{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slicing images with nipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is useful to slice out image edges or subsample images.   For example, your functional images might have a lot of empty space around the brain that will take up memory during processing, or your structural image might contain too much of the subject's neck, or the resolution may be to high.\n",
    "\n",
    "You can always load the image data and slice away the parts of the image data array that you don't want, but then you will have to work out the effect this should have on the image affine (see http://nipy.org/nibabel/coordinate_systems.html).\n",
    "\n",
    "Luckily nipy has the ability to slice images while working out the effect on the affine matrix for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import the numerical and plotting libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Show plots inside the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# - set gray colormap and nearest neighbor interpolation by default\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "plt.rcParams['image.interpolation'] = 'nearest'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# - make numpy print numbers to 4dp only\n",
    "np.set_printoptions(precision=4, suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load our example functional image again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AffineTransform(\n",
       "   function_domain=CoordinateSystem(coord_names=('i', 'j', 'k', 't'), name='voxels', coord_dtype=float64),\n",
       "   function_range=CoordinateSystem(coord_names=('scanner-x=L->R', 'scanner-y=P->A', 'scanner-z=I->S', 't'), name='scanner', coord_dtype=float64),\n",
       "   affine=array([[  -4.    ,    0.    ,    0.    ,    0.    ,  124.244 ],\n",
       "                 [   0.    ,    3.9345,    0.7207,    0.    , -103.4497],\n",
       "                 [   0.    ,   -0.7207,    3.9346,    0.    ,  -33.4929],\n",
       "                 [   0.    ,    0.    ,    0.    ,    2.5   ,    0.    ],\n",
       "                 [   0.    ,    0.    ,    0.    ,    0.    ,    1.    ]])\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = nipy.load_image('ds114_sub009_t2r1.nii')\n",
    "img.coordmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The functional data has a lot of empty space around the brain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 64, 30, 173)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = img.get_data()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x108bd6b10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C5xBNhKzjR52Q9fEnnniiVMc529/73veW6rj/6L7L/cdkE6wfR32f1xui6yvr0fH+sf6d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\nQvQMTXohesb/B+8+cnleZYtEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106156890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data[:, :, 15, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x108e3cc90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7y+rKpkIDZfiM4URxwXCiuGA4UVq3MbIz3XW1M3X6AIfPOYkXOPzAGIZXQtVF27JlS97W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\nXDCcKC4YTpT/Aw+ne5CcJ0aKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1061b7ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data[32, :, :, 0])  # A sagittal slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe we would look to slice off the empty space to left and right and front and back of the brain.\n",
    "\n",
    "Maybe we also want to halve the resolution in the bottom-top direction.  We do that by slicing the image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "small_img = img[15:50, 8:55, ::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The slicing we did on the image shows up when we get the data from the image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35, 47, 15, 173)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_data = small_img.get_data()\n",
    "small_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x108f0d250>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rLD7L1pdpMMMUbW4D9N05y702tW/fSZqZCZ4kzcwET5JmZoInSTMzwZOkmZng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\nDIcjggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108b8eb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(small_data[:, :, 7, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sagittal view now looks more squashed up because we've removed every second slice with the image slicing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x109047f10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108f83890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(small_data[17, :, :, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nipy adjusted the image affine to the slicing.  Notice that the last column of the affine, reflecting the different translations to go from voxel 0, 0, 0 to the position in millimeters (voxel 0, 0, 0 is a different voxel after the slicing).  Also, the second to last column contains the size of the voxel in the z (bottom to top) direction, and this is now around 8 instead of 4 previously.  That is because we threw away every second slice, so the remaining slices are twice the distance apart from each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -4.    ,   0.    ,   0.    ,   0.    ,  64.244 ],\n",
       "       [  0.    ,   3.9345,   1.4414,   0.    , -71.9734],\n",
       "       [  0.    ,  -0.7207,   7.8691,   0.    , -39.2586],\n",
       "       [  0.    ,   0.    ,   0.    ,   2.5   ,   0.    ],\n",
       "       [  0.    ,   0.    ,   0.    ,   0.    ,   1.    ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_img.affine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the notebook on diagnostics, we saw that the first few scans were different from the rest.  This is the T1 effect, where the signal is largest when the radiofrequency stimulation first starts.   We can drop the first four scans with slicing, to get rid of this effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "img = img[..., 4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64, 64, 30, 169)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can save this image back out to disk for further processing by other packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mb312/Library/Python/2.7/lib/python/site-packages/nipy/io/files.py:145: FutureWarning: Default `strict` currently False; this will change to True in a future version of nipy\n",
      "  ni_img = nipy2nifti(img, data_dtype = io_dtype)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Image(\n",
       "  data=memmap([[[[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]],\n",
       "       \n",
       "               [[ 8, 10, ..., 10,  7],\n",
       "                [11, 10, ...,  8, 10],\n",
       "                ..., \n",
       "                [19, 11, ..., 20, 21],\n",
       "                [28, 23, ..., 19, 35]],\n",
       "       \n",
       "               ..., \n",
       "               [[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]],\n",
       "       \n",
       "               [[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]]],\n",
       "       \n",
       "       \n",
       "              [[[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]],\n",
       "       \n",
       "               [[10, 10, ..., 10,  7],\n",
       "                [11, 11, ...,  9, 11],\n",
       "                ..., \n",
       "                [17, 19, ..., 24, 14],\n",
       "                [24, 41, ..., 21, 22]],\n",
       "       \n",
       "               ..., \n",
       "               [[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]],\n",
       "       \n",
       "               [[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]]],\n",
       "       \n",
       "       \n",
       "              ..., \n",
       "              [[[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]],\n",
       "       \n",
       "               [[ 1,  1, ...,  2,  1],\n",
       "                [ 1,  1, ...,  1,  1],\n",
       "                ..., \n",
       "                [22, 19, ..., 12, 19],\n",
       "                [14, 14, ..., 28, 23]],\n",
       "       \n",
       "               ..., \n",
       "               [[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]],\n",
       "       \n",
       "               [[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]]],\n",
       "       \n",
       "       \n",
       "              [[[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]],\n",
       "       \n",
       "               [[ 1,  1, ...,  2,  1],\n",
       "                [ 1,  1, ...,  0,  1],\n",
       "                ..., \n",
       "                [14, 15, ..., 17, 18],\n",
       "                [20, 12, ..., 16, 13]],\n",
       "       \n",
       "               ..., \n",
       "               [[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]],\n",
       "       \n",
       "               [[ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                ..., \n",
       "                [ 0,  0, ...,  0,  0],\n",
       "                [ 0,  0, ...,  0,  0]]]], dtype=int16),\n",
       "  coordmap=AffineTransform(\n",
       "            function_domain=CoordinateSystem(coord_names=('i', 'j', 'k', 't'), name='input-slice', coord_dtype=float64),\n",
       "            function_range=CoordinateSystem(coord_names=('scanner-x=L->R', 'scanner-y=P->A', 'scanner-z=I->S', 't'), name='scanner', coord_dtype=float64),\n",
       "            affine=array([[  -4.      ,    0.      ,    0.      ,    0.      ,  124.244003],\n",
       "                          [   0.      ,    3.934535,    0.720723,    0.      , -103.449684],\n",
       "                          [   0.      ,   -0.72072 ,    3.934551,    0.      ,  -33.492859],\n",
       "                          [   0.      ,    0.      ,    0.      ,    2.5     ,   10.      ],\n",
       "                          [   0.      ,    0.      ,    0.      ,    0.      ,    1.      ]])\n",
       "         ))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nipy.save_image(img, 'fixed.nii')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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