Calc adjacency count.ipynb

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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
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      "text/plain": [
       "array([[5, 3, 4, ..., 5, 5, 5],\n",
       "       [4, 4, 2, ..., 2, 5, 1],\n",
       "       [1, 5, 2, ..., 5, 5, 4],\n",
       "       ..., \n",
       "       [2, 3, 2, ..., 1, 2, 2],\n",
       "       [5, 2, 3, ..., 3, 5, 2],\n",
       "       [4, 4, 2, ..., 2, 2, 2]])"
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     "metadata": {},
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   "source": [
    "data = np.random.randint(1,6, size=(1000,1000))\n",
    "data"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "source": [
    "xstep=1;ystep=1; xsize=3; ysize=3"
   ]
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   "execution_count": 4,
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     "data": {
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       "(1000, 1000)"
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     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "data.shape"
   ]
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 49 ms\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py:482: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "window_view = np.lib.stride_tricks.as_strided(data, ((data.shape[0] - xsize + 1) / xstep, (data.shape[1] - ysize + 1) / ystep, xsize, ysize), (data.strides[0] * xstep, data.strides[1] * ystep, data.strides[0], data.strides[1]))\n",
    "window_view_flat = window_view.reshape(data.shape[0]-2, data.shape[1]-2, 9)\n",
    "window_view_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 3, 4, 4, 4, 2, 1, 5, 2])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_pix = window_view_flat[0][0]\n",
    "single_pix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False, False,  True,  True,  True,  True], dtype=bool)"
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "single_pix!=single_pix[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 3, 2, 1, 5, 2])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_pix[single_pix!=single_pix[4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "np.unique(single_pix[single_pix!=single_pix[4]]).size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def unique_adjacency(arr):\n",
    "    return np.unique(arr[arr!=arr[4]]).size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 12.8 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[4, 3, 3, ..., 3, 2, 3],\n",
       "       [3, 3, 2, ..., 4, 3, 4],\n",
       "       [3, 2, 3, ..., 3, 2, 3],\n",
       "       ..., \n",
       "       [4, 4, 3, ..., 3, 3, 2],\n",
       "       [4, 4, 4, ..., 3, 3, 3],\n",
       "       [3, 4, 4, ..., 3, 3, 3]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "np.apply_along_axis(unique_adjacency, 2, window_view_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  }
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