Convolve-edge-detection.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flipped</th>\n",
       "      <th>raw</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   flipped    raw\n",
       "0       -1   True\n",
       "1        0   True\n",
       "2        1  False\n",
       "3        0  False\n",
       "4        0  False\n",
       "5       -1   True\n",
       "6        0   True\n",
       "7        0   True\n",
       "8        1  False\n",
       "9        0  False"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    "arr = np.array([True, True, False, False, False, True, True, True, False, False])\n",
    "con_arr = np.convolve(arr,[-1,1])\n",
    "pd.DataFrame({'raw':arr, 'flipped':con_arr[:-1]})"
   ]
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   "execution_count": 6,
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flipped</th>\n",
       "      <th>raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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      "text/plain": [
       "   flipped    raw\n",
       "0        0   True\n",
       "1        0   True\n",
       "2        1  False\n",
       "3        0  False"
      ]
     },
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     "metadata": {},
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   ],
   "source": [
    "arr = np.array([True, True, True, False, False])\n",
    "con_arr = np.convolve(arr,[-1,1])\n",
    "pd.DataFrame({'raw':arr[1:], 'flipped':con_arr[1:-1]})"
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flipped</th>\n",
       "      <th>raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
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       "      <td>True</td>\n",
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       "      <th>3</th>\n",
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      "text/plain": [
       "   flipped    raw\n",
       "0        0  False\n",
       "1        0  False\n",
       "2       -1   True\n",
       "3        0   True"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "arr = np.array([False, False, False, True, True])\n",
    "con_arr = np.convolve(arr,[-1,1])\n",
    "pd.DataFrame({'raw':arr[1:], 'flipped':con_arr[1:-1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "outputs": [
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     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hare</th>\n",
       "      <th>hound</th>\n",
       "      <th>hh_diff</th>\n",
       "      <th>hh_diff_change</th>\n",
       "      <th>convolve</th>\n",
       "      <th>hh_diff_edge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-20</td>\n",
       "      <td>-20</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>-9</td>\n",
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       "      <td>1</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>-18</td>\n",
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       "      <td>0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>-27</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>-25</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>-22</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>-17</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>-9</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30</td>\n",
       "      <td>55</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>89</td>\n",
       "      <td>59</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hare  hound  hh_diff hh_diff_change  convolve hh_diff_edge\n",
       "0    -20    -20        0          False         0        False\n",
       "1     -5      5       10           True        -1         True\n",
       "2     10      1       -9          False         1         True\n",
       "3     20      2      -18          False         0        False\n",
       "4     30      3      -27          False         0        False\n",
       "5     30      5      -25          False         0        False\n",
       "6     30      8      -22          False         0        False\n",
       "7     30     13      -17          False         0        False\n",
       "8     30     21       -9          False         0        False\n",
       "9     30     34        4           True        -1         True\n",
       "10    30     55       25           True         0        False\n",
       "11    30     89       59           True         0        False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'hare' :[-20, -5, 10, 20, 30, 30, 30, 30, 30, 30, 30, 30], \n",
    "                   'hound':[-20, 5, 1,  2,   3,  5,  8, 13, 21, 34, 55, 89]})\n",
    "df['hh_diff'] =  df['hound'] - df['hare']\n",
    "df['hh_diff_change'] = df['hh_diff'] > 0\n",
    "df['convolve'] = np.convolve(df['hh_diff_change'], [-1, 1])[:-1]\n",
    "df['hh_diff_edge'] = np.abs(df['convolve']) == 1\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114b59c90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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JTRXg7V0hUwUUl/MH9gsXjLSHs2fD008byclLciNUKSMilyK4x8fD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114b59b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}