by the default formatters. Individual types for which a formatter\n", " can be set are::\n", " \n", " - 'bool'\n", " - 'int'\n", " - 'timedelta' : a `numpy.timedelta64`\n", " - 'datetime' : a `numpy.datetime64`\n", " - 'float'\n", " - 'longfloat' : 128-bit floats\n", " - 'complexfloat'\n", " - 'longcomplexfloat' : composed of two 128-bit floats\n", " - 'numpy_str' : types `numpy.string_` and `numpy.unicode_`\n", " - 'str' : all other strings\n", " \n", " Other keys that can be used to set a group of types at once are::\n", " \n", " - 'all' : sets all types\n", " - 'int_kind' : sets 'int'\n", " - 'float_kind' : sets 'float' and 'longfloat'\n", " - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'\n", " - 'str_kind' : sets 'str' and 'numpystr'\n", " \n", " See Also\n", " --------\n", " get_printoptions, set_string_function, array2string\n", " \n", " Notes\n", " -----\n", " `formatter` is always reset with a call to `set_printoptions`.\n", " \n", " Examples\n", " --------\n", " Floating point precision can be set:\n", " \n", " >>> np.set_printoptions(precision=4)\n", " >>> print np.array([1.123456789])\n", " [ 1.1235]\n", " \n", " Long arrays can be summarised:\n", " \n", " >>> np.set_printoptions(threshold=5)\n", " >>> print np.arange(10)\n", " [0 1 2 ..., 7 8 9]\n", " \n", " Small results can be suppressed:\n", " \n", " >>> eps = np.finfo(float).eps\n", " >>> x = np.arange(4.)\n", " >>> x**2 - (x + eps)**2\n", " array([ -4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00])\n", " >>> np.set_printoptions(suppress=True)\n", " >>> x**2 - (x + eps)**2\n", " array([-0., -0., 0., 0.])\n", " \n", " A custom formatter can be used to display array elements as desired:\n", " \n", " >>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})\n", " >>> x = np.arange(3)\n", " >>> x\n", " array([int: 0, int: -1, int: -2])\n", " >>> np.set_printoptions() # formatter gets reset\n", " >>> x\n", " array([0, 1, 2])\n", " \n", " To put back the default options, you can use:\n", " \n", " >>> np.set_printoptions(edgeitems=3,infstr='inf',\n", " ... linewidth=75, nanstr='nan', precision=8,\n", " ... suppress=False, threshold=1000, formatter=None)\n", " \n", " set_string_function(f, repr=True)\n", " Set a Python function to be used when pretty printing arrays.\n", " \n", " Parameters\n", " ----------\n", " f : function or None\n", " Function to be used to pretty print arrays. The function should expect\n", " a single array argument and return a string of the representation of\n", " the array. If None, the function is reset to the default NumPy function\n", " to print arrays.\n", " repr : bool, optional\n", " If True (default), the function for pretty printing (``__repr__``)\n", " is set, if False the function that returns the default string\n", " representation (``__str__``) is set.\n", " \n", " See Also\n", " --------\n", " set_printoptions, get_printoptions\n", " \n", " Examples\n", " --------\n", " >>> def pprint(arr):\n", " ... return 'HA! - What are you going to do now?'\n", " ...\n", " >>> np.set_string_function(pprint)\n", " >>> a = np.arange(10)\n", " >>> a\n", " HA! - What are you going to do now?\n", " >>> print a\n", " [0 1 2 3 4 5 6 7 8 9]\n", " \n", " We can reset the function to the default:\n", " \n", " >>> np.set_string_function(None)\n", " >>> a\n", " array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n", " \n", " `repr` affects either pretty printing or normal string representation.\n", " Note that ``__repr__`` is still affected by setting ``__str__``\n", " because the width of each array element in the returned string becomes\n", " equal to the length of the result of ``__str__()``.\n", " \n", " >>> x = np.arange(4)\n", " >>> np.set_string_function(lambda x:'random', repr=False)\n", " >>> x.__str__()\n", " 'random'\n", " >>> x.__repr__()\n", " 'array([ 0, 1, 2, 3])'\n", " \n", " setbufsize(size)\n", " Set the size of the buffer used in ufuncs.\n", " \n", " Parameters\n", " ----------\n", " size : int\n", " Size of buffer.\n", " \n", " setdiff1d(ar1, ar2, assume_unique=False)\n", " Find the set difference of two arrays.\n", " \n", " Return the sorted, unique values in `ar1` that are not in `ar2`.\n", " \n", " Parameters\n", " ----------\n", " ar1 : array_like\n", " Input array.\n", " ar2 : array_like\n", " Input comparison array.\n", " assume_unique : bool\n", " If True, the input arrays are both assumed to be unique, which\n", " can speed up the calculation. Default is False.\n", " \n", " Returns\n", " -------\n", " setdiff1d : ndarray\n", " Sorted 1D array of values in `ar1` that are not in `ar2`.\n", " \n", " See Also\n", " --------\n", " numpy.lib.arraysetops : Module with a number of other functions for\n", " performing set operations on arrays.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([1, 2, 3, 2, 4, 1])\n", " >>> b = np.array([3, 4, 5, 6])\n", " >>> np.setdiff1d(a, b)\n", " array([1, 2])\n", " \n", " seterr(all=None, divide=None, over=None, under=None, invalid=None)\n", " Set how floating-point errors are handled.\n", " \n", " Note that operations on integer scalar types (such as `int16`) are\n", " handled like floating point, and are affected by these settings.\n", " \n", " Parameters\n", " ----------\n", " all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n", " Set treatment for all types of floating-point errors at once:\n", " \n", " - ignore: Take no action when the exception occurs.\n", " - warn: Print a `RuntimeWarning` (via the Python `warnings` module).\n", " - raise: Raise a `FloatingPointError`.\n", " - call: Call a function specified using the `seterrcall` function.\n", " - print: Print a warning directly to ``stdout``.\n", " - log: Record error in a Log object specified by `seterrcall`.\n", " \n", " The default is not to change the current behavior.\n", " divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n", " Treatment for division by zero.\n", " over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n", " Treatment for floating-point overflow.\n", " under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n", " Treatment for floating-point underflow.\n", " invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional\n", " Treatment for invalid floating-point operation.\n", " \n", " Returns\n", " -------\n", " old_settings : dict\n", " Dictionary containing the old settings.\n", " \n", " See also\n", " --------\n", " seterrcall : Set a callback function for the 'call' mode.\n", " geterr, geterrcall, errstate\n", " \n", " Notes\n", " -----\n", " The floating-point exceptions are defined in the IEEE 754 standard [1]:\n", " \n", " - Division by zero: infinite result obtained from finite numbers.\n", " - Overflow: result too large to be expressed.\n", " - Underflow: result so close to zero that some precision\n", " was lost.\n", " - Invalid operation: result is not an expressible number, typically\n", " indicates that a NaN was produced.\n", " \n", " .. [1] http://en.wikipedia.org/wiki/IEEE_754\n", " \n", " Examples\n", " --------\n", " >>> old_settings = np.seterr(all='ignore') #seterr to known value\n", " >>> np.seterr(over='raise')\n", " {'over': 'ignore', 'divide': 'ignore', 'invalid': 'ignore',\n", " 'under': 'ignore'}\n", " >>> np.seterr(**old_settings) # reset to default\n", " {'over': 'raise', 'divide': 'ignore', 'invalid': 'ignore', 'under': 'ignore'}\n", " \n", " >>> np.int16(32000) * np.int16(3)\n", " 30464\n", " >>> old_settings = np.seterr(all='warn', over='raise')\n", " >>> np.int16(32000) * np.int16(3)\n", " Traceback (most recent call last):\n", " File \"\", line 1, in \n", " FloatingPointError: overflow encountered in short_scalars\n", " \n", " >>> old_settings = np.seterr(all='print')\n", " >>> np.geterr()\n", " {'over': 'print', 'divide': 'print', 'invalid': 'print', 'under': 'print'}\n", " >>> np.int16(32000) * np.int16(3)\n", " Warning: overflow encountered in short_scalars\n", " 30464\n", " \n", " seterrcall(func)\n", " Set the floating-point error callback function or log object.\n", " \n", " There are two ways to capture floating-point error messages. The first\n", " is to set the error-handler to 'call', using `seterr`. Then, set\n", " the function to call using this function.\n", " \n", " The second is to set the error-handler to 'log', using `seterr`.\n", " Floating-point errors then trigger a call to the 'write' method of\n", " the provided object.\n", " \n", " Parameters\n", " ----------\n", " func : callable f(err, flag) or object with write method\n", " Function to call upon floating-point errors ('call'-mode) or\n", " object whose 'write' method is used to log such message ('log'-mode).\n", " \n", " The call function takes two arguments. The first is the\n", " type of error (one of \"divide\", \"over\", \"under\", or \"invalid\"),\n", " and the second is the status flag. The flag is a byte, whose\n", " least-significant bits indicate the status::\n", " \n", " [0 0 0 0 invalid over under invalid]\n", " \n", " In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.\n", " \n", " If an object is provided, its write method should take one argument,\n", " a string.\n", " \n", " Returns\n", " -------\n", " h : callable, log instance or None\n", " The old error handler.\n", " \n", " See Also\n", " --------\n", " seterr, geterr, geterrcall\n", " \n", " Examples\n", " --------\n", " Callback upon error:\n", " \n", " >>> def err_handler(type, flag):\n", " ... print \"Floating point error (%s), with flag %s\" % (type, flag)\n", " ...\n", " \n", " >>> saved_handler = np.seterrcall(err_handler)\n", " >>> save_err = np.seterr(all='call')\n", " \n", " >>> np.array([1, 2, 3]) / 0.0\n", " Floating point error (divide by zero), with flag 1\n", " array([ Inf, Inf, Inf])\n", " \n", " >>> np.seterrcall(saved_handler)\n", " \n", " >>> np.seterr(**save_err)\n", " {'over': 'call', 'divide': 'call', 'invalid': 'call', 'under': 'call'}\n", " \n", " Log error message:\n", " \n", " >>> class Log(object):\n", " ... def write(self, msg):\n", " ... print \"LOG: %s\" % msg\n", " ...\n", " \n", " >>> log = Log()\n", " >>> saved_handler = np.seterrcall(log)\n", " >>> save_err = np.seterr(all='log')\n", " \n", " >>> np.array([1, 2, 3]) / 0.0\n", " LOG: Warning: divide by zero encountered in divide\n", " \n", " array([ Inf, Inf, Inf])\n", " \n", " >>> np.seterrcall(saved_handler)\n", " <__main__.Log object at 0x...>\n", " >>> np.seterr(**save_err)\n", " {'over': 'log', 'divide': 'log', 'invalid': 'log', 'under': 'log'}\n", " \n", " seterrobj(...)\n", " seterrobj(errobj)\n", " \n", " Set the object that defines floating-point error handling.\n", " \n", " The error object contains all information that defines the error handling\n", " behavior in Numpy. `seterrobj` is used internally by the other\n", " functions that set error handling behavior (`seterr`, `seterrcall`).\n", " \n", " Parameters\n", " ----------\n", " errobj : list\n", " The error object, a list containing three elements:\n", " [internal numpy buffer size, error mask, error callback function].\n", " \n", " The error mask is a single integer that holds the treatment information\n", " on all four floating point errors. The information for each error type\n", " is contained in three bits of the integer. If we print it in base 8, we\n", " can see what treatment is set for \"invalid\", \"under\", \"over\", and\n", " \"divide\" (in that order). The printed string can be interpreted with\n", " \n", " * 0 : 'ignore'\n", " * 1 : 'warn'\n", " * 2 : 'raise'\n", " * 3 : 'call'\n", " * 4 : 'print'\n", " * 5 : 'log'\n", " \n", " See Also\n", " --------\n", " geterrobj, seterr, geterr, seterrcall, geterrcall\n", " getbufsize, setbufsize\n", " \n", " Notes\n", " -----\n", " For complete documentation of the types of floating-point exceptions and\n", " treatment options, see `seterr`.\n", " \n", " Examples\n", " --------\n", " >>> old_errobj = np.geterrobj() # first get the defaults\n", " >>> old_errobj\n", " [10000, 0, None]\n", " \n", " >>> def err_handler(type, flag):\n", " ... print \"Floating point error (%s), with flag %s\" % (type, flag)\n", " ...\n", " >>> new_errobj = [20000, 12, err_handler]\n", " >>> np.seterrobj(new_errobj)\n", " >>> np.base_repr(12, 8) # int for divide=4 ('print') and over=1 ('warn')\n", " '14'\n", " >>> np.geterr()\n", " {'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'}\n", " >>> np.geterrcall() is err_handler\n", " True\n", " \n", " setxor1d(ar1, ar2, assume_unique=False)\n", " Find the set exclusive-or of two arrays.\n", " \n", " Return the sorted, unique values that are in only one (not both) of the\n", " input arrays.\n", " \n", " Parameters\n", " ----------\n", " ar1, ar2 : array_like\n", " Input arrays.\n", " assume_unique : bool\n", " If True, the input arrays are both assumed to be unique, which\n", " can speed up the calculation. Default is False.\n", " \n", " Returns\n", " -------\n", " setxor1d : ndarray\n", " Sorted 1D array of unique values that are in only one of the input\n", " arrays.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([1, 2, 3, 2, 4])\n", " >>> b = np.array([2, 3, 5, 7, 5])\n", " >>> np.setxor1d(a,b)\n", " array([1, 4, 5, 7])\n", " \n", " shape(a)\n", " Return the shape of an array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " \n", " Returns\n", " -------\n", " shape : tuple of ints\n", " The elements of the shape tuple give the lengths of the\n", " corresponding array dimensions.\n", " \n", " See Also\n", " --------\n", " alen\n", " ndarray.shape : Equivalent array method.\n", " \n", " Examples\n", " --------\n", " >>> np.shape(np.eye(3))\n", " (3, 3)\n", " >>> np.shape([[1, 2]])\n", " (1, 2)\n", " >>> np.shape([0])\n", " (1,)\n", " >>> np.shape(0)\n", " ()\n", " \n", " >>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])\n", " >>> np.shape(a)\n", " (2,)\n", " >>> a.shape\n", " (2,)\n", " \n", " show_config = show()\n", " \n", " sinc(x)\n", " Return the sinc function.\n", " \n", " The sinc function is :math:`\\sin(\\pi x)/(\\pi x)`.\n", " \n", " Parameters\n", " ----------\n", " x : ndarray\n", " Array (possibly multi-dimensional) of values for which to to\n", " calculate ``sinc(x)``.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " ``sinc(x)``, which has the same shape as the input.\n", " \n", " Notes\n", " -----\n", " ``sinc(0)`` is the limit value 1.\n", " \n", " The name sinc is short for \"sine cardinal\" or \"sinus cardinalis\".\n", " \n", " The sinc function is used in various signal processing applications,\n", " including in anti-aliasing, in the construction of a Lanczos resampling\n", " filter, and in interpolation.\n", " \n", " For bandlimited interpolation of discrete-time signals, the ideal\n", " interpolation kernel is proportional to the sinc function.\n", " \n", " References\n", " ----------\n", " .. [1] Weisstein, Eric W. \"Sinc Function.\" From MathWorld--A Wolfram Web\n", " Resource. http://mathworld.wolfram.com/SincFunction.html\n", " .. [2] Wikipedia, \"Sinc function\",\n", " http://en.wikipedia.org/wiki/Sinc_function\n", " \n", " Examples\n", " --------\n", " >>> x = np.linspace(-4, 4, 41)\n", " >>> np.sinc(x)\n", " array([ -3.89804309e-17, -4.92362781e-02, -8.40918587e-02,\n", " -8.90384387e-02, -5.84680802e-02, 3.89804309e-17,\n", " 6.68206631e-02, 1.16434881e-01, 1.26137788e-01,\n", " 8.50444803e-02, -3.89804309e-17, -1.03943254e-01,\n", " -1.89206682e-01, -2.16236208e-01, -1.55914881e-01,\n", " 3.89804309e-17, 2.33872321e-01, 5.04551152e-01,\n", " 7.56826729e-01, 9.35489284e-01, 1.00000000e+00,\n", " 9.35489284e-01, 7.56826729e-01, 5.04551152e-01,\n", " 2.33872321e-01, 3.89804309e-17, -1.55914881e-01,\n", " -2.16236208e-01, -1.89206682e-01, -1.03943254e-01,\n", " -3.89804309e-17, 8.50444803e-02, 1.26137788e-01,\n", " 1.16434881e-01, 6.68206631e-02, 3.89804309e-17,\n", " -5.84680802e-02, -8.90384387e-02, -8.40918587e-02,\n", " -4.92362781e-02, -3.89804309e-17])\n", " \n", " >>> plt.plot(x, np.sinc(x))\n", " []\n", " >>> plt.title(\"Sinc Function\")\n", " \n", " >>> plt.ylabel(\"Amplitude\")\n", " \n", " >>> plt.xlabel(\"X\")\n", " \n", " >>> plt.show()\n", " \n", " It works in 2-D as well:\n", " \n", " >>> x = np.linspace(-4, 4, 401)\n", " >>> xx = np.outer(x, x)\n", " >>> plt.imshow(np.sinc(xx))\n", " \n", " \n", " size(a, axis=None)\n", " Return the number of elements along a given axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " axis : int, optional\n", " Axis along which the elements are counted. By default, give\n", " the total number of elements.\n", " \n", " Returns\n", " -------\n", " element_count : int\n", " Number of elements along the specified axis.\n", " \n", " See Also\n", " --------\n", " shape : dimensions of array\n", " ndarray.shape : dimensions of array\n", " ndarray.size : number of elements in array\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1,2,3],[4,5,6]])\n", " >>> np.size(a)\n", " 6\n", " >>> np.size(a,1)\n", " 3\n", " >>> np.size(a,0)\n", " 2\n", " \n", " sometrue(a, axis=None, out=None, keepdims=False)\n", " Check whether some values are true.\n", " \n", " Refer to `any` for full documentation.\n", " \n", " See Also\n", " --------\n", " any : equivalent function\n", " \n", " sort(a, axis=-1, kind='quicksort', order=None)\n", " Return a sorted copy of an array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array to be sorted.\n", " axis : int or None, optional\n", " Axis along which to sort. If None, the array is flattened before\n", " sorting. The default is -1, which sorts along the last axis.\n", " kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n", " Sorting algorithm. Default is 'quicksort'.\n", " order : list, optional\n", " When `a` is a structured array, this argument specifies which fields\n", " to compare first, second, and so on. This list does not need to\n", " include all of the fields.\n", " \n", " Returns\n", " -------\n", " sorted_array : ndarray\n", " Array of the same type and shape as `a`.\n", " \n", " See Also\n", " --------\n", " ndarray.sort : Method to sort an array in-place.\n", " argsort : Indirect sort.\n", " lexsort : Indirect stable sort on multiple keys.\n", " searchsorted : Find elements in a sorted array.\n", " partition : Partial sort.\n", " \n", " Notes\n", " -----\n", " The various sorting algorithms are characterized by their average speed,\n", " worst case performance, work space size, and whether they are stable. A\n", " stable sort keeps items with the same key in the same relative\n", " order. The three available algorithms have the following\n", " properties:\n", " \n", " =========== ======= ============= ============ =======\n", " kind speed worst case work space stable\n", " =========== ======= ============= ============ =======\n", " 'quicksort' 1 O(n^2) 0 no\n", " 'mergesort' 2 O(n*log(n)) ~n/2 yes\n", " 'heapsort' 3 O(n*log(n)) 0 no\n", " =========== ======= ============= ============ =======\n", " \n", " All the sort algorithms make temporary copies of the data when\n", " sorting along any but the last axis. Consequently, sorting along\n", " the last axis is faster and uses less space than sorting along\n", " any other axis.\n", " \n", " The sort order for complex numbers is lexicographic. If both the real\n", " and imaginary parts are non-nan then the order is determined by the\n", " real parts except when they are equal, in which case the order is\n", " determined by the imaginary parts.\n", " \n", " Previous to numpy 1.4.0 sorting real and complex arrays containing nan\n", " values led to undefined behaviour. In numpy versions >= 1.4.0 nan\n", " values are sorted to the end. The extended sort order is:\n", " \n", " * Real: [R, nan]\n", " * Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj]\n", " \n", " where R is a non-nan real value. Complex values with the same nan\n", " placements are sorted according to the non-nan part if it exists.\n", " Non-nan values are sorted as before.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1,4],[3,1]])\n", " >>> np.sort(a) # sort along the last axis\n", " array([[1, 4],\n", " [1, 3]])\n", " >>> np.sort(a, axis=None) # sort the flattened array\n", " array([1, 1, 3, 4])\n", " >>> np.sort(a, axis=0) # sort along the first axis\n", " array([[1, 1],\n", " [3, 4]])\n", " \n", " Use the `order` keyword to specify a field to use when sorting a\n", " structured array:\n", " \n", " >>> dtype = [('name', 'S10'), ('height', float), ('age', int)]\n", " >>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),\n", " ... ('Galahad', 1.7, 38)]\n", " >>> a = np.array(values, dtype=dtype) # create a structured array\n", " >>> np.sort(a, order='height') # doctest: +SKIP\n", " array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),\n", " ('Lancelot', 1.8999999999999999, 38)],\n", " dtype=[('name', '|S10'), ('height', '>> np.sort(a, order=['age', 'height']) # doctest: +SKIP\n", " array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38),\n", " ('Arthur', 1.8, 41)],\n", " dtype=[('name', '|S10'), ('height', '>> np.sort_complex([5, 3, 6, 2, 1])\n", " array([ 1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])\n", " \n", " >>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])\n", " array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])\n", " \n", " source(object, output=)\n", " Print or write to a file the source code for a Numpy object.\n", " \n", " The source code is only returned for objects written in Python. Many\n", " functions and classes are defined in C and will therefore not return\n", " useful information.\n", " \n", " Parameters\n", " ----------\n", " object : numpy object\n", " Input object. This can be any object (function, class, module,\n", " ...).\n", " output : file object, optional\n", " If `output` not supplied then source code is printed to screen\n", " (sys.stdout). File object must be created with either write 'w' or\n", " append 'a' modes.\n", " \n", " See Also\n", " --------\n", " lookfor, info\n", " \n", " Examples\n", " --------\n", " >>> np.source(np.interp) #doctest: +SKIP\n", " In file: /usr/lib/python2.6/dist-packages/numpy/lib/function_base.py\n", " def interp(x, xp, fp, left=None, right=None):\n", " \"\"\".... (full docstring printed)\"\"\"\n", " if isinstance(x, (float, int, number)):\n", " return compiled_interp([x], xp, fp, left, right).item()\n", " else:\n", " return compiled_interp(x, xp, fp, left, right)\n", " \n", " The source code is only returned for objects written in Python.\n", " \n", " >>> np.source(np.array) #doctest: +SKIP\n", " Not available for this object.\n", " \n", " split(ary, indices_or_sections, axis=0)\n", " Split an array into multiple sub-arrays.\n", " \n", " Parameters\n", " ----------\n", " ary : ndarray\n", " Array to be divided into sub-arrays.\n", " indices_or_sections : int or 1-D array\n", " If `indices_or_sections` is an integer, N, the array will be divided\n", " into N equal arrays along `axis`. If such a split is not possible,\n", " an error is raised.\n", " \n", " If `indices_or_sections` is a 1-D array of sorted integers, the entries\n", " indicate where along `axis` the array is split. For example,\n", " ``[2, 3]`` would, for ``axis=0``, result in\n", " \n", " - ary[:2]\n", " - ary[2:3]\n", " - ary[3:]\n", " \n", " If an index exceeds the dimension of the array along `axis`,\n", " an empty sub-array is returned correspondingly.\n", " axis : int, optional\n", " The axis along which to split, default is 0.\n", " \n", " Returns\n", " -------\n", " sub-arrays : list of ndarrays\n", " A list of sub-arrays.\n", " \n", " Raises\n", " ------\n", " ValueError\n", " If `indices_or_sections` is given as an integer, but\n", " a split does not result in equal division.\n", " \n", " See Also\n", " --------\n", " array_split : Split an array into multiple sub-arrays of equal or\n", " near-equal size. Does not raise an exception if\n", " an equal division cannot be made.\n", " hsplit : Split array into multiple sub-arrays horizontally (column-wise).\n", " vsplit : Split array into multiple sub-arrays vertically (row wise).\n", " dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).\n", " concatenate : Join arrays together.\n", " hstack : Stack arrays in sequence horizontally (column wise).\n", " vstack : Stack arrays in sequence vertically (row wise).\n", " dstack : Stack arrays in sequence depth wise (along third dimension).\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(9.0)\n", " >>> np.split(x, 3)\n", " [array([ 0., 1., 2.]), array([ 3., 4., 5.]), array([ 6., 7., 8.])]\n", " \n", " >>> x = np.arange(8.0)\n", " >>> np.split(x, [3, 5, 6, 10])\n", " [array([ 0., 1., 2.]),\n", " array([ 3., 4.]),\n", " array([ 5.]),\n", " array([ 6., 7.]),\n", " array([], dtype=float64)]\n", " \n", " sqrt(x)\n", " Compute the square root of x.\n", " \n", " For negative input elements, a complex value is returned\n", " (unlike `numpy.sqrt` which returns NaN).\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " The input value(s).\n", " \n", " Returns\n", " -------\n", " out : ndarray or scalar\n", " The square root of `x`. If `x` was a scalar, so is `out`,\n", " otherwise an array is returned.\n", " \n", " See Also\n", " --------\n", " numpy.sqrt\n", " \n", " Examples\n", " --------\n", " For real, non-negative inputs this works just like `numpy.sqrt`:\n", " \n", " >>> np.lib.scimath.sqrt(1)\n", " 1.0\n", " >>> np.lib.scimath.sqrt([1, 4])\n", " array([ 1., 2.])\n", " \n", " But it automatically handles negative inputs:\n", " \n", " >>> np.lib.scimath.sqrt(-1)\n", " (0.0+1.0j)\n", " >>> np.lib.scimath.sqrt([-1,4])\n", " array([ 0.+1.j, 2.+0.j])\n", " \n", " squeeze(a, axis=None)\n", " Remove single-dimensional entries from the shape of an array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input data.\n", " axis : None or int or tuple of ints, optional\n", " .. versionadded:: 1.7.0\n", " \n", " Selects a subset of the single-dimensional entries in the\n", " shape. If an axis is selected with shape entry greater than\n", " one, an error is raised.\n", " \n", " Returns\n", " -------\n", " squeezed : ndarray\n", " The input array, but with all or a subset of the\n", " dimensions of length 1 removed. This is always `a` itself\n", " or a view into `a`.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([[[0], [1], [2]]])\n", " >>> x.shape\n", " (1, 3, 1)\n", " >>> np.squeeze(x).shape\n", " (3,)\n", " >>> np.squeeze(x, axis=(2,)).shape\n", " (1, 3)\n", " \n", " std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n", " Compute the standard deviation along the specified axis.\n", " \n", " Returns the standard deviation, a measure of the spread of a distribution,\n", " of the array elements. The standard deviation is computed for the\n", " flattened array by default, otherwise over the specified axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Calculate the standard deviation of these values.\n", " axis : int, optional\n", " Axis along which the standard deviation is computed. The default is\n", " to compute the standard deviation of the flattened array.\n", " dtype : dtype, optional\n", " Type to use in computing the standard deviation. For arrays of\n", " integer type the default is float64, for arrays of float types it is\n", " the same as the array type.\n", " out : ndarray, optional\n", " Alternative output array in which to place the result. It must have\n", " the same shape as the expected output but the type (of the calculated\n", " values) will be cast if necessary.\n", " ddof : int, optional\n", " Means Delta Degrees of Freedom. The divisor used in calculations\n", " is ``N - ddof``, where ``N`` represents the number of elements.\n", " By default `ddof` is zero.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " standard_deviation : ndarray, see dtype parameter above.\n", " If `out` is None, return a new array containing the standard deviation,\n", " otherwise return a reference to the output array.\n", " \n", " See Also\n", " --------\n", " var, mean, nanmean, nanstd, nanvar\n", " numpy.doc.ufuncs : Section \"Output arguments\"\n", " \n", " Notes\n", " -----\n", " The standard deviation is the square root of the average of the squared\n", " deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``.\n", " \n", " The average squared deviation is normally calculated as\n", " ``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is specified,\n", " the divisor ``N - ddof`` is used instead. In standard statistical\n", " practice, ``ddof=1`` provides an unbiased estimator of the variance\n", " of the infinite population. ``ddof=0`` provides a maximum likelihood\n", " estimate of the variance for normally distributed variables. The\n", " standard deviation computed in this function is the square root of\n", " the estimated variance, so even with ``ddof=1``, it will not be an\n", " unbiased estimate of the standard deviation per se.\n", " \n", " Note that, for complex numbers, `std` takes the absolute\n", " value before squaring, so that the result is always real and nonnegative.\n", " \n", " For floating-point input, the *std* is computed using the same\n", " precision the input has. Depending on the input data, this can cause\n", " the results to be inaccurate, especially for float32 (see example below).\n", " Specifying a higher-accuracy accumulator using the `dtype` keyword can\n", " alleviate this issue.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1, 2], [3, 4]])\n", " >>> np.std(a)\n", " 1.1180339887498949\n", " >>> np.std(a, axis=0)\n", " array([ 1., 1.])\n", " >>> np.std(a, axis=1)\n", " array([ 0.5, 0.5])\n", " \n", " In single precision, std() can be inaccurate:\n", " \n", " >>> a = np.zeros((2,512*512), dtype=np.float32)\n", " >>> a[0,:] = 1.0\n", " >>> a[1,:] = 0.1\n", " >>> np.std(a)\n", " 0.45172946707416706\n", " \n", " Computing the standard deviation in float64 is more accurate:\n", " \n", " >>> np.std(a, dtype=np.float64)\n", " 0.44999999925552653\n", " \n", " sum(a, axis=None, dtype=None, out=None, keepdims=False)\n", " Sum of array elements over a given axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Elements to sum.\n", " axis : None or int or tuple of ints, optional\n", " Axis or axes along which a sum is performed.\n", " The default (`axis` = `None`) is perform a sum over all\n", " the dimensions of the input array. `axis` may be negative, in\n", " which case it counts from the last to the first axis.\n", " \n", " .. versionadded:: 1.7.0\n", " \n", " If this is a tuple of ints, a sum is performed on multiple\n", " axes, instead of a single axis or all the axes as before.\n", " dtype : dtype, optional\n", " The type of the returned array and of the accumulator in which\n", " the elements are summed. By default, the dtype of `a` is used.\n", " An exception is when `a` has an integer type with less precision\n", " than the default platform integer. In that case, the default\n", " platform integer is used instead.\n", " out : ndarray, optional\n", " Array into which the output is placed. By default, a new array is\n", " created. If `out` is given, it must be of the appropriate shape\n", " (the shape of `a` with `axis` removed, i.e.,\n", " ``numpy.delete(a.shape, axis)``). Its type is preserved. See\n", " `doc.ufuncs` (Section \"Output arguments\") for more details.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " sum_along_axis : ndarray\n", " An array with the same shape as `a`, with the specified\n", " axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar\n", " is returned. If an output array is specified, a reference to\n", " `out` is returned.\n", " \n", " See Also\n", " --------\n", " ndarray.sum : Equivalent method.\n", " \n", " cumsum : Cumulative sum of array elements.\n", " \n", " trapz : Integration of array values using the composite trapezoidal rule.\n", " \n", " mean, average\n", " \n", " Notes\n", " -----\n", " Arithmetic is modular when using integer types, and no error is\n", " raised on overflow.\n", " \n", " Examples\n", " --------\n", " >>> np.sum([0.5, 1.5])\n", " 2.0\n", " >>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)\n", " 1\n", " >>> np.sum([[0, 1], [0, 5]])\n", " 6\n", " >>> np.sum([[0, 1], [0, 5]], axis=0)\n", " array([0, 6])\n", " >>> np.sum([[0, 1], [0, 5]], axis=1)\n", " array([1, 5])\n", " \n", " If the accumulator is too small, overflow occurs:\n", " \n", " >>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)\n", " -128\n", " \n", " swapaxes(a, axis1, axis2)\n", " Interchange two axes of an array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " axis1 : int\n", " First axis.\n", " axis2 : int\n", " Second axis.\n", " \n", " Returns\n", " -------\n", " a_swapped : ndarray\n", " If `a` is an ndarray, then a view of `a` is returned; otherwise\n", " a new array is created.\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([[1,2,3]])\n", " >>> np.swapaxes(x,0,1)\n", " array([[1],\n", " [2],\n", " [3]])\n", " \n", " >>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])\n", " >>> x\n", " array([[[0, 1],\n", " [2, 3]],\n", " [[4, 5],\n", " [6, 7]]])\n", " \n", " >>> np.swapaxes(x,0,2)\n", " array([[[0, 4],\n", " [2, 6]],\n", " [[1, 5],\n", " [3, 7]]])\n", " \n", " take(a, indices, axis=None, out=None, mode='raise')\n", " Take elements from an array along an axis.\n", " \n", " This function does the same thing as \"fancy\" indexing (indexing arrays\n", " using arrays); however, it can be easier to use if you need elements\n", " along a given axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " The source array.\n", " indices : array_like\n", " The indices of the values to extract.\n", " \n", " .. versionadded:: 1.8.0\n", " \n", " Also allow scalars for indices.\n", " axis : int, optional\n", " The axis over which to select values. By default, the flattened\n", " input array is used.\n", " out : ndarray, optional\n", " If provided, the result will be placed in this array. It should\n", " be of the appropriate shape and dtype.\n", " mode : {'raise', 'wrap', 'clip'}, optional\n", " Specifies how out-of-bounds indices will behave.\n", " \n", " * 'raise' -- raise an error (default)\n", " * 'wrap' -- wrap around\n", " * 'clip' -- clip to the range\n", " \n", " 'clip' mode means that all indices that are too large are replaced\n", " by the index that addresses the last element along that axis. Note\n", " that this disables indexing with negative numbers.\n", " \n", " Returns\n", " -------\n", " subarray : ndarray\n", " The returned array has the same type as `a`.\n", " \n", " See Also\n", " --------\n", " compress : Take elements using a boolean mask\n", " ndarray.take : equivalent method\n", " \n", " Examples\n", " --------\n", " >>> a = [4, 3, 5, 7, 6, 8]\n", " >>> indices = [0, 1, 4]\n", " >>> np.take(a, indices)\n", " array([4, 3, 6])\n", " \n", " In this example if `a` is an ndarray, \"fancy\" indexing can be used.\n", " \n", " >>> a = np.array(a)\n", " >>> a[indices]\n", " array([4, 3, 6])\n", " \n", " If `indices` is not one dimensional, the output also has these dimensions.\n", " \n", " >>> np.take(a, [[0, 1], [2, 3]])\n", " array([[4, 3],\n", " [5, 7]])\n", " \n", " tensordot(a, b, axes=2)\n", " Compute tensor dot product along specified axes for arrays >= 1-D.\n", " \n", " Given two tensors (arrays of dimension greater than or equal to one),\n", " `a` and `b`, and an array_like object containing two array_like\n", " objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s\n", " elements (components) over the axes specified by ``a_axes`` and\n", " ``b_axes``. The third argument can be a single non-negative\n", " integer_like scalar, ``N``; if it is such, then the last ``N``\n", " dimensions of `a` and the first ``N`` dimensions of `b` are summed\n", " over.\n", " \n", " Parameters\n", " ----------\n", " a, b : array_like, len(shape) >= 1\n", " Tensors to \"dot\".\n", " axes : variable type\n", " * integer_like scalar\n", " Number of axes to sum over (applies to both arrays); or\n", " * (2,) array_like, both elements array_like of the same length\n", " List of axes to be summed over, first sequence applying to `a`,\n", " second to `b`.\n", " \n", " See Also\n", " --------\n", " dot, einsum\n", " \n", " Notes\n", " -----\n", " When there is more than one axis to sum over - and they are not the last\n", " (first) axes of `a` (`b`) - the argument `axes` should consist of\n", " two sequences of the same length, with the first axis to sum over given\n", " first in both sequences, the second axis second, and so forth.\n", " \n", " Examples\n", " --------\n", " A \"traditional\" example:\n", " \n", " >>> a = np.arange(60.).reshape(3,4,5)\n", " >>> b = np.arange(24.).reshape(4,3,2)\n", " >>> c = np.tensordot(a,b, axes=([1,0],[0,1]))\n", " >>> c.shape\n", " (5, 2)\n", " >>> c\n", " array([[ 4400., 4730.],\n", " [ 4532., 4874.],\n", " [ 4664., 5018.],\n", " [ 4796., 5162.],\n", " [ 4928., 5306.]])\n", " >>> # A slower but equivalent way of computing the same...\n", " >>> d = np.zeros((5,2))\n", " >>> for i in range(5):\n", " ... for j in range(2):\n", " ... for k in range(3):\n", " ... for n in range(4):\n", " ... d[i,j] += a[k,n,i] * b[n,k,j]\n", " >>> c == d\n", " array([[ True, True],\n", " [ True, True],\n", " [ True, True],\n", " [ True, True],\n", " [ True, True]], dtype=bool)\n", " \n", " An extended example taking advantage of the overloading of + and \\*:\n", " \n", " >>> a = np.array(range(1, 9))\n", " >>> a.shape = (2, 2, 2)\n", " >>> A = np.array(('a', 'b', 'c', 'd'), dtype=object)\n", " >>> A.shape = (2, 2)\n", " >>> a; A\n", " array([[[1, 2],\n", " [3, 4]],\n", " [[5, 6],\n", " [7, 8]]])\n", " array([[a, b],\n", " [c, d]], dtype=object)\n", " \n", " >>> np.tensordot(a, A) # third argument default is 2\n", " array([abbcccdddd, aaaaabbbbbbcccccccdddddddd], dtype=object)\n", " \n", " >>> np.tensordot(a, A, 1)\n", " array([[[acc, bdd],\n", " [aaacccc, bbbdddd]],\n", " [[aaaaacccccc, bbbbbdddddd],\n", " [aaaaaaacccccccc, bbbbbbbdddddddd]]], dtype=object)\n", " \n", " >>> np.tensordot(a, A, 0) # \"Left for reader\" (result too long to incl.)\n", " array([[[[[a, b],\n", " [c, d]],\n", " ...\n", " \n", " >>> np.tensordot(a, A, (0, 1))\n", " array([[[abbbbb, cddddd],\n", " [aabbbbbb, ccdddddd]],\n", " [[aaabbbbbbb, cccddddddd],\n", " [aaaabbbbbbbb, ccccdddddddd]]], dtype=object)\n", " \n", " >>> np.tensordot(a, A, (2, 1))\n", " array([[[abb, cdd],\n", " [aaabbbb, cccdddd]],\n", " [[aaaaabbbbbb, cccccdddddd],\n", " [aaaaaaabbbbbbbb, cccccccdddddddd]]], dtype=object)\n", " \n", " >>> np.tensordot(a, A, ((0, 1), (0, 1)))\n", " array([abbbcccccddddddd, aabbbbccccccdddddddd], dtype=object)\n", " \n", " >>> np.tensordot(a, A, ((2, 1), (1, 0)))\n", " array([acccbbdddd, aaaaacccccccbbbbbbdddddddd], dtype=object)\n", " \n", " test(self, label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, raise_warnings=None) method of numpy.testing.nosetester.NoseTester instance\n", " Run tests for module using nose.\n", " \n", " Parameters\n", " ----------\n", " label : {'fast', 'full', '', attribute identifier}, optional\n", " Identifies the tests to run. This can be a string to pass to\n", " the nosetests executable with the '-A' option, or one of several\n", " special values. Special values are:\n", " * 'fast' - the default - which corresponds to the ``nosetests -A``\n", " option of 'not slow'.\n", " * 'full' - fast (as above) and slow tests as in the\n", " 'no -A' option to nosetests - this is the same as ''.\n", " * None or '' - run all tests.\n", " attribute_identifier - string passed directly to nosetests as '-A'.\n", " verbose : int, optional\n", " Verbosity value for test outputs, in the range 1-10. Default is 1.\n", " extra_argv : list, optional\n", " List with any extra arguments to pass to nosetests.\n", " doctests : bool, optional\n", " If True, run doctests in module. Default is False.\n", " coverage : bool, optional\n", " If True, report coverage of NumPy code. Default is False.\n", " (This requires the `coverage module:\n", " `_).\n", " raise_warnings : str or sequence of warnings, optional\n", " This specifies which warnings to configure as 'raise' instead\n", " of 'warn' during the test execution. Valid strings are:\n", " \n", " - \"develop\" : equals ``(DeprecationWarning, RuntimeWarning)``\n", " - \"release\" : equals ``()``, don't raise on any warnings.\n", " \n", " Returns\n", " -------\n", " result : object\n", " Returns the result of running the tests as a\n", " ``nose.result.TextTestResult`` object.\n", " \n", " Notes\n", " -----\n", " Each NumPy module exposes `test` in its namespace to run all tests for it.\n", " For example, to run all tests for numpy.lib:\n", " \n", " >>> np.lib.test() #doctest: +SKIP\n", " \n", " Examples\n", " --------\n", " >>> result = np.lib.test() #doctest: +SKIP\n", " Running unit tests for numpy.lib\n", " ...\n", " Ran 976 tests in 3.933s\n", " \n", " OK\n", " \n", " >>> result.errors #doctest: +SKIP\n", " []\n", " >>> result.knownfail #doctest: +SKIP\n", " []\n", " \n", " tile(A, reps)\n", " Construct an array by repeating A the number of times given by reps.\n", " \n", " If `reps` has length ``d``, the result will have dimension of\n", " ``max(d, A.ndim)``.\n", " \n", " If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new\n", " axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication,\n", " or shape (1, 1, 3) for 3-D replication. If this is not the desired\n", " behavior, promote `A` to d-dimensions manually before calling this\n", " function.\n", " \n", " If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it.\n", " Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as\n", " (1, 1, 2, 2).\n", " \n", " Parameters\n", " ----------\n", " A : array_like\n", " The input array.\n", " reps : array_like\n", " The number of repetitions of `A` along each axis.\n", " \n", " Returns\n", " -------\n", " c : ndarray\n", " The tiled output array.\n", " \n", " See Also\n", " --------\n", " repeat : Repeat elements of an array.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([0, 1, 2])\n", " >>> np.tile(a, 2)\n", " array([0, 1, 2, 0, 1, 2])\n", " >>> np.tile(a, (2, 2))\n", " array([[0, 1, 2, 0, 1, 2],\n", " [0, 1, 2, 0, 1, 2]])\n", " >>> np.tile(a, (2, 1, 2))\n", " array([[[0, 1, 2, 0, 1, 2]],\n", " [[0, 1, 2, 0, 1, 2]]])\n", " \n", " >>> b = np.array([[1, 2], [3, 4]])\n", " >>> np.tile(b, 2)\n", " array([[1, 2, 1, 2],\n", " [3, 4, 3, 4]])\n", " >>> np.tile(b, (2, 1))\n", " array([[1, 2],\n", " [3, 4],\n", " [1, 2],\n", " [3, 4]])\n", " \n", " trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None)\n", " Return the sum along diagonals of the array.\n", " \n", " If `a` is 2-D, the sum along its diagonal with the given offset\n", " is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.\n", " \n", " If `a` has more than two dimensions, then the axes specified by axis1 and\n", " axis2 are used to determine the 2-D sub-arrays whose traces are returned.\n", " The shape of the resulting array is the same as that of `a` with `axis1`\n", " and `axis2` removed.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array, from which the diagonals are taken.\n", " offset : int, optional\n", " Offset of the diagonal from the main diagonal. Can be both positive\n", " and negative. Defaults to 0.\n", " axis1, axis2 : int, optional\n", " Axes to be used as the first and second axis of the 2-D sub-arrays\n", " from which the diagonals should be taken. Defaults are the first two\n", " axes of `a`.\n", " dtype : dtype, optional\n", " Determines the data-type of the returned array and of the accumulator\n", " where the elements are summed. If dtype has the value None and `a` is\n", " of integer type of precision less than the default integer\n", " precision, then the default integer precision is used. Otherwise,\n", " the precision is the same as that of `a`.\n", " out : ndarray, optional\n", " Array into which the output is placed. Its type is preserved and\n", " it must be of the right shape to hold the output.\n", " \n", " Returns\n", " -------\n", " sum_along_diagonals : ndarray\n", " If `a` is 2-D, the sum along the diagonal is returned. If `a` has\n", " larger dimensions, then an array of sums along diagonals is returned.\n", " \n", " See Also\n", " --------\n", " diag, diagonal, diagflat\n", " \n", " Examples\n", " --------\n", " >>> np.trace(np.eye(3))\n", " 3.0\n", " >>> a = np.arange(8).reshape((2,2,2))\n", " >>> np.trace(a)\n", " array([6, 8])\n", " \n", " >>> a = np.arange(24).reshape((2,2,2,3))\n", " >>> np.trace(a).shape\n", " (2, 3)\n", " \n", " transpose(a, axes=None)\n", " Permute the dimensions of an array.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Input array.\n", " axes : list of ints, optional\n", " By default, reverse the dimensions, otherwise permute the axes\n", " according to the values given.\n", " \n", " Returns\n", " -------\n", " p : ndarray\n", " `a` with its axes permuted. A view is returned whenever\n", " possible.\n", " \n", " See Also\n", " --------\n", " rollaxis\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(4).reshape((2,2))\n", " >>> x\n", " array([[0, 1],\n", " [2, 3]])\n", " \n", " >>> np.transpose(x)\n", " array([[0, 2],\n", " [1, 3]])\n", " \n", " >>> x = np.ones((1, 2, 3))\n", " >>> np.transpose(x, (1, 0, 2)).shape\n", " (2, 1, 3)\n", " \n", " trapz(y, x=None, dx=1.0, axis=-1)\n", " Integrate along the given axis using the composite trapezoidal rule.\n", " \n", " Integrate `y` (`x`) along given axis.\n", " \n", " Parameters\n", " ----------\n", " y : array_like\n", " Input array to integrate.\n", " x : array_like, optional\n", " If `x` is None, then spacing between all `y` elements is `dx`.\n", " dx : scalar, optional\n", " If `x` is None, spacing given by `dx` is assumed. Default is 1.\n", " axis : int, optional\n", " Specify the axis.\n", " \n", " Returns\n", " -------\n", " trapz : float\n", " Definite integral as approximated by trapezoidal rule.\n", " \n", " See Also\n", " --------\n", " sum, cumsum\n", " \n", " Notes\n", " -----\n", " Image [2]_ illustrates trapezoidal rule -- y-axis locations of points\n", " will be taken from `y` array, by default x-axis distances between\n", " points will be 1.0, alternatively they can be provided with `x` array\n", " or with `dx` scalar. Return value will be equal to combined area under\n", " the red lines.\n", " \n", " \n", " References\n", " ----------\n", " .. [1] Wikipedia page: http://en.wikipedia.org/wiki/Trapezoidal_rule\n", " \n", " .. [2] Illustration image:\n", " http://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png\n", " \n", " Examples\n", " --------\n", " >>> np.trapz([1,2,3])\n", " 4.0\n", " >>> np.trapz([1,2,3], x=[4,6,8])\n", " 8.0\n", " >>> np.trapz([1,2,3], dx=2)\n", " 8.0\n", " >>> a = np.arange(6).reshape(2, 3)\n", " >>> a\n", " array([[0, 1, 2],\n", " [3, 4, 5]])\n", " >>> np.trapz(a, axis=0)\n", " array([ 1.5, 2.5, 3.5])\n", " >>> np.trapz(a, axis=1)\n", " array([ 2., 8.])\n", " \n", " tri(N, M=None, k=0, dtype=)\n", " An array with ones at and below the given diagonal and zeros elsewhere.\n", " \n", " Parameters\n", " ----------\n", " N : int\n", " Number of rows in the array.\n", " M : int, optional\n", " Number of columns in the array.\n", " By default, `M` is taken equal to `N`.\n", " k : int, optional\n", " The sub-diagonal at and below which the array is filled.\n", " `k` = 0 is the main diagonal, while `k` < 0 is below it,\n", " and `k` > 0 is above. The default is 0.\n", " dtype : dtype, optional\n", " Data type of the returned array. The default is float.\n", " \n", " Returns\n", " -------\n", " tri : ndarray of shape (N, M)\n", " Array with its lower triangle filled with ones and zero elsewhere;\n", " in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise.\n", " \n", " Examples\n", " --------\n", " >>> np.tri(3, 5, 2, dtype=int)\n", " array([[1, 1, 1, 0, 0],\n", " [1, 1, 1, 1, 0],\n", " [1, 1, 1, 1, 1]])\n", " \n", " >>> np.tri(3, 5, -1)\n", " array([[ 0., 0., 0., 0., 0.],\n", " [ 1., 0., 0., 0., 0.],\n", " [ 1., 1., 0., 0., 0.]])\n", " \n", " tril(m, k=0)\n", " Lower triangle of an array.\n", " \n", " Return a copy of an array with elements above the `k`-th diagonal zeroed.\n", " \n", " Parameters\n", " ----------\n", " m : array_like, shape (M, N)\n", " Input array.\n", " k : int, optional\n", " Diagonal above which to zero elements. `k = 0` (the default) is the\n", " main diagonal, `k < 0` is below it and `k > 0` is above.\n", " \n", " Returns\n", " -------\n", " tril : ndarray, shape (M, N)\n", " Lower triangle of `m`, of same shape and data-type as `m`.\n", " \n", " See Also\n", " --------\n", " triu : same thing, only for the upper triangle\n", " \n", " Examples\n", " --------\n", " >>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)\n", " array([[ 0, 0, 0],\n", " [ 4, 0, 0],\n", " [ 7, 8, 0],\n", " [10, 11, 12]])\n", " \n", " tril_indices(n, k=0, m=None)\n", " Return the indices for the lower-triangle of an (n, m) array.\n", " \n", " Parameters\n", " ----------\n", " n : int\n", " The row dimension of the arrays for which the returned\n", " indices will be valid.\n", " k : int, optional\n", " Diagonal offset (see `tril` for details).\n", " m : int, optional\n", " .. versionadded:: 1.9.0\n", " \n", " The column dimension of the arrays for which the returned\n", " arrays will be valid.\n", " By default `m` is taken equal to `n`.\n", " \n", " \n", " Returns\n", " -------\n", " inds : tuple of arrays\n", " The indices for the triangle. The returned tuple contains two arrays,\n", " each with the indices along one dimension of the array.\n", " \n", " See also\n", " --------\n", " triu_indices : similar function, for upper-triangular.\n", " mask_indices : generic function accepting an arbitrary mask function.\n", " tril, triu\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.4.0\n", " \n", " Examples\n", " --------\n", " Compute two different sets of indices to access 4x4 arrays, one for the\n", " lower triangular part starting at the main diagonal, and one starting two\n", " diagonals further right:\n", " \n", " >>> il1 = np.tril_indices(4)\n", " >>> il2 = np.tril_indices(4, 2)\n", " \n", " Here is how they can be used with a sample array:\n", " \n", " >>> a = np.arange(16).reshape(4, 4)\n", " >>> a\n", " array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11],\n", " [12, 13, 14, 15]])\n", " \n", " Both for indexing:\n", " \n", " >>> a[il1]\n", " array([ 0, 4, 5, 8, 9, 10, 12, 13, 14, 15])\n", " \n", " And for assigning values:\n", " \n", " >>> a[il1] = -1\n", " >>> a\n", " array([[-1, 1, 2, 3],\n", " [-1, -1, 6, 7],\n", " [-1, -1, -1, 11],\n", " [-1, -1, -1, -1]])\n", " \n", " These cover almost the whole array (two diagonals right of the main one):\n", " \n", " >>> a[il2] = -10\n", " >>> a\n", " array([[-10, -10, -10, 3],\n", " [-10, -10, -10, -10],\n", " [-10, -10, -10, -10],\n", " [-10, -10, -10, -10]])\n", " \n", " tril_indices_from(arr, k=0)\n", " Return the indices for the lower-triangle of arr.\n", " \n", " See `tril_indices` for full details.\n", " \n", " Parameters\n", " ----------\n", " arr : array_like\n", " The indices will be valid for square arrays whose dimensions are\n", " the same as arr.\n", " k : int, optional\n", " Diagonal offset (see `tril` for details).\n", " \n", " See Also\n", " --------\n", " tril_indices, tril\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.4.0\n", " \n", " trim_zeros(filt, trim='fb')\n", " Trim the leading and/or trailing zeros from a 1-D array or sequence.\n", " \n", " Parameters\n", " ----------\n", " filt : 1-D array or sequence\n", " Input array.\n", " trim : str, optional\n", " A string with 'f' representing trim from front and 'b' to trim from\n", " back. Default is 'fb', trim zeros from both front and back of the\n", " array.\n", " \n", " Returns\n", " -------\n", " trimmed : 1-D array or sequence\n", " The result of trimming the input. The input data type is preserved.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))\n", " >>> np.trim_zeros(a)\n", " array([1, 2, 3, 0, 2, 1])\n", " \n", " >>> np.trim_zeros(a, 'b')\n", " array([0, 0, 0, 1, 2, 3, 0, 2, 1])\n", " \n", " The input data type is preserved, list/tuple in means list/tuple out.\n", " \n", " >>> np.trim_zeros([0, 1, 2, 0])\n", " [1, 2]\n", " \n", " triu(m, k=0)\n", " Upper triangle of an array.\n", " \n", " Return a copy of a matrix with the elements below the `k`-th diagonal\n", " zeroed.\n", " \n", " Please refer to the documentation for `tril` for further details.\n", " \n", " See Also\n", " --------\n", " tril : lower triangle of an array\n", " \n", " Examples\n", " --------\n", " >>> np.triu([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)\n", " array([[ 1, 2, 3],\n", " [ 4, 5, 6],\n", " [ 0, 8, 9],\n", " [ 0, 0, 12]])\n", " \n", " triu_indices(n, k=0, m=None)\n", " Return the indices for the upper-triangle of an (n, m) array.\n", " \n", " Parameters\n", " ----------\n", " n : int\n", " The size of the arrays for which the returned indices will\n", " be valid.\n", " k : int, optional\n", " Diagonal offset (see `triu` for details).\n", " m : int, optional\n", " .. versionadded:: 1.9.0\n", " \n", " The column dimension of the arrays for which the returned\n", " arrays will be valid.\n", " By default `m` is taken equal to `n`.\n", " \n", " \n", " Returns\n", " -------\n", " inds : tuple, shape(2) of ndarrays, shape(`n`)\n", " The indices for the triangle. The returned tuple contains two arrays,\n", " each with the indices along one dimension of the array. Can be used\n", " to slice a ndarray of shape(`n`, `n`).\n", " \n", " See also\n", " --------\n", " tril_indices : similar function, for lower-triangular.\n", " mask_indices : generic function accepting an arbitrary mask function.\n", " triu, tril\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.4.0\n", " \n", " Examples\n", " --------\n", " Compute two different sets of indices to access 4x4 arrays, one for the\n", " upper triangular part starting at the main diagonal, and one starting two\n", " diagonals further right:\n", " \n", " >>> iu1 = np.triu_indices(4)\n", " >>> iu2 = np.triu_indices(4, 2)\n", " \n", " Here is how they can be used with a sample array:\n", " \n", " >>> a = np.arange(16).reshape(4, 4)\n", " >>> a\n", " array([[ 0, 1, 2, 3],\n", " [ 4, 5, 6, 7],\n", " [ 8, 9, 10, 11],\n", " [12, 13, 14, 15]])\n", " \n", " Both for indexing:\n", " \n", " >>> a[iu1]\n", " array([ 0, 1, 2, 3, 5, 6, 7, 10, 11, 15])\n", " \n", " And for assigning values:\n", " \n", " >>> a[iu1] = -1\n", " >>> a\n", " array([[-1, -1, -1, -1],\n", " [ 4, -1, -1, -1],\n", " [ 8, 9, -1, -1],\n", " [12, 13, 14, -1]])\n", " \n", " These cover only a small part of the whole array (two diagonals right\n", " of the main one):\n", " \n", " >>> a[iu2] = -10\n", " >>> a\n", " array([[ -1, -1, -10, -10],\n", " [ 4, -1, -1, -10],\n", " [ 8, 9, -1, -1],\n", " [ 12, 13, 14, -1]])\n", " \n", " triu_indices_from(arr, k=0)\n", " Return the indices for the upper-triangle of arr.\n", " \n", " See `triu_indices` for full details.\n", " \n", " Parameters\n", " ----------\n", " arr : ndarray, shape(N, N)\n", " The indices will be valid for square arrays.\n", " k : int, optional\n", " Diagonal offset (see `triu` for details).\n", " \n", " Returns\n", " -------\n", " triu_indices_from : tuple, shape(2) of ndarray, shape(N)\n", " Indices for the upper-triangle of `arr`.\n", " \n", " See Also\n", " --------\n", " triu_indices, triu\n", " \n", " Notes\n", " -----\n", " .. versionadded:: 1.4.0\n", " \n", " typename(char)\n", " Return a description for the given data type code.\n", " \n", " Parameters\n", " ----------\n", " char : str\n", " Data type code.\n", " \n", " Returns\n", " -------\n", " out : str\n", " Description of the input data type code.\n", " \n", " See Also\n", " --------\n", " dtype, typecodes\n", " \n", " Examples\n", " --------\n", " >>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',\n", " ... 'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']\n", " >>> for typechar in typechars:\n", " ... print typechar, ' : ', np.typename(typechar)\n", " ...\n", " S1 : character\n", " ? : bool\n", " B : unsigned char\n", " D : complex double precision\n", " G : complex long double precision\n", " F : complex single precision\n", " I : unsigned integer\n", " H : unsigned short\n", " L : unsigned long integer\n", " O : object\n", " Q : unsigned long long integer\n", " S : string\n", " U : unicode\n", " V : void\n", " b : signed char\n", " d : double precision\n", " g : long precision\n", " f : single precision\n", " i : integer\n", " h : short\n", " l : long integer\n", " q : long long integer\n", " \n", " union1d(ar1, ar2)\n", " Find the union of two arrays.\n", " \n", " Return the unique, sorted array of values that are in either of the two\n", " input arrays.\n", " \n", " Parameters\n", " ----------\n", " ar1, ar2 : array_like\n", " Input arrays. They are flattened if they are not already 1D.\n", " \n", " Returns\n", " -------\n", " union1d : ndarray\n", " Unique, sorted union of the input arrays.\n", " \n", " See Also\n", " --------\n", " numpy.lib.arraysetops : Module with a number of other functions for\n", " performing set operations on arrays.\n", " \n", " Examples\n", " --------\n", " >>> np.union1d([-1, 0, 1], [-2, 0, 2])\n", " array([-2, -1, 0, 1, 2])\n", " \n", " unique(ar, return_index=False, return_inverse=False, return_counts=False)\n", " Find the unique elements of an array.\n", " \n", " Returns the sorted unique elements of an array. There are two optional\n", " outputs in addition to the unique elements: the indices of the input array\n", " that give the unique values, and the indices of the unique array that\n", " reconstruct the input array.\n", " \n", " Parameters\n", " ----------\n", " ar : array_like\n", " Input array. This will be flattened if it is not already 1-D.\n", " return_index : bool, optional\n", " If True, also return the indices of `ar` that result in the unique\n", " array.\n", " return_inverse : bool, optional\n", " If True, also return the indices of the unique array that can be used\n", " to reconstruct `ar`.\n", " return_counts : bool, optional\n", " .. versionadded:: 1.9.0\n", " If True, also return the number of times each unique value comes up\n", " in `ar`.\n", " \n", " Returns\n", " -------\n", " unique : ndarray\n", " The sorted unique values.\n", " unique_indices : ndarray, optional\n", " The indices of the first occurrences of the unique values in the\n", " (flattened) original array. Only provided if `return_index` is True.\n", " unique_inverse : ndarray, optional\n", " The indices to reconstruct the (flattened) original array from the\n", " unique array. Only provided if `return_inverse` is True.\n", " unique_counts : ndarray, optional\n", " .. versionadded:: 1.9.0\n", " The number of times each of the unique values comes up in the\n", " original array. Only provided if `return_counts` is True.\n", " \n", " See Also\n", " --------\n", " numpy.lib.arraysetops : Module with a number of other functions for\n", " performing set operations on arrays.\n", " \n", " Examples\n", " --------\n", " >>> np.unique([1, 1, 2, 2, 3, 3])\n", " array([1, 2, 3])\n", " >>> a = np.array([[1, 1], [2, 3]])\n", " >>> np.unique(a)\n", " array([1, 2, 3])\n", " \n", " Return the indices of the original array that give the unique values:\n", " \n", " >>> a = np.array(['a', 'b', 'b', 'c', 'a'])\n", " >>> u, indices = np.unique(a, return_index=True)\n", " >>> u\n", " array(['a', 'b', 'c'],\n", " dtype='|S1')\n", " >>> indices\n", " array([0, 1, 3])\n", " >>> a[indices]\n", " array(['a', 'b', 'c'],\n", " dtype='|S1')\n", " \n", " Reconstruct the input array from the unique values:\n", " \n", " >>> a = np.array([1, 2, 6, 4, 2, 3, 2])\n", " >>> u, indices = np.unique(a, return_inverse=True)\n", " >>> u\n", " array([1, 2, 3, 4, 6])\n", " >>> indices\n", " array([0, 1, 4, 3, 1, 2, 1])\n", " >>> u[indices]\n", " array([1, 2, 6, 4, 2, 3, 2])\n", " \n", " unpackbits(...)\n", " unpackbits(myarray, axis=None)\n", " \n", " Unpacks elements of a uint8 array into a binary-valued output array.\n", " \n", " Each element of `myarray` represents a bit-field that should be unpacked\n", " into a binary-valued output array. The shape of the output array is either\n", " 1-D (if `axis` is None) or the same shape as the input array with unpacking\n", " done along the axis specified.\n", " \n", " Parameters\n", " ----------\n", " myarray : ndarray, uint8 type\n", " Input array.\n", " axis : int, optional\n", " Unpacks along this axis.\n", " \n", " Returns\n", " -------\n", " unpacked : ndarray, uint8 type\n", " The elements are binary-valued (0 or 1).\n", " \n", " See Also\n", " --------\n", " packbits : Packs the elements of a binary-valued array into bits in a uint8\n", " array.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[2], [7], [23]], dtype=np.uint8)\n", " >>> a\n", " array([[ 2],\n", " [ 7],\n", " [23]], dtype=uint8)\n", " >>> b = np.unpackbits(a, axis=1)\n", " >>> b\n", " array([[0, 0, 0, 0, 0, 0, 1, 0],\n", " [0, 0, 0, 0, 0, 1, 1, 1],\n", " [0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8)\n", " \n", " unravel_index(...)\n", " unravel_index(indices, dims, order='C')\n", " \n", " Converts a flat index or array of flat indices into a tuple\n", " of coordinate arrays.\n", " \n", " Parameters\n", " ----------\n", " indices : array_like\n", " An integer array whose elements are indices into the flattened\n", " version of an array of dimensions ``dims``. Before version 1.6.0,\n", " this function accepted just one index value.\n", " dims : tuple of ints\n", " The shape of the array to use for unraveling ``indices``.\n", " order : {'C', 'F'}, optional\n", " .. versionadded:: 1.6.0\n", " \n", " Determines whether the indices should be viewed as indexing in\n", " C (row-major) order or FORTRAN (column-major) order.\n", " \n", " Returns\n", " -------\n", " unraveled_coords : tuple of ndarray\n", " Each array in the tuple has the same shape as the ``indices``\n", " array.\n", " \n", " See Also\n", " --------\n", " ravel_multi_index\n", " \n", " Examples\n", " --------\n", " >>> np.unravel_index([22, 41, 37], (7,6))\n", " (array([3, 6, 6]), array([4, 5, 1]))\n", " >>> np.unravel_index([31, 41, 13], (7,6), order='F')\n", " (array([3, 6, 6]), array([4, 5, 1]))\n", " \n", " >>> np.unravel_index(1621, (6,7,8,9))\n", " (3, 1, 4, 1)\n", " \n", " unwrap(p, discont=3.141592653589793, axis=-1)\n", " Unwrap by changing deltas between values to 2*pi complement.\n", " \n", " Unwrap radian phase `p` by changing absolute jumps greater than\n", " `discont` to their 2*pi complement along the given axis.\n", " \n", " Parameters\n", " ----------\n", " p : array_like\n", " Input array.\n", " discont : float, optional\n", " Maximum discontinuity between values, default is ``pi``.\n", " axis : int, optional\n", " Axis along which unwrap will operate, default is the last axis.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Output array.\n", " \n", " See Also\n", " --------\n", " rad2deg, deg2rad\n", " \n", " Notes\n", " -----\n", " If the discontinuity in `p` is smaller than ``pi``, but larger than\n", " `discont`, no unwrapping is done because taking the 2*pi complement\n", " would only make the discontinuity larger.\n", " \n", " Examples\n", " --------\n", " >>> phase = np.linspace(0, np.pi, num=5)\n", " >>> phase[3:] += np.pi\n", " >>> phase\n", " array([ 0. , 0.78539816, 1.57079633, 5.49778714, 6.28318531])\n", " >>> np.unwrap(phase)\n", " array([ 0. , 0.78539816, 1.57079633, -0.78539816, 0. ])\n", " \n", " vander(x, N=None, increasing=False)\n", " Generate a Vandermonde matrix.\n", " \n", " The columns of the output matrix are powers of the input vector. The\n", " order of the powers is determined by the `increasing` boolean argument.\n", " Specifically, when `increasing` is False, the `i`-th output column is\n", " the input vector raised element-wise to the power of ``N - i - 1``. Such\n", " a matrix with a geometric progression in each row is named for Alexandre-\n", " Theophile Vandermonde.\n", " \n", " Parameters\n", " ----------\n", " x : array_like\n", " 1-D input array.\n", " N : int, optional\n", " Number of columns in the output. If `N` is not specified, a square\n", " array is returned (``N = len(x)``).\n", " increasing : bool, optional\n", " Order of the powers of the columns. If True, the powers increase\n", " from left to right, if False (the default) they are reversed.\n", " \n", " .. versionadded:: 1.9.0\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Vandermonde matrix. If `increasing` is False, the first column is\n", " ``x^(N-1)``, the second ``x^(N-2)`` and so forth. If `increasing` is\n", " True, the columns are ``x^0, x^1, ..., x^(N-1)``.\n", " \n", " See Also\n", " --------\n", " polynomial.polynomial.polyvander\n", " \n", " Examples\n", " --------\n", " >>> x = np.array([1, 2, 3, 5])\n", " >>> N = 3\n", " >>> np.vander(x, N)\n", " array([[ 1, 1, 1],\n", " [ 4, 2, 1],\n", " [ 9, 3, 1],\n", " [25, 5, 1]])\n", " \n", " >>> np.column_stack([x**(N-1-i) for i in range(N)])\n", " array([[ 1, 1, 1],\n", " [ 4, 2, 1],\n", " [ 9, 3, 1],\n", " [25, 5, 1]])\n", " \n", " >>> x = np.array([1, 2, 3, 5])\n", " >>> np.vander(x)\n", " array([[ 1, 1, 1, 1],\n", " [ 8, 4, 2, 1],\n", " [ 27, 9, 3, 1],\n", " [125, 25, 5, 1]])\n", " >>> np.vander(x, increasing=True)\n", " array([[ 1, 1, 1, 1],\n", " [ 1, 2, 4, 8],\n", " [ 1, 3, 9, 27],\n", " [ 1, 5, 25, 125]])\n", " \n", " The determinant of a square Vandermonde matrix is the product\n", " of the differences between the values of the input vector:\n", " \n", " >>> np.linalg.det(np.vander(x))\n", " 48.000000000000043\n", " >>> (5-3)*(5-2)*(5-1)*(3-2)*(3-1)*(2-1)\n", " 48\n", " \n", " var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False)\n", " Compute the variance along the specified axis.\n", " \n", " Returns the variance of the array elements, a measure of the spread of a\n", " distribution. The variance is computed for the flattened array by\n", " default, otherwise over the specified axis.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " Array containing numbers whose variance is desired. If `a` is not an\n", " array, a conversion is attempted.\n", " axis : int, optional\n", " Axis along which the variance is computed. The default is to compute\n", " the variance of the flattened array.\n", " dtype : data-type, optional\n", " Type to use in computing the variance. For arrays of integer type\n", " the default is `float32`; for arrays of float types it is the same as\n", " the array type.\n", " out : ndarray, optional\n", " Alternate output array in which to place the result. It must have\n", " the same shape as the expected output, but the type is cast if\n", " necessary.\n", " ddof : int, optional\n", " \"Delta Degrees of Freedom\": the divisor used in the calculation is\n", " ``N - ddof``, where ``N`` represents the number of elements. By\n", " default `ddof` is zero.\n", " keepdims : bool, optional\n", " If this is set to True, the axes which are reduced are left\n", " in the result as dimensions with size one. With this option,\n", " the result will broadcast correctly against the original `arr`.\n", " \n", " Returns\n", " -------\n", " variance : ndarray, see dtype parameter above\n", " If ``out=None``, returns a new array containing the variance;\n", " otherwise, a reference to the output array is returned.\n", " \n", " See Also\n", " --------\n", " std , mean, nanmean, nanstd, nanvar\n", " numpy.doc.ufuncs : Section \"Output arguments\"\n", " \n", " Notes\n", " -----\n", " The variance is the average of the squared deviations from the mean,\n", " i.e., ``var = mean(abs(x - x.mean())**2)``.\n", " \n", " The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.\n", " If, however, `ddof` is specified, the divisor ``N - ddof`` is used\n", " instead. In standard statistical practice, ``ddof=1`` provides an\n", " unbiased estimator of the variance of a hypothetical infinite population.\n", " ``ddof=0`` provides a maximum likelihood estimate of the variance for\n", " normally distributed variables.\n", " \n", " Note that for complex numbers, the absolute value is taken before\n", " squaring, so that the result is always real and nonnegative.\n", " \n", " For floating-point input, the variance is computed using the same\n", " precision the input has. Depending on the input data, this can cause\n", " the results to be inaccurate, especially for `float32` (see example\n", " below). Specifying a higher-accuracy accumulator using the ``dtype``\n", " keyword can alleviate this issue.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([[1,2],[3,4]])\n", " >>> np.var(a)\n", " 1.25\n", " >>> np.var(a, axis=0)\n", " array([ 1., 1.])\n", " >>> np.var(a, axis=1)\n", " array([ 0.25, 0.25])\n", " \n", " In single precision, var() can be inaccurate:\n", " \n", " >>> a = np.zeros((2,512*512), dtype=np.float32)\n", " >>> a[0,:] = 1.0\n", " >>> a[1,:] = 0.1\n", " >>> np.var(a)\n", " 0.20405951142311096\n", " \n", " Computing the variance in float64 is more accurate:\n", " \n", " >>> np.var(a, dtype=np.float64)\n", " 0.20249999932997387\n", " >>> ((1-0.55)**2 + (0.1-0.55)**2)/2\n", " 0.20250000000000001\n", " \n", " vdot(...)\n", " vdot(a, b)\n", " \n", " Return the dot product of two vectors.\n", " \n", " The vdot(`a`, `b`) function handles complex numbers differently than\n", " dot(`a`, `b`). If the first argument is complex the complex conjugate\n", " of the first argument is used for the calculation of the dot product.\n", " \n", " Note that `vdot` handles multidimensional arrays differently than `dot`:\n", " it does *not* perform a matrix product, but flattens input arguments\n", " to 1-D vectors first. Consequently, it should only be used for vectors.\n", " \n", " Parameters\n", " ----------\n", " a : array_like\n", " If `a` is complex the complex conjugate is taken before calculation\n", " of the dot product.\n", " b : array_like\n", " Second argument to the dot product.\n", " \n", " Returns\n", " -------\n", " output : ndarray\n", " Dot product of `a` and `b`. Can be an int, float, or\n", " complex depending on the types of `a` and `b`.\n", " \n", " See Also\n", " --------\n", " dot : Return the dot product without using the complex conjugate of the\n", " first argument.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([1+2j,3+4j])\n", " >>> b = np.array([5+6j,7+8j])\n", " >>> np.vdot(a, b)\n", " (70-8j)\n", " >>> np.vdot(b, a)\n", " (70+8j)\n", " \n", " Note that higher-dimensional arrays are flattened!\n", " \n", " >>> a = np.array([[1, 4], [5, 6]])\n", " >>> b = np.array([[4, 1], [2, 2]])\n", " >>> np.vdot(a, b)\n", " 30\n", " >>> np.vdot(b, a)\n", " 30\n", " >>> 1*4 + 4*1 + 5*2 + 6*2\n", " 30\n", " \n", " vsplit(ary, indices_or_sections)\n", " Split an array into multiple sub-arrays vertically (row-wise).\n", " \n", " Please refer to the ``split`` documentation. ``vsplit`` is equivalent\n", " to ``split`` with `axis=0` (default), the array is always split along the\n", " first axis regardless of the array dimension.\n", " \n", " See Also\n", " --------\n", " split : Split an array into multiple sub-arrays of equal size.\n", " \n", " Examples\n", " --------\n", " >>> x = np.arange(16.0).reshape(4, 4)\n", " >>> x\n", " array([[ 0., 1., 2., 3.],\n", " [ 4., 5., 6., 7.],\n", " [ 8., 9., 10., 11.],\n", " [ 12., 13., 14., 15.]])\n", " >>> np.vsplit(x, 2)\n", " [array([[ 0., 1., 2., 3.],\n", " [ 4., 5., 6., 7.]]),\n", " array([[ 8., 9., 10., 11.],\n", " [ 12., 13., 14., 15.]])]\n", " >>> np.vsplit(x, np.array([3, 6]))\n", " [array([[ 0., 1., 2., 3.],\n", " [ 4., 5., 6., 7.],\n", " [ 8., 9., 10., 11.]]),\n", " array([[ 12., 13., 14., 15.]]),\n", " array([], dtype=float64)]\n", " \n", " With a higher dimensional array the split is still along the first axis.\n", " \n", " >>> x = np.arange(8.0).reshape(2, 2, 2)\n", " >>> x\n", " array([[[ 0., 1.],\n", " [ 2., 3.]],\n", " [[ 4., 5.],\n", " [ 6., 7.]]])\n", " >>> np.vsplit(x, 2)\n", " [array([[[ 0., 1.],\n", " [ 2., 3.]]]),\n", " array([[[ 4., 5.],\n", " [ 6., 7.]]])]\n", " \n", " vstack(tup)\n", " Stack arrays in sequence vertically (row wise).\n", " \n", " Take a sequence of arrays and stack them vertically to make a single\n", " array. Rebuild arrays divided by `vsplit`.\n", " \n", " Parameters\n", " ----------\n", " tup : sequence of ndarrays\n", " Tuple containing arrays to be stacked. The arrays must have the same\n", " shape along all but the first axis.\n", " \n", " Returns\n", " -------\n", " stacked : ndarray\n", " The array formed by stacking the given arrays.\n", " \n", " See Also\n", " --------\n", " hstack : Stack arrays in sequence horizontally (column wise).\n", " dstack : Stack arrays in sequence depth wise (along third dimension).\n", " concatenate : Join a sequence of arrays together.\n", " vsplit : Split array into a list of multiple sub-arrays vertically.\n", " \n", " Notes\n", " -----\n", " Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that\n", " are at least 2-dimensional.\n", " \n", " Examples\n", " --------\n", " >>> a = np.array([1, 2, 3])\n", " >>> b = np.array([2, 3, 4])\n", " >>> np.vstack((a,b))\n", " array([[1, 2, 3],\n", " [2, 3, 4]])\n", " \n", " >>> a = np.array([[1], [2], [3]])\n", " >>> b = np.array([[2], [3], [4]])\n", " >>> np.vstack((a,b))\n", " array([[1],\n", " [2],\n", " [3],\n", " [2],\n", " [3],\n", " [4]])\n", " \n", " where(...)\n", " where(condition, [x, y])\n", " \n", " Return elements, either from `x` or `y`, depending on `condition`.\n", " \n", " If only `condition` is given, return ``condition.nonzero()``.\n", " \n", " Parameters\n", " ----------\n", " condition : array_like, bool\n", " When True, yield `x`, otherwise yield `y`.\n", " x, y : array_like, optional\n", " Values from which to choose. `x` and `y` need to have the same\n", " shape as `condition`.\n", " \n", " Returns\n", " -------\n", " out : ndarray or tuple of ndarrays\n", " If both `x` and `y` are specified, the output array contains\n", " elements of `x` where `condition` is True, and elements from\n", " `y` elsewhere.\n", " \n", " If only `condition` is given, return the tuple\n", " ``condition.nonzero()``, the indices where `condition` is True.\n", " \n", " See Also\n", " --------\n", " nonzero, choose\n", " \n", " Notes\n", " -----\n", " If `x` and `y` are given and input arrays are 1-D, `where` is\n", " equivalent to::\n", " \n", " [xv if c else yv for (c,xv,yv) in zip(condition,x,y)]\n", " \n", " Examples\n", " --------\n", " >>> np.where([[True, False], [True, True]],\n", " ... [[1, 2], [3, 4]],\n", " ... [[9, 8], [7, 6]])\n", " array([[1, 8],\n", " [3, 4]])\n", " \n", " >>> np.where([[0, 1], [1, 0]])\n", " (array([0, 1]), array([1, 0]))\n", " \n", " >>> x = np.arange(9.).reshape(3, 3)\n", " >>> np.where( x > 5 )\n", " (array([2, 2, 2]), array([0, 1, 2]))\n", " >>> x[np.where( x > 3.0 )] # Note: result is 1D.\n", " array([ 4., 5., 6., 7., 8.])\n", " >>> np.where(x < 5, x, -1) # Note: broadcasting.\n", " array([[ 0., 1., 2.],\n", " [ 3., 4., -1.],\n", " [-1., -1., -1.]])\n", " \n", " Find the indices of elements of `x` that are in `goodvalues`.\n", " \n", " >>> goodvalues = [3, 4, 7]\n", " >>> ix = np.in1d(x.ravel(), goodvalues).reshape(x.shape)\n", " >>> ix\n", " array([[False, False, False],\n", " [ True, True, False],\n", " [False, True, False]], dtype=bool)\n", " >>> np.where(ix)\n", " (array([1, 1, 2]), array([0, 1, 1]))\n", " \n", " who(vardict=None)\n", " Print the Numpy arrays in the given dictionary.\n", " \n", " If there is no dictionary passed in or `vardict` is None then returns\n", " Numpy arrays in the globals() dictionary (all Numpy arrays in the\n", " namespace).\n", " \n", " Parameters\n", " ----------\n", " vardict : dict, optional\n", " A dictionary possibly containing ndarrays. Default is globals().\n", " \n", " Returns\n", " -------\n", " out : None\n", " Returns 'None'.\n", " \n", " Notes\n", " -----\n", " Prints out the name, shape, bytes and type of all of the ndarrays\n", " present in `vardict`.\n", " \n", " Examples\n", " --------\n", " >>> a = np.arange(10)\n", " >>> b = np.ones(20)\n", " >>> np.who()\n", " Name Shape Bytes Type\n", " ===========================================================\n", " a 10 40 int32\n", " b 20 160 float64\n", " Upper bound on total bytes = 200\n", " \n", " >>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str',\n", " ... 'idx':5}\n", " >>> np.who(d)\n", " Name Shape Bytes Type\n", " ===========================================================\n", " y 3 24 float64\n", " x 2 16 float64\n", " Upper bound on total bytes = 40\n", " \n", " zeros(...)\n", " zeros(shape, dtype=float, order='C')\n", " \n", " Return a new array of given shape and type, filled with zeros.\n", " \n", " Parameters\n", " ----------\n", " shape : int or sequence of ints\n", " Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n", " dtype : data-type, optional\n", " The desired data-type for the array, e.g., `numpy.int8`. Default is\n", " `numpy.float64`.\n", " order : {'C', 'F'}, optional\n", " Whether to store multidimensional data in C- or Fortran-contiguous\n", " (row- or column-wise) order in memory.\n", " \n", " Returns\n", " -------\n", " out : ndarray\n", " Array of zeros with the given shape, dtype, and order.\n", " \n", " See Also\n", " --------\n", " zeros_like : Return an array of zeros with shape and type of input.\n", " ones_like : Return an array of ones with shape and type of input.\n", " empty_like : Return an empty array with shape and type of input.\n", " ones : Return a new array setting values to one.\n", " empty : Return a new uninitialized array.\n", " \n", " Examples\n", " --------\n", " >>> np.zeros(5)\n", " array([ 0., 0., 0., 0., 0.])\n", " \n", " >>> np.zeros((5,), dtype=numpy.int)\n", " array([0, 0, 0, 0, 0])\n", " \n", " >>> np.zeros((2, 1))\n", " array([[ 0.],\n", " [ 0.]])\n", " \n", " >>> s = (2,2)\n", " >>> np.zeros(s)\n", " array([[ 0., 0.],\n", " [ 0., 0.]])\n", " \n", " >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype\n", " array([(0, 0), (0, 0)],\n", " dtype=[('x', '>> x = np.arange(6)\n", " >>> x = x.reshape((2, 3))\n", " >>> x\n", " array([[0, 1, 2],\n", " [3, 4, 5]])\n", " >>> np.zeros_like(x)\n", " array([[0, 0, 0],\n", " [0, 0, 0]])\n", " \n", " >>> y = np.arange(3, dtype=np.float)\n", " >>> y\n", " array([ 0., 1., 2.])\n", " >>> np.zeros_like(y)\n", " array([ 0., 0., 0.])\n", "\n", "DATA\n", " ALLOW_THREADS = 1\n", " BUFSIZE = 8192\n", " CLIP = 0\n", " ERR_CALL = 3\n", " ERR_DEFAULT = 521\n", " ERR_IGNORE = 0\n", " ERR_LOG = 5\n", " ERR_PRINT = 4\n", " ERR_RAISE = 2\n", " ERR_WARN = 1\n", " FLOATING_POINT_SUPPORT = 1\n", " FPE_DIVIDEBYZERO = 1\n", " FPE_INVALID = 8\n", " FPE_OVERFLOW = 2\n", " FPE_UNDERFLOW = 4\n", " False_ = False\n", " Inf = inf\n", " Infinity = inf\n", " MAXDIMS = 32\n", " NAN = nan\n", " NINF = -inf\n", " NZERO = -0.0\n", " NaN = nan\n", " PINF = inf\n", " PZERO = 0.0\n", " RAISE = 2\n", " SHIFT_DIVIDEBYZERO = 0\n", " SHIFT_INVALID = 9\n", " SHIFT_OVERFLOW = 3\n", " SHIFT_UNDERFLOW = 6\n", " ScalarType = (, , , \n", " add = \n", " arccosh = \n", " arcsinh = \n", " arctan = \n", " arctan2 = \n", " bitwise_and = \n", " bitwise_not = \n", " bitwise_or = \n", " bitwise_xor = \n", " c_ = \n", " cast = {: at 0x7f...plex256'>: <...\n", " ceil = \n", " conj = \n", " conjugate = \n", " copysign = \n", " cos = \n", " cosh = \n", " deg2rad = \n", " degrees = \n", " divide = \n", " e = 2.718281828459045\n", " equal = \n", " euler_gamma = 0.5772156649015329\n", " exp = \n", " exp2 = \n", " expm1 = \n", " fabs = \n", " floor = \n", " floor_divide = \n", " fmax = \n", " fmin = \n", " fmod = \n", " frexp = \n", " greater = \n", " greater_equal = \n", " hypot = \n", " index_exp = \n", " inf = inf\n", " infty = inf\n", " invert = \n", " isfinite = \n", " isinf = \n", " isnan = \n", " ldexp = \n", " left_shift = \n", " less = \n", " less_equal = \n", " little_endian = True\n", " log1p = \n", " logaddexp = \n", " logaddexp2 = \n", " logical_and = \n", " logical_not = \n", " logical_or = \n", " logical_xor = \n", " maximum = \n", " mgrid = \n", " minimum = \n", " mod = \n", " modf = \n", " multiply = \n", " nan = nan\n", " nbytes = {: 0, : 8...py.float12...\n", " negative = \n", " newaxis = None\n", " nextafter = \n", " not_equal = \n", " ogrid = \n", " pi = 3.141592653589793\n", " r_ = \n", " rad2deg = \n", " radians = \n", " reciprocal = \n", " remainder = \n", " right_shift = \n", " rint = \n", " s_ = \n", " sctypeDict = {0: , 1: , 2: , '...\n", " sctypes = {'complex': [, \n", " signbit = \n", " sin = \n", " sinh = \n", " spacing = \n", " square = \n", " subtract = \n", " tan = \n", " tanh = \n", " true_divide = \n", " trunc = \n", " typeDict = {0: , 1: , 2: , 'Co...\n", " typecodes = {'All': '?bhilqpBHILQPefdgFDGSUVOMm', 'AllFloat': 'efdgFDG...\n", "\n", "VERSION\n", " 0.14.0\n", "\n", "\n" ] } ], "prompt_number": 4 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Integrate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although not completely unrelated, scipy.integrate contains the functions for quadrature and for ode integration." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "heading", "level": 5, "metadata": {}, "source": [ "Quadrature" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy import integrate" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "dir(integrate)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "['IntegrationWarning',\n", " 'Tester',\n", " '__all__',\n", " '__builtins__',\n", " '__doc__',\n", " '__file__',\n", " '__name__',\n", " '__package__',\n", " '__path__',\n", " '_dop',\n", " '_ode',\n", " '_odepack',\n", " '_quadpack',\n", " 'absolute_import',\n", " 'complex_ode',\n", " 'cumtrapz',\n", " 'dblquad',\n", " 'division',\n", " 'fixed_quad',\n", " 'lsoda',\n", " 'newton_cotes',\n", " 'nquad',\n", " 'ode',\n", " 'odeint',\n", " 'odepack',\n", " 'print_function',\n", " 'quad',\n", " 'quad_explain',\n", " 'quadpack',\n", " 'quadrature',\n", " 'romb',\n", " 'romberg',\n", " 's',\n", " 'simps',\n", " 'test',\n", " 'tplquad',\n", " 'trapz',\n", " 'vode']" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.integrate import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's calculate an integral of the following type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\int ^a_b f(x)dx $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by a simple example, **$f(x)=x^2$**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def f(x):\n", " return x**2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define $a$ and $b$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "a, b = -1, 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "quad(f,a,b)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "(0.6666666666666666, 7.401486830834376e-15)" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "def f2(x,c,d):\n", " return c*x**2+d" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "quad(f2,a,b,(3,1,))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "(3.999999999999999, 4.440892098500625e-14)" ] } ], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trapezoidal Rule" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Image('http://upload.wikimedia.org/wikipedia/commons/4/42/Composite_trapezoidal_rule_illustration.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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L9xUxVDv7N/DtLMYHUgeRJKmVshjH\nZDGeSmO76Qso/jtnSw2MXWzw9DWcffG2rP3sDV/ebnp64OJSpXZZqVKbMWFESZIkSZKaba3UAZpg\nBmCZ1CEkSVJzWGTsXB39UEmS1DylSm1AqVI7BrgUmLyIMVd54Ta2evTiIoZqZy8Aq2QxPpU6iCSp\n32r7H5BlMb6RxbgtsArwWOo8fTV85Ifsc/uJnHLVHizwZv3Lb20GPFWq1DZJFE2SJEmSpKbJQxgG\nrJw6R5OsmzqAJElqDouMncsVGSVJ/6NUqQ0H/gIcUtSY2dsvsN9tJxQ1XDsaCRwFLJrF+ELqMJIk\ntYMsxruBpYE9gPcTx+mz0pt1TrlyD/a8/WSmGvmf3bNnAq4oVWoXlSq16RLGkyRJkiSpaCsDw1KH\naJL1UgeQJEnNYZGxc1lklCT9l1KlthBwPwV+iR/+2fsccePhDBszqqgh2831wCJZjEdmMY5MHUaS\npHaSxTg2i3EEsCDwezp8Z4ABRNZ79jrOufjHrPv0tYTY9cVbWwNPlyq1DRLGkyRJkiSpSOXUAZpo\nqTyEQnajkiRJ7cUiY+eyyChJ+o9SpbY+jRJjqagxB44dw6E3HclMH79Z1JDtpA5slMX43SzGF1OH\nkSSpnWUxvpXFuAOwAvBw6jx9NdWoj9jrzlM49YrdKb3x7BcvzwJcU6rUzitVatMkjCdJkiRJUhHm\nTR2giQYAs6YOIUmSimeRsXNZZJQkAVCq1H4GXA1MXeS4u919Gov8+6kih+yVOnA28FLfh3oNOAlY\nJotxoSzGq/s+pCRJk44sxvuBZYFdgXcTx+mzBd7+OydftSf73Pprhn/2n92ztwWeKlVq6ySMJkmS\nJElSX82UOkCTzZI6gCRJKl6IsaN3hppkhRAWAp792gO7ryvGOLDA8SRJTVaq1KYAzgM2K3rs9Z7+\nC3veeWrRw/bYZ/De6jDtW+P+fSFgbWBFYDZgRmDw+E8dA7wOvAI8AVwK3JHF/99DUpKkVgshDAI+\n7+FpR8UYj2xCnD7JQ5geOAbYkX7wI8mPh0zB+ctuz/ULr0/XgP98NT4b2K9eLX+UMJokSZIkST2W\nh/AksEjqHE20aRbjlalDSJKkYllk7FAhhBLwXIFDxhhjxz98kqRJRalSmxf4M7BY0WMv8toTHHvt\ngQzqGlv00D018kg45g/w84kdND2Nn5bOCHwILA0rHwL3ZTEm/y8gSdKX9aci4xfyEJYGRtBYqbHj\nvTj9fJy+8p48M+t/nvW8BGxXr5ZvTRhLkiRJkqQeyUN4g/69KuPuWYynpw4hSZKKZZGxQ4UQFqSx\n22ZhYoxuLy1JHaBUqa0B/IlGh69QM330BqdesRvTjPyg6KF7Y5v5YCzwxx6et2CM8e/NCCRJUl/0\nxyIjQB5CALYHfgXMkDhOn0WgtuBanLP8zrw/+bRfvPRr4NB6tTw6aThJkiRJkr5GHsICNJ4j9+dn\nv0dnMR6eOoQkSSqWK/B1LhuokjQJKlVqewM30YQS49DPR3L4jYe3S4nxxCzGP9C7z7v+PDkjSVLb\nyWKMWYznAAsCp9P4IULHCsCaz9/M2Rdvy0ZPXMGArrEBOBC4v1SpLZw4niRJkiRJE5SHsA3wKP1/\nnnya1AEkSVLxLDJ2rsKLjKGxioYkqQ2VKrWhpUrtPOAUYFAzrrHfbScw3zsvNmPonroJOHjcn3vz\neef9jSSpXfXr71xZjO9lMe4OLAPckzpPX03x+afsck+VEZftwqKvPQ7wLeChUqW2e+JokiRJkiT9\nlzyEqfMQ/gicD0yROk8LvJs6gCRJKp4P+jtXM1Zk7NcP1SSpU5UqtdmBO4Btm3WNrR7+I6u+eHuz\nhu+JvwNbZTF+sZJTVy/G8PNMkqSEshgfBVYGfgy8kTZN383z3j85/pr9OehvxzDdJ29PBvy2VKld\nX6rUZk6dTZIkSZKkPITlaKzC+P3UWVrordQBJElS8Swydi6LjJI0CShVaisCDwHLNusay/3zXrZ5\n8NxmDd8THwIbZjG+/6XX3FpakqQONG676fOBEvAbOny7aYDVX7iFsy7Zjk0f+xMDx45ZF3iyVKlt\nkDqXJEmSJGnSlIcwIA/hEOAuIEudp8XeTB1AkiQVzyJj57LIKEn9XKlS2wq4FZilWdeY692XOKh2\nTDt8AHQBP8hifPYrr1tklCSpg2UxfpDFuDewBHBn6jx9Nfnnn7HjfWdy+mU7s/i/Hp0RuKZUqVVL\nldrkqbNJkiRJkiYdeQizA38DjgEGJY6TgisySpLUD1lk7FwWGSWpHytVagcBFwFDmnWNKUd9xBE3\nHsbkn3/WrEv0xGFZjNeO53WLjJIk9QNZjE9mMa4K/BB4PXWevprr/Zf51V8O5JCbj2aGj9/aFXik\nVKktlTqXJEmSJKn/y0P4HvA4sHrqLAlZZJQkqR+yyNi5LDJKUj9UqtQGliq1EcBxNPHv5QFdY/nJ\nzb9gtg9fa9YleuLSLMZjJvCeRUZJkvqRLMY/0thu+iRgTOI4fbbqi7dz1iXbscWjF5cGjf383lKl\n9pNSpeZciyRJkiSpcHkIw/IQRgBXA9OnzpNQF/Bq6hCSJKl4Tq7ryyx+SFJC47YkvBLYrdnX2uG+\ns1jq1YebfZnueBTYfiLvd/ViTO9vJElqY1mMH2Ux7g8sDtyaOk9fDRszku3uP4fqn3YavOQrDx0L\n3Fqq1OZKnUuSJEmS1H/kIXwTeJAWPD/oAI9lMX6QOoQkSSqeD/o7lysySlI/UqrUZqTxIP97zb5W\nuf5XNnni8mZfpjveAjbKYvx0Ise4IqMkSf1UFuMzWYxrAFsB/0qdp6/m+OBVfnndTzj0piNWnfnD\n158qVWpbp84kSZIkSep8eQgVGiXGRVJnaRM3pw4gSZKawyJj57LIKEn9RKlSWwC4F1i22dda8I3n\n2Ov2k5t9me74HNg0i/HlrznOIqMkSf1cFuOlwELA8TTuETraSv+4mzMu3WGqrR6+8KLFdrzuolKl\nNjx1JkmSJElS58lDmC4P4SrgdGCy1HnaiEVGSZL6KYuMncsioyT1A6VKbQUaJcb5mn2taT95h8Nv\nOoIhXW3RD9gzi/HObhxnkVGS1J/4GTUBWYwfZzEeDCxKP3ggMXTsaLZ98DxOv2ynrVfM73q+VKmt\nkjqTJEmSJKlz5CGsBjwBbJQ4SrsZCdydOoQkSWoOi4ydyyKjJHW4UqW2CXALMH2zrzV47GgOv+kI\npv/0nWZfqjuqWYxndPNYi4ySJE1CshjrWYzfATYDvm7l5rY324evc9hfj5zpyBsOvX3DjU8bUarU\nBqfOJEmSJElqX3kIg/IQfgHUgNlT52lDd2YxjkwdQpIkNYdFxs5lkVGSOlipUtsbuAwY1orr7Xn7\nKSz05nOtuNTXuQPYuwfHW2SUJGkSlMV4BfAN4BhgVOI4fbbcS/eF4/6y/2473336P9fe4qxFU+eR\nJEmSJLWfPIR5gTuBn+Fz/Am5JHUASZLUPN4AdS6LjJLUgUqVWihVaicBp9Ciz+GNnriCtZ7/aysu\n9XVeAjbLYuzJ3tZdvbiO9zeSJPUDWYyfZjH+DFgEuCF1nr4aMvZzNn7yytmOvOGwx49dcusRqfNI\nkiRJktpHHsLWwGPA8qmztLFXgT+kDiFJkprHB/2dyyKjJHWYUqU2DPgTsG+rrrnEKw+z473d3cW5\nqT4FNspifKuH57kio2LiXqYAACAASURBVCRJk7gsxheyGNcDNgL+mThOn8388Rthy0cv2e36mb/x\n7wuyVZZJnUeSJEmSlE4ewpR5COcCFwFTp87T5k7s4UIJkiSpw1hk7FwWGSWpg5QqtemAvwGbteqa\ns37wGofcfDQDY28WNSzcj7MYH+vFeRYZJUkSAFmMVwMLAz8HRiaO02cLvfnczMu9dN/9N88w/wV5\nCJOnziNJkiRJaq08hKWAR4AfJ47SCd4BzkwdQpIkNZdFxs5lkVGSOkSpUpsXuAdYqVXXnGz0pxxx\n46FMNfrjVl1yYn6ZxXhZL8+1yChJkv4ji/GzLMYjgG8Cf0mdp68Gd40J873z4o8+HjLFa/WBgzdP\nnUeSJEmS1Hx5CCEP4QAazw0WSJ2nQ5yaxfhp6hCSJKm5LDJ2LouMktQBSpXa0sC9QKllF42Rg2rH\nMvd7L7fskhNxDXBYH863yChJkv5HFmOexfg9YH3gxdR5+mrK0Z8MH9w15k/PDhp2ex5C6+4bJUmS\nJEktlYcwC3AjcAIwJHGcTvEG8NvUISRJUvNZZOxcFhklqc2VKrX1gduAmVt53W0ePI/lX7q3lZec\nkGeAH2Yx9uUzyyKjJEmaoCzG62iszng48FniOH02dOyoVbsIT+Uh/CoPYcrUeSRJkiRJxclDWBd4\nHPhO6iwdJALbZjG+lzqIJElqPouMncsioyS1sVKltgvwZ2CKVl53lRdvZ+tH/tjKS07Ie8CGWYwf\n9XGcrl6c4/2NJEmTkCzGUVmMRwPfAK5KnaevBhAHAQdHqOchbJk6jyRJkiSpb/IQhuQhnAxcB8yU\nOk+HOTmL8abUISRJUmv4oL9zWWSUpDZUqtRCqVI7BvgdMLCV15737RfZ79bjW3nJCRkLbJnF+EIB\nY7kioySpP/EzqomyGF/KYtwEWAd4PnWevgowG3BJHsIteQgLp84jSZIkSeq5PIQScD+wD84L9NQj\nwCGpQ0iSpNaxyNi5mlFklCT1QalSGwL8gQRfrKf+7AOOuPEwho0Z1epLj8+BWYw3FzSWRUZJktQj\n41ZqWJTGPdknieMUYXXg8TyEE/MQpkodRpIkSZLUPXkIO9Ao430rdZYO9AmwdRbj6NRBJElS61hk\n7FyuyChJbaRUqQ0HbgR+0OprDxw7hp/99Shm/vjNVl96fC7IYjy5wPEsMkqSpB7LYhydxfgrGttN\nX5Y6TwEGAfvR2G665febkiRJkqTuy0OYJg/hT8DZwOSp83Sgz4DNsxg7frcFSZLUMxYZO5dFRklq\nE6VKbTbgbhqr5bTcrnePYLHXn0hx6a+6H9i54DEtMkqSpF7LYnwli3ELYE3g2dR5CjArcGEewh15\nCIumDiNJkiRJ+m95CCsBjwGbp87SoT4E1s5ivCF1EEmS1HoWGTuXRUZJagOlSm0e4E7gmymuv+4z\n17L+M39Jcemveh3YJIux6L2te/N55/2NJEn6L1mMNWBx4EDg48RxirAK8Ggewql5CMNTh5EkSZKk\nSV0ewsA8hMOB24G5U+fpro+GTJk6wpe9BayexXhn6iCSJCkNH/R3LouMkpRYqVJbCLgLyFJc/5uv\nP8lud52W4tJfNQrYOIvxtSaM3dWLc/w8kyRJ/yOL8fMsxl8DJeDi1HkKMBDYi8Z209vmIXgPJEmS\nJEkJ5CHMCdwKHEXju1pHuH7h7/LDbS7lnOV2asqD5x56BVgli/GR1EEkSVI6Fhk7l0VGSUqoVKl9\nC7gDmD3F9Wf86A0OvekoBnWNTXH5r9oli/H+Jo3t1tKSJKlQWYyvZTF+H1gNeCpxnCLMDJwH3JWH\nsETiLJIkSZI0SclD2BR4nMbK+R3hoyFTcvR3juC0Vfdl9KChXL7Elhy71mF8PGSKVJH+CCyTxVhP\nFUCSJLUHi4ydyyKjJCVSqtRWoPHryhlTXH/ImFEcfuMRTDPy/RSX/6qTsxjPb+L4FhklSVJTZDHe\nDiwB7At8mDhOEVYEHspDGJGHMG3qMJIkSZLUn+UhTJ6HcCZwOdAx38GenHVRdtviTO7J/rt3eed8\n344HbHTKPsAIYEyL4jxDYyvpH2YxvtGia0qSpDZmkbFzWWSUpARKlVoZuBmYJlWG/W49gfnfeSHV\n5b/sZuDAJl/DIqMkSWqaLMYxWYyn0Nhu+g+p8xRgALAb8Hwewg5uNy1JkiRJxctDWBx4CNgpdZbu\nGhsGcMEy23Lw907k7Sln+urbEdjlr5fueGoW4x7AYsANTYzzCXAw8K0sxtuaeB1JktRhQozN6MOp\n2UIIkwGfFjzsnDHGVwseU5L6jVKl9j3gT8DQVBm2eOQitnvg96ku/2UvAMtmMb7XzIuEEJYD7uvh\naevGGG9sRh5JkvoiNEply/XwtFf9ntY6eQgrA78FFk+dpSAPALtnMT6UOogkSZIk9Qd5CHsBx5Pw\nOUFPvTHlTBy35s94dpZvju/tCOxWr5Z/99U38hDWeh/2GQzrFbTp9APAxcDFrsAoSZLGxyJjhwoh\nDAM+K3jYuWKMrxQ8piT1C6VK7fvA+cCgVBmWfelejrjhcAY0ZVHeHvkIWD6L8ZlmXyiEsAyNyY2e\n+G6M8fpm5JEkSf1fHsJAoAIcTcJVuAvUBZwN/DSL8Z3UYSRJkiSpE+UhzAicC3w3dZaeuGO+b3Pq\nqvvy6dApJ3TI7vVq+fQJvRlCmH8I/H0VYB2gDAzvWYSnaJQXL8lizHt2qiRJmtRYZOxQIYShwMiC\nh507xvhywWNKUscrVWo7A1UaW/UlMcd7L3PKlXswxedFL8bbYxHYKIvxmlZcLISwNPBgD09bP8Z4\nXTPySJKkSUcewkzAr4AfA/1hi+Z3gZ8BZ2YxdqUOI0mSJEmdIg9hTeACYNbUWbpr5KBhVFfenb8u\ntO7EDtuzXi3/dmIHhBDmB/7+xb8PAuYAZgFm+tJ/Tg28BbwGzAUn7Qh/AF7xB3WSJKknLDJ2qBDC\nEGBUwcPOE2N8qeAxJamjlSq1/YFfp8wwxaiPOfXK3Zn9g3+ljPGFw7IYf9Gqi4UQlgJ6uhXiBjHG\na5uRR5IkTXryEJYHRgBLps5SkIeBPbIY70sdRJIkSZLaWR7CYOAXwIF00A/cXph+fn611s/41zRz\nTuywvevV8m++bqwQwnzACz2M8KMY44U9PEeSJCndylLqs2Y0UDvmBlySWqFUqR1F4hLjgK6xHHLz\nL9qlxHhZK0uM4/Tm887PM0mSVJhxhb9laGw3/W7iOEVYCrgnD+GccVujSZIkSZK+Im+sRHgPcBAd\nMuccgSsX24x9Nznt60qM+3WnxPilYXuqI/73kiRJ7cciY+eyyChJTVSq1E4CDk+dY7v7z2apV3u6\nIGFTPE5jW8VWc5JEkiQll8XYlcX4O2BB4Cyg07dmDsD2wPN5CHvkIQxMHUiSJEmS2kUewjbAo8DS\nqbN013uTTcNh6x3DWSvuypiBgyd26AH1avnkHgztHL0kSWoZi4ydyyKjJDVBqVIbUKrUzgL2TZ1l\njef/xmaPX5Y6BsBbwIZZjJ8muLaTJJIkqW1kMb6TxbgzsDzwYOo8BZgGOA14OA9hpdRhJEmSJCml\nPISp8xAuBM4Hpkydp7senmNpdtv8LB6ea9mvO/SgerV8YisySZIk9YZFxs5lkVGSClaq1AYDFwE7\nps6y4JvPsfftbTGf8DmweRbjS4mu35vVjry/kSRJTZXF+CCwHLAT8HbiOEVYHLgzD+H8PISZU4eR\nJEmSpFbLQ1iOxiqMP0idpbs+HzCIM1fYhUO/eyzvTz7t1x1+aL1aPqEXl3GxAUmS1DI+6O9cFhkl\nqUClSm0YcCWwZeos0376LofddARDxn6eOgrA3lmMtye8vpMkkiSpLWUxxizGs2lsN12lf2w3vQ2N\n7ab3yUMYlDqQJEmSJDVbHsKAPIRDgLuALHWe7np1+Bzsu/FpXLX45hC+dkr8gHq1/MteXso5ekmS\n1DIWGTtUjNEioyQVpFSpTQlcB6yfOsvgsaM57KYj4gyfvJM6CsAZWYzVxBmcJJEkSW0ti/G9LMbd\ngKWBe1PnKcDUwMnAo3kI304dRpIkSZKaJQ9hNuBm4BigY37MdVNpHfbYrMqLMy7wdYd2ATv3cTtp\n5+glSVLLWGTUl3lTKWmSU6rUpqUxUbFG6iwAe91+8phvvPFsO/x9fCewZ+oQOEkiSZI6RBbjo8BK\nwHbAm4njFGER4LY8hIvGPdyTJEmSpH4jD+F7wBO0ybOB7vhkyBQcu+bPOGX1Axg1eLKvO/xz4Pv1\navmsFkSTJEkqhEXGzlb0qowWPyRNUkqV2kzArcDyqbMAbPbYpR+u+fzN7fCrz5eBzbIY22Fva4uM\nkiSpY4zbbvo8GttNnxZhbOJIRdgaeC4P4YA8hMGpw0iSJElSX+QhDMtD+C1wNTB96jzd9czMC7Pb\nZmdwx/yrd+fwz4CN6tXypQVc2jl6SZLUMu1QllDvRYq9EfSmUtIko1SpzQn8jcZD5uSWfem+V7e/\n76xZU+cAPgU2ymJsl1WEnCSRJPUbIYQBwHd6eNoLMcYXmpFHzZPF+AGwVx7C2cAIYOXEkfpqKuAE\nYPs8hD2zGGupA0mSJElST+UhfBO4GFg0dZbu6iJwyZI/4I9L/4iuAQO7c8pHwAb1avn2giI4Ry9J\nklrGIqO+zJtKSZOEUqU2P40S49ypswDM+d7Lzx5xw2GzBujWLESTbT9uW8R20dWLc1xxWpLUroYA\nN/TwnKOAI4uPolbIYnwCWCUP4YddYcBJA2LXjKkz9dE3gL/lIVwG7JfF+GrqQJIkSZLUHXkIuwIn\nAV+7J3O7eGuKGTi+/FOemm2x7p7yDrBOvVp+qMAYFhklSVLL+KC/s7m1tCT10LgS4x20SYlx8lEf\nP1j9044MIE6TOgtwbBZjEVtNFMlJEkmS1PGyGC8cELvm7yKc0kXozQ812s3mNLab/kkewpDUYSRJ\nkiRpQvIQpstDuAqo0kElxrvnXZndtjirJyXG14BVCy4xSpIktZRFxs5mkVGSeqBUqc0L3AK0wxbO\nEOOtl56/6RsDY9c3UkcBrgUOTR1iPCwySpKkfiGL8cP5Y9e+A4iLfTh06sdS5ynAFMCxwJN5CGun\nDiNJkiRJX5WH8G3gcWCj1Fm6a9TAIfxmlX34xdpH8vHQqbp7Wg6sUq+Wn2lCJOfoJUlSy1hk7GwW\nGSWpm0qV2jzAbcCcaZP8x3VXn73evYO6xq6fOgjwLPCDLMZ2XB3ISRJJktSvZDE+/a2RHyzxxpQz\n7/TB0KlHps5TgAWBG/MQrsxDaItVzyVJkiRN2vIQBuUh/ILGwgZzpM7TXfl087LXplVu+GaPHhs8\nQ6PEmDcplnP0kiSpZSwydjaLjJLUDaVKbS7gVmCu1FnG+dNVZ3/3giFjPz8kdRDgfWDDLMYPUweZ\nACdJJElSv7TCR/8+e/ioD2d8ZI6lbhkTBqaOU4SNgWfzEA7NQxiaOowkSZKkSVMewjzAHcDP6KBn\n4VcvshH7bDqCl6fr0e/DHqKxnfRrTYoFztFLkqQW6pibN42XRUZJ+hqlSm0OGiXGeRJH+cK5fz5r\nvWOHjRn1e9L/vTsW2CqL8e+Jc0xMbz7rvL+RJEkdIYvx481eeaj8x6W32eypWRb5NHWeAkwGHA08\nnYfw3dRhJEmSJE1a8hC2Ah4DVkidpbs+GDY1R6x7NL9beQ8+HzikJ6feDqxRr5bfaVI0SZKklvNB\nf2ezyChJE1Gq1GanUWLMUmcZ57Qrz17/4KFjR18FTJE6DHBwFuNNqUN8jd5sd+3nmSRJ6ihHPfD7\nKw7c6JRZqivtdtdbU8yYOk4R5gOuzUO4Jg+hXe7FJUmSJPVTeQhT5CGcC1wMDE+dp7senX0JKpuf\nxQNz97h3eT2wbr1a/qgJsb7KFRklSVLLDEodQH1ikVGSJqBUqc0K3ALMnzrLOMfc8Ls1jwBupj1W\nh/xDFuOJqUN0w7+B1WgUGuOX/umawJ8j8FKKoJIkSX3ReABVXmX5bb+960ZPXPmbjZ+4YvDgrjGp\nY/XVBsBaeQjHAcdlMX6WOpAkSZKk/iUPYUngEmCB1Fm6a8yAgVywzHZc9q0tIfT48eylwI/q1fLn\nTYg2PhYZJUlSy4QYi+7CqVVCCJ8Akxc45BIxxscKHE+SkihVajMDtwELJY7yhUPq1fKv8hB+C+ye\nOgzwILBqFuPI1EEkSZqUhBCGAT0tch0VYzyyCXHUxkqV2jfmfvcff97x3jMXXPqVB1PHKco/gX2y\nGK9OHUSSJElS58tDCMB+wDFAj/ZkTum1qWfluDV/xvMz9erxxanAfvVquTc7GfVKCGEm4I0enrZ7\njPH0ZuSRJEn9m0XGDhZC+JhityZdMsb4aIHjSVLLlSq1mWhsJ71w6iw0fqm4Z71aHpGHsCNwVupA\nNFY4XDqL8V+pg0iSNKmxyKieKFVqw4CTV/jH3bvucs/pzPxRT58bta0bgL2yGF9IHUSSJElSZ8pD\nmBk4H1g7dZaeqC1QZsQqe/PZkB6vUxOB/evV8slNiDVRIYQZgTd7eJpFRkmS1CtuLd3Z3Fpakr6k\nVKnNANRojxLjWGCHerV8fh7CSsCI1IGAUcDGlhglSZLaX71aHglUShVqD8+59O+3fOTiqTZ7/FKG\njG3V7mFNsy7wVB7CicAvsxg/TR1IkiRJUufIQ1gXOA+YKXGUbvt08GSMWGVvbllwzd6cPpLGVtKX\nFxyru9xaWpIktcyA1AHUJxYZJWmcUqU2PY0S4yKpswCfA1uNKzHOCVxBe2xtUclivC91CEmSJHVf\nvVq+fPSgoUv/YdkfP7XLludw/1zLp45UhKHAT4Fn8xA2TR1GkiRJUvvLQxiSh3AScB0dVGKsz1Ri\nj83O6G2J8V1gzYQlRrDIKEmSWsgiY2ezyChJQKlSmw74G7BY6iw0tovcsF4tX56HMBlwFTBz4kwA\np2Yxnps6hCRJknquXi0/Dyz376lnu+DI9X7BEesezetTzZo6VhHmAi7PQ/hrHkIpdRhJkiRJ7Wnc\n94X7gH3pkOeZXQQu/dZW7L/hqbw+fLbeDPEPYMV6tXx3wdFaoSP+P5IkSe3HImNns8goaZJXqtSm\nAW4GvpU6C/ARsF69Wr5h3L+fAyyVMM8XasD+qUNIkiSp9+rV8qf1anlbYJcH5l5h1C5bnsMflt6W\nUQPbYeHvPlsLeDIP4bg8hClTh5EkSZLUPvIQdgAeBpZInaW73pl8en66wfGct/yOjB04qDdDPASs\nUK+W6wVH642in0dLkiRNkEXGzmaRUdIkrVSpDQf+CiyZOgvwHo0tHm4DyEM4GNg6aaKGHNgii3Fs\n6iCSJEnqu3q1fCaw4ueDhvzjoqV/xM5b/p575lkxdawiDAYOAp7LQ9gydRhJkiRJaeUhTJOHcClw\nNjBF6jzddd/cy7Pb5mfy+Oy97l1eB6xWr5bfKDBWX7i1tCRJahmLjJ3NIqOkSVapUpsauAlYJnUW\n4A0aEwsPAOQhrAcckzYSAB8D38tifDd1EEmSJBWnXi0/QmPl77+8OfUsHL3Ozzl0vWP41/DZU0cr\nwuzAJXkIt+QhLJw6jCRJkqTWy0NYCXgM2CJ1lu4aPXAwp6+0B0et+ws+nGx4b4c5A9iwXi1/UmC0\nvrLIKEmSWsYiY2ezyChpklSq1KYCbgSWS50FeAVYtV4tPwGQh1ACLiL9Z2wEfpTF+HTiHJIkSWqC\nerX8HrAhcAgw9uG5lmXXLc7mvGW3Z+SgoYnTFWJ14PE8hBPzEKZKHUaSJElS8+UhDMxDOBy4HZg7\ndZ7uemnaudh7kxH8ZdGNejtEBH5Wr5Z3rVfL7q4kSZImWalLFuobi4ySJjmlSm1K4HpghdRZgBeB\nVerV8vMAeQjDgWuAXv/cskBHZjH+OXUISZIkNU+9Wo71avlXwJrAG2MGDubSJb/PTludy53ZKqnj\nFWEQsB9Qz0P4QeowkiRJkponD2FO4FbgKGBg4jjddt3C67PXplX+OX3W2yFGA9vUq+V22OVpfFyR\nUZIktYxFxs5mkVHSJKVUqU0OXAesnDoL8DSNEuNLAHkIA4CLgQWTpmq4Ajg6dQhJkiS1Rr1avg1Y\nArgT4O0pZ+KY7xzBIesfxyvTzJk0W0FmBS7MQ7gjD2Gx1GEkSZIkFSsPYVPgcaBjfpH10dCpOPo7\nR/LbVfdhdO9Xxf8AWLdeLV9YYLSiWWSUJEktE2IsugunVgkhvAnMWOCQK8cY7y5wPEkqTKlSm4xG\niXH11FlolBhXr1fLb33xQh7C8cCB6SL9xxPAilmMn6QOUoQQwkAak1cDaEx+fPHPxP79qRhjniSw\nJEkTEUIYBnzWw9OOijEe2YQ46odKldog4FjggC9eGzh2DBs9eQU/eOgPTDZmZLpwxRkLjAAOz2L8\nIHUYSZIkSb2XhzA5cDKwc+osPfHErItxQvkQ3p6yT49pX6VRYnyqoFhNEUKYCviwh6ftH2M8qRl5\nJElS/2aRsYOFEN4AZipwyFVijHcVOJ4kFaJUqQ0DrgXKqbMAzwKr1avlN794Ydw2d+3wi8m3gWWy\nGP+ZOkhRQmO77vd7eNq+McZTmpFHkqS+sMioVilVahsB5wHDv3ht+o/fZsd7f8dqL96WKlbR3gQO\nAi7InNySJEmSOs641dYvAb6ROkt3jQ0DuHDpbbh0ye8TQ582PnwCWK9eLf+roGhNY5FRkiS1kltL\ndza3lpbU740rMV5Ne5QYnwPW+EqJcWng7HSR/mMMsHl/KjGO47YVkiRJPVSvlv8MLAU89sVr70w5\nA8etdSgHbfBrXpp27nThijMTjbLmXXkISyTOIkmSJKkH8hD2BB6gg0qMb0w1MwdseDKXLPXDvpYY\n/wqs0gklxnGco5ckSS1jkbGzWWSU1K+VKrXBwBXAd1JnAZ6nUWL89xcv5CHMDFwFDEuW6v/tk8V4\nW+oQTdCbzzrvbyRJ0iSvXi2/CKwA/P7Lrz85+7fYbfMzOXOFXflk8ORpwhVrReChPIQReQjTpg4j\nSZIkacLyEGbIQ/gL8BtgaOo83XX7fKux22Zn8Nws3+zrUKfRWImxpyscpmSRUZIktcyg1AHUJxYZ\nJfVbpUot0FhhZb3EUQBeoFFifP2LF/IQhgBXAnMkS/X/zspiHJE6RJN09eIcP88kSZKAerU8Etih\nVKndBYwAJgPoGjCQqxbfjNsWWIMd7j2D8t9rSXMWYACwG7BFHsIhwDluNy1JkiS1lzyENYELgFlT\nZ+mukYOGcfrKe3DzQuv0dajPgT3q1fKZBcRqNYuMkiSpZVyxqLNZZJTUn/0G+H7qEEAOrD6ebR5O\np7H6S2p3A3ukDtFETpJIkiT1Ub1aPpfG6owvfPn19yafjl+XD+GADU8mn27eNOGKNQNwFnBfHsLS\nqcNIkiRJgjyEwXkIx9HYUrljSowvzDA/e2xWLaLE+DawVoeWGCVJklrKImNns8goqV8qVWpH0h7l\nvH/QKDG++uUX8xD2BHZIE+m/vAJsmsU4OnWQJrLIKEmSVIB6tfw4sDRw1Vffe3rWRdlzs99RXWl3\nPhkyRevDFW9Z4P48hDPyEKZPHUaSJEmaVOUhzEfjx/gH0SHzthG4YrHN2Hfj0/jXNHP2dbingWXr\n1fLtfU+WjHP0kiSpZSwydjaLjJL6nVKltidwROocwEs0Sowvf/nFPIQ1gJPSRPovnwEbZTG+kTpI\nkzlJIkmSVJB6tfxBvVreBDgQGPPl97oGDOSaRTdmh63P4+bSdwqfcEhgALAz8Hwewq55CM6BSZIk\nSS2Uh/Aj4FFgmdRZuuu9yabh0O8ey9kr7sqYgYP7OtxfgBXq1fI/CoiWknP0kiSpZUKM/WBqehIV\nQngZ6PNPgb5kzRhjrcDxJKlHSpXaD4ELSP8l9xXg21+dYMhDmBd4EGiHVV2+n8V4ceoQzRZCGAqM\n7OFph8QYf9WMPJIk9UUIYRiNHyP0xFExxiObEEeTuFKltgpwKRPY2m2hfz/N7neexvzvvDC+tzvR\nw8AeWYz3pQ4iSZIk9Wd5CFMBVeAHqbP0xENzLsOv1ziIDyabtojhjgcOqVfLXUUMllIIIdBY9R4a\npcbYjT+/FmN8s2UhJUlSv2GRsYOFEF4C5ipwyLVijH8rcDxJ6rZSpfZd4M/AoMRRXqVRYsy//GIe\nwpTAPcCiSVL9t+OyGH+SOkQrhBCGAKN6eNpPY4zHNiOPJEl9YZFR7aZUqc0MXAysPr73Q+xivWeu\nZdv7f89Uoz9ubbjmiMC5wE+yGN9KHUaSJEnqb/IQlqXxHSNLnaW7Ph8wiHOX25GrFtsUQp/XWBgF\n7FSvlv9QQDRJkqRJjtvqdDa3lpbUL4xbDeYy0pcYX6OxnfRXS4wBOJ/2KDFeD/w0dYgWctsKSZKk\nJqlXy28AawHHMp77rhgGcN03v8eOW5/PjQut2x+2mw7A9jS2m94jD2Fg6kCSJElSf5CHMCAP4SfA\n3XRQifHV4XOw7yancdXimxVRYvw3sJolRkmSpN6zyNjZLDJK6nilSu1bwF+AyRJHeZ1GiXF8e+cd\nDmzS4jzjU6expXTHb0fRAxYZJUmSmqheLY+tV8s/Bb4HvDe+Yz6cbDinrrY/+2z8W+ozllobsDmm\nAU4DHs5DWCl1GEmSJKmT5SHMBtxM4wdSqRcr6LYbF1qHPTar8uIMCxQx3KPAMvVq+b4iBpMkSZpU\nWWTsbBYZJXW0UqU2P3AjMDxxlH8Da9Sr5ee/+kYewsbAEa2P9D8+AL6XxfhB6iAtZpFRkiSpBerV\n8rXAUsDDEzrm+ZkXYt9NTuPUVfflw6FTty5c8ywO3JWHcEEewiypw0iSJEmdJg9hA+BxYI3UWbrr\n4yFTcMyah3LqagcwanAh6ytcDqxcr5ZfLWIwSZKkSZlFxs5mkVFSxypVal/8SnPmxFHepFFifO6r\nb+QhLApcQPq/H7uArbMY/6doOQmwyChJktQi9Wr5H8BKwBkTOiaGAdy48HfZYevzuG7h9en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MaszvXA4mty4OSL+NK6+/HCmAmzHi69DYD7y4gvlhFzpw4jSZKktMqIBcqI64GzgPGp87Tr+0u/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\n4DVt829DSZI0clhkbG8WGTUiFcvVLuBEcnyMWe75x/neOQeGAOPzmvM17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"prompt_number": 24, "text": [ "" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "x = linspace(-1,1,100)\n", "y = f(x)\n", "trapz(y,x)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "0.66680270720674772" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check convergence of Trapezoidal Rule easily" ] }, { "cell_type": "code", "collapsed": false, "input": [ "N = 10**arange(2,6)\n", "\n", "trap_res = []\n", "for n in N:\n", " x = linspace(-1,1,n)\n", " y = f(x)\n", " trap_res.append(trapz(y,x))\n", "err_trap = abs(2./3-asarray(trap_res))\n", "plot(N,err_trap)\n", "semilogy()\n", "semilogx()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 29, "text": [ "[]" ] } ], "prompt_number": 29 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Simpson's Rule" ] }, { "cell_type": "code", "collapsed": false, "input": [ "N = 10**arange(2,6)\n", "\n", "simps_res = []\n", "for n in N:\n", " x = linspace(-1,1,n)\n", " y = f(x)\n", " simps_res.append(simps(y,x))\n", "err_simps = abs(2./3-asarray(simps_res))\n", "plot(N,err_simps)\n", "semilogy()\n", "semilogx()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 30, "text": [ "[]" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Romb Integration" ] }, { "cell_type": "code", "collapsed": false, "input": [ "romb_res = []\n", "Nromb = 2**arange(2,10)+1\n", "for n in Nromb:\n", " x, dx = linspace(-1,1,n,retstep=True)\n", " y = f(x)\n", " romb_res.append(romb(y,dx))\n", "err_romb = abs(2./3-array(romb_res))\n", "plot(Nromb,err_romb)\n", "semilogy()\n", "semilogx()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "[]" ] } ], "prompt_number": 24 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(N,err_trap,'r',N,err_simps,'b',Nromb,err_romb,'g')\n", "semilogy()\n", "semilogx()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "[]" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indefinite intervals are also possible" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ \\int _{-\\infty}^{\\infty} e^{-x^2} dx $$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "quad(lambda x: exp(-x ** 2), -Inf, Inf)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 31, "text": [ "(1.7724538509055159, 1.4202636780944923e-08)" ] } ], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Double integral" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def integrand(x, y):\n", " return exp(-x**2-y**2)\n", "\n", "x_lower = 0 \n", "x_upper = 10\n", "y_lower = 0\n", "y_upper = 10\n", "\n", "dblquad(integrand, x_lower, x_upper, lambda x : y_lower, lambda x: y_upper)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "(0.7853981633974476, 1.638229942140971e-13)" ] } ], "prompt_number": 33 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Triple Integral" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def integrand(x, y,z):\n", " return exp(-x**2-y**2-z**2)\n", "\n", "x_lower = 0 \n", "x_upper = 10\n", "y_lower = 0\n", "y_upper = 10\n", "z_lower = 0\n", "z_upper = 10\n", "\n", "tplquad(integrand, x_lower, x_upper, lambda x : y_lower, lambda x: y_upper,\\\n", " lambda x,y: z_lower, lambda x,y: z_upper)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "(0.6960409996039545, 1.4506309421028255e-13)" ] } ], "prompt_number": 34 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exercise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numerical Integration" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy as scp\n", "from pylab import *\n", "%matplotlib\n", "from scipy.integrate import odeint, ode" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Using matplotlib backend: Qt4Agg\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Way 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first way is to use *odeint*. scipy.integrate.odeint is a wrapper to LSODA (Fortran Library). It chooses which solver to use depending on the stiffness of the problem. Using the docstring of odeint, we obtain the following information:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Parameters\n", " ----------\n", " func : callable(y, t0, ...)\n", " Computes the derivative of y at t0.\n", " y0 : array\n", " Initial condition on y (can be a vector).\n", " t : array\n", " A sequence of time points for which to solve for y. The initial\n", " value point should be the first element of this sequence.\n", " args : tuple, optional\n", " Extra arguments to pass to function.\n", " Dfun : callable(y, t0, ...)\n", " Gradient (Jacobian) of `func`.\n", " col_deriv : bool, optional\n", " True if `Dfun` defines derivatives down columns (faster),\n", " otherwise `Dfun` should define derivatives across rows.\n", " full_output : bool, optional\n", " True if to return a dictionary of optional outputs as the second output\n", " printmessg : bool, optional\n", " Whether to print the convergence message\n", "\n", " Returns\n", " -------\n", " y : array, shape (len(t), len(y0))\n", " Array containing the value of y for each desired time in t,\n", " with the initial value `y0` in the first row.\n", " infodict : dict, only returned if full_output == True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us try and solve the equations of motion of a simple pendulum" ] }, { "cell_type": "code", "collapsed": false, "input": [ "g = 9.8\n", "L = 0.5" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 35 }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$\\dot{F}=F(t,x)$$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def f(x, t, g, l):\n", " '''RHS of pendulum'''\n", " x1, x2 = x\n", " dx1 = x2\n", " dx2 = - g/l*sin(x1)\n", " \n", " return [dx1, dx2]" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 36 }, { "cell_type": "code", "collapsed": false, "input": [ "x0 = [pi/4, 0]\n", "t = linspace(0, 10, 250)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 38 }, { "cell_type": "code", "collapsed": false, "input": [ "x = odeint(f, x0, t, (g,L,))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 39 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, ax = subplots(1,2)\n", "ax[0].plot(t, x[:, 0], 'r', label=\"theta1\")\n", "x1 = + L*sin(x[:, 0])\n", "y1 = - L*cos(x[:, 0])\n", "ax[1].plot(x1,y1,'b')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 40, "text": [ "[]" ] } ], "prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "from matplotlib.widgets import Slider" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 41 }, { "cell_type": "code", "collapsed": false, "input": [ "axanim = axes([0.1, 0.25, 0.8, 0.6])\n", "pl, = axanim.plot([0,x1[0]],[0,y1[0]],'k')\n", "pl2, = plot(x1[0],y1[0],'bo')\n", "pl3, = plot(x1[0],y1[0],'r',alpha=0.25)\n", "xlim([-1.5*L, 1.5*L])\n", "ylim([-1.5*L, 0])\n", "xlabel('x')\n", "\n", "axsl = axes([0.1, 0.1, 0.8, 0.1])\n", "sl = Slider(axsl,'Time',t.min(), t.max(),valinit=0, valfmt='%.2f s')\n", "\n", "def update(data):\n", " it = abs(t-data).argmin()\n", " pl.set_data([0,x1[it]],[0,y1[it]])\n", " pl2.set_data(x1[it],y1[it])\n", " pl3.set_data(x1[:it+1],y1[:it+1])\n", " draw()\n", "\n", "sl.on_changed(update)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 45, "text": [ "0" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "Way 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another possibility is to use the **ode** package. They differ in the control they give the user. Let's run the simple pendulum example." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.integrate import ode" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "g = 9.8\n", "L = 0.5" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 48 }, { "cell_type": "code", "collapsed": false, "input": [ "def f(x, t, g, l):\n", " '''RHS of pendulum'''\n", " x1, x2 = x\n", " dx1 = x2\n", " dx2 = - g/l*sin(x1)\n", " \n", " return [dx1, dx2]\n", "\n", "x0 = [pi/4, 0]\n", "t = linspace(0, 10, 250)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 49 }, { "cell_type": "code", "collapsed": false, "input": [ "# solver = ode(f)\n", "# solver.set_f_params(g,L)\n", "# solver.set_initial_value(x0,t[0])\n", "\n", "solver = ode(f).set_f_params(g,L).set_initial_value(x0,t[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "dt = 0.01\n", "while solver.successful() and solver.t < 1:\n", " solver.integrate(solver.t+dt)\n", " print (solver.t, solver.y)" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'float' object is not iterable", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.01\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mwhile\u001b[0m \u001b[0msolver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuccessful\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0msolver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0msolver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintegrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msolver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msolver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msolver\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/jpsilva/anaconda/lib/python2.7/site-packages/scipy/integrate/_ode.pyc\u001b[0m in \u001b[0;36mintegrate\u001b[1;34m(self, t, step, relax)\u001b[0m\n\u001b[0;32m 386\u001b[0m self._y, self.t = mth(self.f, self.jac or (lambda: None),\n\u001b[0;32m 387\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 388\u001b[1;33m self.f_params, self.jac_params)\n\u001b[0m\u001b[0;32m 389\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mSystemError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 390\u001b[0m \u001b[1;31m# f2py issue with tuple returns, see ticket 1187.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/jpsilva/anaconda/lib/python2.7/site-packages/scipy/integrate/_ode.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m 735\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire_new_handle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 736\u001b[0m y1, t, istate = self.runner(*(args[:5] + tuple(self.call_args) +\n\u001b[1;32m--> 737\u001b[1;33m args[5:]))\n\u001b[0m\u001b[0;32m 738\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mistate\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 739\u001b[0m warnings.warn('vode: ' +\n", "\u001b[1;32m\u001b[0m in \u001b[0;36mf\u001b[1;34m(x, t, g, l)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;34m'''RHS of pendulum'''\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mx1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mdx1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdx2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0msin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: 'float' object is not iterable" ] } ], "prompt_number": 52 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The reason for this error is the arguments' order convention. Unline **odeint** which asks for a callable f(x,t...), **ode** calls for f(t,x,...). We can either define a new function or use a lambda function to solve this" ] }, { "cell_type": "code", "collapsed": false, "input": [ "solver = ode(lambda t, x: f(x,t,g,L)).set_initial_value(x0,t[0])\n", "# Note that we eliminated the need for extra arguments" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 67 }, { "cell_type": "code", "collapsed": false, "input": [ "dt = 0.01\n", "timey = []\n", "while solver.successful() and solver.t < 1:\n", " solver.integrate(solver.t+dt)\n", " timey.append(solver.y)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 68 }, { "cell_type": "code", "collapsed": false, "input": [ "plot(array(timey))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 70, "text": [ "[,\n", " ]" ] } ], "prompt_number": 70 }, { "cell_type": "code", "collapsed": false, "input": [ "solver.y" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 57, "text": [ "array([-0.34914328, 3.01934742])" ] } ], "prompt_number": 57 }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to store the solution for all time steps, we have to explicitly store it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "heading", "level": 6, "metadata": {}, "source": [ "Version Information" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%load_ext version_information\n", "%version_information scipy, matplotlib" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "
SoftwareVersion
Python2.7.8 |Anaconda 2.1.0 (64-bit)| (default, Aug 21 2014, 18:22:21) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
IPython2.3.1
OSposix [linux2]
scipy0.14.0
matplotlib1.4.2
Fri Dec 05 10:14:13 2014 CET
" ], "json": [ "{ \"Software versions\" : [{ \"module\" : \"Python\", \"version\" : \"2.7.8 |Anaconda 2.1.0 (64-bit)| (default, Aug 21 2014, 18:22:21) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\" }, { \"module\" : \"IPython\", \"version\" : \"2.3.1\" }, { \"module\" : \"OS\", \"version\" : \"posix [linux2]\" }, { \"module\" : \"scipy\", \"version\" : \"0.14.0\" }, { \"module\" : \"matplotlib\", \"version\" : \"1.4.2\" } ] }" ], "latex": [ "\\begin{tabular}{|l|l|}\\hline\n", "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n", "Python & 2.7.8 |Anaconda 2.1.0 (64-bit)| (default, Aug 21 2014, 18:22:21) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] \\\\ \\hline\n", "IPython & 2.3.1 \\\\ \\hline\n", "OS & posix [linux2] \\\\ \\hline\n", "scipy & 0.14.0 \\\\ \\hline\n", "matplotlib & 1.4.2 \\\\ \\hline\n", "\\hline \\multicolumn{2}{|l|}{Fri Dec 05 10:14:13 2014 CET} \\\\ \\hline\n", "\\end{tabular}\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 11, "text": [ "Software versions\n", "Python 2.7.8 |Anaconda 2.1.0 (64-bit)| (default, Aug 21 2014, 18:22:21) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n", "IPython 2.3.1\n", "OS posix [linux2]\n", "scipy 0.14.0\n", "matplotlib 1.4.2\n", "\n", "Fri Dec 05 10:14:13 2014 CET" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import HTML\n", "def css_styling():\n", " styles = open(\"./styles/custom.css\", \"r\").read()\n", " return HTML(styles)\n", "css_styling()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "\n", "\n", "\n", "\n", "\n" ], "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }