{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is part of [**Python for Geosciences**](https://github.com/koldunovn/python_for_geosciences) notes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"================"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](\"files/numpy.png\")
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- a powerful N-dimensional array object\n",
"- sophisticated (broadcasting) functions\n",
"- tools for integrating C/C++ and Fortran code\n",
"- useful linear algebra, Fourier transform, and random number capabilities\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"set_printoptions(precision=3 , suppress= True) # this is just to make the output look better"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Load data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I am going to use some real data as an example of array manipulations. This will be the AO index downloaded by wget through a system call (you have to be on Linux of course):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!wget www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is how data in the file look like (we again use system call for *head* command):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!head monthly.ao.index.b50.current.ascii"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1950 1 -0.60310E-01\r\n",
" 1950 2 0.62681E+00\r\n",
" 1950 3 -0.81275E-02\r\n",
" 1950 4 0.55510E+00\r\n",
" 1950 5 0.71577E-01\r\n",
" 1950 6 0.53857E+00\r\n",
" 1950 7 -0.80248E+00\r\n",
" 1950 8 -0.85101E+00\r\n",
" 1950 9 0.35797E+00\r\n",
" 1950 10 -0.37890E+00\r\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load data in to a variable:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao = loadtxt('monthly.ao.index.b50.current.ascii')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"array([[ 1950. , 1. , -0.06 ],\n",
" [ 1950. , 2. , 0.627],\n",
" [ 1950. , 3. , -0.008],\n",
" ..., \n",
" [ 2013. , 7. , -0.011],\n",
" [ 2013. , 8. , 0.154],\n",
" [ 2013. , 9. , -0.461]])"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"(765, 3)"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So it's a *row-major* order. Matlab and Fortran use *column-major* order for arrays."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(ao)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"numpy.ndarray"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Numpy arrays are statically typed, which allow faster operations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao.dtype"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"dtype('float64')"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can't assign value of different type to element of the numpy array:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao[0,0] = 'Year'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "could not convert string to float: Year",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mao\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Year'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mValueError\u001b[0m: could not convert string to float: Year"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Slicing works similarly to Matlab:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao[0:5,:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"array([[ 1950. , 1. , -0.06 ],\n",
" [ 1950. , 2. , 0.627],\n",
" [ 1950. , 3. , -0.008],\n",
" [ 1950. , 4. , 0.555],\n",
" [ 1950. , 5. , 0.072]])"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One can look at the data. This is done by matplotlib module and you have to start IPython with *--pylab inline* option to make it work:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(ao[:,2])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
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dYELuDz4oXpd37BCjTekmpiXn+PH54hZUlzRlrXzcsWPFPB9Bk3rxQS68TMBc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\n",
"text": [
""
]
}
],
"prompt_number": 14
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Index slicing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In general it is similar to Matlab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First 12 elements of **second** column (months). Remember that indexing starts with 0:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao[0:12,1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.,\n",
" 12.])"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First raw:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao[0,:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"array([ 1950. , 1. , -0.06])"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can create mask, selecting all raws where values in second raw (months) equals 10 (October):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mask = (ao[:,1]==10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we apply this mask and show only first 5 rowd of the array:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao[mask][:5,:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"array([[ 1950. , 10. , -0.379],\n",
" [ 1951. , 10. , -0.213],\n",
" [ 1952. , 10. , -0.437],\n",
" [ 1953. , 10. , -0.194],\n",
" [ 1954. , 10. , 0.513]])"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You don't have to create separate variable for mask, but apply it directly. Here instead of first five rows I show five last rows:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao[ao[:,1]==10][-5:,:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"array([[ 2008. , 10. , 1.676],\n",
" [ 2009. , 10. , -1.54 ],\n",
" [ 2010. , 10. , -0.467],\n",
" [ 2011. , 10. , 0.8 ],\n",
" [ 2012. , 10. , -1.514]])"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can combine conditions. In this case we select October-December data (only first 10 elements are shown):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao[(ao[:,1]>=10)&(ao[:,1]<=12)][0:10,:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"array([[ 1950. , 10. , -0.379],\n",
" [ 1950. , 11. , -0.515],\n",
" [ 1950. , 12. , -1.928],\n",
" [ 1951. , 10. , -0.213],\n",
" [ 1951. , 11. , -0.069],\n",
" [ 1951. , 12. , 1.987],\n",
" [ 1952. , 10. , -0.437],\n",
" [ 1952. , 11. , -1.891],\n",
" [ 1952. , 12. , -1.827],\n",
" [ 1953. , 10. , -0.194]])"
]
}
],
"prompt_number": 21
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Basic operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create example array from first 12 values of second column and perform some basic operations:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"months = ao[0:12,1]\n",
"months"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.,\n",
" 12.])"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"months+10"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"array([ 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.,\n",
" 22.])"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"months*20"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"array([ 20., 40., 60., 80., 100., 120., 140., 160., 180.,\n",
" 200., 220., 240.])"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"months*months"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"array([ 1., 4., 9., 16., 25., 36., 49., 64., 81.,\n",
" 100., 121., 144.])"
]
}
],
"prompt_number": 25
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Basic statistics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create *ao_values* that will contain onlu data values:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao_values = ao[:,2]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Simple statistics:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao_values.min()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"-4.2656999999999998"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao_values.max()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"3.4952999999999999"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao_values.mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"-0.13462109949019607"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao_values.std()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"1.0054168027600721"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao_values.sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"-102.98514111"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also use *sum* function:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sum(ao_values)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"-102.98514111"
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One can make operations on the subsets:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mean(ao[ao[:,1]==1,2]) # January monthly mean"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"-0.40406150000000002"
]
}
],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Result will be the same if we use method on our selected data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ao[ao[:,1]==1,2].mean()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"-0.40406150000000002"
]
}
],
"prompt_number": 34
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Saving data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can save your data as a text file"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"savetxt('ao_only_values.csv',ao[:, 2], fmt='%.4f')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Head of resulting file:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!head ao_only_values.csv"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-0.0603\r\n",
"0.6268\r\n",
"-0.0081\r\n",
"0.5551\r\n",
"0.0716\r\n",
"0.5386\r\n",
"-0.8025\r\n",
"-0.8510\r\n",
"0.3580\r\n",
"-0.3789\r\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also save it as binary:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f=open('ao_only_values.bin', 'w')\n",
"ao[:,2].tofile(f)\n",
"f.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
}
],
"metadata": {}
}
]
}