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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - compatibility with Python 3\n",
      "from __future__ import print_function  # print('me') instead of print 'me'\n",
      "from __future__ import division  # 1/2 == 0.5, not 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - show figures inside the notebook\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# - import common modules\n",
      "import numpy as np  # the Python array package\n",
      "import matplotlib.pyplot as plt  # the Python plotting package"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Repeating data over axes with `np.tile`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "``np.tile`` can be used to replicate data within an array."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For example, let's say I have a row vector `R`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "R = np.array([[1, 2]])\n",
      "R"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([[1, 2]])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I might want to make a new array, where I repeat the row 4 times, to make a 4 by 2 array.  I specify the number of repeats on each axis.  In this case I want to repeat the first axis 4 times, and the second axis I don't want to repeat - so I repeat 1 time:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.tile(R, [4, 1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "array([[1, 2],\n",
        "       [1, 2],\n",
        "       [1, 2],\n",
        "       [1, 2]])"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In fact, we don't often use `np.tile` because we can usually get the same effect with *broadcasting*."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Basic broadcasting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Numpy has a very useful but somewhat advanced feature called *broadcasting*.  Broadcasting is a system that numpy uses when trying to match the shapes of two arrays, that do not have the same shape.\n",
      "\n",
      "Let's say you have two arrays, `A` and `B`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = np.array([[2, 3], [4, 5], [6, 7]])\n",
      "A"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([[2, 3],\n",
        "       [4, 5],\n",
        "       [6, 7]])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "B = np.array([1, 3])\n",
      "B"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([1, 3])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`A` has shape `(3, 2)` and `B` has shape `(2,)`.  What will happen if I try and add the arrays `A` and `B`?\n",
      "\n",
      "One thing that could happen, is that numpy raises an error.  Another thing that might happen, is that numpy might realize that you meant to match the only axis of `B` to the last axis of `A`, and make enough copies of the only axis of `B` to match the 3 columns of `A`.  Something like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_B = np.tile(B, (3, 1))\n",
      "new_B"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([[1, 3],\n",
        "       [1, 3],\n",
        "       [1, 3]])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Then the subtraction would be:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A - new_B"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "array([[1, 0],\n",
        "       [3, 2],\n",
        "       [5, 4]])"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In fact, that is exactly what numpy does by default, using broadcasting:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A - B"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([[1, 0],\n",
        "       [3, 2],\n",
        "       [5, 4]])"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A simplified version of the rules that numpy uses is the following:\n",
      "\n",
      "* If `B` has fewer dimensions than `A`, prepend as many new 1-length dimensions to `B` as necessary (shape of `B` goes from `(2,)` to `(1, 2)` in this case;\n",
      "* If there are any 1-length dimensions in the new `B` shape, and the corresponding dimension in `A` is not 1, make as many repeats of the data along this axis as needed to fill out the 1-length dimension to the length of the matching dimension in `A`.\n",
      "* Do the operation on the original `A` and the modified `B`.\n",
      "\n",
      "So, in our case, that would look like:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Prepend as many 1-length dimensions as necessary \n",
      "# to give B same number of dimensions as A\n",
      "B_1 = B.reshape(1, 2)\n",
      "B_1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "array([[1, 3]])"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# For each 1-length dimension of B, where matching dimension of A has >1 length:\n",
      "# Replicate data to extend 1-length dimension to same length as A dimension\n",
      "B_2 = np.tile(B_1, [3, 1])\n",
      "B_2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([[1, 3],\n",
        "       [1, 3],\n",
        "       [1, 3]])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Do the operation on A and new B\n",
      "A - B_2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([[1, 0],\n",
        "       [3, 2],\n",
        "       [5, 4]])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Dictionaries - key, value containers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The *dictionary* is a very useful fundamental type in Python.\n",
      "\n",
      "The dictionary is made up of key, value pairs.\n",
      "\n",
      "The *key* is the thing that allows you to look up a *value*.  Like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "some_scores = {'matthew': 11, 'jb': 21}\n",
      "some_scores"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "{'jb': 21, 'matthew': 11}"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here `jb` and `matthew` are *keys*.  If I want the `jb` *value*, I can look it up, using the `jb` key:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "some_scores['jb']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "21"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Same for the `matthew` value:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "some_scores['matthew']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "11"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I can add a key, value pair, by assigning like this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "some_scores['stefan'] = 2  # oh dear"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "some_scores"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "{'jb': 21, 'matthew': 11, 'stefan': 2}"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The keys are often strings, as here, but the keys can be lots of different things, such as numbers or tuples:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "some_scores[99] = 7  # 0 is just a number, doesn't refer to position\n",
      "some_scores"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "{99: 7, 'jb': 21, 'matthew': 11, 'stefan': 2}"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As you may see from the output above, the order of the keys is unpredictable, because Python arranges them as it likes.  Only the relationship between the keys and the values is guaranteed."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll see dictionaries coming up often as we use Python more."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Setting defaults for plotting"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We often want to display images with a gray colormap, and with nearest neighbor interpolation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nibabel as nib\n",
      "img = nib.load('ds114_sub009_t2r1.nii')\n",
      "data = img.get_data()\n",
      "# Not like this:\n",
      "plt.imshow(data[:, :, 15, 0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.image.AxesImage at 0x10a212f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xd1N2LADQtGgfI996DWPJrNMoGjIyGoIDMKf2kn7FgisnzUXRak1rNUdc8kRe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\nwQbPGDZCv8EGzxj+PwGZBsHa0iiPAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106001890>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# But like this:\n",
      "plt.imshow(data[:, :, 15, 0], cmap='gray', interpolation='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.image.AxesImage at 0x10a379110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vs04ffX3TT23SL6DNsXVVz6ULU1EXTOKPX9Ocgln/TT3k2qCttNJtjHMSL6Em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\nQvQMTXohesb/B+8+cnleZYtEAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a24c050>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Notice this second image is - grayscale - but also - less blurry - showing us the individual pixels without interpolating between them."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can tell matplotlib to use these settings by default, by using the `rcParams` dictionary:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set gray colormap and nearest neighbor interpolation by default\n",
      "plt.rcParams['image.cmap'] = 'gray'\n",
      "plt.rcParams['image.interpolation'] = 'nearest'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we don't need to set these setting specifically:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.imshow(data[:, :, 15, 0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "<matplotlib.image.AxesImage at 0x10a6ccd90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vs04ffX3TT23SL6DNsXVVz6ULU1EXTOKPX9Ocgln/TT3k2qCttNJtjHMSL6Em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\nQvQMTXohesb/B+8+cnleZYtEAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a389d90>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Arrays are *mutable*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr = np.arange(10)\n",
      "arr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can make another variable point to this, but that's probably not a good idea:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr2 = arr\n",
      "arr2[0] = 42\n",
      "arr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([42,  1,  2,  3,  4,  5,  6,  7,  8,  9])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Oops, we have modified the original array."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Copying arrays"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To avoid stepping on the toes of another array during processing, it is often a good idea to make an explicit copy:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr3 = arr.copy()  # The copy method"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can modify the new copy without modifying the original:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr3[0] = 99\n",
      "arr3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "array([99,  1,  2,  3,  4,  5,  6,  7,  8,  9])"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arr"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "array([42,  1,  2,  3,  4,  5,  6,  7,  8,  9])"
       ]
      }
     ],
     "prompt_number": 28
    }
   ],
   "metadata": {}
  }
 ]
}