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Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/app_python_sql_exceptions.ipynb | 1 | 2670 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python SQL Exceptions\n",
"Generic rule for SQL exceptions in Python according to PEP 249"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: Python SQL Exceptions\n",
" id: 19aefed0-ffd4-47dc-a7fc-f8b1425e84f9\n",
" description: Generic rule for SQL exceptions in Python according to PEP 249\n",
" author: Thomas Patzke\n",
" references:\n",
" - https://www.python.org/dev/peps/pep-0249/#exceptions\n",
" logsource:\n",
" category: application\n",
" product: python\n",
" service: null\n",
" detection:\n",
" exceptions:\n",
" - DataError\n",
" - IntegrityError\n",
" - ProgrammingError\n",
" - OperationalError\n",
" condition: exceptions\n",
" falsepositives:\n",
" - Application bugs\n",
" - Penetration testing\n",
" level: medium\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying Elasticsearch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from elasticsearch import Elasticsearch\n",
"from elasticsearch_dsl import Search\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize Elasticsearch client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"es = Elasticsearch(['http://helk-elasticsearch:9200'])\n",
"searchContext = Search(using=es, index='logs-*', doc_type='doc')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run Elasticsearch Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = searchContext.query('query_string', query='\\*.keyword:(*DataError* OR *IntegrityError* OR *ProgrammingError* OR *OperationalError*)')\n",
"response = s.execute()\n",
"if response.success():\n",
" df = pd.DataFrame((d.to_dict() for d in s.scan()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
mfkasim91/mfkasim91.github.io | assets/notebooks/Optimal transport for AB test.ipynb | 1 | 90405 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"from scipy.spatial.distance import cdist\n",
"import matplotlib.pyplot as plt\n",
"import ot\n",
"\n",
"# load the data\n",
"samples_old = np.loadtxt(\"control.txt\")\n",
"samples_new = np.loadtxt(\"new.txt\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f1c1e652210>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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SOz9fIzeXzQOSSsjdEPR+RHy3ln1XkpsG+L0U43kQeDoiZqTVZg2fczIwLCIu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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1c1e6a4c10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(samples_old, bins=100, alpha=0.5, normed=True)\n",
"plt.hist(samples_new, bins=100, alpha=0.5, normed=True)\n",
"plt.legend([\"Old design\", \"New design\"])\n",
"plt.xlabel(\"Online time (minutes)\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nbins = 100\n",
"yhist_old, xhist_old = np.histogram(samples_old, bins=nbins)\n",
"yhist_new, xhist_new = np.histogram(samples_new, bins=nbins)\n",
"\n",
"# normalization\n",
"yhist_old = yhist_old * 1.0 / np.sum(yhist_old)\n",
"yhist_new = yhist_new * 1.0 / np.sum(yhist_new)\n",
"\n",
"# get the centre of the bins\n",
"xhist_old = (xhist_old[1:] + xhist_old[:-1]) / 2.0\n",
"xhist_new = (xhist_new[1:] + xhist_new[:-1]) / 2.0"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# get the distance matrix\n",
"M = cdist(xhist_old[:,None], xhist_new[:,None], \"sqeuclidean\")\n",
"\n",
"# compute the transport matrix\n",
"T = ot.emd(yhist_old, yhist_new, M)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f1c1e2f99d0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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hy7M04zD/CH0QWl7Gwx3r0rtpTcJDbelnY4rDl0SwU0R6ASoi4cB9wHr/hmWM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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1c581ba0d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Tnorm = T / np.sum(T,axis=1)[:,None] # row normalization\n",
"xt = np.matmul(Tnorm, xhist_new[:,None]).flatten()\n",
"plt.plot(xhist_old, xt)\n",
"plt.plot(xhist_old, xhist_old, '--')\n",
"plt.xlabel(\"Original online time\")\n",
"plt.ylabel(\"New online time\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f1c1e0719d0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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IfKDxDImFvIw0/veiAYy9+Xg656Tzi7/P5pz7P+HThdHrGRU5XAcGwtMCntwX\nVe5eDtwMvAN8Abzs7nPN7C4zOy9c7AmgrZktBG4Dfh5MtBKv+he04eXrh/PgZYPZvqecyx+fzMWP\nTOSzhRs021wajV37ygFIT41kZagjE/0a6uDu44Bx1Y7dUeX9HqCG0T+R2DEzzjkqn1N65/HilOX8\n5eNFXPb4ZEo6ZfHDU7tzfLcczfeQQO3cF2pppKcG+8itiFSRlpzIVcd15pKhRbxSuoKHPlrE956Y\nwqCiTK4f0ZX+BW3Iz0iLyUqjIlXt2luOGbSIwUC4kobIN5SWnMj3hhdz8dGFvDq1jIc+XMQNz4am\nMqUkJVCU3ZLiti3p1Dad4px0TumVR4fMFgFHLc3Zjr0VpKckxaTFq6QhcphSkxK5/JhOXDSkkNJl\nm1i6YRfLNu5k6cadLNu4i08XbmT3/gruMDi+Ww4XDingjL7tY/JYpMSXLbv3kZEWm/+dK2mIHKGU\npASO7ZrDsV0PPu7uLN24i39MX8lrU8v44YszaJ2WxLkDOnDRkAIGFmZqLEQaxKotu2PWmlXSEIkS\nM6NzTjq3ndaDW0/pzqTFG3llahl/n1bG85OXM7Awk1tP7c6JPXKVPOSIrNqyh4GFmTGpS0lDJAYS\nEoxju+VwbLccfjOqL69PX8nDHy/mqr9+zoDCTG49pTsn9VTykMOzYcdeclunxqSuICf3icSljLRk\nvje8mA9vP4l7vtOfjTv2cvVTnzPqwU95/4u1mv8h38ie/RXs2ldBdnpKTOpT0hAJSEpSApcMLeLD\n20/i3guOYsuu/Vz7dCln3vcJz09e/vWELZG6bNy5D4C2Shoi8SE5MYGLjy7k/R+fyB8uGkBSovHL\nf8xm2G/f57fjvtB2tVKnTTtCSSNWLQ2NaYg0EsmJCVwwpIDvDO5I6bLNPPXZUp6YsITHP1nMKb3b\ncd6ADgzulEWHNmka+5CvrdkWWm4/LyMtJvUpaYg0MmbG0cXZHF2czeqtu3lu0nKen7Kcd+etBSCv\ndSqDi7IYVJTJoKIsjipoo7kfcexAS7QwS4/cisS9/DYtuP2Mnvzw1O58uXo701dsZtqyzUxfsYW3\n564BQmMjQ4uzOaF7Dsd3z6F3+wwtZRJHVmzeRcuURHVPici/JScm0L+gDf0L2nDl8GIANu7Yy/Tl\nW5i4eCMTFmzgd299CW9BTqsUjuuWw2l92nFq73ZqhTRzZZt3U5DVImZdlkoaIk1U21apnNqnHaf2\naQfA2m0iTwjHAAAPaUlEQVR7mLBgA58sWM+EhRt4fcYqWqcmcVb/9nx7UAHHdM5WC6QZWrN1T0zX\nNlPSEGkm2mWkccGQAi4YUkBFpTN58Ub+Pn0lb85azculZXTMbMGogR24cEgBXXJbBR2uNJDVW3fT\nr+Ph7dZ3OJQ0RJqhxCoz0O8e1Y9/zVvDP6av5JHxi3noo0UM7ZzNpUMLOatfvrqvmrC95RVs2LGP\n9hlqaYhIA2mRksiogR0ZNbAj67bv4dWpZbz0+Qp+9NJM7hwzj28P6sglQwsPe29pCc7qLaHHbfMz\nY/O4LShpiMSVvNZp3HhSN24Y0ZVJizfywucreH7ycp76bCm98zM4d0A+5x7VgcLslkGHKhFYsG4H\nAN3yYtfdqKQhEoeqLqC4aec+/jl9JWNnreLet+dz79vzGVSUyXkDOnBO//yYTRqTb+6rtdsB6K6k\nISKxkp2ewjXHd+aa4zuzYtMuxs5axdiZq/nN2Hnc9cY8ji7O5ux+7Tmrfz7tlEAala/WbqdjZgta\npyXHrE5rbitqlpSUeGlpadBhiDR5C9dtZ+zM1bw1ZzVfrd2BGQwpyuLs/vkMKsokv00LclqlkJSo\nJeyCMuLeD+nVvjWPXllyxNcys6nuXu+F1NIQkRp1y2vNj05rzY9O68HCddsZN3sN42av5q435n1d\nJsEgt3Uq7TPSKGqbzkk9cvlWrzyyYjQ7OZ6t3rqb5Zt28R/HFse0XiUNEalXt7zW3HJKa245pTtL\nN+xk4bodrNm2h7Xb9rBm6x7WbNvD5MUbGTtzFYkJxtHFWZzWpz2n92mnQfUo+Wj+egCGFmfHtF4l\nDRH5Ropz0inOST/keGWlM3vlVt6dt5Z/zVvD3W/M4+435tG/YxvOHZDPyKM6xHTmcnO2v6KShz5a\nSN8OGTGd2Aca0xCRKFm2cSfvzF3DG7NWM6tsKwBHF2dx7oAOnN0/n5xWsdmetDmasGADVzwxmb9c\nPpiz+uc3yDUjHdNQ0hCRqFu6YSdvhJ/Kmr92O4kJxok9cjl/UEdO76NFFb+pu9+Yx98mLWPGHafR\nMqVhOow0EC4ijUZxTjo3f6s7N3+rO/PXbOcf01fy+oyVfPDlOlqlJnFWv/aMHNCBYzpnK4HUw935\n8Mt1DOvStsESxjehpCEiMdWzfWt+flYvfnJGz68XVRw3ezWvTC0jNSmBYV3aMqJHLif2yKVrbrp2\nKaxiX3klj32ymMUbdnLdiC6BxKDuKREJ3J79FUxcvJHxX63n46/Ws3j9TgDaZaRSUpxNSacsji7O\nplf71nE7L2Tx+h1c9PBENu7cx9n92/PApYMbdKl7dU+JSJORlpzIyT3zOLlnHhDawnT8gvVMWbKJ\n0qWbeXPWagDSUxIZUJjJ4KIsBnfKZFBhVlzMCamodH766izKK52HrxjMKb3bBbY3SiBJw8yygZeA\nYmApcLG7b66hXAUwO/xxubufF6sYRSQ4hdktufyYTlx+TCcAVm3ZTemyzZQu3cS05Zv5y8eLqKgM\n9ZJ0yU3nuK6hrW6HdWlLmxaxW1IjVu577ytKl23mjxcP4Mx+DfO01OEKpHvKzO4FNrn7PWb2cyDL\n3X9WQ7kd7v6NVuJS95RI87drXzmzyrYybflmPl+yiclLNrFrXwUJBkcVZDK8a1uOLs5iSKfsJptE\nNu/cx6eLNlC6dDNPfbaU75YUcs8F/aM2xtOoH7k1s/nASe6+2szygY/cvWcN5ZQ0RKRe+8ormbFi\nCxMWbmDCgvXMKttKeaVjBj3btWZIpyz6dmhD3w4Z9GzfutE+obV++17emLWK975Yy6TFm6gI38PI\nozrwp4sHRHU8p7EnjS3unhl+b8DmA5+rlSsHZgDlwD3u/s9arjcaGA1QVFQ0ZNmyZVGLXUQav937\nKpixYgufL93E50s3MWPFFrbvKQdCuxp2y21F17x0uuS0oktuOl1zWwWeTGau2MK1T3/Ohh376Jqb\nzul9Q8uw9M7PiElcgScNM3sPaF/DqV8BT1dNEma22d2zarhGR3dfaWZdgA+AU9x9UV31qqUhItW5\nOys27Wbuqq3MXbWNL1ZvY/GGnSzftOvrsZEEg665rejTIYPe+Rl0z2tF97zWdMxqQWKUBp3dnSUb\ndvLR/PX8/p35tG2VwiPfG0LfDm2iUl9dAn96yt1Pre2cma01s/wq3VPrarnGyvDXxWb2ETAIqDNp\niIhUZ2YUtW1JUduWBy27sa+8kuWbdrFg7Xa+WL2Neau3MWXJJl6fserrMqlJCXTOSadrXiu6Hvia\nG3q1SDm0BVBR6ezZX8Ge/RXsq6hk7/5KNu3ax8Yd+1i3fQ9lm3dTtjm0Qu3yjTvZvGs/AAMLM3n0\nyiHktW7ce5YE1T31e2BjlYHwbHf/abUyWcAud99rZjnARGCUu8+r4ZJfU0tDRI7U1l37Wbh+OwvX\n7eCrtTtYvH4HizfsZMWmXYQbJphBhzYtSEiAvfsrQ4mivJJ95ZV1XjspweiQ2YKi7FAS65OfwXHd\ncihu2zLQiYyBtzTqcQ/wspldCywDLgYwsxLgBnf/PtAbeMTMKoEEQmMadSYMEZGG0KZlMkM6ZTOk\n08HLju8tr2DZxl0sXLeDBWt3sGTDDsyMtOQEUhITSEtJpGVyEmnJCaQlJ5KcmEBqUgLZ6Sm0bZVC\nbutU8lqnRa27KxY0I1xERCJuacTnfHwRETksShoiIhIxJQ0REYmYkoaIiERMSUNERCKmpCEiIhFT\n0hARkYgpaYiISMSa3eQ+M1tPaJb54coBNjRQOE1FvN1zvN0v6J7jxZHccyd3z62vULNLGkfKzEoj\nmRXZnMTbPcfb/YLuOV7E4p7VPSUiIhFT0hARkYgpaRzq0aADCEC83XO83S/onuNF1O9ZYxoiIhIx\ntTRERCRicZk0zOxMM5tvZgvDOwdWP59qZi+Fz082s+LYR9mwIrjn28xsnpnNMrP3zaxTEHE2pPru\nuUq5C8zMw5uANWmR3LOZXRz+t55rZs/HOsaGFsHPdpGZfWhm08M/32cHEWdDMbMnzWydmc2p5byZ\n2f3h/x6zzGxwgwbg7nH1AhIJ7TPeBUgBZgJ9qpW5EXg4/P4S4KWg447BPZ8MtAy//0E83HO4XGtg\nPDAJKAk67hj8O3cHpgNZ4c95Qccdg3t+FPhB+H0fYGnQcR/hPY8ABgNzajl/NvAWYMAwYHJD1h+P\nLY2hwEJ3X+zu+4AXgVHVyowCng6/fxU4xYLcvPfI1XvP7v6hu+8Kf5wEFMQ4xoYWyb8zwN3A/wB7\nYhlclERyz9cBD7r7ZgB3XxfjGBtaJPfsQEb4fRtgVQzja3DuPh7YVEeRUcAzHjIJyDSz/IaqPx6T\nRkdgRZXPZeFjNZZx93JgK9A2JtFFRyT3XNW1hP5Sacrqvedws73Q3d+MZWBRFMm/cw+gh5l9amaT\nzOzMmEUXHZHc853AFWZWBowD/l9sQgvMN/19/0aSGupC0jyY2RVACXBi0LFEk5klAH8Ergo4lFhL\nItRFdRKh1uR4M+vv7lsCjSq6LgWecvc/mNlw4G9m1s/dK4MOrCmKx5bGSqCwyueC8LEay5hZEqEm\n7caYRBcdkdwzZnYq8CvgPHffG6PYoqW+e24N9AM+MrOlhPp+xzTxwfBI/p3LgDHuvt/dlwBfEUoi\nTVUk93wt8DKAu08E0git0dRcRfT7frjiMWl8DnQ3s85mlkJooHtMtTJjgP8Iv78Q+MDDI0xNVL33\nbGaDgEcIJYym3s8N9dyzu2919xx3L3b3YkLjOOe5e2kw4TaISH62/0molYGZ5RDqrlocyyAbWCT3\nvBw4BcDMehNKGutjGmVsjQGuDD9FNQzY6u6rG+ricdc95e7lZnYz8A6hJy+edPe5ZnYXUOruY4An\nCDVhFxIacLokuIiPXIT3/HugFfBKeMx/ubufF1jQRyjCe25WIrznd4DTzWweUAH8xN2bbCs6wnv+\nMfCYmf2I0KD4VU35j0Aze4FQ4s8Jj9P8GkgGcPeHCY3bnA0sBHYBVzdo/U34v52IiMRYPHZPiYjI\nYVLSEBGRiClpiIhIxJQ0REQkYkoaIiISMSUNaTbCK5meUe3YrWb2FzPrYGav1vP9n4W/FpvZZdGM\nNZrM7Hwz61Pl813hiZuY2UdNfAKjBExJQ5qTFzh0Ts0lwAvuvsrdL6zrm9392PDbYuAbJY3wygGN\nxfmEVnMFwN3vcPf3AoxHmhElDWlOXgXOCc8MJrwPSgfgk3DrYU74eF8zm2JmM8L7DXQPH98Rvs49\nwAnh8z8yszQz+6uZzQ7vyXByuPxVZjbGzD4A3jezfDMbH/6+OWZ2QvUAw3s/fGlm08J7HrwRPn6n\nmd1epdyccPyY2T/NbKqF9r8YXaXMDjP7bzObGV58sJ2ZHQucB/w+HEdXM3vKzA5JmGZ2uplNDMfy\nipm1OrL//BIPlDSk2XD3TcAU4KzwoUuAl2uY/XsD8Gd3H0hoccayaud/Dnzi7gPd/U/ATaHLe39C\ni989bWZp4bKDgQvd/URCrZN3wtcdAMyoetHw9zwGnAsMAdpHeGvXuPuQcKy3mNmBFZfTgUnuPoDQ\nniDXuftnhJaR+Ek4/kU1XTC8hMh/Aqe6+2CgFLgtwngkjilpSHNTtYvqkvDn6iYCvzSznwGd3H13\nPdc8HngWwN2/BJYRWrMJ4N1wsoLQOkhXm9mdQH93317tOr2AJe6+IJzIno3wnm4xs5mE1scq5N8L\nDO4D3gi/n0qoWy1Swwh1YX1qZjMIrbXW5HdrlOhT0pDm5nVCm2YNJrQT4dTqBdz9eUJdOLuBcWb2\nrSOob2eV644ntKvaSuApM7vyG1ynnIN/H9MAzOwk4FRgeLhFMf3AOWB/lVZUBd9sLTkjlPAGhl99\n3P3ab/D9EqeUNKRZcfcdwIfAk9TcysDMugCL3f1+QknmqGpFthNaOv2AT4DLw9/bAygC5tdw3U7A\nWnd/DHicUNdVVV8CxWbWNfz50irnlh4oH054ncPH2wCb3X2XmfUi1EKoT/X4azIJOM7MuoXrTA/f\nm0idlDSkOXqB0JhCjUkDuBiYE+6W6Qc8U+38LKAiPMD8I+AhIMHMZgMvEVoltab9Rk4CZprZdOC7\nwJ+rnnT3PcBo4E0zmwZUXYL+NSDbzOYCNxPa5wLgbSDJzL4gNEA/qb6bJ7Tl6U/Cg/Zdayrg7usJ\nbUD1gpnNItRl1yuCa0uc0yq3IgEJdz3d7u4jg45FJFJqaYiISMTU0hARkYippSEiIhFT0hARkYgp\naYiISMSUNEREJGJKGiIiEjElDRERidj/B8NDL9CAjiOGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1c1e5b8990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qtile = np.cumsum(yhist_old)\n",
"displacement = xt - xhist_old\n",
"plt.plot(qtile, displacement)\n",
"plt.plot(qtile, np.zeros_like(qtile), '--')\n",
"plt.xlabel(\"Visitors quantile\")\n",
"plt.ylabel(\"Online time increment\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Uncertainty"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reg = 0.5 # regularization factor\n",
"Treg = ot.sinkhorn(yhist_old, yhist_new, M, reg)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f1c1dc67d10>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AfnxjM9vaBshzOzhrURXnLpmaEXWADiYvy8GUwqyM2TpyoojIemNM/aGOi6dr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\nm7oYtu1Y2xMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1c1df79890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Treg_norm = Treg / np.sum(Treg, axis=1)[:,None]\n",
"xreg_t_mean = np.matmul(Treg_norm, xhist_new[:,None]).flatten()\n",
"xreg_t_var = np.matmul(Treg_norm, (xhist_new-xreg_t_mean)[:,None]** 2).flatten()\n",
"xreg_t_std = np.sqrt(xreg_t_var)\n",
"\n",
"# plot\n",
"plt.plot(xhist_old, xreg_t_mean)\n",
"plt.fill_between(xhist_old, xreg_t_mean-xreg_t_std,\n",
" xreg_t_mean+xreg_t_std, alpha=0.2)\n",
"plt.plot(xhist_old, xhist_old, '--')\n",
"plt.xlabel(\"Original online time\")\n",
"plt.ylabel(\"New online time\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f1c1dd72810>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Nh46w4/AQ0ZCP3962nFde3GU1BmPMhKoZZYTTXPSyWZdkEfJ6hO7WCD7v3IeRru+M8olX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\nQDDGGOOygGCMMQawgGCMMcb1/wPX8fziYVqdmgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1c1de61150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"displacement_mean = xreg_t_mean - xhist_old\n",
"plt.plot(qtile, displacement_mean)\n",
"plt.fill_between(qtile, displacement_mean - xreg_t_std,\n",
" displacement_mean + xreg_t_std, alpha=0.2)\n",
"plt.plot(qtile, np.zeros_like(qtile), '--')\n",
"plt.xlabel(\"Visitors quantile\")\n",
"plt.ylabel(\"Online time increment\")"
]
},
{
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jazracherif/algorithms | 2SAT/2SAT.ipynb | 1 | 16095 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2SAT Problem\n",
"\n",
"In this assignment you will implement one or more algorithms for the 2SAT problem. Here are 6 different 2SAT instances:\n",
"\n",
"The file format is as follows. In each instance, the number of variables and the number of clauses is the same, and this number is specified on the first line of the file. Each subsequent line specifies a clause via its two literals, with a number denoting the variable and a \"-\" sign denoting logical \"not\". For example, the second line of the first data file is \"-16808 75250\", which indicates the clause ¬x16808∨x75250.\n",
"\n",
"Your task is to determine which of the 6 instances are satisfiable, and which are unsatisfiable. In the box below, enter a 6-bit string, where the ith bit should be 1 if the ith instance is satisfiable, and 0 otherwise. For example, if you think that the first 3 instances are satisfiable and the last 3 are not, then you should enter the string 111000 in the box below.\n",
"\n",
"DISCUSSION: This assignment is deliberately open-ended, and you can implement whichever 2SAT algorithm you want. For example, 2SAT reduces to computing the strongly connected components of a suitable graph (with two vertices per variable and two directed edges per clause, you should think through the details). This might be an especially attractive option for those of you who coded up an SCC algorithm in Part 2 of this specialization. Alternatively, you can use Papadimitriou's randomized local search algorithm. (The algorithm from lecture is probably too slow as stated, so you might want to make one or more simple modifications to it --- even if this means breaking the analysis given in lecture --- to ensure that it runs in a reasonable amount of time.) A third approach is via backtracking. In lecture we mentioned this approach only in passing; see Chapter 9 of the Dasgupta-Papadimitriou-Vazirani book, for example, for more details"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n",
"n= 32\n",
"graph #items: 25\n",
"vertices idx {'27', '13', '24', '11', '6', '10', '31', '9', '22', '14', '26', '8', '4', '1', '15', '16', '3', '19', '21', '25', '17', '5', '7', '12', '2', '20', '28', '29', '30', '32', '23', '18'}\n",
"idx_to_v {'23': '-6', '28': '-8', '29': '-18', '27': '8', '15': '12', '30': '18', '13': '-12', '11': '5', '16': '-2', '31': '-15', '19': '13', '21': '19', '25': '16', '5': '-9', '17': '-13', '6': '-20', '10': '4', '7': '9', '12': '-4', '9': '-5', '2': '-10', '20': '17', '14': '2', '24': '6', '26': '-16', '32': '15', '8': '20', '22': '-19', '3': '3', '4': '10', '1': '-3', '18': '-17'}\n",
"v_to_idx {'-2': '16', '-9': '5', '-12': '13', '15': '32', '-6': '23', '13': '19', '-4': '12', '18': '30', '16': '25', '-17': '18', '-13': '17', '19': '21', '-8': '28', '10': '4', '17': '20', '6': '24', '5': '11', '-10': '2', '9': '7', '2': '14', '20': '8', '-15': '31', '-18': '29', '12': '15', '-5': '9', '-3': '1', '-19': '22', '-20': '6', '4': '10', '-16': '26', '3': '3', '8': '27'}\n",
"graph defaultdict(<class 'list'>, {'27': ['4'], '20': ['17'], '30': ['22'], '15': ['14', '27'], '13': ['2', '15', '15'], '11': ['10', '23', '20'], '16': ['13', '21'], '18': ['9'], '19': ['18'], '21': ['3', '29'], '25': ['26', '26'], '6': ['3'], '7': ['6', '5', '5'], '12': ['9'], '22': ['14', '11'], '2': ['28'], '28': ['13'], '24': ['9'], '26': ['2'], '32': ['2'], '8': ['5'], '9': ['21'], '4': ['1', '25', '15', '31'], '1': ['22', '8'], '3': ['2']})\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def reverse_graph(graph):\n",
" rev = {}\n",
" \n",
" for v, k in graph.items():\n",
" for i in k:\n",
" rev.setdefault(i, [])\n",
" rev[i].append(v)\n",
" \n",
" return rev\n",
"\n",
"class dfs_obj(object):\n",
" UNEXPLORED = 0\n",
" EXPLORING = 1\n",
" EXPLORED = 2\n",
"\n",
"\n",
" def __init__(self, graph, rev_graph, vertices):\n",
" \n",
" self.graph = graph\n",
" self.rev_graph = rev_graph\n",
" self.vertices = vertices\n",
" \n",
" self.state = {}\n",
" self.leaders = {}\n",
" self.finish_time = {} #old label -> new label\n",
" self.finish_time_rev = {} #new label -> old label\n",
"\n",
"\n",
" self.t = 0\n",
" self.s = None\n",
" \n",
" def init_state(self):\n",
" state = {}\n",
" for k in self.vertices:\n",
" state[k] = dfs_obj.UNEXPLORED\n",
" \n",
" return state\n",
"\n",
" \"\"\"\n",
" ITERATIVE IMPLEMENTATION \n",
" \"\"\" \n",
" def DFS_ITERATIVE_1(self, i):\n",
"\n",
" #print (\"node {} explored\".format(i))\n",
" #self.leaders[i] = self.s \n",
"\n",
" stack = []\n",
" stack.append(i)\n",
"\n",
" while (stack):\n",
" v = stack.pop() # remove last\n",
"\n",
" if self.state[v] == dfs_obj.EXPLORING: \n",
" self.state[v] = dfs_obj.EXPLORED\n",
" self.t += 1\n",
" self.finish_time[v] = str(self.t)\n",
"\n",
" if self.state[v] == dfs_obj.UNEXPLORED:\n",
" self.state[v] = dfs_obj.EXPLORING \n",
" stack.append(v)\n",
"\n",
" if v in self.rev_graph:\n",
" for j in self.rev_graph[v]:\n",
" if self.state[j] == dfs_obj.UNEXPLORED:\n",
" stack.append(j) \n",
"\n",
" def DFS_ITERATIVE_LOOP1(self):\n",
" n = len(self.vertices)\n",
" \n",
" for i in range(n, 0, -1):\n",
" i = str(i)\n",
" \n",
" if self.state[i] != dfs_obj.EXPLORED:\n",
" #print(\"loop1 explore\", i)\n",
" self.s = i\n",
" self.DFS_ITERATIVE_1(i)\n",
" \n",
" \n",
" def DFS_ITERATIVE_2(self,i):\n",
" # i is the new label\n",
" \n",
" stack = []\n",
" stack.append(i)\n",
"\n",
" while (stack):\n",
" v = stack.pop() # remove last\n",
"\n",
" if self.state[v] == dfs_obj.EXPLORING: \n",
" self.state[v] = dfs_obj.EXPLORED\n",
" \n",
" # leaders refer to old label\n",
" self.leaders[self.finish_time_rev[v]] = self.finish_time_rev[self.s]\n",
"\n",
"\n",
" if self.state[v] == dfs_obj.UNEXPLORED:\n",
" self.state[v] = dfs_obj.EXPLORING \n",
" stack.append(v)\n",
"\n",
" if v in self.finish_time_rev and self.finish_time_rev[v] in self.graph:\n",
" for j in self.graph[self.finish_time_rev[v]]:\n",
" j = self.finish_time[j]\n",
"\n",
" if self.state[j] == dfs_obj.UNEXPLORED:\n",
" stack.append(j) \n",
"\n",
"\n",
" def DFS_ITERATIVE_LOOP2(self):\n",
" n = len(self.vertices)\n",
" \n",
" for i in range(n, 0, -1):\n",
" # iterate of the *new* labels\n",
" i = str(i)\n",
" \n",
" if self.state[i] != dfs_obj.EXPLORED:\n",
" self.s = i\n",
" self.DFS_ITERATIVE_2(i)\n",
" \"\"\"\n",
" RECURSIVE IMPLEMENTATION \n",
" \"\"\" \n",
" def DFS1(self, i):\n",
"\n",
" # Set Explored \n",
" self.state[i] = 1\n",
" \n",
" #print (\"node {} explored\".format(i)) \n",
" if i in self.rev_graph:\n",
" for j in self.rev_graph[i]:\n",
" if self.state[j] == 0:\n",
" self.DFS1(j)\n",
"\n",
" self.t += 1\n",
" self.finish_time[i] = str(self.t)\n",
" \n",
"\n",
" def DFS_LOOP1(self):\n",
" self.t = 0\n",
" n = len(self.vertices)\n",
" \n",
" for i in range(n, 0, -1):\n",
" i = str(i)\n",
" \n",
" if self.state[i] == 0:\n",
" self.DFS1(i)\n",
"\n",
" \n",
" def DFS2(self,i):\n",
"\n",
" self.state[i] = 1\n",
" #print (\"node {} explored\".format(i))\n",
" \n",
" self.leaders[self.finish_time_rev[i]] = self.finish_time_rev[self.s]\n",
"\n",
" for j in self.graph[self.finish_time_rev[i]]:\n",
" #print (self.finish_time)\n",
" j = self.finish_time[j]\n",
" \n",
" if self.state[j] == 0:\n",
" self.DFS2(j)\n",
"\n",
"\n",
" def DFS_LOOP2(self):\n",
" n = len(self.vertices)\n",
" \n",
" for i in range(n, 0, -1):\n",
" # iterate of the *new* labels\n",
" i = str(i)\n",
" \n",
" if self.state[i] == 0:\n",
" self.s = i\n",
" self.DFS2(i)\n",
"\n",
" def run_DFS_LOOP(self):\n",
" print (self.graph, self.rev_graph)\n",
" print(\"step 1\")\n",
" self.state = self.init_state()\n",
" self.DFS_LOOP1()\n",
"\n",
" print (\"finish_time\", self.finish_time)\n",
" \n",
" print (\"step 2\")\n",
" self.finish_time_rev = {str(d):str(k) for k,d in self.finish_time.items()}\n",
"\n",
" self.state = self.init_state()\n",
"# print (self.finish_time_rev)\n",
" self.DFS_LOOP2()\n",
" \n",
" def run_DFS_LOOP_ITERATIVE(self):\n",
" #print(\"step 1\")\n",
" self.state = self.init_state()\n",
" \n",
" self.DFS_ITERATIVE_LOOP1()\n",
" \n",
" #print (\"step 2\")\n",
" self.finish_time_rev = {str(d):str(k) for k,d in self.finish_time.items()}\n",
"\n",
" self.state = self.init_state()\n",
"\n",
"# print (self.finish_time_rev)\n",
" self.DFS_ITERATIVE_LOOP2()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n",
"Not Satisfiable 8\n",
"satisfiable False\n",
"40\n",
"satisfiable True\n",
"100000\n",
"satisfiable True\n",
"200000\n",
"Not Satisfiable -162741\n",
"satisfiable False\n",
"400000\n",
"satisfiable True\n",
"600000\n",
"satisfiable True\n",
"800000\n",
"Not Satisfiable -618692\n",
"satisfiable False\n",
"1000000\n",
"Not Satisfiable 410420\n",
"satisfiable False\n"
]
}
],
"source": [
"# Use SCC and verify than no Connected Component contains itself and its negation\n",
"import collections\n",
"\n",
"FILES = [\"input_beaunus_10_20.txt\", \n",
" \"input_beaunus_11_40.txt\",\n",
" \"2sat1.txt\", \n",
" \"2sat2.txt\", \n",
" \"2sat3.txt\", \n",
" \"2sat4.txt\",\n",
" \"2sat5.txt\",\n",
" \"2sat6.txt\"]\n",
"\n",
"expected = [False,\n",
" True,\n",
" True,\n",
" False,\n",
" True,\n",
" True,\n",
" False,\n",
" False]\n",
"\n",
"for f in range(len(FILES)):\n",
"\n",
" fp = open(FILES[f], 'r')\n",
" data = fp.readlines()\n",
"\n",
" n = int(data[0])\n",
" graph = collections.defaultdict(list)\n",
" vertices = set()\n",
" v_to_idx = {}\n",
" idx_to_v = {}\n",
" i = 1\n",
" def get_idx(v):\n",
" # Generate a unique id for a node if it hasn't been seen before\n",
" global i\n",
" if v in v_to_idx:\n",
" return v_to_idx[v]\n",
" else:\n",
" v_to_idx[v] = str(i)\n",
" idx_to_v[str(i)] = v\n",
" i += 1\n",
"\n",
" return v_to_idx[v]\n",
"\n",
" # 1. Create Graph\n",
" for row in data[1:]:\n",
" v1, v2 = row.strip().split(\" \")\n",
"\n",
" notv1 = str(-1*int(v1))\n",
" notv2 = str(-1*int(v2))\n",
"\n",
" v1 = get_idx(v1)\n",
" v2 = get_idx(v2)\n",
" notv1 = get_idx(notv1)\n",
" notv2 = get_idx(notv2)\n",
"\n",
" vertices.add(v1)\n",
" vertices.add(v2)\n",
" vertices.add(notv1)\n",
" vertices.add(notv2)\n",
"\n",
" graph[notv1].append(v2)\n",
" graph[notv2].append(v1)\n",
"\n",
" # 2. Run SCC\n",
" rev_graph = reverse_graph(graph)\n",
" g = dfs_obj(graph, rev_graph, vertices)\n",
" g.run_DFS_LOOP_ITERATIVE()\n",
"\n",
" def check_satisfiable(leaders):\n",
" leader_list = collections.defaultdict(list)\n",
" for l in leaders:\n",
" leader_list[int(idx_to_v[leaders[l]])].append(int(idx_to_v[l]))\n",
"\n",
" # Check whether a node and its complement exist in the same Connected Component\n",
" for l in leader_list:\n",
" items = leader_list[l]\n",
" for i in range(0,len(items)-1):\n",
" for j in range(i,len(items)):\n",
" if items[i] == -items[j]:\n",
" print (\"Not Satisfiable\", items[i])\n",
" return False \n",
" return True\n",
"\n",
" # 3. Test Satisfiability\n",
" sat = check_satisfiable(g.leaders)\n",
" print (\"satisfiable\", sat) \n",
" assert sat == expected[f]\n",
"\n",
"print \"PASS\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
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{
"cell_type": "code",
"execution_count": null,
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"collapsed": true
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"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"pygments_lexer": "ipython3",
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"nbformat": 4,
"nbformat_minor": 2
}
| mit |
probml/pyprobml | notebooks/book1/20/binary_fa_demo.ipynb | 1 | 9471 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "66837348",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import multivariate_normal\n",
"from jax import vmap\n",
"\n",
"\n",
"class BinaryFA:\n",
" def __init__(self, input_dim, latent, max_iter, conv_tol=1e-4, compute_ll=True):\n",
" self.W = 0.1 * np.random.randn(latent, input_dim) # 2x16\n",
" self.b = 0.01 * np.random.randn(input_dim, 1) # 16x1\n",
" self.mu_prior = np.zeros((latent, 1)) # 2x1\n",
" self.sigma_prior = np.eye(latent) # 2x2\n",
" self.input_dim = input_dim\n",
" self.latent = latent\n",
" self.max_iter = max_iter\n",
" self.compute_ll = compute_ll\n",
" if compute_ll:\n",
" self.ll_hist = np.zeros((max_iter + 1, 1)) # 51x1\n",
"\n",
" def variational_em(self, data):\n",
" ll_hist = np.zeros((self.max_iter + 1, 1))\n",
" i = 0\n",
" while i < 3:\n",
" S1, S2, ll = self.estep(data)\n",
" ll_hist[i, 0] = ll\n",
" self.mstep(S1, S2)\n",
" if i != 0:\n",
" delta_fval = abs(ll_hist[i] - ll_hist[i - 1])\n",
" avg_fval = (abs(ll_hist[i]) + abs(ll_hist[i - 1]) + np.finfo(float).eps) / 2\n",
" if (delta_fval / avg_fval) < conv_tol:\n",
" break\n",
" i += 1\n",
" return ll_hist[:i]\n",
"\n",
" def estep(self, data):\n",
" S1 = np.zeros((self.latent + 1, self.input_dim)) # 3x16\n",
" S2 = np.zeros((self.latent + 1, self.latent + 1, self.input_dim)) # 3x3x16\n",
" W, b, mu_prior = self.W, self.b, self.mu_prior\n",
" ll = 0\n",
" for i in range(data.T.shape[1]):\n",
" mu_post, sigma_post, logZ, lambd = self.compute_latent_posterior_statistics(data.T[:, i], max_iter=3)\n",
" ll += logZ\n",
" EZZ = np.zeros((self.latent + 1, self.latent + 1))\n",
" EZZ[: self.latent, : self.latent] = sigma_post + np.outer(mu_post, mu_post)\n",
" EZZ[self.latent, : self.latent] = mu_post.T\n",
" EZZ[: self.latent, self.latent] = np.squeeze(np.asarray(mu_post))\n",
" EZZ[self.latent, self.latent] = 1\n",
" EZ = np.append(mu_post, np.ones((1, 1)))\n",
" for j in range(self.input_dim):\n",
" S1[:, j] = S1[:, j] + (data.T[j, i] - 0.5) * EZ\n",
" S2[:, :, j] = S2[:, :, j] - 2 * lambd[j] * EZZ\n",
" return S1, S2, ll\n",
"\n",
" def mstep(self, S1, S2):\n",
" for i in range(self.input_dim):\n",
" what = np.linalg.lstsq(S2[:, :, i], S1[:, i])[0]\n",
" self.W[:, i] = what[: self.latent]\n",
" self.b[i] = what[self.latent]\n",
"\n",
" def compute_latent_posterior_statistics(self, y, output=[0, 0, 0, 0], max_iter=3):\n",
" W, b = np.copy(self.W), np.copy(self.b)\n",
" y = y.reshape((-1, 1))\n",
" # variational parameters\n",
" mu_prior = self.mu_prior\n",
" xi = (2 * y - 1) * (W.T @ mu_prior + b)\n",
" xi[xi == 0] = 0.01 * np.random.rand(np.count_nonzero(xi == 0)) # 16x1\n",
" sigma_inv, iter = np.linalg.inv(self.sigma_prior), 0\n",
" for iter in range(max_iter):\n",
" lambd = (0.5 - sigmoid(xi)) / (2 * xi)\n",
" tmp = W @ np.diagflat(lambd) @ W.T # 2x2\n",
" sigma_post = np.linalg.inv(sigma_inv - (2 * tmp))\n",
" tmp = y - 0.5 + 2 * lambd * b\n",
" tmp2 = np.sum(W @ np.diagflat(tmp), axis=1).reshape((2, 1))\n",
" mu_post = sigma_post @ (sigma_inv @ mu_prior + tmp2)\n",
"\n",
" tmp = np.diag(W.T @ (sigma_post + mu_post @ mu_post.T) @ W)\n",
" tmp = tmp.reshape((tmp.shape[0], 1))\n",
" tmp2 = 2 * (W @ np.diagflat(b)).T @ mu_post\n",
" xi = np.sqrt(tmp + tmp2 + b**2)\n",
" logZ = 0\n",
" if self.compute_ll:\n",
" lam = -lambd\n",
" A = np.diagflat(2 * lam)\n",
" invA = np.diagflat(1 / (2 * lam))\n",
" bb = -0.5 * np.ones((y.shape[0], 1))\n",
" c = -lam * xi**2 - 0.5 * xi + np.log(1 + np.exp(xi))\n",
" ytilde = invA @ (bb + y)\n",
" B = W.T\n",
" logconst1 = -0.5 * np.sum(np.log(lam / np.pi))\n",
" logconst2 = 0.5 * ytilde.T @ A @ ytilde - np.sum(c)\n",
" gauss = multivariate_normal.logpdf(\n",
" np.squeeze(np.asarray(ytilde)),\n",
" mean=np.squeeze(np.asarray(B @ mu_prior + b)),\n",
" cov=(invA + B @ sigma_post @ B.T),\n",
" )\n",
" logZ = logconst1 + logconst2 + gauss\n",
" output = [mu_post, sigma_post, logZ, lambd]\n",
" return output\n",
"\n",
" def predict_missing(self, y):\n",
" N, T = y.shape # 150 x 16\n",
" prob_on = np.zeros(y.shape) # 150 x 16\n",
" post_pred = np.zeros((N, T, 2)) # 150 x 16 x 2\n",
" L, p = self.W.shape # 16 x 3\n",
" B = np.c_[np.copy(self.b), self.W.T] # 16 x 3\n",
" for n in range(N):\n",
" mu_post, sigma_post, logZ, lambd = self.compute_latent_posterior_statistics(y[n, :].T, False)\n",
" mu1 = np.r_[np.ones((1, 1)), mu_post]\n",
" sigma1 = np.zeros((L + 1, L + 1))\n",
" sigma1[1:, 1:] = sigma_post\n",
" prob_on[n, :] = sigmoid_times_gauss(B, mu1, sigma1)\n",
"\n",
" return prob_on\n",
"\n",
" def infer_latent(self, y):\n",
" N, T = y.shape\n",
" W, b, mu_prior = self.W, self.b, self.mu_prior\n",
" K, T2 = self.W.shape\n",
" mu_post, loglik = np.zeros((K, N)), np.zeros((1, N))\n",
" sigma_post = np.zeros((K, K, N))\n",
" for n in range(N):\n",
" mu_p, sigma_p, loglik[0, n], _ = self.compute_latent_posterior_statistics(y[n, :].T)\n",
" mu_post[:, n] = np.squeeze(np.asarray(mu_p))\n",
" sigma_post[:, :, n] = np.squeeze(np.asarray(sigma_p))\n",
" return mu_post, sigma_post, loglik\n",
"\n",
"\n",
"def sigmoid_times_gauss(X, wMAP, C):\n",
" vv = lambda x, y: jnp.vdot(x, y)\n",
" mv = vmap(vv, (None, 0), 0)\n",
" mm = vmap(mv, (0, None), 0)\n",
" vm = vmap(vv, (0, 0), 0)\n",
"\n",
" mu = X @ wMAP\n",
" n = X.shape[1]\n",
" if n < 1000:\n",
" sigma2 = np.diag(X @ C @ X.T)\n",
" else:\n",
" sigma2 = vm(X, mm(C, X))\n",
" kappa = 1 / np.sqrt(1 + np.pi * sigma2 / 8)\n",
" p = sigmoid(kappa * mu.reshape(kappa.shape))\n",
" return p\n",
"\n",
"\n",
"np.random.seed(1)\n",
"\n",
"max_iter, conv_tol = 50, 1e-4\n",
"sigmoid = lambda x: 1 / (1 + np.exp(-1 * x))\n",
"d, k, m = 16, 3, 50\n",
"noise_level = 0.5\n",
"\n",
"proto = np.random.rand(d, k) < noise_level\n",
"src = np.concatenate((np.tile(proto[:, 0], (1, m)), np.tile(proto[:, 1], (1, m)), np.tile(proto[:, 2], (1, m))), axis=1)\n",
"clean_data = np.concatenate(\n",
" (np.tile(proto[:, 0], (m, 1)), np.tile(proto[:, 1], (m, 1)), np.tile(proto[:, 2], (m, 1))), axis=0\n",
")\n",
"n = clean_data.shape[0]\n",
"\n",
"\n",
"mask, noisy_data, missing_data, = (\n",
" np.random.rand(n, d) < 0.05,\n",
" np.copy(clean_data),\n",
" np.copy(clean_data),\n",
")\n",
"\n",
"noisy_data[mask] = 1 - noisy_data[mask]\n",
"missing_data[mask] = np.nan\n",
"\n",
"plt.figure()\n",
"ax = plt.gca()\n",
"plt.imshow(noisy_data, aspect=\"auto\", interpolation=\"none\", origin=\"lower\", cmap=\"gray\")\n",
"plt.title(\"Noisy Binary Data\")\n",
"plt.show()\n",
"\n",
"binaryFA = BinaryFA(d, 2, 50, 1e-4, True)\n",
"binaryFA.variational_em(noisy_data)\n",
"\n",
"mu_post, sigma_post, loglik = binaryFA.infer_latent(noisy_data)\n",
"\n",
"symbols = [\"ro\", \"gs\", \"k*\"]\n",
"plt.figure()\n",
"plt.plot(mu_post[0, :m], mu_post[1, 0:m], symbols[0])\n",
"plt.plot(mu_post[0, m : 2 * m], mu_post[1, m : 2 * m], symbols[1])\n",
"plt.plot(mu_post[0, 2 * m :], mu_post[1, 2 * m :], symbols[2])\n",
"plt.title(\"Latent Embedding\")\n",
"plt.show()\n",
"\n",
"prob_on = binaryFA.predict_missing(noisy_data)\n",
"plt.figure()\n",
"plt.imshow(prob_on, aspect=\"auto\", interpolation=\"none\", origin=\"lower\", cmap=\"gray\")\n",
"plt.title(\"Posterior Predictive\")\n",
"plt.show()\n",
"\n",
"plt.figure()\n",
"plt.imshow(prob_on > 0.5, aspect=\"auto\", interpolation=\"none\", origin=\"lower\", cmap=\"gray\")\n",
"plt.title(\"Reconstruction\")\n",
"plt.show()"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
| mit |
aje/POT | notebooks/plot_otda_mapping.ipynb | 1 | 90571 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# OT mapping estimation for domain adaptation\n",
"\n",
"\n",
"This example presents how to use MappingTransport to estimate at the same\n",
"time both the coupling transport and approximate the transport map with either\n",
"a linear or a kernelized mapping as introduced in [8].\n",
"\n",
"[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n",
" \"Mapping estimation for discrete optimal transport\",\n",
" Neural Information Processing Systems (NIPS), 2016.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Remi Flamary <[email protected]>\n",
"# Stanislas Chambon <[email protected]>\n",
"#\n",
"# License: MIT License\n",
"\n",
"import numpy as np\n",
"import matplotlib.pylab as pl\n",
"import ot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate data\n",
"-------------\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_source_samples = 100\n",
"n_target_samples = 100\n",
"theta = 2 * np.pi / 20\n",
"noise_level = 0.1\n",
"\n",
"Xs, ys = ot.datasets.get_data_classif(\n",
" 'gaussrot', n_source_samples, nz=noise_level)\n",
"Xs_new, _ = ot.datasets.get_data_classif(\n",
" 'gaussrot', n_source_samples, nz=noise_level)\n",
"Xt, yt = ot.datasets.get_data_classif(\n",
" 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n",
"\n",
"# one of the target mode changes its variance (no linear mapping)\n",
"Xt[yt == 2] *= 3\n",
"Xt = Xt + 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot data\n",
"---------\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fb0178b1208>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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TqlUr1q1bx/bt22nTpg0ul4vx48cTDpduIajI22+/TSQSYdWqVaxevbpU8jRo0CCefPLJ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb019d04d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pl.figure(1, (10, 5))\n",
"pl.clf()\n",
"pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n",
"pl.legend(loc=0)\n",
"pl.title('Source and target distributions')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate the different transport algorithms and fit them\n",
"-----------------------------------------------------------\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"It. |Loss |Delta loss\n",
"--------------------------------\n",
" 0|4.210546e+03|0.000000e+00\n",
" 1|4.194392e+03|-3.836611e-03\n",
" 2|4.194053e+03|-8.094371e-05\n",
" 3|4.193924e+03|-3.056965e-05\n",
" 4|4.193849e+03|-1.797118e-05\n",
" 5|4.193801e+03|-1.140070e-05\n",
" 6|4.193776e+03|-5.930168e-06\n",
"It. |Loss |Delta loss\n",
"--------------------------------\n",
" 0|4.245881e+02|0.000000e+00\n",
" 1|4.181680e+02|-1.512078e-02\n",
" 2|4.178974e+02|-6.472597e-04\n",
" 3|4.177550e+02|-3.406786e-04\n",
" 4|4.176586e+02|-2.307406e-04\n",
" 5|4.175879e+02|-1.692203e-04\n",
" 6|4.175338e+02|-1.295518e-04\n",
" 7|4.174909e+02|-1.028089e-04\n",
" 8|4.174570e+02|-8.123852e-05\n",
" 9|4.174309e+02|-6.257777e-05\n",
" 10|4.174083e+02|-5.401101e-05\n"
]
}
],
"source": [
"# MappingTransport with linear kernel\n",
"ot_mapping_linear = ot.da.MappingTransport(\n",
" kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n",
" max_iter=20, verbose=True)\n",
"\n",
"ot_mapping_linear.fit(Xs=Xs, Xt=Xt)\n",
"\n",
"# for original source samples, transform applies barycentric mapping\n",
"transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n",
"\n",
"# for out of source samples, transform applies the linear mapping\n",
"transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n",
"\n",
"\n",
"# MappingTransport with gaussian kernel\n",
"ot_mapping_gaussian = ot.da.MappingTransport(\n",
" kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n",
" max_iter=10, verbose=True)\n",
"ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n",
"\n",
"# for original source samples, transform applies barycentric mapping\n",
"transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n",
"\n",
"# for out of source samples, transform applies the gaussian mapping\n",
"transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot transported samples\n",
"------------------------\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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7eZHN9B+Lx6idWsXerQ1oGGadLSrrKvDTATMWTGX1P9cRK4pRXlWKBiF+KqS8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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb0177ef7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pl.figure(2)\n",
"pl.clf()\n",
"pl.subplot(2, 2, 1)\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
" label='Target samples', alpha=.2)\n",
"pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n",
" label='Mapped source samples')\n",
"pl.title(\"Bary. mapping (linear)\")\n",
"pl.legend(loc=0)\n",
"\n",
"pl.subplot(2, 2, 2)\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
" label='Target samples', alpha=.2)\n",
"pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n",
" c=ys, marker='+', label='Learned mapping')\n",
"pl.title(\"Estim. mapping (linear)\")\n",
"\n",
"pl.subplot(2, 2, 3)\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
" label='Target samples', alpha=.2)\n",
"pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n",
" marker='+', label='barycentric mapping')\n",
"pl.title(\"Bary. mapping (kernel)\")\n",
"\n",
"pl.subplot(2, 2, 4)\n",
"pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n",
" label='Target samples', alpha=.2)\n",
"pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n",
" marker='+', label='Learned mapping')\n",
"pl.title(\"Estim. mapping (kernel)\")\n",
"pl.tight_layout()\n",
"\n",
"pl.show()"
]
}
],
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
Pyomo/PyomoGallery | asl_io/asl_io.ipynb | 1 | 18212 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading ASL Results into a Model\n",
"\n",
"## Summary\n",
"\n",
"In this scripting example we break apart the work flow that occurs when a Pyomo model is solved using the ASL solver plugin. The ASL solver plugin is a generic interface designed for solvers that utilize the AMPL Solver Library. This library takes model input in the form of an NL file and provides a solver solution in the form of an SOL file. As such, it provides a single unifying framework for interacting with a wide array of optimization solvers.\n",
"\n",
"Pyomo includes separate tools for writing NL files and reading SOL files. In this example, we will show how to use these tools directly, as an alternative to calling the ASL solver plugin. In particular, we show how to save information about the symbol map created by the NL writer to a file so that it can be recovered at a later time. The symbol map that is recovered can be used to load a solution from the SOL file reader into any Pyomo model with component names that match those on the model used by the NL writer.\n",
"\n",
"## Solving With ASL\n",
"\n",
"Consider the case below where we solve a simple Pyomo model using Ipopt through the ASL solver plugin and then verify that the solver termination condition is optimal before loading the solution into the model. Note that this example assumes Pyomo version 4.1 or later is installed. Since Pyomo 4.1, the **_load_\\__solutions_** keyword must be assigned a value of _False_ when calling the _solve_ method on a solver plugin in order to prevent the solution from being automatically loaded into the model. This allows us to check the solver termination condition before manually loading the solution via the call to _model.solutions.load_\\__from_."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Objective: 0.9999999925059035\n"
]
}
],
"source": [
"# %load script.py\n",
"from pyomo.environ import *\n",
"from pyomo.opt import SolverFactory, TerminationCondition\n",
"\n",
"def create_model():\n",
" model = ConcreteModel()\n",
" model.x = Var()\n",
" model.o = Objective(expr=model.x)\n",
" model.c = Constraint(expr=model.x >= 1)\n",
" model.x.set_value(1.0)\n",
" return model\n",
"\n",
"if __name__ == \"__main__\":\n",
"\n",
" with SolverFactory(\"ipopt\") as opt:\n",
" model = create_model()\n",
" results = opt.solve(model, load_solutions=False)\n",
" if results.solver.termination_condition != TerminationCondition.optimal:\n",
" raise RuntimeError('Solver did not report optimality:\\n%s'\n",
" % (results.solver))\n",
" model.solutions.load_from(results)\n",
" print(\"Objective: %s\" % (model.o()))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The basic work flow that takes place above can be summarized as:\n",
" 1. Create an ASL solver plugin that uses the _ipopt_ executable appearing in the shell search PATH.\n",
" 2. Construct a Pyomo model.\n",
" 3. Solve the Pyomo model.\n",
" 1. Output the Pyomo model as an NL file.\n",
" 2. Invoke the solver (which produces an SOL file).\n",
" 3. Read the SOL file into a Pyomo results object.\n",
" 4. Check the solver termination condition stored in the results object.\n",
" 5. Load the solution stored in the results object into the Pyomo model.\n",
"\n",
"The remainder of this example shows how to implement step 3 without the use of the ASL solver plugin.\n",
"\n",
"### A note about using the **_with_** statement\n",
"\n",
"In the code provided with this example we make use of Python's **_with_** statement when dealing with objects returned from Pyomo _Factory_ functions such as SolverFactory and ReaderFactory. Pyomo makes use of a Plugin system to instantiate these objects. As a result, they must be deactivated before going out of scope in order to prevent a memory leak. Deactivation of Plugins is managed automatically by the **_with_** statement, but can also be done by calling the _deactivate_ method directly on the Plugin object.\n",
"\n",
"## Writing the NL File\n",
"\n",
"The code block below defines the function **_write_\\__nl_** that outputs a Pyomo model as an NL file and saves the pertinent symbol map data to a file using pickle. This symbol map data will allow a solution stored in an SOL file to be loaded into any Pyomo model with matching component names. The last section of this code block shows how this function can be used with a small example model."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" NL File: example.nl\n",
"Symbol Map File: example.nl.symbol_map.pickle\n"
]
}
],
"source": [
"# %load write.py\n",
"import pyomo.environ\n",
"from pyomo.core import ComponentUID\n",
"from pyomo.opt import ProblemFormat\n",
"# use fast version of pickle (python 2 or 3)\n",
"from six.moves import cPickle as pickle\n",
"\n",
"def write_nl(model, nl_filename, **kwds):\n",
" \"\"\"\n",
" Writes a Pyomo model in NL file format and stores\n",
" information about the symbol map that allows it to be\n",
" recovered at a later time for a Pyomo model with\n",
" matching component names.\n",
" \"\"\"\n",
" symbol_map_filename = nl_filename+\".symbol_map.pickle\"\n",
"\n",
" # write the model and obtain the symbol_map\n",
" _, smap_id = model.write(nl_filename,\n",
" format=ProblemFormat.nl,\n",
" io_options=kwds)\n",
" symbol_map = model.solutions.symbol_map[smap_id]\n",
"\n",
" # save a persistent form of the symbol_map (using pickle) by\n",
" # storing the NL file label with a ComponentUID, which is\n",
" # an efficient lookup code for model components (created\n",
" # by John Siirola)\n",
" tmp_buffer = {} # this makes the process faster\n",
" symbol_cuid_pairs = tuple(\n",
" (symbol, ComponentUID(var_weakref(), cuid_buffer=tmp_buffer))\n",
" for symbol, var_weakref in symbol_map.bySymbol.items())\n",
" with open(symbol_map_filename, \"wb\") as f:\n",
" pickle.dump(symbol_cuid_pairs, f)\n",
"\n",
" return symbol_map_filename\n",
"\n",
"if __name__ == \"__main__\":\n",
" from script import create_model\n",
"\n",
" model = create_model()\n",
" nl_filename = \"example.nl\"\n",
" symbol_map_filename = write_nl(model, nl_filename)\n",
" print(\" NL File: %s\" % (nl_filename))\n",
" print(\"Symbol Map File: %s\" % (symbol_map_filename))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first argument to this function is the Pyomo model. The second argument is the name to use for the NL file. Along with the NL file, another file with the suffix \".symbol_map.pickle\" will be created that contains information that can be used to efficiently rebuild the symbol map for any Pyomo model with component names matching those used to build the NL file. Additional options can be passed to the NL writer as keywords to this function. These include:\n",
"* **show_section_timing**: Print timing after writing major sections of the NL file. (default=_False_) \n",
"* **skip_trivial_constraints**: Skip writing constraints whose body section is fixed (i.e., no variables). (default=_False_)\n",
"* **file_determinism**: Sets the level of effort placed on ensuring the NL file is written deterministically. The value of this keyword will affect the row and column ordering assigned to Pyomo constraints and variables in the NLP matrix, respectively.\n",
" * 0: declaration order only \n",
" * 1: sort index sets of indexed components after declaration order (default)\n",
" * 2: sort component names (overriding declaration order) as well as index sets\n",
"* **symbolic_solver_labels**: Generate .row and .col files identifying constraint and variable indices in the NLP matrix. (default=_False_)\n",
"* **include_all_variable_bounds**: Include all variables that are on active blocks of the Pyomo model in the bounds section of the NL file. This includes variables that do not appear in any objective or constraint expressions. (default=_False_)\n",
"* **output_fixed_variable_bounds**: Allow variables that are fixed to appear in the body of preprocessed expressions. Fixing takes place by using a variable's current value as the upper and lower bound in the bounds section of the NL file. This option is experimental. (default=_False_)\n",
"\n",
"The **symbolic_solver_labels** option, when set to _True_, outputs files containing similar information to what is output by this function to recover the symbol map. The difference is that this function outputs component lookup codes (the ComponentUID class) that are meant to allow efficient recovery of components on models that make use of index Sets and/or Blocks. The .row and .col files are meant as debugging tools and use human readable names that are not efficient for recovering model components.\n",
"\n",
"## Invoking the Solver\n",
"\n",
"The solver can be invoked directly from the command shell or by using Python's built-in utilities for executing shell commands. For most ASL-based solvers, we need to use an additional command-line option such as \"-s\" (before the input file) or \"-AMPL\" (after the input file) in order to tell the AMPL Solver Library we want it to store the solution into an SOL file. The code block below issues a bash command that uses the _ipopt_ executable to solve our example model and generate an SOL file. This command requires that the _ipopt_ executable can be found in the shell search PATH and that the code block from the previous section has been executed."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"******************************************************************************\n",
"This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
" Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
" For more information visit http://projects.coin-or.org/Ipopt\n",
"******************************************************************************\n",
"\n",
"This is Ipopt version 3.12.3, running with linear solver ma27.\n",
"\n",
"Number of nonzeros in equality constraint Jacobian...: 0\n",
"Number of nonzeros in inequality constraint Jacobian.: 1\n",
"Number of nonzeros in Lagrangian Hessian.............: 0\n",
"\n",
"Total number of variables............................: 1\n",
" variables with only lower bounds: 0\n",
" variables with lower and upper bounds: 0\n",
" variables with only upper bounds: 0\n",
"Total number of equality constraints.................: 0\n",
"Total number of inequality constraints...............: 1\n",
" inequality constraints with only lower bounds: 1\n",
" inequality constraints with lower and upper bounds: 0\n",
" inequality constraints with only upper bounds: 0\n",
"\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
" 0 1.0000000e+00 0.00e+00 0.00e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
" 1 1.0001504e+00 0.00e+00 1.50e-09 -3.8 9.85e-03 - 1.00e+00 1.00e+00h 1\n",
" 2 1.0000018e+00 0.00e+00 1.84e-11 -5.7 1.49e-04 - 1.00e+00 1.00e+00f 1\n",
" 3 9.9999999e-01 0.00e+00 2.51e-14 -8.6 1.84e-06 - 1.00e+00 1.00e+00f 1\n",
"\n",
"Number of Iterations....: 3\n",
"\n",
" (scaled) (unscaled)\n",
"Objective...............: 9.9999999250590355e-01 9.9999999250590355e-01\n",
"Dual infeasibility......: 2.5091040356528538e-14 2.5091040356528538e-14\n",
"Constraint violation....: 0.0000000000000000e+00 0.0000000000000000e+00\n",
"Complementarity.........: 2.5059035957398297e-09 2.5059035957398297e-09\n",
"Overall NLP error.......: 2.5059035957398297e-09 2.5059035957398297e-09\n",
"\n",
"\n",
"Number of objective function evaluations = 4\n",
"Number of objective gradient evaluations = 4\n",
"Number of equality constraint evaluations = 0\n",
"Number of inequality constraint evaluations = 4\n",
"Number of equality constraint Jacobian evaluations = 0\n",
"Number of inequality constraint Jacobian evaluations = 4\n",
"Number of Lagrangian Hessian evaluations = 3\n",
"Total CPU secs in IPOPT (w/o function evaluations) = 0.001\n",
"Total CPU secs in NLP function evaluations = 0.000\n",
"\n",
"EXIT: Optimal Solution Found.\n",
" \n",
"Ipopt 3.12.3: Optimal Solution Found\n"
]
}
],
"source": [
"%%bash\n",
"ipopt -s example.nl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading the SOL File\n",
"\n",
"The code block below defines the function **_read_\\__sol_** that produces a results object that can be loaded into a Pyomo model from a given SOL file. The function first calls Pyomo's built-in SOL file reader to produce a bare results object. Symbols used by the SOL file reader take the form < type character >< index >, where < type character > is one of 'o' (objective), 'v' (variable), or 'c' (constraint) and < index > is the row / column index for constraints / variables in the NLP matrix and 0 for the objective (e.g., 'o0', 'v3', 'c1'). These symbols are mapped to component identifiers in the symbol map file created by the **_write_\\__nl_** function from above. The results object returned from the **_read_\\__sol_** function can be loaded into a Pyomo model just like that returned from the _solve_ method on a Pyomo solver plugin when the **_load_\\__solutions_** keyword is set to _False_. The last section of this code block shows how this function can be used to load a solution into a _copy_ of the model used in the section on writing the NL file. It assumes the code blocks in the previous two sections have been executed."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Objective: 0.9999999925059035\n"
]
}
],
"source": [
"# %load read.py\n",
"import pyomo.environ\n",
"from pyomo.core import SymbolMap\n",
"from pyomo.opt import (ReaderFactory,\n",
" ResultsFormat)\n",
"# use fast version of pickle (python 2 or 3)\n",
"from six.moves import cPickle as pickle\n",
"\n",
"def read_sol(model, sol_filename, symbol_map_filename, suffixes=[\".*\"]):\n",
" \"\"\"\n",
" Reads the solution from the SOL file and generates a\n",
" results object with an appropriate symbol map for\n",
" loading it into the given Pyomo model. By default all\n",
" suffixes found in the NL file will be extracted. This\n",
" can be overridden using the suffixes keyword, which\n",
" should be a list of suffix names or regular expressions\n",
" (or None).\n",
" \"\"\"\n",
" if suffixes is None:\n",
" suffixes = []\n",
"\n",
" # parse the SOL file\n",
" with ReaderFactory(ResultsFormat.sol) as reader:\n",
" results = reader(sol_filename, suffixes=suffixes)\n",
"\n",
" # regenerate the symbol_map for this model\n",
" with open(symbol_map_filename, \"rb\") as f:\n",
" symbol_cuid_pairs = pickle.load(f)\n",
" symbol_map = SymbolMap()\n",
" symbol_map.addSymbols((cuid.find_component(model), symbol)\n",
" for symbol, cuid in symbol_cuid_pairs)\n",
"\n",
" # tag the results object with the symbol_map\n",
" results._smap = symbol_map\n",
"\n",
" return results\n",
"\n",
"if __name__ == \"__main__\":\n",
" from pyomo.opt import TerminationCondition\n",
" from script import create_model\n",
"\n",
" model = create_model()\n",
" sol_filename = \"example.sol\"\n",
" symbol_map_filename = \"example.nl.symbol_map.pickle\"\n",
" results = read_sol(model, sol_filename, symbol_map_filename)\n",
" if results.solver.termination_condition != \\\n",
" TerminationCondition.optimal:\n",
" raise RuntimeError(\"Solver did not terminate with status = optimal\")\n",
" model.solutions.load_from(results)\n",
" print(\"Objective: %s\" % (model.o()))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-2-clause |
Ruediger-Braun/compana16 | ak_18/bsp-buchberger.ipynb | 1 | 15686 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ausgeführtes Beispiel für den Buchberger-Algorithmus"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sympy import *\n",
"init_printing()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zuerst wird ein Polynomring geschaffen. `QQ` ist $\\mathbb Q$. Alternative Termordnungen sind `grlex` und `grevlex`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"P, erzeuger = xring('x,y', QQ, lex)\n",
"x, y = erzeuger"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def S_poly(p, q):\n",
" kgv = p.leading_monom().lcm(q.leading_monom())\n",
" t1 = kgv / p.leading_term() * p\n",
" t2 = kgv / q.leading_term() * q\n",
" return t1 - t2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[x*y**2 - x, x - y**3]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f1 = x*y**2-x\n",
"f2 = x - y**3\n",
"G = [f1, f2]\n",
"G"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-x + y**5"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = S_poly(f1, f2)\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([0, -1], y**5 - y**3)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.div(G)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"y**5 - y**3"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f3 = s.div(G)[1]\n",
"f3"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAAoAAAAOBAMAAADkjZCYAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAiXZmMs1UEN0i77ur\nRJlR0qN3AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAUUlEQVQIHWNgYFQWYWBgCGOomMDAvICBMYCB\n+wAD23cG/gMMvN8Y6h8w8H5imC/AwAIkHzCwfISKAGXZvjFwb2Bg/g7VxdDGUOXAwFCodIQBAG3H\nFgUteuAKAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$0$$"
],
"text/plain": [
"0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"G.append(f3)\n",
"S_poly(f1, f3)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"x*y**3 - y**8"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s = S_poly(f2, f3)\n",
"s"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([y, y, -y**3 - y], 0)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.div(G)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[x*y**2 - x, x - y**3, y**5 - y**3]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"G"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Es gibt auch zwei Funktionen, welche die reduzierte Gröbner-Basis direkt bestimmen. Zuerst die Funktion für den Fall, dass die Variablen wie oben einem vorab definierten Polynomring angehören."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sympy.polys import groebnertools as gt"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[x - y**3, y**5 - y**3]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gt.groebner(G, P)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Die zweite Funktion akzeptiert Polynome aus Symbolen. Hier muss man die Termordnung explizit angeben. Wenn alle Koeffizienten ganz sind, arbeitet der Algorithmus über $\\mathbb Z$; eine Gröbner-Basis über $\\mathbb Z$ ist i.a. über $\\mathbb Q$ nicht reduziert. Diese Funktion ist durch den allgemeinen import in der ersten Zeile bereits geladen."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAALQAAAAcBAMAAAA6rIiCAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nQzf0D13wm87uiPDlAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\left ( 3 x y^{2} - x, \\quad x - y^{3}\\right )$$"
],
"text/plain": [
"⎛ 2 3⎞\n",
"⎝3⋅x⋅y - x, x - y ⎠"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = Symbol('x')\n",
"y = Symbol('y')\n",
"f1 = 3*x*y**2-x\n",
"f2 = x - y**3\n",
"f1, f2"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAAWBAMAAADA7BwNAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHa7q5lmIonvRN3N\nVDIpFvc5AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAIuUlEQVRYCe1Zf2xbVxX+nh3nOc+xY6WtWOkf\n8cxStaOQrAtshU3xUlYEq0S2td0YZTMVK6ChzUKUDUbJIxSpyira7mfpNNUKTKiqRDJUWCg/agQD\nNsrmSUgT/FPzY2L/TEm70nbZQvnOvff5PT+/NFEa4J8dte+ce853zzn38/V91y3wjrzDwP+QAeff\nUcXGalHeSF9Ugihf5OT5Oe0i1rw4P+hior54bNGyJU5FpborG+WN9EUloO8VBbbec+MW2w3NW/Jk\nyKGGn71w46ary8FIS68ZLQFGgoHZbHv3t6o65luR2N9mXzXAyDCdD+y+fs6if38scna97UC0bVAN\n2t7s29xfAYyOmj9LUp0gdrzWmLSlJOPVRTifLAYiynwu7FDjR4BEwxerpWhwv5tzwRq4A+mzYcuk\naFSHLpxvdIRH9iBOFuf8aGfC09S43nYgOqnYwFgV1k+zMDpzLoCom9FJTYL76jAa9FkV6iXP8HGX\nrkDLk8hEGRLk/MuDBHXr4DxZ7qniLT3Rt4KJ6vbvB5p6qseUEZtBx/hcLDuRx0BjIjPapfWngfYn\naGqdLJhoUM2S1CS4NQgV3zD/Ps/PDUeCEbGjE8X4ybaeDmNlnCIl8zoxDpfxtk7gW1EJUYj0BpzO\nQXRU5iqaHA/MuLj5Sx0uAP01mlqnxAzLLElNgoeDcPEt5wGgNu2rwYjY0Yni+zhH9n6TPEjPRVj+\nm5qQyYnyTgzP0jEFAPRAngXjCSsfgXmcGO2D4fmzje2KjtSQmBZL669rZ+MzOqlJYAdPVOVrL2NI\nfdjvwugNo3mSm78DuCx/G9oHuqvOrvvztwDWcHfJueLd+RzQUUDmaBb27bfsF+y96GQcwytr6KYm\ny51XfG0VcXVh3CYUf1KedEHUCleeItqSmNWdw1ezNDRQnhu23clnXSIQsCb8j9av7NXk1B3Dk1X2\n112WpQxkhj8O3TshZnH1/EDJs/cWtSXa9pzCjOZIJQ2Q4iFMgtYpzaCV/+josPIlB7E3p1HuddV1\nsI+hq+Q8iwfQ9SvrXDK+lkMcKdluMv5H2ceTG7Z9mG0fLrUz1Yv4ANzDPMVq8UF8kFlGYLmtP0pV\ndEL1ZDw2hVb3NacAfD7jfp83gx9nDUBZOpZ09qGHp5EC6udVGCoboKgIhP3QzXWWA5V1TZnjPIH+\nLB4sm6VciT+b3gkxiyNqzwkR/23vHYmelkyaGcWRThogJZSA1xfF4GW4J/dzNbe1AnPvyFSP4odI\nufyzvGrdiZNZnH6dhA3VrKewrPZ6quDUgB6+MR+F9TgSvbCn8Fe7uhUYQiaLXzDfCJLZ5CmnpFKr\nh8TjvbC2ju4pA8teWPUC3bGDHkAsHduZqOBqb2DgiI97QOpIxOqyd0z5lU1Nmbq8CPbHDT+hluLi\nPt27QPTiAgXq5slBbXpajYSZ7yiOVNIgKfWJ2mjLQTH4TezN/kC50uM4ROPuE9da1jQNXsE7Ch/b\ntjLL7qzz2a4y+2p5PP8ZiElZx789Vd60UzlYb94BC3wVdj3G2EFGuJeFU5KldseJGscPoyNHxzpC\neJ0RFPDdrFLGUrEse2MqAzTwBN+19VSRiLb9HsumsqQwNWlyOROQ3wpnpH9unPUySuWkbb04gsLi\nnNGHh6d1XJi5weNoooGUUALe3RSDWdk2SjJTOC7G3jLSU9S8Dg/lWAV4Ds4MTgIb5Sym0KRIqCfX\n1ouuGvC9C71IcwlO/xseyzy5c4T4wqvKZI3QXXKiONeuL+J+vsKrCmAsHRvLZuStoQfybKkgOa1w\n5tGEsEuIz3gsByqbmpz2CMgLu7VPS/+fAvnXvUvbanHBAsaerACDtD2t3YoZwxGTBkkx8zy1WtPE\noWwbEbKsbsov8a5R4TY4T8JLfABn5VPn1+2s1eHKmCbh8nviE+UuV1rcCd7r4gXYVTlI9InB3moC\nrAuzsmry2ddaxoGHvuHehAtZj2VtmdhWxPZ5QOVqq6hdWM/EDkKIjlMNLNcr65qcyOWQF3abPCf9\nT8A5Z+ne2bZenKTfo755vxGTYj/J7VD0tXIaZgxHTBogJZygz4JiEGrbyHRnHEMu2XubPyipyS0O\nyDkWw7Qcage4V5M8kZCjSZHrcuYNdFWxMV36A8AlVN32cXTAvP3S2R7EsgI1wuX0ifk0xOsMWvJ1\nOa4RnqViffKB1YF0JXgHr4jHkyYEt2XqlLeXA5W9mrBm+I14paMoazsg2zpR2al7Z9t6cV5yX6/Y\nz8Js0NMmopgRjnTSACn+VG2dTWoGX+Zr/3Llaq0gzX39DxI7VKNnE3hdWAPrNmzgBcKeRrJyb6yC\nZSV9CWzfx3f0VYjnWo8mcQ1iOYzZxXgJvAJ+m7NH+Ku0H1+hVZdEwZH9H7jJbZf7cleFPmV5sU0Y\nK9L2b3L8/fyFkng8aUK0FvV92eIeMZWVqWoqay3uKbiJgl4Kd2C76+re2bZenJfc18+XkPk1h54m\nr5JfMaM4UkkDpPhTtTXdqRh0ZuJTdlG5Ei7f/H23F/9ifv0lh0e4Y7dsp3dVUXa6vaGK7u3KBFpe\nOrN5/fUsO/D+L1+JL618H13bYee3VfX0ESzNd27RmVV6gXZz/wfF2XxrGaln6FKWF1qaD//I37Hr\nZi+odDPi6c0fko8WP+PWM5XF1DW1858DZexeVZalxAaR3lLSvbNts7iGEhy0vPWR6w5xY3ha4pJK\nMXNERkvzkrROirga5G5XMWgNXN5NHkXaa0otwiNVUgtuziRnWbO83OziyTmHRCLIcposGNGm1Aw4\nveBC9aWmWrHQwk3zZvnXoo3oKjdh6XBDzpYKj82LymwIspz0J4qpawacfnhh1qWmGl5Y2ahZ10Tu\n5Z9gbRTYyoW8qWI87Jongix3+lAxdc2A0w8vzLrEVFZlYWWjZi2JZHm0uxYFbg07nXzDP1qEwxzP\nhiDLRR8upq4ZcPrhhVmXmIrXg0UTvk/f+3/4H6k1xxZtBf+tRJ8D/gMEsd3KQQgtcAAAAABJRU5E\nrkJggg==\n",
"text/latex": [
"$$GroebnerBasis([x - y**3, 3*y**5 - y**3], x, y, domain='ZZ', order='lex')$$"
],
"text/plain": [
" ⎛⎡ 3 5 3⎤ ⎞\n",
"GroebnerBasis⎝⎣x - y , 3⋅y - y ⎦, x, y, domain=ℤ, order=lex⎠"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"G = groebner([f1, f2], x, y, order='lex')\n",
"G"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAnAAAAAcBAMAAAATlJ+YAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nghcueXfhscsauQmr5frwf2//lyV14H8JCKzjP9qx6gAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\left [ \\operatorname{Poly}{\\left( x - y^{3}, x, y, domain=\\mathbb{Z} \\right)}, \\quad \\operatorname{Poly}{\\left( 3 y^{5} - y^{3}, x, y, domain=\\mathbb{Z} \\right)}\\right ]$$"
],
"text/plain": [
"[Poly(x - y**3, x, y, domain='ZZ'), Poly(3*y**5 - y**3, x, y, domain='ZZ')]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"G.polys"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Achtung: Die Klasse `Poly` ist wieder eine andere Klasse."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
nswa17/DA.jl | DA2.ipynb | 1 | 652448 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# one to many\n",
"## 中田竜明"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DA"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"include(\"da.jl\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ソースコードはこちら [ソースコード](https://github.com/nswa17/DA_alg.jl/blob/master/da.jl)\n",
"\n",
"実行例:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"([1,2],[1,2],[1,2,3])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DA.call_match([1 2; 2 1; 0 0], [1 2; 2 1; 0 0], [1, 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"引数２つだと1 to 1バージョンが呼ばれます."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"([1,2],[1,2])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DA.call_match([1 2; 2 1; 0 0], [1 2; 2 1; 0 0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"stableかどうかチェックする関数も中身を変更しました."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"false"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DA.stable_matching([1, 2], [1, 2], [1, 2, 3], [2 1; 1 2; 0 0], [2 1; 1 2; 0 0])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"false"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DA.stable_matching([1, 2], [1, 2], [2 1; 1 2; 0 0], [2 1; 1 2; 0 0])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"true"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DA.stable_matching([2, 1], [1, 2], [1, 2, 3], [2 1; 1 2; 0 0], [2 1; 1 2; 0 0])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"true"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DA.stable_matching([2, 1], [1, 2], [2 1; 1 2; 0 0], [2 1; 1 2; 0 0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"テストを行います"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Summary: | Pass Total\n",
"Testing da.jl | 10 10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: replacing module DA\n"
]
}
],
"source": [
"deferred_acceptance = DA.call_match\n",
"include(\"test_deferred_acceptance.jl\")\n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"通ったようです."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"またスピードを計測してみます. まずは応募が実質1 to 1となるケースにおいて, 様々な人数に対して1tomanyと1to1のスピードを計測します."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"one2one_times = Float64[]\n",
"one2many_times = Float64[]\n",
"for i in 1:20\n",
" m = i * 100\n",
" n = i * 100\n",
" m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
" caps = ones(Int, n)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs, caps)\n",
" push!(one2many_times, elapsedtime)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs)\n",
" push!(one2one_times, elapsedtime)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"using PyPlot"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31cfe3ac8>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"PyObject <matplotlib.legend.Legend object at 0x32741b940>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot(collect(1:100:2000), one2one_times, label=\"1 to 1\")\n",
"plot(collect(1:100:2000), one2many_times, label=\"one to many\")\n",
"PyPlot.xlabel(\"students num (= colleges num)\")\n",
"legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"マッチングの人数が2000以下の時にはほぼ等しいようです. つぎは人数を固定してそれぞれのアルゴリズムを計測します. まずは300人のとき,"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: using Matching.deferred_acceptance in module Main conflicts with an existing identifier.\n"
]
}
],
"source": [
"using Matching"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"speedtest_plot (generic function with 1 method)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function speedtest_plot(m, n)\n",
" one2one_times = Float64[]\n",
" one2many_times = Float64[]\n",
" for i in 1:20\n",
" m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
" caps = ones(Int, n)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs, caps)\n",
" push!(one2many_times, elapsedtime)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs)\n",
" push!(one2one_times, elapsedtime)\n",
" end\n",
" plot(one2one_times, label=\"1 to 1\")\n",
" plot(one2many_times, label=\"one to many\")\n",
" PyPlot.xlabel(\"test No.\")\n",
" legend()\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x3278e95c0>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"PyObject <matplotlib.legend.Legend object at 0x323da5e48>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"speedtest_plot(300, 300)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"one2oneのアルゴリズムのほうが少しだけ早いみたいです..? 次は4000人の時,"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x323db0f60>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"PyObject <matplotlib.legend.Legend object at 0x311151828>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"speedtest_plot(4000, 4000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"one2oneのほうが少し早いようです."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"次はone2manyの受け入れ側の数を固定して計測します."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"speedtest_plot2 (generic function with 1 method)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function speedtest_plot2(ms, n)\n",
" timess = Float64[]\n",
" for m in ms\n",
" times = Float64[]\n",
" s_prefs, c_prefs, caps = random_prefs(m, n, ReturnCaps)\n",
" for i in 1:10\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(s_prefs, c_prefs, caps)\n",
" push!(times, elapsedtime)\n",
" end\n",
" push!(timess, mean(times))\n",
" end\n",
" plot(ms, timess)\n",
" PyPlot.xlabel(\"students\")\n",
" legend()\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31bd1a550>)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"speedtest_plot2(100:200:2000, 100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"生徒側の人数にほぼ比例しているようです."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"今まで定員をオーバーした時に大学にとって一番望ましくない人を弾くのに, 生徒の大学にとってのランキングを格納した[ヒープ](https://ja.wikipedia.org/wiki/%E3%83%92%E3%83%BC%E3%83%97)を使っていたのですが, ヒープを使わない時にはどれくらいの速度なのかを測るために新しいバージョンを作りました.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DA"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"include(\"da.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Summary: | Pass Total\n",
"Testing da.jl | 10 10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: replacing module DA\n"
]
}
],
"source": [
"deferred_acceptance = DA.call_match\n",
"include(\"test_deferred_acceptance.jl\")\n",
"print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"テストに通ったので速度を計測します. 事実上one2one, つまり```caps=ones(Int, n)```の形のマッチングに対して大学側、生徒側の数を等しく100から2000まで変えたものについて計測します."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"one2one_times = Float64[]\n",
"one2many_times = Float64[]\n",
"for i in 1:20\n",
" m = i * 100\n",
" n = i * 100\n",
" m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
" caps = ones(Int, n)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs, caps)\n",
" push!(one2many_times, elapsedtime)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs)\n",
" push!(one2one_times, elapsedtime)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31407fe10>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"PyObject <matplotlib.legend.Legend object at 0x313ed39e8>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using PyPlot\n",
"plot(collect(1:100:2000), one2one_times, label=\"1 to 1\")\n",
"plot(collect(1:100:2000), one2many_times, label=\"one to many\")\n",
"PyPlot.xlabel(\"students num (= colleges num)\")\n",
"legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"one2oneがヒープを使用するone2manyとほぼ等しかったことから, かなり多くなったことがわかると思います..."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Variables:\n",
" m_prefs::Array{Int64,2}\n",
" f_prefs::Array{Int64,2}\n",
" caps::Array{Int64,1}\n",
" m::Int64\n",
" n::Int64\n",
" f_ranks::Array{Int64,2}\n",
" m_pointers::Array{Int64,1}\n",
" f_pointers::Array{Int64,2}\n",
" f_holding::Array{Int64,1}\n",
" m_matched_tf::BitArray{1}\n",
" m_offers::Array{Int64,2}\n",
" ##dims#8343::Tuple{Int64}\n",
" ##dims#8344::Tuple{Int64,Int64}\n",
" ##dims#8345::Tuple{Int64}\n",
" ##args#8346::Tuple{Int64}\n",
" ##dims#8347::Tuple{Int64,Int64}\n",
" ##I#8348::Tuple{}\n",
"\n",
"Body:\n",
" begin # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 10:\n",
" GenSym(0) = (Base.arraysize)(m_prefs::Array{Int64,2},2)::Int64\n",
" m = GenSym(0) # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 11:\n",
" GenSym(1) = (Base.arraysize)(f_prefs::Array{Int64,2},2)::Int64\n",
" n = GenSym(1) # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 13:\n",
" f_ranks = (DA.get_ranks)(f_prefs::Array{Int64,2})::Array{Int64,2} # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 14:\n",
" m_pointers = (Base.fill!)((Base.Array)(DA.Int,m::Int64)::Array{Int64,1},0)::Array{Int64,1} # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 15:\n",
" f_pointers = (Base.fill!)((Base.Array)(DA.Int,m::Int64,n::Int64)::Array{Int64,2},0)::Array{Int64,2} # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 16:\n",
" f_holding = (Base.fill!)((Base.Array)(DA.Int,n::Int64)::Array{Int64,1},0)::Array{Int64,1} # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 18:\n",
" m_matched_tf = (Base.fill!)((Base.BitArray)(m::Int64)::BitArray{1},false)::BitArray{1} # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 19:\n",
" m_offers = (Base.fill!)((Base.Array)(DA.Int,2,(Base.box)(Base.Int,(Base.add_int)(m::Int64,1)))::Array{Int64,2},0)::Array{Int64,2} # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 20:\n",
" (Base.arrayset)(m_offers::Array{Int64,2},1,1,1)::Array{Int64,2} # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 22:\n",
" (DA.da_match)(m::Int64,n::Int64,f_ranks::Array{Int64,2},m_prefs::Array{Int64,2},f_prefs::Array{Int64,2},m_pointers::Array{Int64,1},f_pointers::Array{Int64,2},m_matched_tf::BitArray{1},m_offers::Array{Int64,2},caps::Array{Int64,1},f_holding::Array{Int64,1})::Void # /Users/neon/Desktop/S/zemi/DA_alg.jl/da.jl, line 23:\n",
" return (DA.convert_pointer_to_list)(m::Int64,n::Int64,f_pointers::Array{Int64,2},f_prefs::Array{Int64,2},caps::Array{Int64,1},f_holding::Array{Int64,1})::Tuple{Array{Int64,1},Array{Int64,1},Array{Int64,1}}\n",
" end::Tuple{Array{Int64,1},Array{Int64,1},Array{Int64,1}}\n"
]
}
],
"source": [
"@code_warntype DA.call_match(m_prefs, f_prefs, caps)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"338 task.jl; anonymous; line: 447\n",
" 338 .../IJulia/src/IJulia.jl; eventloop; line: 143\n",
" 338 ...rc/execute_request.jl; execute_request_0x535c5df2; line: 183\n",
" 338 loading.jl; include_string; line: 282\n",
" 337 profile.jl; anonymous; line: 16\n",
" 21 ...zemi/DA_alg.jl/da.jl; call_match; line: 13\n",
" 13 ...zemi/DA_alg.jl/da.jl; get_ranks; line: 45\n",
" 6 multidimensional.jl; _unsafe_getindex; line: 193\n",
" 2 multidimensional.jl; _unsafe_getindex; line: 194\n",
" 2 multidimensional.jl; _unsafe_getindex; line: 195\n",
" 6 ...zemi/DA_alg.jl/da.jl; get_ranks; line: 47\n",
" 2 ...zemi/DA_alg.jl/da.jl; get_ranks; line: 49\n",
" 1 ...zemi/DA_alg.jl/da.jl; call_match; line: 16\n",
" 1 ...a/lib/julia/sys.dylib; call; (unknown line)\n",
" 315 ...zemi/DA_alg.jl/da.jl; call_match; line: 22\n",
" 48 ...emi/DA_alg.jl/da.jl; da_match; line: 156\n",
" 2 ...zemi/DA_alg.jl/da.jl; proceed_pointer!; line: 87\n",
" 10 ...zemi/DA_alg.jl/da.jl; proceed_pointer!; line: 88\n",
" 2 ...zemi/DA_alg.jl/da.jl; proceed_pointer!; line: 89\n",
" 28 ...zemi/DA_alg.jl/da.jl; proceed_pointer!; line: 91\n",
" 1 ...zemi/DA_alg.jl/da.jl; proceed_pointer!; line: 93\n",
" 5 ...zemi/DA_alg.jl/da.jl; proceed_pointer!; line: 94\n",
" 32 ...emi/DA_alg.jl/da.jl; da_match; line: 157\n",
" 1 ...emi/DA_alg.jl/da.jl; create_offers!; line: 102\n",
" 26 ...emi/DA_alg.jl/da.jl; create_offers!; line: 104\n",
" 1 ...emi/DA_alg.jl/da.jl; create_offers!; line: 106\n",
" 4 ...emi/DA_alg.jl/da.jl; create_offers!; line: 107\n",
" 235 ...emi/DA_alg.jl/da.jl; da_match; line: 158\n",
" 2 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 133\n",
" 1 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 134\n",
" 6 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 137\n",
" 1 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 138\n",
" 222 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 139\n",
" 52 array.jl; findmax; line: 837\n",
" 1 array.jl; findmax; line: 838\n",
" 6 array.jl; findmax; line: 839\n",
" 8 array.jl; findmax; line: 841\n",
" 2 array.jl; findmax; line: 844\n",
" 40 multidimensional.jl; _unsafe_getindex; line: 193\n",
" 22 multidimensional.jl; _unsafe_getindex; line: 194\n",
" 84 multidimensional.jl; _unsafe_getindex; line: 195\n",
" 1 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 141\n",
" 2 ...emi/DA_alg.jl/da.jl; decide_to_accept!; line: 148\n"
]
}
],
"source": [
"Profile.clear()\n",
"@profile DA.call_match(m_prefs, f_prefs, caps)\n",
"Profile.print()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 30.673545 seconds (3.04 M allocations: 39.778 GB, 13.88% gc time)\n",
" 3.486124 seconds (69.00 k allocations: 1.492 GB, 11.10% gc time)\n",
" 29.057338 seconds (3.04 M allocations: 39.778 GB, 14.20% gc time)\n",
" 3.467252 seconds (69.00 k allocations: 1.492 GB, 10.56% gc time)\n",
" 27.840235 seconds (3.04 M allocations: 39.778 GB, 14.06% gc time)\n",
" 3.650230 seconds (69.00 k allocations: 1.492 GB, 9.94% gc time)\n"
]
},
{
"data": {
"text/plain": [
"([1673,4791,5677,4357,7571,971,9945,1215,8145,6782 … 2094,6830,5329,8388,5873,6920,9480,889,5601,4409],[0,2802,2047,1823,9309,6860,4617,6915,4647,5482 … 1316,7622,1617,8298,2606,8863,3005,6802,3523,3544])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m, n = 10000, 10000\n",
"m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
"caps = ones(Int, n)\n",
"@time DA.call_match(m_prefs, f_prefs, caps)\n",
"@time DA.call_match(m_prefs, f_prefs)\n",
"@time DA.call_match(m_prefs, f_prefs, caps)\n",
"@time DA.call_match(m_prefs, f_prefs)\n",
"@time DA.call_match(m_prefs, f_prefs, caps)\n",
"@time DA.call_match(m_prefs, f_prefs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"findmaxが遅いみたいなので, その部分を実装しなおしました."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"DA"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"include(\"da.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Summary: | Pass Total\n",
"Testing da.jl | 10 10\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: replacing module DA\n"
]
}
],
"source": [
"deferred_acceptance = DA.call_match\n",
"include(\"test_deferred_acceptance.jl\")\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"one2one_times = Float64[]\n",
"one2many_times = Float64[]\n",
"for i in 1:20\n",
" m = i * 100\n",
" n = i * 100\n",
" m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
" caps = ones(Int, n)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs, caps)\n",
" push!(one2many_times, elapsedtime)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs)\n",
" push!(one2one_times, elapsedtime)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31cf68048>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"PyObject <matplotlib.legend.Legend object at 0x3219ad940>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using PyPlot\n",
"plot(collect(1:100:2000), one2one_times, label=\"1 to 1\")\n",
"plot(collect(1:100:2000), one2many_times, label=\"one to many\")\n",
"PyPlot.xlabel(\"students num (= colleges num)\")\n",
"legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ヒープを使うバージョンとそれほど変わらないようです. もう少しだけ見てみます. 生徒数に対して大学数が少ない時,ヒープを使うバージョンでは,"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: replacing module DA\n"
]
},
{
"data": {
"text/plain": [
"DA"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"include(\"da.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31ea5cac8>)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for k in 1:5\n",
" one2many_times = Float64[]\n",
" for i in 1:30\n",
" m = i * 1000\n",
" n = 10\n",
" m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
" caps = fill(div(m, n), n)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs, caps)\n",
" push!(one2many_times, elapsedtime)\n",
" end\n",
" plot(collect(1000:1000:30000), one2many_times)\n",
"end\n",
"PyPlot.xlabel(\"students num (= colleges num)\")\n",
"legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ヒープを使わないバージョンでは,"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: replacing module DA\n"
]
},
{
"data": {
"text/plain": [
"DA"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"include(\"da.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31dd96ef0>)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for k in 1:5\n",
" one2many_times = Float64[]\n",
" for i in 1:30\n",
" m = i * 1000\n",
" n = 10\n",
" m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
" caps = fill(div(m, n), n)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs, caps)\n",
" push!(one2many_times, elapsedtime)\n",
" end\n",
" plot(collect(1000:1000:30000), one2many_times)\n",
"end\n",
"PyPlot.xlabel(\"students num (= colleges num)\")\n",
"legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"差が出てきました."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"標準ライブラリのヒープの速度が不安だったので自分で実装してみたところ,"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: replacing module DA\n"
]
},
{
"data": {
"text/plain": [
"DA"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"include(\"da.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x324cd86a0>)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"using PyPlot\n",
"for k in 1:5\n",
" one2many_times = Float64[]\n",
" for i in 1:30\n",
" m = i * 1000\n",
" n = 10\n",
" m_prefs, f_prefs = DA.generate_random_preference_data(m, n)\n",
" caps = fill(div(m, n), n)\n",
" _, elapsedtime, _, _, _ = @timed DA.call_match(m_prefs, f_prefs, caps)\n",
" push!(one2many_times, elapsedtime)\n",
" end\n",
" plot(collect(1000:1000:30000), one2many_times)\n",
"end\n",
"PyPlot.xlabel(\"students num (= colleges num)\")\n",
"legend()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"標準ライブラリのヒープと同じか少し遅いようです."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.5",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
nkmk/python-snippets | notebook/pandas_view_copy.ipynb | 1 | 18416 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 3\n",
"1 1 4\n",
"2 2 5\n"
]
}
],
"source": [
"df_homo = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5]})\n",
"print(df_homo)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a int64\n",
"b int64\n",
"dtype: object\n"
]
}
],
"source": [
"print(df_homo.dtypes)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 3\n",
"1 1 4\n"
]
}
],
"source": [
"df_homo_slice = df_homo.iloc[:2]\n",
"print(df_homo_slice)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.shares_memory(df_homo, df_homo_slice))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(df_homo_slice._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 3\n",
"1 1 4\n"
]
}
],
"source": [
"df_homo_list = df_homo.iloc[[0, 1]]\n",
"print(df_homo_list)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.shares_memory(df_homo, df_homo_list))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(df_homo_list._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 True\n",
"1 True\n",
"2 False\n",
"Name: a, dtype: bool\n"
]
}
],
"source": [
"print(df_homo['a'] < 2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 3\n",
"1 1 4\n"
]
}
],
"source": [
"df_homo_bool = df_homo.loc[df_homo['a'] < 2]\n",
"print(df_homo_bool)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.shares_memory(df_homo, df_homo_bool))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(df_homo_bool._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a 0\n",
"b 3\n",
"Name: 0, dtype: int64\n"
]
}
],
"source": [
"s_homo_scalar = df_homo.iloc[0]\n",
"print(s_homo_scalar)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.shares_memory(df_homo, s_homo_scalar))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(s_homo_scalar._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0\n",
"1 1\n",
"2 2\n",
"Name: a, dtype: int64\n"
]
}
],
"source": [
"s_homo_col = df_homo['a']\n",
"print(s_homo_col)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(np.shares_memory(df_homo, s_homo_col))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(s_homo_col._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 3\n",
"1 1 4\n",
"2 2 5\n"
]
}
],
"source": [
"df_homo_col_multi = df_homo[['a', 'b']]\n",
"print(df_homo_col_multi)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.shares_memory(df_homo, df_homo_col_multi))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(df_homo_col_multi._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 100 3\n",
"1 1 4\n",
"2 2 5\n"
]
}
],
"source": [
"df_homo.iat[0, 0] = 100\n",
"print(df_homo)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 100 3\n",
"1 1 4\n"
]
}
],
"source": [
"print(df_homo_slice)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 3\n",
"1 1 4\n"
]
}
],
"source": [
"print(df_homo_list)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 3\n",
"1 1 4\n"
]
}
],
"source": [
"print(df_homo_bool)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a 100\n",
"b 3\n",
"Name: 0, dtype: int64\n"
]
}
],
"source": [
"print(s_homo_scalar)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 100\n",
"1 1\n",
"2 2\n",
"Name: a, dtype: int64\n"
]
}
],
"source": [
"print(s_homo_col)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 3\n",
"1 1 4\n",
"2 2 5\n"
]
}
],
"source": [
"print(df_homo_col_multi)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n",
"2 2 z\n"
]
}
],
"source": [
"df_hetero = pd.DataFrame({'a': [0, 1, 2], 'b': ['x', 'y', 'z']})\n",
"print(df_hetero)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a int64\n",
"b object\n",
"dtype: object\n"
]
}
],
"source": [
"print(df_hetero.dtypes)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n"
]
}
],
"source": [
"df_hetero_slice = df_hetero.iloc[:2]\n",
"print(df_hetero_slice)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.shares_memory(df_hetero, df_hetero_slice))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(df_hetero_slice._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n"
]
}
],
"source": [
"df_hetero_slice2 = df_hetero.iloc[:2, 0:]\n",
"print(df_hetero_slice2)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.shares_memory(df_hetero, df_hetero_slice2))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(df_hetero_slice2._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n"
]
}
],
"source": [
"df_hetero_list = df_hetero.iloc[[0, 1]]\n",
"print(df_hetero_list)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.shares_memory(df_hetero, df_hetero_list))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(df_hetero_list._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n"
]
}
],
"source": [
"df_hetero_bool = df_hetero.loc[df_hetero['a'] < 2]\n",
"print(df_hetero_bool)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(df_hetero_bool._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(df_hetero_bool._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a 0\n",
"b x\n",
"Name: 0, dtype: object\n"
]
}
],
"source": [
"s_hetero_scalar = df_hetero.iloc[0]\n",
"print(s_hetero_scalar)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.shares_memory(df_hetero, s_hetero_scalar))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(s_hetero_scalar._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0\n",
"1 1\n",
"2 2\n",
"Name: a, dtype: int64\n"
]
}
],
"source": [
"s_hetero_col = df_hetero['a']\n",
"print(s_hetero_col)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.shares_memory(df_hetero, s_hetero_col))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(s_hetero_col._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n",
"2 2 z\n"
]
}
],
"source": [
"df_hetero_col_multi = df_hetero[['a', 'b']]\n",
"print(df_hetero_col_multi)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(np.shares_memory(df_hetero, df_hetero_col_multi))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(df_hetero_col_multi._is_view)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 100 x\n",
"1 1 y\n",
"2 2 z\n"
]
}
],
"source": [
"df_hetero.iat[0, 0] = 100\n",
"print(df_hetero)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 100 x\n",
"1 1 y\n"
]
}
],
"source": [
"print(df_hetero_slice)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n"
]
}
],
"source": [
"print(df_hetero_slice2)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n"
]
}
],
"source": [
"print(df_hetero_list)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n"
]
}
],
"source": [
"print(df_hetero_bool)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a 0\n",
"b x\n",
"Name: 0, dtype: object\n"
]
}
],
"source": [
"print(s_hetero_scalar)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 100\n",
"1 1\n",
"2 2\n",
"Name: a, dtype: int64\n"
]
}
],
"source": [
"print(s_hetero_col)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" a b\n",
"0 0 x\n",
"1 1 y\n",
"2 2 z\n"
]
}
],
"source": [
"print(df_hetero_col_multi)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
yandexdataschool/gumbel_lstm | demo_gumbel_sigmoid.ipynb | 1 | 203142 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from gumbel_sigmoid import GumbelSigmoid,GumbelSigmoidLayer\n",
"import theano.tensor as T\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple demo\n",
"* Sample from gumbel-softmax\n",
"* Average over samples"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"temperature = 0.1\n",
"logits = np.linspace(-5,5,10).reshape([1,-1])\n",
"gumbel_sigm = GumbelSigmoid(t=temperature)(logits)\n",
"sigm = T.nnet.sigmoid(logits)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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VLbOJ1xvDFXThW1ikmN2a9JCoVqs0Gg38fj+usIu6PvlmfGePHSNdqZCMJ/HEfDQKVYrF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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe77cdae4d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fe77554ea90>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/nosXL2a7nbNnz1K6dGkqVarEuXPnLrvPZF4J/ejRo3z33XecO3eOUqVKUbZs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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7783877d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.title('gumbel-sigmoid samples')\n",
"for i in range(10):\n",
" plt.plot(range(10),gumbel_sigm.eval()[0],marker='o',alpha=0.25)\n",
"plt.ylim(0,1)\n",
"plt.show()\n",
"\n",
"plt.title('average over samples')\n",
"plt.plot(range(10),np.mean([gumbel_sigm.eval()[0] for _ in range(500)],axis=0),\n",
" marker='o',label='gumbel-sigmoid average')\n",
"\n",
"plt.plot(sigm.eval()[0],marker='+',label='regular softmax')\n",
"plt.legend(loc='best')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoencoder with gumbel-softmax\n",
"\n",
"* We do not use any bayesian regularization, simply optimizer by backprop\n",
"* Hidden layer contains 32 units"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.datasets import load_digits\n",
"X = load_digits().data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import lasagne\n",
"from lasagne.layers import *\n",
"import theano\n",
"\n",
"#graph inputs and shareds\n",
"input_var = T.matrix()\n",
"temp = theano.shared(np.float32(1),'temperature',allow_downcast=True)\n",
"\n",
"#architecture: encoder\n",
"nn = l_in = InputLayer((None,64),input_var)\n",
"nn = DenseLayer(nn,64,nonlinearity=T.tanh)\n",
"nn = DenseLayer(nn,32,nonlinearity=T.tanh)\n",
"\n",
"#bottleneck\n",
"nn = DenseLayer(nn,32,nonlinearity=None)#or nonlinearity=GumbelSigmoid(t=temp)\n",
"bottleneck = nn = GumbelSigmoidLayer(nn,t=temp)\n",
"\n",
"#decoder\n",
"nn = DenseLayer(nn,32,nonlinearity=T.tanh)\n",
"nn = DenseLayer(nn,64,nonlinearity=T.tanh)\n",
"nn = DenseLayer(nn,64,nonlinearity=None)\n",
"\n",
"#loss and updates\n",
"loss = T.mean((get_output(nn)-input_var)**2)\n",
"updates = lasagne.updates.adam(loss,get_all_params(nn))\n",
"\n",
"#compile\n",
"train_step = theano.function([input_var],loss,updates=updates)\n",
"evaluate = theano.function([input_var],loss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training loop\n",
"* We gradually reduce temperature from 1 to 0.01 over time"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"59.725 21.948 18.904 18.790 18.781 18.784 18.793 18.790 18.783 18.792 18.786 18.787 18.790 18.784 18.791 18.788 18.667 18.148 17.381 16.659 16.151 15.521 14.731 13.898 13.334 12.917 12.266 11.772 11.461 11.161 10.893 10.671 10.522 10.318 10.111 9.950 9.789 9.664 9.483 9.422 9.331 9.245 9.124 9.033 8.934 8.815 8.792 8.708 8.667 8.538 8.522 8.443 8.378 8.304 8.257 8.194 8.134 8.064 8.026 7.982 7.854 7.859 7.788 7.801 7.722 7.741 7.664 7.673 7.649 7.556 7.562 7.516 7.518 7.420 7.406 7.353 7.509 7.396 7.412 7.439 7.341 7.372 7.283 7.296 7.242 7.205 7.210 7.364 7.306 7.156 7.266 7.287 7.251 7.124 7.174 7.136 7.171 7.281 7.427 7.363\n"
]
}
],
"source": [
"for i,t in enumerate(np.logspace(0,-2,10000)):\n",
" sample = X[np.random.choice(len(X),32)]\n",
" temp.set_value(t)\n",
" mse = train_step(sample)\n",
" if i %100 ==0:\n",
" print '%.3f'%evaluate(X),"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING (theano.tensor.blas): We did not found a dynamic library into the library_dir of the library we use for blas. If you use ATLAS, make sure to compile it with dynamics library.\n"
]
}
],
"source": [
"#functions for visualization\n",
"get_sample = theano.function([input_var],get_output(nn))\n",
"get_sample_hard = theano.function([input_var],get_output(nn,hard_max=True))\n",
"get_code = theano.function([input_var],get_output(bottleneck,hard_max=False))\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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QFvIWZUXuAsBYU+0G8FpJ90fEmk40BugQ8hZlRe4CmPGmWqyeIOnrnWgI0EHk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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe766aa42d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Mi9V3Sbq0HR0B2oi8RVmRuwDmvNzFqu15ko6X9K32dQdIi7xFWZG7AFA1k3lW\n3yzp5xHxaLs6A7RB3bwdGhoaX69UKurp6elUv1BSETG+/utf/7qdTdXN3eHh4fH1SqXC/IyoKyIm\n5C5QJjM5up0s6bJ2dQRok7p529fXN75QqCIP2+PLfvvt186m6uZub2/v+EKhikZsj3+oIV9QNrky\n1vbOqg70v6q93QHSIW9RVuQu5jLbi2xfYXuD7btsH9HtPqG7cg0DiIhnJL2wzX0BkiJvUVbkLua4\nf5F0TUS83XavpJ273SF010zGrAIAALSN7V0lvSEiTpWkiBiW9ER3e4VuK93AldoLYlqxcePGJHF+\n8pOfJIlz8803J4kjSY8//niyWHPdyMhIkjip8laS7rjjjiRx1qxZkyTO+vXrk8TZtm1bkjgplfmC\nlNHR0SRxUuXunXfemSSOlO54+Ytf/CJJnCeffDJJnFTKnLeS9pX0qO2v2L7V9pdtL+h2p9BdpStW\na6+AbcXg4GCSOD/96U+TxElZrBbxj35ZFe0PvpSuWE2Vc7fddluSOORtWqlyN9UxN2Wx+qMf/ShJ\nnFTF6lNPPZUkTiolL1Z7JR0m6byIOEzSM5I+PnmjFStWjC8DAwMd7iLyGpuFotXZKBgGAAAAiuIB\nSQ9ExC3Zz1dommIVxWd7ws/NFqxJitU8U/6Mjo42nC4jTxzbDbeb/Oa0ul1R5J1uZGyKknpS/c7y\nyBMn1dftM9XofRgZGWm4TZ73Mu8crnlycmzapE5I1Z9U71HKvM3z2hr9/rs5BVCncjfVMbeTeTvW\nXp5typa7eXOuUXupzrynFhEP295s+8CI+JWqs2KkOy2PUnKrXxfYLvX3DSiOiOjopwdyF6mQuyij\nTudtXrYPlnShpHmS7pF0WkQ8UfN8dKrY7vQH0rKdRJupbDjAjF9ky8UqAABAp1CsllezxWrpLrAC\nAADA3EGxCgAAgMKiWAUAAEBhtb1YtX2s7bttb7T9sRbiXGx7q+3bW+zPYts32r7T9h22T28yzk62\n19pen8VZ0WK/emyvs/2dFmJssv2LLM5/tBAnyX2ZbR+U9WVseaLZ97sbUuQueZs7TmFyl7wdj0Pu\n5otD7gLtNnnC1pSLpB5Jg5KWSOqTtF7Sy5uM9QZJh0q6vcU+7SnpkGx9oaRfttCnBdm/vZJ+Kunw\nFvp1pqSvS7q6hRj3Sto9we/tEkl/UfPadk0QsyJpi6TF7cy5VEuq3CVvc8cpZO7O1bzNYpG7+eKQ\nux1eJMVqPgbwAAAINElEQVTo6GhHFkkdXWzP6kVSNPM7b/eZ1WWSBiNiU0QMSbpc0onNBIqINZJa\nvo9oRDwcEeuz9aclbZC0V5Oxns1W56n6h6GpyxNt7yPpOFWn6mj1UsCW9vdz92W+WKrelzlqpgxp\nwZsk3RMRmxPE6oQkuUvezixkSzu3J3fnZN5K5O5MQ7a0M7kL1NXuYnVvSbX/UR7IHisE20tUPXOw\ntsn9K7bXS9oq6bp47o4bM/XPkv5GTR54a4SkH9j+me33NhmjXfdlfpekSxPE6ZTC5u4szFupuLlL\n3iZE7k6L3AXqaHexWthJXG0vVPU2bh/NPu3PWESMRsQhkvaRdLjtVzbRj7dIeiQi1qn1T/jLI+JQ\nSW+W9CHbb2giRq77Ms+E7XmSjpf0rVbidFghc3eW5q1UwNwlb9Mid+sid4E62l2sPihpcc3Pi1X9\npN9VtvskXSnpaxGxutV42dc1N0o6tondXy/pBNv3SrpM0u/b/mqT/diS/fuopFWqfiU4U1Pdl/mw\nZvpT482Sfp71qywKl7uzNW+zvhQxd8nbRMjdhshdoI52F6s/k3SA7SXZJ713Srq6zW3WZduSLpJ0\nV0SsbCHOC20vytbnSzpa1bFYMxIRn4yIxRGxr6pf29wQEe9uoj8LbO+Sre8s6RhJM76KNyIelrTZ\n9oHZQynuy3yyqn8UyqRQuTtb8zbrR1Fzl7xNgNzN1Sdyd4Zsd2QZGRnp6NLui9O6vTSrN2Hu7CAi\nhm1/WNL3Vb1K9aKImPHBRZJsXybpSEkvsL1Z0mci4itNhFou6RRJv7C9LnvsExHxvRnGebGkS2z3\nqFr0fyMirmmiP5M1+9vcQ9Kq6t8F9Ur6ekRc12Ssj0j6evbH7h5JpzUZZ+wA/iZJzY7l6opUuUve\n5lK43J3reSuRuzmRu0AHuJVKFwAAoJNsR6dql9HRFNfg5dfT09PR9rohImY8Vpw7WAEAAKCwKFYB\nAABQWBSrAAAAKCyKVQAAABQWxSoAAAAKi2IVAAAAhUWxCgAACsH2QbbX1SxP2D692/1CdzHPKgAA\nKBzbFVVvIbwsIjbXPM48qyXGPKsAAGC2eJOke2oLVcxNFKsAAKCI3iXp0m53At3HMAAAAFAotuep\nOgTgFRHx6KTnGAZQYs0MA+htR0cAAABa8GZJP59cqI5ZsWLF+Hp/f7/6+/s70yt0BWdWAQBAodi+\nXNK1EXHJFM9xZrXEmjmzSrEKAAAKw/bOku6TtG9EPDXF8xSrJUaxCgAAZjWK1XJj6ioAAADMKhSr\nAAAAKCyKVQAAABQWxSoAAAAKi2IVAAAAhUWxCgAAgMKiWAUAALPawMBAKfbD1ChWAQDArNZs8XjT\nTTd1dD9MjWIVAAAAhdXb7Q4AAABA2nPPPbXXXnvNeL+HHnqoFPvdeuutM95H4narAACgRGxTuJRY\nM7dbpVgFAABAYTFmFQAAAIVFsQoAAIDColgFAACFZHt329fb/pXt62wvmma7TbZ/YXvQ9nbbG21/\nbJptv5A9f5vtQ7PHjrV993T72e63/YTtddnyadsX295q+/Y6/Z+qrbr7TdVW9vhi2zfavtP2HbZP\nz9Nmnv2meX072V5re32234qc7TXcb7rXOK2IYGFhYWFhYWEp3CLpHyX9bbb+MUmfn2a7eyW9UNKg\npCWS+iStl/TySdsdJ+mabP1wST+V1JNjv35JV0967A2SDpV0+zR92qGtnPvt0Fb2+J6SDsnWF0r6\nZc7Xl2e/6dpckP3bm8U6POdrbLTflO1Nt3BmFQAAFNUJki7J1i+R9NY6275G0mBEbIqIIUmXSzpx\nungRsVbSIkl/kGM/SZpwFXtErJH0eJ6+j7Vle48c++3QVhbj4YhYn60/LWmDpMnzR031+iLHftO1\n+Wy2Ok/VQn4052tstN+U7U2HYhUAABTVHhGxNVvfKmmPabYLSV+U9Brb780ee0DS3pO221vS5pqf\nH5D0qikem7xfSHp99lX3NbZfkaPvU7W1T479GrZle4mqZ2fXzqTNOvtN2abtiu31qr7310XELXna\ny7HfjN5PbgoAAAC6xvb1qn5VPdmnan+IiKgzx+pySa9X9czrh2zfXa/JST9PddZvslslLY6IZ22/\nWdJqSQfm2G9yW3nmC63blu2Fkq6Q9NHsTGmuNhvsN2WbETEq6RDbu0paZfuVEXFno/Zy7Dej95Mz\nqwAAoGsi4uiI+J0plqslbbW9pyTZfrGkR6aJsUXSg5JeJGmVpGWSFqt6pq/Wg9njY/aRdOekx3bY\nLyKeGvtqOyKuldRne/cGL22qth5ssE/dtmz3SbpS0tciYnXeNhvt1+j1RcQTkm6UdOxMXuN0+830\n/aRYBQAARXW1pFOz9VNVPQM3ge0FtneR9DNVz84dr+q4zHdm+0+O9+5svyMkbZN0vaQDbC+xPW+q\n/WzvYdvZ+jJVb6r0WI6+T2irZkjDtKZrK3vsIkl3RcTKvG2qWuDX3W+qNiVVnM2+YHu+pKNVfV8b\ntTfSaL+Zvp8MAwAAAEX1eUnftP2XkjZJ+mNJsr2XpC9HxB+qOoTgqmz7iqSXSvqCpIsiYoPt90tS\nRFwQEdfYPs72oKRnJJ0WEcO2Pyzp+6rODLDDfpLeLukDtoclPSvpXbYvk3SkpBfa3izpLFUvJpq2\nrazvdfebqq3stS2XdIqkX9helz32SUkvadBmw/2mafPFki6x3ZO9r9/I4td9P/PsV+c1TonbrQIA\nAKCwGAYAAACAwqJYBQAAQGFRrAIAAKCwKFYBAABQWBSrAAAAKCyKVQAAABQWxSoAAAAKi2IVAAAA\nhfX/ARlfDCxzUHFdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe766125790>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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7aturba+yfXi3c0J3NTQMICKelbRnm3MBkqJuUVTULqa5r0i6ISLea7tX0s7d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\nAAC5RbMKAACA3KJZBQAAQG79fwSg3wZHOIFsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe765e90c50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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77dnJ1qxZkyTO0UcfnSTOW9/61iRxkNbWrVuTxfrMZz6TJM7ZZ5+dJE6q/8nN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\ngML6/w1k5r4V01RYAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe765ba7fd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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0zvDw8FjdPPzww51cVdPa3bZt29jtSqXCyeLR1Pbt25NdBALotuls3U6WdGWn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\nAACFRbMKAACAwvr/18IYlMLevBMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe765842c10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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kZHy+UqmoUuECGWguIsYvpv+zn/2sm001zd3G60zaJnfR1OjoqGq1Wr+7AbRl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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe76555bf90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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7sIj4qbKzYtzb7XGhu9zq4XPbHH9HEhHR0T+PyS5SIbuook7ntijbvynpXyT1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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7651fba90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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TACLiBUn7tHgsQFLkLcqK3MUU9wVJN0XEibanSdq90wNCZ41nzioAAEDL2N5L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\nAAAAKCyKVQAAABQWxSoAAAAK6/8DfsT5IYKTiNYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe765631650>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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99FArh6J2kUx93QJlM5FmdbGkS1qVCNAi49Ztu04sjR1Hd3f30FWEDjnkEK1b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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe765936f90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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AoLAoVgEAAFBYFKsAAAAoLG4JBAAApdKtETBGRka6sp9Ro8PidUtZRhLhzCoAAAAKK8mZ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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe765757b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in range(10):\n",
" X_sample = X[np.random.randint(len(X)),None,:]\n",
" plt.figure(figsize=[12,4])\n",
" plt.subplot(1,4,1)\n",
" plt.title(\"original\")\n",
" plt.imshow(X_sample.reshape([8,8]),interpolation='none',cmap='gray')\n",
" plt.subplot(1,4,2)\n",
" plt.title(\"gumbel\")\n",
" plt.imshow(get_sample(X_sample).reshape([8,8]),interpolation='none',cmap='gray')\n",
" plt.subplot(1,4,3)\n",
" plt.title(\"hard-max\")\n",
" plt.imshow(get_sample_hard(X_sample).reshape([8,8]),interpolation='none',cmap='gray')\n",
" plt.subplot(1,4,4)\n",
" plt.title(\"code\")\n",
" plt.imshow(get_code(X_sample).reshape(8,4),interpolation='none',cmap='gray')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
M-R-Houghton/euroscipy_2015 | scikit_image/lectures/stackoverflow_challenges.ipynb | 1 | 16137 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import division, print_function\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from skimage import (filter as filters, io, color,\n",
" exposure, segmentation, morphology, img_as_float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Snakes\n",
"\n",
"Based on http://stackoverflow.com/questions/8686926/python-image-processing-help-needed-for-corner-detection-in-preferably-pil-or/9173430#9173430\n",
"\n",
"<img src=\"../images/snakes.png\" width=\"200px\" style=\"float: left; padding-right: 1em;\"/>\n",
"\n",
"Consider the zig-zaggy snakes on the left (``../images/snakes.png``). Write some code to find the begin- and end-points of each.\n",
"\n",
"<div style=\"clear: both;\"></div>\n",
"\n",
"*Hints:*\n",
"\n",
"1. Binarize and skeletonize (``morphology.skeletonize``)\n",
"2. Locate corners via convolution (``scipy.signal.convolve2d``)\n",
"3. Find intersections between corners and snakes (``np.logical_and``)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy.signal import convolve2d\n",
"\n",
"img = color.rgb2gray(io.imread('../images/snakes.png'))\n",
"\n",
"# Reduce all lines to one pixel thickness\n",
"snakes = morphology.skeletonize(img < 1)\n",
"\n",
"# Find pixels with only one neighbor\n",
"corners = convolve2d(snakes, [[1, 1, 1],\n",
" [1, 0, 1],\n",
" [1, 1, 1]], mode='same') == 1\n",
"corners = corners & snakes\n",
"\n",
"# Those are the start and end positions of the segments\n",
"y, x = np.where(corners)\n",
"\n",
"plt.figure(figsize=(10, 5))\n",
"plt.imshow(img, cmap=plt.cm.gray, interpolation='nearest')\n",
"plt.scatter(x, y)\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parameters of a pill\n",
"\n",
"(Based on StackOverflow http://stackoverflow.com/questions/28281742/fitting-a-circle-to-a-binary-image)\n",
"\n",
"<img src=\"../images/round_pill.jpg\" width=\"200px\" style=\"float: left; padding-right: 1em;\"/>\n",
"Consider a pill from the [NLM Pill Image Recognition Pilot](http://pir.nlm.nih.gov/pilot/instructions.html) (``../images/round_pill.jpg``). Fit a circle to the pill outline and compute its area.\n",
"\n",
"<div style=\"clear: both;\"></div>\n",
"\n",
"*Hints:*\n",
"\n",
"1. Equalize (``exposure.equalize_*``)\n",
"2. Detect edges (``filter.canny`` or ``feature.canny``--depending on your version)\n",
"3. Fit the ``CircleModel`` using ``measure.ransac``."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"image = io.imread(\"../images/round_pill.jpg\")\n",
"image_equalized = exposure.equalize_adapthist(image)\n",
"edges = filters.canny(color.rgb2gray(image_equalized))\n",
"\n",
"f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(15, 8))\n",
"ax0.imshow(image)\n",
"ax1.imshow(image_equalized)\n",
"ax2.imshow(edges, cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from skimage import measure\n",
"\n",
"coords = np.column_stack(np.nonzero(edges))\n",
"\n",
"model, inliers = measure.ransac(coords, measure.CircleModel,\n",
" min_samples=3, residual_threshold=1,\n",
" max_trials=500)\n",
"\n",
"print('Circle parameters:', model.params)\n",
"\n",
"row, col, radius = model.params\n",
"\n",
"f, ax = plt.subplots()\n",
"ax.imshow(image, cmap='gray');\n",
"circle = plt.Circle((col, row), radius=radius, edgecolor='green', linewidth=2, fill=False)\n",
"ax.add_artist(circle);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Viscous fingers\n",
"\n",
"Based on StackOverflow: http://stackoverflow.com/questions/23121416/long-boundary-detection-in-a-noisy-image\n",
"\n",
"<img src=\"../images/fingers.png\" width=\"200px\" style=\"float: left; padding-right: 1em;\"/>\n",
"\n",
"Consider the fluid experiment on the right. Determine any kind of meaningful boundary.\n",
"\n",
"<div style=\"clear: both;\"></div>\n",
"\n",
"*Hints:*\n",
"\n",
"1. Convert to grayscale\n",
"2. Try edge detection (``filters.canny``)\n",
"3. If edge detection fails, denoising is needed (try ``restoration.denoise_tv_bregman``)\n",
"4. Try edge detection (``filters.canny``)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from skimage import restoration, color, io, filter as filters, morphology\n",
"\n",
"image = color.rgb2gray(io.imread('../images/fingers.png'))\n",
"denoised = restoration.denoise_tv_bregman(image, 1)\n",
"edges = filters.canny(denoised, low_threshold=0.01, high_threshold=0.21)\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(15, 10))\n",
"axes[0].imshow(denoised, cmap='gray')\n",
"axes[1].imshow(edges, cmap='gray')\n",
"for ax in axes:\n",
" ax.set_axis_off()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Counting coins\n",
"\n",
"Based on StackOverflow http://stackoverflow.com/questions/28242274/count-number-of-objects-using-watershed-algorithm-scikit-image\n",
"\n",
"Consider the coins image from the scikit-image example dataset, shown below.\n",
"Write a function to count the number of coins.\n",
"\n",
"The procedure outlined here is a bit simpler than in the notebook lecture (and works just fine!)\n",
"\n",
"<div style=\"clear: both;\"></div>\n",
"\n",
"*Hint:*\n",
"\n",
"1. Equalize\n",
"2. Threshold (``filter.otsu`` or ``filters.otsu``, depending on version)\n",
"3. Remove objects touching boundary (``segmentation.clear_border``)\n",
"4. Apply morphological closing (``morphology.closing``)\n",
"5. Remove small objects (``measure.regionprops``)\n",
"6. Visualize (potentially using ``color.label2rgb``)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from skimage import data\n",
"plt.imshow(data.coins(), cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy import ndimage\n",
"from skimage import segmentation\n",
"\n",
"image = data.coins()\n",
"equalized = exposure.equalize_adapthist(image)\n",
"edges = equalized > filters.threshold_otsu(equalized)\n",
"edges = segmentation.clear_border(edges)\n",
"edges = morphology.closing(edges, morphology.square(3))\n",
"\n",
"f, (ax0, ax1) = plt.subplots(1, 2)\n",
"ax0.imshow(image, cmap='gray')\n",
"ax1.imshow(edges, cmap='gray');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"labels = measure.label(edges)\n",
"for region in measure.regionprops(labels):\n",
" if region.area < 200:\n",
" rows, cols = region.coords.T\n",
" labels[rows, cols] = 0\n",
"\n",
"print(\"Number of coins:\", len(np.unique(labels)) - 1)\n",
" \n",
"out = color.label2rgb(labels, image, bg_label=0)\n",
"plt.imshow(out);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Color wheel\n",
"\n",
"Based on http://stackoverflow.com/questions/21618252/get-blue-colored-contours-using-scikit-image-opencv/21661395#21661395\n",
"\n",
"<img src=\"../images/color-wheel.jpg\" width=\"200px\" style=\"float: left; padding-right: 1em;\"/>\n",
"<img src=\"../images/balloon.jpg\" width=\"200px\" style=\"float: right; padding-left: 1em;\"/>\n",
" \n",
"Consider the color wheel (``../images/color-wheel.jpg``) or the balloon (``../images/balloon.jpg``). Isolate all the blue-ish colors in the top quadrant."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from skimage import img_as_float\n",
"\n",
"image = img_as_float(io.imread('../images/color-wheel.jpg'))\n",
"\n",
"blue_lab = color.rgb2lab([[[0, 0, 1.]]])\n",
"light_blue_lab = color.rgb2lab([[[0, 1, 1.]]])\n",
"red_lab = color.rgb2lab([[[1, 0, 0.]]])\n",
"image_lab = color.rgb2lab(image)\n",
"\n",
"distance_blue = color.deltaE_cmc(blue_lab, image_lab, kL=0.5, kC=0.5)\n",
"distance_light_blue = color.deltaE_cmc(light_blue_lab, image_lab, kL=0.5, kC=0.5)\n",
"distance_red = color.deltaE_cmc(red_lab, image_lab, kL=0.5, kC=0.5)\n",
"distance = distance_blue + distance_light_blue - distance_red\n",
"distance = exposure.rescale_intensity(distance)\n",
"\n",
"image_blue = image.copy()\n",
"image_blue[distance > 0.3] = 0\n",
"\n",
"f, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(10, 5))\n",
"ax0.imshow(image)\n",
"ax1.imshow(distance, cmap='gray')\n",
"ax2.imshow(image_blue)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hand-coin\n",
"\n",
"Based on StackOverflow http://stackoverflow.com/questions/27910187/how-do-i-calculate-the-measurements-of-a-hand-using-scikit-image\n",
"\n",
"<img src=\"../images/hand-coin.jpg\" width=\"200px\" style=\"float: left; padding-right: 1em;\"/>\n",
"\n",
"Consider the image of the hand and the coin (``../images/hand-coin.jpg``). Roughly isolate the region of the hand and plot its orientation.\n",
"\n",
"<div style=\"clear: both;\"></div>\n",
"\n",
"*Hint:*\n",
"\n",
"1. Segment the image, using ``segmentation.slic``\n",
"2. Compute the region properties of the resulting labeled image\n",
"3. Select the largest and second largest (non-background) region--the hand and the coin\n",
"4. For the hand, use ``region.major_axis_length`` and ``region.orientation`` (where region\n",
" is your region property) to plot its orientation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"image = io.imread(\"../images/hand-coin.jpg\")\n",
"\n",
"label_image = segmentation.slic(image, n_segments=2)\n",
"label_image = measure.label(label_image)\n",
"\n",
"regions = measure.regionprops(label_image)\n",
"areas = [r.area for r in regions]\n",
"ix = np.argsort(areas)\n",
"\n",
"hand = regions[ix[-1]]\n",
"coin = regions[ix[-2]]\n",
"\n",
"selected_labels = np.zeros_like(image[..., 0], dtype=np.uint8)\n",
"\n",
"fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(8, 8))\n",
"\n",
"for n, region in enumerate([hand, coin]):\n",
" selected_labels[region.coords[:, 0], region.coords[:, 1]] = n + 2\n",
"\n",
" y0, x0 = region.centroid\n",
" orientation = region.orientation\n",
"\n",
" x1 = x0 + np.cos(orientation) * 0.5 * region.major_axis_length\n",
" y1 = y0 - np.sin(orientation) * 0.5 * region.major_axis_length\n",
" x2 = x0 - np.sin(orientation) * 0.5 * region.minor_axis_length\n",
" y2 = y0 - np.cos(orientation) * 0.5 * region.minor_axis_length\n",
"\n",
" ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)\n",
" ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)\n",
" ax.plot(x0, y0, '.g', markersize=15)\n",
"\n",
"image_label_overlay = color.label2rgb(selected_labels, image=image, bg_label=0)\n",
"ax.imshow(image_label_overlay, cmap='gray')\n",
"ax.axis('image')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"<div style=\"height: 400px;\"></div>"
]
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {
"collapsed": false
},
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| mit |
ramabrahma/data-sci-int-capstone | data-exploration-life-insurance.ipynbw3r8ey/data-exploration-life-insurance.ipynb | 1 | 73901 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploration of Prudential Life Insurance Data\n",
"\n",
"### Data retrieved from: \n",
"https://www.kaggle.com/c/prudential-life-insurance-assessment\n",
"\n",
"\n",
"###### File descriptions:\n",
"\n",
"* train.csv - the training set, contains the Response values\n",
"* test.csv - the test set, you must predict the Response variable for all rows in this file\n",
"* sample_submission.csv - a sample submission file in the correct format\n",
"\n",
"###### Data fields:\n",
"\n",
"Variable |\tDescription\n",
"-------- | ------------\n",
"Id |\tA unique identifier associated with an application.\n",
"Product_Info_1-7 |\tA set of normalized variables relating to the product applied for\n",
"Ins_Age |\tNormalized age of applicant\n",
"Ht |\tNormalized height of applicant\n",
"Wt |\tNormalized weight of applicant\n",
"BMI |\tNormalized BMI of applicant\n",
"Employment_Info_1-6 |\tA set of normalized variables relating to the employment history of the applicant.\n",
"InsuredInfo_1-6 |\tA set of normalized variables providing information about the applicant.\n",
"Insurance_History_1-9 |\tA set of normalized variables relating to the insurance history of the applicant.\n",
"Family_Hist_1-5 |\tA set of normalized variables relating to the family history of the applicant.\n",
"Medical_History_1-41 |\tA set of normalized variables relating to the medical history of the applicant.\n",
"Medical_Keyword_1-48 |\tA set of dummy variables relating to the presence of/absence of a medical keyword being associated with the application.\n",
"Response |\tThis is the target variable, an ordinal variable relating to the final decision associated with an application\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following variables are all categorical (nominal):\n",
"\n",
"Product_Info_1, Product_Info_2, Product_Info_3, Product_Info_5, Product_Info_6, Product_Info_7, Employment_Info_2, Employment_Info_3, Employment_Info_5, InsuredInfo_1, InsuredInfo_2, InsuredInfo_3, InsuredInfo_4, InsuredInfo_5, InsuredInfo_6, InsuredInfo_7, Insurance_History_1, Insurance_History_2, Insurance_History_3, Insurance_History_4, Insurance_History_7, Insurance_History_8, Insurance_History_9, Family_Hist_1, Medical_History_2, Medical_History_3, Medical_History_4, Medical_History_5, Medical_History_6, Medical_History_7, Medical_History_8, Medical_History_9, Medical_History_11, Medical_History_12, Medical_History_13, Medical_History_14, Medical_History_16, Medical_History_17, Medical_History_18, Medical_History_19, Medical_History_20, Medical_History_21, Medical_History_22, Medical_History_23, Medical_History_25, Medical_History_26, Medical_History_27, Medical_History_28, Medical_History_29, Medical_History_30, Medical_History_31, Medical_History_33, Medical_History_34, Medical_History_35, Medical_History_36, Medical_History_37, Medical_History_38, Medical_History_39, Medical_History_40, Medical_History_41\n",
"\n",
"The following variables are continuous:\n",
"\n",
"Product_Info_4, Ins_Age, Ht, Wt, BMI, Employment_Info_1, Employment_Info_4, Employment_Info_6, Insurance_History_5, Family_Hist_2, Family_Hist_3, Family_Hist_4, Family_Hist_5\n",
"\n",
"The following variables are discrete:\n",
"\n",
"Medical_History_1, Medical_History_10, Medical_History_15, Medical_History_24, Medical_History_32\n",
"\n",
"Medical_Keyword_1-48 are dummy variables."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Convert variable data into categorical, continuous, discrete, \n",
"# and dummy variable lists the following into a dictionary list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The following variables are all categorical (nominal):"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s = [\"Product_Info_1, Product_Info_2, Product_Info_3, Product_Info_5, Product_Info_6, Product_Info_7, Employment_Info_2, Employment_Info_3, Employment_Info_5, InsuredInfo_1, InsuredInfo_2, InsuredInfo_3, InsuredInfo_4, InsuredInfo_5, InsuredInfo_6, InsuredInfo_7, Insurance_History_1, Insurance_History_2, Insurance_History_3, Insurance_History_4, Insurance_History_7, Insurance_History_8, Insurance_History_9, Family_Hist_1, Medical_History_2, Medical_History_3, Medical_History_4, Medical_History_5, Medical_History_6, Medical_History_7, Medical_History_8, Medical_History_9, Medical_History_11, Medical_History_12, Medical_History_13, Medical_History_14, Medical_History_16, Medical_History_17, Medical_History_18, Medical_History_19, Medical_History_20, Medical_History_21, Medical_History_22, Medical_History_23, Medical_History_25, Medical_History_26, Medical_History_27, Medical_History_28, Medical_History_29, Medical_History_30, Medical_History_31, Medical_History_33, Medical_History_34, Medical_History_35, Medical_History_36, Medical_History_37, Medical_History_38, Medical_History_39, Medical_History_40, Medical_History_41\",\n",
" \"Product_Info_4, Ins_Age, Ht, Wt, BMI, Employment_Info_1, Employment_Info_4, Employment_Info_6, Insurance_History_5, Family_Hist_2, Family_Hist_3, Family_Hist_4, Family_Hist_5\",\n",
" \"Medical_History_1, Medical_History_10, Medical_History_15, Medical_History_24, Medical_History_32\"]\n",
"\n",
"varTypes = dict()\n",
"varTypes['categorical'] = s[0].split(', ')\n",
"varTypes['continuous'] = s[1].split(', ')\n",
"varTypes['discrete'] = s[2].split(', ')\n",
"l = list()\n",
"for i in range(1,49): l.append(\"Medical_Keyword_\"+str(i))\n",
"varTypes['dummy'] = l\n"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Checking over the variable types\n",
"#for i in iter(varTypes['dummy']):\n",
" #print i"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"%matplotlib inline\n",
"import pandas as pd \n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv('prud_files/train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>Product_Info_1</th>\n",
" <th>Product_Info_2</th>\n",
" <th>Product_Info_3</th>\n",
" <th>Product_Info_4</th>\n",
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" <th>Medical_Keyword_46</th>\n",
" <th>Medical_Keyword_47</th>\n",
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" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> D3</td>\n",
" <td> 10</td>\n",
" <td> 0.076923</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 0.641791</td>\n",
" <td> 0.581818</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 5</td>\n",
" <td> 1</td>\n",
" <td> A1</td>\n",
" <td> 26</td>\n",
" <td> 0.076923</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.059701</td>\n",
" <td> 0.600000</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> E1</td>\n",
" <td> 26</td>\n",
" <td> 0.076923</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.029851</td>\n",
" <td> 0.745455</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 7</td>\n",
" <td> 1</td>\n",
" <td> D4</td>\n",
" <td> 10</td>\n",
" <td> 0.487179</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.164179</td>\n",
" <td> 0.672727</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 8</td>\n",
" <td> 1</td>\n",
" <td> D2</td>\n",
" <td> 26</td>\n",
" <td> 0.230769</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 0.417910</td>\n",
" <td> 0.654545</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 128 columns</p>\n",
"</div>"
],
"text/plain": [
" Id Product_Info_1 Product_Info_2 Product_Info_3 Product_Info_4 \\\n",
"0 2 1 D3 10 0.076923 \n",
"1 5 1 A1 26 0.076923 \n",
"2 6 1 E1 26 0.076923 \n",
"3 7 1 D4 10 0.487179 \n",
"4 8 1 D2 26 0.230769 \n",
"\n",
" Product_Info_5 Product_Info_6 Product_Info_7 Ins_Age Ht \\\n",
"0 2 1 1 0.641791 0.581818 \n",
"1 2 3 1 0.059701 0.600000 \n",
"2 2 3 1 0.029851 0.745455 \n",
"3 2 3 1 0.164179 0.672727 \n",
"4 2 3 1 0.417910 0.654545 \n",
"\n",
" ... Medical_Keyword_40 Medical_Keyword_41 Medical_Keyword_42 \\\n",
"0 ... 0 0 0 \n",
"1 ... 0 0 0 \n",
"2 ... 0 0 0 \n",
"3 ... 0 0 0 \n",
"4 ... 0 0 0 \n",
"\n",
" Medical_Keyword_43 Medical_Keyword_44 Medical_Keyword_45 \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Medical_Keyword_46 Medical_Keyword_47 Medical_Keyword_48 Response \n",
"0 0 0 0 8 \n",
"1 0 0 0 4 \n",
"2 0 0 0 8 \n",
"3 0 0 0 8 \n",
"4 0 0 0 8 \n",
"\n",
"[5 rows x 128 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>Product_Info_1</th>\n",
" <th>Product_Info_3</th>\n",
" <th>Product_Info_4</th>\n",
" <th>Product_Info_5</th>\n",
" <th>Product_Info_6</th>\n",
" <th>Product_Info_7</th>\n",
" <th>Ins_Age</th>\n",
" <th>Ht</th>\n",
" <th>Wt</th>\n",
" <th>...</th>\n",
" <th>Medical_Keyword_40</th>\n",
" <th>Medical_Keyword_41</th>\n",
" <th>Medical_Keyword_42</th>\n",
" <th>Medical_Keyword_43</th>\n",
" <th>Medical_Keyword_44</th>\n",
" <th>Medical_Keyword_45</th>\n",
" <th>Medical_Keyword_46</th>\n",
" <th>Medical_Keyword_47</th>\n",
" <th>Medical_Keyword_48</th>\n",
" <th>Response</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td>...</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 39507.211515</td>\n",
" <td> 1.026355</td>\n",
" <td> 24.415655</td>\n",
" <td> 0.328952</td>\n",
" <td> 2.006955</td>\n",
" <td> 2.673599</td>\n",
" <td> 1.043583</td>\n",
" <td> 0.405567</td>\n",
" <td> 0.707283</td>\n",
" <td> 0.292587</td>\n",
" <td>...</td>\n",
" <td> 0.056954</td>\n",
" <td> 0.010054</td>\n",
" <td> 0.045536</td>\n",
" <td> 0.010710</td>\n",
" <td> 0.007528</td>\n",
" <td> 0.013691</td>\n",
" <td> 0.008488</td>\n",
" <td> 0.019905</td>\n",
" <td> 0.054496</td>\n",
" <td> 5.636837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 22815.883089</td>\n",
" <td> 0.160191</td>\n",
" <td> 5.072885</td>\n",
" <td> 0.282562</td>\n",
" <td> 0.083107</td>\n",
" <td> 0.739103</td>\n",
" <td> 0.291949</td>\n",
" <td> 0.197190</td>\n",
" <td> 0.074239</td>\n",
" <td> 0.089037</td>\n",
" <td>...</td>\n",
" <td> 0.231757</td>\n",
" <td> 0.099764</td>\n",
" <td> 0.208479</td>\n",
" <td> 0.102937</td>\n",
" <td> 0.086436</td>\n",
" <td> 0.116207</td>\n",
" <td> 0.091737</td>\n",
" <td> 0.139676</td>\n",
" <td> 0.226995</td>\n",
" <td> 2.456833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td>...</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 19780.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 26.000000</td>\n",
" <td> 0.076923</td>\n",
" <td> 2.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.238806</td>\n",
" <td> 0.654545</td>\n",
" <td> 0.225941</td>\n",
" <td>...</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 39487.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 26.000000</td>\n",
" <td> 0.230769</td>\n",
" <td> 2.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.402985</td>\n",
" <td> 0.709091</td>\n",
" <td> 0.288703</td>\n",
" <td>...</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 59211.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 26.000000</td>\n",
" <td> 0.487179</td>\n",
" <td> 2.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.567164</td>\n",
" <td> 0.763636</td>\n",
" <td> 0.345188</td>\n",
" <td>...</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
" <td> 8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 79146.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 38.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td>...</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 8.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 127 columns</p>\n",
"</div>"
],
"text/plain": [
" Id Product_Info_1 Product_Info_3 Product_Info_4 \\\n",
"count 59381.000000 59381.000000 59381.000000 59381.000000 \n",
"mean 39507.211515 1.026355 24.415655 0.328952 \n",
"std 22815.883089 0.160191 5.072885 0.282562 \n",
"min 2.000000 1.000000 1.000000 0.000000 \n",
"25% 19780.000000 1.000000 26.000000 0.076923 \n",
"50% 39487.000000 1.000000 26.000000 0.230769 \n",
"75% 59211.000000 1.000000 26.000000 0.487179 \n",
"max 79146.000000 2.000000 38.000000 1.000000 \n",
"\n",
" Product_Info_5 Product_Info_6 Product_Info_7 Ins_Age \\\n",
"count 59381.000000 59381.000000 59381.000000 59381.000000 \n",
"mean 2.006955 2.673599 1.043583 0.405567 \n",
"std 0.083107 0.739103 0.291949 0.197190 \n",
"min 2.000000 1.000000 1.000000 0.000000 \n",
"25% 2.000000 3.000000 1.000000 0.238806 \n",
"50% 2.000000 3.000000 1.000000 0.402985 \n",
"75% 2.000000 3.000000 1.000000 0.567164 \n",
"max 3.000000 3.000000 3.000000 1.000000 \n",
"\n",
" Ht Wt ... Medical_Keyword_40 \\\n",
"count 59381.000000 59381.000000 ... 59381.000000 \n",
"mean 0.707283 0.292587 ... 0.056954 \n",
"std 0.074239 0.089037 ... 0.231757 \n",
"min 0.000000 0.000000 ... 0.000000 \n",
"25% 0.654545 0.225941 ... 0.000000 \n",
"50% 0.709091 0.288703 ... 0.000000 \n",
"75% 0.763636 0.345188 ... 0.000000 \n",
"max 1.000000 1.000000 ... 1.000000 \n",
"\n",
" Medical_Keyword_41 Medical_Keyword_42 Medical_Keyword_43 \\\n",
"count 59381.000000 59381.000000 59381.000000 \n",
"mean 0.010054 0.045536 0.010710 \n",
"std 0.099764 0.208479 0.102937 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" Medical_Keyword_44 Medical_Keyword_45 Medical_Keyword_46 \\\n",
"count 59381.000000 59381.000000 59381.000000 \n",
"mean 0.007528 0.013691 0.008488 \n",
"std 0.086436 0.116207 0.091737 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 0.000000 \n",
"50% 0.000000 0.000000 0.000000 \n",
"75% 0.000000 0.000000 0.000000 \n",
"max 1.000000 1.000000 1.000000 \n",
"\n",
" Medical_Keyword_47 Medical_Keyword_48 Response \n",
"count 59381.000000 59381.000000 59381.000000 \n",
"mean 0.019905 0.054496 5.636837 \n",
"std 0.139676 0.226995 2.456833 \n",
"min 0.000000 0.000000 1.000000 \n",
"25% 0.000000 0.000000 4.000000 \n",
"50% 0.000000 0.000000 6.000000 \n",
"75% 0.000000 0.000000 8.000000 \n",
"max 1.000000 1.000000 8.000000 \n",
"\n",
"[8 rows x 127 columns]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"?df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...], dtype='int64')"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.index"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'Id', u'Product_Info_1', u'Product_Info_2', u'Product_Info_3', u'Product_Info_4', u'Product_Info_5', u'Product_Info_6', u'Product_Info_7', u'Ins_Age', u'Ht', u'Wt', u'BMI', u'Employment_Info_1', u'Employment_Info_2', u'Employment_Info_3', u'Employment_Info_4', u'Employment_Info_5', u'Employment_Info_6', u'InsuredInfo_1', u'InsuredInfo_2', u'InsuredInfo_3', u'InsuredInfo_4', u'InsuredInfo_5', u'InsuredInfo_6', u'InsuredInfo_7', u'Insurance_History_1', u'Insurance_History_2', u'Insurance_History_3', u'Insurance_History_4', u'Insurance_History_5', u'Insurance_History_7', u'Insurance_History_8', u'Insurance_History_9', u'Family_Hist_1', u'Family_Hist_2', u'Family_Hist_3', u'Family_Hist_4', u'Family_Hist_5', u'Medical_History_1', u'Medical_History_2', u'Medical_History_3', u'Medical_History_4', u'Medical_History_5', u'Medical_History_6', u'Medical_History_7', u'Medical_History_8', u'Medical_History_9', u'Medical_History_10', u'Medical_History_11', u'Medical_History_12', u'Medical_History_13', u'Medical_History_14', u'Medical_History_15', u'Medical_History_16', u'Medical_History_17', u'Medical_History_18', u'Medical_History_19', u'Medical_History_20', u'Medical_History_21', u'Medical_History_22', u'Medical_History_23', u'Medical_History_24', u'Medical_History_25', u'Medical_History_26', u'Medical_History_27', u'Medical_History_28', u'Medical_History_29', u'Medical_History_30', u'Medical_History_31', u'Medical_History_32', u'Medical_History_33', u'Medical_History_34', u'Medical_History_35', u'Medical_History_36', u'Medical_History_37', u'Medical_History_38', u'Medical_History_39', u'Medical_History_40', u'Medical_History_41', u'Medical_Keyword_1', u'Medical_Keyword_2', u'Medical_Keyword_3', u'Medical_Keyword_4', u'Medical_Keyword_5', u'Medical_Keyword_6', u'Medical_Keyword_7', u'Medical_Keyword_8', u'Medical_Keyword_9', u'Medical_Keyword_10', u'Medical_Keyword_11', u'Medical_Keyword_12', u'Medical_Keyword_13', u'Medical_Keyword_14', u'Medical_Keyword_15', u'Medical_Keyword_16', u'Medical_Keyword_17', u'Medical_Keyword_18', u'Medical_Keyword_19', u'Medical_Keyword_20', u'Medical_Keyword_21', ...], dtype='object')"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.loc??"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Product_Info_1</th>\n",
" <th>Product_Info_3</th>\n",
" <th>Product_Info_5</th>\n",
" <th>Product_Info_6</th>\n",
" <th>Product_Info_7</th>\n",
" <th>Employment_Info_2</th>\n",
" <th>Employment_Info_3</th>\n",
" <th>Employment_Info_5</th>\n",
" <th>InsuredInfo_1</th>\n",
" <th>InsuredInfo_2</th>\n",
" <th>...</th>\n",
" <th>Medical_History_31</th>\n",
" <th>Medical_History_33</th>\n",
" <th>Medical_History_34</th>\n",
" <th>Medical_History_35</th>\n",
" <th>Medical_History_36</th>\n",
" <th>Medical_History_37</th>\n",
" <th>Medical_History_38</th>\n",
" <th>Medical_History_39</th>\n",
" <th>Medical_History_40</th>\n",
" <th>Medical_History_41</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td>...</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" <td> 59381.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 1.026355</td>\n",
" <td> 24.415655</td>\n",
" <td> 2.006955</td>\n",
" <td> 2.673599</td>\n",
" <td> 1.043583</td>\n",
" <td> 8.641821</td>\n",
" <td> 1.300904</td>\n",
" <td> 2.142958</td>\n",
" <td> 1.209326</td>\n",
" <td> 2.007427</td>\n",
" <td>...</td>\n",
" <td> 2.985265</td>\n",
" <td> 2.804618</td>\n",
" <td> 2.689076</td>\n",
" <td> 1.002055</td>\n",
" <td> 2.179468</td>\n",
" <td> 1.938398</td>\n",
" <td> 1.004850</td>\n",
" <td> 2.830720</td>\n",
" <td> 2.967599</td>\n",
" <td> 1.641064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.160191</td>\n",
" <td> 5.072885</td>\n",
" <td> 0.083107</td>\n",
" <td> 0.739103</td>\n",
" <td> 0.291949</td>\n",
" <td> 4.227082</td>\n",
" <td> 0.715034</td>\n",
" <td> 0.350033</td>\n",
" <td> 0.417939</td>\n",
" <td> 0.085858</td>\n",
" <td>...</td>\n",
" <td> 0.170989</td>\n",
" <td> 0.593798</td>\n",
" <td> 0.724661</td>\n",
" <td> 0.063806</td>\n",
" <td> 0.412633</td>\n",
" <td> 0.240574</td>\n",
" <td> 0.069474</td>\n",
" <td> 0.556665</td>\n",
" <td> 0.252427</td>\n",
" <td> 0.933361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td>...</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 1.000000</td>\n",
" <td> 26.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 9.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td>...</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 1.000000</td>\n",
" <td> 26.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 9.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td>...</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 1.000000</td>\n",
" <td> 26.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 9.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td>...</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 1.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 2.000000</td>\n",
" <td> 38.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 38.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td>...</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" <td> 3.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 59 columns</p>\n",
"</div>"
],
"text/plain": [
" Product_Info_1 Product_Info_3 Product_Info_5 Product_Info_6 \\\n",
"count 59381.000000 59381.000000 59381.000000 59381.000000 \n",
"mean 1.026355 24.415655 2.006955 2.673599 \n",
"std 0.160191 5.072885 0.083107 0.739103 \n",
"min 1.000000 1.000000 2.000000 1.000000 \n",
"25% 1.000000 26.000000 2.000000 3.000000 \n",
"50% 1.000000 26.000000 2.000000 3.000000 \n",
"75% 1.000000 26.000000 2.000000 3.000000 \n",
"max 2.000000 38.000000 3.000000 3.000000 \n",
"\n",
" Product_Info_7 Employment_Info_2 Employment_Info_3 \\\n",
"count 59381.000000 59381.000000 59381.000000 \n",
"mean 1.043583 8.641821 1.300904 \n",
"std 0.291949 4.227082 0.715034 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 1.000000 9.000000 1.000000 \n",
"50% 1.000000 9.000000 1.000000 \n",
"75% 1.000000 9.000000 1.000000 \n",
"max 3.000000 38.000000 3.000000 \n",
"\n",
" Employment_Info_5 InsuredInfo_1 InsuredInfo_2 ... \\\n",
"count 59381.000000 59381.000000 59381.000000 ... \n",
"mean 2.142958 1.209326 2.007427 ... \n",
"std 0.350033 0.417939 0.085858 ... \n",
"min 2.000000 1.000000 2.000000 ... \n",
"25% 2.000000 1.000000 2.000000 ... \n",
"50% 2.000000 1.000000 2.000000 ... \n",
"75% 2.000000 1.000000 2.000000 ... \n",
"max 3.000000 3.000000 3.000000 ... \n",
"\n",
" Medical_History_31 Medical_History_33 Medical_History_34 \\\n",
"count 59381.000000 59381.000000 59381.000000 \n",
"mean 2.985265 2.804618 2.689076 \n",
"std 0.170989 0.593798 0.724661 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 3.000000 3.000000 3.000000 \n",
"50% 3.000000 3.000000 3.000000 \n",
"75% 3.000000 3.000000 3.000000 \n",
"max 3.000000 3.000000 3.000000 \n",
"\n",
" Medical_History_35 Medical_History_36 Medical_History_37 \\\n",
"count 59381.000000 59381.000000 59381.000000 \n",
"mean 1.002055 2.179468 1.938398 \n",
"std 0.063806 0.412633 0.240574 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 1.000000 2.000000 2.000000 \n",
"50% 1.000000 2.000000 2.000000 \n",
"75% 1.000000 2.000000 2.000000 \n",
"max 3.000000 3.000000 3.000000 \n",
"\n",
" Medical_History_38 Medical_History_39 Medical_History_40 \\\n",
"count 59381.000000 59381.000000 59381.000000 \n",
"mean 1.004850 2.830720 2.967599 \n",
"std 0.069474 0.556665 0.252427 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 1.000000 3.000000 3.000000 \n",
"50% 1.000000 3.000000 3.000000 \n",
"75% 1.000000 3.000000 3.000000 \n",
"max 2.000000 3.000000 3.000000 \n",
"\n",
" Medical_History_41 \n",
"count 59381.000000 \n",
"mean 1.641064 \n",
"std 0.933361 \n",
"min 1.000000 \n",
"25% 1.000000 \n",
"50% 1.000000 \n",
"75% 3.000000 \n",
"max 3.000000 \n",
"\n",
"[8 rows x 59 columns]"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Exploration of the categorical data\n",
"catD = df.loc[:,varTypes['categorical']]\n",
"catD.describe()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Product_Info_1</th>\n",
" <th>Product_Info_2</th>\n",
" <th>Product_Info_3</th>\n",
" <th>Product_Info_5</th>\n",
" <th>Product_Info_6</th>\n",
" <th>Product_Info_7</th>\n",
" <th>Employment_Info_2</th>\n",
" <th>Employment_Info_3</th>\n",
" <th>Employment_Info_5</th>\n",
" <th>InsuredInfo_1</th>\n",
" <th>...</th>\n",
" <th>Medical_History_31</th>\n",
" <th>Medical_History_33</th>\n",
" <th>Medical_History_34</th>\n",
" <th>Medical_History_35</th>\n",
" <th>Medical_History_36</th>\n",
" <th>Medical_History_37</th>\n",
" <th>Medical_History_38</th>\n",
" <th>Medical_History_39</th>\n",
" <th>Medical_History_40</th>\n",
" <th>Medical_History_41</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> D3</td>\n",
" <td> 10</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 12</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td>...</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td> A1</td>\n",
" <td> 26</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td>...</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1</td>\n",
" <td> E1</td>\n",
" <td> 26</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td>...</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 1</td>\n",
" <td> D4</td>\n",
" <td> 10</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td>...</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1</td>\n",
" <td> D2</td>\n",
" <td> 26</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td>...</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 60 columns</p>\n",
"</div>"
],
"text/plain": [
" Product_Info_1 Product_Info_2 Product_Info_3 Product_Info_5 \\\n",
"0 1 D3 10 2 \n",
"1 1 A1 26 2 \n",
"2 1 E1 26 2 \n",
"3 1 D4 10 2 \n",
"4 1 D2 26 2 \n",
"\n",
" Product_Info_6 Product_Info_7 Employment_Info_2 Employment_Info_3 \\\n",
"0 1 1 12 1 \n",
"1 3 1 1 3 \n",
"2 3 1 9 1 \n",
"3 3 1 9 1 \n",
"4 3 1 9 1 \n",
"\n",
" Employment_Info_5 InsuredInfo_1 ... Medical_History_31 \\\n",
"0 3 1 ... 3 \n",
"1 2 1 ... 3 \n",
"2 2 1 ... 3 \n",
"3 3 2 ... 3 \n",
"4 2 1 ... 3 \n",
"\n",
" Medical_History_33 Medical_History_34 Medical_History_35 \\\n",
"0 1 3 1 \n",
"1 3 1 1 \n",
"2 3 3 1 \n",
"3 3 3 1 \n",
"4 3 3 1 \n",
"\n",
" Medical_History_36 Medical_History_37 Medical_History_38 \\\n",
"0 2 2 1 \n",
"1 2 2 1 \n",
"2 3 2 1 \n",
"3 2 2 1 \n",
"4 3 2 1 \n",
"\n",
" Medical_History_39 Medical_History_40 Medical_History_41 \n",
"0 3 3 3 \n",
"1 3 3 1 \n",
"2 3 3 1 \n",
"3 3 3 1 \n",
"4 3 3 1 \n",
"\n",
"[5 rows x 60 columns]"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"catD.head(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d = catD.loc[:,key]\n",
".groupby('colour').size().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'numpy.ndarray' object is not callable",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-113-464223f80026>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Histogram: '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: 'numpy.ndarray' object is not callable"
]
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x2d89f080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"AABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x2d89fb00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x2d89f7f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"#Iterate through each categorical column of data\n",
"#Perform a 2D histogram later\n",
"plt.figure()\n",
"fig, axes = plt.subplots(nrows = 1, ncols = 2)\n",
"i=0\n",
"\n",
"for key in varTypes['categorical']:\n",
" \n",
" #Select the data in each key iteration \n",
" d = catD.loc[:,key]\n",
" l = d.value_counts()\n",
" #print \"The category is: {0} with value_counts: {1} and detailed tuple: {2} \".format(key, l.count(), l)\n",
" \n",
" plt.figure()\n",
" \n",
" ax = axes[i,0]\n",
" \n",
" plt.title('Histogram: ' + str(key), ax = ax)\n",
" plt.xlabel('Category: '+str(key), ax = ax)\n",
" plt.ylabel('Frequency', ax = ax)\n",
" d.value_counts().hist(alpha=0.5, ax = ax)\n",
" \n",
" ax = axes[i,1]\n",
" plt.title('Cumulative Histogram: ' + str(key), ax = ax)\n",
" plt.xlabel('Category: '+str(key), ax = ax)\n",
" plt.ylabel('Frequency', ax = ax)\n",
" d.value_counts().hist(alpha=0.5, cumulative=True, ax = ax)\n",
" i+=1\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in range(catD):\n",
" print catD[i].columns\n",
" catD[i].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 57816., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 1565.]),\n",
" array([ 1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0xbddd400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#fig, axes = plt.subplots(1, 2, figsize(12,4))\n",
"#fig.tight_layout()\n",
"num_bins = 50\n",
"\n",
"plt.hist(catD['Product_Info_1'], num_bins, facecolor='green',alpha=0.5)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"disD = df.loc[:,varTypes['discrete']]\n",
"contD = df.loc[:,varTypes['continuous']]\n",
"respD = df.loc[:,['id','Response']]"
]
},
{
"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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
oliverlee/antlia | python/notebooks/filter_steer_angle.ipynb | 1 | 15010218 | null | bsd-2-clause |
GEMScienceTools/rmtk | notebooks/vulnerability/model_generator/SPBELA_approach/SPBELA.ipynb | 1 | 6400 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Generation of capacity curves using SP-BELA\n",
"The Simplified Pushover-based Earthquake Loss Assessment (SP-BELA) methodology allows the calculation of the displacement capacity (i.e. spectral displacement) and collapse multiplier (i.e. spectral acceleration) using a simplified mechanics-based procedure, similar to what has been proposed by [Cosenza et al. 2005](http://www.tandfonline.com/doi/abs/10.1080/13632460509350531). The methodology currently implemented in the Risk Modeller's Toolkit only supports reinforced concrete frames.\n",
"\n",
"<img src=\"../../../../figures/synthethic_capacity_curves.png\" width=\"350\" align=\"middle\">\n",
"\n",
"**Note**: To run the code in a cell:\n",
"\n",
"1. Click on the cell to select it.\n",
"2. Press `SHIFT+ENTER` on your keyboard or press the play button (<button class='fa fa-play icon-play btn btn-xs btn-default'></button>) in the toolbar above."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from rmtk.vulnerability.model_generator.SPBELA_approach import SPBELA\n",
"from rmtk.vulnerability.common import utils\n",
"%matplotlib inline "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"source": [
"---\n",
"### Load geometric and material properties\n",
"\n",
"In order to use this methodology it is necessary to define a building model, which specifies the probabilistic distribution of the geometrical and material properties. These models need to be defined according to the format described in the [RMTK manual](../../../../../rmtk-docs.pdf). Please specify below the paths for the input files containing the building model and damage model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"building_model_file = \"../../../../../rmtk_data/SPBELA/bare_frames.csv\"\n",
"damage_model_file = \"../../../../../rmtk_data/damage_model_spbela.csv\""
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Number of samples\n",
"\n",
"The parameter `no_assets` below controls the number of synthetic structural models or assets (each one with unique geometrical and material properties) that will be generated using a Monte Carlo sampling process:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"no_assets = 200"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Generate the capacity curves"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [],
"source": [
"building_class_model = SPBELA.read_building_class_model(building_model_file)\n",
"assets = SPBELA.generate_assets(building_class_model, no_assets)\n",
"damage_model = utils.read_damage_model(damage_model_file)\n",
"capacity_curves = SPBELA.generate_capacity_curves(assets, damage_model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Plot the capacity curves"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"utils.plot_capacity_curves(capacity_curves)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Adding additional information\n",
"Additional information can be added to the capacity curves generated using the above method. For instance, by setting appropriate values for the parameters `gamma` and `yielding_point_index` in the cell below, the `add_information` function can be used to include this data in the previously generated capacity curves."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"gamma = 1.2\n",
"yielding_point_index = 1.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"capacity_curves = utils.add_information(capacity_curves, \"gamma\", \"value\", gamma)\n",
"capacity_curves = utils.add_information(capacity_curves, \"yielding point\", \"point\", yielding_point_index)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Save capacity curves\n",
"Please specify below the path for the output file to save the capacity curves:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"output_file = \"../../../../../rmtk_data/capacity_curves_spbela.csv\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"utils.save_SdSa_capacity_curves(capacity_curves, output_file)"
]
}
],
"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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| agpl-3.0 |
pdamodaran/yellowbrick | examples/bbengfort/testing.ipynb | 2 | 361053 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Visual Diagnosis of Text Analysis with Baleen \n",
"\n",
"This notebook has been created as part of the [Yellowbrick user study](http://www.scikit-yb.org/en/latest/evaluation.html). I hope to explore how visual methods might improve the workflow of text classification on a small to medium sized corpus. \n",
"\n",
"## Dataset \n",
"\n",
"The dataset used in this study is a sample of the [Baleen Corpus](http://baleen.districtdatalabs.com/). The Baleen corpus has been ingesting RSS feeds on the hour from a variety of topical feeds since March 2016, including news, hobbies, and political documents and currently has over 1.2M posts from 373 feeds. [Baleen](https://github.com/bbengfort/baleen) (an open source system) has a sister library called [Minke](https://github.com/bbengfort/minke) that provides multiprocessing support for dealing with Gigabytes worth of text. \n",
"\n",
"The dataset I'll use in this study is a sample of the larger data set that contains 68,052 or roughly 6% of the total corpus. For this test, I've chosen to use the preprocessed corpus, which means I won't have to do any tokenization, but can still apply normalization techniques. The corpus is described as follows:\n",
"\n",
"Baleen corpus contains 68,052 files in 12 categories.\n",
"Structured as:\n",
"\n",
"- 1,200,378 paragraphs (17.639 mean paragraphs per file)\n",
"- 2,058,635 sentences (1.715 mean sentences per paragraph).\n",
"\n",
"Word count of 44,821,870 with a vocabulary of 303,034 (147.910 lexical diversity).\n",
"\n",
"Category Counts: \n",
"\n",
"- books: 1,700 docs\n",
"- business: 9,248 docs\n",
"- cinema: 2,072 docs\n",
"- cooking: 733 docs\n",
"- data science: 692 docs\n",
"- design: 1,259 docs\n",
"- do it yourself: 2,620 docs\n",
"- gaming: 2,884 docs\n",
"- news: 33,253 docs\n",
"- politics: 3,793 docs\n",
"- sports: 4,710 docs\n",
"- tech: 5,088 docs\n",
"\n",
"This is quite a lot of data, so for now we'll simply create a classifier for the \"hobbies\" categories: e.g. books, cinema, cooking, diy, gaming, and sports. \n",
"\n",
"Note: this data set is not currently publically available, but I am happy to provide it on request. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import os \n",
"import sys \n",
"import nltk\n",
"import pickle\n",
"\n",
"# To import yellowbrick \n",
"sys.path.append(\"../..\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Loading Data \n",
"\n",
"In order to load data, I'd typically use a `CorpusReader`. However, for the sake of simplicity, I'll load data using some simple Python generator functions. I need to create two primary methods, the first loads the documents using pickle, and the second returns the vector of targets for supervised learning. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"CORPUS_ROOT = os.path.join(os.getcwd(), \"data\") \n",
"CATEGORIES = [\"books\", \"cinema\", \"cooking\", \"diy\", \"gaming\", \"sports\"]\n",
"\n",
"def fileids(root=CORPUS_ROOT, categories=CATEGORIES): \n",
" \"\"\"\n",
" Fetch the paths, filtering on categories (pass None for all). \n",
" \"\"\"\n",
" for name in os.listdir(root):\n",
" dpath = os.path.join(root, name)\n",
" if not os.path.isdir(dpath):\n",
" continue \n",
" \n",
" if categories and name in categories: \n",
" for fname in os.listdir(dpath):\n",
" yield os.path.join(dpath, fname)\n",
"\n",
"\n",
"def documents(root=CORPUS_ROOT, categories=CATEGORIES):\n",
" \"\"\"\n",
" Load the pickled documents and yield one at a time. \n",
" \"\"\"\n",
" for path in fileids(root, categories):\n",
" with open(path, 'rb') as f:\n",
" yield pickle.load(f)\n",
"\n",
"\n",
"def labels(root=CORPUS_ROOT, categories=CATEGORIES):\n",
" \"\"\"\n",
" Return a list of the labels associated with each document. \n",
" \"\"\" \n",
" for path in fileids(root, categories):\n",
" dpath = os.path.dirname(path) \n",
" yield dpath.split(os.path.sep)[-1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Feature Extraction and Normalization \n",
"\n",
"In order to conduct analyses with Scikit-Learn, I'll need some helper transformers to modify the loaded data into a form that can be used by the `sklearn.feature_extraction` text transformers. I'll be mostly using the `CountVectorizer` and `TfidfVectorizer`, so these normalizer transformers and identity functions help a lot. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from nltk.corpus import wordnet as wn\n",
"from nltk.stem import WordNetLemmatizer \n",
"from unicodedata import category as ucat\n",
"from nltk.corpus import stopwords as swcorpus\n",
"from sklearn.base import BaseEstimator, TransformerMixin \n",
"\n",
"\n",
"def identity(args):\n",
" \"\"\"\n",
" The identity function is used as the \"tokenizer\" for \n",
" pre-tokenized text. It just passes back it's arguments. \n",
" \"\"\"\n",
" return args \n",
"\n",
"\n",
"def is_punctuation(token):\n",
" \"\"\"\n",
" Returns true if all characters in the token are\n",
" unicode punctuation (works for most punct). \n",
" \"\"\"\n",
" return all(\n",
" ucat(c).startswith('P')\n",
" for c in token \n",
" )\n",
"\n",
"\n",
"def wnpos(tag):\n",
" \"\"\"\n",
" Returns the wn part of speech tag from the penn treebank tag. \n",
" \"\"\"\n",
" return {\n",
" \"N\": wn.NOUN,\n",
" \"V\": wn.VERB,\n",
" \"J\": wn.ADJ, \n",
" \"R\": wn.ADV, \n",
" }.get(tag[0], wn.NOUN)\n",
"\n",
"\n",
"class TextNormalizer(BaseEstimator, TransformerMixin):\n",
" \n",
" def __init__(self, stopwords='english', lowercase=True, lemmatize=True, depunct=True):\n",
" self.stopwords = frozenset(swcorpus.words(stopwords)) if stopwords else frozenset()\n",
" self.lowercase = lowercase \n",
" self.depunct = depunct \n",
" self.lemmatizer = WordNetLemmatizer() if lemmatize else None \n",
" \n",
" def fit(self, docs, labels=None):\n",
" return self\n",
"\n",
" def transform(self, docs): \n",
" for doc in docs: \n",
" yield list(self.normalize(doc)) \n",
" \n",
" def normalize(self, doc):\n",
" for paragraph in doc:\n",
" for sentence in paragraph:\n",
" for token, tag in sentence: \n",
" if token.lower() in self.stopwords:\n",
" continue \n",
" \n",
" if self.depunct and is_punctuation(token):\n",
" continue \n",
" \n",
" if self.lowercase:\n",
" token = token.lower() \n",
" \n",
" if self.lemmatizer:\n",
" token = self.lemmatizer.lemmatize(token, wnpos(tag))\n",
" \n",
" yield token "
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Corpus Analysis \n",
"\n",
"At this stage, I'd like to get a feel for what was in my corpus, so that I can start thinking about how to best vectorize the text and do different types of counting. With the Yellowbrick 0.3.3 release, support has been added for two text visualizers, which I think I will test out at scale using this corpus. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
},
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute 'transform'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-3514380b0c82>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m ])\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocuments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnamed_steps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'viz'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fit_transform'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 303\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mlast_step\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 304\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mlast_step\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mXt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'transform'"
]
},
{
"data": {
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fiBEjkiRvvfXWoNUBAACUqur3watUKknS7cVXTqipqRm0OgAAgFJV/b4Bo0ePTpK0trZ2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\noVAoFAqFQqG8I/wf27lDuEaJvt4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116ca22e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.pipeline import Pipeline \n",
"from sklearn.feature_extraction.text import CountVectorizer \n",
"from yellowbrick.text import FreqDistVisualizer\n",
"\n",
"visualizer = Pipeline([\n",
" ('norm', TextNormalizer()),\n",
" ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n",
" ('viz', FreqDistVisualizer())\n",
"])\n",
"\n",
"visualizer.fit_transform(documents(), labels())\n",
"visualizer.named_steps['viz'].poof()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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Aq6OsV/mU/hcMCZMAcF6pB7zatWtLko4fP+62Lz09XVWq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x9/4NKlSzhy5Ah+++037N+/H61atcKYMWNga2vLqWtZWlpyOzHJyclwcHCQOz8+NzcXixcvliu4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\nIIiPBArwCIIgCIIgCIIgPhL+D4YIpwprbr3zAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11752cd68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"vect = Pipeline([\n",
" ('norm', TextNormalizer()),\n",
" ('count', CountVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n",
"])\n",
"\n",
"docs = vect.fit_transform(documents(), labels())\n",
"viz = FreqDistVisualizer() \n",
"viz.fit(docs, vect.named_steps['count'].get_feature_names())\n",
"viz.poof()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11b5e0c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.pipeline import Pipeline \n",
"from sklearn.feature_extraction.text import TfidfVectorizer \n",
"from yellowbrick.text import TSNEVisualizer\n",
"\n",
"vect = Pipeline([\n",
" ('norm', TextNormalizer()),\n",
" ('tfidf', TfidfVectorizer(tokenizer=lambda x: x, preprocessor=None, lowercase=False)),\n",
"])\n",
"\n",
"docs = vect.fit_transform(documents(), labels())\n",
"\n",
"viz = TSNEVisualizer() \n",
"viz.fit(docs, labels())\n",
"viz.poof()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classification \n",
"\n",
"The primary task for this kind of corpus is classification - sentiment analysis, etc. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split as tts \n",
"\n",
"docs_train, docs_test, labels_train, labels_test = tts(docs, list(labels()), test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression \n",
"from yellowbrick.classifier import ClassBalance, ClassificationReport, ROCAUC\n",
"\n",
"logit = LogisticRegression()\n",
"logit.fit(docs_train, labels_train)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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C+SvhwSTAnwcjbgCKVGRkpL799lv16dNHlSpVUmJiouLj43X27Fm1aNFCkydP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\nvy9JCgkJ0SeffKLGjRtLksq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GjNGWLVv0wAMPqGnTpurVq5d27Nih7OxsPfvs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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x146a61940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"logit_balance = ClassBalance(logit, classes=set(labels_test))\n",
"logit_balance.score(docs_test, labels_test)\n",
"logit_balance.poof()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-27-7b911cc79c37>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mva\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x12ee61da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"logit_balance = ClassificationReport(logit, classes=set(labels_test))\n",
"logit_balance.score(docs_test, labels_test)\n",
"logit_balance.poof()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-29-3e54aae07a7d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mClassificationReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 135\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 136\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, y, y_pred)\u001b[0m\n\u001b[1;32m 158\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mva\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'nearest'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x13466ad30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"logit_balance = ClassificationReport(LogisticRegression())\n",
"logit_balance.fit(docs_train, labels_train)\n",
"logit_balance.score(docs_test, labels_test)\n",
"logit_balance.poof()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Data is not binary and pos_label is not specified",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-28-5eccb2a02e03>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mlogit_balance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mROCAUC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdocs_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mlogit_balance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/benjamin/Repos/tmp/yellowbrick/yellowbrick/classifier.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 311\u001b[0m \"\"\"\n\u001b[1;32m 312\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthresholds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroc_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 314\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroc_auc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36mroc_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight, drop_intermediate)\u001b[0m\n\u001b[1;32m 503\u001b[0m \"\"\"\n\u001b[1;32m 504\u001b[0m fps, tps, thresholds = _binary_clf_curve(\n\u001b[0;32m--> 505\u001b[0;31m y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m 506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 507\u001b[0m \u001b[0;31m# Attempt to drop thresholds corresponding to points in between and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.5/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 313\u001b[0m array_equal(classes, [1]))):\n\u001b[0;32m--> 314\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Data is not binary and pos_label is not specified\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 315\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Data is not binary and pos_label is not specified"
]
}
],
"source": [
"logit_balance = ROCAUC(logit)\n",
"logit_balance.score(docs_test, labels_test)\n",
"logit_balance.poof()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
IS-ENES-Data/submission_forms | test/forms/test/.ipynb_checkpoints/test_ki_12345-checkpoint.ipynb | 1 | 2712 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generic DKRZ national archive form \n",
"\n",
"\n",
"This form is intended to provide a generic template for interactive forms e.g. for testing \n",
"\n",
"... to be finalized ..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# please edit the (red) information below: Name, email and project the data belongs to\n",
"from dkrz_forms import form_handler\n",
"my_project = \"DKRZ_CDP\"\n",
"\n",
"my_first_name = \"....\" # example: sf.first_name = \"Harold\"\n",
"my_last_name = \"....\" # example: sf.last_name = \"Mitty\"\n",
"my_email = \"....\" # example: sf.email = \"[email protected]\"\n",
"my_keyword = \"....\" # example: sf.keyword = \"mymodel_myrunid\"\n",
"\n",
"sf = form_handler.init_form(my_project,my_first_name,my_last_name,my_email,my_keyword)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Edit form information"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sf.myattribute = \"myinformation\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Save your form\n",
"\n",
"your form will be stored (the form name consists of your last name plut your keyword)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"form_handler.save_form(sf,\"..my comment..\") # edit my comment info "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# officially submit your form\n",
"the form will be submitted to the DKRZ team to process\n",
"you also receive a confirmation email with a reference to your online form for future modifications "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"form_handler.email_form_info(sf)\n",
"form_handler.form_submission(sf)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
FlyRanch/figurefirst | examples/svgitems/svgitem_editing_example.ipynb | 1 | 33429 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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" <sodipodi:namedview bordercolor=\"#666666\" borderopacity=\"1.0\" id=\"base\" inkscape:current-layer=\"layer1\" inkscape:cx=\"65.519287\" inkscape:cy=\"202.14359\" inkscape:document-units=\"in\" inkscape:pageopacity=\"0.0\" inkscape:pageshadow=\"2\" inkscape:window-height=\"955\" inkscape:window-maximized=\"0\" inkscape:window-width=\"1833\" inkscape:window-x=\"1286\" inkscape:window-y=\"44\" inkscape:zoom=\"0.99278098\" pagecolor=\"#ffffff\" showgrid=\"false\" units=\"in\"/>\n",
" <defs id=\"defs4\"/>\n",
" <metadata id=\"metadata7\">\n",
" <rdf:RDF>\n",
" <cc:Work rdf:about=\"\">\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
" <dc:title/>\n",
" </cc:Work>\n",
" </rdf:RDF>\n",
" </metadata>\n",
" <g id=\"layer1\" inkscape:groupmode=\"layer\" inkscape:label=\"Layer 1\" transform=\"translate(0,-692.36221)\">\n",
" <rect height=\"152.90111\" id=\"rect4155\" style=\"opacity:1;fill:#916f6f;fill-opacity:1;stroke:none;stroke-width:0.36000001;stroke-miterlimit:10;stroke-dasharray:0.53999999, 1.07999999;stroke-dashoffset:0;stroke-opacity:1\" width=\"234.57027\" x=\"62.230171\" y=\"789.03369\">\n",
" <figurefirst:axis figurefirst:name=\"test_ax\"/>\n",
" </rect>\n",
" <g id=\"g4137\" transform=\"matrix(2.5160983,0,0,2.5160983,-38.917192,-1051.0966)\">\n",
" <rect height=\"15.852816\" id=\"rect4167-3\" style=\"opacity:1;fill:#916f6f;fill-opacity:1;stroke:none;stroke-width:0.36000001;stroke-miterlimit:10;stroke-dasharray:0.53999999, 1.07999999;stroke-dashoffset:0;stroke-opacity:1\" width=\"15.852816\" x=\"197.3313\" y=\"732.66376\">\n",
" <figurefirst:svgitem figurefirst:name=\"r1\"/>\n",
" </rect>\n",
" <rect figurefirst:name=\"r2\" height=\"15.852816\" id=\"rect4167-0\" style=\"opacity:1;fill:#916f6f;fill-opacity:1;stroke:none;stroke-width:0.36000001;stroke-miterlimit:10;stroke-dasharray:0.53999999, 1.07999999;stroke-dashoffset:0;stroke-opacity:1\" width=\"15.852816\" x=\"168.68225\" y=\"732.66376\">\n",
" <figurefirst:svgitem figurefirst:name=\"r2\"/>\n",
" </rect>\n",
" <rect figurefirst:name=\"r3\" height=\"15.852816\" id=\"rect4167-11\" style=\"opacity:1;fill:#916f6f;fill-opacity:1;stroke:none;stroke-width:0.36000001;stroke-miterlimit:10;stroke-dasharray:0.53999999, 1.07999999;stroke-dashoffset:0;stroke-opacity:1\" width=\"15.852816\" x=\"140.0332\" y=\"732.66376\">\n",
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" <g id=\"g4155\" transform=\"matrix(2.5160983,0,0,2.5160983,-38.917192,-1051.0966)\">\n",
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" <figurefirst:svgitem figurefirst:name=\"path3\"/>\n",
" </path>\n",
" <figurefirst:svggroup figurefirst:name=\"pathgroup\"/>\n",
" </g>\n",
" <text id=\"text4142-7\" sodipodi:linespacing=\"125%\" style=\"font-style:normal;font-weight:normal;font-size:40px;line-height:125%;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#ff7f2a;fill-opacity:1;stroke:none;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\" x=\"320.78781\" xml:space=\"preserve\" y=\"782.27643\"><tspan id=\"tspan4144-9\" sodipodi:role=\"line\" x=\"320.78781\" y=\"782.27643\">l1</tspan><figurefirst:svgitem figurefirst:name=\"l1\"/></text>\n",
" </g>\n",
"</svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
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},
"metadata": {},
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},
{
"data": {
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" <sodipodi:namedview bordercolor=\"#666666\" borderopacity=\"1.0\" id=\"base\" inkscape:current-layer=\"layer1\" inkscape:cx=\"65.519287\" inkscape:cy=\"202.14359\" inkscape:document-units=\"in\" inkscape:pageopacity=\"0.0\" inkscape:pageshadow=\"2\" inkscape:window-height=\"955\" inkscape:window-maximized=\"0\" inkscape:window-width=\"1833\" inkscape:window-x=\"1286\" inkscape:window-y=\"44\" inkscape:zoom=\"0.99278098\" pagecolor=\"#ffffff\" showgrid=\"false\" units=\"in\"/>\n",
" <defs id=\"defs4\"/>\n",
" <metadata id=\"metadata7\">\n",
" <rdf:RDF>\n",
" <cc:Work rdf:about=\"\">\n",
" <dc:format>image/svg+xml</dc:format>\n",
" <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
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" </rdf:RDF>\n",
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" <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"49.7841358781\" xlink:href=\"#m8ce01e3208\" y=\"77.3371861483\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_24\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-width:0.5;\" x=\"237.440348403\" xlink:href=\"#mff27b9aa69\" y=\"77.3371861483\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_12\">\n",
" <!-- 1.0 -->\n",
" <g transform=\"translate(29.8810108781 80.0965611483)scale(0.1 -0.1)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
"</g></svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import figurefirst\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import display,SVG\n",
"\n",
"layout = figurefirst.FigureLayout('example_svgitem_layout.svg')\n",
"layout.make_mplfigures()\n",
"cdict1 = {'r1':0.3,'r2':0.1,'r3':0.9}\n",
"cdict2 = {'path1':0.3,'path2':0.1,'path3':0.9}\n",
"for key,patch in layout.svgitems['svggroup'].items():\n",
" clev = cdict1[key]\n",
" hexi = matplotlib.colors.rgb2hex(plt.cm.viridis(clev))\n",
" patch.style['fill'] = str(hexi)\n",
"\n",
"for key,patch in layout.svgitems['pathgroup'].items():\n",
" clev = cdict2[key]\n",
" hexi = matplotlib.colors.rgb2hex(plt.cm.viridis(clev))\n",
" patch.style['fill'] = str(hexi)\n",
" \n",
"layout.svgitems['l1'].style['fill'] = str(hexi)\n",
"layout.svgitems['l1'].text = str(clev)\n",
"\n",
"layout.apply_svg_attrs()\n",
"layout.save('svgitem_testoutput.svg')\n",
"display(SVG('example_svgitem_layout.svg'))\n",
"display(SVG('svgitem_testoutput.svg'))"
]
},
{
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
RajeshThevar/Image-Classification | image-classification/dlnd_image_classification.ipynb | 1 | 58514 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Image Classification\n",
"In this project, you'll classify images from the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html). The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded. You'll get to apply what you learned and build a convolutional, max pooling, dropout, and fully connected layers. At the end, you'll get to see your neural network's predictions on the sample images.\n",
"## Get the Data\n",
"Run the following cell to download the [CIFAR-10 dataset for python](https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All files found!\n"
]
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"from urllib.request import urlretrieve\n",
"from os.path import isfile, isdir\n",
"from tqdm import tqdm\n",
"import problem_unittests as tests\n",
"import tarfile\n",
"\n",
"cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
"\n",
"# Use Floyd's cifar-10 dataset if present\n",
"floyd_cifar10_location = '/input/cifar-10/python.tar.gz'\n",
"if isfile(floyd_cifar10_location):\n",
" tar_gz_path = floyd_cifar10_location\n",
"else:\n",
" tar_gz_path = 'cifar-10-python.tar.gz'\n",
"\n",
"class DLProgress(tqdm):\n",
" last_block = 0\n",
"\n",
" def hook(self, block_num=1, block_size=1, total_size=None):\n",
" self.total = total_size\n",
" self.update((block_num - self.last_block) * block_size)\n",
" self.last_block = block_num\n",
"\n",
"if not isfile(tar_gz_path):\n",
" with DLProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
" urlretrieve(\n",
" 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
" tar_gz_path,\n",
" pbar.hook)\n",
"\n",
"if not isdir(cifar10_dataset_folder_path):\n",
" with tarfile.open(tar_gz_path) as tar:\n",
" tar.extractall()\n",
" tar.close()\n",
"\n",
"\n",
"tests.test_folder_path(cifar10_dataset_folder_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Data\n",
"The dataset is broken into batches to prevent your machine from running out of memory. The CIFAR-10 dataset consists of 5 batches, named `data_batch_1`, `data_batch_2`, etc.. Each batch contains the labels and images that are one of the following:\n",
"* airplane\n",
"* automobile\n",
"* bird\n",
"* cat\n",
"* deer\n",
"* dog\n",
"* frog\n",
"* horse\n",
"* ship\n",
"* truck\n",
"\n",
"Understanding a dataset is part of making predictions on the data. Play around with the code cell below by changing the `batch_id` and `sample_id`. The `batch_id` is the id for a batch (1-5). The `sample_id` is the id for a image and label pair in the batch.\n",
"\n",
"Ask yourself \"What are all possible labels?\", \"What is the range of values for the image data?\", \"Are the labels in order or random?\". Answers to questions like these will help you preprocess the data and end up with better predictions."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Stats of batch 2:\n",
"Samples: 10000\n",
"Label Counts: {0: 984, 1: 1007, 2: 1010, 3: 995, 4: 1010, 5: 988, 6: 1008, 7: 1026, 8: 987, 9: 985}\n",
"First 20 Labels: [1, 6, 6, 8, 8, 3, 4, 6, 0, 6, 0, 3, 6, 6, 5, 4, 8, 3, 2, 6]\n",
"\n",
"Example of Image 985:\n",
"Image - Min Value: 2 Max Value: 224\n",
"Image - Shape: (32, 32, 3)\n",
"Label - Label Id: 6 Name: frog\n"
]
},
{
"data": {
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wRQ8AhZVdr7uY5FarlpcuhTMr68n1qdk0nJnMz1K31nODUO1HH90KZx4f59b8Hj8+D2dO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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ea38240>"
]
},
"metadata": {
"image/png": {
"height": 250,
"width": 253
}
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import helper\n",
"import numpy as np\n",
"\n",
"# Explore the dataset\n",
"batch_id = 2\n",
"sample_id = 985\n",
"helper.display_stats(cifar10_dataset_folder_path, batch_id, sample_id)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import sklearn\n",
"from sklearn import preprocessing\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implement Preprocess Functions\n",
"### Normalize\n",
"In the cell below, implement the `normalize` function to take in image data, `x`, and return it as a normalized Numpy array. The values should be in the range of 0 to 1, inclusive. The return object should be the same shape as `x`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def normalize(x):\n",
" \"\"\"\n",
" Normalize a list of sample image data in the range of 0 to 1\n",
" : x: List of image data. The image shape is (32, 32, 3)\n",
" : return: Numpy array of normalize data\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" normalize = []\n",
" for image in x:\n",
" normalized_values = (image - np.min(image)) / (np.max(image) - np.min(image))\n",
" normalize.append(normalized_values)\n",
" return np.asarray(normalize)\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_normalize(normalize)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### One-hot encode\n",
"Just like the previous code cell, you'll be implementing a function for preprocessing. This time, you'll implement the `one_hot_encode` function. The input, `x`, are a list of labels. Implement the function to return the list of labels as One-Hot encoded Numpy array. The possible values for labels are 0 to 9. The one-hot encoding function should return the same encoding for each value between each call to `one_hot_encode`. Make sure to save the map of encodings outside the function.\n",
"\n",
"Hint: Don't reinvent the wheel."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"label_encode = preprocessing.LabelBinarizer()\n",
"label_encode.fit(range(10))\n",
"\n",
"\n",
"def one_hot_encode(x):\n",
" \"\"\"\n",
" One hot encode a list of sample labels. Return a one-hot encoded vector for each label.\n",
" : x: List of sample Labels\n",
" : return: Numpy array of one-hot encoded labels\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" encoded = label_encode.transform(x)\n",
" return encoded\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_one_hot_encode(one_hot_encode)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Randomize Data\n",
"As you saw from exploring the data above, the order of the samples are randomized. It doesn't hurt to randomize it again, but you don't need to for this dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preprocess all the data and save it\n",
"Running the code cell below will preprocess all the CIFAR-10 data and save it to file. The code below also uses 10% of the training data for validation."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"# Preprocess Training, Validation, and Testing Data\n",
"helper.preprocess_and_save_data(cifar10_dataset_folder_path, normalize, one_hot_encode)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Point\n",
"This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import pickle\n",
"import problem_unittests as tests\n",
"import helper\n",
"\n",
"# Load the Preprocessed Validation data\n",
"valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the network\n",
"For the neural network, you'll build each layer into a function. Most of the code you've seen has been outside of functions. To test your code more thoroughly, we require that you put each layer in a function. This allows us to give you better feedback and test for simple mistakes using our unittests before you submit your project.\n",
"\n",
">**Note:** If you're finding it hard to dedicate enough time for this course each week, we've provided a small shortcut to this part of the project. In the next couple of problems, you'll have the option to use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages to build each layer, except the layers you build in the \"Convolutional and Max Pooling Layer\" section. TF Layers is similar to Keras's and TFLearn's abstraction to layers, so it's easy to pickup.\n",
"\n",
">However, if you would like to get the most out of this course, try to solve all the problems _without_ using anything from the TF Layers packages. You **can** still use classes from other packages that happen to have the same name as ones you find in TF Layers! For example, instead of using the TF Layers version of the `conv2d` class, [tf.layers.conv2d](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d), you would want to use the TF Neural Network version of `conv2d`, [tf.nn.conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d). \n",
"\n",
"Let's begin!\n",
"\n",
"### Input\n",
"The neural network needs to read the image data, one-hot encoded labels, and dropout keep probability. Implement the following functions\n",
"* Implement `neural_net_image_input`\n",
" * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
" * Set the shape using `image_shape` with batch size set to `None`.\n",
" * Name the TensorFlow placeholder \"x\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
"* Implement `neural_net_label_input`\n",
" * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
" * Set the shape using `n_classes` with batch size set to `None`.\n",
" * Name the TensorFlow placeholder \"y\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
"* Implement `neural_net_keep_prob_input`\n",
" * Return a [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder) for dropout keep probability.\n",
" * Name the TensorFlow placeholder \"keep_prob\" using the TensorFlow `name` parameter in the [TF Placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder).\n",
"\n",
"These names will be used at the end of the project to load your saved model.\n",
"\n",
"Note: `None` for shapes in TensorFlow allow for a dynamic size."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image Input Tests Passed.\n",
"Label Input Tests Passed.\n",
"Keep Prob Tests Passed.\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"\n",
"def neural_net_image_input(image_shape):\n",
" \"\"\"\n",
" Return a Tensor for a batch of image input\n",
" : image_shape: Shape of the images\n",
" : return: Tensor for image input.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" batch_size = None\n",
" image_input = tf.placeholder(tf.float32, shape=[batch_size, image_shape[0], image_shape[1], image_shape[2]], name='x')\n",
" return image_input\n",
"\n",
"\n",
"def neural_net_label_input(n_classes):\n",
" \"\"\"\n",
" Return a Tensor for a batch of label input\n",
" : n_classes: Number of classes\n",
" : return: Tensor for label input.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" label_input = tf.placeholder(tf.float32, shape=[None, n_classes], name='y')\n",
" return label_input\n",
"\n",
"\n",
"def neural_net_keep_prob_input():\n",
" \"\"\"\n",
" Return a Tensor for keep probability\n",
" : return: Tensor for keep probability.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" keep_probability = tf.placeholder(tf.float32, name='keep_prob')\n",
" return keep_probability\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tf.reset_default_graph()\n",
"tests.test_nn_image_inputs(neural_net_image_input)\n",
"tests.test_nn_label_inputs(neural_net_label_input)\n",
"tests.test_nn_keep_prob_inputs(neural_net_keep_prob_input)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convolution and Max Pooling Layer\n",
"Convolution layers have a lot of success with images. For this code cell, you should implement the function `conv2d_maxpool` to apply convolution then max pooling:\n",
"* Create the weight and bias using `conv_ksize`, `conv_num_outputs` and the shape of `x_tensor`.\n",
"* Apply a convolution to `x_tensor` using weight and `conv_strides`.\n",
" * We recommend you use same padding, but you're welcome to use any padding.\n",
"* Add bias\n",
"* Add a nonlinear activation to the convolution.\n",
"* Apply Max Pooling using `pool_ksize` and `pool_strides`.\n",
" * We recommend you use same padding, but you're welcome to use any padding.\n",
"\n",
"**Note:** You **can't** use [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) for **this** layer, but you can still use TensorFlow's [Neural Network](https://www.tensorflow.org/api_docs/python/tf/nn) package. You may still use the shortcut option for all the **other** layers."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Placeholder_5:0\", shape=(?, 32, 32, 5), dtype=float32)\n",
"<tf.Variable 'Variable_8:0' shape=(2, 2, 5, 10) dtype=float32_ref>\n",
"Tests Passed\n"
]
}
],
"source": [
"def conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides):\n",
" \"\"\"\n",
" Apply convolution then max pooling to x_tensor\n",
" :param x_tensor: TensorFlow Tensor\n",
" :param conv_num_outputs: Number of outputs for the convolutional layer\n",
" :param conv_ksize: kernal size 2-D Tuple for the convolutional layer\n",
" :param conv_strides: Stride 2-D Tuple for convolution\n",
" :param pool_ksize: kernal size 2-D Tuple for pool\n",
" :param pool_strides: Stride 2-D Tuple for pool\n",
" : return: A tensor that represents convolution and max pooling of x_tensor\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" print(x_tensor)\n",
" weight = tf.Variable(tf.truncated_normal([conv_ksize[0], conv_ksize[1], int(x_tensor.shape[3]), conv_num_outputs])) #int(x_tensor.shape[3]) #x_tensor.get_shape().as_list()[-1]\n",
" print(weight)\n",
" bias = tf.Variable(tf.random_normal([conv_num_outputs]))\n",
" conv = tf.nn.conv2d(x_tensor, weight, strides= [1, conv_strides[0], conv_strides[1], 1], padding='SAME')\n",
" add = tf.nn.bias_add(conv, bias)\n",
" activation = tf.nn.relu(add)\n",
" max_pooling = tf.nn.max_pool(activation, ksize=[1, pool_ksize[0], pool_ksize[1], 1], strides=[1, pool_strides[0], pool_strides[1], 1], padding='SAME')\n",
" \n",
" return max_pooling \n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_con_pool(conv2d_maxpool)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Flatten Layer\n",
"Implement the `flatten` function to change the dimension of `x_tensor` from a 4-D tensor to a 2-D tensor. The output should be the shape (*Batch Size*, *Flattened Image Size*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Placeholder_15:0\", shape=(?, 10, 30, 6), dtype=float32)\n",
"1800\n",
"Tensor(\"Flatten/Reshape:0\", shape=(?, 1800), dtype=float32)\n",
"Tests Passed\n"
]
}
],
"source": [
"def flatten(x_tensor):\n",
" \"\"\"\n",
" Flatten x_tensor to (Batch Size, Flattened Image Size)\n",
" : x_tensor: A tensor of size (Batch Size, ...), where ... are the image dimensions.\n",
" : return: A tensor of size (Batch Size, Flattened Image Size).\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
"# batch_size = x_tensor.get_shape()[-1]\n",
" batch_size = x_tensor.shape[0]\n",
" print(x_tensor)\n",
" width = x_tensor.shape[1]\n",
" breadth = x_tensor.shape[2]\n",
" height = x_tensor.shape[3]\n",
" image_size = width * breadth * height\n",
" print(image_size)\n",
"# flattened_1 = tf.reshape(x_tensor, [batch_size , image_size])\n",
" flattened = tf.contrib.layers.flatten(x_tensor)\n",
" print(flattened)\n",
" return flattened\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_flatten(flatten)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fully-Connected Layer\n",
"Implement the `fully_conn` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Placeholder_17:0\", shape=(?, 128), dtype=float32)\n",
"40\n",
"Tests Passed\n"
]
}
],
"source": [
"def fully_conn(x_tensor, num_outputs):\n",
" \"\"\"\n",
" Apply a fully connected layer to x_tensor using weight and bias\n",
" : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
" : num_outputs: The number of output that the new tensor should be.\n",
" : return: A 2-D tensor where the second dimension is num_outputs.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" print(x_tensor)\n",
" print(num_outputs)\n",
" weights = tf.Variable(tf.random_normal((int(x_tensor.get_shape().as_list()[1]), num_outputs), stddev = 0.01))\n",
" bias = tf.Variable(tf.zeros(num_outputs))\n",
" fully_connected = tf.add(tf.matmul(x_tensor, weights), bias)\n",
" fully_connected = tf.nn.relu(fully_connected)\n",
"# fully_connected = tf.contrib.layers.fully_connected(inputs= x_tensor, num_outputs= num_outputs )\n",
" return fully_connected\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_fully_conn(fully_conn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Output Layer\n",
"Implement the `output` function to apply a fully connected layer to `x_tensor` with the shape (*Batch Size*, *num_outputs*). Shortcut option: you can use classes from the [TensorFlow Layers](https://www.tensorflow.org/api_docs/python/tf/layers) or [TensorFlow Layers (contrib)](https://www.tensorflow.org/api_guides/python/contrib.layers) packages for this layer. For more of a challenge, only use other TensorFlow packages.\n",
"\n",
"**Note:** Activation, softmax, or cross entropy should **not** be applied to this."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def output(x_tensor, num_outputs):\n",
" \"\"\"\n",
" Apply a output layer to x_tensor using weight and bias\n",
" : x_tensor: A 2-D tensor where the first dimension is batch size.\n",
" : num_outputs: The number of output that the new tensor should be.\n",
" : return: A 2-D tensor where the second dimension is num_outputs.\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" weights = tf.Variable(tf.random_normal((int(x_tensor.get_shape().as_list()[1]), num_outputs), stddev = 0.01))\n",
" bias = tf.Variable(tf.zeros(num_outputs))\n",
" dense = tf.add(tf.matmul(x_tensor, weights), bias)\n",
"# dense = tf.layers.dense(inputs= x_tensor, units= num_outputs)\n",
" return dense\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_output(output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Convolutional Model\n",
"Implement the function `conv_net` to create a convolutional neural network model. The function takes in a batch of images, `x`, and outputs logits. Use the layers you created above to create this model:\n",
"\n",
"* Apply 1, 2, or 3 Convolution and Max Pool layers\n",
"* Apply a Flatten Layer\n",
"* Apply 1, 2, or 3 Fully Connected Layers\n",
"* Apply an Output Layer\n",
"* Return the output\n",
"* Apply [TensorFlow's Dropout](https://www.tensorflow.org/api_docs/python/tf/nn/dropout) to one or more layers in the model using `keep_prob`. "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"x:0\", shape=(?, 32, 32, 3), dtype=float32)\n",
"Tensor(\"x:0\", shape=(?, 32, 32, 3), dtype=float32)\n",
"<tf.Variable 'Variable:0' shape=(2, 2, 3, 64) dtype=float32_ref>\n",
"Tensor(\"dropout/Identity:0\", shape=(?, 32, 32, 64), dtype=float32)\n",
"65536\n",
"Tensor(\"Flatten/Reshape:0\", shape=(?, 65536), dtype=float32)\n",
"Tensor(\"Flatten/Reshape:0\", shape=(?, 65536), dtype=float32)\n",
"128\n",
"Tensor(\"Relu_1:0\", shape=(?, 128), dtype=float32)\n",
"Tensor(\"Placeholder:0\", shape=(?, 32, 32, 3), dtype=float32)\n",
"Tensor(\"Placeholder:0\", shape=(?, 32, 32, 3), dtype=float32)\n",
"<tf.Variable 'Variable_6:0' shape=(2, 2, 3, 64) dtype=float32_ref>\n",
"Tensor(\"dropout_3/Identity:0\", shape=(?, 32, 32, 64), dtype=float32)\n",
"65536\n",
"Tensor(\"Flatten_1/Reshape:0\", shape=(?, 65536), dtype=float32)\n",
"Tensor(\"Flatten_1/Reshape:0\", shape=(?, 65536), dtype=float32)\n",
"128\n",
"Tensor(\"Relu_3:0\", shape=(?, 128), dtype=float32)\n",
"Neural Network Built!\n"
]
}
],
"source": [
"def conv_net(x, keep_prob):\n",
" \"\"\"\n",
" Create a convolutional neural network model\n",
" : x: Placeholder tensor that holds image data.\n",
" : keep_prob: Placeholder tensor that hold dropout keep probability.\n",
" : return: Tensor that represents logits\n",
" \"\"\"\n",
" # TODO: Apply 1, 2, or 3 Convolution and Max Pool layers\n",
" # Play around with different number of outputs, kernel size and stride\n",
" # Function Definition from Above:\n",
" # conv2d_maxpool(x_tensor, conv_num_outputs, conv_ksize, conv_strides, pool_ksize, pool_strides)\n",
" print(x)\n",
" apply_1 = conv2d_maxpool(x, conv_num_outputs=64, conv_ksize= [2,2], conv_strides= [1,1], pool_ksize= [2,2], pool_strides= [1,1])\n",
" dropping_out = tf.layers.dropout(apply_1, rate= keep_prob)\n",
" \n",
" \n",
"\n",
" # TODO: Apply a Flatten Layer\n",
" # Function Definition from Above:\n",
" # flatten(x_tensor)\n",
" apply_2 = flatten(dropping_out)\n",
" \n",
"\n",
" # TODO: Apply 1, 2, or 3 Fully Connected Layers\n",
" # Play around with different number of outputs\n",
" # Function Definition from Above:\n",
" # fully_conn(x_tensor, num_outputs)\n",
" apply_3 = fully_conn(apply_2, num_outputs=128)\n",
" dropping_out_2 = tf.layers.dropout(apply_3, rate= keep_prob)\n",
" print(apply_3)\n",
" \n",
" \n",
" # TODO: Apply an Output Layer\n",
" # Set this to the number of classes\n",
" # Function Definition from Above:\n",
" # output(x_tensor, num_outputs)\n",
" logits = output(dropping_out_2, 10)\n",
" \n",
" \n",
" # TODO: return output\n",
" return logits\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"\n",
"##############################\n",
"## Build the Neural Network ##\n",
"##############################\n",
"\n",
"# Remove previous weights, bias, inputs, etc..\n",
"tf.reset_default_graph()\n",
"\n",
"# Inputs\n",
"x = neural_net_image_input((32, 32, 3))\n",
"y = neural_net_label_input(10)\n",
"keep_prob = neural_net_keep_prob_input()\n",
"\n",
"# Model\n",
"logits = conv_net(x, keep_prob)\n",
"\n",
"# Name logits Tensor, so that is can be loaded from disk after training\n",
"logits = tf.identity(logits, name='logits')\n",
"\n",
"# Loss and Optimizer\n",
"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y))\n",
"optimizer = tf.train.AdamOptimizer().minimize(cost)\n",
"\n",
"# Accuracy\n",
"correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')\n",
"\n",
"tests.test_conv_net(conv_net)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Neural Network\n",
"### Single Optimization\n",
"Implement the function `train_neural_network` to do a single optimization. The optimization should use `optimizer` to optimize in `session` with a `feed_dict` of the following:\n",
"* `x` for image input\n",
"* `y` for labels\n",
"* `keep_prob` for keep probability for dropout\n",
"\n",
"This function will be called for each batch, so `tf.global_variables_initializer()` has already been called.\n",
"\n",
"Note: Nothing needs to be returned. This function is only optimizing the neural network."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def train_neural_network(session, optimizer, keep_probability, feature_batch, label_batch):\n",
" \"\"\"\n",
" Optimize the session on a batch of images and labels\n",
" : session: Current TensorFlow session\n",
" : optimizer: TensorFlow optimizer function\n",
" : keep_probability: keep probability\n",
" : feature_batch: Batch of Numpy image data\n",
" : label_batch: Batch of Numpy label data\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
"# images, label = feature_batch, label_batch\n",
"# session.run(images, label)\n",
" session.run(optimizer, feed_dict= {x: feature_batch, y: label_batch, keep_prob : keep_probability})\n",
" \n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_train_nn(train_neural_network)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show Stats\n",
"Implement the function `print_stats` to print loss and validation accuracy. Use the global variables `valid_features` and `valid_labels` to calculate validation accuracy. Use a keep probability of `1.0` to calculate the loss and validation accuracy."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def print_stats(session, feature_batch, label_batch, cost, accuracy):\n",
" \"\"\"\n",
" Print information about loss and validation accuracy\n",
" : session: Current TensorFlow session\n",
" : feature_batch: Batch of Numpy image data\n",
" : label_batch: Batch of Numpy label data\n",
" : cost: TensorFlow cost function\n",
" : accuracy: TensorFlow accuracy function\n",
" \"\"\"\n",
" # TODO: Implement Function\n",
" \n",
" loss = session.run(cost, feed_dict={ x: feature_batch, y:label_batch, keep_prob: 1.0})\n",
" validation_accuracy = session.run(accuracy, feed_dict={x: valid_features, y:valid_labels, keep_prob: 1.0})\n",
" print(\"cost: {}, accuracy: {}\".format(loss, validation_accuracy))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Hyperparameters\n",
"Tune the following parameters:\n",
"* Set `epochs` to the number of iterations until the network stops learning or start overfitting\n",
"* Set `batch_size` to the highest number that your machine has memory for. Most people set them to common sizes of memory:\n",
" * 64\n",
" * 128\n",
" * 256\n",
" * ...\n",
"* Set `keep_probability` to the probability of keeping a node using dropout"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# TODO: Tune Parameters\n",
"epochs = 100\n",
"batch_size = 64\n",
"keep_probability = 0.8"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train on a Single CIFAR-10 Batch\n",
"Instead of training the neural network on all the CIFAR-10 batches of data, let's use a single batch. This should save time while you iterate on the model to get a better accuracy. Once the final validation accuracy is 50% or greater, run the model on all the data in the next section."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checking the Training on a Single Batch...\n",
"Epoch 1, CIFAR-10 Batch 1: cost: 2.0899670124053955, accuracy: 0.25619998574256897\n",
"speed (in seconds) = 64.3204071521759\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
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"\u001b[0;32m/Users/rajesh/anaconda3/envs/py35/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 776\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 777\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 779\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 780\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/rajesh/anaconda3/envs/py35/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 980\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 981\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m 983\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/rajesh/anaconda3/envs/py35/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1031\u001b[0m return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m 1033\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1034\u001b[0m return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
"\u001b[0;32m/Users/rajesh/anaconda3/envs/py35/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1038\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1040\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1041\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/rajesh/anaconda3/envs/py35/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1019\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[1;32m 1020\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1023\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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}
],
"source": [
"import time\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"print('Checking the Training on a Single Batch...')\n",
"with tf.Session() as sess:\n",
" # Initializing the variables\n",
" sess.run(tf.global_variables_initializer())\n",
" start = time.time()\n",
" # Training cycle\n",
" for epoch in range(epochs):\n",
" batch_i = 1\n",
" for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
" train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
" \n",
" print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')\n",
" print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
" elapsed_time = float(time.time() - start)\n",
"# epoch_second = epoch / elapsed_time if elapsed_time > 0 else 0\n",
" print('speed (in seconds) = {}'.format(elapsed_time))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fully Train the Model\n",
"Now that you got a good accuracy with a single CIFAR-10 batch, try it with all five batches."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"save_model_path = './image_classification'\n",
"\n",
"print('Training...')\n",
"with tf.Session() as sess:\n",
" # Initializing the variables\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" # Training cycle\n",
" for epoch in range(epochs):\n",
" # Loop over all batches\n",
" n_batches = 5\n",
" for batch_i in range(1, n_batches + 1):\n",
" for batch_features, batch_labels in helper.load_preprocess_training_batch(batch_i, batch_size):\n",
" train_neural_network(sess, optimizer, keep_probability, batch_features, batch_labels)\n",
" print('Epoch {:>2}, CIFAR-10 Batch {}: '.format(epoch + 1, batch_i), end='')\n",
" print_stats(sess, batch_features, batch_labels, cost, accuracy)\n",
" \n",
" # Save Model\n",
" saver = tf.train.Saver()\n",
" save_path = saver.save(sess, save_model_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Checkpoint\n",
"The model has been saved to disk.\n",
"## Test Model\n",
"Test your model against the test dataset. This will be your final accuracy. You should have an accuracy greater than 50%. If you don't, keep tweaking the model architecture and parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import tensorflow as tf\n",
"import pickle\n",
"import helper\n",
"import random\n",
"\n",
"# Set batch size if not already set\n",
"try:\n",
" if batch_size:\n",
" pass\n",
"except NameError:\n",
" batch_size = 64\n",
"\n",
"save_model_path = './image_classification'\n",
"n_samples = 4\n",
"top_n_predictions = 3\n",
"\n",
"def test_model():\n",
" \"\"\"\n",
" Test the saved model against the test dataset\n",
" \"\"\"\n",
"\n",
" test_features, test_labels = pickle.load(open('preprocess_test.p', mode='rb'))\n",
" loaded_graph = tf.Graph()\n",
"\n",
" with tf.Session(graph=loaded_graph) as sess:\n",
" # Load model\n",
" loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
" loader.restore(sess, save_model_path)\n",
"\n",
" # Get Tensors from loaded model\n",
" loaded_x = loaded_graph.get_tensor_by_name('x:0')\n",
" loaded_y = loaded_graph.get_tensor_by_name('y:0')\n",
" loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
" loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
" loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
" \n",
" # Get accuracy in batches for memory limitations\n",
" test_batch_acc_total = 0\n",
" test_batch_count = 0\n",
" \n",
" for test_feature_batch, test_label_batch in helper.batch_features_labels(test_features, test_labels, batch_size):\n",
" test_batch_acc_total += sess.run(\n",
" loaded_acc,\n",
" feed_dict={loaded_x: test_feature_batch, loaded_y: test_label_batch, loaded_keep_prob: 1.0})\n",
" test_batch_count += 1\n",
"\n",
" print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
"\n",
" # Print Random Samples\n",
" random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
" random_test_predictions = sess.run(\n",
" tf.nn.top_k(tf.nn.softmax(loaded_logits), top_n_predictions),\n",
" feed_dict={loaded_x: random_test_features, loaded_y: random_test_labels, loaded_keep_prob: 1.0})\n",
" helper.display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
"\n",
"\n",
"test_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Why 50-80% Accuracy?\n",
"You might be wondering why you can't get an accuracy any higher. First things first, 50% isn't bad for a simple CNN. Pure guessing would get you 10% accuracy. However, you might notice people are getting scores [well above 80%](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130). That's because we haven't taught you all there is to know about neural networks. We still need to cover a few more techniques.\n",
"## Submitting This Project\n",
"When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_image_classification.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
bsmithyman/dotgraph | DotGraph Example.ipynb | 1 | 11303 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DotGraph Example\n",
"\n",
"Testing example created in the [Zephyr](https://github.com/uwoseis/zephyr) project using `pyreverse` from the `pylint` project. The file `Example/packages_zephyr.dot` was generated by:\n",
"\n",
"```bash\n",
"pyreverse -my -A -o dot -p zephyr ../zephyr/**/**.py\n",
"```\n",
"\n",
"## Rendered using pyreverse"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DotGraph Example"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from dotgraph import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dg = DotGraph('./Example/packages_zephyr.dot')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" .node text {\n",
" color: black;\n",
" font: 10px sans-serif;\n",
" }\n",
" .link {\n",
" fill: none;\n",
" stroke: #bbb;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" var graph = {\"directed\": true, \"graph\": {\"node\": {}, \"graph\": {\"rankdir\": \"BT\", \"charset\": \"\\\"utf-8\\\"\"}, \"edge\": {}, \"name\": \"\\\"packages_zephyr\\\"\"}, \"nodes\": [{\"shape\": \"\\\"box\\\"\", \"id\": \"11\", \"label\": \"\\\"zephyr.middleware.fields\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"10\", \"label\": \"\\\"zephyr.middleware\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"13\", \"label\": \"\\\"zephyr.middleware.survey\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"12\", \"label\": \"\\\"zephyr.middleware.problem\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"14\", \"label\": \"\\\"zephyr.middleware.util\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"1\", \"label\": \"\\\"zephyr.backend.analytical\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"0\", \"label\": \"\\\"zephyr.backend\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"3\", \"label\": \"\\\"zephyr.backend.eurus\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"2\", \"label\": \"\\\"zephyr.backend.discretization\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"5\", \"label\": \"\\\"zephyr.backend.minizephyr\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"4\", \"label\": \"\\\"zephyr.backend.meta\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"7\", \"label\": \"\\\"zephyr.backend.source\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"6\", \"label\": \"\\\"zephyr.backend.solver\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"9\", \"label\": \"\\\"zephyr.frontend.cli\\\"\"}, {\"shape\": \"\\\"box\\\"\", \"id\": \"8\", \"label\": \"\\\"zephyr.frontend\\\"\"}], \"links\": [{\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 2, \"key\": 0, \"source\": 1}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 3, \"key\": 0, \"source\": 1}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 0, \"key\": 0, \"source\": 3}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 2, \"key\": 0, \"source\": 3}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 5, \"key\": 0, \"source\": 6}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 7, \"key\": 0, \"source\": 6}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 8, \"key\": 0, \"source\": 6}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 9, \"key\": 0, \"source\": 6}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 10, \"key\": 0, \"source\": 6}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 11, \"key\": 0, \"source\": 6}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 8, \"key\": 0, \"source\": 7}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 10, \"key\": 0, \"source\": 8}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 12, \"key\": 0, \"source\": 8}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 8, \"key\": 0, \"source\": 9}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 9, \"key\": 0, \"source\": 9}, {\"arrowtail\": \"\\\"none\\\"\", \"arrowhead\": \"\\\"open\\\"\", \"target\": 10, \"key\": 0, \"source\": 11}], \"multigraph\": true};\n",
" \n",
" require.config({paths: {d3: \"https://d3js.org/d3.v3.min\"}});\n",
" \n",
" require([\"d3\"], function(d3) {\n",
" \n",
" var width = 1000, height = 450;\n",
" var rwidth = 150, rheight=20;\n",
" var rr = 10;\n",
" var color = d3.scale.category10();\n",
" var domain = [0, 1, 2, 3];\n",
" color.domain(domain);\n",
" \n",
" var force = d3.layout.force()\n",
" .charge(-200)\n",
" .linkDistance(50)\n",
" .linkStrength(2)\n",
" .size([width, height]);\n",
" \n",
" var svg = d3.select(\"#Graph2376206610\").select(\"svg\");\n",
" \n",
" if (svg.empty()) {\n",
" svg = d3.select(\"#Graph2376206610\").append(\"svg\")\n",
" .attr(\"width\", width)\n",
" .attr(\"height\", height);\n",
" }\n",
" \n",
" var nodes = graph.nodes.slice(),\n",
" links = [],\n",
" bilinks = [];\n",
" \n",
" graph.links.forEach(function(link) {\n",
" var s = nodes[link.source],\n",
" t = nodes[link.target],\n",
" i = {}; // intermediate node\n",
" nodes.push(i);\n",
" links.push({source: s, target: i}, {source: i, target: t});\n",
" bilinks.push([s, i, t]);\n",
" });\n",
" \n",
" force.nodes(nodes)\n",
" .links(links)\n",
" .start();\n",
" \n",
" var link = svg.selectAll(\".link\")\n",
" .data(bilinks)\n",
" .enter().append(\"path\")\n",
" .attr(\"class\", \"link\");\n",
" \n",
" var node = svg.selectAll(\".node\")\n",
" .data(graph.nodes)\n",
" .enter().append(\"g\")\n",
" .attr(\"class\", \"node\")\n",
" .call(force.drag);\n",
" \n",
" node.append(\"rect\")\n",
" .attr(\"x\", -rwidth/2)\n",
" .attr(\"y\", -rheight/2)\n",
" .attr(\"width\", rwidth)\n",
" .attr(\"height\", rheight)\n",
" .attr(\"rx\", rr)\n",
" .attr(\"ry\", rr)\n",
" .style(\"fill\", \"white\")\n",
" .style(\"stroke\", \"black\");\n",
" \n",
" node.append(\"text\")\n",
" .attr(\"dx\", 0)\n",
" .attr(\"dy\", 3)\n",
" .attr(\"text-anchor\", \"middle\")\n",
" .text(function(d) { return d.label; });\n",
" \n",
" node.append(\"title\")\n",
" .text(function(d) { return d.label; });\n",
" \n",
" force.on(\"tick\", function() {\n",
" link.attr(\"d\", function(d) {\n",
" return \"M\" + d[0].x + \",\" + d[0].y + \"S\" + d[1].x + \",\" + d[1].y + \" \" + d[2].x + \",\" + d[2].y;\n",
" });\n",
" \n",
" node.attr(\"transform\", function(d) { return \"translate(\" + d.x + \",\" + d.y + \")\"; });\n",
" });\n",
" });\n",
" </script>\n",
" "
],
"text/plain": [
"<dotgraph.DotGraph at 0x7f67dd2cb310>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dg"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on class DotGraph in module dotgraph:\n",
"\n",
"class DotGraph(__builtin__.object)\n",
" | Class that returns various representations of the directed graph\n",
" | in a 'dot' file. This includes converting to NetworkX graph object,\n",
" | Python dictionary, JSON, and HTML with d3.js rendering (which can\n",
" | be displayed inline in an IPython/Jupyter notebook).\n",
" | \n",
" | Methods defined here:\n",
" | \n",
" | __init__(self, infile, template=None)\n",
" | Initialize DotGraph\n",
" | \n",
" | Args:\n",
" | infile (str): Input file in dot format\n",
" | template (str): Input file for HTML template\n",
" | \n",
" | Returns:\n",
" | new DotGraph instance\n",
" | \n",
" | render(self)\n",
" | Returns IPython display representation\n",
" | \n",
" | ----------------------------------------------------------------------\n",
" | Data descriptors defined here:\n",
" | \n",
" | __dict__\n",
" | dictionary for instance variables (if defined)\n",
" | \n",
" | __weakref__\n",
" | list of weak references to the object (if defined)\n",
" | \n",
" | dict\n",
" | Returns dictionary representation\n",
" | \n",
" | graph\n",
" | Returns NetworkX graph representation\n",
" | \n",
" | html\n",
" | Returns HTML representation\n",
" | \n",
" | json\n",
" | Returns JSON representation\n",
"\n"
]
}
],
"source": [
"help(DotGraph)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "python2",
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tensorflow/tfx-addons | tfx_addons/feature_selection/nb/Example.ipynb | 1 | 2472 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "408bf10c",
"metadata": {},
"outputs": [],
"source": [
"from component import FeatureSelection\n",
"from tfx.components import CsvExampleGen\n",
"from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "95533af7",
"metadata": {},
"outputs": [],
"source": [
"context = InteractiveContext()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1e35dbe",
"metadata": {},
"outputs": [],
"source": [
"import urllib.request\n",
"import tempfile\n",
"import os\n",
"\n",
"# getting data and setup CsvExampleGen\n",
"DATA_ROOT = tempfile.mkdtemp(prefix='tfx-data') # Create a temporary directory.\n",
"_data_url = 'https://raw.githubusercontent.com/tensorflow/tfx/master/tfx/examples/penguin/data/labelled/penguins_processed.csv'\n",
"_data_filepath = os.path.join(DATA_ROOT, \"data.csv\")\n",
"urllib.request.urlretrieve(_data_url, _data_filepath)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36c3d298",
"metadata": {},
"outputs": [],
"source": [
"example_gen = CsvExampleGen(input_base=DATA_ROOT)\n",
"context.run(example_gen)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fa28bcd8",
"metadata": {},
"outputs": [],
"source": [
"# give path to the module file\n",
"feature_selector = FeatureSelection(orig_examples = example_gen.outputs['examples'],\n",
" module_file=\"module_file\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9afcfe7f",
"metadata": {},
"outputs": [],
"source": [
"context.run(feature_selector)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b088c2c8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
readywater/caltrain-predict | 00getdata.ipynb | 2 | 16668 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Grab as many tweets as we can from the @caltrain_news twitter feed, export to a _raw_ CSV, and to a formatted _csv_ for import into another notebook"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys\n",
"import re\n",
"import time\n",
"import datetime\n",
"# Requires for ipython to pick up on twitter?\n",
"sys.path.append('/Library/Python/2.7/site-packages/')\n",
"import twitter\n",
"import pandas as pd\n",
"import func\n",
"\n",
"# inline plot\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"keys = pd.read_csv('keys.csv') # hidden from github\n",
"\n",
"api = twitter.Api(consumer_key=keys.iloc[0].string,\n",
" consumer_secret=keys.iloc[1].string,\n",
" access_token_key=keys.iloc[2].string,\n",
" access_token_secret=keys.iloc[3].string)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<twitter.user.User at 0x1180f6850>"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"api.VerifyCredentials()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"api.GetUserTimeline?"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"twt = []\n",
"for i in range(3200/200): # defined by api\n",
" if i is 0:\n",
" # If the first call, just start from most recent\n",
" twt.append(api.GetUserTimeline(screen_name='caltrain_news', count=200))\n",
" else:\n",
" # If not the first in the lot, grab the last tweet id and continue\n",
" next_id = re.search('\\\"id\\\":\\s([0-9]{18})',str(twt[-1][-1]))\n",
" twt.append(api.GetUserTimeline(screen_name='caltrain_news', count=200, max_id=next_id.group(1)))\n",
" time.sleep(.5)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"200"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This is a bit clumsy. I'm grabbing batches of up to 200 tweets,\n",
"# and then need to resort into a flat list, and convert to dicts\n",
"# on the fly\n",
"df = pd.DataFrame([t.AsDict() for call in twt for t in call])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>created_at</th>\n",
" <th>favorite_count</th>\n",
" <th>favorited</th>\n",
" <th>hashtags</th>\n",
" <th>id</th>\n",
" <th>in_reply_to_screen_name</th>\n",
" <th>in_reply_to_status_id</th>\n",
" <th>in_reply_to_user_id</th>\n",
" <th>lang</th>\n",
" <th>media</th>\n",
" <th>place</th>\n",
" <th>retweet_count</th>\n",
" <th>retweeted</th>\n",
" <th>retweeted_status</th>\n",
" <th>source</th>\n",
" <th>text</th>\n",
" <th>truncated</th>\n",
" <th>urls</th>\n",
" <th>user</th>\n",
" <th>user_mentions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Tue Jan 26 20:32:15 +0000 2016</td>\n",
" <td>6</td>\n",
" <td>False</td>\n",
" <td>[SanFrancisco]</td>\n",
" <td>692082643022680064</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>en</td>\n",
" <td>[{u'expanded_url': u'http://twitter.com/Caltra...</td>\n",
" <td>NaN</td>\n",
" <td>7</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td><a href=\"https://about.twitter.com/products/tw...</td>\n",
" <td>NOTICE: Ped &amp; Bike detours in place for Ma...</td>\n",
" <td>False</td>\n",
" <td>{u'https://t.co/hcYGYF5L5S': u'https://www.sfm...</td>\n",
" <td>{u'id': 456808166, u'verified': True, u'profil...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Tue Jan 26 19:41:32 +0000 2016</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>692069881559134208</td>\n",
" <td>therealwall</td>\n",
" <td>6.920673e+17</td>\n",
" <td>46136761</td>\n",
" <td>en</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td><a href=\"https://about.twitter.com/products/tw...</td>\n",
" <td>@therealwall After the end of the concert we w...</td>\n",
" <td>False</td>\n",
" <td>{u'https://t.co/3f9VEAaGTY': u'http://www.calt...</td>\n",
" <td>{u'id': 456808166, u'verified': True, u'profil...</td>\n",
" <td>[{u'screen_name': u'therealwall', u'id': 46136...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Tue Jan 26 19:28:52 +0000 2016</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>[SB50]</td>\n",
" <td>692066695838498816</td>\n",
" <td>AemalTheAFGHAN</td>\n",
" <td>6.920578e+17</td>\n",
" <td>291505788</td>\n",
" <td>en</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td><a href=\"https://about.twitter.com/products/tw...</td>\n",
" <td>@AemalTheAFGHAN @BKDenverSports We're glad to ...</td>\n",
" <td>False</td>\n",
" <td>{u'https://t.co/fgMOSXplzZ': u'http://www.calt...</td>\n",
" <td>{u'id': 456808166, u'verified': True, u'profil...</td>\n",
" <td>[{u'screen_name': u'AemalTheAFGHAN', u'id': 29...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" created_at favorite_count favorited hashtags \\\n",
"0 Tue Jan 26 20:32:15 +0000 2016 6 False [SanFrancisco] \n",
"1 Tue Jan 26 19:41:32 +0000 2016 NaN False NaN \n",
"2 Tue Jan 26 19:28:52 +0000 2016 NaN False [SB50] \n",
"\n",
" id in_reply_to_screen_name in_reply_to_status_id \\\n",
"0 692082643022680064 NaN NaN \n",
"1 692069881559134208 therealwall 6.920673e+17 \n",
"2 692066695838498816 AemalTheAFGHAN 6.920578e+17 \n",
"\n",
" in_reply_to_user_id lang \\\n",
"0 NaN en \n",
"1 46136761 en \n",
"2 291505788 en \n",
"\n",
" media place retweet_count \\\n",
"0 [{u'expanded_url': u'http://twitter.com/Caltra... NaN 7 \n",
"1 NaN NaN NaN \n",
"2 NaN NaN NaN \n",
"\n",
" retweeted retweeted_status \\\n",
"0 False NaN \n",
"1 False NaN \n",
"2 False NaN \n",
"\n",
" source \\\n",
"0 <a href=\"https://about.twitter.com/products/tw... \n",
"1 <a href=\"https://about.twitter.com/products/tw... \n",
"2 <a href=\"https://about.twitter.com/products/tw... \n",
"\n",
" text truncated \\\n",
"0 NOTICE: Ped & Bike detours in place for Ma... False \n",
"1 @therealwall After the end of the concert we w... False \n",
"2 @AemalTheAFGHAN @BKDenverSports We're glad to ... False \n",
"\n",
" urls \\\n",
"0 {u'https://t.co/hcYGYF5L5S': u'https://www.sfm... \n",
"1 {u'https://t.co/3f9VEAaGTY': u'http://www.calt... \n",
"2 {u'https://t.co/fgMOSXplzZ': u'http://www.calt... \n",
"\n",
" user \\\n",
"0 {u'id': 456808166, u'verified': True, u'profil... \n",
"1 {u'id': 456808166, u'verified': True, u'profil... \n",
"2 {u'id': 456808166, u'verified': True, u'profil... \n",
"\n",
" user_mentions \n",
"0 NaN \n",
"1 [{u'screen_name': u'therealwall', u'id': 46136... \n",
"2 [{u'screen_name': u'AemalTheAFGHAN', u'id': 29... "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1509.000000</td>\n",
" <td>3199</td>\n",
" <td>3.199000e+03</td>\n",
" <td>1.270000e+03</td>\n",
" <td>1.300000e+03</td>\n",
" <td>1946.000000</td>\n",
" <td>3199</td>\n",
" <td>3199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.513585</td>\n",
" <td>0</td>\n",
" <td>6.484877e+17</td>\n",
" <td>6.474381e+17</td>\n",
" <td>6.174353e+08</td>\n",
" <td>18.703494</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6.693903</td>\n",
" <td>0</td>\n",
" <td>2.243800e+16</td>\n",
" <td>2.323574e+16</td>\n",
" <td>1.026750e+09</td>\n",
" <td>467.130516</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>False</td>\n",
" <td>6.069953e+17</td>\n",
" <td>6.069949e+17</td>\n",
" <td>3.632000e+03</td>\n",
" <td>1.000000</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>0</td>\n",
" <td>6.308106e+17</td>\n",
" <td>6.265296e+17</td>\n",
" <td>2.001160e+07</td>\n",
" <td>1.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.000000</td>\n",
" <td>0</td>\n",
" <td>6.497296e+17</td>\n",
" <td>6.494642e+17</td>\n",
" <td>1.069600e+08</td>\n",
" <td>3.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.000000</td>\n",
" <td>0</td>\n",
" <td>6.664330e+17</td>\n",
" <td>6.626031e+17</td>\n",
" <td>5.472453e+08</td>\n",
" <td>4.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>116.000000</td>\n",
" <td>False</td>\n",
" <td>6.920826e+17</td>\n",
" <td>6.920673e+17</td>\n",
" <td>4.265436e+09</td>\n",
" <td>18479.000000</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" favorite_count favorited id in_reply_to_status_id \\\n",
"count 1509.000000 3199 3.199000e+03 1.270000e+03 \n",
"mean 3.513585 0 6.484877e+17 6.474381e+17 \n",
"std 6.693903 0 2.243800e+16 2.323574e+16 \n",
"min 1.000000 False 6.069953e+17 6.069949e+17 \n",
"25% 1.000000 0 6.308106e+17 6.265296e+17 \n",
"50% 2.000000 0 6.497296e+17 6.494642e+17 \n",
"75% 3.000000 0 6.664330e+17 6.626031e+17 \n",
"max 116.000000 False 6.920826e+17 6.920673e+17 \n",
"\n",
" in_reply_to_user_id retweet_count retweeted truncated \n",
"count 1.300000e+03 1946.000000 3199 3199 \n",
"mean 6.174353e+08 18.703494 0 0 \n",
"std 1.026750e+09 467.130516 0 0 \n",
"min 3.632000e+03 1.000000 False False \n",
"25% 2.001160e+07 1.000000 0 0 \n",
"50% 1.069600e+08 3.000000 0 0 \n",
"75% 5.472453e+08 4.000000 0 0 \n",
"max 4.265436e+09 18479.000000 False False "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"filename = \"./data/raw-twt{date}.csv\".format(date=datetime.datetime.now().strftime(\"%Y-%m-%d-%H:%M:%S\"))\n",
"df.to_csv(filename, sep='\\t', encoding='utf-8')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
mne-tools/mne-tools.github.io | 0.23/_downloads/1b3716673f2aeae3f2b0c6c336812aba/80_fix_bem_in_blender.ipynb | 1 | 12456 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Editing BEM surfaces in Blender\n\nSometimes when creating a BEM model the surfaces need manual correction because\nof a series of problems that can arise (e.g. intersection between surfaces).\nHere, we will see how this can be achieved by exporting the surfaces to the 3D\nmodeling program `Blender <https://blender.org>`_, editing them, and\nre-importing them.\n\nThis tutorial is based on https://github.com/ezemikulan/blender_freesurfer by\nEzequiel Mikulan.\n :depth: 2\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Marijn van Vliet <[email protected]>\n# Ezequiel Mikulan <[email protected]>\n# Manorama Kadwani <[email protected]>\n#\n# License: BSD (3-clause)\n\n\nimport os\nimport os.path as op\nimport shutil\nimport mne\n\ndata_path = mne.datasets.sample.data_path()\nsubjects_dir = op.join(data_path, 'subjects')\nbem_dir = op.join(subjects_dir, 'sample', 'bem', 'flash')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exporting surfaces to Blender\n\nIn this tutorial, we are working with the MNE-Sample set, for which the\nsurfaces have no issues. To demonstrate how to fix problematic surfaces, we\nare going to manually place one of the inner-skull vertices outside the\nouter-skill mesh.\n\nWe then convert the surfaces to `.obj\n<https://en.wikipedia.org/wiki/Wavefront_.obj_file>`_ files and create a new\nfolder called ``conv`` inside the FreeSurfer subject folder to keep them in.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Put the converted surfaces in a separate 'conv' folder\nconv_dir = op.join(subjects_dir, 'sample', 'conv')\nos.makedirs(conv_dir, exist_ok=True)\n\n# Load the inner skull surface and create a problem\n# The metadata is empty in this example. In real study, we want to write the\n# original metadata to the fixed surface file. Set read_metadata=True to do so.\ncoords, faces = mne.read_surface(op.join(bem_dir, 'inner_skull.surf'))\ncoords[0] *= 1.1 # Move the first vertex outside the skull\n\n# Write the inner skull surface as an .obj file that can be imported by\n# Blender.\nmne.write_surface(op.join(conv_dir, 'inner_skull.obj'), coords, faces,\n overwrite=True)\n\n# Also convert the outer skull surface.\ncoords, faces = mne.read_surface(op.join(bem_dir, 'outer_skull.surf'))\nmne.write_surface(op.join(conv_dir, 'outer_skull.obj'), coords, faces,\n overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Editing in Blender\n\nWe can now open Blender and import the surfaces. Go to *File > Import >\nWavefront (.obj)*. Navigate to the ``conv`` folder and select the file you\nwant to import. Make sure to select the *Keep Vert Order* option. You can\nalso select the *Y Forward* option to load the axes in the correct direction\n(RAS):\n\n<img src=\"file://../../_static/blender_import_obj/blender_import_obj1.jpg\" width=\"800\" alt=\"Importing .obj files in Blender\">\n\nFor convenience, you can save these settings by pressing the ``+`` button\nnext to *Operator Presets*.\n\nRepeat the procedure for all surfaces you want to import (e.g. inner_skull\nand outer_skull).\n\nYou can now edit the surfaces any way you like. See the\n`Beginner Blender Tutorial Series\n<https://www.youtube.com/playlist?list=PLxLGgWrla12dEW5mjO09kR2_TzPqDTXdw>`_\nto learn how to use Blender. Specifically, `part 2\n<http://www.youtube.com/watch?v=RaT-uG5wgUw&t=5m30s>`_ will teach you how to\nuse the basic editing tools you need to fix the surface.\n\n<img src=\"file://../../_static/blender_import_obj/blender_import_obj2.jpg\" width=\"800\" alt=\"Editing surfaces in Blender\">\n\n## Using the fixed surfaces in MNE-Python\n\nIn Blender, you can export a surface as an .obj file by selecting it and go\nto *File > Export > Wavefront (.obj)*. You need to again select the *Y\nForward* option and check the *Keep Vertex Order* box.\n\n<img src=\"file://../../_static/blender_import_obj/blender_import_obj3.jpg\" width=\"200\" alt=\"Exporting .obj files in Blender\">\n\n\nEach surface needs to be exported as a separate file. We recommend saving\nthem in the ``conv`` folder and ending the file name with ``_fixed.obj``,\nalthough this is not strictly necessary.\n\nIn order to be able to run this tutorial script top to bottom, we here\nsimulate the edits you did manually in Blender using Python code:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"coords, faces = mne.read_surface(op.join(conv_dir, 'inner_skull.obj'))\ncoords[0] /= 1.1 # Move the first vertex back inside the skull\nmne.write_surface(op.join(conv_dir, 'inner_skull_fixed.obj'), coords, faces,\n overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Back in Python, you can read the fixed .obj files and save them as\nFreeSurfer .surf files. For the :func:`mne.make_bem_model` function to find\nthem, they need to be saved using their original names in the ``surf``\nfolder, e.g. ``bem/inner_skull.surf``. Be sure to first backup the original\nsurfaces in case you make a mistake!\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Read the fixed surface\ncoords, faces = mne.read_surface(op.join(conv_dir, 'inner_skull_fixed.obj'))\n\n# Backup the original surface\nshutil.copy(op.join(bem_dir, 'inner_skull.surf'),\n op.join(bem_dir, 'inner_skull_orig.surf'))\n\n# Overwrite the original surface with the fixed version\n# In real study you should provide the correct metadata using ``volume_info=``\n# This could be accomplished for example with:\n#\n# _, _, vol_info = mne.read_surface(op.join(bem_dir, 'inner_skull.surf'),\n# read_metadata=True)\n# mne.write_surface(op.join(bem_dir, 'inner_skull.surf'), coords, faces,\n# volume_info=vol_info, overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Editing the head surfaces\n\nSometimes the head surfaces are faulty and require manual editing. We use\n:func:`mne.write_head_bem` to convert the fixed surfaces to ``.fif`` files.\n\n### Low-resolution head\n\nFor EEG forward modeling, it is possible that ``outer_skin.surf`` would be\nmanually edited. In that case, remember to save the fixed version of\n``-head.fif`` from the edited surface file for coregistration.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Load the fixed surface\ncoords, faces = mne.read_surface(op.join(bem_dir, 'outer_skin.surf'))\n\n# Make sure we are in the correct directory\nhead_dir = op.dirname(bem_dir)\n\n# Remember to backup the original head file in advance!\n# Overwrite the original head file\n#\n# mne.write_head_bem(op.join(head_dir, 'sample-head.fif'), coords, faces,\n# overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### High-resolution head\n\nWe use :func:`mne.read_bem_surfaces` to read the head surface files. After\nediting, we again output the head file with :func:`mne.write_head_bem`.\nHere we use ``-head.fif`` for speed.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# If ``-head-dense.fif`` does not exist, you need to run\n# ``mne make_scalp_surfaces`` first.\n# [0] because a list of surfaces is returned\nsurf = mne.read_bem_surfaces(op.join(head_dir, 'sample-head.fif'))[0]\n\n# For consistency only\ncoords = surf['rr']\nfaces = surf['tris']\n\n# Write the head as an .obj file for editing\nmne.write_surface(op.join(conv_dir, 'sample-head.obj'),\n coords, faces, overwrite=True)\n\n# Usually here you would go and edit your meshes.\n#\n# Here we just use the same surface as if it were fixed\n# Read in the .obj file\ncoords, faces = mne.read_surface(op.join(conv_dir, 'sample-head.obj'))\n\n# Remember to backup the original head file in advance!\n# Overwrite the original head file\n#\n# mne.write_head_bem(op.join(head_dir, 'sample-head.fif'), coords, faces,\n# overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it! You are ready to continue with your analysis pipeline (e.g.\nrunning :func:`mne.make_bem_model`).\n\n#### What if you still get an error?\n\nWhen editing BEM surfaces/meshes in Blender, make sure to use\ntools that do not change the number or order of vertices, or the geometry\nof triangular faces. For example, avoid the extrusion tool, because it\nduplicates the extruded vertices.\n\nBelow are some examples of errors you might encounter when running the\n`mne.make_bem_model` function, and the likely causes of those errors.\n\n\n1. Cannot decimate to requested ico grade\n\n This error is caused by having too few or too many vertices. The full\n error is something like:\n\n .. code-block:: console\n\n RuntimeError: Cannot decimate to requested ico grade 4. The provided\n BEM surface has 20516 triangles, which cannot be isomorphic with a\n subdivided icosahedron. Consider manually decimating the surface to a\n suitable density and then use ico=None in make_bem_model.\n\n2. Surface inner skull has topological defects\n\n This error can occur when trying to match the original number of\n triangles by removing vertices. The full error looks like:\n\n .. code-block:: console\n\n RuntimeError: Surface inner skull has topological defects: 12 / 20484\n vertices have fewer than three neighboring triangles [733, 1014, 2068,\n 7732, 8435, 8489, 10181, 11120, 11121, 11122, 11304, 11788]\n\n3. Surface inner skull is not complete\n\n This error (like the previous error) reflects a problem with the surface\n topology (i.e., the expected pattern of vertices/edges/faces is\n disrupted).\n\n .. code-block:: console\n\n RuntimeError: Surface inner skull is not complete (sum of solid\n angles yielded 0.999668, should be 1.)\n\n4. Triangle ordering is wrong\n\n This error reflects a mismatch between how the surface is represented in\n memory (the order of the vertex/face definitions) and what is expected by\n MNE-Python. The full error is:\n\n .. code-block:: console\n\n RuntimeError: The source surface has a matching number of\n triangles but ordering is wrong\n\n\nFor any of these errors, it is usually easiest to start over with the\nunedited BEM surface and try again, making sure to only *move* vertices and\nfaces without *adding* or *deleting* any. For example,\nselect a circle of vertices, then press :kbd:`G` to drag them to the desired\nlocation. Smoothing a group of selected vertices in Blender (by\nright-clicking and selecting \"Smooth Vertices\") can also be helpful.\n\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
MridulS/BinPy | BinPy/examples/notebook/Combinational/Decoder.ipynb | 1 | 3521 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Example for Decoder class"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Imports\n",
"from __future__ import print_function\n",
"from BinPy.Combinational.combinational import *"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Initializing the Decoder class\n",
"\n",
"decoder = Decoder(0, 1)\n",
"\n",
"# Output of decoder\n",
"\n",
"print (decoder.output())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0, 1, 0, 0]\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Input changes\n",
"\n",
"# Input at index 1 is changed to 0\n",
"\n",
"decoder.setInput(1, 0)\n",
"\n",
"# New Output of the decoder\n",
"\n",
"print (decoder.output())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 0, 0, 0]\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Changing the number of inputs\n",
"# No need to set the number, just change the inputs\n",
"# Input must be power of 2\n",
"\n",
"decoder.setInputs(1, 0, 0)\n",
"\n",
"# To get the input states\n",
"\n",
"print (decoder.getInputStates())\n",
"\n",
"# New output of decoder\n",
"\n",
"print (decoder.output())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 0, 0]\n",
"[0, 0, 0, 0, 1, 0, 0, 0]\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Using Connectors as the input lines\n",
"\n",
"conn = Connector()\n",
"\n",
"# Set Output of decoder to Connector conn\n",
"\n",
"decoder.setOutput(1, conn)\n",
"\n",
"# Put this connector as the input to gate1\n",
"\n",
"gate1 = AND(conn, 1)\n",
"\n",
"# Output of the gate1\n",
"\n",
"print (gate1.output())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Information about decoder instance can be found by\n",
"\n",
"print (decoder)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Decoder Gate; Output: [0, 0, 0, 0, 1, 0, 0, 0]; Inputs: [1, 0, 0];\n"
]
}
],
"prompt_number": 6
}
],
"metadata": {}
}
]
} | bsd-3-clause |
ESO-python/ESOPythonTutorials | notebooks/MarchApril2016_TutorialSession/Astroquery_Day2_Part3.ipynb | 1 | 117153 | {
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied (use --upgrade to upgrade): astroquery in /Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages\n",
"Requirement already satisfied (use --upgrade to upgrade): beautifulsoup4>=4.3.2 in /Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages (from astroquery)\n",
"Requirement already satisfied (use --upgrade to upgrade): keyring>=4.0 in /Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages (from astroquery)\n",
"Requirement already satisfied (use --upgrade to upgrade): requests>=2.4.3 in /Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages (from astroquery)\n",
"Requirement already satisfied (use --upgrade to upgrade): html5lib>=0.999 in /Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages (from astroquery)\n",
"Requirement already satisfied (use --upgrade to upgrade): astropy>=0.4.1 in /Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages (from astroquery)\n",
"Requirement already satisfied (use --upgrade to upgrade): six in /Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages (from html5lib>=0.999->astroquery)\n",
"Requirement already satisfied (use --upgrade to upgrade): numpy>=1.6.0 in /Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages (from astropy>=0.4.1->astroquery)\n"
]
}
],
"source": [
"%%bash\n",
"pip install astroquery"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from astroquery.eso import Eso\n",
"from astropy import coordinates\n",
"from astropy import units as u"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rslt = Eso.query_instrument('naco',target='Sgr A*')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"coords = coordinates.SkyCoord(rslt['Target Ra Dec'], unit=(u.hour, u.deg))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<SkyCoord (ICRS): (ra, dec) in deg\n",
" [(266.41658333, -29.00666667), (266.41483333, -29.00661111),\n",
" (266.4145, -29.00755556), (266.41725, -29.00911111),\n",
" (266.41695833, -29.00708333), (266.41620833, -29.00677778),\n",
" (266.41641667, -29.00883333), (266.41554167, -29.00858333),\n",
" (266.41433333, -29.00858333), (266.41733333, -29.00638889),\n",
" (266.41675, -29.00775), (266.41616667, -29.00661111),\n",
" (266.415625, -29.008), (266.41529167, -29.00677778),\n",
" (266.41779167, -29.00725), (266.416, -29.00872222),\n",
" (266.41733333, -29.00861111), (266.41820833, -29.00897222),\n",
" (266.41704167, -29.00661111), (266.418125, -29.00813889),\n",
" (266.41833333, -29.00658333), (266.41670833, -29.00786111),\n",
" (266.41666667, -29.00908333), (266.415625, -29.00763889),\n",
" (266.415625, -29.0065), (266.41633333, -29.00722222),\n",
" (266.4175, -29.00741667), (266.415375, -29.00913889),\n",
" (266.41825, -29.00897222), (266.41645833, -29.00647222),\n",
" (266.416375, -29.00630556), (266.41779167, -29.00563889),\n",
" (266.41745833, -29.00666667), (266.41654167, -29.00711111),\n",
" (266.41591667, -29.00511111), (266.415125, -29.00722222),\n",
" (266.41533333, -29.00588889), (266.41691667, -29.00572222),\n",
" (266.41720833, -29.005), (266.417875, -29.00752778),\n",
" (266.414875, -29.00511111), (266.41645833, -29.00630556),\n",
" (266.41770833, -29.00561111), (266.41741667, -29.00666667),\n",
" (266.416875, -29.00766667), (266.41595833, -29.00694444),\n",
" (266.41508333, -29.00722222), (266.41583333, -29.00552778),\n",
" (266.41508333, -29.00638889), (266.41641667, -29.00508333)]>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coords"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x10ee25b38>]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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TSYejr8CQZF2SG5PsTXJDkmMWKLclyZ4k9ye5rNf6SU5KMpPkHf20U5pUa9d2LmTbscML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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10db656a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import pylab as pl\n",
"pl.plot(coords.ra.deg, coords.dec.deg, '.')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages/matplotlib/artist.py:221: MatplotlibDeprecationWarning: This has been deprecated in mpl 1.5, please use the\n",
"axes property. A removal date has not been set.\n",
" warnings.warn(_get_axes_msg, mplDeprecation, stacklevel=1)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO: Auto-setting vmin to 4.221e+02 [aplpy.core]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: FITSFixedWarning: LONPOLE2= 180.000000000 /lonpole \n",
"invalid alternate code, keyword resembles LONPOLEa but isn't. [astropy.wcs.wcs]\n",
"WARNING: FITSFixedWarning: LATPOLE2= 0.00000000000 /latpole \n",
"invalid alternate code, keyword resembles LATPOLEa but isn't. [astropy.wcs.wcs]\n"
]
},
{
"data": {
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lbmoPqF7ddBQhnFtZzun85z/1Pq3GjfX+rYgIXVzVqHHlvS++CPfd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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c4b3dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import aplpy\n",
"F = aplpy.FITSFigure('gc_2mass_k.fits')\n",
"F.show_grayscale(vmax=1000)\n",
"F.show_contour('gc_bolocam_gps.fits', convention='calabretta')\n",
"F.show_markers(coords.ra.deg, coords.dec.deg)\n",
"sgrastar = coordinates.SkyCoord.from_name('Sgr A*')\n",
"F.recenter(sgrastar.ra.deg, sgrastar.dec.deg, radius=0.01)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# load table from previous section"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# crossmatch with... 2mass"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from astroquery.irsa import Irsa"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'a1763t2': 'Abell 1763 Source Catalog',\n",
" 'a1763t3': 'Abell 1763 MIPS 70 micron Catalog',\n",
" 'acmccat': 'ACMC Catalog',\n",
" 'acs_iphot_sep07': 'COSMOS ACS I-band photometry catalog September 2007',\n",
" 'akari_fis': 'Akari/FIS Bright Source Catalogue',\n",
" 'akari_irc': 'Akari/IRC Point Source Catalogue',\n",
" 'astsight': 'IRAS Minor Planet Survey',\n",
" 'bolocam_gps_v1_0_1': 'BOLOCAM Galactic Plane Survey Catalog',\n",
" 'bolocamdist': 'BOLOCAM Galactic Plane Survey Distance Catalog',\n",
" 'bolocamv21': 'BOLOCAM Galactic Plane Survey Catalog v2.1',\n",
" 'brava': 'BRAVA Catalog',\n",
" 'cf_info': '2MASS Calibration Merged Point Source Information Table',\n",
" 'cf_link': '2MASS Calibration Merged Point Source Link Table',\n",
" 'chandra_160_cat_f05': \"SWIRE CDFS Region 160um Fall '05 SWIRE Spitzer Catalog\",\n",
" 'chandra_24_cat_f05': \"SWIRE CDFS Region 24um Fall '05 Spitzer Catalog\",\n",
" 'chandra_70_cat_f05': \"SWIRE CDFS Region 70um Fall '05 Spitzer Catalog\",\n",
" 'chandra_cat_f05': \"SWIRE CDFS Region Fall '05 Spitzer Catalog\",\n",
" 'clash36_v2': 'CLASH 3.6 micron Catalog',\n",
" 'clash45_v2': 'CLASH 4.5 micron Catalog',\n",
" 'clash58_v2': 'CLASH 5.8 micron Catalog',\n",
" 'clash80_v2': 'CLASH 80 micron Catalog',\n",
" 'coadd_dat': '2MASS Survey Atlas Image Info',\n",
" 'coadd_dat_6x2': '2MASS 6X w/LMC/SMC Atlas Image Info',\n",
" 'coadd_dat_c': '2MASS Calibration Atlas Image Info',\n",
" 'coadd_dat_sc': '2MASS LMC/SMC Calibration Atlas Image Info',\n",
" 'columns': 'Available columns at NASA/IPAC Infrared Science Archive',\n",
" 'com_pccs1_030': 'Planck PCCS 30GHz Catalog',\n",
" 'com_pccs1_044': 'Planck PCCS 44GHz Catalog',\n",
" 'com_pccs1_070': 'Planck PCCS 70GHz Catalog',\n",
" 'com_pccs1_100': 'Planck PCCS 100GHz Catalog',\n",
" 'com_pccs1_143': 'Planck PCCS 143GHz Catalog',\n",
" 'com_pccs1_217': 'Planck PCCS 217GHz Catalog',\n",
" 'com_pccs1_353': 'Planck PCCS 353GHz Catalog',\n",
" 'com_pccs1_545': 'Planck PCCS 545GHz Catalog',\n",
" 'com_pccs1_857': 'Planck PCCS 857GHz Catalog',\n",
" 'com_pccs1_sz_mmf1': 'Planck Sunyaev-Zeldovich Cluster MMF1 List',\n",
" 'com_pccs1_sz_mmf3': 'Planck Sunyaev-Zeldovich Cluster MMF3 List',\n",
" 'com_pccs1_sz_pws': 'Planck Sunyaev-Zeldovich Cluster PwS List',\n",
" 'com_pccs1_sz_union2': 'Planck Sunyaev-Zeldovich Cluster UNION List v2.1',\n",
" 'com_pccs2_030': 'Planck PCCS2 30GHz Catalog',\n",
" 'com_pccs2_044': 'Planck PCCS2 44GHz Catalog',\n",
" 'com_pccs2_070': 'Planck PCCS2 70GHz Catalog',\n",
" 'com_pccs2_100': 'Planck PCCS2 100GHz Catalog',\n",
" 'com_pccs2_143': 'Planck PCCS2 143GHz Catalog',\n",
" 'com_pccs2_217': 'Planck PCCS2 217GHz Catalog',\n",
" 'com_pccs2_353': 'Planck PCCS2 353GHz Catalog',\n",
" 'com_pccs2_545': 'Planck PCCS2 545GHz Catalog',\n",
" 'com_pccs2_857': 'Planck PCCS2 857GHz Catalog',\n",
" 'com_pccs2_gcc': 'Planck Catalog of Galactic Cold Clumps',\n",
" 'com_pccs2_sz_mmf1': 'Planck PR2 Sunyaev-Zeldovich Cluster MMF1 List',\n",
" 'com_pccs2_sz_mmf3': 'Planck PR2 Sunyaev-Zeldovich Cluster MMF3 List',\n",
" 'com_pccs2_sz_pws': 'Planck PR2 Sunyaev-Zeldovich Cluster PwS List',\n",
" 'com_pccs2_sz_union': 'Planck PR2 Sunyaev-Zeldovich Cluster UNION List',\n",
" 'com_pccs2e_100': 'Planck PCCS2E 100GHz Catalog (lower reliability)',\n",
" 'com_pccs2e_143': 'Planck PCCS2E 143GHz Catalog (lower reliability)',\n",
" 'com_pccs2e_217': 'Planck PCCS2E 217GHz Catalog (lower reliability)',\n",
" 'com_pccs2e_353': 'Planck PCCS2E 353GHz Catalog (lower reliability)',\n",
" 'com_pccs2e_545': 'Planck PCCS2E 545GHz Catalog (lower reliability)',\n",
" 'com_pccs2e_857': 'Planck PCCS2E 857GHz Catalog (lower reliability)',\n",
" 'comsight': 'IRAS Asteroid and Comet Survey',\n",
" 'cosmos327': 'COSMOS VLA 327 MHz Catalog',\n",
" 'cosmos_chandra_bsc21': 'Chandra-COSMOS Bright Source Catalog v2.1',\n",
" 'cosmos_ib_phot': 'COSMOS Intermediate and Broad Band Photometry Catalog 2008',\n",
" 'cosmos_morph_cassata_1_1': 'COSMOS Cassata Morphology Catalog v1.1',\n",
" 'cosmos_morph_col_1': 'COSMOS Zamojski Morphology Catalog v1.0',\n",
" 'cosmos_morph_tasca_1_1': 'COSMOS Tasca Morphology Catalog v1.1',\n",
" 'cosmos_morph_zurich_1': 'COSMOS Zurich Structure and Morphology Catalog v1.0',\n",
" 'cosmos_phot': 'COSMOS Photometry Catalog January 2006',\n",
" 'cosmos_vla_deep_may2010': 'COSMOS VLA Deep Catalog May 2010',\n",
" 'cosmos_xgal': 'COSMOS X-ray Group Member Catalog',\n",
" 'cosmos_xgroups': 'COSMOS X-ray Group Catalog',\n",
" 'cosmos_xmm_2': 'COSMOS XMM Point-like Source Catalog v2.0',\n",
" 'cosmos_zphot_mag25': 'COSMOS Photometric Redshift Catalog Fall 2008 (README - mag 25 limited)',\n",
" 'csi2264t1': 'CSI 2264 Object Table',\n",
" 'csi2264t2': 'CSI 2264 CoRoT Light Curves',\n",
" 'csi2264t3': 'CSI 2264 Spitzer Light Curves',\n",
" 'cygx_arch': 'Cygnus-X Archive',\n",
" 'cygx_cat': 'Cygnus-X Catalog',\n",
" 'deepcal_src': '2MASS Combined Calibration Field Source Table',\n",
" 'deepglimpsea': 'Deep GLIMPSE Archive (more complete, less reliable)',\n",
" 'deepglimpsec': 'Deep GLIMPSE Catalog (highly reliable)',\n",
" 'denis3': 'DENIS 3rd Release (Sep. 2005)',\n",
" 'dr4_MM': \"C2D Fall '07 Millimeter (MM) Sources Catalog (OPH, PER, SER Clouds)\",\n",
" 'dr4_clouds_full': \"C2D Fall '07 Full CLOUDS Catalog (CHA_II, LUP, OPH, PER, SER)\",\n",
" 'dr4_clouds_hrel': \"C2D Fall '07 High Reliability (HREL) CLOUDS Catalog (CHA_II, LUP, OPH, PER, SER)\",\n",
" 'dr4_clouds_ysoc': \"C2D Fall '07 candidate Young Stellar Objects (YSO) CLOUDS Catalog (CHA_II, LUP, OPH, PER, SER)\",\n",
" 'dr4_cores_full': \"C2D Fall '07 Full CORES Catalog\",\n",
" 'dr4_cores_hrel': \"C2D Fall '07 High Reliability (HREL) CORES Catalog\",\n",
" 'dr4_cores_ysoc': \"C2D Fall '07 candidate Young Stellar Objects (YSO) CORES Catalog\",\n",
" 'dr4_off_cloud_full': \"C2D Fall '07 Full OFF-CLOUD Catalog (CHA_II, LUP, OPH, PER, SER)\",\n",
" 'dr4_off_cloud_hrel': \"C2D Fall '07 High Reliability (HREL) OFF-CLOUD Catalog (CHA_II, LUP, OPH, PER, SER)\",\n",
" 'dr4_off_cloud_ysoc': \"C2D Fall '07 candidate Young Stellar Objects (YSO) OFF-CLOUD Catalog (CHA_II, LUP, OPH, PER, SER)\",\n",
" 'dr4_stars_full': \"C2D Fall '07 Full STARS Catalog\",\n",
" 'dr4_stars_hrel': \"C2D Fall '07 High Reliability (HREL) STARS Catalog\",\n",
" 'dr4_stars_ysoc': \"C2D Fall '07 candidate Young Stellar Objects (YSO) STARS Catalog\",\n",
" 'dr4_trans1_full': \"C2D Fall '07 Perseus Epoch 1 Transient Sources FULL Catalog\",\n",
" 'dr4_trans2_full': \"C2D Fall '07 Perseus Epoch 2 Transient Sources FULL Catalog\",\n",
" 'dunes': 'DUNES Catalog',\n",
" 'dustingsfull': 'DUSTiNGS Full Catalog',\n",
" 'dustingsgsc': 'DUSTiNGS Good Source Catalog',\n",
" 'ecc': 'Planck Early Cold Core Source List (ECC)',\n",
" 'ecf_info': '2MASS Calibration Merged Extended Source Information Table',\n",
" 'ecf_link': '2MASS Calibration Merged Extended Source Link Table',\n",
" 'elaisn1_160_cat_s05': \"SWIRE ELAIS N1 Region 160um Spring '05 Spitzer Catalog\",\n",
" 'elaisn1_24_cat_s05': \"SWIRE ELAIS N1 Region 24um Spring '05 Spitzer Catalog\",\n",
" 'elaisn1_70_cat_s05': \"SWIRE ELAIS N1 Region 70um Spring '05 Spitzer Catalog\",\n",
" 'elaisn1_cat_s05': \"SWIRE ELAIS N1 Region Spring '05 Spitzer Catalog\",\n",
" 'elaisn2_160_cat_s05': \"SWIRE ELAIS N2 Region 160um Spring '05 Spitzer Catalog\",\n",
" 'elaisn2_24_cat_s05': \"SWIRE ELAIS N2 Region 24um Spring '05 Spitzer Catalog\",\n",
" 'elaisn2_70_cat_s05': \"SWIRE ELAIS N2 Region 70um Spring '05 Spitzer Catalog\",\n",
" 'elaisn2_cat_s05': \"SWIRE ELAIS N2 Region Spring '05 Spitzer Catalog\",\n",
" 'elaiss1_160_cat_f05': \"SWIRE ELAIS S1 Region 160um Fall '05 Spitzer Catalog\",\n",
" 'elaiss1_24_cat_f05': \"SWIRE ELAIS S1 Region 24um Fall '05 Spitzer Catalog\",\n",
" 'elaiss1_70_cat_f05': \"SWIRE ELAIS S1 Region 70um Fall '05 Spitzer Catalog\",\n",
" 'elaiss1_cat_f05': \"SWIRE ELAIS S1 Region Fall '05 SWIRE Spitzer Catalog\",\n",
" 'ercsc_f030_e': 'Planck ERCSC 30GHz Catalog',\n",
" 'ercsc_f044_e': 'Planck ERCSC 44GHz Catalog',\n",
" 'ercsc_f070_e': 'Planck ERCSC 70GHz Catalog',\n",
" 'ercsc_f100_e': 'Planck ERCSC 100GHz Catalog',\n",
" 'ercsc_f143_e': 'Planck ERCSC 143GHz Catalog',\n",
" 'ercsc_f217_e': 'Planck ERCSC 217GHz Catalog',\n",
" 'ercsc_f353_e': 'Planck ERCSC 353GHz Catalog',\n",
" 'ercsc_f545_e': 'Planck ERCSC 545GHz Catalog',\n",
" 'ercsc_f857_e': 'Planck ERCSC 857GHz Catalog',\n",
" 'escf_info': '2MASS LMC/SMC Calibration Merged Extended Source Information Table',\n",
" 'escf_link': '2MASS LMC/SMC Calibration Merged Extended Source Link Table',\n",
" 'esixxf_info': '2MASS 6X w/LMC/SMC Merged Extended Source Information Table',\n",
" 'esixxf_link': '2MASS 6X w/LMC/SMC Merged Extended Source Link Table',\n",
" 'esz': 'Planck Early Sunyaev-Zeldovich Cluster List (ESZ)',\n",
" 'ewsdbf_info': '2MASS Survey Merged Extended Source Information Table',\n",
" 'ewsdbf_link': '2MASS Survey Merged Extended Source Link Table',\n",
" 'ext_src_6x2': '2MASS 6X w/LMC/SMC Extended Source Working Database / Catalog ( Read Me! )',\n",
" 'ext_src_c': '2MASS Calibration Extended Source Working Database',\n",
" 'ext_src_cat': '2MASS Second Incremental Release Extended Source Catalog (XSC)',\n",
" 'ext_src_cat1': '2MASS First Incremental Release Extended Source Catalog (XSC)',\n",
" 'ext_src_rej': '2MASS Survey Extended Source Reject Table',\n",
" 'ext_src_sc': '2MASS LMC/SMC Calibration Extended Source Working Database',\n",
" 'exts_samp_cat': '2MASS Sampler Extended Source Catalog (XSC)',\n",
" 'feps_phot': 'FEPS Photometry Catalog',\n",
" 'fls_release_v2_mmt_spectra': 'FLS MMT/Hectospec Spectroscopic Catalog (V2)',\n",
" 'fls_release_v2_photom': 'FLS SDSS and MIPS Astrometric and Photometric Catalog (V2)',\n",
" 'fls_release_v2_sdss_spectra': 'FLS SDSS Spectroscopic Catalog (V2)',\n",
" 'fp_coadd_dat': '2MASS All-Sky Survey Atlas Image Info',\n",
" 'fp_psc': '2MASS All-Sky Point Source Catalog (PSC)',\n",
" 'fp_scan_dat': '2MASS All-Sky Survey Scan Info (Read Me!)',\n",
" 'fp_xsc': '2MASS All-Sky Extended Source Catalog (XSC)',\n",
" 'galcen_psc': 'Point Source in a Spitzer/IRAC Survey of the Galactic Center (Ramirez et al. 2008)',\n",
" 'galex_emphot_v3': 'GALEX/COSMOS Prior-based Photometry Catalog June 2008',\n",
" 'glimpse2_v2arc': \"GLIMPSE II Spring '08 Archive (more complete, less reliable)\",\n",
" 'glimpse2_v2cat': \"GLIMPSE II Spring '08 Catalog (highly reliable)\",\n",
" 'glimpse2ep1a08': \"GLIMPSE II Epoch 1 December '08 Archive (more complete, less reliable)\",\n",
" 'glimpse2ep1c08': \"GLIMPSE II Epoch 1 December '08 Catalog (highly reliable)\",\n",
" 'glimpse2ep2a09': \"GLIMPSE II Epoch 2 November '09 Archive (more complete, less reliable)\",\n",
" 'glimpse2ep2mra09': \"GLIMPSE II Epoch 2 November '09 More Reliable Archive (more reliable)\",\n",
" 'glimpse2sub': 'GLIMPSEII Subarray Source List',\n",
" 'glimpse360a': 'GLIMPSE360 Archive (more complete, less reliable)',\n",
" 'glimpse360c': 'GLIMPSE360 Catalog (highly reliable)',\n",
" 'glimpse3d_v1cat_tbl': 'GLIMPSE 3D, 2007-2009 Catalog (highly reliable)',\n",
" 'glimpse3d_v2arc': 'GLIMPSE 3D, 2007-2009 Archive (more complete, less reliable),(Erratum)',\n",
" 'glimpse3dep1a': 'GLIMPSE 3D Epoch 1 Archive (more complete, less reliable)',\n",
" 'glimpse3dep1c': 'GLIMPSE 3D Epoch 1 Catalog (highly reliable)',\n",
" 'glimpse3dep2a': 'GLIMPSE 3D Epoch 2 Archive (more complete, less reliable)',\n",
" 'glimpse3dep2mra': 'GLIMPSE 3D Epoch 2 More Reliable Archive (more complete, less reliable)',\n",
" 'glimpse_s07': \"GLIMPSE I Spring '07 Catalog (highly reliable)\",\n",
" 'glimpse_s07_ar': \"GLIMPSE I Spring '07 Archive (more complete, less reliable)\",\n",
" 'glimpsesmoga': 'SMOG Archive (more complete, less reliable)',\n",
" 'glimpsesmogc': 'SMOG Catalog (highly reliable)',\n",
" 'goods_mips24': 'GOODS-S MIPS 24 micron Photometry Catalog',\n",
" 'goodsn_mips24': 'GOODS-N MIPS 24 micron Photometry Catalog',\n",
" 'heritagel100': 'HERITAGE LMC PACS 100 micron Catalog',\n",
" 'heritagel160': 'HERITAGE LMC PACS 160 micron Catalog',\n",
" 'heritagel250': 'HERITAGE LMC SPIRE 250 micron Catalog',\n",
" 'heritagel350': 'HERITAGE LMC SPIRE 350 micron Catalog',\n",
" 'heritagel500': 'HERITAGE LMC SPIRE 500 micron Catalog',\n",
" 'heritagelclass': 'HERITAGE LMC Band-Matched Classification Table',\n",
" 'heritagelphot': 'HERITAGE LMC Band-Matched Catalog',\n",
" 'heritages100': 'HERITAGE SMC PACS 100 micron Catalog',\n",
" 'heritages160': 'HERITAGE SMC PACS 160 micron Catalog',\n",
" 'heritages250': 'HERITAGE SMC SPIRE 250 micron Catalog',\n",
" 'heritages350': 'HERITAGE SMC SPIRE 350 micron Catalog',\n",
" 'heritages500': 'HERITAGE SMC SPIRE 500 micron Catalog',\n",
" 'heritagesclass': 'HERITAGE SMC Band-Matched Classification Table',\n",
" 'heritagesphot': 'HERITAGE SMC Band-Matched Catalog',\n",
" 'hgoodsn': 'GOODS North Catalog',\n",
" 'hgoodss': 'GOODS South Catalog',\n",
" 'iras_ao': 'IRAS Additional Observations (AO) Catalog',\n",
" 'irascatalog': 'IRAS 1.2-Jy Redshift Survey',\n",
" 'irasfsc': 'IRAS Faint Source Catalog v2.0 (FSC)',\n",
" 'irasfscr': 'IRAS Faint Source Catalog Rejects',\n",
" 'irasgal': 'IRAS Cataloged Galaxies and Quasars',\n",
" 'iraspsc': 'IRAS Point Source Catalog v2.1 (PSC)',\n",
" 'iraspsch': 'IRAS PSC joined with HCON and WSDB',\n",
" 'iraspscr': 'IRAS Point Source Catalog Rejects',\n",
" 'iraspscw': 'IRAS PSC joined with WSDB',\n",
" 'irasssc': 'IRAS Serendipitous Survey Catalog',\n",
" 'irassss': 'IRAS Small Scale Structure Catalog',\n",
" 'irs_enhv211': 'IRS Enhanced Products',\n",
" 'key_columns': 'Available Key Columns at NASA/IPAC Infrared Science Archive',\n",
" 'keys': 'Available Keys at NASA/IPAC Infrared Science Archive',\n",
" 'lga_v2': 'The 2MASS Large Galaxy Atlas',\n",
" 'lockman_160_cat_s05': \"SWIRE Lockman Region 160um Spring '05 Spitzer Catalog\",\n",
" 'lockman_24_cat_s05': \"SWIRE Lockman Region 24um Spring '05 Spitzer Catalog\",\n",
" 'lockman_70_cat_s05': \"SWIRE Lockman Region 70um Spring '05 Spitzer Catalog\",\n",
" 'lockman_cat_s05': \"SWIRE Lockman Region Spring '05 SWIRE Spitzer Catalog\",\n",
" 'mipsgala': 'MIPSGAL Archive',\n",
" 'mipsgalc': 'MIPSGAL Catalog',\n",
" 'mipslg': 'MIPS Local Galaxies Catalog',\n",
" 'morphology_2005': 'COSMOS Morphology Catalog 2005',\n",
" 'msxc6': 'The Midcourse Space Experiment (MSXC6)',\n",
" 'msxc6_rej': 'The Midcourse Space Experiment (MSXC6) Rejects',\n",
" 'musyc_phot': 'MUSYC Photometry Catalog',\n",
" 'musyc_photz': 'MUSYC Photometric Redshift Catalog',\n",
" 'neowiser_p1ba_mch': 'NEOWISE-R Known Solar System Object Possible Association List ( Caution )',\n",
" 'neowiser_p1bl_lod': 'NEOWISE-R Single Exposure (L1b) Scan Inventory Table',\n",
" 'neowiser_p1bm_frm': 'NEOWISE-R Single Exposure (L1b) Image Inventory Table',\n",
" 'neowiser_p1bs_frm': 'NEOWISE-R Single Exposure (L1b) Frame Metadata Table',\n",
" 'neowiser_p1bs_psd': 'NEOWISE-R Single Exposure (L1b) Source Table',\n",
" 'pep100': 'PEP PACS 100 micron Catalog',\n",
" 'pep160': 'PEP PACS 160 micron Catalog',\n",
" 'pep250': 'PEP SPIRE 250 micron Catalog',\n",
" 'pep350': 'PEP SPIRE 350 micron Catalog',\n",
" 'pep500': 'PEP SPIRE 500 micron Catalog',\n",
" 'peplh24': 'PEP Lockman Hole MIPS 24 micron Comparison Catalog',\n",
" 'pepprior': 'PEP PACS Extractions Using MIPS 24 micron Priors Catalog',\n",
" 'pepxid': 'PEP PACS and MIPS Cross-IDs Catalog',\n",
" 'ppmxl': 'PPMXL: A Proper Motion Catalog Combining USNO-B and 2MASS',\n",
" 'prelim_2band_p1ba_mch': 'WISE Preliminary Post-Cryo Solar System Object Possible Association List ( Caution , Superseded)',\n",
" 'prelim_2band_p1bl_lod': 'WISE Preliminary Post-Cryo Single Exposure (L1b) Scan Inventory Table (Superseded)',\n",
" 'prelim_2band_p1bm_frm': 'WISE Preliminary Post-Cryo Single Exposure (L1b) Image Inventory Table (Superseded)',\n",
" 'prelim_2band_p1bs_frm': 'WISE Preliminary Post-Cryo Single Exposure (L1b) Frame Metadata Table (Superseded)',\n",
" 'prelim_2band_p1bs_psd': 'WISE Preliminary Post-Cryo Single Exposure (L1b) Source Table (Superseded)',\n",
" 'prelim_p1ba_mch': 'WISE Preliminary Release Known Solar System Object Possible Association List ( Caution , Superseded)',\n",
" 'prelim_p1bm_frm': 'WISE Preliminary Release Single Exposure (L1b) Image Inventory Table (Superseded)',\n",
" 'prelim_p1bs_frm': 'WISE Preliminary Release Single Exposure (L1b) Frame Metadata Table (Superseded)',\n",
" 'prelim_p1bs_psd': 'WISE Preliminary Release Single Exposure (L1b) Source Table (Superseded)',\n",
" 'prelim_p3al_lod': 'WISE Preliminary Release Atlas Inventory Table (Superseded)',\n",
" 'prelim_p3am_cdd': 'WISE Preliminary Release Atlas Image Inventory Table (Superseded)',\n",
" 'prelim_p3am_xrf': 'WISE Preliminary Release Frame Cross-Reference Table (Superseded)',\n",
" 'prelim_p3as_cdd': 'WISE Preliminary Release Atlas Metadata Table (Superseded)',\n",
" 'prelim_p3as_psd': 'WISE Preliminary Release Source Catalog (Superseded)',\n",
" 'pscan_dat': '2MASS Survey Scan Info',\n",
" 'pscan_dat_6x2': '2MASS 6X w/LMC/SMC Scan Info',\n",
" 'pscan_dat_c': '2MASS Calibration Scan Info',\n",
" 'pscan_dat_sc': '2MASS LMC/SMC Calibration Scan Info',\n",
" 'pt_src_6x2': '2MASS 6X w/LMC/SMC Point Source Working Database /Catalog ( Read Me! )',\n",
" 'pt_src_c': '2MASS Calibration Point Source Working Database',\n",
" 'pt_src_cat': '2MASS Second Incremental Release Point Source Catalog (PSC)',\n",
" 'pt_src_cat1': '2MASS First Incremental Release Point Source Catalog (PSC)',\n",
" 'pt_src_rej': '2MASS Survey Point Source Reject Table',\n",
" 'pt_src_sc': '2MASS LMC/SMC Calibration Point Source Working Database',\n",
" 'ptfphotcalcat': 'PTF Photometric Calibrator Catalog',\n",
" 'pts_samp_cat': '2MASS Sampler Point Source Catalog (PSC)',\n",
" 's4gcat': 'Spitzer Survey of Stellar Structure in Galaxies (S4G)',\n",
" 'safires160': 'Spitzer Archival Far-Infrared Extragalactic Survey (SAFIRES) MIPS 160 micron Catalog',\n",
" 'safires70': 'Spitzer Archival Far-Infrared Extragalactic Survey (SAFIRES) MIPS 70 micron Catalog',\n",
" 'sage_ar_irac': 'SAGE IRAC Single Frame + Mosaic Photometry Archive (more complete, less reliable)',\n",
" 'sage_ar_irac_e1e2': 'SAGE IRAC Epoch 1 and Epoch 2 Archive (more complete, less reliable)',\n",
" 'sage_ar_irac_match': 'SAGE IRAC Matched Epoch Catalog (more complete, less reliable)',\n",
" 'sage_ar_irac_off': 'SAGE IRAC Offset Position Epoch 1 and Epoch 2 Archive (more complete, less reliable)',\n",
" 'sage_cat_irac': 'SAGE IRAC Single Frame + Mosaic Photometry Catalog (more reliable)',\n",
" 'sage_cat_irac_e1e2': 'SAGE IRAC Epoch 1 and Epoch 2 Catalog (more reliable)',\n",
" 'sage_cat_irac_match': 'SAGE IRAC Matched Epoch Archive (more reliable)',\n",
" 'sage_cat_irac_off': 'SAGE IRAC Offset Position Epoch 1 and Epoch 2 Catalog (more reliable)',\n",
" 'sage_cat_m160': 'SAGE MIPS 160 um Combined Epoch Catalog (more reliable)',\n",
" 'sage_cat_m24': 'SAGE MIPS 24 um Epoch 1 and Epoch 2 Catalog (more reliable)',\n",
" 'sage_cat_m24_match': 'SAGE MIPS 24 um Matched Epoch Catalog (more reliable)',\n",
" 'sage_cat_m70': 'SAGE MIPS 70 um Combined Epoch Catalog (more reliable)',\n",
" 'sage_full_m160': 'SAGE MIPS 160 um Combined Epoch Catalog (more complete, less reliable)',\n",
" 'sage_full_m24': 'SAGE MIPS 24 um Epoch 1 and Epoch 2 Full List (more complete, less reliable)',\n",
" 'sage_full_m24_match': 'SAGE MIPS 24 um Matched Epoch Full List (more complete, less reliable)',\n",
" 'sage_full_m70': 'SAGE MIPS 70 um Combined Epoch Full List (more complete, less reliable)',\n",
" 'sagearciracv2': \"SAGE Winter '08 IRAC Epoch 1 and Epoch 2 Archive (more complete, less reliable)\",\n",
" 'sagecatiracv2': \"SAGE Winter '08 IRAC Epoch 1 and Epoch 2 Catalog (more reliable)\",\n",
" 'sagecatmips24v2': \"SAGE Winter '08 MIPS 24 um Epoch 1 and Epoch 2 Catalog (more reliable)\",\n",
" 'sagefull': 'SAGE-Var LMC Full Catalog',\n",
" 'sagesmc_iraca': 'SAGE-SMC IRAC Epoch 0, Epoch 1, and Epoch 2 Archive (less reliable)',\n",
" 'sagesmc_iracadr3': 'SAGE-SMC IRAC Single Frame + Mosaic Photometry Archive v1.5',\n",
" 'sagesmc_iracc': 'SAGE-SMC IRAC Epoch 0, Epoch 1, and Epoch 2 Catalog (more reliable)',\n",
" 'sagesmc_iraccdr3': 'SAGE-SMC IRAC Single Frame + Mosaic Photometry Catalog v1.5',\n",
" 'sagesmc_iracep1a': 'SAGE-SMC IRAC Epoch 1 Archive (less reliable)',\n",
" 'sagesmc_iracep1c': 'SAGE-SMC IRAC Epoch 1 Catalog (more reliable)',\n",
" 'sagesmc_mips160c': 'SAGE-SMC MIPS 160um Combined Epoch Catalog (more reliable)',\n",
" 'sagesmc_mips160f': 'SAGE-SMC MIPS 160um Combined Epoch Full List (more complete, less reliable)',\n",
" 'sagesmc_mips24c': 'SAGE-SMC MIPS 24 um Epoch 0, Epoch 1, and Epoch 2 Catalog (more reliable)',\n",
" 'sagesmc_mips24ep1c': 'SAGE-SMC MIPS 24um Epoch 1 Catalog (more reliable)',\n",
" 'sagesmc_mips24ep1f': 'SAGE-SMC MIPS 24um Epoch 1 Full List (less reliable)',\n",
" 'sagesmc_mips24f': 'SAGE-SMC MIPS 24 um Epoch 0, Epoch 1, and Epoch 2 Full List (more complete, less reliable)',\n",
" 'sagesmc_mips70c': 'SAGE-SMC MIPS 70um Combined Epoch Catalog (more reliable)',\n",
" 'sagesmc_mips70f': 'SAGE-SMC MIPS 70um Combined Epoch Full List (more complete, less reliable)',\n",
" 'sagesmcfull': 'SAGE-Var SMC Full Catalog',\n",
" 'sagesmcvar': 'SAGE-Var SMC Variable Catalog',\n",
" 'sagevar': 'SAGE-Var LMC Variable Catalog',\n",
" 'sass_v3': 'SASS October 2011 Catalog',\n",
" 'scan_dat': '2MASS First Incremental Release Survey Scan Info',\n",
" 'scan_dat_2': '2MASS Second Incremental Release Survey Scan Info',\n",
" 'scf_info': '2MASS LMC/SMC Calibration Merged Point Source Information Table',\n",
" 'scf_link': '2MASS LMC/SMC Calibration Merged Point Source Link Table',\n",
" 'schemas': 'Available schemas at NASA/IPAC Infrared Science Archive',\n",
" 'scosmos_irac_0407': 'S-COSMOS IRAC 4-channel Photometry Catalog June 2007 (README)',\n",
" 'scosmos_mips_160_v3': 'S-COSMOS MIPS 160um Photometry Catalog v3 Jan 2009',\n",
" 'scosmos_mips_24_go2': 'S-COSMOS MIPS 24um MAIN Photometry Catalog June 2007 ((Aug 2008: Important Flux-correction Note))',\n",
" 'scosmos_mips_24_go2_deep': 'S-COSMOS MIPS 24um DEEP Photometry Catalog June 2007 ((Aug 2008: Important Flux-correction Note))',\n",
" 'scosmos_mips_24_go3': 'S-COSMOS MIPS 24 Photometry Catalog October 2008',\n",
" 'scosmos_mips_70_v3': 'S-COSMOS MIPS 70um Photometry Catalog v3 Jan 2009',\n",
" 'sdwfs_ch1_epoch1': \"SDWFS Aug '09 DR1.1 IRAC 3.6um-Selected 3x30sec Coadd, epoch 1 (Jan '04)\",\n",
" 'sdwfs_ch1_epoch2': \"SDWFS Aug '09 DR1.1 IRAC 3.6um-Selected 3x30sec Coadd, epoch 2 (Aug '07)\",\n",
" 'sdwfs_ch1_epoch3': \"SDWFS Aug '09 DR1.1 IRAC 3.6um-Selected 3x30sec Coadd, epoch 3 (Feb '08)\",\n",
" 'sdwfs_ch1_epoch4': \"SDWFS Aug '09 DR1.1 IRAC 3.6um-Selected 3x30sec Coadd, epoch 4 (Mar '08)\",\n",
" 'sdwfs_ch1_stack': \"SDWFS Aug'09 DR1.1 IRAC 3.6um-Selected Total Coadd Stack\",\n",
" 'sdwfs_ch2_epoch1': \"SDWFS Aug '09 DR1.1 IRAC 4.5um-Selected 3x30sec Coadd, epoch 1 (Jan '04)\",\n",
" 'sdwfs_ch2_epoch2': \"SDWFS Aug '09 DR1.1 IRAC 4.5um-Selected 3x30sec Coadd, epoch 2 (Aug '07)\",\n",
" 'sdwfs_ch2_epoch3': \"SDWFS Aug '09 DR1.1 IRAC 4.5um-Selected 3x30sec Coadd, epoch 3 (Feb '08)\",\n",
" 'sdwfs_ch2_epoch4': \"SDWFS Aug '09 DR1.1 IRAC 4.5um-Selected 3x30sec Coadd, epoch 4 (Mar '08)\",\n",
" 'sdwfs_ch2_stack': \"SDWFS Aug '09 DR1.1 IRAC 4.5um-Selected Total Coadd Stack\",\n",
" 'sdwfs_ch3_epoch1': \"SDWFS Aug '09 DR1.1 IRAC 5.8um-Selected 3x30sec Coadd, epoch 1 (Jan '04)\",\n",
" 'sdwfs_ch3_epoch2': \"SDWFS Aug '09 DR1.1 IRAC 5.8um-Selected 3x30sec Coadd, epoch 2 (Aug '07)\",\n",
" 'sdwfs_ch3_epoch3': \"SDWFS Aug '09 DR1.1 IRAC 5.8um-Selected 3x30sec Coadd, epoch 3 (Feb '08)\",\n",
" 'sdwfs_ch3_epoch4': \"SDWFS Aug '09 DR1.1 IRAC 5.8um-Selected 3x30sec Coadd, epoch 4 (Mar '08)\",\n",
" 'sdwfs_ch3_stack': \"SDWFS Aug '09 DR1.1 IRAC 5.8um-Selected Total Coadd Stack\",\n",
" 'sdwfs_ch4_epoch1': \"SDWFS Aug '09 DR1.1 IRAC 8.0um-Selected 3x30sec Coadd, epoch 1 (Jan '04)\",\n",
" 'sdwfs_ch4_epoch2': \"SDWFS Aug '09 DR1.1 IRAC 8.0um-Selected 3x30sec Coadd, epoch 2 (Aug '07)\",\n",
" 'sdwfs_ch4_epoch3': \"SDWFS Aug '09 DR1.1 IRAC 8.0um-Selected 3x30sec Coadd, epoch 3 (Feb '08)\",\n",
" 'sdwfs_ch4_epoch4': \"SDWFS Aug '09 DR1.1 IRAC 8.0um-Selected 3x30sec Coadd, epoch 4 (Mar '08)\",\n",
" 'sdwfs_ch4_stack': \"SDWFS Aug '09 DR1.1 IRAC 8.0um-Selected Total Coadd Stack\",\n",
" 'sdwfs_lcurve': 'SDWFS Light Curve Catalog',\n",
" 'sdwfs_var': 'SDWFS Variability Catalog',\n",
" 'sepirac': 'SEP IRAC-based Multiwavelength Photometric Catalog',\n",
" 'sepm24': 'SEP MIPS 24 micron Point Source Catalog',\n",
" 'sepm70': 'SEP MIPS 70 micron Point Source Catalog',\n",
" 'sepmext': 'SEP MIPS Extended Source Catalog',\n",
" 'servscdfsi1': 'SERVS CDFS 3.6 micron Catalog',\n",
" 'servscdfsi12': 'SERVS CDFS 2-band Catalog (highly reliable)',\n",
" 'servscdfsi2': 'SERVS CDFS 4.5 micron Catalog',\n",
" 'servseni1': 'SERVS ELAIS N1 3.6 micron Catalog',\n",
" 'servseni12': 'SERVS ELAIS N1 2-band Catalog (highly reliable)',\n",
" 'servseni2': 'SERVS ELAIS N1 4.5 micron Catalog',\n",
" 'servsesi1': 'SERVS ELAIS S1 3.6 micron Catalog',\n",
" 'servsesi12': 'SERVS ELAIS S1 2-band Catalog (highly reliable)',\n",
" 'servsesi2': 'SERVS ELAIS S1 4.5 micron Catalog',\n",
" 'servslhi1': 'SERVS Lockman Hole 3.6 micron Catalog',\n",
" 'servslhi12': 'SERVS Lockman Hole 2-band Catalog (highly reliable)',\n",
" 'servslhi2': 'SERVS Lockman Hole 4.5 micron Catalog',\n",
" 'servsxmmi1': 'SERVS XMM-LSS 3.6 micron Catalog',\n",
" 'servsxmmi12': 'SERVS XMM-LSS 2-band Catalog (highly reliable)',\n",
" 'servsxmmi2': 'SERVS XMM-LSS 4.5 micron Catalog',\n",
" 'shelacomb': 'SHELA Combined Epoch IRAC Catalog',\n",
" 'shelaep1': 'SHELA Epoch 1 IRAC Catalog',\n",
" 'shelaep2': 'SHELA Epoch 2 IRAC Catalog',\n",
" 'shelaep3': 'SHELA Epoch 3 IRAC Catalog',\n",
" 'shelasdss': 'SHELA-SDSS Stripe 82 Catalog',\n",
" 'simple': 'SIMPLE Photometry Catalog',\n",
" 'sixxf_info': '2MASS 6X w/LMC/SMC Merged Point Source Information Table',\n",
" 'sixxf_link': '2MASS 6X w/LMC/SMC Merged Point Source Link Table',\n",
" 'slicovv2': 'SEIP IRAC Coverage Table',\n",
" 'slmcovv2': 'SEIP MIPS Coverage Table',\n",
" 'slphotdr4': 'SEIP Source List',\n",
" 'sltracev2': 'SEIP Traceback Table',\n",
" 'spuds_irac': 'SpUDS IRAC Catalog',\n",
" 'spuds_mips': 'SpUDS MIPS Catalog',\n",
" 'ssdf1': 'SSDF IRAC Ch1 Catalog',\n",
" 'ssdf2': 'SSDF IRAC Ch2 Catalog',\n",
" 'ssid2': 'SAGE-Spec ID Search',\n",
" 'summary': 'IRAS Large Galaxies Catalog',\n",
" 'swire_lhisod': 'SWIRE Lockman Hole ISOCAM Deep Field Catalog',\n",
" 'swire_lhisos': 'SWIRE Lockman Hole ISOCAM Shallow Field Catalog',\n",
" 'tables': 'Available tables at NASA/IPAC Infrared Science Archive',\n",
" 'taurus_2008_2_1': 'Taurus Catalog October 2008 v2.1',\n",
" 'ucac4_sources': 'USNO CCD Astrograph Catalog (UCAC4)',\n",
" 'urat1': 'The First USNO Robotic Astrometric Telescope Catalog (URAT1)',\n",
" 'usno_b1': 'USNO-B1 (United States Naval Observatory B1.0 Catalog)',\n",
" 'velcara': 'Vela-Carina Archive (more complete, less reliable)',\n",
" 'velcarc': 'Vela-Carina Catalog (highly reliable)',\n",
" 'wise_allsky_2band_p1ba_mch': 'WISE Post-Cryo Single Exposure (L1b) Known SSO Possible Association List ( Caution )',\n",
" 'wise_allsky_2band_p1bl_lod': 'WISE Post-Cryo Single Exposure (L1b) Scan Inventory Table',\n",
" 'wise_allsky_2band_p1bm_frm': 'WISE Post-Cryo Single Exposure (L1b) Image Inventory Table',\n",
" 'wise_allsky_2band_p1bs_frm': 'WISE Post-Cryo Single Exposure (L1b) Frame Metadata Table',\n",
" 'wise_allsky_2band_p1bs_psd': 'WISE Post-Cryo Single Exposure (L1b) Source Table',\n",
" 'wise_allsky_3band_p1ba_mch': 'WISE 3-Band Cryo Known Solar System Object Possible Association List ( Caution )',\n",
" 'wise_allsky_3band_p1bl_lod': 'WISE 3-Band Cryo Single Exposure (L1b) Scan Inventory Table',\n",
" 'wise_allsky_3band_p1bm_frm': 'WISE 3-Band Cryo Single Exposure (L1b) Image Inventory Table',\n",
" 'wise_allsky_3band_p1bs_frm': 'WISE 3-Band Cryo Single Exposure (L1b) Frame Metadata Table',\n",
" 'wise_allsky_3band_p1bs_psd': 'WISE 3-Band Cryo Single Exposure (L1b) Source Table',\n",
" 'wise_allsky_3band_p3al_lod': 'WISE 3-Band Cryo Atlas Inventory Table',\n",
" 'wise_allsky_3band_p3am_cdd': 'WISE 3-Band Cryo Atlas Image Inventory Table',\n",
" 'wise_allsky_3band_p3am_xrf': 'WISE 3-Band Cryo Frame Cross-Reference Table',\n",
" 'wise_allsky_3band_p3as_cdd': 'WISE 3-Band Cryo Atlas Metadata Table',\n",
" 'wise_allsky_3band_p3as_psd': 'WISE 3-Band Cryo Source Working Database ( Readme)',\n",
" 'wise_allsky_4band_p1ba_mch': 'WISE All-Sky Known Solar System Object Possible Association List ( Caution )',\n",
" 'wise_allsky_4band_p1bl_lod': 'WISE All-Sky Single Exposure (L1b) Scan Inventory Table',\n",
" 'wise_allsky_4band_p1bm_frm': 'WISE All-Sky Single Exposure (L1b) Image Inventory Table',\n",
" 'wise_allsky_4band_p1bs_frm': 'WISE All-Sky Single Exposure (L1b) Frame Metadata Table',\n",
" 'wise_allsky_4band_p1bs_psd': 'WISE All-Sky Single Exposure (L1b) Source Table',\n",
" 'wise_allsky_4band_p3al_lod': 'WISE All-Sky Atlas Inventory Table',\n",
" 'wise_allsky_4band_p3am_cdd': 'WISE All-Sky Atlas Image Inventory Table',\n",
" 'wise_allsky_4band_p3am_xrf': 'WISE All-Sky Frame Cross-Reference Table',\n",
" 'wise_allsky_4band_p3as_cdd': 'WISE All-Sky Atlas Metadata Table',\n",
" 'wise_allsky_4band_p3as_psd': 'WISE All-Sky Source Catalog',\n",
" 'wise_allsky_4band_p3as_psr': 'WISE All-Sky Reject Table',\n",
" 'wise_allwise_p3al_lod': 'AllWISE Atlas Inventory Table',\n",
" 'wise_allwise_p3am_cdd': 'AllWISE Atlas Image Inventory Table',\n",
" 'wise_allwise_p3am_xrf': 'AllWISE Frame Cross-Reference Table',\n",
" 'wise_allwise_p3as_cdd': 'AllWISE Atlas Metadata Table',\n",
" 'wise_allwise_p3as_mep': 'AllWISE Multiepoch Photometry Table',\n",
" 'wise_allwise_p3as_psd': 'AllWISE Source Catalog',\n",
" 'wise_allwise_p3as_psr': 'AllWISE Reject Table',\n",
" 'wise_prelim_p1bl_lod': 'WISE Preliminary Release Single Exposure (L1b) Scan Inventory Table (Superseded)',\n",
" 'wsdb_info': '2MASS Survey Merged Point Source Information Table',\n",
" 'wsdb_link': '2MASS Survey Merged Point Source Link Table',\n",
" 'xfls_i1m': 'Extragalactic FLS IRAC Channel 1 Main Field Catalog',\n",
" 'xfls_i1v': 'Extragalactic FLS IRAC Channel 1 Verification Field Catalog',\n",
" 'xfls_i2m': 'Extragalactic FLS IRAC Channel 2 Main Field Catalog',\n",
" 'xfls_i2v': 'Extragalactic FLS IRAC Channel 2 Verification Field Catalog',\n",
" 'xfls_i3m': 'Extragalactic FLS IRAC Channel 3 Main Field Catalog',\n",
" 'xfls_i3v': 'Extragalactic FLS IRAC Channel 3 Verification Field Catalog',\n",
" 'xfls_i4m': 'Extragalactic FLS IRAC Channel 4 Main Field Catalog',\n",
" 'xfls_i4v': 'Extragalactic FLS IRAC Channel 4 Verification Field Catalog',\n",
" 'xfls_iallm': 'Extragalactic FLS IRAC Bandmerged Main Field Catalog',\n",
" 'xfls_iallv': 'Extragalactic FLS IRAC Bandmerged Verification Field Catalog',\n",
" 'xfls_kpno': 'Extragalactic FLS KPNO R-band Source List',\n",
" 'xfls_m1t2': 'Extragalactic FLS MIPS 24 micron Extended Source Catalog',\n",
" 'xfls_m1t4': 'Extragalactic FLS MIPS 24 micron Calibration Star Catalog',\n",
" 'xfls_m1t5': 'Extragalactic FLS MIPS 24 micron Point Source Catalog',\n",
" 'xfls_m2': 'Extragalactic FLS MIPS 70 micron Catalog',\n",
" 'xfls_m3': 'Extragalactic FLS MIPS 160 micron Catalog',\n",
" 'xfls_w2': 'Extragalactic FLS WIYN/Hydra Spectroscopic Catalog',\n",
" 'xfls_w3': 'Extragalactic FLS WIYN/Hydra Line Strength and Equivalent Width Catalog',\n",
" 'xfls_w4': 'Extragalactic FLS WIYN/Hydra Line Ratios and Extinction Catalog',\n",
" 'xmm_160_cat_s05': \"SWIRE XMM_LSS Region 160um Spring '05 Spitzer Catalog\",\n",
" 'xmm_24_cat_s05': \"SWIRE XMM_LSS Region 24um Spring '05 Spitzer Catalog\",\n",
" 'xmm_70_cat_s05': \"SWIRE XMM_LSS Region 70um Spring '05 Spitzer Catalog\",\n",
" 'xmm_cat_s05': \"SWIRE XMM_LSS Region Spring '05 Spitzer Catalog\",\n",
" 'ysoggd1215lc': 'YSOVAR GGD 12-15 Light Curve Table',\n",
" 'ysoggd1215obj': 'YSOVAR GGD 12-15 Object Table',\n",
" 'ysoi20050lc': 'YSOVAR IRAS 20050+2720 Light Curve Table',\n",
" 'ysoi20050obj': 'YSOVAR IRAS 20050+2720 Object Table',\n",
" 'ysol1688lc': 'YSOVAR L1688 Light Curve Table',\n",
" 'ysol1688obj': 'YSOVAR L1688 Object Table',\n",
" 'yson1333lc': 'YSOVAR NGC1333 Light Curve Table',\n",
" 'yson1333obj': 'YSOVAR NGC1333 Object Table'}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Irsa.list_catalogs()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<Table masked=True length=500>\n",
"<table id=\"table4537893328\">\n",
"<thead><tr><th>ra</th><th>dec</th><th>clon</th><th>clat</th><th>err_maj</th><th>err_min</th><th>err_ang</th><th>designation</th><th>j_m</th><th>j_msig</th><th>h_m</th><th>h_msig</th><th>k_m</th><th>k_msig</th><th>rd_flg</th><th>bl_flg</th><th>cc_flg</th><th>extd_flg</th><th>mp_flg</th><th>id_opt</th><th>b_m_opt</th><th>r_m_opt</th><th>dist_opt</th><th>phi_opt</th><th>nopt_mchs</th><th>dist</th><th>angle</th><th>id</th></tr></thead>\n",
"<thead><tr><th>deg</th><th>deg</th><th></th><th></th><th>arcs</th><th>arcs</th><th>deg</th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>arcs</th><th>deg</th><th></th><th>arcs</th><th>deg</th><th></th></tr></thead>\n",
"<thead><tr><th>float64</th><th>float64</th><th>object</th><th>object</th><th>float64</th><th>float64</th><th>int32</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>object</th><th>object</th><th>object</th><th>int32</th><th>int32</th><th>object</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>int32</th><th>float64</th><th>float64</th><th>object</th></tr></thead>\n",
"<tr><td>266.541</td><td>-28.941</td><td>17h46m09.85s</td><td>-28d56m27.33s</td><td>0.13</td><td>0.13</td><td>90</td><td>1746098-285627</td><td>13.709</td><td>0.027</td><td>13.159000000000001</td><td>0.062</td><td>12.868</td><td>--</td><td>220</td><td>110</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>459.33338199999997</td><td>58.420993000000003</td><td>0</td></tr>\n",
"<tr><td>266.539</td><td>-28.939</td><td>17h46m09.27s</td><td>-28d56m21.04s</td><td>0.13</td><td>0.13</td><td>90</td><td>1746092-285621</td><td>13.92</td><td>--</td><td>12.699999999999999</td><td>0.043999999999999997</td><td>10.49</td><td>0.036999999999999998</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>456.28099500000002</td><td>57.248691000000001</td><td>1</td></tr>\n",
"<tr><td>266.539</td><td>-28.933</td><td>17h46m09.44s</td><td>-28d55m59.05s</td><td>0.13</td><td>0.13</td><td>90</td><td>1746094-285559</td><td>15.788</td><td>--</td><td>12.417999999999999</td><td>0.050000000000000003</td><td>10.247999999999999</td><td>0.041000000000000002</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>470.31552099999999</td><td>55.138218000000002</td><td>2</td></tr>\n",
"<tr><td>266.542</td><td>-28.934</td><td>17h46m10.05s</td><td>-28d56m02.59s</td><td>0.14999999999999999</td><td>0.14999999999999999</td><td>90</td><td>1746100-285602</td><td>14.343999999999999</td><td>0.031</td><td>14.471</td><td>--</td><td>12.868</td><td>--</td><td>200</td><td>100</td><td>000</td><td>0</td><td>0</td><td>U</td><td>18.100000000000001</td><td>15.9</td><td>0.02</td><td>82</td><td>1</td><td>474.96516400000002</td><td>56.045482999999997</td><td>3</td></tr>\n",
"<tr><td>266.535</td><td>-28.937</td><td>17h46m08.50s</td><td>-28d56m13.33s</td><td>0.14999999999999999</td><td>0.14999999999999999</td><td>90</td><td>1746084-285613</td><td>16.152999999999999</td><td>0.16200000000000001</td><td>12.058999999999999</td><td>0.032000000000000001</td><td>10.144</td><td>0.047</td><td>222</td><td>111</td><td>BBC</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>452.05897099999999</td><td>55.728765000000003</td><td>4</td></tr>\n",
"<tr><td>266.535</td><td>-28.935</td><td>17h46m08.43s</td><td>-28d56m06.89s</td><td>0.13</td><td>0.13</td><td>90</td><td>1746084-285606</td><td>17.212</td><td>--</td><td>12.978999999999999</td><td>0.067000000000000004</td><td>10.943</td><td>0.042000000000000003</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>454.981067</td><td>54.994160999999998</td><td>5</td></tr>\n",
"<tr><td>266.538</td><td>-28.938</td><td>17h46m09.09s</td><td>-28d56m16.60s</td><td>0.13</td><td>0.13</td><td>90</td><td>1746090-285616</td><td>15.949</td><td>--</td><td>12.242000000000001</td><td>0.048000000000000001</td><td>9.7810000000000006</td><td>0.044999999999999998</td><td>022</td><td>011</td><td>00C</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>456.69620300000003</td><td>56.617438999999997</td><td>6</td></tr>\n",
"<tr><td>266.541</td><td>-28.938</td><td>17h46m09.83s</td><td>-28d56m15.78s</td><td>0.13</td><td>0.13</td><td>90</td><td>1746098-285615</td><td>17.518999999999998</td><td>--</td><td>12.279999999999999</td><td>0.029999999999999999</td><td>10.356</td><td>0.029000000000000001</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>465.33253999999999</td><td>57.196688000000002</td><td>7</td></tr>\n",
"<tr><td>266.539</td><td>-28.935</td><td>17h46m09.40s</td><td>-28d56m07.08s</td><td>0.13</td><td>0.13</td><td>90</td><td>1746093-285607</td><td>17.335000000000001</td><td>--</td><td>12.542999999999999</td><td>0.052999999999999999</td><td>10.422000000000001</td><td>0.053999999999999999</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>465.347711</td><td>55.914127999999998</td><td>8</td></tr>\n",
"<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
"<tr><td>266.482</td><td>-28.855</td><td>17h45m55.80s</td><td>-28d51m19.63s</td><td>0.13</td><td>0.13</td><td>90</td><td>1745557-285119</td><td>17.079000000000001</td><td>--</td><td>12.720000000000001</td><td>0.050999999999999997</td><td>10.917</td><td>0.045999999999999999</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>586.17899</td><td>20.684032999999999</td><td>490</td></tr>\n",
"<tr><td>266.479</td><td>-28.859</td><td>17h45m55.04s</td><td>-28d51m33.11s</td><td>0.14999999999999999</td><td>0.14999999999999999</td><td>90</td><td>1745550-285133</td><td>17.456</td><td>--</td><td>13.688000000000001</td><td>--</td><td>11.387</td><td>0.040000000000000001</td><td>002</td><td>001</td><td>00S</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>570.07834300000002</td><td>20.228916999999999</td><td>491</td></tr>\n",
"<tr><td>266.482</td><td>-28.858</td><td>17h45m55.63s</td><td>-28d51m28.68s</td><td>0.14999999999999999</td><td>0.14999999999999999</td><td>90</td><td>1745556-285128</td><td>15.763999999999999</td><td>--</td><td>13.058</td><td>--</td><td>11.468</td><td>0.094</td><td>002</td><td>001</td><td>00S</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>576.90266899999995</td><td>20.789037</td><td>492</td></tr>\n",
"<tr><td>266.466</td><td>-28.849</td><td>17h45m51.94s</td><td>-28d50m55.81s</td><td>0.13</td><td>0.13</td><td>90</td><td>1745519-285055</td><td>16.576000000000001</td><td>--</td><td>12.868</td><td>0.096000000000000002</td><td>10.775</td><td>0.034000000000000002</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>593.19416100000001</td><td>15.276467</td><td>493</td></tr>\n",
"<tr><td>266.468</td><td>-28.851</td><td>17h45m52.33s</td><td>-28d51m05.20s</td><td>0.14999999999999999</td><td>0.14999999999999999</td><td>90</td><td>1745523-285105</td><td>16.530999999999999</td><td>--</td><td>12.647</td><td>--</td><td>11.092000000000001</td><td>0.065000000000000002</td><td>002</td><td>001</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>585.55087500000002</td><td>16.007439999999999</td><td>494</td></tr>\n",
"<tr><td>266.473</td><td>-28.850</td><td>17h45m53.62s</td><td>-28d50m59.85s</td><td>0.14999999999999999</td><td>0.14999999999999999</td><td>90</td><td>1745536-285059</td><td>14.464</td><td>0.041000000000000002</td><td>11.606</td><td>--</td><td>9.9949999999999992</td><td>--</td><td>200</td><td>100</td><td>000</td><td>0</td><td>0</td><td>U</td><td>19.300000000000001</td><td>17.0</td><td>0.34000000000000002</td><td>282</td><td>1</td><td>595.54726400000004</td><td>17.434287000000001</td><td>495</td></tr>\n",
"<tr><td>266.474</td><td>-28.850</td><td>17h45m53.72s</td><td>-28d51m00.45s</td><td>0.13</td><td>0.13</td><td>90</td><td>1745537-285100</td><td>14.271000000000001</td><td>--</td><td>11.709</td><td>0.033000000000000002</td><td>10.012</td><td>0.035999999999999997</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td>U</td><td>19.300000000000001</td><td>17.0</td><td>1.77</td><td>292</td><td>1</td><td>595.37115900000003</td><td>17.571944999999999</td><td>496</td></tr>\n",
"<tr><td>266.472</td><td>-28.852</td><td>17h45m53.36s</td><td>-28d51m06.85s</td><td>0.13</td><td>0.13</td><td>90</td><td>1745533-285106</td><td>15.561999999999999</td><td>--</td><td>12.105</td><td>0.039</td><td>10.282999999999999</td><td>0.029999999999999999</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>587.84205599999996</td><td>17.320962000000002</td><td>497</td></tr>\n",
"<tr><td>266.468</td><td>-28.849</td><td>17h45m52.26s</td><td>-28d50m57.71s</td><td>0.13</td><td>0.13</td><td>90</td><td>1745522-285057</td><td>16.184999999999999</td><td>--</td><td>12.465999999999999</td><td>0.053999999999999999</td><td>10.6</td><td>0.048000000000000001</td><td>022</td><td>011</td><td>000</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>592.51437699999997</td><td>15.725013000000001</td><td>498</td></tr>\n",
"<tr><td>266.467</td><td>-28.861</td><td>17h45m51.97s</td><td>-28d51m41.08s</td><td>0.13</td><td>0.13</td><td>90</td><td>1745519-285141</td><td>15.756</td><td>--</td><td>12.212999999999999</td><td>0.034000000000000002</td><td>10.458</td><td>0.035000000000000003</td><td>022</td><td>011</td><td>00D</td><td>0</td><td>0</td><td></td><td>--</td><td>--</td><td>--</td><td>--</td><td>0</td><td>549.77417200000002</td><td>16.562427</td><td>499</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table masked=True length=500>\n",
" ra dec clon ... dist angle id \n",
" deg deg ... arcs deg \n",
"float64 float64 object ... float64 float64 object\n",
"------- ------- ------------ ... ------------------ ------------------ ------\n",
"266.541 -28.941 17h46m09.85s ... 459.33338199999997 58.420993000000003 0\n",
"266.539 -28.939 17h46m09.27s ... 456.28099500000002 57.248691000000001 1\n",
"266.539 -28.933 17h46m09.44s ... 470.31552099999999 55.138218000000002 2\n",
"266.542 -28.934 17h46m10.05s ... 474.96516400000002 56.045482999999997 3\n",
"266.535 -28.937 17h46m08.50s ... 452.05897099999999 55.728765000000003 4\n",
"266.535 -28.935 17h46m08.43s ... 454.981067 54.994160999999998 5\n",
"266.538 -28.938 17h46m09.09s ... 456.69620300000003 56.617438999999997 6\n",
"266.541 -28.938 17h46m09.83s ... 465.33253999999999 57.196688000000002 7\n",
"266.539 -28.935 17h46m09.40s ... 465.347711 55.914127999999998 8\n",
" ... ... ... ... ... ... ...\n",
"266.482 -28.855 17h45m55.80s ... 586.17899 20.684032999999999 490\n",
"266.479 -28.859 17h45m55.04s ... 570.07834300000002 20.228916999999999 491\n",
"266.482 -28.858 17h45m55.63s ... 576.90266899999995 20.789037 492\n",
"266.466 -28.849 17h45m51.94s ... 593.19416100000001 15.276467 493\n",
"266.468 -28.851 17h45m52.33s ... 585.55087500000002 16.007439999999999 494\n",
"266.473 -28.850 17h45m53.62s ... 595.54726400000004 17.434287000000001 495\n",
"266.474 -28.850 17h45m53.72s ... 595.37115900000003 17.571944999999999 496\n",
"266.472 -28.852 17h45m53.36s ... 587.84205599999996 17.320962000000002 497\n",
"266.468 -28.849 17h45m52.26s ... 592.51437699999997 15.725013000000001 498\n",
"266.467 -28.861 17h45m51.97s ... 549.77417200000002 16.562427 499"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rslt = Irsa.query_region('Sgr A*', radius=10*u.arcmin, catalog='pt_src_cat')\n",
"rslt"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"twomass_coords = coordinates.SkyCoord(rslt['ra'], rslt['dec'], frame='fk5', unit=(u.deg, u.deg))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "Can only get separation to another SkyCoord or a coordinate frame with data",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-852d265fcf65>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwomass_coords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatch_to_catalog_sky\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/adam/anaconda/envs/esopython2016/lib/python3.5/site-packages/astropy/coordinates/sky_coordinate.py\u001b[0m in \u001b[0;36mmatch_to_catalog_sky\u001b[0;34m(self, catalogcoord, nthneighbor)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[0mself_in_catalog_frame\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform_to\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcatalogcoord\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 743\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m raise TypeError('Can only get separation to another SkyCoord or a '\n\u001b[0m\u001b[1;32m 745\u001b[0m 'coordinate frame with data')\n\u001b[1;32m 746\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Can only get separation to another SkyCoord or a coordinate frame with data"
]
}
],
"source": [
"twomass_coords.match_to_catalog_sky(...)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
apryor6/apryor6.github.io | visualizations/bokeh/notebooks/glyphs/asterisk.ipynb | 1 | 1668 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bokeh Asterisk Glyph"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from bokeh.plotting import figure, output_file, show\n",
"from bokeh.models import Range1d\n",
"from bokeh.io import export_png\n",
"\n",
"line_color = '#91bfdb'\n",
"output_file(\"../../figures/asterisk.html\")\n",
"\n",
"p = figure(plot_width=400, plot_height=400)\n",
"p.asterisk(x=0,y=0,size=100,line_alpha=1,\n",
" line_color=line_color, line_dash='dashed', line_width=5)\n",
"p.asterisk(x=0,y=1,size=100,line_alpha=1,\n",
" line_color=line_color, line_dash='dotdash', line_width=8)\n",
"p.asterisk(x=1,y=0,size=100, line_alpha=1,\n",
" line_color=line_color, line_dash='dotted', line_width=13)\n",
"p.asterisk(x=1,y=1,size=100, line_alpha=1,\n",
" line_color=line_color, line_dash='solid', line_width=17)\n",
"p.x_range = Range1d(-0.5,1.5, bounds=(-1,2))\n",
"p.y_range = Range1d(-0.5,1.5, bounds=(-1,2))\n",
"show(p)\n",
"export_png(p, filename=\"../../figures/asterisk.png\");"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jeicher/NMRPy | nmrpy/docs/quickstart_tutorial.ipynb | 1 | 18606 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"marked": false
}
},
"source": [
"# NMRPy quickstart tutorial"
]
},
{
"cell_type": "markdown",
"metadata": {
"run_control": {
"marked": false
}
},
"source": [
"Refer to https://nmrpy.readthedocs.io/en/latest/quickstart.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"import matplotlib\n",
"%matplotlib ipympl\n",
"import nmrpy\n",
"import numpy as np\n",
"import scipy as sp\n",
"from matplotlib import pyplot as plt\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The basic NMR project object used in NMRPy is the `FidArray`, which consists of a set of `Fid` objects, each representing a single spectrum in an array of spectra.\n",
"\n",
"The simplest way to instantiate an `FidArray` is by using the `from_path()` method, and specifying the path of the .fid directory:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fname = os.path.join(os.path.dirname(nmrpy.__file__),'tests','test_data','test1.fid')\n",
"fid_array = nmrpy.from_path(fname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will notice that the `fid_array` object is instantiated and now owns several attributes, most of which are of the form `fidXX` where XX is a number starting at 00. These are the individual arrayed `Fid` objects."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Apodisation and Fourier transform"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To quickly visualise the imported data, we can use the plotting functions owned by each `Fid` instance. This will not display the imaginary portion of the data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.fid00.plot_ppm()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now perform apodisation of the FIDs using the default value of 5 Hz, and visualise the result:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.emhz_fids()\n",
"fid_array.fid00.plot_ppm()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we zero-fill and Fourier-transform the data into the frequency domain:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.zf_fids()\n",
"fid_array.ft_fids()\n",
"fid_array.fid00.plot_ppm()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Phase-correction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is clear from the data visualisation that at this stage the spectra require phase-correction. NMRPy provides a number of GUI widgets for manual processing of data. In this case we will use the `phaser()` method on `fid00`.\n",
"\n",
"Dragging with the left mouse button and right mouse button will apply zero- and first-order phase-correction, respectively. The cumulative phase correction for the zero-order (`p0`) and first-order (`p1`) phase angles is displayed at the bottom of the plot so that these can be applied programatically to all `Fid` objects in the `FidArray` using the `ps_fids()` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.fid00.phaser()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, *automatic* phase-correction can be applied at either the `FidArray` or `Fid` level. We will apply it to the whole array:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.phase_correct_fids()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this stage it is useful to discard the imaginary component of our data, and possibly normalise the data (by the maximum data value amongst the `Fid` objects):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.real_fids()\n",
"fid_array.norm_fids()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And plot an array of the phase-corrected data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
},
"scrolled": false
},
"outputs": [],
"source": [
"fid_array.plot_array()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zooming in on the relevant peaks, changing the view perspective, and filling the spectra produces a more interesting plot:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.plot_array(upper_ppm=7, lower_ppm=-1, filled=True, azim=-76, elev=23)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calibration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The spectra may need calibration by assigning a chemical shift to a reference peak of a known standard and adjusting the spectral offset accordingly. To this end, a `calibrate()` convenience method exists that allows the user to easily select a peak and specify the PPM. This method can be applied at either the `FidArray` or `Fid` level. We will apply it to the whole array.\n",
"\n",
"Left-clicking selects a peak and its current ppm value is displayed below the spectrum. The new ppm value can be entered in a text box, and hitting `Enter` completes the calibration process. Here we have chosen triethyl phosphate (TEP) as reference peak and assigned its chemical shift value of 0.44 ppm (the original value was 0.57 ppm, and the offset of all the spectra in the array has been adjusted by 0.13 ppm after the calibration)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fid_array.calibrate()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Peak-picking"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To begin the process of integrating peaks by deconvolution, we will need to pick some peaks. The `peaks` attribute of a `Fid` is an array of peak positions, and `ranges` is an array of range boundaries. These two objects are used in deconvolution to integrate the data by fitting Lorentzian/Gaussian peak shapes to the spectra. `peaks` and `ranges` may be specified programatically, or picked using the interactive GUI widget.\n",
"\n",
"Left-clicking specifies a peak selection with a vertical red line. Dragging with a right-click specifies a range to fit independently with a grey rectangle. Inadvertent wrongly selected peaks can be deleted with Ctrl+left-click; wrongly selected ranges can be deleted with Ctrl+right-click. Once you are done selecting peaks and ranges, these need to be assigned to the `FidArray`; this is achieved with a Ctrl+Alt+right-click."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
},
"scrolled": false
},
"outputs": [],
"source": [
"fid_array.peakpicker(fid_number=10, assign_only_to_index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ranges divide the data into smaller portions, which significantly speeds up the process of fitting of peakshapes to the data. Range-specification also prevents incorrect peaks from being fitted by the fitting algorithm.\n",
"\n",
"Having used the `peakpicker()` `FidArray` method (as opposed to the `peakpicker()` on each individual `Fid` instance), the peak and range selections have now been assigned to each Fid in the array:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"print(fid_array.fid00.peaks)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"print(fid_array.fid00.ranges)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Peak-picking trace selector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes peaks are subject to drift so that the chemical shift changes over time; this can happen, e.g., when the pH of the reaction mixture changes as the reaction proceeds. NMRPy offers a convenient trace selector, `peakpicker_traces()`, with which the drift of the peaks can be traced over time and the chemical shift selected accordingly as appropriate for the particular `Fid`.\n",
"\n",
"As for the `peakpicker()`, ranges are selected by dragging the right mouse button and can be deleted with Ctrl+right-click. A peak trace is initiated by left-clicking below the peak underneath the first `Fid` in the series. This selects a point and anchors the trace line, which is displayed in red as the mouse is moved. The trace will attempt to follow the highest peak. Further trace points can be added by repeated left-clicking, thus tracing the peak through the individual `Fid`s in the series. It is not necessary to add an anchor point for every `Fid`, only when the trace needs to change direction. Once the trace has traversed all the `Fid`s, select a final trace point (left-click) and then finalize the trace with a right-click. The trace will change colour from red to blue to indicate that it has been finalized.\n",
"\n",
"Additional peaks can then be selected by initiating a new trace. Wrongly selected traces can be deleted by Ctrl+left-click at the bottom of the trace that should be removed. Note that the interactive buttons on the matplotlib toolbar for the figure can be used to zoom and pan into a region of interest of the spectra. As previously, peaks and ranges need to be assigned to the `FidArray` with Ctrl+Alt+right-click. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
},
"scrolled": false
},
"outputs": [],
"source": [
"fid_array.peakpicker_traces(voff=0.08)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If these trace lines don't run exactly vertically, individual peaks have different chemical shifts for the different `Fid`s, although in this particular case the drift in the spectra is not significant so that `peakpicker_traces()` need not have been used and `peakpicker()` would have been sufficient. This is merely for illustrative purposes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"print(fid_array.fid00.peaks)\n",
"print(fid_array.fid10.peaks)\n",
"print(fid_array.fid20.peaks)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deconvolution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:**\n",
"\n",
"If you *have not* assigned peaks and ranges using any of the peakpicker widgets above,\n",
"**uncomment the following cell** to assign peaks and ranges to continue with the deconvolution."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"# peaks = [ 4.73, 4.63, 4.15, 0.55]\n",
"# ranges = [[ 5.92, 3.24], [ 1.19, -0.01]]\n",
"# for fid in fid_array.get_fids():\n",
"# fid.peaks = peaks\n",
"# fid.ranges = ranges"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Individual `Fid` objects can be deconvoluted with `deconv()`. `FidArray` objects can be deconvoluted with `deconv_fids()`. By default this is a multiprocessed method (`mp=True`), which will fit pure Lorentzian lineshapes (`frac_gauss=0.0`) to the `peaks` and `ranges` specified in each `Fid`.\n",
"\n",
"We shall fit the whole array at once:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.deconv_fids()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And visualise the deconvoluted spectra:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.fid10.plot_deconv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zooming-in to a set of peaks makes clear the fitting result:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
},
"scrolled": true
},
"outputs": [],
"source": [
"fid_array.fid10.plot_deconv(upper_ppm=5.5, lower_ppm=3.5)\n",
"fid_array.fid10.plot_deconv(upper_ppm=0.9, lower_ppm=0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" *Black*: original data; \n",
" *Blue*: individual peak shapes (and peak numbers above); \n",
" *Red*: summed peak shapes; \n",
" *Green*: residual (original data - summed peakshapes)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, peaks 0 and 1 belong to glucose-6-phosphate, peak 2 belongs to fructose-6-phosphate, and peak 3 belongs to triethyl-phosphate (internal standard).\n",
"\n",
"We can view the deconvolution result for the whole array using `plot_deconv_array()`. Fitted peaks appear in red:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.plot_deconv_array(upper_ppm=6, lower_ppm=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Peak integrals of the complete `FidArray` are stored in `deconvoluted_integrals`, or in each individual `Fid` as `deconvoluted_integrals`.\n",
"\n",
"The species integrals can easily be plotted using the following code:\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"integrals = fid_array.deconvoluted_integrals.transpose()\n",
"\n",
"g6p = integrals[0] + integrals[1]\n",
"f6p = integrals[2]\n",
"tep = integrals[3]\n",
"\n",
"#scale species by internal standard tep (5 mM)\n",
"g6p = 5.0*g6p/tep.mean()\n",
"f6p = 5.0*f6p/tep.mean()\n",
"tep = 5.0*tep/tep.mean()\n",
"\n",
"species = {'g6p': g6p,\n",
" 'f6p': f6p,\n",
" 'tep': tep}\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"for k, v in species.items():\n",
" ax.plot(fid_array.t, v, label=k)\n",
"\n",
"ax.set_xlabel('min')\n",
"ax.set_ylabel('mM')\n",
"ax.legend(loc=0, frameon=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Saving / Loading"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The current state of any `FidArray` object can be saved to file using the `save_to_file()` method:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array.save_to_file(filename='fidarray.nmrpy')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And reloaded using `from_path()`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"run_control": {
"marked": false
}
},
"outputs": [],
"source": [
"fid_array = nmrpy.data_objects.FidArray.from_path(fid_path='fidarray.nmrpy')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
},
"toc": {
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"toc_cell": false,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": true
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
google-research/google-research | dialogue_ope/airdialogue_ope/analysis_tfboard.ipynb | 1 | 52048 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Copyright 2020 Google LLC. \n",
"# \n",
"# Licensed under the Apache License, Version 2.0 (the \"License\"); \n",
"# you may not use this file except in compliance with the License. \n",
"# You may obtain a copy of the License at \n",
"# \n",
"# http://www.apache.org/licenses/LICENSE-2.0 \n",
"# \n",
"# Unless required by applicable law or agreed to in writing, software \n",
"# distributed under the License is distributed on an \"AS IS\" BASIS, \n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n",
"# See the License for the specific language governing permissions and \n",
"# limitations under the License. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os,sys\n",
"import pandas as pd\n",
"from tqdm import tqdm\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm\n",
"import tensorflow as tf\n",
"from tensorflow.python.summary.summary_iterator import summary_iterator\n",
"from scipy import stats\n",
"\n",
"import seaborn as sns\n",
"sns.set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Define Helper functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extract tf board data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Extraction function\n",
"def sum_log(path,config,model,runlog):\n",
"# try:\n",
" r={}\n",
" for e in summary_iterator(path):\n",
" for v in e.summary.value:\n",
" if v.tag not in r:\n",
" r[v.tag] = {'Model': model, 'config': config, 'Metric': v.tag, 'Values': [], 'Steps': []}\n",
" r[v.tag]['Values'].append(v.simple_value)\n",
" r[v.tag]['Steps'].append(e.step)\n",
" for k,v in r.items():\n",
" runlog = runlog.append(v, ignore_index=True)\n",
" \n",
" return runlog\n",
"\n",
"# load tf logs\n",
"def log_tflogs_from_path(tfboardroot, trim_config=None, trim_model=None):\n",
" models = os.listdir(tfboardroot)\n",
"\n",
" all_log = pd.DataFrame(columns=['Model', 'config', 'Metric', 'Values', 'Steps'])\n",
"\n",
" modellist = set()\n",
" configlist = set()\n",
"\n",
" for m in tqdm(models):\n",
" modelroot = os.path.join(tfboardroot,m)\n",
" modellist.add(m)\n",
" for config in os.listdir(modelroot):\n",
" configlist.add(config)\n",
" logpath = os.path.join(modelroot,config,'log')\n",
" for f in os.listdir(logpath):\n",
" if f.startswith('events'):\n",
" logpath = os.path.join(logpath,f)\n",
" break\n",
"\n",
" all_log = sum_log(logpath,config,m,all_log)\n",
" if trim_config is not None:\n",
" all_log['config'] = all_log['config'].apply(trim_config)\n",
" configlist = set(all_log['config'])\n",
" if trim_model is not None:\n",
" all_log['Model'] = all_log['Model'].apply(trim_model)\n",
" modellist = set(all_log['Model'])\n",
" return all_log, modellist, configlist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Curve plot helper"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_plot_df(sub_human_stats, sub_log, metric, std_scale = 1.645/10):\n",
" steps = sub_log.iloc[0]['Steps']\n",
" sub_human_stats['Steps'] = [steps] * sub_human_stats.shape[0]\n",
" sub_human_stats = sub_human_stats.explode('Steps')\n",
" sub_log = sub_log[['Model','Values','Steps']]\n",
" \n",
" y = sub_log.iloc[0]['Values']\n",
" \n",
" sub_log = sub_log.set_index(['Model']).apply(pd.Series.explode).reset_index()\n",
" sub_log = sub_log.infer_objects()\n",
" value_mean = sub_log.groupby(['Model'])['Values'].apply(lambda x: x.ewm(halflife=10).mean())\n",
" value_std = sub_log.groupby(['Model'])['Values'].apply(lambda x: x.ewm(halflife=10).std()) \n",
" \n",
" plot_log = sub_log.copy()\n",
"\n",
" plot_log['Evaluation']='OPE'\n",
" \n",
" sub_human_stats['Evaluation']='Truth'\n",
" new_plot_log = sub_human_stats.copy()\n",
" new_plot_log['Values'] = new_plot_log[metric+'-mean']\n",
" plot_log = plot_log.append(new_plot_log[['Model','Values','Steps','Evaluation']], ignore_index=True)\n",
" new_plot_log = sub_human_stats.copy()\n",
" if std_scale > 0:\n",
" new_plot_log['Values'] = new_plot_log[metric+'-mean'] + new_plot_log[metric+'-std']*std_scale #90% confidenen interval\n",
" plot_log = plot_log.append(new_plot_log[['Model','Values','Steps','Evaluation']], ignore_index=True)\n",
" new_plot_log = sub_human_stats.copy()\n",
" new_plot_log['Values'] = new_plot_log[metric+'-mean'] - new_plot_log[metric+'-std']*std_scale\n",
" plot_log = plot_log.append(new_plot_log[['Model','Values','Steps','Evaluation']], ignore_index=True)\n",
" \n",
" return plot_log"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate OPE helper"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def gather_ope_logs_by_config(\n",
" all_log, \n",
" plot_metrics, \n",
" config, \n",
" last_n = 100,\n",
" trim_metric= lambda x: 'est_reward_dual_{}_normalized'.format(x),\n",
" final_n = None\n",
"):\n",
"\n",
" all_plotlog = None\n",
" for metric in plot_metrics:\n",
" plot_log = human_stats[['Model', metric+'-mean', metric+'-std']]\n",
" plot_log = plot_log.rename(columns={metric+'-mean': 'Human-mean', metric+'-std': 'Human-std'})\n",
" sub_log = all_log.loc[all_log['config']==config]\n",
" sub_log = sub_log.loc[sub_log['Metric']==trim_metric(metric)]\n",
" sub_log = sub_log[['Model','Values']]\n",
" if final_n is None:\n",
" sub_log['Values'] = sub_log['Values'].apply(lambda x: np.array(x[-last_n:]).mean())\n",
" else:\n",
" sub_log['Values'] = sub_log['Values'].apply(lambda x: np.array(x[-last_n:-final_n]).mean())\n",
" sub_log = sub_log.rename(columns={'Values':'OPE'})\n",
"\n",
" plot_log = plot_log.set_index('Model')\n",
" sub_log = sub_log.set_index('Model')\n",
"\n",
" plot_log = plot_log.join(sub_log)\n",
" plot_log['Metric'] = metric\n",
" if all_plotlog is None:\n",
" all_plotlog = plot_log\n",
" else:\n",
" all_plotlog = all_plotlog.append(plot_log)\n",
" all_plotlog['Error'] = (all_plotlog['OPE']-all_plotlog['Human-mean']).apply(lambda x: abs(x))\n",
"\n",
" return all_plotlog"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AirDialogue Rule Based"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotroot=\"outputs/plotdir/air_ope/rule/\"\n",
"os.makedirs(plotroot,exist_ok=True)\n",
"tfboardroot = 'outputs/syn_air_ope/syn_ope_data_500'\n",
"default_config = 'epoch_500'\n",
"\n",
"human_stats = {\n",
" 'Model': ['L0', 'L1', 'L2', 'L3', 'L4', 'L5'], \n",
" 'reward-mean': [0.4928, 0.5914, 0.6917, 0.7958, 0.8973, 1.0000], \n",
" 'reward-std': [0.3292, 0.3589, 0.3574, 0.3261, 0.2525, 0.0001]\n",
"}\n",
"human_stats = pd.DataFrame(data=human_stats)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"human_stats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_log, modellist, configlist = log_tflogs_from_path(tfboardroot, \n",
" trim_config = lambda x: 'epoch_'+x.split('epoch_')[-1].split('_')[0],\n",
" trim_model = lambda x: x.replace('tgt_', ''))\n",
"\n",
"print(modellist)\n",
"print(configlist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_log"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Curve"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sub_log = all_log[all_log['config']==default_config]\n",
"sub_log = sub_log[sub_log['Metric']=='est_reward_dual_normalized']\n",
"\n",
"plot_log = get_plot_df(human_stats, sub_log, 'reward', std_scale=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_log"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_log = plot_log.sort_values(by='Model')\n",
"g = sns.lineplot(x='Steps', y='Values', hue='Model', style='Evaluation', data=plot_log)\n",
"g.legend(loc='center left', bbox_to_anchor=(1.0, 0.5), ncol=1)\n",
"g.set_position([0.15,0.15,0.6,0.8])\n",
"g.figure.savefig(plotroot+'learning_curve.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Human vs OPE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_plotlog = gather_ope_logs_by_config(all_log, ['reward'], default_config, trim_metric=lambda x:'est_reward_dual_normalized')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_plotlog"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def r2(x, y):\n",
" return stats.pearsonr(x, y)[0] ** 2\n",
"all_plotlog = all_plotlog.rename(columns={'Human-mean': 'Reward', 'OPE': 'OPE'})\n",
"g = sns.jointplot('Reward', 'OPE', kind=\"reg\", data=all_plotlog, stat_func=r2,\n",
" xlim=[0.4,1.1],ylim=[0.4,1.1])\n",
"g.savefig(plotroot+'ope_vs_human.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AirDialogue Model-Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotroot=\"outputs/plotdir/air_ope/model/\"\n",
"os.makedirs(plotroot,exist_ok=True)\n",
"\n",
"# load stats\n",
"statspath = 'data/selfplay_opedata/orig/stats.csv'\n",
"\n",
"human_stats = pd.read_csv(statspath)\n",
"human_stats = human_stats.rename(columns={'model_name': 'Model'}).set_index('Model')\n",
"\n",
"human_stats_auto = {\n",
" 'Model': ['5K','10K','20K','30K','40K','50K','75K','100K','150K','200K','250K','full',\n",
" '5K_w','10K_w','20K_w','30K_w','40K_w','50K_w','75K_w','100K_w','150K_w','200K_w','250K_w','full_w'], \n",
" 'ppl': [2.671, 2.368, 2.141, 2.053, 2.121, 1.919, 1.843, 2.084, 2.021, 2.076, 1.949, 1.954,\n",
" 2.673, 2.369, 2.141, 2.053, 2.121, 1.919, 1.843, 2.084, 2.021, 2.076, 1.949, 1.954], \n",
" 'BLEU': [12.53, 19.07, 23.72, 24.49, 19.64, 29.98, 31.70, 20.10, 21.95, 20.24, 26.04, 25.41,\n",
" 12.53, 19.07, 23.71, 24.49, 19.64, 29.98, 31.70, 20.11, 21.95, 20.23, 26.04, 25.41], \n",
"}\n",
"human_stats_auto = pd.DataFrame(data=human_stats_auto).set_index('Model')\n",
"human_stats = pd.concat([human_stats_auto, human_stats], axis=1, join='inner').reset_index()\n",
"\n",
"metriclist = ['flight_score', 'reward', 'status_score']\n",
"print(metriclist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"human_stats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tfboardroot = \"outputs/selfplay_air_ope_all\"\n",
"default_config = 'roberta-base_fix_false_share_true_freeze_true_epoch_300_invsqrt_adam_lr_2e-4_C_1_Q_2_L_10x100_BERT_1_warmup_30_mom_0.5_MAXNORM_1_WD_1e-4_BS_20x1_Linit_0_alphaR_0_C_1_Q_0_L_0_A_0_regfunC_square_Q_abs_cut20_L_square_actC_square_Q_no_tag__seed_0'\n",
"all_log, modellist, configlist = log_tflogs_from_path(tfboardroot)\n",
"\n",
"print(modellist)\n",
"print(configlist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_log"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Curve"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_metrics = metriclist\n",
"# plot_metrics = ['reward','avoid_rep']\n",
"\n",
"num_models = human_stats.shape[0]\n",
"print('num_models: ', num_models)\n",
"\n",
"all_plotlog = None\n",
"\n",
"for metric in plot_metrics:\n",
" sub_human_stats = human_stats[['Model', metric+'-mean', metric+'-std']]\n",
" sub_log = all_log.loc[all_log['config']==default_config]\n",
" sub_log = sub_log.loc[sub_log['Metric']=='est_reward_dual_{}_normalized'.format(metric)]\n",
" sub_human_stats = sub_human_stats.sort_values(by=[metric+'-mean', metric+'-std'])\n",
" sub_human_stats = sub_human_stats.iloc[[0,6,12,23]]\n",
" \n",
" selected_models = list(sub_human_stats['Model'])\n",
" \n",
" sub_log.index = sub_log['Model']\n",
" sub_log = sub_log.loc[selected_models]\n",
" \n",
" # change model name\n",
" model_dict = {\n",
" selected_models[0]: 'Model 0%',\n",
" selected_models[1]: 'Model 25%',\n",
" selected_models[2]: 'Model 50%',\n",
" selected_models[3]: 'Model 100%',\n",
" }\n",
" sub_log['Model'] = sub_log['Model'].apply(lambda x: model_dict[x])\n",
" sub_human_stats['Model'] = sub_human_stats['Model'].apply(lambda x: model_dict[x])\n",
" plot_log = get_plot_df(sub_human_stats, sub_log, metric)\n",
" plot_log['Metric'] = metric\n",
" if all_plotlog is None:\n",
" all_plotlog = plot_log\n",
" else:\n",
" all_plotlog = all_plotlog.append(plot_log)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.FacetGrid(all_plotlog, col='Metric', col_wrap=3,\n",
" height=3, aspect=1.5,sharey=False)\n",
"def mylineplot(x, y, h, s, **kwargs):\n",
" sns.lineplot(x=x, y=y, hue=h, style=s, **kwargs)\n",
"g.map(mylineplot, \n",
" 'Steps', 'Values','Model','Evaluation',\n",
" ci='sd')\n",
"g.add_legend();\n",
"g.savefig(plotroot+'learning_curve.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Human vs. OPE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_plotlog = gather_ope_logs_by_config(all_log, metriclist, default_config)\n",
"bleu_ppl_stats = human_stats[['Model', 'BLEU', 'ppl']].set_index('Model')\n",
"all_plotlog = all_plotlog.join(bleu_ppl_stats)\n",
"all_plotlog"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ope vs human\n",
"g = sns.lmplot(x=\"Human-mean\", y='OPE', col='Metric', hue='Metric',\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'ope_vs_human.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for m in all_plotlog.Metric.unique():\n",
" tempdf = all_plotlog[all_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['OPE']\n",
" X = X[~y.isna()]\n",
" y = y[~y.isna()]\n",
"\n",
"# model = sm.OLS(y, X)\n",
"# results = model.fit()\n",
"# print('{} R2: {:.4f}'.format(m, results.rsquared))\n",
" print('{} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"# print(results.summary())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# \n",
"from scipy import stats\n",
"def r2(x, y):\n",
" return stats.pearsonr(x, y)[0] ** 2\n",
"g = sns.jointplot('Human-mean', 'OPE', kind=\"reg\", data=all_plotlog, stat_func=r2)\n",
"g.savefig(plotroot+'ope_vs_human_all.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## vs. BLEU PPL"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.lmplot(x=\"Human-mean\", y='BLEU', col='Metric', hue='Metric',\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'bleu_vs_human.pdf')\n",
"for m in all_plotlog.Metric.unique():\n",
" tempdf = all_plotlog[all_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['BLEU']\n",
" print('BLEU {} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"g = sns.lmplot(x=\"Human-mean\", y='ppl', col='Metric', hue='Metric',\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'ppl_vs_human.pdf')\n",
"for m in all_plotlog.Metric.unique():\n",
" tempdf = all_plotlog[all_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['ppl']\n",
" print('ppl {} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"# g = sns.jointplot(metric+\"-mean\", \"ppl\", kind=\"reg\", data=all_plotlog, stat_func=r2)\n",
"# g.savefig(plotroot+'ppl_vs_human.pdf')\n",
"# g = sns.jointplot(metric+\"-mean\", 'OPE', kind=\"reg\", data=all_plotlog, stat_func=r2)\n",
"# g.savefig(plotroot+'ope_vs_human.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Error Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.lmplot(x=\"Human-mean\", y='Error', col='Metric', hue='Metric', order=2,\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.set(ylim=(-0.008, None))\n",
"for ax, (_, subdata) in zip(g.axes, all_plotlog.groupby('Metric')):\n",
" ax2=ax.twinx()\n",
" sns.distplot(subdata[\"Human-mean\"], ax=ax2,color='#95a5a6')\n",
" plt.setp(ax2.get_yticklabels(), visible=False)\n",
" plt.setp(ax2.get_yticklines(), visible=False)\n",
" ax.patch.set_visible(True)\n",
" \n",
"g.savefig(plotroot+'error_analysis.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AirDialogue Model-Human"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotroot=\"outputs/plotdir/air_ope/human/\"\n",
"os.makedirs(plotroot,exist_ok=True)\n",
"\n",
"# load stats\n",
"statspath = 'data/human_opedata/orig/stats.csv'\n",
"\n",
"human_stats = pd.read_csv(statspath)\n",
"human_stats = human_stats.rename(columns={'model_name': 'Model'}).set_index('Model')\n",
"\n",
"# load SP stats\n",
"statspath = 'data/selfplay_opedata/orig/stats.csv'\n",
"human_stats_sp = pd.read_csv(statspath).add_prefix('SP-')\n",
"human_stats_sp = human_stats_sp.rename(columns={'SP-model_name': 'Model'}).set_index('Model')\n",
"human_stats = pd.concat([human_stats, human_stats_sp], axis=1, join='inner')\n",
"\n",
"human_stats_auto = {\n",
" 'Model': ['5K','10K','20K','30K','40K','50K','75K','100K','150K','200K','250K','full',\n",
" '5K_w','10K_w','20K_w','30K_w','40K_w','50K_w','75K_w','100K_w','150K_w','200K_w','250K_w','full_w'], \n",
" 'ppl': [2.671, 2.368, 2.141, 2.053, 2.121, 1.919, 1.843, 2.084, 2.021, 2.076, 1.949, 1.954,\n",
" 2.673, 2.369, 2.141, 2.053, 2.121, 1.919, 1.843, 2.084, 2.021, 2.076, 1.949, 1.954], \n",
" 'BLEU': [12.53, 19.07, 23.72, 24.49, 19.64, 29.98, 31.70, 20.10, 21.95, 20.24, 26.04, 25.41,\n",
" 12.53, 19.07, 23.71, 24.49, 19.64, 29.98, 31.70, 20.11, 21.95, 20.23, 26.04, 25.41], \n",
"}\n",
"human_stats_auto = pd.DataFrame(data=human_stats_auto).set_index('Model')\n",
"human_stats = pd.concat([human_stats_auto, human_stats], axis=1, join='inner').reset_index()\n",
"\n",
"\n",
"metriclist = ['flight_score', 'reward', 'status_score']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"human_stats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tfboardroot = \"outputs/human_air_ope_all\"\n",
"default_config = 'roberta-base_fix_false_share_true_freeze_true_epoch_500_invsqrt_adam_lr_1.5e-4_C_1_Q_2_L_10x100_BERT_1_warmup_30_mom_0.5_MAXNORM_1_WD_1e-4_BS_20x1_Linit_0_alphaR_0_C_1_Q_0_L_0_A_0_regfunC_square_Q_abs_cut20_L_square_actC_square_Q_no_tag__seed_0'\n",
"all_log, modellist, configlist = log_tflogs_from_path(tfboardroot)\n",
"\n",
"print(modellist)\n",
"print(configlist)\n",
"all_log"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Learning Curve"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_metrics = metriclist\n",
"# plot_metrics = ['reward','avoid_rep']\n",
"\n",
"num_models = human_stats.shape[0]\n",
"print('num_models: ', num_models)\n",
"\n",
"all_plotlog = None\n",
"\n",
"for metric in plot_metrics:\n",
" sub_human_stats = human_stats[['Model', metric+'-mean', metric+'-std']]\n",
" sub_log = all_log.loc[all_log['config']==default_config]\n",
" sub_log = sub_log.loc[sub_log['Metric']=='est_reward_dual_{}_normalized'.format(metric)]\n",
" sub_human_stats = sub_human_stats.sort_values(by=[metric+'-mean', metric+'-std'])\n",
" sub_human_stats = sub_human_stats.iloc[[0,6,12,23]]\n",
" \n",
" selected_models = list(sub_human_stats['Model'])\n",
" \n",
" sub_log.index = sub_log['Model']\n",
" sub_log = sub_log.loc[selected_models]\n",
" \n",
" # change model name\n",
" model_dict = {\n",
" selected_models[0]: 'Model 0%',\n",
" selected_models[1]: 'Model 25%',\n",
" selected_models[2]: 'Model 50%',\n",
" selected_models[3]: 'Model 100%',\n",
" }\n",
" sub_log['Model'] = sub_log['Model'].apply(lambda x: model_dict[x])\n",
" sub_human_stats['Model'] = sub_human_stats['Model'].apply(lambda x: model_dict[x])\n",
" plot_log = get_plot_df(sub_human_stats, sub_log, metric)\n",
" plot_log['Metric'] = metric\n",
" if all_plotlog is None:\n",
" all_plotlog = plot_log\n",
" else:\n",
" all_plotlog = all_plotlog.append(plot_log)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.FacetGrid(all_plotlog, col='Metric', col_wrap=3,\n",
" height=3, aspect=1.5,sharey=False)\n",
"def mylineplot(x, y, h, s, **kwargs):\n",
" sns.lineplot(x=x, y=y, hue=h, style=s, **kwargs)\n",
"g.map(mylineplot, \n",
" 'Steps', 'Values','Model','Evaluation',\n",
" ci='sd')\n",
"g.add_legend();\n",
"g.savefig(plotroot+'learning_curve.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Human vs. OPE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_plotlog = gather_ope_logs_by_config(all_log, metriclist, default_config)\n",
"bleu_ppl_stats = human_stats[['Model', 'BLEU', 'ppl']].set_index('Model')\n",
"all_plotlog = all_plotlog.join(bleu_ppl_stats)\n",
"all_splog = None\n",
"for m in metriclist:\n",
" sp_log = human_stats[['Model', 'SP-'+m+'-mean', 'SP-'+m+'-std']].set_index('Model')\n",
" sp_log = sp_log.rename(columns = {'SP-'+m+'-mean': 'SP-mean', 'SP-'+m+'-std': 'SP-std'})\n",
" sp_log['Metric'] = m\n",
" if all_splog is None:\n",
" all_splog = sp_log\n",
" else:\n",
" all_splog = all_splog.append(sp_log)\n",
"\n",
"all_plotlog = all_plotlog.reset_index().set_index(['Model', 'Metric'])\n",
"all_splog = all_splog.reset_index().set_index(['Model', 'Metric'])\n",
"all_plotlog = all_plotlog.join(all_splog).reset_index()\n",
"all_plotlog"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ope vs human\n",
"g = sns.lmplot(x=\"Human-mean\", y='OPE', col='Metric', hue='Metric',\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'ope_vs_human.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for m in all_plotlog.Metric.unique():\n",
" tempdf = all_plotlog[all_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['OPE']\n",
" X = X[~y.isna()]\n",
" y = y[~y.isna()]\n",
"\n",
"# model = sm.OLS(y, X)\n",
"# results = model.fit()\n",
"# print('{} R2: {:.4f}'.format(m, results.rsquared))\n",
" print('{} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"# print(results.summary())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# \n",
"from scipy import stats\n",
"def r2(x, y):\n",
" return stats.pearsonr(x, y)[0] ** 2\n",
"g = sns.jointplot('Human-mean', 'OPE', kind=\"reg\", data=all_plotlog, stat_func=r2)\n",
"g.savefig(plotroot+'ope_vs_human_all.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## vs. BLEU PPL"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.lmplot(x=\"Human-mean\", y='BLEU', col='Metric', hue='Metric',\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'bleu_vs_human.pdf')\n",
"for m in all_plotlog.Metric.unique():\n",
" tempdf = all_plotlog[all_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['BLEU']\n",
" print('BLEU {} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"g = sns.lmplot(x=\"Human-mean\", y='ppl', col='Metric', hue='Metric',\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'ppl_vs_human.pdf')\n",
"for m in all_plotlog.Metric.unique():\n",
" tempdf = all_plotlog[all_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['ppl']\n",
" print('ppl {} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"g = sns.lmplot(x=\"Human-mean\", y='SP-mean', col='Metric', hue='Metric',\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'selfplay_vs_human.pdf')\n",
"for m in all_plotlog.Metric.unique():\n",
" tempdf = all_plotlog[all_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['SP-mean']\n",
" print('Selfplay {} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"# g = sns.jointplot(metric+\"-mean\", \"ppl\", kind=\"reg\", data=all_plotlog, stat_func=r2)\n",
"# g.savefig(plotroot+'ppl_vs_human.pdf')\n",
"# g = sns.jointplot(metric+\"-mean\", 'OPE', kind=\"reg\", data=all_plotlog, stat_func=r2)\n",
"# g.savefig(plotroot+'ope_vs_human.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## vs. Selfplay flight_score > 0.50, reward > 0.65, status_score > 0.7"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sub_plotlog = all_plotlog[(all_plotlog['Metric']=='flight_score') & (all_plotlog['Human-mean']>0.51)]\n",
"sub_plotlog = sub_plotlog.append(all_plotlog[(all_plotlog['Metric']=='reward') & (all_plotlog['Human-mean']>0.65)])\n",
"sub_plotlog = sub_plotlog.append(all_plotlog[(all_plotlog['Metric']=='status_score') & (all_plotlog['Human-mean']>0.7)])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.lmplot(x=\"Human-mean\", y='BLEU', col='Metric', hue='Metric',\n",
" data=sub_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'bleu_vs_human_top.pdf')\n",
"for m in sub_plotlog.Metric.unique():\n",
" tempdf = sub_plotlog[sub_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['BLEU']\n",
" print('BLEU {} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"g = sns.lmplot(x=\"Human-mean\", y='ppl', col='Metric', hue='Metric',\n",
" data=sub_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'ppl_vs_human_top.pdf')\n",
"for m in sub_plotlog.Metric.unique():\n",
" tempdf = sub_plotlog[sub_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['ppl']\n",
" print('ppl {} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"g = sns.lmplot(x=\"Human-mean\", y='OPE', col='Metric', hue='Metric',\n",
" data=sub_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'ope_vs_human_top.pdf')\n",
"for m in sub_plotlog.Metric.unique():\n",
" tempdf = sub_plotlog[sub_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['OPE']\n",
" print('OPE {} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"g = sns.lmplot(x=\"Human-mean\", y='SP-mean', col='Metric', hue='Metric',\n",
" data=sub_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'selfplay_vs_human_top.pdf')\n",
"for m in sub_plotlog.Metric.unique():\n",
" tempdf = sub_plotlog[sub_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['SP-mean']\n",
" print('Selfplay {} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"# g = sns.jointplot(metric+\"-mean\", \"ppl\", kind=\"reg\", data=sub_plotlog, stat_func=r2)\n",
"# g.savefig(plotroot+'ppl_vs_human.pdf')\n",
"# g = sns.jointplot(metric+\"-mean\", 'OPE', kind=\"reg\", data=sub_plotlog, stat_func=r2)\n",
"# g.savefig(plotroot+'ope_vs_human.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Error Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# sp_all_plotlog = all_plotlog.copy()\n",
"# sp_all_plotlog['Error'] = (sp_all_plotlog['SP-mean'] - sp_all_plotlog['Human-mean'])\n",
"# sp_all_plotlog['Evaluation'] = 'Selfplay'\n",
"# ope_all_plotlog = all_plotlog.copy()\n",
"# ope_all_plotlog['Evaluation'] = 'OPE'\n",
"# error_all_plotlog = ope_all_plotlog.append(sp_all_plotlog)\n",
"g = sns.lmplot(x=\"Human-mean\", y='Error', col='Metric', hue='Metric', order=2,\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.set(ylim=(-0.008, None))\n",
"for ax, (_, subdata) in zip(g.axes, all_plotlog.groupby('Metric')):\n",
" ax2=ax.twinx()\n",
" sns.distplot(subdata[\"Human-mean\"], ax=ax2,color='#95a5a6')\n",
" plt.setp(ax2.get_yticklabels(), visible=False)\n",
" plt.setp(ax2.get_yticklines(), visible=False)\n",
" ax.patch.set_visible(True)\n",
" \n",
"g.savefig(plotroot+'error_analysis.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Covai2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# plotroot=\"outputs/plotdir/convai2/hard/\"\n",
"# tfboardroot = 'outputs/convai2_ope_all_hard'\n",
"\n",
"plotroot=\"outputs/plotdir/convai2/all/\"\n",
"tfboardroot = 'outputs/convai2_ope_all'\n",
"default_config = 'roberta-base_fix_false_share_true_freeze_true_epoch_300_invsqrt_adam_lr_1e-4_C_1_Q_2_L_10x100_BERT_1_warmup_30_mom_0.5_MAXNORM_1_WD_1e-4_BS_20x1_Linit_-0.01_alphaR_0_C_1_Q_0_L_0_A_0_regfunC_square_Q_abs_cut20_L_square_actC_square_Q_no_tag__seed_0'\n",
"\n",
"os.makedirs(plotroot,exist_ok=True)\n",
"\n",
"# load stats\n",
"statspath = 'data/convai2/orig/stats.csv'\n",
"\n",
"human_stats = pd.read_csv(statspath)\n",
"human_stats = human_stats.rename(columns={'model_name': 'Model'})\n",
"human_stats = human_stats.loc[human_stats['Model'] != 'human_eval']\n",
"metriclist = [i.replace('-mean','') for i in list(human_stats.columns) if i.endswith('-mean')]\n",
"print(metriclist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"human_stats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_log, modellist, configlist = log_tflogs_from_path(tfboardroot)\n",
"\n",
"print(modellist)\n",
"print(configlist)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_log"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot Training Curves"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_metrics = metriclist\n",
"# plot_metrics = ['reward','avoid_rep']\n",
"\n",
"num_models = human_stats.shape[0]\n",
"print('num_models: ', num_models)\n",
"\n",
"all_plotlog = None\n",
"\n",
"for metric in plot_metrics:\n",
" sub_human_stats = human_stats[['Model', metric+'-mean', metric+'-std']]\n",
" sub_log = all_log.loc[all_log['config']==default_config]\n",
" sub_log = sub_log.loc[sub_log['Metric']=='est_reward_dual_{}_normalized'.format(metric)]\n",
" sub_human_stats = sub_human_stats.sort_values(by=[metric+'-mean', metric+'-std'])\n",
" sub_human_stats = sub_human_stats.iloc[[0,6,13,27]]\n",
" \n",
" selected_models = list(sub_human_stats['Model'])\n",
" \n",
" sub_log.index = sub_log['Model']\n",
" sub_log = sub_log.loc[selected_models]\n",
" \n",
" # change model name\n",
" model_dict = {\n",
" selected_models[0]: 'Model 0%',\n",
" selected_models[1]: 'Model 25%',\n",
" selected_models[2]: 'Model 50%',\n",
" selected_models[3]: 'Model 100%',\n",
" }\n",
" sub_log['Model'] = sub_log['Model'].apply(lambda x: model_dict[x])\n",
" sub_human_stats['Model'] = sub_human_stats['Model'].apply(lambda x: model_dict[x])\n",
" plot_log = get_plot_df(sub_human_stats, sub_log, metric)\n",
" plot_log['Metric'] = metric\n",
" if all_plotlog is None:\n",
" all_plotlog = plot_log\n",
" else:\n",
" all_plotlog = all_plotlog.append(plot_log)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.FacetGrid(all_plotlog, col='Metric', col_wrap=5,\n",
" height=3, aspect=1.5,sharey=False)\n",
"def mylineplot(x, y, h, s, **kwargs):\n",
" sns.lineplot(x=x, y=y, hue=h, style=s, **kwargs)\n",
"g.map(mylineplot, \n",
" 'Steps', 'Values','Model','Evaluation',\n",
" ci='sd')\n",
"g.add_legend();\n",
"g.savefig(plotroot+'learning_curve.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Human vs OPE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_plotlog = gather_ope_logs_by_config(all_log, metriclist, default_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ope vs human\n",
"g = sns.lmplot(x=\"Human-mean\", y='OPE', col='Metric', hue='Metric',\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.savefig(plotroot+'ope_vs_human.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for m in all_plotlog.Metric.unique():\n",
" tempdf = all_plotlog[all_plotlog.Metric == m]\n",
" X = tempdf['Human-mean']\n",
" y = tempdf['OPE']\n",
"\n",
"# model = sm.OLS(y, X)\n",
"# results = model.fit()\n",
"# print('{} R2: {:.4f}'.format(m, results.rsquared))\n",
" print('{} R2: {:.4f}'.format(m, stats.pearsonr(X, y)[0] ))\n",
"# print(results.summary())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# \n",
"from scipy import stats\n",
"def r2(x, y):\n",
" return stats.pearsonr(x, y)[0] ** 2\n",
"g = sns.jointplot('Human-mean', 'OPE', kind=\"reg\", data=all_plotlog, stat_func=r2)\n",
"g.savefig(plotroot+'ope_vs_human_all.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Error Analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# g = sns.FacetGrid(all_plotlog, col='Metric', col_wrap=5,\n",
"# height=3, aspect=1,sharey=False,sharex=False)\n",
"# g.map(sns.jointplot, \n",
"# 'Human-mean', 'OPE', kind=\"reg\")\n",
"g = sns.lmplot(x=\"Human-mean\", y='Error', col='Metric', hue='Metric', order=2,\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.set(ylim=(-0.008, None))\n",
"for ax, (_, subdata) in zip(g.axes, all_plotlog.groupby('Metric')):\n",
" ax2=ax.twinx()\n",
" sns.distplot(subdata[\"Human-mean\"], ax=ax2,color='#95a5a6')\n",
" plt.setp(ax2.get_yticklabels(), visible=False)\n",
" plt.setp(ax2.get_yticklines(), visible=False)\n",
" ax.patch.set_visible(True)\n",
" \n",
"g.savefig(plotroot+'error_analysis.pdf')\n",
" \n",
"#g = sns.FacetGrid(all_plotlog, hue='Metric', col='Metric', col_wrap=5, ).map(sns.distplot, \"Human-mean\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## vs. Auxilary Loss"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"default_plotlog = gather_ope_logs_by_config(all_log, metriclist, default_config)\n",
"default_plotlog['config'] = 'default'\n",
"aux_plotlog = gather_ope_logs_by_config(all_log, metriclist, config = 'roberta-base_fix_false_share_true_freeze_true_epoch_300_invsqrt_adam_lr_1e-4_C_1_Q_2_L_10x100_BERT_1_warmup_30_mom_0.5_MAXNORM_1_WD_1e-4_BS_20x1_Linit_-0.01_alphaR_0_C_1_Q_0_L_0_A_1_regfunC_square_Q_abs_cut20_L_square_actC_square_Q_no_tag__seed_0')\n",
"aux_plotlog['config'] = 'with Aux. Loss'\n",
"all_plotlog = default_plotlog.append(aux_plotlog)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.lmplot(x=\"Human-mean\", y='Error', col='Metric', hue=\"config\", order=2,\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.set(ylim=(-0.008, None))\n",
"g.savefig(plotroot+'error_vs_auxloss.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Auxilary loss is useless"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## vs Data Size"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_log_half, _, _ = log_tflogs_from_path('outputs/convai2_ope_all_half/')\n",
"config_half = \"roberta-base_fix_false_share_true_freeze_true_epoch_300_invsqrt_adam_lr_1e-4_C_1_Q_2_L_10x100_BERT_1_warmup_30_mom_0.5_MAXNORM_1_WD_1e-4_BS_20x1_Linit_-0.01_alphaR_0_C_1_Q_0_L_0_A_0_regfunC_square_Q_abs_cut20_L_square_actC_square_Q_no_tag__seed_0\"\n",
"plotlog_half = gather_ope_logs_by_config(all_log_half, metriclist, config_half, last_n=100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ope vs human half\n",
"g = sns.lmplot(x=\"Human-mean\", y='OPE', col='Metric', hue='Metric',\n",
" data=plotlog_half, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False,robust=True)\n",
"g.savefig(plotroot+'ope_vs_human_half.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for m in plotlog_half.Metric.unique():\n",
" tempdf = plotlog_half[plotlog_half.Metric == m]\n",
" tempdf = tempdf[~tempdf.OPE.isnull()]\n",
" X = np.array(tempdf['Human-mean'])\n",
" y = np.array(tempdf['OPE'])\n",
"\n",
" print('{} R2: {:.4f}'.format(m, stats.pearsonr(y, X)[0] ))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_log_small, _, _ = log_tflogs_from_path('outputs/convai2_ope_all_small/')\n",
"config_small = \"roberta-base_fix_false_share_true_freeze_true_epoch_1000_invsqrt_adam_lr_1e-4_C_1_Q_2_L_10x100_BERT_1_warmup_30_mom_0.5_MAXNORM_1_WD_1e-4_BS_20x1_Linit_-0.01_alphaR_0_C_1_Q_0_L_0_A_0_regfunC_square_Q_abs_cut20_L_square_actC_square_Q_no_tag__seed_0\"\n",
"plotlog_small = gather_ope_logs_by_config(all_log_small, metriclist, config_small, last_n=500)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ope vs human small\n",
"g = sns.lmplot(x=\"Human-mean\", y='OPE', col='Metric', hue='Metric',\n",
" data=plotlog_small, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False,robust=True)\n",
"g.savefig(plotroot+'ope_vs_human_small.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for m in plotlog_small.Metric.unique():\n",
" tempdf = plotlog_small[plotlog_half.Metric == m]\n",
" tempdf = tempdf[~tempdf.OPE.isnull()]\n",
" X = np.array(tempdf['Human-mean'])\n",
" y = np.array(tempdf['OPE'])\n",
"\n",
" print('{} R2: {:.4f}'.format(m, stats.pearsonr(y, X)[0] ))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"all_plotlog = gather_ope_logs_by_config(all_log, metriclist, default_config)\n",
"all_plotlog['Data'] = '100%'\n",
"plotlog_50 = plotlog_half.copy()\n",
"plotlog_50['Data'] = '50%'\n",
"all_plotlog = all_plotlog.append(plotlog_50)\n",
"plotlog_10 = plotlog_small.copy()\n",
"plotlog_10['Data'] = '10%'\n",
"all_plotlog = all_plotlog.append(plotlog_10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.lmplot(x=\"Human-mean\", y='Error', col='Metric', hue='Data', order=2,\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.set(ylim=(-0.008, None))\n",
"g.savefig(plotroot+'error_vs_datasize.pdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hard Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotroot=\"outputs/plotdir/convai2/hard/\"\n",
"all_log_hard, _, _ = log_tflogs_from_path('outputs/convai2_ope_all_hard/')\n",
"os.makedirs(plotroot,exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotlog_hard = gather_ope_logs_by_config(all_log_hard, metriclist, default_config, last_n=300)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ope vs human\n",
"g = sns.lmplot(x=\"Human-mean\", y='OPE', col='Metric', hue='Metric',\n",
" data=plotlog_hard, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False,robust=True)\n",
"g.savefig(plotroot+'ope_vs_human.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for m in plotlog_hard.Metric.unique():\n",
" tempdf = plotlog_hard[plotlog_hard.Metric == m]\n",
" tempdf = tempdf[~tempdf.OPE.isnull()]\n",
" X = np.array(tempdf['Human-mean'])\n",
" y = np.array(tempdf['OPE'])\n",
"\n",
"# model = sm.OLS(y, X)\n",
"# results = model.fit()\n",
"# print('Parameters: ', results.params)\n",
"# print('{} R2: {:.4f}'.format(m, results.rsquared))\n",
"# print(results.summary())\n",
"\n",
" print('{} R2: {:.4f}'.format(m, stats.pearsonr(y, X)[0] ))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def r2(x, y):\n",
" return stats.pearsonr(x, y)[0] ** 2\n",
"g = sns.jointplot('Human-mean', 'OPE', kind=\"reg\", data=plotlog_hard, stat_func=r2)\n",
"g.savefig(plotroot+'ope_vs_human_all.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.lmplot(x=\"Human-mean\", y='Error', col='Metric', hue='Metric', order=2,\n",
" data=plotlog_hard, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.set(ylim=(-0.008, None))\n",
"for ax, (_, subdata) in zip(g.axes, plotlog_hard.groupby('Metric')):\n",
" ax2=ax.twinx()\n",
" sns.distplot(subdata[\"Human-mean\"], ax=ax2,color='#95a5a6')\n",
" plt.setp(ax2.get_yticklabels(), visible=False)\n",
" plt.setp(ax2.get_yticklines(), visible=False)\n",
" ax.patch.set_visible(True)\n",
" \n",
"g.savefig(plotroot+'error_analysis.pdf')\n",
" \n",
"#g = sns.FacetGrid(all_plotlog, hue='Metric', col='Metric', col_wrap=5, ).map(sns.distplot, \"Human-mean\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"default_plotlog = gather_ope_logs_by_config(all_log, metriclist, default_config)\n",
"default_plotlog['Data'] = 'Normal'\n",
"plotlog_hard = gather_ope_logs_by_config(all_log_hard, metriclist, default_config)\n",
"plotlog_hard['Data'] = 'Hard'\n",
"all_plotlog = default_plotlog.append(plotlog_hard)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.lmplot(x=\"Human-mean\", y='Error', col='Metric', hue='Data', order=2,\n",
" data=all_plotlog, col_wrap=5, height=3,sharey=False,sharex=False,truncate=False)\n",
"g.set(ylim=(-0.008, None))\n",
"for ax, (_, subdata) in zip(g.axes, all_plotlog.groupby('Metric')):\n",
" ax2=ax.twinx()\n",
" sns.distplot(subdata[\"Human-mean\"], ax=ax2,color='#95a5a6')\n",
" plt.setp(ax2.get_yticklabels(), visible=False)\n",
" plt.setp(ax2.get_yticklines(), visible=False)\n",
" ax.patch.set_visible(True)\n",
"g.savefig(plotroot+'error_normal_vs_hard_data.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": []
},
{
"cell_type": "code",
"execution_count": null,
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}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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"toc-autonumbering": true,
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},
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| apache-2.0 |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/04_advanced_preprocessing/labs/taxicab_traffic/deploy.ipynb | 2 | 9789 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"PROJECT_ID = 'qwiklabs-gcp-da02053fb2a13c97' # CHANGE THIS\n",
"BUCKET = 'qwiklabs-gcp-da02053fb2a13c97' # CHANGE THIS\n",
"MODEL_BASE = 'taxi_trained/export/exporter'\n",
"MODEL_PATH = os.path.join(MODEL_BASE,os.listdir(MODEL_BASE)[-1])\n",
"MODEL_NAME = 'taxifare'\n",
"VERSION_NAME = 'v1'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy for Online Prediction\n",
"\n",
"To get our predictions, in addition to the features provided by the client, we also need to fetch the latest traffic information from BigQuery. We then combine these and invoke our tensorflow model. This is visualized by the 'on-demand' portion (red arrows) in the below diagram:\n",
"\n",
"<img src=\"../../taxicab_traffic/assets/architecture.png\" >\n",
"\n",
"To do this we'll take advantage of [AI Platforms Custom Prediction Routines](https://cloud.google.com/ml-engine/docs/tensorflow/custom-prediction-routines) which allows us to execute custom python code in response to every online prediction request. There are 5 steps to creating a custom prediction routine:\n",
"\n",
"1. Upload Model Artifacts to GCS\n",
"2. Implement Predictor interface \n",
"3. Package the prediction code and dependencies\n",
"4. Deploy\n",
"5. Invoke API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Upload Model Artifacts to GCS\n",
"\n",
"Here we upload our model weights so that AI Platform can access them."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil cp -r $MODEL_PATH/* gs://$BUCKET/taxifare/model/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Implement Predictor Interface\n",
"\n",
"Interface Spec: https://cloud.google.com/ml-engine/docs/tensorflow/custom-prediction-routines#predictor-class\n",
"\n",
"This tells AI Platform how to load the model artifacts, and is where we specify our custom prediction code.\n",
"\n",
"**Excercise 1:** Complete the SQL `query_string` to return the latest (proxy) traffic information. To check your answer reference the solution.\n",
"\n",
"Note: the correct PROJECT_ID will automatically be inserted using the bash `sed` command in the subsequent cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile predictor.py\n",
"import tensorflow as tf\n",
"from google.cloud import bigquery\n",
"\n",
"PROJECT_ID = 'will_be_replaced'\n",
"\n",
"class TaxifarePredictor(object):\n",
" def __init__(self, predict_fn):\n",
" self.predict_fn = predict_fn \n",
" \n",
" def predict(self, instances, **kwargs):\n",
" bq = bigquery.Client(PROJECT_ID)\n",
" query_string = \"\"\"\n",
" ###TODO###\n",
" \"\"\"\n",
" trips = bq.query(query_string).to_dataframe()['trips_last_5min'][0]\n",
" instances['trips_last_5min'] = [trips for _ in range(len(list(instances.items())[0][1]))]\n",
" predictions = self.predict_fn(instances)\n",
" return predictions['predictions'].tolist() # convert to list so it is JSON serialiable (requirement)\n",
" \n",
"\n",
" @classmethod\n",
" def from_path(cls, model_dir):\n",
" predict_fn = tf.contrib.predictor.from_saved_model(model_dir,'predict')\n",
" return cls(predict_fn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!sed -i -e 's/will_be_replaced/{PROJECT_ID}/g' predictor.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test Predictor Class Works Locally"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import predictor\n",
"\n",
"instances = {'dayofweek' : [6,5],\n",
" 'hourofday' : [12,11],\n",
" 'pickuplon' : [-73.99,-73.99], \n",
" 'pickuplat' : [40.758,40.758],\n",
" 'dropofflat' : [40.742,40.758],\n",
" 'dropofflon' : [-73.97,-73.97]}\n",
"\n",
"predictor = predictor.TaxifarePredictor.from_path(MODEL_PATH)\n",
"predictor.predict(instances)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Package Predictor Class and Dependencies\n",
"\n",
"We must package the predictor as a tar.gz source distribution package. Instructions for this are specified [here](http://cloud.google.com/ml-engine/docs/custom-prediction-routines#predictor-tarball). The AI Platform runtime comes preinstalled with several packages [listed here](https://cloud.google.com/ml-engine/docs/tensorflow/runtime-version-list). However it does not come with `google-cloud-bigquery` so we list that as a dependency below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile setup.py\n",
"from setuptools import setup\n",
"\n",
"setup(\n",
" name='taxifare_custom_predict_code',\n",
" version='0.1',\n",
" scripts=['predictor.py'],\n",
" install_requires=[\n",
" 'google-cloud-bigquery==1.16.0',\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!python setup.py sdist --formats=gztar"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gsutil cp dist/taxifare_custom_predict_code-0.1.tar.gz gs://$BUCKET/taxifare/predict_code/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Deploy\n",
"\n",
"This is similar to how we deploy standard models to AI Platform, with a few extra command line arguments.\n",
"\n",
"Note the use of the `--service-acount` parameter below.\n",
"\n",
"The default service account does not have permissions to read from BigQuery, so we [specify a different service account](https://cloud.google.com/ml-engine/docs/tensorflow/deploying-models#service-account) that does have permission.\n",
"\n",
"Specifically we use the [Compute Engine default service account](https://cloud.google.com/compute/docs/access/service-accounts#compute_engine_default_service_account) which has the IAM project editor role."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!gcloud beta ai-platform models create $MODEL_NAME --regions us-central1 --enable-logging --enable-console-logging\n",
"#!gcloud ai-platform versions delete $VERSION_NAME --model taxifare --quiet\n",
"!gcloud beta ai-platform versions create $VERSION_NAME \\\n",
" --model $MODEL_NAME \\\n",
" --origin gs://$BUCKET/taxifare/model \\\n",
" --service-account $(gcloud projects list --filter=\"$PROJECT_ID\" --format=\"value(PROJECT_NUMBER)\")[email protected] \\\n",
" --runtime-version 1.14 \\\n",
" --python-version 3.5 \\\n",
" --package-uris gs://$BUCKET/taxifare/predict_code/taxifare_custom_predict_code-0.1.tar.gz \\\n",
" --prediction-class predictor.TaxifarePredictor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Invoke API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Warning:** You will see `ImportError: file_cache is unavailable when using oauth2client >= 4.0.0 or google-auth` when you run this. While it looks like an error this is actually just a warning and is safe to ignore, the subsequent cell will still work."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import googleapiclient.discovery\n",
"\n",
"instances = {'dayofweek' : [6], \n",
" 'hourofday' : [12],\n",
" 'pickuplon' : [-73.99], \n",
" 'pickuplat' : [40.758],\n",
" 'dropofflat' : [40.742],\n",
" 'dropofflon' : [-73.97]}\n",
"\n",
"service = googleapiclient.discovery.build('ml', 'v1')\n",
"name = 'projects/{}/models/{}/versions/{}'.format(PROJECT_ID, MODEL_NAME, VERSION_NAME)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = service.projects().predict(\n",
" name=name,\n",
" body={'instances': instances}\n",
").execute()\n",
"\n",
"if 'error' in response:\n",
" raise RuntimeError(response['error'])\n",
"else:\n",
" print(response['predictions'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try re-running the query again after 15 seconds (the windowing period for DataFlow), note how the prediction changes in response to the new traffic data!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
theandygross/CancerData | Notebooks/HNSCC_Imports.ipynb | 1 | 11360 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Global Imports"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### External Package Imports"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os as os\n",
"import pickle as pickle\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Module Imports"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from Stats.Scipy import *\n",
"from Stats.Survival import *\n",
"from Helpers.Pandas import *\n",
"\n",
"from Figures.FigureHelpers import *\n",
"from Figures.Pandas import *\n",
"from Figures.Boxplots import *\n",
"from Figures.Survival import draw_survival_curve, survival_and_stats\n",
"from Figures.Survival import draw_survival_curves\n",
"from Figures.Survival import survival_stat_plot"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import Data.Firehose as FH"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tweaking Display Parameters"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pd.set_option('precision', 3)\n",
"pd.set_option('display.width', 300)\n",
"plt.rcParams['font.size'] = 12"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''Color schemes for paper taken from http://colorbrewer2.org/'''\n",
"colors = plt.rcParams['axes.color_cycle']\n",
"colors_st = ['#CA0020', '#F4A582', '#92C5DE', '#0571B0']\n",
"colors_th = ['#E66101', '#FDB863', '#B2ABD2', '#5E3C99']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Function to Pull a Firehose Run Container"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_run(firehose_dir, version='Latest'):\n",
" '''\n",
" Helper to get a run from the file-system. \n",
" '''\n",
" path = '{}/ucsd_analyses'.format(firehose_dir)\n",
" if version is 'Latest':\n",
" version = sorted(os.listdir(path))[-1]\n",
" run = pickle.load(open('{}/{}/RunObject.p'.format(path, version), 'rb'))\n",
" return run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Read In Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we read in the pre-processed data that we downloaded and initialized in the [download_data notebook](download_data.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"populating namespace with data\n"
]
}
],
"source": [
"print 'populating namespace with data'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"OUT_PATH = '/cellar/users/agross/TCGA_Code/CancerData/Data'\n",
"RUN_DATE = '2014_07_15'\n",
"VERSION = 'all'\n",
"CANCER = 'HNSC'\n",
"FIGDIR = '../Figures/'\n",
"if not os.path.isdir(FIGDIR):\n",
" os.makedirs(FIGDIR)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"run_path = '{}/Firehose__{}/'.format(OUT_PATH, RUN_DATE)\n",
"run = get_run(run_path, 'Run_' + VERSION)\n",
"run.data_path = run_path \n",
"run.result_path = run_path + 'ucsd_analyses'\n",
"run.report_path = run_path + 'ucsd_analyses/Run_all'\n",
"\n",
"cancer = run.load_cancer(CANCER)\n",
"cancer.path = '{}/{}'.format(run.report_path , cancer.name)\n",
"clinical = cancer.load_clinical()\n",
"\n",
"mut = cancer.load_data('Mutation')\n",
"mut.uncompress()\n",
"cn = cancer.load_data('CN_broad')\n",
"cn.uncompress()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rna = FH.read_rnaSeq(run.data_path, cancer.name, tissue_code='All')\n",
"mirna = FH.read_miRNASeq(run.data_path, cancer.name, tissue_code='All')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"keepers_o = pd.read_csv('/cellar/users/agross/TCGA_Code/TCGA_Working/Data/Firehose__2014_04_16/' + 'old_keepers.csv', index_col=0,\n",
" squeeze=True)\n",
"keepers_o = array(keepers_o)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Update Clinical Data"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(508, 56)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from Processing.ProcessClinicalDataPortal import update_clinical_object\n",
"\n",
"p = '/cellar/users/agross/TCGA_Code/TCGA/Data'\n",
"path = p + '/Followup_R9/HNSC/'\n",
"clinical = update_clinical_object(clinical, path)\n",
"clinical.clinical.shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#hpv = clinical.hpv\n",
"surv = clinical.survival.survival_5y\n",
"age = clinical.clinical.age.astype(float)\n",
"old = pd.Series(1.*(age>=75), name='old')"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p = '/cellar/users/agross/TCGA_Code/TCGA/Data'\n",
"f = p + '/MAFs/PR_TCGA_HNSC_PAIR_Capture_All_Pairs_QCPASS_v4.aggregated.capture.tcga.uuid.automated.somatic.maf.txt'\n",
"mut_new = pd.read_table(f, skiprows=4, low_memory=False)\n",
"keep = (mut_new.Variant_Classification.isin(['Silent', 'Intron', \"3'UTR\", \"5'UTR\"])==False)\n",
"mut_new = mut_new[keep]\n",
"mut_new['barcode'] = mut_new.Tumor_Sample_Barcode.map(lambda s: s[:12])\n",
"mut_new = mut_new.groupby(['barcode','Hugo_Symbol']).size().unstack().fillna(0).T\n",
"mut_new = mut.df.combine_first(mut_new).fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gistic = FH.get_gistic_gene_matrix(run.data_path, cancer.name)\n",
"del_3p = gistic.ix['3p14.2'].median(0)\n",
"del_3p.name = '3p_deletion'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### HPV Data"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p = '/cellar/users/agross/TCGA_Code/TCGA/'\n",
"hpv_all = pd.read_csv(p + '/Extra_Data/hpv_summary_3_20_13_distribute.csv', index_col=0)\n",
"hpv = hpv_all.Molecular_HPV.map({0:'HPV-', 1:'HPV+'})\n",
"hpv.name = 'HPV'\n",
"hpv_seq = hpv"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"status = clinical.clinical[['hpvstatusbyishtesting','hpvstatusbyp16testing']]\n",
"status = status.replace('[Not Evaluated]', nan)\n",
"hpv_clin = (status.dropna() == 'Positive').sum(1)\n",
"hpv_clin = hpv_clin.map({2: 'HPV+', 0:'HPV-', 1:nan}).dropna()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"HPV- 65\n",
"HPV+ 18\n",
"dtype: int64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hpv_clin.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"HPV- 24\n",
"HPV+ 14\n",
"dtype: int64"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hpv_clin.ix[hpv_clin.index.diff(hpv_seq.index)].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hpv_new = pd.read_table(p + '/Data/Followup_R6/HNSC/auxiliary_hnsc.txt',\n",
" skiprows=[1], index_col=0, na_values=['[Not Available]'])\n",
"hpv_new = hpv_new['hpv_status']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hpv = (hpv_seq.dropna() == 'HPV+').combine_first(hpv_new == 'Positive')\n",
"hpv.name = 'HPV'"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False 450\n",
"True 78\n",
"dtype: int64"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hpv.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n = ti(hpv==False)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"odds_ratio 6.79e+00\n",
"p 5.46e-07\n",
"dtype: float64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fisher_exact_test(del_3p<0, mut_new.ix['TP53'].ix[n.diff(keepers_o)]>0)"
]
}
],
"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": "2.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
mne-tools/mne-tools.github.io | 0.17/_downloads/fd79fe12dec0d8ba3f96e5d55db03054/plot_ecog.ipynb | 1 | 4650 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Working with ECoG data\n\n\nMNE supports working with more than just MEG and EEG data. Here we show some\nof the functions that can be used to facilitate working with\nelectrocorticography (ECoG) data.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Eric Larson <[email protected]>\n# Chris Holdgraf <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.io import loadmat\nfrom mayavi import mlab\n\nimport mne\nfrom mne.viz import plot_alignment, snapshot_brain_montage\n\nprint(__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's load some ECoG electrode locations and names, and turn them into\na :class:`mne.channels.DigMontage` class.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mat = loadmat(mne.datasets.misc.data_path() + '/ecog/sample_ecog.mat')\nch_names = mat['ch_names'].tolist()\nelec = mat['elec'] # electrode positions given in meters\ndig_ch_pos = dict(zip(ch_names, elec))\nmon = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)\nprint('Created %s channel positions' % len(ch_names))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have our electrode positions in MRI coordinates, we can create\nour measurement info structure.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"info = mne.create_info(ch_names, 1000., 'ecog', montage=mon)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can then plot the locations of our electrodes on our subject's brain.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>These are not real electrodes for this subject, so they\n do not align to the cortical surface perfectly.</p></div>\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"subjects_dir = mne.datasets.sample.data_path() + '/subjects'\nfig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir,\n surfaces=['pial'])\nmlab.view(200, 70)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sometimes it is useful to make a scatterplot for the current figure view.\nThis is best accomplished with matplotlib. We can capture an image of the\ncurrent mayavi view, along with the xy position of each electrode, with the\n`snapshot_brain_montage` function.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We'll once again plot the surface, then take a snapshot.\nfig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir,\n surfaces='pial')\nmlab.view(200, 70)\nxy, im = snapshot_brain_montage(fig, mon)\n\n# Convert from a dictionary to array to plot\nxy_pts = np.vstack(xy[ch] for ch in info['ch_names'])\n\n# Define an arbitrary \"activity\" pattern for viz\nactivity = np.linspace(100, 200, xy_pts.shape[0])\n\n# This allows us to use matplotlib to create arbitrary 2d scatterplots\n_, ax = plt.subplots(figsize=(10, 10))\nax.imshow(im)\nax.scatter(*xy_pts.T, c=activity, s=200, cmap='coolwarm')\nax.set_axis_off()\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
alexbarcelo/pythoncoursetgk | notebook/20-primercaspractic.ipynb | 1 | 3963 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Python Course - Primer cas pràctic\n",
"<img src=\"http://www.telecogresca.com/logo_mail.png\"></img>\n",
"## Exercici fortament sintètic\n",
"(En part de https://wiki.python.org/moin/SimplePrograms, en part collita pròpia)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Considerem un diccionari de preus, amb productes bàsics:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"prices = {'apple': 0.40, 'banana': 0.50, 'entrada_promocional': 10, 'entrada_simple': 17}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I considerem una llista d'objectes que ha comprat un cert \"client\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"compra_marcos = [\n",
" 'apple',\n",
" 'banana',\n",
" 'apple',\n",
" 'entrada_promocional',\n",
" 'apple',\n",
" 'banana',\n",
" 'entrada_promocional', # segur que voldrà un canvi de nom d'aquesta\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Compra del client\n",
"Imaginem que volem saber de forma una mica més ben estructurada què ha comprat aquest client.\n",
"\n",
"### Primera versió - C++ programmer fent desastres"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"result = dict()\n",
"\n",
"for i in range(len(compra_marcos)):\n",
" producte = compra_marcos[i]\n",
" previous_value = result.get(producte)\n",
" if previous_value == None:\n",
" result[producte] = 1\n",
" else:\n",
" result[producte] = previous_value + 1\n",
"\n",
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Segona versió - Ara com a mínim sap Python"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tercera versió - No reinventeu la roda"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from collections import Counter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Càlcul del total\n",
"\n",
"Ara volem calcular el total de la compra del client.\n",
"\n",
"### Primera versió - Somewhat Python"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"total = 0\n",
"\n",
"for producte, quantitat in result.items():\n",
" total += prices[producte] * quantitat\n",
" \n",
"total"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Segona versió - Now with comprehensions!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
nicolasm/lastfm-notebooks | year-tops/lastfm-top-year-2008.ipynb | 1 | 3685 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import MySQLdb\n",
"import netrc\n",
"import pandas\n",
"import lastfmDb as lf\n",
"import datetime\n",
"\n",
"from plotly import __version__\n",
"from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
"\n",
"init_notebook_mode(connected=True)\n",
"\n",
"login = netrc.netrc().authenticators('lastfm.mysql')\n",
"if not login:\n",
" raise netrc.NetrcParseError('No authenticators for lastfm.mysql')\n",
"\n",
"mysql = MySQLdb.connect(user=login[0],passwd=login[2],db=login[1], charset='utf8')\n",
"cursor = mysql.cursor()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Last execution date and time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"today = datetime.datetime.now()\n",
"print today.strftime('Generated on the %d %b %Y at %H:%M:%S')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Play count for year"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"forYear = 2008\n",
"lf.retrieve_total_play_count_for_year(cursor, forYear)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Top 10 artists for year"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = lf.retrieve_top_artists_for_year_as_dataframe(cursor, forYear, 20)\n",
"top10 = df.head(10)\n",
"\n",
"iplot(lf.create_figure_for_year(top10.Artist, top10.Artist, top10.PlayCount, 'artists', forYear))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Top 10 albums for year"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = lf.retrieve_top_albums_for_year_as_dataframe(cursor, forYear, 20)\n",
"top10 = df.head(10)\n",
"\n",
"iplot(lf.create_figure_for_year(top10.Album, top10.Album, top10.PlayCount, 'albums', forYear))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Top 10 tracks for year"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = lf.retrieve_top_tracks_for_year_as_dataframe(cursor, forYear, 20)\n",
"top10 = df.head(10)\n",
"\n",
"iplot(lf.create_figure_for_year(top10.Track, top10.Track, top10.PlayCount, 'tracks', forYear))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df"
]
}
],
"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": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
alimanfoo/petl | notes/case_study_1.ipynb | 4 | 53801 | {
"metadata": {
"name": "",
"signature": "sha256:3bbbcfb0d62b99e5755186306ff0cf10e99bf3c5a78c08cf76b27d3ad4da5512"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"sys.version_info"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"sys.version_info(major=3, minor=4, micro=2, releaselevel='final', serial=0)"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"[petl](http://petl.readthedocs.org) Case Study 1 - Comparing Tables"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This case study illustrates the use of the [petl](http://petl.readthedocs.org) package for doing some simple profiling and comparison of data from\n",
"two tables.\n"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The files used in this case study can be downloaded from the following\n",
"link:\n",
"\n",
"* http://aliman.s3.amazonaws.com/petl/petl-case-study-1-files.zip\n",
"\n",
"Download and unzip the files:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!wget http://aliman.s3.amazonaws.com/petl/petl-case-study-1-files.zip\n",
"!unzip -o petl-case-study-1-files.zip"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"--2015-01-19 17:37:39-- http://aliman.s3.amazonaws.com/petl/petl-case-study-1-files.zip\r\n",
"Resolving aliman.s3.amazonaws.com (aliman.s3.amazonaws.com)... 54.231.9.241\r\n",
"Connecting to aliman.s3.amazonaws.com (aliman.s3.amazonaws.com)|54.231.9.241|:80... "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"connected.\r\n",
"HTTP request sent, awaiting response... "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"200 OK\r\n",
"Length: 3076773 (2.9M) [application/zip]\r\n",
"Saving to: \u2018petl-case-study-1-files.zip\u2019\r\n",
"\r\n",
"\r",
" 0% [ ] 0 --.-K/s "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" 2% [ ] 75,696 276KB/s "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" 8% [==> ] 265,496 484KB/s "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
"22% [=======> ] 688,896 838KB/s "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
"50% [==================> ] 1,567,816 1.39MB/s "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
"100%[======================================>] 3,076,773 2.34MB/s in 1.3s \r\n",
"\r\n",
"2015-01-19 17:37:41 (2.34 MB/s) - \u2018petl-case-study-1-files.zip\u2019 saved [3076773/3076773]\r\n",
"\r\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Archive: petl-case-study-1-files.zip\r\n",
" inflating: popdata.csv "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n",
" inflating: snpdata.csv "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first file (`snpdata.csv`) contains a list of locations in the\n",
"genome of the malaria parasite *P. falciparum*, along with some basic\n",
"data about genetic variations found at those locations.\n",
"\n",
"The second file (`popdata.csv`) is supposed to contain the same list\n",
"of genome locations, along with some additional data such as allele\n",
"frequencies in different populations.\n",
"\n",
"The main point for this case study is that the first file\n",
"(`snpdata.csv`) contains the canonical list of genome locations, and\n",
"the second file (`popdata.csv`) contains some additional data about\n",
"the same genome locations and therefore should be consistent with the\n",
"first file. We want to check whether this second file is in fact\n",
"consistent with the first file."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Preparing the data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start by importing the petl package:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import petl as etl\n",
"etl.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"'1.0.0'"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To save some typing, let ***a*** be the table of data extracted from the\n",
"first file (`snpdata.csv`), and let ***b*** be the table of data extracted\n",
"from the second file (`popdata.csv`), using the `fromcsv()`\n",
"function:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = etl.fromtsv('snpdata.csv')\n",
"b = etl.fromtsv('popdata.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Examine the header from each file:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a.header()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"('Chr',\n",
" 'Pos',\n",
" 'Ref',\n",
" 'Nref',\n",
" 'Der',\n",
" 'Mut',\n",
" 'isTypable',\n",
" 'GeneId',\n",
" 'GeneAlias',\n",
" 'GeneDescr')"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b.header()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"('Chromosome',\n",
" 'Coordinates',\n",
" 'Ref. Allele',\n",
" 'Non-Ref. Allele',\n",
" 'Outgroup Allele',\n",
" 'Ancestral Allele',\n",
" 'Derived Allele',\n",
" 'Ref. Aminoacid',\n",
" 'Non-Ref. Aminoacid',\n",
" 'Private Allele',\n",
" 'Private population',\n",
" 'maf AFR',\n",
" 'maf PNG',\n",
" 'maf SEA',\n",
" 'daf AFR',\n",
" 'daf PNG',\n",
" 'daf SEA',\n",
" 'nraf AFR',\n",
" 'nraf PNG',\n",
" 'nraf SEA',\n",
" 'Mutation type',\n",
" 'Gene',\n",
" 'Gene Aliases',\n",
" 'Gene Description',\n",
" 'Gene Information')"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is a common set of 9 fields that is present in both tables, and\n",
"we would like focus on comparing these common fields, however\n",
"different field names have been used in the two files. To simplify\n",
"comparison, use `rename()` to rename some fields in the\n",
"second file:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b_renamed = b.rename({'Chromosome': 'Chr', \n",
" 'Coordinates': 'Pos', \n",
" 'Ref. Allele': 'Ref', \n",
" 'Non-Ref. Allele': 'Nref', \n",
" 'Derived Allele': 'Der', \n",
" 'Mutation type': 'Mut', \n",
" 'Gene': 'GeneId', \n",
" 'Gene Aliases': 'GeneAlias', \n",
" 'Gene Description': 'GeneDescr'})\n",
"b_renamed.header()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"('Chr',\n",
" 'Pos',\n",
" 'Ref',\n",
" 'Nref',\n",
" 'Outgroup Allele',\n",
" 'Ancestral Allele',\n",
" 'Der',\n",
" 'Ref. Aminoacid',\n",
" 'Non-Ref. Aminoacid',\n",
" 'Private Allele',\n",
" 'Private population',\n",
" 'maf AFR',\n",
" 'maf PNG',\n",
" 'maf SEA',\n",
" 'daf AFR',\n",
" 'daf PNG',\n",
" 'daf SEA',\n",
" 'nraf AFR',\n",
" 'nraf PNG',\n",
" 'nraf SEA',\n",
" 'Mut',\n",
" 'GeneId',\n",
" 'GeneAlias',\n",
" 'GeneDescr',\n",
" 'Gene Information')"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use `cut()` to extract only the fields we're interested in\n",
"from both tables:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"common_fields = ['Chr', 'Pos', 'Ref', 'Nref', 'Der', 'Mut', 'GeneId', 'GeneAlias', 'GeneDescr']\n",
"a_common = a.cut(common_fields)\n",
"b_common = b_renamed.cut(common_fields)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inspect the data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_common"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>Chr</th>\n",
"<th>Pos</th>\n",
"<th>Ref</th>\n",
"<th>Nref</th>\n",
"<th>Der</th>\n",
"<th>Mut</th>\n",
"<th>GeneId</th>\n",
"<th>GeneAlias</th>\n",
"<th>GeneDescr</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>91099</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>S</td>\n",
"<td>PFA0095c</td>\n",
"<td>MAL1P1.10</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>91104</td>\n",
"<td>A</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0095c</td>\n",
"<td>MAL1P1.10</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>93363</td>\n",
"<td>T</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>hypothetical protein, conserved in P. falciparum</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>93382</td>\n",
"<td>T</td>\n",
"<td>G</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>hypothetical protein, conserved in P. falciparum</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>93384</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>hypothetical protein, conserved in P. falciparum</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"+--------+---------+-----+------+-----+-----+------------+-------------+----------------------------------------------------+\n",
"| Chr | Pos | Ref | Nref | Der | Mut | GeneId | GeneAlias | GeneDescr |\n",
"+========+=========+=====+======+=====+=====+============+=============+====================================================+\n",
"| 'MAL1' | '91099' | 'G' | 'A' | '-' | 'S' | 'PFA0095c' | 'MAL1P1.10' | 'rifin' |\n",
"+--------+---------+-----+------+-----+-----+------------+-------------+----------------------------------------------------+\n",
"| 'MAL1' | '91104' | 'A' | 'T' | '-' | 'N' | 'PFA0095c' | 'MAL1P1.10' | 'rifin' |\n",
"+--------+---------+-----+------+-----+-----+------------+-------------+----------------------------------------------------+\n",
"| 'MAL1' | '93363' | 'T' | 'A' | '-' | 'N' | 'PFA0100c' | 'MAL1P1.11' | 'hypothetical protein, conserved in P. falciparum' |\n",
"+--------+---------+-----+------+-----+-----+------------+-------------+----------------------------------------------------+\n",
"| 'MAL1' | '93382' | 'T' | 'G' | '-' | 'N' | 'PFA0100c' | 'MAL1P1.11' | 'hypothetical protein, conserved in P. falciparum' |\n",
"+--------+---------+-----+------+-----+-----+------------+-------------+----------------------------------------------------+\n",
"| 'MAL1' | '93384' | 'G' | 'A' | '-' | 'N' | 'PFA0100c' | 'MAL1P1.11' | 'hypothetical protein, conserved in P. falciparum' |\n",
"+--------+---------+-----+------+-----+-----+------------+-------------+----------------------------------------------------+\n",
"..."
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b_common"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>Chr</th>\n",
"<th>Pos</th>\n",
"<th>Ref</th>\n",
"<th>Nref</th>\n",
"<th>Der</th>\n",
"<th>Mut</th>\n",
"<th>GeneId</th>\n",
"<th>GeneAlias</th>\n",
"<th>GeneDescr</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>91099</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>SYN</td>\n",
"<td>PFA0095c</td>\n",
"<td>MAL1P1.10,RIF</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>91104</td>\n",
"<td>A</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td>NON</td>\n",
"<td>PFA0095c</td>\n",
"<td>MAL1P1.10,RIF</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>93363</td>\n",
"<td>T</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>NON</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>Plasmodium exported protein (PHISTa), unknown function</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>93382</td>\n",
"<td>T</td>\n",
"<td>G</td>\n",
"<td>-</td>\n",
"<td>NON</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>Plasmodium exported protein (PHISTa), unknown function</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>93384</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>NON</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>Plasmodium exported protein (PHISTa), unknown function</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"+--------+---------+-----+------+-----+-------+------------+-----------------+----------------------------------------------------------+\n",
"| Chr | Pos | Ref | Nref | Der | Mut | GeneId | GeneAlias | GeneDescr |\n",
"+========+=========+=====+======+=====+=======+============+=================+==========================================================+\n",
"| 'MAL1' | '91099' | 'G' | 'A' | '-' | 'SYN' | 'PFA0095c' | 'MAL1P1.10,RIF' | 'rifin' |\n",
"+--------+---------+-----+------+-----+-------+------------+-----------------+----------------------------------------------------------+\n",
"| 'MAL1' | '91104' | 'A' | 'T' | '-' | 'NON' | 'PFA0095c' | 'MAL1P1.10,RIF' | 'rifin' |\n",
"+--------+---------+-----+------+-----+-------+------------+-----------------+----------------------------------------------------------+\n",
"| 'MAL1' | '93363' | 'T' | 'A' | '-' | 'NON' | 'PFA0100c' | 'MAL1P1.11' | 'Plasmodium exported protein (PHISTa), unknown function' |\n",
"+--------+---------+-----+------+-----+-------+------------+-----------------+----------------------------------------------------------+\n",
"| 'MAL1' | '93382' | 'T' | 'G' | '-' | 'NON' | 'PFA0100c' | 'MAL1P1.11' | 'Plasmodium exported protein (PHISTa), unknown function' |\n",
"+--------+---------+-----+------+-----+-------+------------+-----------------+----------------------------------------------------------+\n",
"| 'MAL1' | '93384' | 'G' | 'A' | '-' | 'NON' | 'PFA0100c' | 'MAL1P1.11' | 'Plasmodium exported protein (PHISTa), unknown function' |\n",
"+--------+---------+-----+------+-----+-------+------------+-----------------+----------------------------------------------------------+\n",
"..."
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `fromucsv()` function does not attempt to parse any of the\n",
"values from the underlying CSV file, so all values are reported as\n",
"strings:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b_common.display(vrepr=repr)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>Chr</th>\n",
"<th>Pos</th>\n",
"<th>Ref</th>\n",
"<th>Nref</th>\n",
"<th>Der</th>\n",
"<th>Mut</th>\n",
"<th>GeneId</th>\n",
"<th>GeneAlias</th>\n",
"<th>GeneDescr</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td>'91099'</td>\n",
"<td>'G'</td>\n",
"<td>'A'</td>\n",
"<td>'-'</td>\n",
"<td>'SYN'</td>\n",
"<td>'PFA0095c'</td>\n",
"<td>'MAL1P1.10,RIF'</td>\n",
"<td>'rifin'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td>'91104'</td>\n",
"<td>'A'</td>\n",
"<td>'T'</td>\n",
"<td>'-'</td>\n",
"<td>'NON'</td>\n",
"<td>'PFA0095c'</td>\n",
"<td>'MAL1P1.10,RIF'</td>\n",
"<td>'rifin'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td>'93363'</td>\n",
"<td>'T'</td>\n",
"<td>'A'</td>\n",
"<td>'-'</td>\n",
"<td>'NON'</td>\n",
"<td>'PFA0100c'</td>\n",
"<td>'MAL1P1.11'</td>\n",
"<td>'Plasmodium exported protein (PHISTa), unknown function'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td>'93382'</td>\n",
"<td>'T'</td>\n",
"<td>'G'</td>\n",
"<td>'-'</td>\n",
"<td>'NON'</td>\n",
"<td>'PFA0100c'</td>\n",
"<td>'MAL1P1.11'</td>\n",
"<td>'Plasmodium exported protein (PHISTa), unknown function'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td>'93384'</td>\n",
"<td>'G'</td>\n",
"<td>'A'</td>\n",
"<td>'-'</td>\n",
"<td>'NON'</td>\n",
"<td>'PFA0100c'</td>\n",
"<td>'MAL1P1.11'</td>\n",
"<td>'Plasmodium exported protein (PHISTa), unknown function'</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, the 'Pos' field should be interpreted as an integer.\n",
"\n",
"Also, the 'Mut' field has a different representation in the two\n",
"tables, which needs to be converted before the data can be compared:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_common.valuecounts('Mut')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>Mut</th>\n",
"<th>count</th>\n",
"<th>frequency</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>N</td>\n",
"<td style='text-align: right'>71162</td>\n",
"<td style='text-align: right'>0.6865804123611875</td>\n",
"</tr>\n",
"<tr>\n",
"<td>S</td>\n",
"<td style='text-align: right'>31535</td>\n",
"<td style='text-align: right'>0.30425386166507473</td>\n",
"</tr>\n",
"<tr>\n",
"<td>-</td>\n",
"<td style='text-align: right'>950</td>\n",
"<td style='text-align: right'>0.009165725973737783</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"+-----+-------+----------------------+\n",
"| Mut | count | frequency |\n",
"+=====+=======+======================+\n",
"| 'N' | 71162 | 0.6865804123611875 |\n",
"+-----+-------+----------------------+\n",
"| 'S' | 31535 | 0.30425386166507473 |\n",
"+-----+-------+----------------------+\n",
"| '-' | 950 | 0.009165725973737783 |\n",
"+-----+-------+----------------------+"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b_common.valuecounts('Mut')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>Mut</th>\n",
"<th>count</th>\n",
"<th>frequency</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>NON</td>\n",
"<td style='text-align: right'>70880</td>\n",
"<td style='text-align: right'>0.6840510336042</td>\n",
"</tr>\n",
"<tr>\n",
"<td>SYN</td>\n",
"<td style='text-align: right'>32738</td>\n",
"<td style='text-align: right'>0.31594896639579995</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"+-------+-------+---------------------+\n",
"| Mut | count | frequency |\n",
"+=======+=======+=====================+\n",
"| 'NON' | 70880 | 0.6840510336042 |\n",
"+-------+-------+---------------------+\n",
"| 'SYN' | 32738 | 0.31594896639579995 |\n",
"+-------+-------+---------------------+"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the `convert()` function to convert the type of the 'Pos'\n",
"field in both tables and the representation of the 'Mut' field in\n",
"table ***b***:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_conv = a_common.convert('Pos', int)\n",
"b_conv = (\n",
" b_common\n",
" .convert('Pos', int)\n",
" .convert('Mut', {'SYN': 'S', 'NON': 'N'})\n",
")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"highlight = 'background-color: yellow'\n",
"a_conv.display(caption='a', vrepr=repr, td_styles={'Pos': highlight})"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<caption>a</caption>\n",
"<thead>\n",
"<tr>\n",
"<th>Chr</th>\n",
"<th>Pos</th>\n",
"<th>Ref</th>\n",
"<th>Nref</th>\n",
"<th>Der</th>\n",
"<th>Mut</th>\n",
"<th>GeneId</th>\n",
"<th>GeneAlias</th>\n",
"<th>GeneDescr</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>91099</td>\n",
"<td>'G'</td>\n",
"<td>'A'</td>\n",
"<td>'-'</td>\n",
"<td>'S'</td>\n",
"<td>'PFA0095c'</td>\n",
"<td>'MAL1P1.10'</td>\n",
"<td>'rifin'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>91104</td>\n",
"<td>'A'</td>\n",
"<td>'T'</td>\n",
"<td>'-'</td>\n",
"<td>'N'</td>\n",
"<td>'PFA0095c'</td>\n",
"<td>'MAL1P1.10'</td>\n",
"<td>'rifin'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>93363</td>\n",
"<td>'T'</td>\n",
"<td>'A'</td>\n",
"<td>'-'</td>\n",
"<td>'N'</td>\n",
"<td>'PFA0100c'</td>\n",
"<td>'MAL1P1.11'</td>\n",
"<td>'hypothetical protein, conserved in P. falciparum'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>93382</td>\n",
"<td>'T'</td>\n",
"<td>'G'</td>\n",
"<td>'-'</td>\n",
"<td>'N'</td>\n",
"<td>'PFA0100c'</td>\n",
"<td>'MAL1P1.11'</td>\n",
"<td>'hypothetical protein, conserved in P. falciparum'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>93384</td>\n",
"<td>'G'</td>\n",
"<td>'A'</td>\n",
"<td>'-'</td>\n",
"<td>'N'</td>\n",
"<td>'PFA0100c'</td>\n",
"<td>'MAL1P1.11'</td>\n",
"<td>'hypothetical protein, conserved in P. falciparum'</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b_conv.display(caption='b', vrepr=repr, td_styles={'Pos': highlight, 'Mut': highlight})"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<caption>b</caption>\n",
"<thead>\n",
"<tr>\n",
"<th>Chr</th>\n",
"<th>Pos</th>\n",
"<th>Ref</th>\n",
"<th>Nref</th>\n",
"<th>Der</th>\n",
"<th>Mut</th>\n",
"<th>GeneId</th>\n",
"<th>GeneAlias</th>\n",
"<th>GeneDescr</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>91099</td>\n",
"<td>'G'</td>\n",
"<td>'A'</td>\n",
"<td>'-'</td>\n",
"<td style='background-color: yellow'>'S'</td>\n",
"<td>'PFA0095c'</td>\n",
"<td>'MAL1P1.10,RIF'</td>\n",
"<td>'rifin'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>91104</td>\n",
"<td>'A'</td>\n",
"<td>'T'</td>\n",
"<td>'-'</td>\n",
"<td style='background-color: yellow'>'N'</td>\n",
"<td>'PFA0095c'</td>\n",
"<td>'MAL1P1.10,RIF'</td>\n",
"<td>'rifin'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>93363</td>\n",
"<td>'T'</td>\n",
"<td>'A'</td>\n",
"<td>'-'</td>\n",
"<td style='background-color: yellow'>'N'</td>\n",
"<td>'PFA0100c'</td>\n",
"<td>'MAL1P1.11'</td>\n",
"<td>'Plasmodium exported protein (PHISTa), unknown function'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>93382</td>\n",
"<td>'T'</td>\n",
"<td>'G'</td>\n",
"<td>'-'</td>\n",
"<td style='background-color: yellow'>'N'</td>\n",
"<td>'PFA0100c'</td>\n",
"<td>'MAL1P1.11'</td>\n",
"<td>'Plasmodium exported protein (PHISTa), unknown function'</td>\n",
"</tr>\n",
"<tr>\n",
"<td>'MAL1'</td>\n",
"<td style='background-color: yellow'>93384</td>\n",
"<td>'G'</td>\n",
"<td>'A'</td>\n",
"<td>'-'</td>\n",
"<td style='background-color: yellow'>'N'</td>\n",
"<td>'PFA0100c'</td>\n",
"<td>'MAL1P1.11'</td>\n",
"<td>'Plasmodium exported protein (PHISTa), unknown function'</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the tables are ready for comparison."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Looking for missing or unexpected rows"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because both tables should contain the same list of genome locations,\n",
"they should have the same number of rows. Use `nrows()` to\n",
"compare:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_conv.nrows()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"103647"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b_conv.nrows()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"103618"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is some discrepancy. First investigate by comparing just the\n",
"genomic locations, defined by the 'Chr' and 'Pos' fields, using\n",
"`complement()`:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_locs = a_conv.cut('Chr', 'Pos')\n",
"b_locs = b_conv.cut('Chr', 'Pos')\n",
"locs_only_in_a = a_locs.complement(b_locs)\n",
"locs_only_in_a.nrows()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"29"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"locs_only_in_a.displayall(caption='a only')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<caption>a only</caption>\n",
"<thead>\n",
"<tr>\n",
"<th>Chr</th>\n",
"<th>Pos</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>216961</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL10</td>\n",
"<td style='text-align: right'>538210</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL10</td>\n",
"<td style='text-align: right'>548779</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL10</td>\n",
"<td style='text-align: right'>1432969</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL11</td>\n",
"<td style='text-align: right'>500289</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL11</td>\n",
"<td style='text-align: right'>1119809</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL11</td>\n",
"<td style='text-align: right'>1278859</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL12</td>\n",
"<td style='text-align: right'>51827</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL13</td>\n",
"<td style='text-align: right'>183727</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL13</td>\n",
"<td style='text-align: right'>398404</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL13</td>\n",
"<td style='text-align: right'>627342</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL13</td>\n",
"<td style='text-align: right'>1216664</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL13</td>\n",
"<td style='text-align: right'>2750149</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL14</td>\n",
"<td style='text-align: right'>1991758</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL14</td>\n",
"<td style='text-align: right'>2297918</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL14</td>\n",
"<td style='text-align: right'>2372268</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL14</td>\n",
"<td style='text-align: right'>2994810</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL2</td>\n",
"<td style='text-align: right'>38577</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL2</td>\n",
"<td style='text-align: right'>64017</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL4</td>\n",
"<td style='text-align: right'>1094258</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL5</td>\n",
"<td style='text-align: right'>1335335</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL5</td>\n",
"<td style='text-align: right'>1338718</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL7</td>\n",
"<td style='text-align: right'>670602</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL7</td>\n",
"<td style='text-align: right'>690509</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL8</td>\n",
"<td style='text-align: right'>489937</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL9</td>\n",
"<td style='text-align: right'>416116</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL9</td>\n",
"<td style='text-align: right'>868677</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL9</td>\n",
"<td style='text-align: right'>1201970</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL9</td>\n",
"<td style='text-align: right'>1475245</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"locs_only_in_b = b_locs.complement(a_locs)\n",
"locs_only_in_b.nrows()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"0"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So it appears that 29 locations are missing from table ***b***. Export\n",
"these missing locations to a CSV file using `toucsv()`:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"locs_only_in_a.tocsv('missing_locations.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An alternative method for finding rows in one table where some key\n",
"value is not present in another table is to use the `antijoin()`\n",
"function:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"locs_only_in_a = a_conv.antijoin(b_conv, key=('Chr', 'Pos'))\n",
"locs_only_in_a.nrows()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"29"
]
}
],
"prompt_number": 23
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Finding conflicts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'd also like to compare the values given in the other fields, to\n",
"find any discrepancies between the two tables.\n",
"\n",
"The simplest way to find conflicts is to `merge()` both tables under a given key:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ab_merge = etl.merge(a_conv, b_conv, key=('Chr', 'Pos'))\n",
"ab_merge.display(caption='ab_merge', \n",
" td_styles=lambda v: highlight if isinstance(v, etl.Conflict) else '')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<caption>ab_merge</caption>\n",
"<thead>\n",
"<tr>\n",
"<th>Chr</th>\n",
"<th>Pos</th>\n",
"<th>Ref</th>\n",
"<th>Nref</th>\n",
"<th>Der</th>\n",
"<th>Mut</th>\n",
"<th>GeneId</th>\n",
"<th>GeneAlias</th>\n",
"<th>GeneDescr</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>91099</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>S</td>\n",
"<td>PFA0095c</td>\n",
"<td style='background-color: yellow'>Conflict({'MAL1P1.10', 'MAL1P1.10,RIF'})</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>91104</td>\n",
"<td>A</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0095c</td>\n",
"<td style='background-color: yellow'>Conflict({'MAL1P1.10', 'MAL1P1.10,RIF'})</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>93363</td>\n",
"<td>T</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td style='background-color: yellow'>Conflict({'Plasmodium exported protein (PHISTa), unknown function', 'hypothetical protein, conserved in P. falciparum'})</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>93382</td>\n",
"<td>T</td>\n",
"<td>G</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td style='background-color: yellow'>Conflict({'Plasmodium exported protein (PHISTa), unknown function', 'hypothetical protein, conserved in P. falciparum'})</td>\n",
"</tr>\n",
"<tr>\n",
"<td>MAL1</td>\n",
"<td>93384</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td style='background-color: yellow'>Conflict({'Plasmodium exported protein (PHISTa), unknown function', 'hypothetical protein, conserved in P. falciparum'})</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From a glance at the conflicts above, it appears there are\n",
"discrepancies in the 'GeneAlias' and 'GeneDescr' fields. There may\n",
"also be conflicts in other fields, so we need to investigate further.\n",
"\n",
"Note that the table ***ab_merge*** will contain all rows, not only those containing conflicts. To find only conflicting rows, use `cat()` then `conflicts()`, e.g.:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ab = etl.cat(a_conv.addfield('source', 'a', index=0), \n",
" b_conv.addfield('source', 'b', index=0))\n",
"ab_conflicts = ab.conflicts(key=('Chr', 'Pos'), exclude='source')\n",
"ab_conflicts.display(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<thead>\n",
"<tr>\n",
"<th>source</th>\n",
"<th>Chr</th>\n",
"<th>Pos</th>\n",
"<th>Ref</th>\n",
"<th>Nref</th>\n",
"<th>Der</th>\n",
"<th>Mut</th>\n",
"<th>GeneId</th>\n",
"<th>GeneAlias</th>\n",
"<th>GeneDescr</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>91099</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>S</td>\n",
"<td>PFA0095c</td>\n",
"<td>MAL1P1.10</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>91099</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>S</td>\n",
"<td>PFA0095c</td>\n",
"<td>MAL1P1.10,RIF</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>91104</td>\n",
"<td>A</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0095c</td>\n",
"<td>MAL1P1.10</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>91104</td>\n",
"<td>A</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0095c</td>\n",
"<td>MAL1P1.10,RIF</td>\n",
"<td>rifin</td>\n",
"</tr>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>93363</td>\n",
"<td>T</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>hypothetical protein, conserved in P. falciparum</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>93363</td>\n",
"<td>T</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>Plasmodium exported protein (PHISTa), unknown function</td>\n",
"</tr>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>93382</td>\n",
"<td>T</td>\n",
"<td>G</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>hypothetical protein, conserved in P. falciparum</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>93382</td>\n",
"<td>T</td>\n",
"<td>G</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>Plasmodium exported protein (PHISTa), unknown function</td>\n",
"</tr>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>93384</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>hypothetical protein, conserved in P. falciparum</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>93384</td>\n",
"<td>G</td>\n",
"<td>A</td>\n",
"<td>-</td>\n",
"<td>N</td>\n",
"<td>PFA0100c</td>\n",
"<td>MAL1P1.11</td>\n",
"<td>Plasmodium exported protein (PHISTa), unknown function</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let's find conflicts in a specific field:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ab_conflicts_mut = ab.conflicts(key=('Chr', 'Pos'), include='Mut')\n",
"ab_conflicts_mut.display(10, caption='Mut conflicts', td_styles={'Mut': highlight})"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table class='petl'>\n",
"<caption>Mut conflicts</caption>\n",
"<thead>\n",
"<tr>\n",
"<th>source</th>\n",
"<th>Chr</th>\n",
"<th>Pos</th>\n",
"<th>Ref</th>\n",
"<th>Nref</th>\n",
"<th>Der</th>\n",
"<th>Mut</th>\n",
"<th>GeneId</th>\n",
"<th>GeneAlias</th>\n",
"<th>GeneDescr</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>99099</td>\n",
"<td>G</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td style='background-color: yellow'>-</td>\n",
"<td>PFA0110w</td>\n",
"<td>MAL1P1.13,Pf155</td>\n",
"<td>ring-infected erythrocyte surface antigen</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>99099</td>\n",
"<td>G</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td style='background-color: yellow'>N</td>\n",
"<td>PFA0110w</td>\n",
"<td>MAL1P1.13,Pf155,RESA</td>\n",
"<td>ring-infected erythrocyte surface antigen</td>\n",
"</tr>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>99211</td>\n",
"<td>C</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td style='background-color: yellow'>-</td>\n",
"<td>PFA0110w</td>\n",
"<td>MAL1P1.13,Pf155</td>\n",
"<td>ring-infected erythrocyte surface antigen</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>99211</td>\n",
"<td>C</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td style='background-color: yellow'>N</td>\n",
"<td>PFA0110w</td>\n",
"<td>MAL1P1.13,Pf155,RESA</td>\n",
"<td>ring-infected erythrocyte surface antigen</td>\n",
"</tr>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>197903</td>\n",
"<td>C</td>\n",
"<td>A</td>\n",
"<td>A</td>\n",
"<td style='background-color: yellow'>S</td>\n",
"<td>PFA0220w</td>\n",
"<td>MAL1P1.34b</td>\n",
"<td>ubiquitin carboxyl-terminal hydrolase, putative</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>197903</td>\n",
"<td>C</td>\n",
"<td>A</td>\n",
"<td>A</td>\n",
"<td style='background-color: yellow'>N</td>\n",
"<td>PFA0220w</td>\n",
"<td>PFA0215w,MAL1P1.34b</td>\n",
"<td>ubiquitin carboxyl-terminal hydrolase, putative</td>\n",
"</tr>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>384429</td>\n",
"<td>C</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td style='background-color: yellow'>N</td>\n",
"<td>PFA0485w</td>\n",
"<td>MAL1P2.26</td>\n",
"<td>dolichol kinase</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>384429</td>\n",
"<td>C</td>\n",
"<td>T</td>\n",
"<td>-</td>\n",
"<td style='background-color: yellow'>S</td>\n",
"<td>-</td>\n",
"<td>-</td>\n",
"<td>-</td>\n",
"</tr>\n",
"<tr>\n",
"<td>a</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>513268</td>\n",
"<td>A</td>\n",
"<td>G</td>\n",
"<td>-</td>\n",
"<td style='background-color: yellow'>N</td>\n",
"<td>PFA0650w</td>\n",
"<td>MAL1P3.12,MAL1P3.12a,PFA0655w</td>\n",
"<td>surface-associated interspersed gene pseudogene, (SURFIN) pseudogene</td>\n",
"</tr>\n",
"<tr>\n",
"<td>b</td>\n",
"<td>MAL1</td>\n",
"<td style='text-align: right'>513268</td>\n",
"<td>A</td>\n",
"<td>G</td>\n",
"<td>-</td>\n",
"<td style='background-color: yellow'>S</td>\n",
"<td>PFA0650w</td>\n",
"<td>MAL1P3.12,PFA0655,MAL1P3.12a,3D7surf1.2,PFA0655w,MAL1P12a</td>\n",
"<td>surface-associated interspersed gene (SURFIN), pseudogene</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>\n",
"<p><strong>...</strong></p>"
],
"metadata": {},
"output_type": "display_data"
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ab_conflicts_mut.nrows()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"3592"
]
}
],
"prompt_number": 27
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For more information about the `petl` package see the [petl online documentation](http://petl.readthedocs.org)."
]
}
],
"metadata": {}
}
]
} | mit |
longyangking/ML | Deep Learning/Keras.ipynb | 1 | 25897 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-class classification"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using CNTK backend\n",
"E:\\Anaconda2\\lib\\site-packages\\keras\\backend\\cntk_backend.py:19: UserWarning: CNTK backend warning: GPU is not detected. CNTK's CPU version is not fully optimized,please run with GPU to get better performance.\n",
" 'CNTK backend warning: GPU is not detected. '\n",
"E:\\Anaconda2\\lib\\site-packages\\cntk\\core.py:351: UserWarning: your data is of type \"float64\", but your input variable (uid \"Input4\") expects \"<type 'numpy.float32'>\". Please convert your data beforehand to speed up training.\n",
" (sample.dtype, var.uid, str(var.dtype)))\n",
"E:\\Anaconda2\\lib\\site-packages\\cntk\\core.py:351: UserWarning: your data is of type \"float64\", but your input variable (uid \"Input67\") expects \"<type 'numpy.float32'>\". Please convert your data beforehand to speed up training.\n",
" (sample.dtype, var.uid, str(var.dtype)))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.9999999404\n"
]
}
],
"source": [
"import keras\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Activation\n",
"from keras.optimizers import SGD\n",
"import numpy as np\n",
"x_train = np.random.random((1000, 20))\n",
"y_train = keras.utils.to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10)\n",
"x_test = np.random.random((100, 20))\n",
"y_test = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(64, activation='relu', input_dim=20))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(64, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer=sgd,\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(x_train, y_train,\n",
" epochs=20,\n",
" batch_size=128,verbose=0)\n",
"score = model.evaluate(x_test, y_test, batch_size=128,verbose=0)\n",
"print score[1]*100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model visualization\n",
"Need to install graphicviz from official website and add its /bin into environmental variable $PATH"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"83\" y=\"-379.8\">dense_4_input: InputLayer</text>\n",
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"<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"83\" y=\"-233.8\">dropout_3: Dropout</text>\n",
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"<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"83\" y=\"-87.8\">dropout_4: Dropout</text>\n",
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"</g>\n",
"<!-- 135902712->135903552 -->\n",
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"</g>\n",
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"<IPython.core.display.SVG object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import SVG\n",
"from keras.utils.vis_utils import model_to_dot\n",
"\n",
"SVG(model_to_dot(model).create(prog='dot', format='svg'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Binary Classification"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"E:\\Anaconda2\\lib\\site-packages\\cntk\\core.py:351: UserWarning: your data is of type \"float64\", but your input variable (uid \"Input536\") expects \"<type 'numpy.float32'>\". Please convert your data beforehand to speed up training.\n",
" (sample.dtype, var.uid, str(var.dtype)))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"55.0000011921\n"
]
}
],
"source": [
"import numpy as np\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout\n",
"\n",
"x_train = np.random.random((1000, 20))\n",
"y_train = np.random.randint(2, size=(1000, 1))\n",
"x_test = np.random.random((100, 20))\n",
"y_test = np.random.randint(2, size=(100, 1))\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(64, input_dim=20, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(64, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(x_train, y_train,\n",
" epochs=20,\n",
" batch_size=128,verbose=0)\n",
"score = model.evaluate(x_test, y_test, batch_size=128,verbose=0)\n",
"print score[1]*100"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"</g>\n",
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"</g>\n",
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"<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"83\" y=\"-87.8\">dropout_4: Dropout</text>\n",
"</g>\n",
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"<text font-family=\"Times New Roman,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"83\" y=\"-14.8\">dense_6: Dense</text>\n",
"</g>\n",
"<!-- 135902712->135903552 -->\n",
"<g class=\"edge\" id=\"edge5\"><title>135902712->135903552</title>\n",
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"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import SVG\n",
"from keras.utils.vis_utils import model_to_dot\n",
"\n",
"SVG(model_to_dot(model).create(prog='dot', format='svg'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# VGG-like convnet"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import keras\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout, Flatten\n",
"from keras.layers import Conv2D, MaxPooling2D\n",
"from keras.optimizers import SGD\n",
"\n",
"\n",
"x_train = np.random.random((100, 100, 100, 3))\n",
"y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)\n",
"x_test = np.random.random((20, 100, 100, 3))\n",
"y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)\n",
"\n",
"model = Sequential()\n",
"# input: 100x100 images with 3 channels -> (100, 100, 3) tensors.\n",
"# this applies 32 convolution filters of size 3x3 each.\n",
"model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))\n",
"model.add(Conv2D(32, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2, 2)))\n",
"model.add(Dropout(0.25))\n",
"\n",
"model.add(Flatten())\n",
"model.add(Dense(256, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
"model.compile(loss='categorical_crossentropy', optimizer=sgd)\n",
"\n",
"from keras.utils import plot_model\n",
"plot_model(model, to_file='VGG.png',show_shapes=True,show_layer_names=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.fit(x_train, y_train, batch_size=32, epochs=10,verbose=0)\n",
"score = model.evaluate(x_test, y_test, batch_size=32,verbose=0)\n",
"print score[1]*100"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Sequence classification with 1D convolutions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using CNTK backend\n",
"E:\\Anaconda2\\lib\\site-packages\\keras\\backend\\cntk_backend.py:19: UserWarning: CNTK backend warning: GPU is not detected. CNTK's CPU version is not fully optimized,please run with GPU to get better performance.\n",
" 'CNTK backend warning: GPU is not detected. '\n",
"E:\\Anaconda2\\lib\\site-packages\\cntk\\core.py:351: UserWarning: your data is of type \"float64\", but your input variable (uid \"Input4\") expects \"<type 'numpy.float32'>\". Please convert your data beforehand to speed up training.\n",
" (sample.dtype, var.uid, str(var.dtype)))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.6910078644752502, 0.65000000000000002]\n"
]
}
],
"source": [
"import keras\n",
"import numpy as np\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout\n",
"from keras.layers import Embedding\n",
"from keras.layers import Conv1D, GlobalAveragePooling1D, MaxPooling1D\n",
"\n",
"samplesize = 1000\n",
"x_train = np.random.random((samplesize, 100, 100))\n",
"y_train = np.random.randint(2, size=(samplesize, 1))\n",
"x_test = np.random.random((20, 100, 100))\n",
"y_test = np.random.randint(2, size=(20, 1))\n",
"\n",
"model = Sequential()\n",
"model.add(Conv1D(64, 3, activation='relu', input_shape=(100, 100)))\n",
"model.add(Conv1D(64, 3, activation='relu'))\n",
"model.add(MaxPooling1D(3))\n",
"model.add(Conv1D(128, 3, activation='relu'))\n",
"model.add(Conv1D(128, 3, activation='relu'))\n",
"model.add(GlobalAveragePooling1D())\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(x_train, y_train, batch_size=16, epochs=10,verbose=0)\n",
"score = model.evaluate(x_test, y_test, batch_size=16, verbose=0)\n",
"print score\n",
"\n",
"from keras.utils import plot_model\n",
"plot_model(model, to_file='sc1c.png',show_shapes=True,show_layer_names=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stacked LSTM"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.0\n"
]
}
],
"source": [
"import keras\n",
"from keras.models import Sequential\n",
"from keras.layers import LSTM, Dense\n",
"import numpy as np\n",
"\n",
"batchsize = 1000\n",
"data_dim = 16\n",
"timesteps = 8\n",
"num_classes = 10\n",
"\n",
"x_train = np.random.random((batchsize, timesteps, data_dim)).astype('float32')\n",
"y_train = keras.utils.to_categorical(np.random.randint(num_classes, size=(batchsize, 1)), num_classes=num_classes).astype('float32')\n",
"x_test = np.random.random((20, timesteps, data_dim)).astype('float32')\n",
"y_test = keras.utils.to_categorical(np.random.randint(num_classes, size=(20, 1)), num_classes=num_classes).astype('float32')\n",
"\n",
"# expected input data shape: (batch_size, timesteps, data_dim)\n",
"model = Sequential()\n",
"model.add(LSTM(32, return_sequences=True,\n",
" input_shape=(timesteps, data_dim))) # returns a sequence of vectors of dimension 32\n",
"model.add(LSTM(32, return_sequences=True)) # returns a sequence of vectors of dimension 32\n",
"model.add(LSTM(32)) # return a single vector of dimension 32\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])\n",
"\n",
"model.fit(x_train, y_train,\n",
" batch_size=64, epochs=5, verbose=0)\n",
"score = model.evaluate(x_test, y_test, batch_size=16, verbose=0)\n",
"print score[1]*100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# RNN"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import LSTM, Dense\n",
"import numpy as np\n",
"\n",
"data_dim = 16\n",
"timesteps = 8\n",
"num_classes = 10\n",
"batch_size = 32\n",
"\n",
"model = Sequential()\n",
"model.add(LSTM(32, return_sequences=True, stateful=True,\n",
" batch_input_shape=(batch_size, timesteps, data_dim)))\n",
"model.add(LSTM(32, return_sequences=True, stateful=True))\n",
"model.add(LSTM(32, stateful=True))\n",
"model.add(Dense(10, activation='softmax'))\n",
"\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])\n",
"\n",
"# Generate dummy training data\n",
"x_train = np.random.random((batch_size * 10, timesteps, data_dim))\n",
"y_train = np.random.random((batch_size * 10, num_classes))\n",
"\n",
"# Generate dummy validation data\n",
"x_val = np.random.random((batch_size * 3, timesteps, data_dim))\n",
"y_val = np.random.random((batch_size * 3, num_classes))\n",
"\n",
"model.fit(x_train, y_train,\n",
" batch_size=batch_size, epochs=5, shuffle=False,verbose=1)\n",
"score = model.evaluate(x_val, y_val, batch_size=16, verbose=0)\n",
"print score[1]*100"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy \n",
"id(numpy.dot) == id(numpy.core.multiarray.dot) "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Keras API"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense_1 (Dense) (None, 64) 1344 \n",
"_________________________________________________________________\n",
"dropout_1 (Dropout) (None, 64) 0 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 64) 4160 \n",
"_________________________________________________________________\n",
"dropout_2 (Dropout) (None, 64) 0 \n",
"_________________________________________________________________\n",
"dense_3 (Dense) (None, 1) 65 \n",
"=================================================================\n",
"Total params: 5,569\n",
"Trainable params: 5,569\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"import numpy as np\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Dropout\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(64, input_dim=20, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(64, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(1, activation='sigmoid'))\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| lgpl-3.0 |
dawenl/cofactor | src/Cofactorization_ML20M.ipynb | 1 | 88425 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fit CoFactor model to the binarized ML20M"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import itertools\n",
"import glob\n",
"import os\n",
"import sys\n",
"os.environ['OPENBLAS_NUM_THREADS'] = '1'\n",
"\n",
"import numpy as np\n",
"import matplotlib\n",
"matplotlib.use('Agg')\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"from scipy import sparse\n",
"import seaborn as sns\n",
"sns.set(context=\"paper\", font_scale=1.5, rc={\"lines.linewidth\": 2}, font='DejaVu Serif')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cofacto\n",
"import rec_eval"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Construct the positive pairwise mutual information (PPMI) matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Change this to wherever you saved the pre-processed data following [this notebook](./preprocess_ML20M.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DATA_DIR = '/hdd2/dawen/data/ml-20m/pro/'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_uid = list()\n",
"with open(os.path.join(DATA_DIR, 'unique_uid.txt'), 'r') as f:\n",
" for line in f:\n",
" unique_uid.append(line.strip())\n",
" \n",
"unique_sid = list()\n",
"with open(os.path.join(DATA_DIR, 'unique_sid.txt'), 'r') as f:\n",
" for line in f:\n",
" unique_sid.append(line.strip())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"111148 11711\n"
]
}
],
"source": [
"n_items = len(unique_sid)\n",
"n_users = len(unique_uid)\n",
"\n",
"print n_users, n_items"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def load_data(csv_file, shape=(n_users, n_items)):\n",
" tp = pd.read_csv(csv_file)\n",
" timestamps, rows, cols = np.array(tp['timestamp']), np.array(tp['uid']), np.array(tp['sid'])\n",
" seq = np.concatenate((rows[:, None], cols[:, None], np.ones((rows.size, 1), dtype='int'), timestamps[:, None]), axis=1)\n",
" data = sparse.csr_matrix((np.ones_like(rows), (rows, cols)), dtype=np.int16, shape=shape)\n",
" return data, seq"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train_data, train_raw = load_data(os.path.join(DATA_DIR, 'train.csv'))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"watches_per_movie = np.asarray(train_data.astype('int64').sum(axis=0)).ravel()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The mean (median) watches per movie is 597 (48)\n"
]
}
],
"source": [
"print(\"The mean (median) watches per movie is %d (%d)\" % (watches_per_movie.mean(), np.median(watches_per_movie)))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"user_activity = np.asarray(train_data.sum(axis=1)).ravel()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The mean (median) movies each user wathced is 62 (33)\n"
]
}
],
"source": [
"print(\"The mean (median) movies each user wathced is %d (%d)\" % (user_activity.mean(), np.median(user_activity)))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vad_data, vad_raw = load_data(os.path.join(DATA_DIR, 'validation.csv'))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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nZ6Nly5YAgPT0dIwePVpqDBUVDAoKShscuzx92r0V7qTl1dimiLjMzQ2bPF9l4VOuAL/y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\n5AkMNCIi8gQGGhERecInh5yK783j73IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f31646b8e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.semilogx(1 + np.arange(n_users), -np.sort(-user_activity), 'o')\n",
"plt.ylabel('Number of items that this user clicked on')\n",
"plt.xlabel('User rank by number of consumed items')\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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kxjzJjzqbrrRy9913s2LFCuX7u+66i4SEBLKysiwShBBCCGFtmgre+fPnqz2+cOFCZcUT\nYTuyK7oQQtSeasELDw9n8+bNuLm5MWjQIHQ6XZW5a+bmxgnrkV3RhRCi9lQL3qJFi5RVSUJDQ6vd\nHuixxx6zXmTCLNkVXQghakfzoJW77767yvH//e9/dOnSxSqB1aeGPmilPsmDdfMkP+okN+ZJftTZ\ndB5edcUO4O9//7tFghBCCCGsTdOgle+++47FixeTk5NDcXGxtWMSQgghLE5TwVu4cCGjRo0iKCgI\nJycnoHzxZbUNW4UQQoiGRlPBa926NU8//XSV44sWLbJ4QEIIIYQ1aHqGFxwczJUrV6oc379/v8UD\nEkIIIaxB9Q5vwYIFytfFxcWMGDGCe++9t9Jmql9//TUzZ860boRCCCGEBagWvEOHDvHoo48q33fq\n1KnKORULNAshhBANnWrBi46OZurUqWYvrtiEVQghhGjoVJ/h3Vrsvvjiiyrty5cvp0OHDtaJSggh\nhLAwTYNW3n///SrHJkyYUO1xIYQQoiEyOy3h8OHDABQWFpKenl5p8eiioiIMBoN1oxN1kp51SRaW\nFkKI25gteBWLQ+t0Ov7yl79UanNxcWHSpElWC0zUjeyGLoQQ1TNb8Co2eH3ssceq3S1BNDwVd3a3\nH5OCJ4Ro7jQ9w1u/fr214xBCCCGsSlPBq1g/UzR81e18LruhCyGExrU0LSUhIYE9e/ZgMpm4evUq\nY8aMUZ4N/vDDD8TFxWFnZ4e7uztLlizB3d1duXbDhg2kpKSg0+kYPnw4U6ZMUdoKCgqIiYmhoKCA\nsrIyYmNjCQoKUtoPHDjAypUrcXBw4J577iEuLq7JTpqX3dCFEKJ6mjaAtZSRI0eyceNGPD09ycnJ\nYfjw4SQmJhIYGMjQoUNZunQpoaGhvPXWW5w4cYI33ngDKF+zc8mSJWzbtg2TycSIESOIiYlhwIAB\nAMybN4/OnTszc+ZMDh06xAsvvMDu3bvR6/Xk5eUxfPhwkpKS8PPzIyYmBi8vL+bPn68ap2wAq042\nqTRP8qNOcmOe5EedTTeAVVPbWrls2TJldZaOHTvi5ubGuXPn2L9/P/b29oSGhgIQFRXF7t27uXr1\nKgBJSUmEh4ej1+txdHQkIiKCxMREAPLz89m5cyeRkZEA9OnTB71ez759+wBISUkhMDAQPz8/ACIj\nI0lOTq517EIIIRq3Oyp4jz/+eK3O79q1q/L1rl27cHZ25uGHH+b48eN07txZaWvXrh0tWrQgMzMT\noEp7QEAAGRnlQ+8zMzNxcnKiXbt2Sru/v7/SXt21BoOBnJycWsXeFKRnXeL1xCO8nniE9KxL9R2O\nEELYlOozvMGDB9d4cXVbBtXkp59+Yu7cufz666+sXr0aFxcXcnNzcXFxqXSem5sbubm5AOTm5lba\npcHV1ZW8vDylraZr27dvX6mt4nh1C2I3VTI/TwjR3KkWPFdXV1588UWgfD7eoUOHiIiIwNPTk7y8\nPLZu3crYsWNr/YZdu3YlNTWVH374gaeeeor4+HigfHL77W7tdqyu3VxbTV2Wza1LU+bnCSGaO9WC\nN3fuXB544AEA/v3vf/PWW29hZ/dbD2hYWBjTpk2rcUcFNd26dWPAgAEkJCTQvn17ZZJ7BYPBQOvW\nrQHw8vKqtIxZYWGh8izQ29ubwsLCKtdWdJ96eXlVaq94HW9vb9XY7O11eHi0qtPP1VA5ONhXe6y2\nP6e9vV2Ty40lSX7USW7Mk/xYn2rBqxgBCXDp0qVKxQ7AwcGB/Px8zW+Un59PWloajzzyiHKsZcuW\nGAwGevTowY4dO5TjFy5coKioiODgYABCQkI4efKk0n7ixAlCQkIACAoKwmg0cvHiRdq2bQtAdna2\nMoglJCREGcBSca27uzsdO3ZUjbW01NTkRks93L0dx05U7oK+y7MlL635FtA+fUFGkpkn+VEnuTFP\n8qPOpqM0W7RowerVqzl//jxFRUWcP3+eVatW0aqV9k8j169fJz4+nps3bwKQl5fHnj17ePDBB+nX\nrx+lpaWkp6cDsGXLFsLCwvDw8ADK9+ZLTU3FaDRSVFRESkoK48aNA8DDw4Nhw4aRnJwMQFpaGiUl\nJfTv3x+AiIgIsrKyOH36tPLaUVFRVQp4U1cxPy+okydBnTwZGtqBXYfPkHnqKpmnrhK/NUMGsggh\nmjRN8/BycnJ4+umnK41s9Pf355133tG8J57RaGTNmjUcOHAAvV5PYWEhYWFhzJgxAyh/Trhw4ULs\n7e1xc3OrMvF806ZNbN++HZ1OR3h4OJMnT1babp94vnDhQgIDA5X2gwcPsmLFChwcHOjUqROLFi0y\nO/G8OczDez3xCJmnrlY6FtTJk79G32v2OvkUap7kR53kxjzJjzpL3eFpnnheWlrK0aNH+eWXX7jr\nrrvo2bNnk71LkoKnTv4ozZP8qJPcmCf5UWfzief29va0atUKd3d3QkJCuHFD/sM0ZrLmphCiudG0\nlualS5eYOXMmx44d4+677+bTTz9l9OjRLFmyhPvuu8/aMQorqG7NTSi/86v4XqYsCCGaEk1dms8+\n+yy9evVi5MiRzJkzh4SEBC5evMgLL7zApk2bbBCmbTWHLs3b3T4xHWBoaAfOXL4G/FYApdvFPMmP\nOsmNeZIfdZbq0tR0h5efn6/sTlAxybtt27bNbvJ2U1bdxPRdh88oX1eszDLk951sGJUQQliOpmd4\nRqOR4uLiSseKi4v59ddfrRKUaJiqK4pCCNFYaCp4DzzwAI899hiffvopBoOBzz//nGnTpvHQQw9Z\nOz5hIzJgRQjR1Gl6hldcXMzrr79OUlISv/76Ky1btiQ6Opp58+ah1+ttEadNNcdneFD+HK/iLq5D\nG5dKXZqA0qXZHHOjlTyHUSe5MU/yo87m8/Aq5OXl4eXlZZE3b6iaa8G73a0FUAataCP5USe5MU/y\no86mg1ZudWuxe+aZZ1izZo1FAhENT+9AH5maIIRoMjQVvB9//JHVq1dz6tQpjEajcrwu++EJIYQQ\n9UFTwXv++efp1asXAwYMwMnJCSjfT+5f//qXVYMTDUdF96aDgz0Pd28nd35CiEZHU8Fr1aoVixYt\nqnK8YjcD0bTdPin92Ikrslu6EKLR0TQtISAgoNq1My9dku1kmgO13dKFEKIxUb3De/vtt5WvnZ2d\nGTNmDH379sXV9bfRMp9++inR0dHWjVAIIYSwANWCl5iYSL9+/ZTvQ0JCMBgMGAwG5VhRUZF1oxMN\nwsBevlW2EpKJ6kKIxka14A0fPpwXX3zR7MWrV6+2eECi4bl1ZwUZtCKEaKxqPfEcykdoViwi3RTJ\nxHN1MjnWPMmPOsmNeZIfdTbdADYxMZGhQ4dy+PBhADIyMoiMjOTs2bMWCUI0LulZl3g98QivJx4h\nPUsGLgkhGgdNBW/r1q3Ex8cTGhoKlD/Pi42NJS4uzqrBiYbn4PELxG/NIPPUVTJPXSV+a4YUPSFE\no6Cp4Dk6OuLv71/pWI8ePWTQSjO061BOlWMyRUEI0RhomnheVFTEtWvXcHFxUY4VFhZy8+ZNzW9U\nUlLC+++/z549e4DyHRhmz57Ngw8+CMDDDz9M586dleeD999/P7Nnz1au37BhAykpKeh0OoYPH65s\nSAtQUFBATEwMBQUFlJWVERsbS1BQkNJ+4MABVq5ciYODA/fccw9xcXE4Ojpqjl2Yl/NLIa8nHlEW\nmBZCiIZIU8EbOnQoo0ePJiIiAm9vb3Jzc/nss88YN26c5je6ePEi77//Ptu2bcPZ2ZkDBw4wffp0\ndu3ahY+PD/369eO1116r9tr9+/eTnJzMtm3bMJlMjBgxgoCAAAYMGABAXFwcwcHBzJw5k0OHDjF9\n+nR2796NXq8nLy+PefPmkZSUhJ+fHzExMaxevZr58+drjl38Zmifjhw7UXkN1es3S5QuTlmBRQjR\nUGnq0pw8eTITJkxgx44dLF26lB07dvDYY4/x+OOPa34jZ2dnZs2ahbOzMwAPPfQQTk5OHDlypMZr\nk5KSCA8PR6/X4+joSEREBImJiQDk5+ezc+dOIiMjAejTpw96vZ59+/YBkJKSQmBgIH5+fgBERkaS\nnJxMHQanCuDBkLuYPrI7QZ08cW5R9fPSv3dmyTM9IUSDpOkOT6fTMXHiRCZOnKgcy8nJqdX0BA8P\nD/785z9XOlZcXIy3tzcA2dnZTJ06lWvXrhEQEMDcuXPx9PQE4Pjx44SHhyvXBQQE8OGHHwKQmZmJ\nk5MT7dq1U9r9/f3JyMggLCyM48eP07lz50rXGgwGcnJy6NSpk6bYRWUV2wa9nnikyoT06zdLiN+a\nwd1tnHFzdpRuTiFEg6HpDu+ZZ56pcmzr1q3Mmzevzm986NAhfH196d27NwBdunRh9erVbN68GRcX\nF5566inl3Nzc3EpLmrm6upKXl6e03fpsEcDNzY3c3Nxqr3Vzc1OOiztjbrWVs5evK6M4/xr/LbHv\nHpJpDEKIeqWp4FW3cPTs2bOxt7ev05sWFRWxatUqli5dqhxbvHgxrVq1AuDZZ58lMzOTY8eOKe3m\n7iSra6upy1K6NO9cxQos1XVt3irPUFSpAMa+e0gKnxDC5sz+n+qxxx5Dp9ORlZVVqTsTwGg01mqU\n5q1iY2N54okn6NatW7XtLVu2xN3dnfPnz9OjRw+8vLwqreFZWFiodHd6e3tTWFhY6XqDwUDXrl2B\n8h3ab22veJ2KrtTq2Nvr8PBoVaeframzt7erlJshv++Es7MTyz/4j+bXOHv5OvFbM+jYzpUxg7vy\nYMhd1gi1XtyeH/EbyY15kh/rM1vw+vTpA8DZs2d54IEHKrW5uLgQFhZW6zdcsmQJPXv25JFHHsFo\nNJKbm0tOTg4uLi50794dKH+2ZzAYaNu2LVA+0f3kyZPKa5w4cYKQkBAAgoKCMBqNXLx4UTk/Oztb\nGcQSEhKiDGCpuNbd3Z2OHTuqxlhaapIlflRUt/xRtw7uylqbhutGzl6+rum1cn4pZPkH/2FoaAfG\nDu5ijXBtTpaHUie5MU/yo85SS4uZLXgzZ84Eyu+GajMFQc26desoKSlh5MiR3Lhxg/Pnz7Nz5058\nfX1JT09n8eLFACQkJODn50ePHj0AiI6OZunSpUyZMgWTyURKSgoxMTFA+WCYYcOGkZyczIwZM0hL\nS6OkpIT+/fsDEBERwZo1azh9+jR+fn5s2bKFqKgo7Ow09eYKjSoGskD50mPbvz2pufDtOnwGf193\nGdwihLCqOi0eXeHll1/m73//u6ZzT506xbBhw5TnbRUjPGfMmEFkZCRvvvkmOTk5lJWV4eLiwksv\nvVTpLmzTpk1s374dnU5HeHg4kydPVtpun3i+cOFCAgMDlfaDBw+yYsUKHBwc6NSpE4sWLTI78VwW\nj1ZXm0+h6VmXlLu+G0Ul5BnUV+ZxbuHA48MCG33Rk0/p6iQ35kl+1FnqDk9TwTMajWzYsIHvv/++\n0gCWrKws0tLSLBJIQyIFT92d/FFqufNr7BPX5X9a6iQ35kl+1Nl0t4RXX32VoqIicnJyGDVqFOHh\n4bRq1YohQ4ZYJAjRPPQO9GHRlD4MDe2geo6syymEsBZNBe/kyZPMnj2b1q1bM2rUKMaMGUN8fDzX\nrl2zdnyiCRo7uIum6QxCCGFJmgqeg0P5/5hKSkooKSkpv9DOjjNnzlgvMtGk9Q704fFhgVWOm5vM\nLoQQd0LTR2x7e3uOHTvG7373O2bMmEH//v1JT09Hr9dbOz7RhFVMXN/+7UmuFhbh6epU3yEJIZow\nTXd4s2fPpqioiFmzZlFcXMzrr7/O6dOnWbRokbXjE83A2cvXuX6zRJmQLquwCCGsQdMdnqOjI8HB\nwUD5vnRCWEp1g1T2fX+uUY/UFEI0TJru8GbOnMl///tfa8cihBBCWI2mgufk5MSBAweYMmUKixYt\n4rvvvqOsrMzasYlmoLpBKobrRunWFEJYnKaJ5z/99JOyGPOpU6fYtWsXW7dupXfv3ppXWmlMZOK5\nOmtMjlWbkN4YJ6HL5GF1khvzJD/qbDrxvGvXrphMJg4fPszmzZtJTEzk7NmzXLokn8LFnesd6IOb\nc9Wl3mQSuhDCkjQNWnnppZf48ssvuXHjBn379mXu3LkMGjSoysarQgghREOlqeC5u7vj5ubG0KFD\nGTVqlLLGwowKAAAgAElEQVQ1jxCWMrCXL5mnrlY5JoQQllKr3RJ+/PFHvvjiCzIyMujUqRNDhw7l\nvvvus2Z89UKe4amz5nOGit0VoLzYNbbndyDPYcyR3Jgn+VFnk/3wKnzxxRc88sgj/O53v6OoqIib\nN2+yZcsWEhMTOXr0qEUCEeLWPfWEEMLSNBW8N954g/T0dL744gsKCgro378/L7/8MgMHDrRyeEII\nIYRlaCp4586dIy8vj7/97W/069ePFi1aWDsuIYQQwqI0FbynnnqKGTNmWDsWIZrEczwhRMOkqeBJ\nsRO2kJ51ifitGcr3maeuNsrJ50KIhknTxHMhbEFtIWkhhLAEm205XVJSwvvvv8+ePXsAKC4uZvbs\n2Tz44IMA/PDDD8TFxWFnZ4e7uztLlizB3d1duX7Dhg2kpKSg0+kYPnw4U6ZMUdoKCgqIiYmhoKCA\nsrIyYmNjCQoKUtoPHDjAypUrcXBw4J577iEuLg5Hx6orewghhGi6bHaHd/HiRd5//33WrFlDQkIC\ns2bNYvr06Vy6dIni4mJmzJjBc889x+bNmwkKCiI2Nla5dv/+/SQnJ/PRRx/x4YcfkpyczFdffaW0\nx8XFERwczObNm5k7dy7Tp0+nuLgYgLy8PObNm8fKlStJTEzEZDKxevVqW/3Yohaqm2guk8+FEJZS\nq4KXl5dHRkYGeXl5tX4jZ2dnZs2ahbOzMwAPPfQQTk5OHDlyhP3792Nvb09oaCgAUVFR7N69m6tX\ny1feSEpKIjw8HL1ej6OjIxERESQmJgKQn5/Pzp07iYyMBKBPnz7o9Xr27dsHQEpKCoGBgfj5+QEQ\nGRlJcnIytZhvL2ykYgf0oE6e3N3GmbvbOLPv+3Oyc4IQwiI0Fbzr168zZ84c+vbtS2RkJH379mXO\nnDlcv3695ov/fx4eHvz5z3+udKy4uBgvLy+OHz9O586dlePt2rWjRYsWZGZmAlRpDwgIICOjfHBD\nZmYmTk5OtGvXTmn39/dX2qu71mAwkJOTozl2YTu9A30Y2MuXs5evc/bydTJPXZVd0IUQFqGp4C1f\nvhydTsemTZtITU1l48aN6HQ6li1bVuc3PnToEL6+voSGhpKbm1tlIWo3Nzdyc3MByM3NxdX1t6Vl\nXF1dlbvM2l7r5uamHBcNU3UDVbZ/e7IeIhFCNCWaBq38+OOPfPjhh8r3/v7+9OnTh3HjxtXpTYuK\nili1ahVLly5Vjul0uirn3drtWF271murI12ajcvZy9dJz7okUxSEEHWmqeDZ29tXOabT6ao9rkVs\nbCyTJ0+mW7duAHh5eZGVlVXpHIPBQOvWrZV2g8GgtBUWFuLp6QmAt7c3hYWFVa6t2LDWy8urUnvF\n63h7e6vGZ2+vw8OjVZ1+tqbO3t7O6rn5U9/OZJ76T5Xjqd/lMOT3naz63nfKFvlprCQ35kl+rE9T\nwWvbti3Lly9n/PjxtG7dmitXrrB582batm1b6zdcsmQJPXv2ZOjQoRiNRnJzcwkJCeHzzz9Xzrlw\n4QJFRUUEBwcDEBISwsmTv3VpnThxQtmiKCgoCKPRyMWLF5V4srOzlUEsISEhygCWimvd3d3p2LGj\naoylpSZZtVyFLVZ079bBnbvbOFfZAT3nl0L+77tTDfouT1a8Vye5MU/yo86mO56/+OKLHD58mCFD\nhtCrVy+GDBlCWloaCxYsqNWbrVu3jpKSEkaOHMmNGzc4ffo0W7ZsoX///pSUlJCeng7Ali1bCAsL\nw8PDA4Do6GhSU1MxGo0UFRWRkpKidKd6eHgwbNgwkpOTAUhLS6OkpIT+/fsDEBERQVZWFqdPn1Ze\nOyoqCjs7mXPfkP257z3VHpeJ6EKIutK8H57JZOLYsWOcP38eX19fQkJCzD5Xu92pU6cYNmyYco3J\nZEKn0zFz5kxmzJihTDy3t7fHzc2tysTzTZs2sX37dnQ6HeHh4UyePFlpu33i+cKFCwkMDFTaDx48\nyIoVK3BwcKBTp04sWrTI7MRz2Q9PnS0/hca+e6jKXV5QJ0/+Gn2vTd6/LuRTujrJjXmSH3WWusOr\n1Qawt3vjjTeYPXu2RQJpSKTgqbPlH+Xta2sCDX5tTfmfljrJjXmSH3U23QD22rVrJCcnc/LkSYxG\no3L866+/bpIFTzQMFRPRt397kquFRXi6OtV3SEKIRkzTg6y5c+fy8ccfVxkNKYQtnL18nes3Szh7\n+bpMQhdC1JmmO7xffvmFzz77rMpAj3/+859WCUqICmo7KDTkbk0hRMOk6Q6vc+fO1Y5qfPjhhy0e\nkBA1MVw31nySEELcRvUO7/Dhw8rXoaGhvPDCCzzyyCPK0lwAr776Kp9++ql1IxTN2sBevmSeulrp\n2NnL1/loz/8YO7hLPUUlhGiMVAvelClTaNOmTaUluG4tgiDrUQrr6x3oU+0k9F2HzwBI0RNCaKZa\n8Hr27ElCQoLZi5955hmLByTE7dycHeFy1Z05dh0+g7+vuzzPE0JoovoM79Zid/DgwWrPWbNmjeUj\nEuI25jaBlV0UhBBaaRq0smDBArZv3861a9esHY8QVfQO9GFoaIdq2yp2URBCiJpompbQpk0bysrK\nePHFFzGZTAwcOJDBgwcra10KYW0Vz+oqnt3dSqYpCCG00FTw1q5di5eXFyNHjuT69evs2LGD8PBw\nunbtyoYNG6wdoxBAedH7/07lVRnAItMUhBBaaOrSrNiPbuvWrcyfP59//OMfAGa32BHCGqrbRUG6\nNYUQWmi6w5s8eTKHDx/Gx8eHsLAwNmzYwH333Ver3RKEsAS1aQrbvz0p3ZpCCLM0FbywsDDs7e3x\n8PDg3nvvJSgoSIqdqDfVTVOouMuToieEUFOr7YEKCgrYs2cPX3/9NXZ2dgwcOJCIiAhrxlcvZHsg\ndQ1hC5Pqtg0CcNLbMeVPQfVa9BpCfhoqyY15kh91Nt3x/IMPPgDA0dGRli1bYjKZ2Lt3LytWrLBI\nEELURkW35u2KisuI35rB8/HfyjM9IUQVmu7wBg8eTGBgIN988w1t2rQhLCyMoUOH0qtXL1vEaHNy\nh6euoXwKVbvLu9XQ0A42X3qsoeSnIZLcmCf5UWfzDWADAgKYMWMGQUFBFnljIe6E2uCVW8l6m0KI\nW2nq0nzuueeYO3euFDvRoFQ3ReF2uw6fke5NIQSgseCNGTPG2nEIUWu9A32YPrI7Xm5OZs9L3Ps/\nG0UkhGjINHVpWlJGRgbz5s1j+vTpjBw5Ujn+8MMP07lzZ0wmEzqdjvvvv5/Zs2cr7Rs2bCAlJQWd\nTsfw4cOZMmWK0lZQUEBMTAwFBQWUlZURGxtb6W70wIEDrFy5EgcHB+655x7i4uJwdHS0zQ8srKp3\noA+9A31Iz7pE4t7/kWcoqnJOnqFI9s8TQti24O3Zs4eUlBRcXFyqtPXr14/XXnut2uv2799PcnIy\n27Ztw2QyMWLECAICAhgwYAAAcXFxBAcHM3PmTA4dOsT06dPZvXs3er2evLw85s2bR1JSEn5+fsTE\nxLB69Wrmz59v1Z9V2FZF4Xs+/ltyqyl68jxPCKGpS9NSgoODWbVqFc7OVYeUm5OUlER4eDh6vR5H\nR0ciIiJITEwEID8/n507dxIZGQlAnz590Ov17Nu3D4CUlBQCAwPx8/MDIDIykuTkZGox/VA0ImMH\nqRe0XYfP8NEe6d4UormqU8FLT08nLS2t1te1a9dOtS07O5upU6cyfvx4YmNjuXr1qtJ2/PhxOnfu\nrHwfEBBARkb5kPTMzEycnJwqvba/v7/SXt21BoOBnJycWscvGj5zWwlBedGb9cbXMpBFiGZIU8F7\n/fXXGThwIEajka1btzJp0iSmTZvGunXrLBZIly5dWL16NZs3b8bFxYWnnnpKacvNzcXV9bd5GK6u\nruTl5Sltt3eRurm5kZubW+21bm5uynHRNI0d3MVs0bv2azHxWzN44+OjNoxKCFHfNBW89PR0duzY\ngaOjI++99x5vv/02X331FTt37rRYIIsXL6ZVq1YAPPvss2RmZnLs2DGl3dzandW11dRlKV2aTdvY\nwV3wrmH05tHsXOI21r6nQgjROGkatNKiRQtatWrFuXPnyM/PZ+DAgQCV7pwsqWXLlri7u3P+/Hl6\n9OihbE9UobCwEE9PTwC8vb0pLCysdL3BYKBr165A+dZGt7ZXvI63t7fq+9vb6/DwaGWxn6cpsbe3\nazS5eSKiO8s/+I/Zc3IuXmPrt6eY9CfLzDFtTPmxNcmNeZIf69NU8IqKijh69Chbtmxh+PDhAFy/\nfh2j0TIbb3733Xe4uLjQvXt3AIqLizEYDLRt2xaAkJAQTp48qZx/4sQJQkJCAAgKCsJoNHLx4kXl\n/OzsbGUQS0hIiDKApeJad3d3s3v5lZaaZIkfFY1p+aNuHdyZPrI77+3K4tqvJarn7fruFCP7drLI\nezam/Nia5MY8yY86my4ePWPGDGbMmMGRI0eYPHkyFy5cYOjQoTzwwAMWCeLChQt8+OGHyvcJCQn4\n+fnRo0cPAKKjo0lNTcVoNFJUVERKSgrjxo0DwMPDg2HDhpGcnAxAWloaJSUl9O/fH4CIiAiysrI4\nffo0AFu2bCEqKgo7O5sOUBX1pHegD2/O7s/Q0A6o/ScvKi6T53lCNAOaFo/OysqiVatWytD+uvrp\np594++23SU9Px9fXl27durFo0SIuXLjA22+/zalTpygrK8PFxYWXXnqp0l3Ypk2b2L59OzqdjvDw\ncCZPnqy03T7xfOHChQQGBirtBw8eZMWKFTg4ONCpUycWLVpkduK5LB6trrF/Co3bmEbOxWvVtnVs\n68Irk+/sQ1xjz481SW7Mk/yos9QdnqaC1717d+bOnVtpdZOmTAqeuqbwR6k2OR3uvOg1hfxYi+TG\nPMmPOpt2ad53333VFjsZ6SgaI3OT03MuXmP6yq9knp4QTZCmghcUFMQvv/xS5fjjjz9u8YCEsLbe\ngT709FcfpXvTWEr81gwpekI0MZpGaV65coVRo0Zx3333KRO3AX7++WerBSaENc2O6mn2eR7Avu/P\n0TvQx4ZRCSGsSVPB++9//8uECROqHHdyMj+xV4iG7JXJD5gteobrlpl2I4RoGDQVvOjoaKZOnVrl\neMXkbyEaK3NF7+zl66RnXZK7PCGaCE3P8CqKncFgIDs7G5PJhMlkqvauT4jG5pXJD6guQ7b925PV\nHhdCND6aCt61a9eYM2cOffr04emnn6agoIDhw4eTnZ1t7fiEsIm2XtUv6fRLngwTF6Kp0FTwFi9e\njJeXFx999BE+Pj54eHjw5ptvsmTJEmvHJ4RNDOzlW+3xklKTLDAtRBOhqeCdPXuW2NhYevTogYND\n+WO/Ll26WGwtTSHqm7l99HIuXpOiJ0QToKngFRcXK19XTDY3mUwUFVW/WoUQjdHYwV1w0lf/JyFF\nT4jGT1PB69q1K7NmzSItLQ2j0cixY8d46aWXKq1XKURToNa1CVL0hGjsNBW8+fPnU1ZWxsSJEzl6\n9ChjxoyhsLCQ559/3trxCWFTYwd3oWNbF9X2nIvXePr1fbIKixCNkKbFoyvk5uZy7tw5fH19zW6g\n2tjJ4tHqmssCtzWtwgIwNLQDYwdXXpezueSnLiQ35kl+1Nl08eibN29y/vx5vL29CQkJ4ZtvvmHr\n1q2UlZVZJAghGppXJj9AC0d7s+fsOnyGj/b8z0YRCSHulKaCt2zZMmbNmkVxcTH//ve/efXVV4mP\nj2f58uXWjk+IevPE8G41niNFT4jGQ1PBy8zMJCkpCb1eT2JiIuvWrSM1NZW0NHmAL5qu3oE+TB/Z\nHb2Dzux5uw6fkS2FhGgENBW8Fi1aYGdnR3Z2NnZ2dvTs2RO9Xo+rq2X6VYVoqHoH+rD2r38wO5AF\nfttS6ODxCzaKTAhRW5oKXmlpKTt27GDVqlVEREQAcOnSJZmHJ5qNVyY/YHYPvQpJe36yQTRCiLrQ\nVPBiYmJ47733KCkpYeLEiVy4cIGJEyfyxz/+0drxCdFgzI7qWeOd3ulfCm0UjRCitmo1LaG5kGkJ\n6mToNLzx8VGOZueqtru20vPGrH42jKhxkN8d8yQ/6mw6LUHNc889V+trMjIyeOSRR9i6dWul4z/8\n8APR0dGMHz+eadOmUVBQUKl9w4YNPProo4wePZp33323UltBQQHTpk1j/PjxREdHk5mZWan9wIED\nREZGEh0dzYIFC2QNUHFHZkf1ZPrI7tipjGUpvFHM/HcO2DYoIUSNNG0AO3HixGqPZ2Vl1erN9uzZ\nQ0pKCi4ulbuFiouLmTFjBkuXLiU0NJS33nqL2NhY3njjDQD2799PcnIy27Ztw2QyMWLECAICAhgw\nYAAAcXFxBAcHM3PmTA4dOsT06dPZvXs3er2evLw85s2bR1JSEn5+fsTExLB69Wrmz59fq9iFuFXv\nQB+eoTvxWzOqbb9ScJM3Pj7K7KieNo5MCKFG0x3e5cuXGTVqlPJv0KBB2NnZMWnSpFq9WXBwMKtW\nrcLZ2bnS8f3792Nvb09oaCgAUVFR7N69m6tXrwKQlJREeHg4er0eR0dHIiIiSExMBCA/P5+dO3cS\nGRkJQJ8+fdDr9ezbtw+AlJQUAgMD8fPzAyAyMpLk5GSkJ1fcqd6BPrR2b6Habq7bUwhhe5oK3osv\nvlip4E2aNIn169fXegPYdu3aVXv8+PHjdO7cudJ5LVq0ULomb28PCAggI6P8k3VmZiZOTk6VXtvf\n319pr+5ag8FATk5OrWIXojrLpj2EvVrfJjD7za9tGI0QwhxNBa9fv6oP4O3t7fnf/yyzwkRubm6V\nbk43Nzdyc3OV9lvn/Lm6upKXl1ena93c3JTjQljC038OVm0rvFHME0v2ymosQjQAmp7hLViwoNL3\nRqORH3/8kbZt21osEJ2u6qfkW7sdq2vXem11pEtTWErFiixqz/OgfDWWXYfPVLvgtBDCNjQVvK+/\n/rrSXZ6zszOjRo0iKirKIkF4eXlVGQBjMBho3bq10m4wGJS2wsJCPD09AfD29qawsLDKtV27dlWu\nvbW94nXM7fZgb6/Dw6PVHfxETZe9vZ3kphpDft+JXYdPk33OYPa8XYfP8H32Fd6ZP9hGkTUc8rtj\nnuTH+jQVvMjISObMmWO1IEJCQvj888+V7y9cuEBRURHBwcFK+8mTJ5X2EydOEBISAkBQUBBGo5GL\nFy8qd5zZ2dnKIJaQkBBlAEvFte7u7nTs2FE1ntJSk8yHUSFzhdT97bHeTF3+JSWl5nsPLub9ytTX\n/o9l0x6yUWQNg/zumCf5UWfTeXjWLHYA/fv3p6SkhPT0dAC2bNlCWFgYHh4eAERHR5OamorRaKSo\nqIiUlBTGjRsHgIeHB8OGDSM5ORmAtLQ0SkpK6N+/PwARERFkZWVx+vRp5bWjoqKws7ujKYhCVGtu\n9H2azrtScFMGtAhhYzZdaeWnn37i7bffJj09HV9fX7p168aiRYuA8jl9CxcuxN7eHjc3N5YsWYK7\nu7ty7aZNm9i+fTs6nY7w8HAmT56stBUUFBATE0NBQQFlZWUsXLiQwMBApf3gwYOsWLECBwcHOnXq\nxKJFi3B0dFSNU1ZaUSefQs3z8GjF/313irXbMyjVsF2k3sGOtX8daPW4GgL53TFP8qPOUnd4srRY\nNaTgqZM/SvNuzc9He/7HrsNnNF3XHAazyO+OeZIfdVbv0kxKSiI1NdUibyJEczR2cBc2xAzStMtC\nxZ56QgjrUS14H374IQ888AAACQkJ1Z5z7tw560QlRBMyO6qnpqJ301gqRU8IK1IteC1atKBNmzYA\n7N69u9pzbp+fJ4SoXm2KXtzGNBtEJETzozotwcfHh0mTJnHXXXfx888/V1vcfv75Z6sGJ0RTMjuq\nJ+lZl8xOUAfIuXiNuI1pvDL5ARtFJkTzoHqHt3z5cv785z/j6+uLo6Mjvr6+Vf45OTnZMlYhGr3e\ngT5siBmE3sH8tJici9dkiyEhLEz1Ds/R0ZFHH31U+Xrq1KnVniOEqL21fx1Y40ayFXP1ZDNZISyj\nVtMS8vLyOH/+PO3bt8fLy8uacdUrmZagToZOm1fb/MRtTCPn4jWz5zSVuXryu2Oe5EedTVdauXHj\nBnPmzKFv375ERkbSt29f5syZw/Xr1y0ShBDN1SuTH6CFo73Zc4pLynhy6V4bRSRE06Wp4C1btgyd\nTsemTZtITU1l48aN6HQ6li1bZu34hGjy4ucNqLHolZmQoifEHdJU8H788UdWrVpFnz598Pf35/e/\n/z0rV67kxx9/tHZ8QjQL8fMG4NpKb/acMhNMWSJFT4i60lTw7O2rfvrU6XTVHhdC1M0bs/rR2r2F\n2XNMwBNS9ISoE00Fr23btixfvpxz585RVFTEuXPnWL58uUU3gBVCwLJpD9VY9EC6N4WoC02jNHNz\nc5k2bRrHjx9XjnXv3p133nlH2aS1KZFRmupkJJl5lspPTVMWADq2dWlUk9Pld8c8yY86m++WYDKZ\nOH78OOfOncPX15eQkBB0Op1FgmhopOCpkz9K8yyZHy1Fb/rI7vQO9LHI+1mb/O6YJ/lRJ9sDWZEU\nPHXyR2mepfOjZYuhxlL05HfHPMmPOpvOwxNC1I+xg7swfWR3s+es2WZ+bU4hRDkpeEI0cL0DfcwO\nZCkzwew3v7ZhREI0TlLwhGgElk17CHNPzAtvFMti00LUQFPBmzlzJqtWrbJ2LEIIM96NGWS2/UrB\nTdKzLtkoGiEaH9XdEm6VmZnJP/7xD6sGsmDBAmUHdZPJhE6nY+3atbRs2RKAH374gbi4OOzs7HB3\nd2fJkiW4u7sr12/YsIGUlBR0Oh3Dhw9nypQpSltBQQExMTEUFBRQVlZGbGwsQUFBVv15hLCGoaEd\nzA5iid+awYYaCqMQzZWmgtetWzc8PDyqHN+yZQujR4+2WDDvvfdetceLi4uZMWMGS5cuJTQ0lLfe\neovY2FjeeOMNAPbv309ycjLbtm3DZDIxYsQIAgICGDBgAABxcXEEBwczc+ZMDh06xPTp09m9ezd6\nvfmlnIRoaMYO7sLeI+coLilTPeeJJXvp6e/N7KieNoxMiIZPU5dmWFgYy5YtIysri/Pnzyv/Pv74\nY2vHB5QXNHt7e0JDQwGIiopi9+7dXL16FYCkpCTCw8PR6/U4OjoSERFBYmIiAPn5+ezcuZPIyEgA\n+vTpg16vZ9++fTaJXQhLW/vXgdjVMAX2aHYuTy370jYBCdFIaCp4MTExbNiwgZEjRzJo0CDl39Gj\nRy0azCuvvMKECROYOnUqhw4dUo4fP36czp07K9+3a9eOFi1akJmZWW17QEAAGRnlQ7UzMzNxcnKi\nXbt2Sru/v7/SLkRjtP6FmrstS8tMsu6mELfQVPBCQ0PJysqq8q93794WC8Tf35+oqCg++OADnn32\nWZ555hmysrKA8qXNXFxcKp3v5uZGbm6u0u7q+tvERFdXV/Ly8jRdK0RjVdP8vApPLNnLR3v+Z+Vo\nhGj4NBU8tRGaa9eutVggTz75JN27l/8Bh4SEMHDgQD766COlvbplzG5dJMbcMmc1XStEY9Q70Ieh\noR00nbvr8BniNqZZOSIhGjZNg1Zat27NiRMn+Pjjj/n111+ZP38+u3btsuiAldvdddddZGdnA+Dl\n5aXc7VUwGAzKwtVeXl4YDAalrbCwEE9PTwC8vb0pLCyscm3Xrl1V39veXoeHRyuL/BxNjb29neTG\nDFvn5+nRPenR1YflH/ynxnNzLl7jyWV7SX413AaRVSW/O+ZJfqxPU8H78ssvee655+jduzcXLlzA\nycmJtLQ0Ll26xLRp0ywSyPr163nyySeV73Nzc/HxKV8fMCQkhM8//1xpu3DhAkVFRQQHByvtJ0+e\nVNpPnDhBSEgIAEFBQRiNRi5evKhsZ5SdnW22WJeWmmRNOxWy3p959ZGfbh3c2RAziCeX7qWsho6L\nsjJ4NCalXnZakN8d8yQ/6my6lub69ev57LPPWLduHR4eHuj1epYsWcLXX1tuOaNNmzYpz93OnDnD\n3r17GTFiBAD9+/enpKSE9PR0oHw6RFhYmDJVIjo6mtTUVIxGI0VFRaSkpDBu3DgAPDw8GDZsGMnJ\nyQCkpaVRUlKiTFkQoqlY/8KgGndNr5Bz8ZosRyaaHU13eHZ2dvj6+gK/PQ+z9I7nU6ZMYcaMGTg4\nOPDrr78SGxurDIpxdHQkPj6ehQsXYm9vj5ubG0uWLFGu7devH9nZ2URHR6PT6YiKiqJ///5Ke2xs\nLDExMYwfP56ysjLeeecdHB0dLRa7EA3FG7P6adphAcqXI3ty6V5NIz6FaAo0bQ80btw4Xn75ZYKC\ngpg4cSLvvfce2dnZvPzyy2zevNkWcdqUbA+kTrpdzGtI+anNlARbbDHUkHLTEEl+1Nl0P7z9+/cz\nY8YMevXqxcmTJwkKCuI///kPb775Jn379rVIIA2JFDx18kdpXkPLT22Kno6a1+u8Ew0tNw2N5Eed\nzTeAzczM5KOPPuLChQu0b9+esWPH0q1bN4sE0dBIwVMnf5TmNcT8aNk5/VZ2Om0T22urIeamIZH8\nqJMdz61ICp46+aM0ryHnpy6rrlhyIeqGnJuGQPKjzqajNEtLS4mPjycsLIyQkBDCwsJYs2YNpaWl\nFglCCGF9dSleTyzZKyu1iCZD0x3ea6+9xv79+4mIiKB169ZcvnyZlJQUBg4cyAsvvGCLOG1K7vDU\nyadQ8xpDfua/c4ArBTfrdO2d3PE1htzUJ8mPOpt2aYaHh5OYmFhpTcpr164RHR1NSkqKRQJpSKTg\nqZM/SvMaU37uZGHpuhS+xpSb+iD5UWfTLs22bdtWWYDZxcVFWdpLCNH4bIgZRGv3FnW6tqKrU4jG\nRFPBGzBgADt27Kh0LDU1VdmfTgjROC2b9hAbYrSv0HK7J5bslUWpRaOh2qU5ePBg5WuTycSlS5dw\ncnLCw8OD/Px8bty4Qfv27dmzZ4/NgrUV6dJUJ90u5jX2/MRtTCPn4rU6XVtTN2djz421SX7UWapL\nUwHIZnAAABh2SURBVHVpMVdXV1588UXVC00mE6+99ppFghBCNAy3Lihd2y7LivMtOZVBCEtSvcP7\n6quvalxgWcs5jZHc4amTT6HmNbX8aF2X83bVTV5varmxNMmPugYx8fyZZ55hzZo1FgmkIZGCp07+\nKM1rqvlJz7pE/NaMOl1bccfXVHNjKZIfdTYteFlZWaxevZqcnByMRiNQ3qWZm5vL0aNHLRJIQyIF\nT538UZrX1PMz+82vKbxRXOfrpbtTXVP/3bkTNi14I0aMICIigqCgIBwcyh/7VTzD27p1q0UCaUik\n4KmTP0rzmkt+7nRKQmv3Fiyb9pCFomkamsvvTl3YtOA99thjJCQkVDn+/fff06tXL4sE0pBIwVMn\nf5TmNaf8WGoentz1lWtOvzu1ZfVRmre69957yc7Oxt/fv9LxTz/9tEkWPCFEzSoK1ZNL91J2B0vQ\nVxROa29PJISmO7zs7GwmTZpE69atcXX9rdJmZWWRltb0Jp3KHZ46+RRqXnPOj6VXXmlud37N+Xen\nJjbt0hw1ahT33nsv3bp1q/QM71//+heff/65RQJpSKTgqZM/SvMkP3c2orM6zeXOT3531Nm0S7NF\nixbExsZWOe7m5maRIIQQTUfvQB/l7swSd32mal6nud39CcvQVPCCgoLIy8vDy8ur0vGsrCyGDBli\nlcCEEI3frYXJkl2eUgBFXWjq0nzuuec4dOgQvXr1qvQM7+uvv+abb76xaoCWYDQaeeWVV/j5558p\nLS1l7ty59O3bV/V86dJUJ90u5kl+1FXk5qllX1J6J6NcaqExFUL53VFn0y7NI0eOEB0dXeW4o6Oj\nRYKwtjfffBOAjz76iFOnTjF27Fg+//zzKnesQgjr+9f8PyhfW3uLIXOvr3ewY+1fB1r1/UXDoqng\nRUdHM3Xq1CrHPT09LR6QpZlMJpKTk3n77bcB6NSpE926dWP79u1MmjSpfoMTopmzVpenFsUlZXV6\nz8Z01ygq01Twqit2AB07drRoMNZw5swZCgoK6Ny5s3IsICCA48eP12NUQojb1Wfxqw1bxyZ3opaj\nqeAdPny42uMrVqzg4YcftmhAlnblyhWASs8eXV1dyc7Orq+QhBA1uP0uqiEXQGsrLinj6df3SdGz\nAE0Fb8qUKbRp04aK8S0Gg4GioiJ8fHysGpwl6XS6St/fwSYRQggbu70AvvHxUY5m59ZTNLZXXFJW\n3yE0CZoK3pAhQ1i5cmWlY/v37+fMmdrvk2Vr3t7eQHmRrhikUlhYqByvjl5vb7FRQU2R5MY8yY86\nS+XmH9Or71mKeG6bRV6/IZLfqzunqeDdXuwA+vfvz5QpU5gwYYLFg7IkPz8/3N3dOXnypFLwTpw4\nwcCBA+s3MCGExX22YkR9hyAaME0F7/z585W+Lyoq4ocffuD06dNWCcqSdDodY8aMYcuWLdx///2c\nOnWKrKwsVqxYUd+hCSGEsCFNE88DAwMrPQMzmUy4urry0ksvMWJEw/9EdfvE8+eee44HH3ywvsMS\nQghhQ5oK3tixYyt1a+r1elq3bo2dnZ1VgxNCCCEsRVPBO336NH5+fraIRwghhLAK1Vu0GTNmKF83\ntWKXkZHBI488wtatWysdNxqNLFiwgLFjxxIZGcm3335bqX3t2rWsXbuWxYsXYzQabRmyzdQ1NwcP\nHmTIkCFVnvc2NXXJz08//cTf/vY31q9fz/z587lxo2mul1iX3Pzyyy/MmTOH9evXM3v2bPLy8mwd\ntk3U9e8K4Msvv2Tw4MG2CrVe1DU/f/zjH5k4cSITJ04kMzOzxvdRHbRy9OhRFixYUOMLvPbaazWe\n05Ds2bOHlJQUXFxcqrSZW3MzOzubU6dO8dprr/HJJ5/wySefVLu+aGNW19wAXL9+nfbt29s0Xlur\na37y8/N54okn8Pf3Z+PGjWzdupXx48fbOnyrqmtuiouLGTt2LA8++CAJCQl89tlnPP7447YO36ru\n9O/q+++/t2m8tnYn+Xn66acZOXKk5vdSvcPz9fVl5syZVf49+eSTXLp0iW3bttGhQ4fa/mz1Ljg4\nmFWrVuHs7FzpeMWam6NHjwYqr7kJkJ6eTlBQEFC+XZLa6jONWV1zA+VzNZv6ZP665ueBBx7A398f\ngLKyMlq2bGnbwG2grrnp0KGDMoDs/PnzjWK5wtq6k7+rjRs3Nvk1f+8kP3v37mXjxo1s3LiR4uLi\nGt9LteA98cQT+Pr6VvpnNBqZN28eWVlZvPvuu0yfPr2uP2O9adeuXbXHa1pzMz8/n1atWgHQqlUr\nCgoKrB+sjdU1N83FneanpKSE77//nj/96U9WjbM+3Gluli5dyrlz5xr8UoV1UdfcHD16lLvvvhtP\nT88m/WHyTn53Zs+ezeTJk/H09OTdd9+t8b1UC97QoUMrfb9t2zZGjx6Ns7Mzn3zySZMb1q+25mbF\nMwUPDw/l2cuNGzdwd3e3fZD1pKbcNHda87N69Wrmzp3baLbVsgStuXnh/7V379FUZm8cwL9KbqNS\nhzFoTTXNTIjQuNSiMRTm0oVVRibU0kUzTblUulAzhjTRbSJTp1rNQiGF0p2sEZMc5FISopJOrikZ\ninh+f1je1YlzaKbSb+zPX73v2We/z/uc3dnvu89r77VrMWvWLAQHB7/V+PpTb7m5cuUKampqwOfz\n0dTUhP3796O9vb1fYu0PfWk7XSMnenp6fRr67fUPz589ewY/Pz/Ex8dj4cKFWL16NQYPHvzKwf+/\nEDfnpqGhIQ4cOAAAuHHjBoyNjd96bP2NzUcqmaT8HDx4EBYWFvjoo4+QkZHxn7tg7I243GRlZUFD\nQwPq6upQV1f/zz/01BNxuXFzc+P2RUdHY8mSJW81rneFuPwUFBSgo6MD+vr6EAqFfXqGQGKHV1ZW\nBnd3d1RXVyMkJATTp0//F2G/23qbc3PcuHEYM2YM9uzZg4aGBnh7e/dbrG9bX+YjjY2NxYMHDxAZ\nGQlXV1coKyv3S6z9obf8ZGRk4NChQ9zQjJ6e3oDp8HrLjYyMDEJCQjB27FiUlpZi2bJl/Rbr29bX\neX737duHv//+G1FRUXB0dHzrcfaX3vKjpKSEkJAQZGZm4s6dO/D09Oy1TrEd3vHjxxEQEICxY8ci\nLi6uxwdUli1bhr179/6jk3nX9GXOzRevuAaSvuTG3t4e9vb2/RRh/+otP1OmTEF6eno/Rth/esuN\nnp4e9PT0+jHC/tPXeX7d3NwG5HdPb/n58MMPX3kIXGyH5+Pjg8GDB2PMmDEICwvrscz169df6WDv\nMjbnpngsN5Kx/IjHciMey41kbyI/YmdaeXk6sZcREVatWoWYmJh/fPD+UFJSgtDQUGRnZ0NDQwNa\nWlr45ZdfALA5N1luJGP5EY/lRjyWG8neZn7EdnjJycm9/mbXlzIMwzAM8y7o01yaDMMwDPP/ji13\nwDAMwwwIrMNjGIZhBgTW4TEMwzADAuvwGIZhmAGBdXgMwzDMgMA6PIZhGGZAYB0ewzAMMyCwDo/p\nUV5eHpydnaGpqSl2Qt/GxkYYGBjA1NQUy5cvfy3H9fPzA5/Pfy11vU7u7u4wMjJCQkJCn8oHBATA\nzMwMoaGhbziyNyc1NRW2traYNm1af4cCoHPWDXd3dzg6OsLOzg5Hjx7t75BEeHl5vVIbYd4+1uEx\nPdLX10dERATk5eWRmpqKkpKSbmXCw8MBAKamptizZ89rOe7atWvfyRWef/vtN2hqava5vK+vL6ZO\nnfoGI3rzzM3NsWHDhv4Og3Pq1Ck8fvwYUVFR4PP53VbIft3Wr1+P9evX97n8jh07XqmNMG8f6/AY\niXR0dKClpYV9+/aJ7G9pacHVq1ehq6v7Wo8nJyc3oBZIZfpOKBRCTU0NAKCiovKfXDmeebN6XQCW\nYZYuXYpVq1bBw8ODWyYqOjoaDg4OiIyM7Fb+r7/+QlhYGAYNGoS2tjY4OTlhxowZiIqKwq5duyAn\nJ4cFCxbA1dUVe/bsQUREBAwMDPD5558jPDwcKioq3N1jU1MTgoKCUFJSgiFDhmDUqFHw8fGBoqJi\nt+OmpaVh+/btaGxsxNKlS5GSkoKrV69i5cqVsLCwQFBQEJqbm9HR0QEej4eNGzdi+PDhuHnzJn76\n6Sfk5+dj165dSEhIQFlZGSwtLcVe4QcFBeHw4cPQ0dHB999/DzMzsx7LPX78GGvXrkVFRQVaWlrg\n6ekJc3NzeHt74+TJk9DU1MSmTZswadIkLFmyBLm5ufjhhx/g6uoqUk98fDz4fD6UlZUxefJkCAQC\nCIVCuLu7Y8aMGaioqMCaNWuQn5+PlJQUqKurw8vLC8nJyTh48CCMjIzA5/MRHR0NAwMDKCkp4caN\nG3j69Cm2bduGEydOIDc3F42Njdi6davInQoRITw8HKmpqaiuroapqSm8vb25haAvXbqE33//HdLS\n0ujo6MDixYthYWGBuro6eHp6IisrC7/++iuSkpJQVFQEbW3tHod66+vrERgYiOrqarS3t2P8+PHw\n9vaGgoIC+Hw+4uLi0NraChcXF1hZWcHZ2Vnk/X5+foiOjoaWlhZ8fX0xadIkeHh4cMPyly9fhp+f\nH2RkZBAeHo6ioiIcOHAAUlJSaG1thaGhIdzd3QEAISEhSEtLAwC4uLhg9OjR8Pf3R1tbG3bv3g2B\nQABZWVm0t7fDxcUFNjY2XBxCoRCrV69GeXk5ZGRksGvXLnzwwQd9as/btm1DdnY25OTk0NHRgUWL\nFsHc3LzHtsX8A8QwEjg7OxMRkY2NDW3cuJGIiFpbW2nhwoVEROTk5ERr1qzhyhcVFZGuri4VFxcT\nEVFVVRUZGRlRamoqERHx+XyytbXlyre2tnLHICKKi4sT2V6+fDmtWLGC2/bz8yN3d3ex8WZmZpKu\nri4lJSUREdHly5fp3LlzVFxczMVARBQbG0u+vr7cdmVlJY0fP55iY2OJiEgoFJKWlhYVFhZyZZyc\nnCg+Pp6IiIKDgykmJkZC5ojWrVtH06ZNo4aGBiIiSk9PpwkTJlBFRQUREc2fP5927tzJlc/KyqLg\n4GCx9cXFxZG+vj6VlJQQEdGJEyfI0NBQ5Bw0NTXp/v373D5LS0sSCATcdkhICJmamlJ9fT0RdebT\nzMyMbt26RURE+/fvJzc3N658ZmYmTZgwgU6fPk1ERE+ePKEvv/yS9uzZQ0REhYWFpKOjw+WpsrKS\nDAwMqLS0lKtj/PjxtHXrViIiamhooB07dnQ7t46ODrK3t6egoCBun4eHh8hnHRISQuvWrRObHyKi\nefPm0R9//EFERM+ePSNDQ0Oys7PjXt+wYQNVVVUREdGFCxfo7t273Gtr1qyhxMREbnvdunXdjhcY\nGEjz58+ntrY2IiJKS0sTaa9OTk60YMECam9vJyIiV1dX8vf3516X1J7T0tLoq6++4l5LSkrq9XyZ\nV8OGNJk+Wbp0KRISElBTU4P4+HjMnj27x3LR0dHQ09PDp59+CgBQVVWFpaUlDh8+DACYM2cObt26\nhYKCAgBAUlISrKyseqyrvr4eycnJcHJy4vbZ2tri/PnzaGxsFBurrKwst4rHlClTYGNjg1GjRiEz\nMxOOjo5wdnZGZGQk8vLyRN4nJSXFXamrqalh5MiRuHPnjkiZ58+fw8fHB++//z6+/fZbsTF0+eKL\nL6CkpASg87dONTU1JCYmAuhcguvYsWPo6OgAAMTExMDBwUFifaNHj8Ynn3wCoHO4uampCXV1dWLL\nUw9zw+vr63MLamppaUFZWRnjxo0DAGhra+Pu3bsi5WVlZfH1118DABQVFTFz5kzEx8dzMevr60Nb\nWxsAoKGhAUNDQ8TGxorUMWvWLACdq1T3tDL1tWvXcO3aNXz33Xfcvnnz5uH8+fN4+PChhIyIsrGx\nwfnz5wEA6enpsLe3R1FREYRCIdrb21FbWwtVVVUAgKamJkJDQ7k2kZOTg9zcXIn1Hz16FHPnzoW0\ndOfgmJmZGVasWCFSxtLSEoMGdX61amtrc22ot/Y8bNgwVFdX49SpU3j69CmmT5/OLZPDvB5sSJOR\nqOsLc9asWQgNDQWfz0dZWRkOHDjQY3mhUAhlZWWRfTweD4WFhQCAkSNHwtraGjExMZg4cSISEhLE\nLuh4//59AMD27dshIyMDKSkpPH/+HBoaGqitrcWwYcN6fF9P+7ds2YLCwkJERkZCQUEBAoGgx+HK\noUOHcv+WkZFBa2uryOv79++Hmpoabt++DWdnZ0hJSfUYQ5euzq4Lj8dDVVUVgM4v582bN+PixYsw\nMjLCkydPuCFjcV48N1lZWQDoFmNvXjxHaWnpbtsv1/fyOSgrK3Pn8ODBA9y+fRsuLi4AOtvLo0eP\nuCG8nuLuSddn/WLbUVZWBhFBKBRyHXRvrKysEBwcjLq6OiQnJ2PVqlXIyMhAUlISPv74Y5iYmHBl\nFy9eDBMTE0RFRQEAQkNDuTh68vDhQ7S0tHSLxcjISOy5ysrKcvnsrT1PnDgRe/fuRUREBPz9/WFi\nYgJ3d3fuYoT591iHx0jU9YUuLS0NV1dXBAYGwtfXl/v95mXq6uooKysT2VdfXw91dXVu28HBAW5u\nbnBwcACPxxP5wn2RhoYGpKSk4OPjg4kTJ3L7Hz16hOHDh7/SeeTl5cHMzAwKCgoAgLa2tld6f5cF\nCxbAzs4Otra2CA0N7XZ1/7KGhgaR7fr6eq4zkJGRgZ2dHaKjo1FZWQlbW9t/FFOXIUOGgIjw7Nkz\nbp+kO+G+evTokch2bW0tdw5dn+uLf0rS1tYmEkNfaGhoAADq6uq4f9fW1kJKSkqk7fSlHi0tLZw5\ncwaPHz8Gj8eDtbU1Lly4gPLycixevBhA5+dy9+5dkYue3trEyJEjIS8vj/r6epH9BQUFIu1TUmyS\n2nNTUxO0tLSwe/duNDc3Y8uWLXBzc0NycnKfz5+RjA1pMhK9OCRmb28PDw8PzJ07V2x5BwcHFBQU\noLi4GABQVVWFlJQUkaEqY2NjqKmpYeXKld2G8IiIOyaPx4OVlRViYmK4faWlpXB0dJQYb0/DeGPH\njkVeXh7a29sBABcvXuzT+16moKAAeXl5bNu2DQcOHEBOTo7EWC5evMh1GOnp6aiqqsLMmTO5Mg4O\nDsjIyEBiYqLYod0X65MUs7KyMoYOHYqioiIAnQ+T9Nbx9FYnEaG5uRmnT58G0PnQxalTpzBnzhwu\n/pycHO4ih4jg6+vLDSv2la6uLnR0dHDkyBGunpiYGNjY2PT57q6LtbU1wsLCuLs5a2tr5Obm4s6d\nO9wdtJKSEng8HrKysgAAz5494x5S6fLee+9xd2e+vr5obm7GvHnzcPz4cW7/uXPnxI52vExce+76\nv5GUlISdO3cC6Gxn+vr63HA383oM/vnnn3/u7yCYd09ZWRnc3d1RXFyMK1euYPbs2ZCWlsZnn33G\n3d0tWrQIN27cgFAoRFFREaytraGiogJdXV1s27YNCQkJOHPmDH788UdYW1uL1P/8+XOUl5eL/J4T\nFRWFQ4cO4d69e7h58yZsbGwwdepUZGZmYt++fUhMTMSVK1cQGBjY45dgTk4ONm/eDKFQiIyMDIwf\nPx4qKioAAD09PaSmpuLgwYPIzMyEoqIisrOzkZ+fj4kTJ2L16tWoqamBQCDAN998Aw8PD9y8eROl\npaVQUVHBoUOHIBAIUFRUBBUVFVy/fh3Xrl3DuXPnUFNT0+1v7gICApCSkgILCwucPXsW4eHhuHTp\nEgICAkSu7pWUlCAQCGBsbAxTU1Oxn8f58+cRFhaGe/fuobKyEpqamvDy8kJNTQ3y8/NhYmICJSUl\nqKqqYvfu3UhNTQWPx0NJSQkEAgFGjRqFtLQ0HDlyBLdv30ZzczMaGxsRGhrK1TlixAgEBARAKBQi\nPz8fQ4cORXBwMGRlZaGmpoawsDCEh4fD3NwcK1aswKBBg6CiogIdHR1s3boVCQkJOHbsGPT09ODi\n4oLm5ma4urriwYMHyM/Ph5SUFPdb38ukpKRgaWnJ5So2NhYaGhrYtGkTZGRkwOfzcezYMZSXl+PP\nP/+EsbGx2GFSHo+HgwcPwt/fH4qKihg5ciTOnj2LadOmwdDQkDuejo4OIiIicPLkSQgEAigqKiIz\nMxNPnjzB5MmTMWLECMTGxiI7Oxvy8vKwsrKCsbExKisrERISgsTERFRUVMDf3x9ycnLw9fXl2oiq\nqiqys7MREREBoVCIiooKWFpaSmzP8vLySElJwZEjRxAfH4/CwkL4+/t3Gx5m/jm24jnD9DMvLy94\nenr2+vsdwzD/DvsNj2H6QUFBAeTl5TFs2DC0tLSwzo5h3gLW4TFMP6itrcWWLVswYsQI9ug5w7wl\nbEiTYRiGGRDYU5oMwzDMgMA6PIZhGGZAYB0ewzAMMyCwDo9hGIYZEFiHxzAMwwwIrMNjGIZhBoT/\nAWwLndy7BvRhAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3163fe9650>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.semilogx(1 + np.arange(n_items), -np.sort(-watches_per_movie), 'o')\n",
"plt.ylabel('Number of users who watched this movie')\n",
"plt.xlabel('Movie rank by number of watches')\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate co-occurrence matrix based on the user's entire watching history"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def _coord_batch(lo, hi, train_data):\n",
" rows = []\n",
" cols = []\n",
" for u in xrange(lo, hi):\n",
" for w, c in itertools.permutations(train_data[u].nonzero()[1], 2):\n",
" rows.append(w)\n",
" cols.append(c)\n",
" np.save(os.path.join(DATA_DIR, 'coo_%d_%d.npy' % (lo, hi)),\n",
" np.concatenate([np.array(rows)[:, None], np.array(cols)[:, None]], axis=1))\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from joblib import Parallel, delayed\n",
"\n",
"batch_size = 5000\n",
"\n",
"start_idx = range(0, n_users, batch_size)\n",
"end_idx = start_idx[1:] + [n_users]\n",
"\n",
"Parallel(n_jobs=8)(delayed(_coord_batch)(lo, hi, train_data) for lo, hi in zip(start_idx, end_idx))\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User 0 to 5000 finished\n",
"User 5000 to 10000 finished\n",
"User 10000 to 15000 finished\n",
"User 15000 to 20000 finished\n",
"User 20000 to 25000 finished\n",
"User 25000 to 30000 finished\n",
"User 30000 to 35000 finished\n",
"User 35000 to 40000 finished\n",
"User 40000 to 45000 finished\n",
"User 45000 to 50000 finished\n",
"User 50000 to 55000 finished\n",
"User 55000 to 60000 finished\n",
"User 60000 to 65000 finished\n",
"User 65000 to 70000 finished\n",
"User 70000 to 75000 finished\n",
"User 75000 to 80000 finished\n",
"User 80000 to 85000 finished\n",
"User 85000 to 90000 finished\n",
"User 90000 to 95000 finished\n",
"User 95000 to 100000 finished\n",
"User 100000 to 105000 finished\n",
"User 105000 to 110000 finished\n",
"User 110000 to 111148 finished\n"
]
}
],
"source": [
"X = sparse.csr_matrix((n_items, n_items), dtype='float32')\n",
"\n",
"for lo, hi in zip(start_idx, end_idx):\n",
" coords = np.load(os.path.join(DATA_DIR, 'coo_%d_%d.npy' % (lo, hi)))\n",
" \n",
" rows = coords[:, 0]\n",
" cols = coords[:, 1]\n",
" \n",
" tmp = sparse.coo_matrix((np.ones_like(rows), (rows, cols)), shape=(n_items, n_items), dtype='float32').tocsr()\n",
" X = X + tmp\n",
" \n",
" print(\"User %d to %d finished\" % (lo, hi))\n",
" sys.stdout.flush()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: Don't forget to delete all the temporary coo_LO_HI.npy files"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.save(os.path.join(DATA_DIR, 'coordinate_co_binary_data.npy'), X.data)\n",
"np.save(os.path.join(DATA_DIR, 'coordinate_co_binary_indices.npy'), X.indices)\n",
"np.save(os.path.join(DATA_DIR, 'coordinate_co_binary_indptr.npy'), X.indptr)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.38557504805354814"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float(X.nnz) / np.prod(X.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Or load the pre-saved co-occurrence matrix"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# or co-occurrence matrix from the entire user history\n",
"dir_predix = DATA_DIR"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = np.load(os.path.join(dir_predix, 'coordinate_co_binary_data.npy'))\n",
"indices = np.load(os.path.join(dir_predix, 'coordinate_co_binary_indices.npy'))\n",
"indptr = np.load(os.path.join(dir_predix, 'coordinate_co_binary_indptr.npy'))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = sparse.csr_matrix((data, indices, indptr), shape=(n_items, n_items))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.38557504805354814"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float(X.nnz) / np.prod(X.shape)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_row(Y, i):\n",
" lo, hi = Y.indptr[i], Y.indptr[i + 1]\n",
" return lo, hi, Y.data[lo:hi], Y.indices[lo:hi]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"count = np.asarray(X.sum(axis=1)).ravel()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_pairs = X.data.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Construct the SPPMI matrix"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M = X.copy()\n",
"\n",
"for i in xrange(n_items):\n",
" lo, hi, d, idx = get_row(M, i)\n",
" M.data[lo:hi] = np.log(d * n_pairs / (count[i] * count[idx]))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"M.data[M.data < 0] = 0\n",
"M.eliminate_zeros()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.237642385093\n"
]
}
],
"source": [
"print float(M.nnz) / np.prod(M.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now $M$ is the PPMI matrix. Depending on the number of negative examples $k$, we can obtain the shifted PPMI matrix as $\\max(M_{wc} - \\log k, 0)$"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# number of negative samples\n",
"k_ns = 1\n",
"\n",
"M_ns = M.copy()\n",
"\n",
"if k_ns > 1:\n",
" offset = np.log(k_ns)\n",
"else:\n",
" offset = 0.\n",
" \n",
"M_ns.data -= offset\n",
"M_ns.data[M_ns.data < 0] = 0\n",
"M_ns.eliminate_zeros()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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jR0SLKNva2iRJbrfbbHO73fL5fEHn/fa3v9VLL72kBx98UKtWrYrkloCj8Q4YjBaWrNB3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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f31616bb890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(M_ns.data, bins=50)\n",
"plt.yscale('log')\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.23764238509276445"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float(M_ns.nnz) / np.prod(M_ns.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train the model"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scale = 0.03\n",
"\n",
"n_components = 100\n",
"max_iter = 20\n",
"n_jobs = 8\n",
"lam_theta = lam_beta = 1e-5 * scale\n",
"lam_gamma = 1e-5\n",
"c0 = 1. * scale\n",
"c1 = 10. * scale\n",
"\n",
"save_dir = os.path.join(DATA_DIR, 'ML20M_ns%d_scale%1.2E' % (k_ns, scale))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reload(cofacto)\n",
"coder = cofacto.CoFacto(n_components=n_components, max_iter=max_iter, batch_size=1000, init_std=0.01, n_jobs=n_jobs, \n",
" random_state=98765, save_params=True, save_dir=save_dir, early_stopping=True, verbose=True, \n",
" lam_theta=lam_theta, lam_beta=lam_beta, lam_gamma=lam_gamma, c0=c0, c1=c1)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ITERATION #0\n",
"\tUpdating user factors: time=7.40\n",
"\tUpdating item factors: time=6.99\n",
"\tUpdating context factors: time=5.63\n",
"\tUpdating bias terms: time=5.99\n",
"\tValidation NDCG@k: 0.19163\n",
"ITERATION #1\n",
"\tUpdating user factors: time=5.65\n",
"\tUpdating item factors: time=7.21\n",
"\tUpdating context factors: time=5.19\n",
"\tUpdating bias terms: time=5.94\n",
"\tValidation NDCG@k: 0.26963\n",
"ITERATION #2\n",
"\tUpdating user factors: time=5.58\n",
"\tUpdating item factors: time=7.58\n",
"\tUpdating context factors: time=5.86\n",
"\tUpdating bias terms: time=5.94\n",
"\tValidation NDCG@k: 0.34485\n",
"ITERATION #3\n",
"\tUpdating user factors: time=5.93\n",
"\tUpdating item factors: time=7.09\n",
"\tUpdating context factors: time=5.57\n",
"\tUpdating bias terms: time=5.90\n",
"\tValidation NDCG@k: 0.35849\n",
"ITERATION #4\n",
"\tUpdating user factors: time=5.62\n",
"\tUpdating item factors: time=7.35\n",
"\tUpdating context factors: time=5.51\n",
"\tUpdating bias terms: time=5.97\n",
"\tValidation NDCG@k: 0.36298\n",
"ITERATION #5\n",
"\tUpdating user factors: time=5.52\n",
"\tUpdating item factors: time=6.91\n",
"\tUpdating context factors: time=5.61\n",
"\tUpdating bias terms: time=5.96\n",
"\tValidation NDCG@k: 0.36325\n",
"ITERATION #6\n",
"\tUpdating user factors: time=5.50\n",
"\tUpdating item factors: time=7.12\n",
"\tUpdating context factors: time=5.42\n",
"\tUpdating bias terms: time=5.92\n",
"\tValidation NDCG@k: 0.36150\n"
]
},
{
"data": {
"text/plain": [
"CoFacto(batch_size=1000, dtype='float32', early_stopping=True, init_std=0.01,\n",
" max_iter=20, n_components=100, n_jobs=8, random_state=98765,\n",
" save_dir='/hdd2/dawen/data/ml-20m/pro/ML20M_ns1_scale3.00E-02',\n",
" save_params=True, verbose=True)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coder.fit(train_data, M_ns, vad_data=vad_data, batch_users=5000, k=100)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_data, _ = load_data(os.path.join(DATA_DIR, 'test.csv'))\n",
"test_data.data = np.ones_like(test_data.data)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_params = len(glob.glob(os.path.join(save_dir, '*.npz')))\n",
"\n",
"params = np.load(os.path.join(save_dir, 'CoFacto_K%d_iter%d.npz' % (n_components, n_params - 1)))\n",
"U, V = params['U'], params['V']"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Recall@20: 0.1448\n",
"Test Recall@50: 0.1765\n",
"Test NDCG@100: 0.1724\n",
"Test MAP@100: 0.0545\n"
]
}
],
"source": [
"print 'Test Recall@20: %.4f' % rec_eval.recall_at_k(train_data, test_data, U, V, k=20, vad_data=vad_data)\n",
"print 'Test Recall@50: %.4f' % rec_eval.recall_at_k(train_data, test_data, U, V, k=50, vad_data=vad_data)\n",
"print 'Test NDCG@100: %.4f' % rec_eval.normalized_dcg_at_k(train_data, test_data, U, V, k=100, vad_data=vad_data)\n",
"print 'Test MAP@100: %.4f' % rec_eval.map_at_k(train_data, test_data, U, V, k=100, vad_data=vad_data)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.savez('CoFactor_K100_ML20M.npz', U=U, V=V)"
]
},
{
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
apetrin/CNOP | Monte-Carlo and Multiprocessing/CNOP8_MC-res-process.ipynb | 1 | 73644 | {
"metadata": {
"name": "",
"signature": "sha256:b434c561bac0a499cdce008a0a784d73e7ca961d0c3fa9e819d5ee72d609f8b3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython import parallel\n",
"clients = parallel.Client(profile='parallel')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print clients.ids\n",
"print \"Total %i cores\"%(len(clients.ids))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0, 1, 2, 3, 4, 5, 6, 15, 16]\n",
"Total 9 cores\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%px --local\n",
"import cPickle as pickle\n",
"import glob\n",
"import os"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%px --local\n",
"\n",
"import sys\n",
"sys.path.append(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\")\n",
"import cPickle as pickle\n",
"import numpy as np\n",
"import pandas as pd\n",
"from statsmodels.tsa.arima_process import arma_generate_sample\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from CNOP import CNOP\n",
"import winsound\n",
"def threshold(x,thresholds=[],values=[-1,0,1]):\n",
" for threshold,val in zip(thresholds,values):\n",
" if x < threshold: \n",
" return val\n",
" return values[-1]\n",
"import time\n",
"from itertools import repeat\n",
"import os\n",
"from datetime import datetime\n",
"import numpy as np\n",
"import numpy.linalg as linalg"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%px --local\n",
"def pickle_folder(path, wait_interactive=True, chunksize=1):\n",
" '''Pickles all objects (except !README.txt) from the folder assynchronically'''\n",
" try:\n",
" print \"INFO FROM FILE:\", file(path+\"!README.txt\").read()\n",
" except:\n",
" print \"NO README file\"\n",
"\n",
" view = clients.load_balanced_view()\n",
" files = os.listdir(path)\n",
" if len(files) < 2:\n",
" raise RuntimeError, \"Less than 2 files\"\n",
" ar = view.map_async(lambda x: pickle.load(file(x)), \n",
" [path+x for x in os.listdir(path) if x != \"!README.txt\"], \n",
" chunksize=chunksize)\n",
" if wait_interactive:\n",
" ar.wait_interactive()\n",
" return ar.get()\n",
" else:\n",
" return ar\n",
"\n",
"def get_folder_info(path):\n",
" print \"INFO FROM FILE:\", file(path+\"!README.txt\").read()\n",
" files = os.listdir(path)\n",
" print \"With %i files\"%(len(files))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150323_212714\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 8 cores, 10000 replications of \"full_overlap\" with 250 observations\n",
"With 10001 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res250Full=pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150323_212714\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10000/10000 tasks finished after 140 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(res250Full)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"10000"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res250Full, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\3Full\\\\res250_CHECKED\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del res250Full"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150324_101209\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 8 cores, 10000 replications of \"full_overlap\" with 500 observations\n",
"With 10001 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res500Full=pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150324_101209\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10000/10000 tasks finished after 133 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(res500Full)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"10000"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res500Full, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\3Full\\\\res500_CHECKED\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150325_051321\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 8 cores, 10000 replications of \"full_overlap\" with 1000 observations\n",
"With 5140 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res1000Full0=pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150325_051321\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5139/5139 tasks finished after 81 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(res1000Full0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 62,
"text": [
"5139"
]
}
],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res1000Full1=pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150326_120353\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4861/4861 tasks finished after 132 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res1000Full0+res1000Full1, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\3Full\\\\res1000_CHECKED\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150323_212714\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 8 cores, 10000 replications of \"full_overlap\" with 250 observations\n",
"With 10001 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150328_094006\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 15 cores, 4225 replications of \"no_overlap\" with 500 observations\n",
"With 4224 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150328_004236\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 17 cores, 10000 replications of \"no_overlap\" with 500 observations\n",
"With 5776 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res500No0=pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150328_094006\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4223/4223 tasks finished after 71 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res500No1=pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150328_004236\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5775/5775 tasks finished after 99 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res500No0 = [[i, \"\"] for i in res500No0]\n",
"res500No1 = [[i, \"\"] for i in res500No1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res500No0+res500No1, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\1No+\\\\res500_CHECKED\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del res100Full"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"\u041e\u0431\u0440\u0430\u0431\u043e\u0442\u043a\u0430 \u0438 \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0434\u0430\u043c\u043f\u043e\u0432"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res1000_NOT_CHECKED=pickle.load(file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\2Partial\\\\res1000_NOT_CHECKED\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(res1000_NOT_CHECKED)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 82,
"text": [
"10000"
]
}
],
"prompt_number": 82
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"MC analysis"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import numpy.linalg as linalg"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def process_dump(path, res_real):\n",
" mc_res = pickle.load(file(path))\n",
" xs = np.array([item[0][0].x for item in mc_res if len(item)==2 and linalg.norm(item[0][0].x, ord=np.inf) < 100 ])\n",
" ses = np.array([item[0][0].se for item in mc_res \n",
" if len(item)==2 and linalg.norm(item[0][0].x, ord=np.inf) < 100 \\\n",
" and \"se\" in item[0][0].keys() and not np.isnan(item[0][0].se).any() \\\n",
" #and linalg.norm(item[0][0].se, ord=np.inf) < 100 ]) \n",
" ]) \n",
" \n",
" xs = pd.Dataframe(xs)\n",
" ses = pd.Dataframe(ses)\n",
" \n",
" rmse = ((res_real - xs) ** 2).mean()\n",
" bias = (res_real - xs).mean()\n",
" a_ratio = (ses.mean()/xs.std()).mean()\n",
" m_ratio = (ses.median()/xs.std()).mean()\n",
" \n",
" print \"FILE: %s\"%(path.split(\"\\\\\")[-1])\n",
" print \"BIAS: %2.3f\"%(bias)\n",
" print \"RMSE: %2.3f\"%(rmse)\n",
" print \"A-ratio: %s\"%(a_ratio)\n",
" print \"M-ratio: %s\"%(m_ratio)\n",
" print\n",
" print \"XS len: %s\" % len(xs)\n",
" print \"SE len: %s\" % len(ses)\n",
" print \"XS variance: %s \"% xs.std(axis=0)\n",
" \n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res1000_NOT_CHECKED = pickle.load(file(u\"C:\\\\Users\\\\User\\\\Dropbox\\\\\u041b\u0435\u043a\u0446\u0438\u0438\\\\\u0412\u041a\u0420\\\\My Monte-carlo\\\\MC 31.03-results\\\\2Partial\\\\res100_CHECKED\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path=u\"C:\\\\Users\\\\User\\\\Dropbox\\\\\u041b\u0435\u043a\u0446\u0438\u0438\\\\\u0412\u041a\u0420\\\\My Monte-carlo\\\\MC 31.03-results\\\\2Partial\\\\res100_CHECKED\"\n",
"process_dump(path, res_real)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res100_CHECKED\n",
"BIAS: -0.102\n",
"RMSE: 6.714\n",
"A-ratio: 5.41139855272e+140\n",
"M-ratio: 0.321047317994\n"
]
}
],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"beta, alpha = [0.6, 0.4, 0.8], [0.85, 1.55]\n",
"gammam, mum = [0.4, 0.3, 0.9], [-1.2, 0.07]\n",
"gammap, mup = [0.2, 0.8, 0.3], [1.28, 2.5]\n",
"res_real_full = beta+alpha+gammam+mum+gammap+mup\n",
"beta, alpha = [0.6, 0.4], [0.9, 1.5]\n",
"gammam, mum = [0.3, 0.9], [-0.67, 0.36]\n",
"gammap, mup = [0.2, 0.3], [0.02, 1.28]\n",
"res_real_partial = beta+alpha+gammam+mum+gammap+mup\n",
"beta, alpha = [0.6], [0.95, 1.45]\n",
"gammam, mum = [0.9], [-1.22, 0.03]\n",
"gammap, mup = [0.8], [-0.03, 1.18]\n",
"res_real_no = beta+alpha+gammam+mum+gammap+mup"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"beta, alpha = [0.6, 0.4], [0.9, 1.5]\n",
"gammam, mum = [0.3, 0.9], [-0.67, 0.36]\n",
"gammap, mup = [0.2, 0.3], [0.02, 1.28]\n",
"res_real_partial = beta+alpha+gammam+mum+gammap+mup"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"beta, alpha = [0.6], [0.95, 1.45]\n",
"gammam, mum = [0.9], [-1.22, 0.03]\n",
"gammap, mup = [0.8], [-0.03, 1.18]\n",
"res_real_no = beta+alpha+gammam+mum+gammap+mup"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"150 obs"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path=u\"W:\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\3Full\\\\\"\n",
"process_dump(path + \"res150_CHECKED\", res_real_full)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res150_CHECKED\n",
"BIAS: -0.280\n",
"RMSE: 10.400\n",
"A-ratio: 35935.1136818\n",
"M-ratio: 0.175399313066\n"
]
}
],
"prompt_number": 191
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path=u\"W:\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\3Full\\\\\"\n",
"process_dump(path + \"res150_CHECKED\", res_real_full)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res150_CHECKED\n",
"BIAS: -0.280\n",
"RMSE: 10.400\n",
"A-ratio: 0.251587434582\n",
"M-ratio: 0.175614207518\n",
"\n",
"XS len: 8514\n",
"SE len: 1195\n",
"XS variance: [ 1.1554774 1.10966607 2.53651001 2.08770918 2.25000791 1.54851286\n",
" 1.61313639 3.41722933 3.93851742 4.94660503 1.40904432 2.392834\n",
" 2.85243582 5.24450798 5.32106036] \n"
]
}
],
"prompt_number": 198
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"process_dump(\"W:\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\2Partial\\\\res150_CHECKED\", res_real_partial)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res150_CHECKED\n",
"BIAS: 0.011\n",
"RMSE: 0.978\n",
"A-ratio: 4.34731895038e+13\n",
"M-ratio: 0.521236619654\n",
"\n",
"XS len: 9972\n",
"SE len: 5466\n",
"XS variance: [ 0.30280266 0.24669125 0.7045185 0.5066124 0.37580024 0.6462535\n",
" 0.66332632 1.0424209 0.56516882 0.79194061 2.33731737 0.87763953] \n"
]
}
],
"prompt_number": 200
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"process_dump(\"W:\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\1No+\\\\res150_CHECKED\", res_real_no)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res150_CHECKED\n",
"BIAS: -0.041\n",
"RMSE: 0.304\n",
"A-ratio: 13.4976140604\n",
"M-ratio: 0.644672743941\n",
"\n",
"XS len: 9999\n",
"SE len: 7315\n",
"XS variance: [ 0.20827329 0.54688004 0.41749412 0.46636337 0.48699761 0.63612905\n",
" 0.52249531 0.88121121 0.44566477] \n"
]
}
],
"prompt_number": 201
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"No Overlap Results:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path_no=u\"W:\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\1No+\\\\\"\n",
"process_dump(path_no + \"res100_CHECKED\", res_real_no)\n",
"print\n",
"process_dump(path_no + \"res250_CHECKED\", res_real_no)\n",
"print\n",
"#process_dump(path_no + \"res500_CHECKED\", res_real_no)\n",
"print\n",
"process_dump(path_no + \"res1000_OK\", res_real_no)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res100_CHECKED\n",
"BIAS: -0.083\n",
"RMSE: 1.297\n",
"A-ratio: 5.23583135829e+58\n",
"M-ratio: 0.504675009963\n",
"\n",
"FILE: res250_CHECKED"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"BIAS: -0.029\n",
"RMSE: 0.090\n",
"A-ratio: 1.10624749965\n",
"M-ratio: 0.923506266575\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"FILE: res1000_OK"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"BIAS: -0.008\n",
"RMSE: 0.020\n",
"A-ratio: 0.988147836321\n",
"M-ratio: 0.974229890311\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path_no=u\"W:\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\1No+\\\\\"\n",
"process_dump2(path_no + \"res500_WRONG_FORMAL\", res_real_no)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res500_WRONG_FORMAL\n",
"BIAS: -0.016\n",
"RMSE: 0.042\n",
"A-ratio: 0.983974826146\n",
"M-ratio: 0.955236960605\n"
]
}
],
"prompt_number": 174
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mc_res = pickle.load(file(path_no+\"res500_WRONG_FORMAL\"))\n",
"res_real = res_real_no"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 162
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mc_res[210][0].x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 168,
"text": [
"array([ 0.50534012, 0.46968066, 1.34641935, 1.35828922, -1.11020074,\n",
" 0.9499147 , 0.81643529, -0.25452674, 0.85107151])"
]
}
],
"prompt_number": 168
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in "
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xs = np.array([item[0].x for item in mc_res if type(item) != IndexError and type(item[0])!=int\n",
" and linalg.norm(item[0].x, ord=np.inf) < 100 \\\n",
" and len(item[0].x) == len(res_real)])\n",
"ses = np.array([item[0].se for item in mc_res \n",
" if type(item) != IndexError and type(item[0])!=int \\\n",
" and linalg.norm(item[0].x, ord=np.inf) < 100 \\\n",
" and \"se\" in item[0].keys() and not np.isnan(item[0].se).any()\\\n",
" and len(item[0].x) == len(res_real)]) \n",
"\n",
"rmse = ((res_real - xs) ** 2).mean()\n",
"bias = (res_real - xs).mean()\n",
"a_ratio = (ses.mean(axis=0)/xs.std(axis=0)).mean()\n",
"m_ratio = (np.median(ses, axis=0)/xs.std(axis=0)).mean()\n",
"\n",
"print \"FILE: %s\"%(path.split(\"\\\\\")[-1])\n",
"print \"BIAS: %2.3f\"%(bias)\n",
"print \"RMSE: %2.3f\"%(rmse)\n",
"print \"A-ratio: %s\"%(a_ratio)\n",
"print \"M-ratio: %s\"%(m_ratio)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'path' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-172-08899174e2a7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[0mm_ratio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mses\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"FILE: %s\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\\\\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 16\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"BIAS: %2.3f\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"RMSE: %2.3f\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrmse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'path' is not defined"
]
}
],
"prompt_number": 172
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(xs)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 175,
"text": [
"9996"
]
}
],
"prompt_number": 175
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mc_res = pickle.load(file(path_no+\"res500_CHECKED\"))\n",
"xs = np.array([item[0][0].x for item in mc_res if len(item)==2 and linalg.norm(item[0][0].x, ord=np.inf) < 100 ])\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "KeyError",
"evalue": "0",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-13-b8f78aefb810>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mmc_res\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_no\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"res500_CHECKED\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mxs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmc_res\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m100\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m: 0"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xs=[]\n",
"for i,item in enumerate(mc_res):\n",
" print i\n",
" if len(item)==2 and linalg.norm(item[0][0].x, ord=np.inf) < 100 : \n",
" xs.append(item[0][0].x)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0\n"
]
},
{
"ename": "KeyError",
"evalue": "0",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-15-b2ed36692c69>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmc_res\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m100\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mxs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyError\u001b[0m: 0"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"process_dump(path_no + \"res500_CHECKED\", res_real_no)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'int' object has no attribute 'x'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-38-f5d2c5f3bf91>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprocess_dump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_no\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"res500_CHECKED\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres_real_no\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-7-b7cc45a6ba54>\u001b[0m in \u001b[0;36mprocess_dump\u001b[1;34m(path, res_real)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprocess_dump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres_real\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mmc_res\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mxs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmc_res\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m100\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m ses = np.array([item[0][0].se for item in mc_res \n\u001b[0;32m 5\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m100\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'int' object has no attribute 'x'"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mc_res = pickle.load(file(path_no+\"res100_CHECKED\"))\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res500No0[1][0][0].x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"array([ 0.62671246, 1.02496687, 1.47209305, 0.89534815, -1.21529914,\n",
" 0.00714367, 0.72925652, 0.19039956, 1.15774118])"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mc_res[2][0][0].x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 57,
"text": [
"array([ 0.84679601, 1.37658051, 2.2288862 , 1.12325832, -0.91816145,\n",
" 0.37881604, 1.62014918, -0.16429745, 1.72127041])"
]
}
],
"prompt_number": 57
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Partial overlap"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path_no=u\"W:\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\2Partial\\\\\"\n",
"process_dump(path_no + \"res100_CHECKED\", res_real_partial)\n",
"print\n",
"process_dump(path_no + \"res250_CHECKED\", res_real_partial)\n",
"print\n",
"process_dump(path_no + \"res500_CHECKED\", res_real_partial)\n",
"print\n",
"process_dump(path_no + \"res1000_CHECKED\", res_real_partial)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res100_CHECKED\n",
"BIAS: -0.073\n",
"RMSE: 6.881\n",
"A-ratio: 5.41139855272e+140\n",
"M-ratio: 0.321047317994\n",
"\n",
"FILE: res250_CHECKED"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"BIAS: 0.049\n",
"RMSE: 0.535\n",
"A-ratio: 2.49132448918\n",
"M-ratio: 0.729842391546\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res500_CHECKED\n",
"BIAS: 0.008\n",
"RMSE: 0.154\n",
"A-ratio: 1.39862764424\n",
"M-ratio: 0.833354113928\n",
"\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res1000_CHECKED\n",
"BIAS: -0.003\n",
"RMSE: 0.037\n",
"A-ratio: 1.01862149737\n",
"M-ratio: 0.908802775334\n"
]
}
],
"prompt_number": 58
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Full overlap\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path_no=u\"W:\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\3Full\\\\\"\n",
"process_dump2(path_no + \"res100_CHECKED\", res_real_full)\n",
"print\n",
"process_dump2(path_no + \"res250_CHECKED\", res_real_full)\n",
"print\n",
"process_dump2(path_no + \"res500_CHECKED\", res_real_full)\n",
"print\n",
"process_dump2(path_no + \"res1000_CHECKED\", res_real_full)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res100_CHECKED\n",
"BIAS: -0.334\n",
"RMSE: 29.428\n",
"A-ratio: 4.06928585651e+52\n",
"M-ratio: 0.18334951266\n",
"\n",
"FILE: res250_CHECKED"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"BIAS: -0.105\n",
"RMSE: 2.185\n",
"A-ratio: 8.71899096624e+15\n",
"M-ratio: 0.449445462746\n",
"\n",
"FILE: res500_CHECKED"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"BIAS: -0.046\n",
"RMSE: 0.934\n",
"A-ratio: 6.4193297699\n",
"M-ratio: 0.644375604958\n",
"\n"
]
},
{
"ename": "AttributeError",
"evalue": "'tuple' object has no attribute 'x'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-156-c1b821479a28>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mprocess_dump2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_no\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"res500_CHECKED\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres_real_full\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mprocess_dump2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_no\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"res1000_CHECKED\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres_real_full\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-153-f0fe0cec8e53>\u001b[0m in \u001b[0;36mprocess_dump2\u001b[1;34m(path, res_real)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mprocess_dump2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mres_real\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mmc_res\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mxs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmc_res\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m100\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres_real\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m ses = np.array([item[0].se for item in mc_res \n\u001b[0;32m 5\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m100\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'x'"
]
}
],
"prompt_number": 156
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path_no=u\"W:\\\\CNOP\\\\dumps\\\\MC 31.03-results\\\\3Full\\\\\"\n",
"\n",
"process_dump2(path_no + \"res1000_CHECKED\", res_real_full)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"FILE: res1000_CHECKED\n",
"BIAS: -0.026\n",
"RMSE: 0.676\n",
"A-ratio: 1.92570525581\n",
"M-ratio: 0.677401473318\n"
]
}
],
"prompt_number": 184
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xs = np.array([item[0].x for item in mc_res if type(item) != IndexError and type(item[0])!=int \n",
" and type(item[0])!=tuple\n",
" and linalg.norm(item[0].x, ord=np.inf) < 100 \\\n",
" and len(item[0].x) == len(res_real)])\n",
"ses = np.array([item[0].se for item in mc_res \n",
" if type(item) != IndexError and type(item[0])!=int \\\n",
" and type(item[0])!=tuple\n",
" and linalg.norm(item[0].x, ord=np.inf) < 100 \\\n",
" and \"se\" in item[0].keys() and not np.isnan(item[0].se).any()\\\n",
" and len(item[0].x) == len(res_real)]) \n",
"\n",
"rmse = ((res_real - xs) ** 2).mean()\n",
"bias = (res_real - xs).mean()\n",
"a_ratio = (ses.mean(axis=0)/xs.std(axis=0)).mean()\n",
"m_ratio = (np.median(ses, axis=0)/xs.std(axis=0)).mean()\n",
"\n",
"print \"FILE: %s\"%(path.split(\"\\\\\")[-1])\n",
"print \"BIAS: %2.3f\"%(bias)\n",
"print \"RMSE: %2.3f\"%(rmse)\n",
"print \"A-ratio: %s\"%(a_ratio)\n",
"print \"M-ratio: %s\"%(m_ratio)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'path' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-179-c0972ff224c4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mm_ratio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mses\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"FILE: %s\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\\\\\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"BIAS: %2.3f\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"RMSE: %2.3f\"\u001b[0m\u001b[1;33m%\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrmse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'path' is not defined"
]
}
],
"prompt_number": 179
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del mc_res"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 183
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def process_dump2(path, res_real):\n",
" mc_res = pickle.load(file(path))\n",
" xs = np.array([item[0].x for item in mc_res if type(item) != IndexError and type(item[0])!=int \n",
" and type(item[0])!=tuple\n",
" and linalg.norm(item[0].x, ord=np.inf) < 100 \\\n",
" and len(item[0].x) == len(res_real)])\n",
" ses = np.array([item[0].se for item in mc_res \n",
" if type(item) != IndexError and type(item[0])!=int \\\n",
" and type(item[0])!=tuple\n",
" and linalg.norm(item[0].x, ord=np.inf) < 100 \\\n",
" and \"se\" in item[0].keys() and not np.isnan(item[0].se).any()\\\n",
" and len(item[0].x) == len(res_real)]) \n",
" \n",
" rmse = ((res_real - xs) ** 2).mean()\n",
" bias = (res_real - xs).mean()\n",
" a_ratio = (ses.mean(axis=0)/xs.std(axis=0)).mean()\n",
" m_ratio = (np.median(ses, axis=0)/xs.std(axis=0)).mean()\n",
" \n",
" print \"FILE: %s\"%(path.split(\"\\\\\")[-1])\n",
" print \"BIAS: %2.3f\"%(bias)\n",
" print \"RMSE: %2.3f\"%(rmse)\n",
" print \"A-ratio: %s\"%(a_ratio)\n",
" print \"M-ratio: %s\"%(m_ratio)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mc_res = pickle.load(file(path_no + \"res100_CHECKED\"))\n",
"xs = np.array([item[0].x for item in mc_res if type(item) != IndexError and linalg.norm(item[0].x, ord=np.inf) < 100 ])\n",
"ses = np.array([item[0].se for item in mc_res \n",
" if type(item) != IndexError and linalg.norm(item[0].x, ord=np.inf) < 100 \\\n",
" and \"se\" in item[0].keys() and not np.isnan(item[0].se).any()]) \n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 98
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xs=[]\n",
"for i, item in enumerate(mc_res):\n",
" print i\n",
" if type(item) != IndexError and linalg.norm(item[0].x, ord=np.inf) < 100:\n",
" xs.append(item[0].x)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xs = [item[0].x for item in mc_res if type(item) != IndexError and linalg.norm(item[0].x, ord=np.inf) < 100 and len(item[0].x) = len(res_resl)]\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 128
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.asarray(xs[0:131])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(xs[130])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 147,
"text": [
"13"
]
}
],
"prompt_number": 147
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arrs = [np.array([1,2,3]), np.array([4,5,6]), np.array([7,8,9])]\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 126
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arrs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 127,
"text": [
"[array([1, 2, 3]), array([4, 5, 6]), array([7, 8, 9])]"
]
}
],
"prompt_number": 127
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Autocorrelation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def process_dump(obj, res_real, cutpoints = (.2, .8)):\n",
" if type(obj) is str:\n",
" mc_res = pickle.load(file(obj))\n",
" else:\n",
" mc_res = obj\n",
" xs = np.array([item[0].x for item in mc_res if type(item)==tuple and item[0].success\n",
" ])\n",
" ses = np.array([np.array(item[0].se)[0] for item in mc_res \n",
" if type(item)==tuple and item[0].success\n",
" #and linalg.norm(item[0][0].x, ord=np.inf) < 100 \n",
" and \"se\" in item[0].keys() #and not np.isnan(item[0][0].se).any() \\\n",
" #and linalg.norm(item[0][0].se, ord=np.inf) < 100\n",
" ])\n",
" ses = np.nan_to_num(ses)\n",
" \n",
" \n",
" xs = pd.DataFrame(xs)\n",
" ses = pd.DataFrame(ses)\n",
"\n",
" \n",
" rmse = ((res_real - xs) ** 2).mean().mean()\n",
" bias = (res_real - xs).mean().mean()\n",
" #a_ratio = (ses.mean()/xs.std()).mean()\n",
" #m_ratio = (ses.median()/xs.std()).mean()\n",
" a_ratio = (ses[(ses<ses.quantile(cutpoints[1]))&(ses>ses.quantile(cutpoints[0]))].mean() \\\n",
" / xs[(xs<xs.quantile(cutpoints[1]))&(xs>xs.quantile(cutpoints[0]))].std()).mean()\n",
" m_ratio = (ses[(ses<ses.quantile(cutpoints[1]))&(ses>ses.quantile(cutpoints[0]))].median() \\\n",
" / xs[(xs<xs.quantile(cutpoints[1]))&(xs>xs.quantile(cutpoints[0]))].std()).mean()\n",
"\n",
" if type(obj) is str:\n",
" print \"FILE: %s\"%(obj.split(\"\\\\\")[-1])\n",
" print \"BIAS: %2.3f\"%(bias)\n",
" print \"RMSE: %2.3f\"%(rmse)\n",
" print \"A-ratio: %2.3f\"%(a_ratio)\n",
" print \"M-ratio: %2.3f\"%(m_ratio)\n",
" print\n",
" print \"XS len: %s\" % len(xs)\n",
" print \"SE len: %s\" % len(ses)\n",
" #print \"SE mean: %s \"% ses.mean()\n",
" #print \"XS variance: %s \"% xs.std()\n",
" \n",
" return xs, ses\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Full"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150412_132614\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 17 cores, 10000 replications of \"autocorrelated_errors\" with rho = 0.30 observations\n",
"With 744 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150412_141301\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 17 cores, 9300 replications of \"autocorrelated_errors\" with rho = 0.30 observations\n",
"With 307 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150412_143656\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 17 cores, 9000 replications of \"autocorrelated_errors\" with rho = 0.30 observations\n",
"With 10046 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res03full = pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150412_143656\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"10045/10045 tasks finished after 332 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res03full, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 16.04 -autocorrelation results\\\\resFull03\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xs, ses = process_dump(res03full, res_real_partial, cutpoints=(0.001,0.9))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"BIAS: 0.035\n",
"RMSE: 0.177\n",
"A-ratio: 1.004\n",
"M-ratio: 1.001\n",
"\n",
"XS len: 10037\n",
"SE len: 10037\n"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del xs, ses, res03full"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"get_folder_info(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150413_005610\\\\\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"INFO FROM FILE: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Doing MC on 13 cores, 10000 replications of \"autocorrelated_errors\" with rho = 0.60 observations\n",
"With 9999 files"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res06full = pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150413_005610\\\\\", chunksize=30)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 334/334 tasks finished after 292 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res06full, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 16.04 -autocorrelation results\\\\resFull06\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"process_dump(res06full, res_real_partial, cutpoints=(0.001,0.9));"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"BIAS: 0.140\n",
"RMSE: 0.311\n",
"A-ratio: 0.992\n",
"M-ratio: 0.980\n",
"\n",
"XS len: 9989\n",
"SE len: 9988\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del res06full"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res09full = pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150413_112135\\\\\", chunksize=30)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 334/334 tasks finished after 329 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res09full, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 16.04 -autocorrelation results\\\\resFull09\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"process_dump(res09full, res_real_partial, cutpoints=(0.001,0.9));"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"BIAS: 0.362\n",
"RMSE: 1.118\n",
"A-ratio: 1.006\n",
"M-ratio: 0.899\n",
"\n",
"XS len: 10013\n",
"SE len: 9988\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del res09full"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res09xs = pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150415_000032\\\\\", chunksize=30)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 334/334 tasks finished after 307 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res09xs, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 16.04 -autocorrelation results\\\\resXs09\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"process_dump(res09xs, res_real_partial, cutpoints=(0.001,0.9));"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"BIAS: 0.197\n",
"RMSE: 0.352\n",
"A-ratio: 1.069\n",
"M-ratio: 1.015\n",
"\n",
"XS len: 9993\n",
"SE len: 9993\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del res09xs"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res06xs = pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150414_085358\\\\\", chunksize=30)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 334/334 tasks finished after 245 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res06xs, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 16.04 -autocorrelation results\\\\resXs06\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"process_dump(res06xs, res_real_partial, cutpoints=(0.001,0.9));"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"BIAS: 0.078\n",
"RMSE: 0.210\n",
"A-ratio: 1.010\n",
"M-ratio: 1.009\n",
"\n",
"XS len: 9990\n",
"SE len: 9990\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del res06xs"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"res03xs = pickle_folder(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\temp\\\\20150413_234307\\\\\", chunksize=30)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 284/284 tasks finished after 275 s"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"done\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pickle.dump(res03xs, file(\"\\\\\\\\DAP-NAS\\\\work\\\\CNOP\\\\dumps\\\\MC 16.04 -autocorrelation results\\\\resXs03\", \"w\"))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"process_dump(res03xs, res_real_partial, cutpoints=(0.001,0.9));"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"BIAS: 0.024\n",
"RMSE: 0.168\n",
"A-ratio: 1.007\n",
"M-ratio: 1.016\n",
"\n",
"XS len: 8498\n",
"SE len: 8498\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(res03full[45]) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"tuple"
]
}
],
"prompt_number": 35
}
],
"metadata": {}
}
]
} | gpl-2.0 |
scitran/client | datashare/descoteaux-run_mrq-download_anatomy.ipynb | 1 | 17487 | {
"cells": [
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello Michael Perry!\n"
]
}
],
"source": [
"import os\n",
"import re\n",
"import flywheel\n",
"from pprint import pprint as pp\n",
"\n",
"# Assumes you are logged in via the CLI\n",
"fw = flywheel.Client()\n",
"print('Hello %s %s!' % (fw.get_current_user().firstname, fw.get_current_user().lastname))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9465\n",
"5cca1d9a7bb46c00364a80cf\n",
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"5cca1d9e7bb46c00364a810f\n"
]
}
],
"source": [
"# Run MRQ gear on each session in the Descoteaux collection\n",
"collection = fw.collections.find('label=Descoteaux')[0]\n",
"sessions = collection.sessions()\n",
"\n",
"# Get the gear we'll be running\n",
"gear = fw.lookup('gears/mrq')\n",
"\n",
"# Modify the default configuration to account for the (non-standard) acquisition labels...\n",
"config = gear.get_default_config()\n",
"config['ir_regex'] = '[0-9].*_[0-9].*_IR.*'\n",
"config['spgr_regex'] = '[0-9].*_[0-9].*_SPGR.*'\n",
"config['session_split'] = ''\n",
"\n",
"# Set an analysis label\n",
"ANALYSIS_LABEL = 'MRQ - %s' % (gear.gear.version)\n",
"\n",
"# Find the session, which will be the destination\n",
"for s in sessions:\n",
" print(s.label)\n",
" # Run the gear/job, which returns the analysis ID\n",
" analysis_id = gear.run(analysis_label=ANALYSIS_LABEL, config=config, destination=s)\n",
" print(analysis_id)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9465\n",
"s163_9465-T1.nii.gz\n",
"/scratch/purge/descoteaux/9465-T1.nii.gz\n",
"9464\n",
"s162_9464-T1.nii.gz\n",
"/scratch/purge/descoteaux/9464-T1.nii.gz\n",
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"/scratch/purge/descoteaux/8893-T1.nii.gz\n",
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"/scratch/purge/descoteaux/8892-T1.nii.gz\n",
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"/scratch/purge/descoteaux/5569-T1.nii.gz\n",
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"/scratch/purge/descoteaux/5477-T1.nii.gz\n",
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"/scratch/purge/descoteaux/5456-T1.nii.gz\n",
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"/scratch/purge/descoteaux/5441-T1.nii.gz\n",
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"/scratch/purge/descoteaux/5362-T1.nii.gz\n",
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"/scratch/purge/descoteaux/5312-T1.nii.gz\n",
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"/scratch/purge/descoteaux/5245-T1.nii.gz\n",
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"/scratch/purge/descoteaux/4275-T1.nii.gz\n"
]
}
],
"source": [
"# Once the MRQ runs are done (verified in FW)\n",
"\n",
"# Download the quantitative T1s\n",
"collection = fw.collections.find('label=Descoteaux')[0]\n",
"\n",
"# Here we have to get the session in order to get the analyses\n",
"sessions = [ fw.get_session(x.id) for x in collection.sessions() ]\n",
"\n",
"download_dir = '/scratch/purge/descoteaux'\n",
"if not os.path.isdir(download_dir):\n",
" os.mkdir(download_dir)\n",
" \n",
"# Regex to match for the t1 file\n",
"rex = re.compile('.*_.*-T1.nii.gz')\n",
"\n",
"# For each session, find the analysis, and download the T1q data\n",
"for s in sessions:\n",
" print(s.label)\n",
" analysis = [ x for x in s.analyses if x.label.startswith('MRQ') ]\n",
" if analysis:\n",
" analysis = analysis[0]\n",
" t1_file = [ x for x in analysis.files if rex.match(x.name) ]\n",
" if t1_file:\n",
" t1_file = t1_file[0]\n",
" print(t1_file.name)\n",
" dl_file = os.path.join(download_dir, t1_file.name.split('_')[1:][0])\n",
" print(dl_file)\n",
" t1_file.download(dl_file)\n",
" else:\n",
" print('%s has no matching analysis!' % s.label)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9465\n",
"s163_9465-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
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"s122_4775-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4655\n",
"s121_4655-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4570\n",
"s115_4570-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4534\n",
"s114_4534-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4533\n",
"s113_4533-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4459\n",
"s112_4459-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4456\n",
"s111_4456-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4451\n",
"s110_4451-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4400\n",
"s109_4400-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4399\n",
"s108_4399-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4377\n",
"s107_4377-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4358\n",
"s106_4358-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4349\n",
"s105_4349-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4322\n",
"4319\n",
"s103_4319-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4303\n",
"s102_4303-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n",
"4275\n",
"s101_4275-mrQ_Output/OutPutFiles_1/SyntheticT1w/T1w1.nii.gz\n"
]
}
],
"source": [
"## Grab the T1w images from the zip file.\n",
"\n",
"# This I decided to do after the fact, when I saw (https://groups.google.com/forum/#!topic/mrq-forum/bT5Msk119zE): \n",
"\n",
"import re\n",
"\n",
"# Download the quantitative T1s and zip\n",
"collection = fw.collections.find('label=Descoteaux')[0]\n",
"\n",
"# Have to get the session in order to get the analyses.\n",
"sessions = [ fw.get_session(x.id) for x in collection.sessions() ]\n",
"\n",
"download_dir = '/scratch/purge/descoteaux'\n",
"if not os.path.isdir(download_dir):\n",
" os.mkdir(download_dir)\n",
" \n",
"# Regex to match for the t1 and zip files\n",
"rex = re.compile('.*T1w1.nii.gz')\n",
"ziprex = re.compile('.*-mrQ_Output.zip')\n",
"\n",
"# For each session, find the analysis, output zip, and download the t1w data\n",
"for s in sessions:\n",
" print(s.label)\n",
" analysis = [ x for x in s.analyses if x.label.startswith('MRQ') ]\n",
" if analysis:\n",
" analysis = analysis[0]\n",
" zip_file = [ x for x in analysis.files if ziprex.match(x.name) ]\n",
" if zip_file: \n",
" zip_file = zip_file[0]\n",
" zip_info = analysis.get_file_zip_info(zip_file.name)\n",
" entry_name = [ x for x in zip_info.members if rex.match(x.path) ][0]\n",
" print(entry_name.path)\n",
" analysis.download_file_zip_member(zip_file.name, entry_name.path, os.path.join(download_dir, s.label + '-T1w.nii.gz'))\n",
" else:\n",
" print('%s has no matching analysis!' % s.label)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NOTES\n",
"\n",
"* 8504 & 4322 did not run through. I shared the SPGR coil-combined images for FA10, acpc-aligned."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "2.7.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
maxhutch/thesis-notebooks | Stokes.ipynb | 1 | 176736 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stokes solver for asymptotic flow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inport away."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib\n",
"matplotlib.rcParams['figure.figsize'] = (10.0, 16.0)\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy as sp\n",
"from scipy import fftpack\n",
"from numpy import fft\n",
"import json\n",
"from functools import partial\n",
"class Foo: pass\n",
"from chest import Chest\n",
"from slict import CachedSlict\n",
"from glopen import glopen, glopen_many"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load a frame from a real simulation."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"name = \"HighAspect/HA_viscosity_4.0E-4/HA_viscosity_4.0E-4\"\n",
"arch = \"alcf#dtn_mira/projects/alpha-nek/experiments\"\n",
"c = Chest(path=\"{:s}-results\".format(name), \n",
" open=partial(glopen, endpoint=arch),\n",
" open_many = partial(glopen_many, endpoint=arch))\n",
"sc = CachedSlict(c)\n",
"c.prefetch(sc[:,'t_xy'].full_keys())\n",
"with glopen(\n",
" \"{:s}.json\".format(name), mode='r',\n",
" endpoint = arch,\n",
" ) as f:\n",
" p = json.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the governing properties from the frame."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"L = 1./p[\"kmin\"]\n",
"Atwood = p[\"atwood\"]\n",
"g = p[\"g\"]\n",
"viscosity = p[\"viscosity\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the last midplane slice of the scalar field and manipulate it into a periodic box."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"T_end = sc[:,'t_xy'].keys()[-1]\n",
"phi_raw = sc[T_end, 't_xy']\n",
"phi_raw = np.concatenate((phi_raw, np.flipud(phi_raw)), axis=0)\n",
"phi_raw = np.concatenate((phi_raw, np.flipud(phi_raw)), axis=0)\n",
"phi_raw = np.concatenate((phi_raw, np.fliplr(phi_raw)), axis=1)\n",
"phi_raw = np.concatenate((phi_raw, np.fliplr(phi_raw)), axis=1)\n",
"raw_shape = phi_raw.shape\n",
"nx = raw_shape[0]\n",
"ny = raw_shape[0]\n",
"phi = phi_raw[nx/8:5*nx/8, ny/8:5*ny/8]\n",
"nx = phi.shape[0]\n",
"ny = phi.shape[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure it looks OK."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/maxhutch/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
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7vV6fla2CSVZa5tu33S58voGn8BL+QOcYrfN6CqxQxeAyWs12O41JZxdIne2lec5WQVYv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\nTNcHH1rWD9GezxPgbtOMppDhZO+Ja7S4D9DY2cfd7p6z2EdO9CfllXr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rnRyyczZpSehi\n+IS2u8ntWkN38RZp69q2N8jgz2Ob/0ehQlTlAChUmH8ereoGsDWIrCcpRtWAOAWeXJzh6nxdzg5/\nSKZ+pdMSmfyOnWCDk8vrvCKXI81eVr6LB+b2FeBzF7lHyLZLXVnaZUmobeGTmFYFa+eJPedIAtvl\ntqfOtbbIH4xPHYPJGkpxXBhVm8rjVDOSfB4m62EA6hWvFrQNZV/fH/R5i3xzFblxQ3a7OMZ2dUlD\nKEkqA8iyFd9aFesrD23b5PY2hpQfe0vddMbKsvWIPmPPaYOygVwbzxbf5+JCG+Dk8hona33SuA1O\n8okZ3aDqR8p37Z9+DpVRdLTqU9bLB6VrMIl5geRquyy9RXC+h3qJ3L/S5i1y1/umTqiRORuk4aOp\n7ahVWHyeQe5NRGUQPULuMXqEekD2JXB9eTKJQUSGMalRFCOfVD52A2itWDdoPECzy7pXwG2baNWz\npG/7IrS/lJq8lrypvuNtNb1Cd8kDqBkRSZIbR9uk8CClQJwUMSk2UW4OyxEVqbNfivxhq9ULXYKN\n5XKgq8vrJSqOszFhQDePkMx7XydNH7/FmG3TUDWtXce3PkZuBG8TIL4UeWy9p3KAwRrlPY7TSmeS\nQnmF16PavxEsXYsZylBX3I1Tn3NNeR57XtNMkkVqT2os9qzXDCxfUnzLmtfHXa/egr4eoRujwuJ8\nIq81zcgh/Elc6Y3MnlnkUvnqjlR82wfmZfXbPiyTpJJllyq5q4x8VasPed62Knrr7OdKrlf13BLs\nG18Kr5E0dOSfcn+nzjppNElDqS090sjxjf6z+e0aXEkzNkiLC7KPY/eS2qO4azU8VhXctQxpZScR\nv+Uj127bIr8fSSwMI9frZjVUdK21vrvNvrpDVuqugQSgekiWKRHLMvVHr74WxMNSJilBFWvk06LU\nnmYchdAMo8RZdr8bQfIWX7C8WxpvrArFNuPXju1a6/ruNjILJjWK8kDrq/qrO+y3nYOo6FbbvlJ1\nn3xuUznvZVgn0JSvXNeHsapI2Y6059VuurbelaSVqe1aA1B1IxXdaEmoa801fCCWtS43OL/dOMHQ\nPD62cmgZsr9LF1mffA+Vh1CVu0s5GHqsVhWX2G7UIo9XQBWAfYH6bNfFoIST4sXJEvtS2Ma7zLS5\niWpeIdl1dmPVp6QzFscoXWtA0wjxdaG5291lN5nasv2tGUYNdukiu5EqLCjyOlk1tROjNkLzCtME\nWjO4uz+TGUXVC0OTuiFkZ6r+HPIYiZdRxhBZL4FbJWvtGJchTteu9G1nutWq25616yG2rZx1Ntqh\n1taxMUjCGNICtWuGksXGFcmLwvPbRauMgcYQeWkAtXmCgOb90PK3T8RCl+0ahyw7sir2vthhU+Xx\nMwBWNpboWdTjGYpGhp3V2nah2TehJ4jqlbmMIbIfbJGL0eYA1edRn1gvj3PP48QgyctpdK2hO98C\naQC1eYLkOt8x7nHuMVoCj0KFqPLzmTz4+g6aMUWFYcRZrZfDxJ6iJJ9x132dh62ki99PL6th9gn0\ndmqfdocP95iWtw21XsvnrB+C2yaVzl1LraouvAhPCwfBygnUloTmUwIc40khc/5g2/w/0gvUJSrB\nrrP7hB67bY/kUF7sWu0OKT/aMfJZaatmrXyvUmB1Caxc3QC5gVusXxev+5AkiHCVrpszVF8452kY\nQFRfB/WhMpJkWqQXSZ7P3UdeumvXS+b8bjNWfd4eqrCnCquPq524+n2VrpFENIqWwOQxRRHSalZq\n2X1WdKltN/mwe7dalg59SUhSfeXWdX+f7HxRD0C93dnmOA45/N1ryP3lvXmKKli3NJTsxsLrUFbh\nygVjT0lpm/BQ6wKzy76887U329Zr27R9tGuGGFpNtx2nlZtQ9Ivr2EuQG7u3N7lxVGrIev0KXXnf\nfZZEevdZOSmj9AxRfc31vdWHpqFk06Gtl9tD9DFCqMI6o6gQwO1iLiOo3WdpEvnfCrBH6J3qz6RG\nke1CK/u9ZaB1UaFvk3wmFNnL3bU63qfDVbtOqE3ia7+4jnq5v0+SXf+XVqX7OgxWwlBSZ20WXXLl\nuQIB35rzXabB3RZyxrvrtW1t1wrt42Pf5SfUptXKlFZWngC4mxSeQKkdMfpFdp1ZUsR5RZ1U+9UC\nrkujiOqr2Jv6xLfPcOgSVORCFfY//2AVVkaR1ZDQYJpESNeT+yBIBybPpdKtnygfVJ6INmfuHOhS\nPct9u4yZ0dq4vu1aT3iXNnGXqt5nLPmOC1WdXbbtUhXv0h7ts98hsHlp26NyvaX01nk05HadVcdF\nwePqXQhU38TqC2ynCvdLJxVWX+pzjB6bpTC5UQSgWRk734mzK9AumSkl1ad61ghVvdp2eS3Xse+r\nnn1tH+2+uSGlLn2c5X2r25CTvu3YvkxdDWt57W6XY5wAj/1iv2sGjgef9mo7uMtUn36tKdRnoQrH\nYScV1n921SCZFZMZRfYFlXGa1ucl2ojvYpSS67QPydW3bgraqufQdp+j3e7vOnH7/ueubWN33670\ndbK35Wnbdt91Qvt33X5ItDxwfQ9u/m8T1Cdw3CCfwLHQVZymiKLmC2HT0MSN5ZWpvjpLUJ+FKhzG\nIBXmi97us3iS+J6EMUW9mdxTFCVOTJEwjLJi1Jk0tn1tHDjb54QWPeDbLvfRqk65f5fByL62rG+f\nMRs1XarNLhEIfffdpV09B0KPx5XYbnXxtJjYtJwE1U7qWOwQJaka5NmIKZKGETLkwb9U3zLV557f\nhSoM01uFADKUk6HWZrQutEYWwcRD8lNEyXU5yVwZ4FlUztrrHBJU61xJ7SKxMaqjLjfTVwX79vEN\nLA4527tW1e71Q8cNCfP0XbPL8XNohx6qTGi4VbL9reXdFrlWVrKVukapqSi5RrRuTt54LUefOdqr\nK628Cqg+e605qK/Lsb5rdzmeKuypwjzY2mpIfK4TzlO0FCb3FAGoWrVp/WOHdrsOfJeppNb1nKGb\n3FZNd2nHyusMceQPeZz0vcaQ8w49rsv2rtfZFe1x2pW2Dh55vm1SjCCUH6uptgRqn5rxQ/XVty9J\nffJaQ85PFeZ0UiHKF8U29DQNfN9afya/Y5EMVVDCFg5Zfe4LVzohdo2EsPic9Nqxh74fQ6vgtmO7\nbO9zrX3Q9THY95yNH0JPke+PuqPPvCcbE6rPf9whoQrHP6ezWNMVvURLYXKjCEBdF6JAyeH4llCb\nte+lDk2o2nTx9Wh33R6SfZdOhDHpWkUeoiqeMv8tu1TJNv2uzyLxGTZtf9ijPf1kVF+37XNSn++6\nPqjCbscCigqHaZDMisljioYy5xC9LnSRZJeqGYF9uj4G2kJRhzBWFdz1XEupinehrTyE0F4Iu1tK\nlsyxq087dwiqsDvDVciYomUwuacotnW1GC0j3Y5jjneZmxy7OPb7hob69unaq77vx90hq98+1zw0\nfdqpXcbBlCvkp9BW7Gt7JLF+XO3KcFcq27swt1y4ieqzUIU5e1GhrqdkmkctDbH+TG4UAWgWIOeF\nopahVcYYcux77T5tCV/YZpd0dKmmtX3nUEX1uadjtnnHuI5ll7Z9n2gXl8a4GEc7nYM8G5W3vIJ7\nxSFQfc1956A+C1WYM4oK66fsqkEyK+ZhFElS5xuHH+w7Rnut7RxtMvalOxTC2Xb+fVc1h7hvlqH1\nzNhtcd/A7D7sEt1Qu76ind4JCV9hjBO2MFf1aTk0J/UNvc6u55gqj9vON5kKq9ORRTK5UWSsm1+4\n+22BsvM6DqXvsYeMlBgabjmGI77rtfZ1Pw45iHuK6Je2aBONXcM+E0c7UlPGd/MSE/AU7drEPTb1\n7eJR6nuttnOMAVXYZGcVVotyVYJcaxPA7rP+TG4UNZjIwp46dHRoW6dPVR26Voi2dIx975ZU9YbY\nJTB6J4bewMlat1Pn3JLVN/S8bVCFO0FP0WKZePQZS06IoXLepb3jS8ehOJaqeG64WuM7kdq4ieqz\nUIX7gJpbBvPzFE3AnKU8dMBu1262qdnFLJ5zvlkm8xYthjnn4rGrz0IVEmKZTK2tcxTtOPJsrEGn\nu17Dx9CxDpJdx9jskpY+jOUPHKP6PXSeAd2jG/r6GLbu/i1/zmquf5wB1bdc9XVJQx+owuZ552v0\nMqaoP/PIzR4akbvuIs9DhhkOOdeub0Qaq8o+NFM+Kvueu694dmmvymN7VdtdhuR3huqrOEb1WajC\n9mN7qHCOWUy8zMMoAjwjYObBFMnZtW3Zp1qby2s+ujKX4rHLDCd7wx350vWYmWqP6hsLqvBgDNEg\nmQ3Tl6QJCs3SJ6Mfo50rmVtUwJzvvYZM764vj9gL3iH5B01FAdVXZ27qs8w5DzRmrsKJbieDu/sz\nvVHUkxj7L19Tj73YVa5dJ36cmn3l45T5MPbYI43p8pHqa2cp6rNQhcOYa36SXVl0zjpvnJmUMdMx\ndMxLG1OPidnn43Qf5WBOY1bmko4Kqq8fU6vPQhUOZy7pIPtkeqOoYwrmVAW77DNdu4x5aWPI1HO7\nnHtMDlUW5lQlu3ROly9TO2c21ZezFPWFrjEmVGHndE3/pCUdmWdWTZiqrtXIVI+IPm9bGsLcIgnm\n8Cju6zs4hPPey64XnrRGoPrmCVXYixk9VdM5JWYhzOeOxThIanaV9xyqB42u6ZpLe2uu97GNIW9U\n0s6x93zoo6cDaY/qm4v6LHO9n20sRIUH0xUZk3lkmZaKCVLW1k6benYVYPfbstRq0GXq+9pWpU7S\nTh2io3nUAKD6lsjU93eWKmwygySQ7kyWXUubabNrdTaXsTlL1eFUHQhDwmDnHOmgYTXXOpv87KD6\nDgtVeCws7Tk7B+an3rj5c4X59rZb5pa+PulZyms+DskMp4RTWUFJ49BEqyei+vozJ/X15WlJAAAg\nAElEQVRZ5naPurBgFc49ycTLpFnXFgQWR8AcGrXH5vR2Gdr+XWI1OzZzaafGLQ1CV2txq7DmUqtT\nfTm+/KAKZ6TClq0zeJiRVuZS83VinwODd6labkq1dFP+p2SXqIR9RjQc/hFA9U3LTfmfGlQhORzz\nMYpioOz+nE+q9k7fxwxlmMP75sFqJ0J3HclRMjdIeyxFu8L7pyK1NLGeGFPUn3lUgVq+teSlbbfu\ne1q50Ln7tN3GSuOu55lLtXToTpFdZphpi2zYt/N+5Xx7GaCjYTUA1TeMuajPQhV2p7MKm8zjKUs6\nMnl2ZTFg5ApRia9ilDFF9q1Ltkgewpnsk/AU1fFYzC09U9NnxpNQtXyoqAYb+uymYyV/OIZQ5p3R\nOkNNfbX95L+h+sZhbumZC0ejwvoutUOy/SaJjMbkRlGJdDUK17+N6w9VJ1p7dddXV+5SfbHqWx5j\nvPFq1ypZE2Pb+WT13OiC3rn7jOojh+QoVDir7rOE3We9mYdRFHuWZ8g+ppjre40uzPw2epnDf1/I\nlHB++uhpQdqj+g7FHO7BwlW4KF0RyaKyK9T+nMMrK4defx+dEaFzTp3ph55ib8j/ncMg39D1D5+H\nVN8455xafRaqsBvzUiHZP/PM1T2mag7Vt2SKgbZ9r9mWHXMfLDy3NuVeq/ld/+hebxTVN776hp73\n0NwgFc7pb5LeTJ59SVQUT3cIcQzEMYCN/1i3ip1blRti7lWYZElp9bGUuXEtbpXdGtXgxi+I+KLE\nF1YQF8HT2rFJ252i+g7DktLaxtGrsKmjMr5omnxsmyCZNLk15cXLORQC/a82hE2L9T80oWLd5XGQ\niA+Zhj55MNaA8LGxGrC6CL7mo1h25yuJ5LBO7bhyBdVHxuYGqFDR1fLeOXgzmdQoquGOgImcYcYC\n9UEgtslTzgFWxfNkTvniGdjb2Me3bWU9Q0NGvrjai0OpoPrImMwpf3ZWoUdLZElMnmVpDKxsIzYS\nnwK3jeq2G+bgtN/X9af+X0tjH1ECcw31lKZJbburoSjXmEqcAnFxdAylEqf6SF+owoaWYuRamwDO\naN2feXmKnN+x0ru262kPzZA20Baskocw9L5N3U4do3zH2ms9+niKGr/ViYt6QvXdPG60CodrkMyG\n+WVZS4pWaJfPHNqvfVlaeufMGFPATUWX9LbuM1TVrcdRfaQrN1yF83uyko7Mx1MEqI1TreixvJGb\nilb2V9oOfUWiHkf1EdKkRYVjOFnJZEyebWl8C4ivmy+ujAGjBPTbdqp8C1Nb29Sddm4fLybQ2HVs\nBdmdrm9V2scsKq0GTIdryqieUBiz0YI6iy61NNbbPrfiFNfacTHgvJEQVB8Zzo1Roa6lONfaFDCm\nqD/zGJJvUYI946g+KNhH2z6TW38KrJIPxxzvdahM9inzsZSRGjAdGJIfPM4dkj80tVQfAeZ5z0dT\nYf2UigY5JH8ZTF5bpXGUe4rkkGL77aROFk85DZjWJjxUZMPQd3nvI21ThyqOzdiFc+jblA419iU0\nCFjuow4KtnpRNJTGemsxkp4i9xNMHdXX/5pLhSrM6aRCXUtxrjWyDCY3igDUK2JneP4qzkczutXv\nkEHC+3DSa+zzGsda9WpM8QapQ72MoK19qq3zeOarOYqkduQObQlxYyDKY1ZiB6rvZqnPQhXW13lV\nWD9l7GyeiITdZ72ZT6B1YOI5WTzHCv2cajzEkAGrnIu3ydB7MuWA6yFlri0iohFkLTXUtT50j/MG\nW1N9REIVNpaDWiJLYF4xRUC9EMXI3+mEuo1+k8oZq+J2btI9khoowz/dylcRSGtMkXqcXUH1kTZu\n0r1SVajvImBM0TKYzCiylXQSRerIszFY2uwY5OYyWll1tRMVGkOlOVs5R3Gi9waMAtVHlsZIZVbR\nVDTRC2FJf+bT7HODPHumTEYsjBHmOfQcu762suu5SJ2+UQihsM2hEQ1jVKk7vT3Mddt3PcGO2qP6\nSA5VOFiDeyKdOgELJOgpMsacGmN+2RjzEWPM88aYv1Gsf84Y8wFjzMeMMT9vjLknjvk+Y8xvG2N+\nyxjzdW0JSBHrlXIRY7SK67H+9luL/2/r+fXt02XboWGV3J853bM+5awtUsdX7st1VjtaXF7srxhj\nO4JBi4OI7ZWpPtKHOd27g6owqKWYo88WQ9AoyrLsEsDXZFn2JQC+GMDXGGO+GsD3AvhAlmVvBvDB\n4jeMMW8B8G0A3gLg6wH8mDGmvYvOFiL5Msuiko+LT2AQZO+quW39PujTTp1TtbI0+ty7Q4Z69imD\nblWsHVNWyYU+akPx5Ui0NltDM4bKit0uUH2kDzdOhflH1RDaNUhmRWt2ZVn2pFg8QV7V/hGAdwB4\nW7H+XQAeIjeMvgXAu7Ms2wJ4wRjzcQBfCeCXtHMniKoAUK1AxflMvasYeCoMbVnefE77tt9t69u2\n7QNWx+MgZ9E5FKFHfNeqOPRbDo6v7ROL2aw9lXKKqDE0N0JSzZ3iPdYUV34qjqT6SBdulApRzmbt\n0WAU5yo8NJzRuj+tJbbw9PxrAH8SwI9nWfabxphXZVn2UrHLSwBeVSy/FnUD6EUAr2tNhWzpau5H\n1IumrCpjjBvNMBX7lstc78s+/QWHmu1kH4T8L9KJX67QPD7SY+SjU+uW6tuNud8XqlCnlwpbvK5k\nKXTxFF0D+BJjzLMA3m+M+Rpne2aMyUKnGJQiUZnHMYBNfbcV/FVZHNh2U5h7NWzRpv+7yYQEqTr5\n5QFdu8zaLh5rK92UUH1+lqI+C1VYp7cKm4s0hBZL56zLsuxlY8zPAvhyAC8ZY16dZdmnjDGvAfDp\nYrdPAHiDOOz1xboG7/+BX8WH8Bk8xCW++fXAgx6Ve6hK7oJbbS+5jaux5P9yqMn8D0WfUOMh52uc\nWHsPWgh3n9ZKneoLcwz/hSrsdz7ntPL0v/sQ+LcPcfkLF/h38Sd3vC45BMHSYYy5DyDJsuyRMeY2\ngLcDeCeA9wH4dgA/XHy/tzjkfQD+sTHmbyHvNnsTgF/Rzv0f/8B/iDfj3+JL8Tm87iMb4CPQA0Tt\nqz4iwM595VbJvrZp18iGEPIY7TpjVOljtauPoToGur9TuwtjOO+HhGH2PafvHD4/jQ2yLl/xIXd2\nus18cQW19zEFu87kCqpP51jUZ6EK9evIfZUBCG4X2uc9AL7iAc4ffAJvjD6MF9757gHpHA5jivrT\nVlJfA+BdRVzRLQA/lWXZB40xHwbwHmPMdwB4AcC3AkCWZc8bY94D4HnkSvjOLMu83WflUGFfgNq6\nSuEqLg/CFvU63P5OsHs1uTTn/7FVxRL3kboUxqr+Y/FbyqJmENkd1/DrCM1h+TFagqxPtRQBVJ/k\nmNVnoQqr3w0VonZPThHUYMwZrRdBsORkWfbrAL5MWf9ZAF/rOeYHAfxg1wQkiJqVulO44qgeO7FK\n8/EwriNfVqljVdNz5Rj/U4gx261zw61+3WW5jzSIYutBdY0Zp1HheylkFKXNY9yKPXH71+xoNKrv\n5kEVVt/SMIr9GiyWo4gG0VLY1ZzemRRRNfJsDeAS9blWomJYfgokog62VbJsr8ptfR33x1p9k+no\n8ujoMiA49m2Li+H4kfKxjYwo0H2GpF55X6LZwk3ssHw3DojqI0tg7yqEdzi+MI6mGI4PsPtsCPN8\nIazP/RijNsO1234F2q28rvvNmZv8+Fjyf/dVq132k0576yWqbVD0Iun8QljvecqrKxupvpvFku/B\naCpErRx30CBfCLsMJjOKai5929r1eS7trL07cIwOX7Jsdi2TsRNQ3dCPoymruVqrVavEGxX6rkYM\n1Ufmys4q9P8MGEhkvkzqKQKU9zLJQuQUqFoLWWFfVS+rdOJjijJnPabVCng1AwTefea2XIPncUeh\n9UnxLlB9pI1JVFjf3qJBBlkvh3nYsNKiti1fZ5ZrE6Mckh9H9WBrQB+zEnvWz4m+6Vuy43os+s6i\nsoQ5dX3pk1WvDbK2GFc3UjtdW6may79xrFFSRPXdbG60CsU2U+3i09CEf9s3yOIYMMY8B+CfAPgT\nKEbBZ1n2yNnnDQD+EYA/jnwi6b+XZdnfDp13Bp4iEWgtA65jZ12MctSNbSW7vb0Sti/J0ghNMVdW\nybGYt0vRiAywtt/+QOu0WXlrw4rL1NhYCje1VB85FjqpEDXjqIOGGE+0F9QX0ztsAXxPlmV/GsBb\nAfwVY8wXhU46j0BrrTDJZTsK7RTl8HwZ7inDN0NO/q7zms61PcN2asVc70XXMhWaJs4d6yLLuR2G\nb07RfGegTz8IBFq3HVvzFtVSAqrvpjPXe7J3FRYfxUsU0CANo9F5B/IX0qP4/vPuDlmWfSrLso8U\nyxcAPor8Ha1eJq+BUkTIYsDIocSXYll6jVANz5czXAP+Qb2aE983T26XgcFtnQL76jSYa/UzJft6\nGUGXHqc2fH6Utnl5fecAKk9pOQxfeofWqA/HL5azuM1TlAGxqSpxOyz/FFWlXmKH52sPF6rvZnLj\nVIhyGL5cJfXSaFhkkxlEvnjCI8H3YnoVY8znAfhSAL8c2m/yO5a43iJ3Wf623dLFy2HtbClt1eC+\nqkpCdqVr1V6rmtt04ix7J2+0quiqvUaqqD5yDAxSYXVoBw1ONU/R0jHGfADAq5VNf13+aHsxvTHm\nDoCfAfDdhcfIy6RGka2sE/eVBe4HYltatZifih4AO38uwHYdWTZup5SljCVyN/g0E+faApqGUWUQ\nJUC8Cp9HTd1T8ZvqI8eGV4VQhRHQIOJcazSMmrzy8EN48vBD3u1Zlr3dt80Y43sxvbvfCsA/BfC/\nZFn2Xm0fyeSeohpduoKPN5iekG745iZCh/U+lhbiQ8hcoHYGc/7gK3D+4CvK359559/rc7jvxfQl\nxhgD4CcAPJ9l2Y92Oenk2ZYiRhoDK9kCdmMm5HDjJI+piGNgtWm2V4G6kzP0jm102DYH2Pb2s6+I\nhrEICUxrf4aiIGIbT2Q3uoHWchn57zRumacoTsOeInm+BKgCrjVvkU2pheq7GdwoFUINsA56ilJM\nNU/Rkb/m44egvJjeGPNaAH8/y7JvAvBnAfwlAP+meJE9AHxflmX/h++kkxtFAJDGt4D4Og8QtWEK\nMdRAa9uFBvET6F51dX3L0tyrajI/uoqpz+Oj4azXDCCpk3X9dxqHB5jeilNcx8gDQ6X23MBRQBEE\n1UfmxkFUWF3KfqReTuu/b8UcdbYPfC+mz7LskwC+qVj+RfQcZT/xkPwYKSKkcdS0stdoFjq7Pqri\nK1ybfoq5TefcRiK7MUW+u1WwnbRxZY0hqw136LyrmRhI4yjXmFOpF8pDFHeYq0hW9rUUUn3kEMxG\nhdX1pCZC2omBKLYKpHG0BCbzFNlKOkWEJIqAeKt3CbjrkH/LYGu3vRqLZV/btGubdQhs5y6XfQrC\nV323OesBZcLGLlqJgSSKShe61ZytnHOjKMG2rRtAeovKVNruM6qPjM1sVVgtd9VMDERxMuGQ/KPu\nPtsLk89oXdLRGArR1maYzAIkxKGtLLa2f6UmQtrpmpg2Y6hVPFQfWRo7qlBqo1NjgiyB+RhFGm6h\nE+tswOk+necsz6Qrh2jbGtdA6WW09CR4DTtxHdVH5sRBVFi/1D41SCZhFtmYIu7XQu2Qaju1HCFL\noZOJEdKDoqG2GW1jLaaoTXutwqL6yFLpoMKeGownDLQ+5hfC7ovJ331WyzRfrIT7wli7e1w/FPXN\ntZ7gNobsS24eXfJem/Ktz75qWXZ3cDWh6aYgj2hovvusNplc1xiJRiq9KXb+YQiqj/RhMhXWF3vo\nJcJ0cUWkH7PoPksR+Y0hzSASy+4INEKOCTvyDEClA7ncopu2QEt19Fmogm8YRlQfOXackWgBr5D2\niTgkf1FMbhTZYfmNQFHfRzwIVnH9XH3am4TMEV/7duWU/a5a0YbjW2LfkPxOrV/fW++pPrJ0vCqs\nVvf4RBNO3kj6M6kv2rZiE+sp0owfWRErXQNA/a1LvmiGoYOAY8/5CLEMFVHb4GD1FZRSH/K3x1iy\n3dNa9xkARFEHo8i9jppaqo9MyQFVGNKGZhRF1RQYh6YtppA0mYGnKK+sMzfvZMVvfzvbY8VbRMix\nUZujSCJ/K9utpnxdaGUlHTsvl3aNH1Vj7DojN4nArNZyuaHRXFuMJ1oOkxtFDdz5VUIPAkJuIm3G\n0dABJ23GELVHSE5IG6pHlSyFeWWdLzWyq2AEg3uf8+kSEmI034rsUg5t73s+3/rRerKoPjI1I6kw\ndr592yeEM1r3ZxaeovxVH85K19oOeY8IuSmENOHoIok6jD5DAsSOtRPyGFF75KYT0kbDg5TUp74g\ns2cWRlGJFkjtxkoIr5FxCmBf+5/z8RLJPvNs17Jp3AEHFi32zl3XBS2Q2mccxUA1q7UvxW1QfURj\n1irUvUM+3bAYLpLJjSJvS9Y1hjyFzE5q5xsgTMhScMtsOTBYK8xaReyRUmugdZdze4OtZUrd9YQs\nDa8K9V07xhIx0Ho5zKL2ShEjjUXV2uaud+r4OAKSA5c5Dha+eUwhlti1ZzT7xqOXNO7wmg+kQJyi\nNgeLdl7vuimUQPXdXCZRYXsSvF1q085RxJii/kzuKWowCzONkAUzVEPUHiHjQC0tlvkZRYQQQggh\nE0B7lhBCCDlC0mt2n/WFniJCCCGEEMzRKGL8JCG7MVRD1B4h40AtLZZZGEUREkSyEPmWLU4w/6FH\nngEs8zeRKfK8Uba1su7RS5SgdeK4BBFqM6e2aa+xbpK7MsE1yTyYQXlr00VtOSpfykyWweQxRd75\nG1I0C5pSGJNinfviAFabZGkkqM/4U7533lcJR85vj5R8GvMO13W15tFetZLqI8eCV4X6rp10Mt3Q\n+KTxqgjSxiw8RSXWEEqUdfI38nWZUwD7vlFpn29g4mNheewzz3Ytm5nUhbRxXGMoUdZ1IUF7JZ/I\n76wlxW1QfURj1ip0NCCWtd8shotkFkZRhDSfP06iGUdyGyE3kZAm3FeYpe0z6aaIgcR9aRp0Y8hd\nJuQmEtKGq48kbp1AlcyLeeWWr8JNW7b3hO/oJlNhnfE7kyBXr8/m6asV3/5ay3gnqD4yNSOp0Gqw\nTTsTkroNHtLKLDxFNVKEvUIzKGiETEpIE65++p435BWi9gjJCWmDXWeLZnKjyLr3jVbIWoyjJAW2\nLHzkyNkWZb3VGHK2W021Blonzhvv24yjciW9PuSmsIU32FouNzSaa4vvIFsOkxpFtrKOkdaD06xB\n5AasaYHYyIvrVixrDK2+aXORNoaWkbayutX2cQOpQ5pJUL6M0jWMbCWdplFTe+7HvU4jtVQfmZoD\nqjCkDeWTpoXWaBgtgsk7HPNZHFK1Qlc/afXteonsT7ZfyVLZogpTkOLcJsBKlH0kyIfkt2glQuqd\nqyhBhDTpYBRpRpJ3GD7VR5aOV4UAVr31kiYRkmgagyjlkPzeTN59BhStWFvZS+NI/pbdA2I5SVkN\nk+NlC1QTOLoeIqkNj25aR5/1MYogvssfVB85dpyus44eImkUkeUwefdZLCttzRhyK39ZNj3l1F3X\npdoesi+5eXTJe1uGhu6rlmV3B1cTmm4KYqRq91ltqHAvL1GnFDv/MATVR/owmQrriz30khYqJPNn\n8u4zoHgVQaeWaWCdA9uvZGlYp32QkB4UDbW+5sPnKWq7VhCqjyyVDirsqcEpZ5Wml6o/s+g+8+IW\nNLHOzmbNeXHJHNhnWbFlPHONlTYDZheC17CzWVN9ZE4cRIX1S+1Tg2QS5mMUua5/tzsAaC10bVU0\nyyyZC21lsdXckJoIaadrYtq6AFrFQ/WRpbGjCgPeIb3LmSyByYwi69bPX/GR6oZQYJ2coyhBvfjK\ncjj2IOEuUAfL5RBtzdB6t+yW0TZFmW/ThbsuTqt4Iqs5+9buFFE+423X+IgycVJxVB8Zm9mqsFru\noZk0YTzRkpg0psgOx48SZZ6ijfP7UqxPq4eEW5T3Vd2GzssIiuMl9EKA0V7ZoZwXqMS5RT4cvxyW\nvyk2XAJYw6+ZBIiSFFHUHJZfKE8ffXbp+X0pU6gZQ1Qf2QezUWH1fQngFNW3TztJHteTFk+7Q5Ns\naYz1ZR6B1sl1VanbT+Is21aw45LsWxV33ZftTdKXBN0E1acaVydvjMSFXJ1sim3FNCtRcp0bTh6u\nrVF0KT6Js+ztBqD6yNw4iAqrS9lvVztCg9cMdl4Uk8cURUgQWYMH0A0gJ7YoS4AkaXd4+ra7zL0K\n3kc76FiY+73pWu7ayuwWeZmvBVv7pq6w+6RAlPhHoCWIAGsUQRwb7DrLuqQ0sL2Zinkz9xI2B+Z+\nj0ZVIWrB1m16SQAk03iJyDBm4Skq8ZVduX7oyy4JORZS1D1FGn1tjbHOQ8hNY8bauU7n9YhfApN6\niuzEjbE7wkxtoVbL7otg7aLnlX2ELArZVq21U4s4usaGQCs1LvaPndZEOXGjDbQOncebOpkQqo8c\nE14VQi3nQU9RrrV0Zj4IojN591lZWTcKktjJ3VagvjBTgVU1mStdymajnHfVSbHsGkSWyjDqeM5w\nqjxQfWTuDFJhdWgHDdIgWg6TG0URUhgZH7Fxlp1A6yxxWswFvupZK+59BxC3na/P9qHMvdd+CvZ1\nT8bI4z5lzHe+xitXCw9p5sYPWZ0oGjKJ//1nKSIgMdV5ZGC1DBwtyaC3lKm+m8uNU2GxLquvcgcm\n1AYpGA7JXxATD8l3vES+QlVU8Nll/mCwQdZ2sztjSp/irTHXtq0dEErm+5hKoIvKXe8b+yLLXgyl\nbCe5Nz6+BMwp6saRTz9oGkZlJd12bJmgDHXFUX3kxqpQnMvUtRLQ4CSGEUe+9WYWnqLGyy09w/Bt\nS9nGE4XGt7D6IksjZDbYbbb81yZydL1Gcl3a5imCv1KvGUX2gVCmxJNS7Z8QshQ6qRDBiRy1uYro\nKVoMkxtFAOoFyNbfznDjTJRNOWljqG061zanpK+rbq5ts0PS9x4soTc/VIZrZV3YN5mrG6mdhlET\nuIC7r+ol8qYocOK5Q/UN50arUGxThue7v5cgBVIyeUltzKGiFayCbZJ3H/iYYj5dcrPZ53y6vvNa\nDcS28RnQDNAyT1F9ReA8baPLqD4yFZOosPjuMFgBitYOBbvPejOZp6g2Ika6++U6S4pGYHVfWLWS\nubFrmUzcl766+nE0ZTVXGwmjtWYbts+uTV2qj8yVnVXo/0lP0SKZtPtMjXVw3Y6iUCVFPMUWzbKm\nzSih0XW/OXOTnfhL/u+2zLVVw9p+trxv4XhMA3qReAOt3Qt7zyNjiqi+m82S78FoKkStHHfQIOOK\nlsHkMUW1QGs5rFh87DB8iTseQNvm+63BtiwZmyHlTvvtq8Zrw/Pdjwi49gdax1UFrgVYJ0A1DF9L\nJdVH5s7eVQgg07UjNMV5ipbD5DkVI60MIk8FXQ7DL5a18S+A3gNwrNWtbasd6/9zWXLbtA0buZCg\n/vYOd/CwHRy8kj3PMbByK+I1apryTt6YRk29NYbjy9axNIKovuP9fz6oQqFCsT4BklVdQ6eo6SlN\nI0ziKErMBBddNhPPU2T7xTwfayihbhDJXVyH/Mg9xLNHSvPYquilVsFuVdoXt3qWZbws84Wdc9tu\nkI0KxY3vBlsn7jxFWiu3liJpGFF9OcesPgtVGFBhwe38y+dtLR9z7D5bAvPoPrPIwNGkWicNol3m\nIh1SbYVmYxl6TpexLNMVlluFScb8H2Pc27Yyt2u5Cp3DV+bs8Pyt7T6TOzsB2N7uMzkyRcZB1C4q\nDSKqz8+xqM9CFerXkfu63lPUNSQOSzkKbDFM3n0GoBmYpr30UjB2e/TY2nhLnnv3mB4rgOpo37n9\nqh4vDSK5rs354u6jGkZuCnaB6ps/VGH7+ZTjNe100eA+WZrzdQbMwyiSKJW7NjdRqNphOWhWa3Ot\npo+t+t2VUHWtVcVJj8ZEp4s3ju/roaH6lqM+C1VYp7cKm4uUwWKZh1HkzF6tuSCVQZDlLpa5Vz0h\nYuxXRzex2ptH4R6GbNsmaLZzLWVMkasZ263WNr+XJ/6h2YVG9Q3nJqrPcmNUqGtwak8R6c2kMUUx\n0uZLYZ2PNhxfC3UbOjA4VJUfupqPsewqZC5McR+HlKM+ZVabHQgQw/KDQdZpYwRairiKc/Aeqw3H\np/pIF26UCuEdll/smiYRh+UvhHnkkhsgKl5mmaSojTrTivbQcMxDVrt9Ig323W49ZvoU6EO2392o\nhtB66bzXtstytCr0sZIvVdYGLPgItm6lAUT1ka7cOBXm+8th+V6v64FhUe7NDEafJXohKip5OYO1\nOw7Graa7VMmhMjKn8jMPa3VZzOme9SlnXcbVaOW+XCe7yxQjx/vusyTyd1kn9spUH+nDnO7dQVUY\n1FLC0WeLYXKjqGRH63rs6Iah5whVCcf4Xum5MOb7zofe97HLXW8zQTVsOh63U8uW6iMAVYjhGiSz\nYTLl21iiOE2bwaAjFaQlh36Sm4XPsd8bVztpobGo0px9B1OaxM39R6vEqT6yNEZSoaKpNJnoUUuj\nrDfzfCGsWLZDjuXYl5uUz2yvtnOT7pHUgDU7ErdFqgik8wth1RVUH2njJt0rVYX6LgK+EHYZzKck\na/EQBaFxLnb3vkzVjh3y1iSZSTfpkRRiaMGdcnD0kHaoDPfUzrN1d3bj8rpexKM9qo/q80MVVsti\nV6+WyBKYh1EkC08qvu0rPtAsW26Yp2UO08rtc/xKKMOOTX9TFM5DXVOrZi2hsS7uTCkJUL3qQ2pH\n7tCWENcJVB7jG4JM9TU5NvVZqML6OsCjwuqn+32sReNImdwoipK0Kjip8+0Upq5jXoDDtUV9g33b\nquZ9vAxg8sycOW3tQ9/9O1S71teGlVW3OxC4tpNHQ1GSqm/oTt3RZ8HgUKovDNXXjaNWoVdPk737\njKF9vZlXTJFSKYdeBCtp22eOxvpNnuf20MzxXofKZJ8yn2jvO3NO3hpTpB5njZ76hVoAACAASURB\nVCCqj4zBHO/5aCqsn1LRIGOKlsHkQ/Kj5FqPf0iKmXrrq0pkMWwrtm6xHysyoo0ubcdje7f23Oh6\nf/fRztfKVJ+Ze+T+bT6aTKmErbcoSq7Vc19LT1EjUVkgpVQf6cONUaGupaTQGlkE8/L5NmIaDleF\nErIEErS8blTRUOcTN46j+ghp0qLCoRrcB10HW5CSeRlFQGtB6tJFusRuVCmxJaZ/Tiy57d9lbEzr\nPkMr49bjqD7SlRuuwjkYRGQQk3eflShuRxsrsUv5mrpsDrE66dQfxtD7NnXLYIzyncgpLfqeWD1u\njOYu1XfzuNEqHK5BMhumLomIZDyR+zJL+Htz3e1Tso+xLPa8ZFrmkAdtg4NrZc/VUFpoTMPGFAGe\n4FCqj8yBOeRFDxW6WkqQa40sgsmNohK3EBVzFGk4sf6NbfKUc8De5Lmkh+TMp/A3B/z6BgUDerq3\nCbAKTIDaevHGcT5Dg+ojY3JUKgSS1TAN7oupr79A5jEkX2acMuJMDoycMo93fd1kLD5kGvrkwT5e\nVzkGctyXaqIoevIOyQ9oj+oj++EGqFDRFYfkL4PJa4hYdp05BSlpqYPdtuwcnPldWVL7ta2QHMN/\nmBtuO7Ut9FN9/1mhrdg3AsW+pFI7tvMwZd/vOXNM6rMc03+ZC71V2NRR6TVa2n+/ucwzp/ao77lV\n3ft8KUHomlOeb+n/d1dGehe3zq43d6+ZQ/XtrzRShf3YowrnZJ/OKS0LYW4lNUgof+dQ3Q4N+XQz\nYYxyPOeM3fcbpMb473MN7bTI6IfDQPUNP+ccoQq7MS8Vkv0zjxwNxjXMi0O8VWkemTINc/jvQ9/O\nNBv66GlB2qP6DsUc7sHCVbgoXRFJp0BrY0xkjPmwMeafFb+fM8Z8wBjzMWPMzxtj7ol9v88Y89vG\nmN8yxnxd55TI/lcRXxQa62LZx7y7u7RRVuJDlsEYebZrfmtltstLNLbyRyqWh44+q12d6iOH4ihU\nOK/RZ6Q3Xc3t7wbwPIC7xe/vBfCBLMv+pjHmrxW/v9cY8xYA3wbgLQBeB+D/NMa8Ocsy/eVLAIxb\naOQcRWKbjPU/VDkLvYMbHdNhJTqHDgZgPo+KJd6PXcc/jYEtc6tiuSxfsjA6gdUNjZUnM/rJ8zMq\nG6i+3ZiL+ixLvC+zVmFzl/K3o7VDQaOsN61GkTHm9QC+EcD/COC/KVa/A8DbiuV3AXiI3DD6FgDv\nzrJsC+AFY8zHAXwlgF8KXkQbHdPyzpat870vQg75IdWzS9/0z61aHcqu/+OQ961NJPvOExvV0Boa\nOkBHwypNqu84oAq701mFTWiYLIounqIfAfBXATwj1r0qy7KXiuWXALyqWH4t6gbQi8g9Ru24rv8b\nwrFVs4eC982D0gXd6Ri1++zYYSnaDd4/FXafHQRjzHMA/gmAPwHgBQDfmmXZI8++EYAPAXgxy7L/\nJHTeYEyRMeabAXw6y7IPA1D9f1mWZQCywGlC23qxz3bpLmF7Mw/5G43Y+dwE5louDt/xQfVNi6s+\nqnD/x7Yxl+7HAMnCPv2wYTxvBvDB4rcPGwLUao+0lZg/A+AdxphvBHAK4BljzE8BeMkY8+osyz5l\njHkNgE8X+38CwBvE8a8v1jX4uR/4EH4Nn8S/eAV4cAI8UPZJ2lz/B2Jfb1eaC0OrjWOaVm4oc2kr\nt2klcnIhaZ1ddy65RvWNc/xc8nMfzEaFzVV/8BB4/0Pgd4Dfu/97h07QseML46nhCQHyElRUlmXf\nD+D7ixO/DcB/m2XZXzbG/E0A3w7gh4vv9xaHvA/APzbG/C3k3WZvAvAr2rm/4Qe+Al+OD+GrP/0p\n4OdRdbo55cqJ658tc5sjd05tyD5p4f3rR+trPvqgnojq688cSw9VuD8Cr/n49x4AX/0A+GbgtV/4\ni/jUO3/ysEk7bnxhPC5aCJCXvqXOup5+CMB7jDHfgaIvDwCyLHveGPMe5G6qBMB3Ft1rDdx3Mc2d\nru3VfUwFFzr/sTFVu3fIfZ1L+7QrVnPLewcT1XdYqEIyD4wxHwDwamXTX5c/sizLjDENW0OGABlj\nHnS5ZudSmGXZvwTwL4vlzwL4Ws9+PwjgB7ueF4CurQkaK/uYGm5u1ehcqpClT7E3y6nlhuhoLk4B\nqm8CqMKDMKXGZqNvD7/+EPiNh97NWZa93bfNGOML45FoIUD/KMuy/8x33pmUGhwsUn/XCIW5zXxi\nmVt120bX9B7jfT5IXg2dvHGvUH3zgircKxx91s6//yD/WH76nX2Ofh/0MJ4STwiQ1yAC5mQUSSYs\nSF1fEem+O/lQ7FvKYxWIsbJwrBlm9pEGH5OKatcbP2klTvWNC1U4CTSEDoUaxmOMeS2Av59l2Tcp\nx+w8+mz/dCxAc2urSPY5Pmaf1fA+M18795h1hXtflnj/d2WLjnnondG6z5XmCtXX7xpU4bh0VOFU\nhtIRG2i+MJ4syz4JoGEQyRCgENMbRTswp6p6zKp5X1XA1Jm9zzDYffgO5lQVD5hHd89Qff2YWn0W\nqnA481MhGZ+5KLUzhzB8uzrxXeYil6Vkqi+du+bxlPlwiHufHOg6+pX3DdV3WKjCYUynQrJfps/V\nCVLQpV0pkzU3D+TYt+wQbw3qQ9v/W3J+TPKo8CVwEvVTfXUOVSKowooJVDj9k5Z0ZD5ZJWetn0+q\nAEwzNdyut2Au7WagX1qGzEYjWVIe7QWpoa4JnLH2qL6xoAoPxhAN7os59XIvhKmzLKdHKqRzfZdI\ngjFmPdlF+mPc+LGq3n0Xgj73KfSfdq2q+6al77nb2CW/5LG90tG2c++TUX05S1GfhSrMmUCF83jK\nko5Mll2ts+o6KetbjXaJTNh11pND37xd5DylLsdqU44x1mWK+9A13/qmrXHelhNYzfWfTZ7qW676\nLFRhN3ZWIVk4c1Dr5Mz5lZNDJbeUjN2l/T/VbDV9YJXZBtU3PVTh0bKst2nNgknVmy6u8jgsc6mS\n29IxZlW4y9ijuVbJc8DVWszasoWlqM9CFc4dam4ZzM8qmShFU8v5kFXw2O2mvufrM/bI0nee467X\n2ieTtU+Hamiy2oDq2x2qUGciFc7vyUo6MnnWZTFgoiIlNsyoSFUcAfEOxnXf9s4h36y072p4ircD\ndb1vQ8I4hzr4DzXnbuiaXdhFiDFyrdROJDSVeYfkZ0BsPKPPdh06c2zqO/SQ76HnOKQKgWGG0jHk\nsXZsXC3KVTFyrZFFMLlR1CByvpEX76HjFYY4gsdo6+xaLe7D+Nl3m2kMI6rrW5aGTjk3dlfgGPd0\nFxHWrq9oZ/eEUH3jXOdQHotDqhAYZigdtQp3P92YzG06qQUwj6yTFjVQVuqrGLVAsaFO9jFmOpnL\nW5C6pKPLPlNlvMyDPlVv17bm0Las7zr7ZMxqeOVop6GpUCJUT1HjCqD6lq4+C1VYMaoK66fsqkEy\nKybPriQqipJ19zsFyRY7X5uzT1U9NHxwX3S5+YeuhseqjnbpButS9fZxyI/xWB6bMR7Fsbvd1U9h\nICU+z1Gc5EcruqufmerbbZ9Dq89CFYbZiwp1PcVz+t8kxMSjz4b6+acPzdyVMarktu1TVcXuObtU\nlYC/uuwSbdKlPMztsdyXXfLJ1Vr/uYrclFB9u1+jy3l2gSocn+H5tZvmBrLkWz0Rk3uKANRTIVyP\ncQxgk6+yUrNF8pBRDmMxpqN+lyr50OMx+jjdQ3nTVuV2rZItSygH2rEr5zdQaKXRBdbhYh7tIZE/\nqL5+2+ekPt91qcLdjlVVOEyDZFZMnl1pXBQvT6W+jwQeumo+VHU8tCredyFw73Wo/dpWXba1V/uM\nYZrCob+v8tz4IfSU+i4ap6h1n3lPNiZUX/fjxoQqbF5zT+dUNLjTMGpyUCY3igDUhg/Ljw0eleNf\nhkhUo0uvel+G3Myh1bDvWmPt3/X4rgNy+3a2bD3XlG/ecvd3rxnaLtnlv7UxZhine96V2Gel6EfG\nFQVPpH2S8syg+rpe69Dqs1CFYQ6mQr+eyGKYPKYojW8Bp9fAGsApgE3xOQXiqF4k5XIRIlqT2S6R\nDoe6EUNDN7X09amCNWdv1/OFCB0j86KPw9zmrXY+3+O0LXqiT/VsmVIcnnEtWKHpvF8h1wpOi42n\nxafQVBrfUmOKbsUprk/F/qcALu23dhUL1TcP9XU5liocTm8VNjVYfG7FKWOKFsLkNmwaR0B8XbVq\nY+SVeQyYU+B2MYGjdbrbdqvPCT/HENB9tkf7VsFtVf5YEQ+y6tQc9zaPujrPZRXdVs33HTezlPIS\nO9ttI/R2lGtFakeO5kxj3V0UxSmu3RatPQ8MgNtiA9XX3H+u6rNQhcPprUIApq4h8YnYfbYYJjOK\ncrs5QhJFQLStG0SXxXfRhbZK6w78GPXQT81pOweJjRWh0KXt6quK3WNdSYfOGbq+xDelW9f2Y5dz\ny2rbV92GHtWhaw/pANoX2r32hHSWjvuy68waRNIwioAkikq9AUD5K06wdQ0i6ykqr+x2n1F981Kf\nhSocj0EqzFdJT1HNKEqm8RSR3kzuKQLQ7Ht1vmNn1y3qVbXGlFXzru29PlVy12q3yzG+64XWbz3b\nfG3UlWfbyjlOWw/oVa7b5h3qMZ66Wu76GHf1UFuQ313iGXzaa14BVF/b9kOrz0IVjsdOKqz/7KrB\nfcLus95MbhSVb+923fh2dYxyWD7QdNpq0Q1T0ac6DlWNofP1rZK1/UPXCrafnV6YJO1WXfsKma9a\ndqtkuR7KNm17mUbPPiGmrpYlvjxbiXWxVgkLDaWeHIitS99zXNNSovraz7E39XnW+2J/JFThbnRS\nYfWlPsfoJVoKkxpFiXXp28JjuwBs99k67xo4Q11WdtkN8/Stg7JtTLpUx2NGLoQkKtdr1bZcV7uG\nfNeckqjYk9BEaYlsE5EGUReM1eniVvFuVS7xtYXdNGnsu1oOlRvfI9k66u3yGYr8KvRSakjGFCFC\n0gi0TvI4B1l5y+6zUwCX9gpUX3PbqOrzrPelwUVzB0iVaF1nVKGelrZtXhVWuomhdJ/lKiTzZwae\noqiq0M+Rv+ssQTlyZrUGbq+B7abZNtGWQxLq0p7qs38bXdt9oWowJEv3PHZ/7dhyfzHVQXm+4rdd\n73qETEspyRytJ4UhtC3WW8PpNqr11ljSwjUT6FWur8puq8rbwk3lfhpdy8EY5Sfkf1ghv4c2/HmF\nXBsrO3LTaui0+C505Zs5PorTqiK/g/zm3IFjFN2G7jWg+gaoTzmnu797TqOkQcN9C7trvNrft8V6\nLb+owgEqRM0ouuN8nzLQeklMahSliLHBSV6B248tO3eqdbc31YNUa4O4Ug2FAYYYWg23HdfmnA9t\nDznlZRUs91mhMmxKQ0cYPnZbzdixnjotQS2lxDh13Kr4vbIGboE1npK0aTBtk7xqSYo8to9ii6yi\n5eXGaD/62q99rzFG9Qvoj0lZFRdVMOIIuG0NoHPkmhG6sZ8NThpdaDFSnESb6hhrFEH8fgRUOeHz\nAFB99X00w0g7pqOx07WGTtzz+brkrPFkFWWXgUp5CajCzipEaWje8X9Oog1iDslfBJMZRUk5Biau\nWrZ3xA7PAHgWwCvAapP/3CbFQ3Sjn9MX5dCl131XQjfS1+5wt7VFIGjtTbcLbBXnBpDr9TGyi9I1\nhoBad4s3sa7DQWrcFV9SfNLqt622V9ZTlNQNJaDKY+lNAupVtKzC3bZnm0Pfrc7bOHTZ8eW7rIqf\nWVd5vDpHrpNnkYvkvPi2xtFp3viQXWhRob4YadWildq7V3wuADxaFSeUd973L260+lDvUpHHewyg\nWHy3ac/3F33OO/s7EesTmRbr9QDqhhJQ5bUbwE0VChWi1pVmNXMPuZbsd+EtijHRPEWkNxN7iiJc\n4aSauPEc1YPUVupFl8AKQHyJ6pVMwjCSkuzTK+4iZdr3xvgk64s8cLdr633S7OIJMrKSlR6gNeqV\nMZz93HXyeB+aceQYRI3vTX5es873WxXr4yjPY/tSaetJsr+tt9CtirV87+ro71tFhxhShrRHqzxe\nVsfPrHPvUGznJhIakYZQ+b0GrnCiTt54giu9+0xU5vnNsQ977S7dSPU5x7meII8RdAq/9mJlnbsc\nQjOOGgaR831pD5CGkr1ognreysTYfL6xKkTVfSbmJnK1I7rPTnBFo2ghTGoUWbK4eDjKQGv7gD5F\nOd+KOc0NozjOvUdSktKpa4uz5pANVdn7qoq1fUOOebte28caRJoxBIiHpdsVJr1EbqtUrpfPzq4t\nVVkvyuvKSlmuc3/H1bEmzvM4SMe6RWtf+qJftLat3E/uG2JIFSyP9T1mSz9E7OSxnMna/hZaytoS\nFGfAqajYL8UFS6MIyB+cMiU3Vn3Qc8n+VgyiWPn4tmnJabs1rgblckiDp8p2AFVej8FRqhC1vJZa\ncbVzilxjU8Hus95MahQlhSM/sQ95t0DZyt2+/gP5w2CVFoZBque5z9kacsLuGt7nu5Fae9O3zddW\nGdxFphk+bQaTm1hfnJG7PRX72IrZnlO2Tu061/AR+5nTvIsts56jNP+vW5nZPRpdsg3r+xtaqKk9\n1hIqC0PKj5sWrYPGLktjuDSIZH6uUddO8UmiSmeSQnlAnADxylOZozKUAOQPAWkY3Dj1oXMXmWb4\n+DxFsbLe3SeE1JerQa0Lza7XGh9lltq8zsQBu3oCj0KFxbfIb00zNWM3qfRGZs+kRhEAXGGNq9Nb\nWK2vc5e/5VkALxfftqvsEmVXyzMAVpe5x+hJsVnqHajXD0CzXSLpcyOGtlG7yE9ua/UISSPInkgz\nitx17vaQB8n3xyT2BrvdaEpcUfl7LZZdd3+xzgZw2/gj27V2G/VAbfvbDcR3ox/cvyEf09pf9D3G\nxy4/7mNZPnqBfMDv7aLbbGUDqG1jwcYUyW/7WQNXp7dwhbWajjWucOv0Ctenq2ZM0aPi2z44L1Hk\nT6m+4nO06kOzdPT0CGnr2ra7ye16a9zuMbusGUX2c9qyvfQQAs2uNRmELX+7htNRqRBlgLU0dGUM\nkYwlKrrPbp1eYY2rHmkjUzKZUZQixhXW2OAET9ZnOH/2onwRbPnAfAWVQfQy8oflJfIYo5eB1Sv5\nyLS7CWqjmJ6mdbla3PDPobS1S7V9XSc8AG9ckF3XiA0CdOMGyrq1WA+xro8hJa/prgP8xpD9lkaP\n3Nf+3jj7Q6xzj0sAk+YeJBQGkLxmlqA2qg2olwkAVTC3OFQrJ+42l7HKjvbIXiF/l5ksE6sY+bB7\na/jYGLyo+P3His9zxedOte+T9Rk2OMEV1uUItBgp1rjCCTY4u/MEF/fO6/MTJciDrK1B9AiVZ+ES\nRfC1jbG4K+7IFsBTLER9nt92ncfwsbuGDJtTsR5iXReDyU1Km3HkM4bsd5thdKmc4zJ0nCkMY6B5\nz7NiJy2vtZFu8GyDss1l7ypE00Aqft9DfU6iewDuOx9rFN0Dzu48wQk2NIwWwqSeojzQeo3HuIN7\nz13kDzxb+ec75KxReYyssXSef1tvEYDyYZpdQh32DTQfkENx5/IJTXiozf9jZGWnGR0hQ8V96ae2\nfxejyHesFlfUp1KWBo3rCepqFIWO3TjHFF6llTWa3LSJ88g5lXzzKUm2zrqxyo0sL40gedk9BtQH\nIlhvkM3Pc+QG0WsB/HEAr6rWb58DHuNOYRA1A63XuMKd88e4uH8vf9DZVi9Q3btTVB4jayzdKb4v\nV8VcRhL3wagZRrs80AC/10fbRzN6WobDhwyVLsZNF6OozTByjw/RxUvkM5R6G0XiOPfa1mBKtPyQ\nyBgbn9EkcQ2jscqPzyBSDGI5IMF6g6Sn6D6A1wN4dfEp129x5/wx1lMFWu9rzssjZkJPUT4k/wlu\n4zHu4tGzF7ifXsAUxk5JhLzifxnVg/EC+cPhAvkDUj5cN8UD8lJ4E1zRa2Wzi858d6ttfh+f0WO/\ntWHyWhcXUK88fd1gp4Fta2e9ZhBp6fSNQHPvsV0OGUbSINK61i4D29yK2rePlhbU51Sqzafk/gf5\n21eXDS0zWkyXXS/j6SCW18grWTvc3ubTs8i9Q9Ygeg7AOZCdAo+evYPHuIsnuK2+EPY2nuAuHuPi\n/iNcJPeBOybXlEy7na/I3t+L4rf1JMmH6iXyB+OleDDu+vwaintvfdrTDBGfQaQZMO62kNF06ll/\nKKPI1eClZ33bNu182j5aWgDU5lQqy8mMyouMDZLrNKMoRuUpsgaR9RSdZrhz/xHu4jFu4wlHny2E\nyYwiAEgKT9EV1rlx9OwGJ+st1qeOnR4jN4yKmKJy+PEFcgPKfVi6XTDVBfWHOJx9XEJ3SW4Ljdzy\neV98D8g2w8j1JkivgnZsrGzTrqMZQb7KWauMXSOlzePjVqgBb1DNYNKu5V4Hzr6+9LplxP0v7nqX\nPmXGNVLles1wtevlkHs3pug5lAZR9seAzRq4Ol3hCW6X+tICrdfF1tvnT7G59xjbyxPgdI2a+qxh\npHmKLuD3PPgejL775VvfVXuh5ZBB5K6T23z7aMYNEPYGLaX7zN2mnU/zLrUZS/Bsc9f7/ou77DJE\ngyGDWMtLd1JGu5/1FFmD6H4GnG6wOs21ZXXGQOtlMKlRZGMcbMzDJloX1vkWp9ZjdAfVA3KNcn6b\nkhgNT1E5YqbNUzS0jGp3bai3KGQY+YwVeZzPuAl1n4VikUJGkVxOxDVCRpESG1Sui9HMG5uXa+jG\nlAzQltfRDF7XINIq2CFeol3LzRBPkTWAnkEVd2cbC3dQdplZg2gTrcuYvfxv1AumnV03j3XYYH2a\ni2wLAHdOK+PHPhjlcH35f1xP0Sn8D03tuy+a9nyGg8/osctDDKMu6/cRU6T9vzbjoYtRpGnQ5p87\nZD9pWd/FIPKl0/0PofW+dV0I5bf97uIpkjFFjbmJcoNofbopFLgpTjOBp4jOqd5MahQBthstxhVO\n8okciwfDerPNu9I2qLdY5LBWOU+LFZx9mLoPVaAuyr5dIr475TMe5O/Ys59mENllzVAB2g0fez9C\nXiT3ur5raQaSRQ5o8nWhaUaR/B07x9n8vYTu2XHjjdquJY/t4inyVcryP7oMLS/avXbzTuaNfJXH\nudhHjDbLTqVBdFJM2hj7331WbD3BFU5OqyDQ7ek670qz3WP2f56i6l6Li2WpPbvsdr3A+e27Z33v\nZchgcA2P0HqfsaQdGzJ8XONHbu9jFLnn7ULIA9NmFLnHS8PWZzD58rfN+Aml012v7acxtNz48lfz\nEknjR3qK3PWnWWkQnZzmKuRs1stiMqPIxjhssMbTIq4IyGf+PIk22Dz3FPeii+oN4PaBaI2kV1CN\nTnO9BYmzvrroYStmn0fA3aYZTSHDyd4T12hxH6Sxs4+73T1nsY+c8C8R6Uqd/xeJeyXfd2jcvID4\ndrs6+8YNacHZWswSnG2aMaydB8p2d9m3j6RLhazludb9addbA0h6iopBCNvn8hiiqsvsBI9xF09x\nG5si0NqNKVpjg9t4irt4DETA1fkJNudrPD3d4CK+B8Sr+gPSGkm260wLyJ2jUeTbN2T4aMv2u22+\noa7Gj7pdBCHH4ob4XigqBZqIP5IY3RgC9DzzrdeODXWfucvueaFs077d7dq2tvVjG0Vy1mrpKbKD\nEe5vcef+o6LLLO//yOOJ8t80jJbBZEbR7zz8Pbzlwf2aQbTBuox1OMEGj599jLvPXuBs8wQnl9eI\n0+KBKw0j9yFpH4Ja95lcHtIt4rtbXbxFbmPdJ0zNk6AZMB0NG1tnpjGQxreQxhGSqEpMihj/z8ME\nb32QPzjd2BPpZXC7YGQfuSv4uHwMp4194zRFlKSIkuvSsLJ1fmeDyl0PZ5vmSZLHwNkvVBkPDbTW\nykso6L7wFD38DeDBl6Oer9YoKgyiLM7z9ur0Fp6sz/AYeVC17TJLEZfayg2jk1oyTqRBBGCNDWyM\n3+b8BI/PH+Pi/l08uTjD9eVJ/sBNTN0w8sWjaA9NeNa523yMYRhp60PGkl3+tYfAlz+ob+ti2MRV\n4b4Vp4jiFJEwdOKi0Ed2W1QvaDXNeAphEtBnmkZIk/wDAIkwoNIkRppEuE6iqpKwRlVXg8pdD8+2\n0HrtHO56ufzxh8AXPND30dDKTZuh7PPqOa/uQJwBcYJbp1c4u/MEd84f4y4el8+vGGmpsdt4Wnaj\nHZS2+0MaTGYUvfDw9/CFD16NJzgDkBtE0po+wQZnRUFar69wsq7eMnxSBK3ZYY5R8TgH6g9coMWb\n4UNu63iHtNcphLwsaXyr+K4bIdZgsRWc2/Vhf9sX6lbr6/u7Bo7c7p7zfQ9fwv0Hr/Ve07cOaBpD\ncl2ZJ84+UVQ8ANbNir86tmlMlS8xVa7buGbqbC/G0ttyUa2vlrXGeLCcSDqWmTYvXBrfwvt/Efji\nt1WxQJVX9aTsas4vGZVGkB2sYCdqlF7YJzhTY4rOiokX19iU3iQgj/F7ijNszk9wdV4ZWUD+HrUE\nEa7SdfnAtQ/b2oPW/YOuF8PHAO2pr1EIeFluCWNEYg2W2Nl++Qv/O06/+k+Vv6MobZRbALWy69ve\nLLeJeo76PmEPg1evUVGfrBWDSdQDbj0h6xB1e2rz26mbHOMrTWJ1+7VznLeclOtEefmfHwLf/KC5\nT57QilDZafHGuQZsXP5Oy7fdnxRzDslG/BpXuI0n5XxE0ht7hidew5bMi8mMomvcKiv3DU7wBGe1\nB5v0NLiBag1DSBS2KEqBCIjW+kPTZeiIALeS8e/nG8vuN3gkrufGvXabAdP0/DTT/Vls8f/iC1rT\n2wd5v/WKvn7f49b95fZmnjUqnMg5j/2tT+7c2bU9dnnRysCLt34Xv4I3lutsHkoDSa6/Et1jSWE6\n2nNJQ8ruX74MFrbx8cQxtuuGmEx/eZ0of+im62ZZ7FKuZfr70vXhEspTn1FdX5fn9b+LP4k3rj/U\nuHbfcqqlu00nu1BvNLXXLZ3rlag41tlFlgXfefrUhxov3n8Br/7CX2zdp2a5YgAADatJREFUT8NX\nbvwNrGZe+jzgbgNdPqus18hqjsybyYwiwIryBChapm2tf0nXAt6VrseNZTSMef6+Dxf3Af0YL+KT\neG3v6w6lr2ExpIW17/77fZaXP8ATPI+3qNtCRnLoAdQckp/aoQ04w1PvsW1Gue96feh63FR5+jn8\nIb4QH/Mcs/+yvA92rTO6HTN+XZngU/gK/Kte5x/7mRB6HoWMbcYULQOTZYd/g68xZsLXBhNCCCHT\nkGVZy5Tq42CMyfDfLexR+z+Yg90fH5N4iqb+04QQQgghLremTgAhhBBCyByYNKaIEEIIIXuCQ/J7\nc3BPkTHm640xv2WM+W1jzF879PVvIsaYf2iMeckY8+ti3XPGmA8YYz5mjPl5Y8w9se37ivz5LWPM\n102T6uPEGPMGY8y/MMb8pjHmN4wx/3WxnvkxAcaYU2PMLxtjPmKMed4Y8zeK9cyPiTDGRMaYDxtj\n/lnxm3lBDsZBjSJjTATg7wD4egBvAfAXjTFfdMg03FB+Evk9l3wvgA9kWfZmAB8sfsMY8xYA34Y8\nf74ewI8ZY9jNOh5bAN+TZdmfBvBWAH+l0ADzYwKyLLsE8DVZln0JgC8G8DXGmK8G82NKvhvA8wBs\nlDDzghyMQxegrwTw8SzLXsiybAvgpwF8y4HTcOPIsuwXAPyRs/odAN5VLL8LwJ8vlr8FwLuzLNtm\nWfYCgI8jzzcyAlmWfSrLso8UyxcAPgrgdWB+TEaWZU+KxRPks+/8EZgfk2CMeT2AbwTwDwDYATnM\nC3IwDh1T9DoAvy9+vwjgqw6cBpLzqizLXiqWXwLwqmL5tQB+Sez3IvJ8IyNjjPk8AF8K4JfB/JiM\nwrvwrwH8SQA/nmXZbxpjmB/T8CMA/iqAZ8Q65sVQtlMnYHkc2lO0sEkTbgZZPllVKG+YbyNjjLkD\n4J8C+O4syx7LbcyPw5Jl2XXRffZ6AP+RMeZrnO3MjwNgjPlmAJ/OsuzDqLxENZgXZN8c2ij6BIA3\niN9vQG7dk8PzkjHm1QBgjHkNgE8X6908en2xjoyEMWaF3CD6qSzL3lusZn5MTJZlLwP4WQBfDubH\nFPwZAO8wxvwOgHcD+HPGmJ8C84IckEMbRR8C8CZjzOcZY06QB8m978BpIDnvA/DtxfK3A3ivWP8X\njDEnxpjPB/AmAL8yQfqOEmOMAfATAJ7PsuxHxSbmxwQYY+7b0UzGmNsA3g7gw2B+HJwsy74/y7I3\nZFn2+QD+AoD/K8uyvwzmxXDShX1mwEFjirIsS4wx3wXg/cgDGn8iy7KPHjINNxFjzLsBvA3AfWPM\n7wP47wH8EID3GGO+A8ALAL4VALIse94Y8x7koz8SAN+ZTfEumOPlzwL4SwD+jTHmw8W67wPzYype\nA+BdRVzRLeTeuw8WecP8mBZ7X6kNcjAmefcZIYQQQvaHMSbD9yzs+f4j07/7jHM6EEIIIYSAr/kg\nhBBCjhO+5qM39BQRQgghhIBGESGEEEIIAHafEUIIIccJu896Q08RIYQQQghoFBFCCCGEAKBRRAgh\nhBACgDFFhBBCyHGynToBy4OeIkIIIYQQ0CgihBBCCAHA7jNCCCHkOJnJm+eXBD1FhBBCCCGgUUQI\nIYQQAoBGESGEEEIIAMYUEUIIIccJX/PRG3qKCCGEEEJAo4gQQgghBAC7zwghhJDjhN1nvaGniBBC\nCCEENIoIIYQQQgDQKCKEEEIIAcCYIkIIIeQ42U6dgOVBTxEhhBBCCGgUEUIIIYQAYPcZIYQQcpyk\nUydgedBTRAghhJBFYYx5zhjzAWPMx4wxP2+MuefZ754x5meMMR81xjxvjHlr6Lw0igghhBCyNL4X\nwAeyLHszgA8WvzX+JwD/PMuyLwLwxQA+GjqpybJs1FQSQgghZFqMMRn+04U93/83gyzLTJddjTG/\nBeBtWZa9ZIx5NYCHWZb9KWefZwF8OMuyN3ZNAmOKCCGEkGPkuF/z8aosy14qll8C8Cpln88H8AfG\nmJ8E8B8A+FcAvjvLsie+k7L7jBBCCCGzo4gZ+nXl8w65X5Z3eWlusRjAlwH4sSzLvgzAK/B3s5UH\nEEIIIYQcls88BP7woXdzlmVv920zxrxkjHl1lmWfMsa8BsCnld1eBPBilmW/Wvz+GdAoIoQQQm4g\nc+8+u/cg/1g+9s4+R78PwLcD+OHi+73uDoXB9PvGmDdnWfYxAF8L4DdDJ2X3GSGEEEKWxg8BeLsx\n5mMA/lzxG8aY1xpjflbs918B+F+NMb+GfPTZD4ZOytFnhBBCyJFhjMnwDQt7vv9c99Fn+4KeIkII\nIYQQMKaIEEIIOU62UydgedBTRAghhBACGkWEEEIIIQDYfUYIIYQcJ+nUCVge9BQRQgghhIBGESGE\nEEIIABpFhBBCCCEAGFNECCGEHCdzf83HDKGniBBCCCEENIoIIYQQQgCw+4wQQgg5Tth91ht6iggh\nhBBCQKOIEEIIIQQAjSJCCCGEEACMKSKEE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"text/plain": [
"<matplotlib.figure.Figure at 0x2ad943f0b240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.imshow(phi)\n",
"plt.colorbar();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stokes is linear and we have periodic boundaries, so we can solve it directly using Fourier transforms and the frequency-space Green's function (which is diagonal)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/maxhutch/anaconda3/lib/python3.4/site-packages/IPython/kernel/__main__.py:16: RuntimeWarning: divide by zero encountered in true_divide\n",
"/home/maxhutch/anaconda3/lib/python3.4/site-packages/IPython/kernel/__main__.py:16: RuntimeWarning: invalid value encountered in true_divide\n"
]
}
],
"source": [
"# Setup the frequencies\n",
"dx = L / ny\n",
"X = np.tile(np.linspace(0, L, nx), (ny, 1))\n",
"Y = np.tile(np.linspace(0, L, ny), (nx, 1)).transpose()\n",
"rfreqs = fft.rfftfreq(nx, dx) * 2 * np.pi;\n",
"cfreqs = fft.fftfreq(nx, dx)* 2 * np.pi;\n",
"rones = np.ones(rfreqs.shape[0]);\n",
"cones = np.ones(cfreqs.shape[0]);\n",
"\n",
"# RHS comes from the forcing\n",
"F = phi * Atwood * g / viscosity\n",
"\n",
"# Transform forward\n",
"p1 = fft.rfftn(F)\n",
"# Green's function\n",
"p1 = p1 / (np.square(np.outer(cfreqs, rones)) + np.square(np.outer(cones, rfreqs)))\n",
"p1[0,0] = 0\n",
"# Transform back\n",
"w = fft.irfftn(p1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Look ok?"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/maxhutch/anaconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": 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3Peb+MUuPspCubcHJ0DMpzI51UbnWMT/jsYjuRd69TO6/P/ybdwu0HoZhHP/UPc60HsP3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\nSQLg/wMuxnVltIKxFQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2ad95f1ea470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"plt.imshow(w)\n",
"plt.colorbar();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we want to turn this into something Darcy-Weisbach-esque. We don't have uniform forcing, so we take an average."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"A_tilde = np.sum(np.abs(phi))/ (nx * ny)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rayleight-Taylor types like the Froude number, which doesn't really make sense:\n",
"$$ \\text{Fr} = \\frac{u}{\\sqrt{A g L}} $$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Froude = np.sum(np.abs(w)) / np.sqrt(g * Atwood * A_tilde * L) / (nx * ny)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead, we normalize \"the right way\" using the viscosity:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Right = np.sum(np.abs(w)) * viscosity / (g * Atwood * A_tilde * L**2)/ (nx*ny)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print everything out."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"L=1.000000, A=0.001000, A_til=0.835020, g=9.800000, nu=0.000400. D-W is 0.008786\n",
" Froude: 2.233357 | Right: 0.009875\n",
" C1 = 101.261227 * C0 \n"
]
}
],
"source": [
"dff = 64 / 16 * 14.227\n",
"print(\"L={:f}, A={:f}, A_til={:f}, g={:f}, nu={:f}. D-W is {:f}\".format(\n",
" L, Atwood, A_tilde, g, viscosity, 1./(dff*2)))\n",
"print(\" Froude: {:10f} | Right: {:10f}\".format(Froude, Right))\n",
"print(\" C1 = {:f} * C0 \".format(1./Right))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
ml6973/Course | assignment/Ravikumar-Puthussery-Abhijith/Assignment_3.ipynb | 2 | 178797 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assignment 3\n",
"\n",
"### Abhijith Ravikumar Puthussery"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Write a TensorFlow Program: Classification\n",
"\n",
"#### Translate the following program to TensorFlow using your elab Cloud Jupyter Notebook (https://github.com/ml6973/Course/blob/master/code/Introduction%20to%20Deep%20Learning.ipynb)\n",
"\n",
"#### Data : Data is taken from sklearn's make_moon dataset. There are two features and and the target is a categorical variable (0/1). The aim is to devise an algorithm that correctly classifies the datapoints. Here is the link to the data set: (https://github.com/ml6973/Course/blob/master/code/data/intro_to_ann.csv)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Import the required packages\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import scipy\n",
"import math\n",
"import random\n",
"import string"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"epochs = 10000 #No of iterations\n",
"learning_rate = 0.01 #Rate of learning"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"random.seed(123)\n",
"# Display plots inline \n",
"%matplotlib inline\n",
"# Define plot's default figure size\n",
"matplotlib.rcParams['figure.figsize'] = (8.0, 6.0)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Feature1 Feature2 Target\n",
"0 2.067788 0.258133 1\n",
"1 0.993994 -0.609145 1\n",
"2 -0.690315 0.749921 0\n",
"3 1.023582 0.529003 0\n",
"4 0.700747 -0.496724 1\n",
"\n",
"[5 rows x 3 columns]\n",
"(500, 2) (500, 1)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3/dist-packages/matplotlib/collections.py:549: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == 'face':\n"
]
},
{
"data": {
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KSa8+faSdt7eYmpqKlZWVHDx48L0cZ+LEiZInTx4Ji7yTIXTGJ6dIrc+/kJo1a8rPP/8s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Ndf8fbxUf3rM21qFDC6CXQNBrMJcmzo6VbGPTzumuP7+9//TpcuXVj08Yf8knGSA8nHePu9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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f75c0332518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"training = pd.read_csv(\"intro_to_ann.csv\") #Read dataset\n",
"print (training.head())\n",
"mti, mtl = np.array(training.ix[:,0:2]), np.array(training.ix[:,2:3]) #mti=image, mtl=layer\n",
"plt.scatter(mti[:,0], mti[:,1], s=40, c=mtl, cmap=plt.cm.BuGn) #Scatter plot\n",
"print(mti.shape, mtl.shape) #dimension of mti & mtl"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32, [None, 2])\n",
"Wh = tf.Variable(tf.random_normal([2, 6])) #Weight of hidden layer\n",
"W = tf.Variable(tf.random_normal([2,6])) #Normal weight\n",
"bh = tf.Variable(tf.random_normal([6])) #Bias of hidden layer\n",
"b = tf.Variable(tf.zeros([1])) #Bias of layer\n",
"y = tf.nn.softmax(tf.add(tf.matmul(x, W) , b)) #Output\n",
"y_= tf.placeholder(tf.float32,[None,1])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Define loss and optimizer\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.688\n"
]
}
],
"source": [
"#Creating and running the session for accuracy\n",
"init = tf.initialize_all_variables()\n",
"errors=[]\n",
"with tf.Session() as sess:\n",
" sess.run(init)\n",
" for i in range(epochs):\n",
" batch_xs = mti\n",
" batch_ys = mtl\n",
" _, cross_entropy_value, y_value = sess.run([optimizer, cross_entropy, y], feed_dict={x: batch_xs, y_: batch_ys})\n",
" accuracy_value = sess.run(accuracy, feed_dict={x: mti, y_: mtl})\n",
" errors.append(1-accuracy_value)\n",
" print (accuracy_value)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/dist-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
" warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n"
]
},
{
"data": {
"image/png": 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we//9yapVVVcCAMBIaXS4TbQmAADUSWPD7axZZb9iRbV1AAAwcoYTbo9LsjzJA0kuTXLg\nEN93QJI1SX7bz2uHJrk8yYNJ/pTkkGHU1ZFHP7rsly8f7U8CAGCsdBpuD09yZpL3J9kryS+SnJ9k\nl0HeNyPJ2Ul+lGR9n9f2S/K1JF9JsmeSc5J8Pck+HdbWkdmzk623Tv7yl9H8FAAAxlKn4fbEJF9M\n8uUkVyV5S5Lrkxw7yPs+m+TcJBcl6enz2glJLkhyepKrk3woyY9bx0dNT08yf37y5z+P5qcAADCW\nOgm3U5IsTAmi7S5Isv8A7zsyyaOSvDcPD7ZJsu8wrjkiHvvY5OqrR/tTAAAYK52E2+2TTExya5/j\nK5LM2cR7dktyWpIjkqzbxDlz+rnmrQNcc8Tsvnty1VWj/SkAAIyV0ZwtYWKS/0xySpJrRvFzhu2x\nj01uvDG5776qKwEAYCRM6uDc25OsTTK7z/HZSW7u5/ypSRalPHj2ydaxCSmtCQ8leU6Snya5ZRPX\nvGWgYk444YTMmDFjo2OLFy/O4sWLB/kxNth997L/85+Tvfce8tsAAGhZsmRJlixZstGxlStXVlRN\n/z2wA7k4ydIkx7cduzzJN5O8u59rL+hz7Pgkz0qZ+uvaJPenzJQwNckL2s47P8mdSV7RTw0Lkyxd\nunRpFi5c2GH5G7vzzjLf7de+lhx++GZdCgCAlmXLlmXRokVJGehcNpaf3cnIbZJ8NGWqrktTgu4x\nSXZOmQ0hKf21c5O8OmXKr8v7vP+2lLls24+fleTnSd6e5NtJXpLkoJR5cUfVdtuV7Zpx2TQBAECn\nOg23X08yK8nJSXZKclmS56dMB5aUh8AGmvN2fR4+z+1FSV6e5AMp8+dek+SwJL/psLZh2Xnn0ncL\nAED36zTcJslnWlt/jhzkve9tbX39V2sbc/PmJTfdVMUnAwAw0kZztoSuMG+ekVsAgLpofLidO1e4\nBQCoi8aH23nzkltvTdasqboSAAA2l3A7L1m3rgRcAAC6m3A7r+y1JgAAdL/Gh9u5c8teuAUA6H6N\nD7fbb59Mnmw6MACAOmh8uJ0wwYwJAAB10fhwm5jrFgCgLoTbGLkFAKgL4TZGbgEA6kK4TTJnTrJi\nRdVVAACwuYTbJLNmJXfdlaxdW3UlAABsDuE2JdyuX5+sXFl1JQAAbA7hNiXcJskdd1RbBwAAm0e4\njXALAFAXwm2EWwCAuhBusyHc3n57tXUAALB5hNskU6YkU6cauQUA6HbCbcusWcItAEC3E25bhFsA\ngO4n3LYItwAA3U+4bRFuAQC6n3Dbsv32ZksAAOh2wm3L7NnJrbdWXQUAAJtDuG2ZM6eM3D70UNWV\nAAAwXMJty+zZyfr1yW23VV0JAADDJdy2zJlT9rfcUm0dAAAMn3DbMm9e2V93XbV1AAAwfMJty5w5\nydZbJ3/5S9WVAAAwXMJtS09PMn9+cs01VVcCAMBwCbdthFsAgO4m3LaZP19bAgBANxNu28yfn/z1\nr8nq1VVXAgDAcAi3bR7zmGTduuTaa6uuBACA4RBu28yfX/b6bgEAupNw22bnnZMtthBuAQC6lXDb\nZsKEZNddhVsAgG4l3PZhxgQAgO4l3PZhrlsAgO4l3PYxf36yfHmyZk3VlQAA0Cnhto/585OHHkqu\nv77qSgAA6JRw28djHlP2WhMAALqPcNvHIx+ZbLVV8vvfV10JAACdEm77mDQp2Xff5Je/rLoSAAA6\nJdz248ADS7hdv77qSgAA6IRw24+nPjW5447kyiurrgQAgE4It/3Yd9+yWpnWBACA7iLc9mPq1GTv\nvZNf/KLqSgAA6IRwuwm9fbcAAHQP4XYTnv70slLZ8uVVVwIAwFAJt5tw0EHJ5MnJd79bdSUAAAyV\ncLsJ06aVWRMuuKDqSgAAGCrhdgALFyZ//GPVVQAAMFTC7QAWLEiuvTZ54IGqKwEAYCiE2wEsWFBW\nKbv66qorAQBgKITbAeyxR9lfcUW1dQAAMDTC7QBmzkxmzxZuAQC6hXA7iAULkiuvrLoKAACGQrgd\nxB57GLkFAOgWwu0gFiwoD5StXVt1JQAADEa4HcSCBcmqVWVKMAAAxjfhdhBmTAAA6B7C7SB23jnZ\ndlvhFgCgGwi3g+jpSXbf3YwJAADdQLgdgkc+MrnhhqqrAABgMMLtEMydm9x0U9VVAAAwGOF2CIRb\nAIDuINwOwdy5yZ13Jg8+WHUlAAAMRLgdgnnzyv7mm6utAwCAgQm3QzB3btlrTQAAGN+E2yEQbgEA\nuoNwOwTTpydbbSXcAgCMd8LtEPT0mDEBAKAbCLdDJNwCAIx/wu0QzZ2b3Hhj1VUAADAQ4XaIdt45\nuf76qqsAAGAgwu0QPeIRJdyuX191JQAAbIpwO0S77JKsWpXcdlvVlQAAsCnC7RA94hFlrzUBAGD8\nEm6HaJddyv6666qtAwCATRNuh2iHHZKtt06WL6+6EgAANkW4HaKenmT+/OTPf666EgAANkW47cD8\n+ck111RdBQAAmyLcdmC33YzcAgCMZ8JtB+bPLw+UrVpVdSUAAPRHuO3AbruVRRz+53+qrgQAgP4I\ntx3Ybbey13cLADA+Cbcd2GmnMh2YvlsAgPFJuO1A73RgRm4BAMYn4bZDe++d/PjHpfcWAIDxRbjt\n0CtekVx9dXLJJVVXAgBAX8Jth571rOSRj0ye/vTkW9+quhoAANoJtx2aOLG0Jcybl3zkI1VXAwBA\nO+F2GB7zmOSUU5Jf/jK58caqqwEAoJdwO0yHHJJMnpx84xtVVwIAQC/hdpimT0+e+9zkvPOqrgQA\ngF7C7WY4/PDk4ouTa6+tuhIAABLhdrO86EXJllsmX/961ZUAAJAIt5tl6tTkBS/QmgAAMF4It5vp\n8MOTZcssyQsAMB4It5vpBS9IttkmecYzkt//vupqAACaTbjdTFtvnXz60yXgvuxlyd/+VnVFAADN\nJdyOgFe9Kvn2t5Mbbkje/vaqqwEAaC7hdoTsvntyxhllFPf736+6GgCAZhJuR9BxxyUHH5wcdZRl\neQEAqjCccHtckuVJHkhyaZIDBzj3wCQXJrk9yf1Jrkjylj7nvCbJuj7b2iRThlFbpXp6ki9/OXnw\nwWTPPZNbbqm6IgCAZuk03B6e5Mwk70+yV5JfJDk/yS6bOP++JB9P8tQkeyT5QOu9r+9z3j1J5rRt\nOyVZ3WFt48K8ecmFFyb33598/vNVVwMA0CydhtsTk3wxyZeTXJUyCnt9kmM3cf7vkpyXMmJ7XZKv\nJvlBkv37nLc+yYo+W9dasCD5x39MlixJ1q+vuhoAgOboJNxOSbIwyQV9jl+Qh4fVTdm7de4P+xzf\nNsm1KUH5Oymjwl3t5S9PrrwyueyyqisBAGiOTsLt9kkmJrm1z/EVKa0EA7khyYMpPbqfTXJu22tX\nJHl1khclWdw678Ik8zuobdx5znOS7bZLvva1qisBAGiOSWP0OQekjM7ul+SMJLck+VzrtUtaW68L\nkyxL8qYk/7ypC55wwgmZMWPGRscWL16cxYsXj1zVm2HKlOTQQ0u4/dd/LQ+bAQDUzZIlS7JkyZKN\njq1cubKiapJOIteUJH9L8o9JvtV2/KwkeyZ55hCv8+6UGRJ2G+CcLySZl+T5/by2MMnSpUuXZuHC\nhUP8yGr85CfJQQclF1+cPOUpVVcDADA2li1blkWLFiXJopRByzHTSVvC6iRLkxzc5/hzkvyqw88c\n6HN7Unpub+rgmuPS05+ezJ6tNQEAYKx0OlvCR5McneTIJAtSpgXbOaWPNklOS/Ifbecfn+SFKaO0\nu7Xe93+SnNN2zikpgXnXlFD7pZSR4M+my02cmBx2WHLeecnatVVXAwBQf5323H49yawkJ6fMRXtZ\nSuvA9a3X52TjOW97UgLvo5OsSXJNknckaZ8Bdnrr+zlJ7k4Zun5aysNnXW/x4uQTn0i+853kkEOq\nrgYAoN668TGnrum5Tco8t897XnL55cmf/pRMnVp1RQAAo6tbem4Zhp6e5LOfTe64I3n3u6uuBgCg\n3oTbMfCoR5XpwD75yeSii6quBgCgvoTbMfKmNyVPelJy9NHJ6tVVVwMAUE/C7RiZODH5whdK762p\nwQAARodwO4ae+MTk2c9OPvrR5KGHqq4GAKB+hNsx9sEPJn/8Y/K+91VdCQBA/Qi3Y+zJT05OPbWE\n3F/9Sv8tAMBIEm4rcNJJyb77JgcckMyYkfzhD1VXBABQD8JtBSZNSs45J1m0KHnggTKDguV5AQA2\nn3BbkV13TS69tLQmXHppmQMXAIDNI9xWbL/9kuOOK6uX/fWvVVcDANDdhNtx4IMfLL23b3xj1ZUA\nAHQ34XYcmDYtOf305L//O3nsY5MjjjAPLgDAcEyqugCKww9P7rknueqq5BOfSHbbLTnllKqrAgDo\nLsLtODFxYvKGN5Svp01L3v/+5PnPL/PiAgAwNNoSxqH3vCfZe+/SnnD//VVXAwDQPYTbcWjy5DIP\n7nXXJccck9x9d9UVAQB0B+F2nNpjj+QjH0m++tVkzz2TlSurrggAYPwTbsex445LLrqojNyaJgwA\nYHDC7Ti3777Jpz5VRnDPO6/qagAAxjfhtgv80z8lL3tZcuyxyY03Vl0NAMD4ZSqwLtDTk3zmM8kT\nnpC86EVlyd5Jkzbenva05O//vupKAQCqJdx2iVmzkq99LTnppNKHu2ZNWcVszZrkvvuSM85Ifve7\n5HGPq7pSAIDqCLdd5GlPS371q4cff/DBZK+9kte9LvnFL5IJmk0AgIYSg2pgyy2Tz32uBN/Pf77q\nagAAqiPc1sTTn54cfXTyjnckt91WdTUAANUQbmvktNOSBx5Izj236koAAKoh3NbI9tsnL35x8pWv\nJOvXV10NAMDYE25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"text/plain": [
"<matplotlib.figure.Figure at 0x7f75c0321e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot([np.mean(errors[i-50:i]) for i in range(len(errors))])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_run_ica.ipynb | 1 | 3435 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Compute ICA components on epochs\n\n\nICA is fit to MEG raw data.\nWe assume that the non-stationary EOG artifacts have already been removed.\nThe sources matching the ECG are automatically found and displayed.\n\nNote that this example does quite a bit of processing, so even on a\nfast machine it can take about a minute to complete.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Authors: Denis Engemann <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.preprocessing import ICA, create_ecg_epochs\nfrom mne.datasets import sample\n\nprint(__doc__)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Read and preprocess the data. Preprocessing consists of:\n\n- MEG channel selection\n\n- 1-30 Hz band-pass filter\n\n- epoching -0.2 to 0.5 seconds with respect to events\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\n\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.pick_types(meg=True, eeg=False, exclude='bads', stim=True)\nraw.filter(1, 30, fir_design='firwin')\n\n# longer + more epochs for more artifact exposure\nevents = mne.find_events(raw, stim_channel='STI 014')\nepochs = mne.Epochs(raw, events, event_id=None, tmin=-0.2, tmax=0.5)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Fit ICA model using the FastICA algorithm, detect and plot components\nexplaining ECG artifacts.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"ica = ICA(n_components=0.95, method='fastica').fit(epochs)\n\necg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5)\necg_inds, scores = ica.find_bads_ecg(ecg_epochs)\n\nica.plot_components(ecg_inds)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Plot properties of ECG components:\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"ica.plot_properties(epochs, picks=ecg_inds)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.14",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
kmunve/TSanalysis | decomp_radiation.ipynb | 1 | 226730 | {
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1254f940>]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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kuaq7L16iDQAAANvWAXsqrKrzkjxgg6KXLi50d1dVb1Bvo3WpqnskeUmm6ZY3r96sHbt2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\nDEqgAwAAGJRABwAAMCiBDgAAYFD/H9OaK9ioYRoUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e659e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import datetime\n",
"import numpy as np\n",
"import scipy as sp\n",
"import scipy.fftpack\n",
"import netCDF4\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"n_start = 0\n",
"n_stop = -1\n",
"#ff = netCDF4.Dataset('./Test/Data/FORCING_nve.nc', 'r') # Data fra Filefjell 2013\n",
"dummy = netCDF4.Dataset('FORCING.nc', 'r') # dummy data set\n",
"\n",
"dates = netCDF4.num2date(dummy.variables['time'][n_start:n_stop], dummy.variables['time'].units)\n",
"\n",
"# compare ff and dummy\n",
"param = 'Rainf'\n",
"#ff_v = ff.variables[param][n_start:n_stop]\n",
"d_v = dummy.variables[param][n_start:n_stop]\n",
"\n",
"plt.figure(figsize=(15,5))\n",
"#plt.plot(dates, ff_v)\n",
"plt.plot(dates, d_v)"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x20189198>]"
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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OG1YJJA+t3LbNdSCNcQeY27aJsNTvosyz54weHf0z+gm5LVv6HLnCj/YlhcKK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\nyJEzcNDZFS58bXc1VZqbgdt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QI1dX587Wxi7k9B+nelawQittITd+vDTdVuyZI61eOmuW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5YtVDycnQfyiro6pTBNE2AVFVkZP3FBcO6OdYucn2tlUVjkmEIeq/b2xnK9jBxJQm7ffc2/33UX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\ns1gsFovFYrFYLJYC4/8DKbso1d7AZnYAAAAASUVORK5CYII=\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,5))\n",
"plt.plot(dates, q_air)\n",
"plt.plot(dates, rainf*10., color='red')"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2390\n",
"Qair [ 0.00061] [ 0.00875]\n",
"Rainf [ 0.] [ 0.00116667]\n"
]
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x202ff9b0>]"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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K4pBahTuGuYqNohFOmkkGSUEQBEEQBCGeNJ1Iq1WftNEWaaZOGi9TKWMlBX+twh1bW+Mv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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sw_rad = ff.variables['DIR_SWdown'][n_start:n_stop]\n",
"lw_rad = ff.variables['LWdown'][n_start:n_stop]\n",
"print len(sw_rad)\n",
"\n",
"q_air = ff.variables['Qair'][n_start:n_stop]\n",
"print 'Qair', min(q_air), max(q_air)\n",
"\n",
"rainf = ff.variables['Rainf'][n_start:n_stop]\n",
"print 'Rainf', min(rainf), max(rainf)\n",
"\n",
"plt.figure(figsize=(15,5))\n",
"plt.plot(dates, sw_rad)\n",
"plt.plot(dates, lw_rad, color='red')"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sw_fft = sp.fftpack.fft(sw_rad)\n",
"sw_psd = np.abs(sw_fft) ** 2\n",
"fftfreq = sp.fftpack.fftfreq(len(sw_psd), 1./24)\n",
"i = fftfreq>0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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OhI03hsceg6FDy06zOBdUkSRJkqR2GjQIPvEJ+OUvy06y/By5kyRJktSrPf44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\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,5));\n",
"plt.plot(fftfreq[i], 10*np.log10(sw_psd[i]));\n",
"#plt.xlim(0, 5);\n",
"plt.xlabel('Frequency (1/day)');\n",
"plt.ylabel('PSD (dB)');"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n = np.arange(72, dtype=float)\n",
"sw_func = ((np.sin(2*np.pi*1/24.*n)+1.)*50.0) * np.exp(n/(max(n))) # W/m2\n",
"# Long-wave radiation\n",
"lw_amp = 75. # amplitude of the long-wave signal\n",
"lw_offset = - (2*np.pi*3./24.) # offset of the daily LW maximum wrt the SW maximum\n",
"lw_mean = 275. # LW minimum in W/m2\n",
"lw_func = (np.sin(2*np.pi*1/24.*n + lw_offset) * lw_amp) + lw_mean # W/m2"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2157b630>]"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RLdyCICgN9AOOBRzwvnPu7SAIWgOPAGsy3vUF59yojI9pATwEpAGNnHNj9vK4\nkSvcvv0Wrr3WzugpWzYyjymJacUKqFoVRo2yQ14ltvz8sy1pHTPGlsCI5Nb69fYz9P77cM01vtPI\nntassS6SffrAlVf6TiPxbMcO6yTbpAk8+KDvNCL/EenCrSRQ0jk3LwiC4sC3wC3AXcBm51znPd6/\nEjAAqAqcAIwDKjjn0vd4v8gUbps3w7nnWoOC2rUP/vFEBg+Gl16yAYFDD/WdRjLt3g0XX2ydJJ95\nxncaCYOUFJt5mzMHSpb0nUYyZZ67d/rp8PrrvtNIGHz3nTUsmTJF+9gl5kS0q6RzbpVzbl7G21uA\nH7CCDGBvn+RmYKBzbrdzbjmwDLggu2FyrGFDO4dLRZtEyl132c+U9krGllat4Kij4OmnfSeRsEhK\ngocfhnr1bKm0xIZu3az1vzrGSqRUrgwvv2ydZXfu9J1G5KBke0NYEARlgXOAGRl3NQyCYH4QBL2C\nIMhcgF4KWJnlw1byT6EXWQMGwIwZ8PbbefLwksC6dLGfrY8/9p1EAMaPh759bdmU9ihIJLVqZSs3\nOnc+8PtK3ps/3y6wBw6EQoV8p5EwadAAypSxxkQicSxbhVvGMsnPgKczZt7eBU4GqgB/Avs7ATfy\n3U9+/tlG3gcOhGLFIv7wkuCKFYNPPrEleT/95DtNYluzxmZE+vaFY4/1nUbCpkABGwR8/XX45hvf\naRLb1q22FPrNN6FcOd9pJGyCAHr1gs8+s33sInGqwIHeIQiCgsAQoL9zbiiAc+6vLP/eExie8dff\ngdJZPvzEjPv+o3Xr1v//dlJSEklJSdlLvHu3TXe/9JJtXhbJC1Wq2M/YPffYuviCBX0nSjzO2Wby\ne++1jp8ieaFsWeje3X6vzJmjva2+NG5sTSTuu893EgmrI4+0zsR16sDcudrbKl6kpKSQkpKS648/\nUHOSAOgLrHPONc5y//HOuT8z3m4MVHXO3ZOlOckF/NOcpNyenUgOqjlJixZ2JsdXX2nZlOQt5+zQ\n1zPPhNde850m8XTtar9kp0zRsinJe488YgODffv6TpJ4Pv3UfrfPmQOHHeY7jYTd//5n2yGSk3WE\nlHgX6a6SNYHJwAL+WfL4AnA3tkzSAb8AjznnVmd8zAvYcQCp2NLK0Xt53NwVbuPHQ926NlKiZVMS\nDWvW2Oxb376a9Ymm+fPt6z1jBpx6qu80kgi2brVjQFq2tFleiY7MY1hGjLA/RfJaairUqgW33ALP\nPec7jSRyYW1eAAAgAElEQVS48B7AnXmuy4cf6gJaomvcONtnNW+eHdoreWvrVlsy9eKLWjYl0TVv\nnp0bpgGD6EhNte6eN90Ezz/vO40kEg0YSIwIZ+GWuWStcmVo3z7vgonsS/PmdhbM8OFaopvX6te3\nls39+vlOIono7behf38t0Y2GVq1g2jQYPVpL1iT6Mpfozp2rva3iTTgLt7ffttbsahIhvuzeDTVr\n2hKqRo18pwmvTz+FF15QkwjxRwOF0TF5sp3BOmcOHH+87zSSqDRQKJ6Fr3DTXheJFT/9BNWrw9ix\ntu9NIitz6crIkbZUUsQXLc3PW+vX22tojx5w3XW+00gi09J88SynhVtsr03IPNflrbdUtIl/p55q\nP4t16tjPpkROaqodvfDccyraxL9jjrGGRPXqWREnkeOcdfC8/XYVbeJf5rmtjRvr3FaJC7E941a/\nPuzapfbMElvq1bMluz17+k4SHmrPLLFIx89EXo8e8N579nw/5BDfaUSM9raKJ+FZKvnhh9Cunfa6\nSOzZvNnahrdoYQdEy8EZOdJG4OfM0YGoElt274aLL7auhy+84DtN/Js1C66/3i6OTzvNdxqRfzhn\nz/MyZaBbN99pJIGEY6nk5MnWGvjLL1W0Sew59FD72WzWDCZN8p0mvi1caDOYn32mok1iT8GC8Pnn\n8O67MGSI7zTx7ddf4dZboVcvFW0Se4IAPvoIJkxQ4SYxLfZm3JYts+59/ftrU7jEtnHjbDPzlClQ\nrpzvNPFn9WqoVs1m1nXgscSyOXPg6qth1CjtwcyNzZvt93rdutCkie80Ivv2889Qowb06QPXXOM7\njSSA+F4quWEDXHghPPMMNGgQ9VwiOdajhzUsmT4djjjCd5r4sX07XHYZXHUVtGnjO43IgQ0dCk89\nZXuzTjzRd5r4kZYGt9xiM+rvv6+9ghL7pkyB226DiRPhjDN8p5GQi9/CbfduuPZaOPNMePPNqGcS\nybXGja2BQXKyzhnMDuesg6RzMHCgLuQkfnTsCAMGwNdfQ/HivtPEhyZN7IDj5GQ1fZD40b8/tGwJ\nM2fCscf6TiMhFp+Fm3M2w/b77zBsGOTPH/VMIrmWlgY33wylSlm3NBUi+9e6tV3ETZwIRYr4TiOS\nfZmt7Neutb1v+l21f++/D5062SylViRIvGnZEsaPt31vhQv7TiMhFZ+F25tv2nriqVPVjETi0+bN\nti7+gQfg2Wd9p4ldAwZYd74ZM9SMROLTrl223+38820GTvZu3DjbuzplCpQv7zuNSM6lp9u5rQUL\n2gycBmUlD8Rf4TZ8ODz2mO0RKlMm6llEIubXX22PZo8ecOONvtPEnunTrd3y+PFw1lm+04jk3vr1\nUL26dT9+5BHfaWLP4sVQqxYMGgRJSb7TiOTetm32M3zjjTYDJxJh8VW4zZ8PV15pxVu1alHPIRJx\nM2fCDTfA2LFQpYrvNLFj+XK46CL44AM7x0kk3i1dame8DRxojXbErF1rRe2LL+qcSwmHVavsGvX1\n16F2bd9pJGTi5xy3P/+00fdu3VS0SXhUqwbdu9vP9p9/+k4TG/7+24rZZs1UtEl4VKgAn3wCd99t\nRZzAzp3Wje/221W0SXiULGlnt2Z2lRXxyN+MW9WqmnqW8HrlFXuhT0mBokV9p/EnNdWK2DJl4J13\ntEdAwqdnT+jQwS7ojjrKdxp/nLNibdMmO6w8n79xYZE88dVX8Oij2tojERU/SyXvvddOqdeFnISR\nc3D//TYCPWhQ4l7ENGoEP/wAI0fqqAQJr+eeg9mzYcyYxG153749DB5sRyUUK+Y7jUjeyGymN2UK\nHHaY7zQSAvFTuG3frvaqEm47dsDll1vDko4dE2+QonNn29M2fTqUKOE7jUjeSUuzJYKHHWYXdQUK\n+E4UXQMG2FLoGTPghBN8pxHJO5nHV61YAV98oSNt5KDFzx43FW0SdoUL27mEkybZ8orUVN+JosM5\nWwLdoweMGqWiTcIvf34rXlatgjvvtEGbRNGtm3XXHDlSRZuEXxDYz/wRR8A118DGjb4TSYJJ0PVb\nIlFy9NF2eOeKFXZBt32770R5KzXVitTkZDuXsWxZ34lEoqNYMRgxwgZsrr46/Bd0mQM0b79tyyPP\nPNN3IpHoKFgQPv4Yzj4bLrkE/vjDdyJJICrcRPLaoYfapuYiRcJ9Qbd9uxWnK1ZYsXrMMb4TiURX\noUKJcUG35wDNySf7TiQSXfnyQZcu1lW2Rg11lpWoUeEmEg2FCkH//nDOOeG8oNu40YrSIkWsSD30\nUN+JRPwI+wWdBmhETBBAixbw0kt24Pzs2b4TSQJQ4SYSLfnywVtvwT33QM2a4bmg++MPK0bPOceK\n00TtqieSKawXdBqgEfmvhx+G996zc0rHjvWdRkLOX1dJD59XJGb07g0vvmhnvVWt6jtN7i1dahu0\nH33UusolWudMkQP58kt45BFbQnnllb7T5N4ff9hz/dJLrSV6oh5xsoeff4Y1a6B4catjM2+J1lhU\nsL2ed9xhA7R33+07jcSJ+DkOQIWbJLp4v6CbPdsO127XDh56yHcakdgV7xd0S5faTNtjj2mAJsMP\nP8DLL8P48bbFb/Pmf98KFfp3IXfooVC+PLRpA6VL+04veWbhQrjuOmjaFJ5+2ncaiQMq3ETiyZQp\ncPvt8XdBN3Ys3Hsv9OxpxZuI7F+8XtBpgOZfFi+2gm3cOHj2WXjyyf+uGHXOtgLuWcyNGwfvvGOn\nJzzzjFaVh9aKFTbQcdtt9rzRQIfshwo3kXjz3Xdw7bV2QdeoUey/yA8caFcdQ4bYXj0RyZ54u6Ab\nMwbuu08DNMCSJVawjR0LjRvDU0/lbovfsmXQsCH8+qsVcbVqRT6rxIC1a23PW+XKtv8t1tfOrlgB\nJ50U+69JIRQ/B3CLiKlc2WbePvjACrhFi3wn2rvly6F2bWu6MH68ijaRnCpTxp7rkyZZx8lZs3wn\n2rvVq6F+fahbFz7/PKGLtiVLrHatWRPOOMMKrxYtct+XpVw5O6u8bVu4/367rVoV2cwSA44+2n5P\nrl5tjbvGj/edaO82brRB4/POgx9/9J1GskGFm0gsKFMG5syxzf+1atmQ7Lp1vlOZLVuskcp550Gl\nSlZYVq7sO5VIfDr6aNvzVr8+3HKLFUe//+47ldm5E15/3SqUww6zdYEJOkCzdKkVVTVrQsWK8NNP\n8MIL9mU5WEFgk66LFkGpUnZ2ebdukJZ28I8tMaR4cRg+3DY21q8PN98cO8VRaqrNBFasCJs22cqf\nChV8p5JsUOEmEisKFbIliD/8YJskTj/dzoPavdtPnvR0+PBDOO00W9czfz60agVFi/rJIxIW+fLB\ngw/adE7p0nDWWbYOb9s2P3mcs5m1SpXsQO1p06BTJyhRwk8ez/r3h4susuvYZcvsVIdIFGx7Kl4c\nOnSwCdjPPrMGwzNmRP7ziEdZq/SLLoILL4QmTWymy5fx4+Hcc23bQ3KyrfYpWdJfHskR7XETiVXf\nf2+731essIuo666L3vrzr7/+Z/f8W29BtWrR+bwiiWj5cuvWOH06tG9vjYqi9VyfO9c2ba1bZ23+\nr7giOp83Rg0YYCvHxo2zOjZanLPP/dxzcMMN0LmzFXYSMqtWQcuW1lW6dWubiYvW/rcff7Qf7oUL\n4Y034NZbtactBkR0j1sQBKWDIJgYBMH3QRB8FwRBo4z7jwyCYGwQBEuDIBgTBEGJLB/TIgiCH4Mg\nWBwEwVW5/6+IJLgzzrDRsM6dbYTummusmMtLy5fDXXdZx8imTW3kXUWbSN4qWxYGDbKjQTp1spH5\nmTPz9nNm7mO75hqoU8cKuAQv2gYNspfaMWOiW7SBXT/fe69NzGzZAnfe6W+xheShkiVthmv0aPuB\nO+ccGyXISxs32g/2hRfaa8uiRTYLqKItLh1oqeRuoLFz7gygOvBkEASnA82Bsc65CsD4jL8TBEEl\noDZQCbgGeCcIAi3HFMmtILCZtsxW4klJ1s5s5crIfp41a/7Zx3bmmba3JZqj/iICF19s7fcfe8wu\nrO6/30bJI7lCZeNGW593xhlw+OG2XLNBg9jvepfHPv3UFhmMGeN3C2+JEtCvH+TPbz8GWpwUUlWq\nwMSJtkT6scesAdCCBZH9hm/ZAj162D62v/+2fWzNmkHhwpH7HBJ1OVoqGQTBUKBbxq2Wc251EAQl\ngRTnXMUgCFoA6c65Dhnvnwy0ds7N2ONxtFRSJDfWrbPlFQMHQsGCtk4983bOOdbkZH/FlnPw55/W\nCCXr7e+/beN0u3Zw4olR+++IyD5s3mzLJnv1skYCWZ/r554Lp5xie+X256+/bCYt63P9r7/gqqvs\nscuXj87/JcZ9/jk88YRNgpx9tu80ZutWuPRSazTcpo3vNJKndu6Et9+Grl3td/E55/z7uV6hglXy\n+7Nhw3+f67/9ZoNB7dtboSgxKc/OcQuCoCwwCagM/OqcOyLj/gBY75w7IgiCrsAM59zHGf/WExjl\nnBuyx2OpcBM5GM7Zi3LmC3TmC/b27f9+wc9sh5b1fdLSbGYt6y+Hk08+8EWgiPix52DL3Ll2oVal\nyj/P4cqV//2aMGeOXf3veRFYvvyBLwITyNChNuGRnGxfqljy11+2sq1ZM1vVKglgzZp/fldn/vnn\nn9bAKPM5fPbZ9sOR9fVg7Vq7f8/f/wUL+v4fyQHkSeEWBEFxrGhr65wbGgTBhszCLePf1zvnjtxH\n4TbSOff5Ho/nWrVq9f9/T0pKIikpKbuZRWRfVq3696jb4sVw6qn/fjE/4QQtgRSJd+vW/fu5/t13\n1qEy63O9bFk91/dj+HB45BE7V+2883yn2bsff4RLLrEz0K+/3nca8WLTJpg375/n+vz5cOyx/36u\nlyunwdc4kZKSQkpKyv//vU2bNpEt3IIgKAh8hc2cvZVx32IgyTm3KgiC44GJGUslmwM459pnvF8y\n0Mo5N3OPx9SMm4iIiHgxYoSdyDBihLXhj2UzZ1qnyZEjYz+riORMpLtKBkAvYFFm0ZbhS6Bextv1\ngKFZ7q8TBEGhIAhOBsoDs7IbRkRERCQvJSdb0TZ8eHwUQtWq2VbHm2+2le8ikrgO1EaqBnAfsCAI\ngrkZ97UA2gODgyB4GFgO3AXgnFsUBMFgYBGQCjyhqTURERGJBWPGQN26trctnk46uekm+OMPO71h\n2jQ45hjfiUTEBx3ALSIiIqE3cSLUrm1dJGvW9J0md158EcaPhwkToGhR32lE5GDlWVfJSFLhJiIi\nItHy11/WdO/jj+Gyy3ynyT3n4IEHrKno558n/PF7InFPhZuIiIhIBufg1lutO3r79r7THLxdu6zD\n5KmnwrvvqnGoSDyLaHMSERERkXj24Yfwyy/hOci6UCEYMsS6TXbs6DuNiESTZtxEREQklJYvt86R\nEybAmWf6ThNZK1faoeEpKXDGGb7TiEhuaMZNREREEl56OtSrB88/H76iDeDEE6FtW3j0Ufu/ikj4\nqXATERGR0HnzTdvf9uyzvpPknUcftT/ff99vDhGJDi2VFBERkVD57ju49FKYNQtOPtl3mrz1/feQ\nlATz50OpUr7TiEhOaKmkiIiIJKxdu+C++6yDZNiLNrD9bY8/Dg0b+k4iInlNhZuIiIiERps2cNJJ\n8NBDvpNEzwsv2Czj0KG+k4hIXtJSSREREQmFadPgttts2eBxx/lOE12TJtlM4/ffw2GH+U4jItmh\nA7hFREQk4WzZAlWq2Nlmt97qO40fjzwCRYpA166+k4hIdqhwExERkYTToAHs2GEHbieq9ettz9sX\nX0D16r7TiMiB5LRwK5CXYURERETy2siRkJxsSyQT2ZFH2jEI9evDnDlQsKDvRCISSWpOIiIiInFr\n3TorVD78EA4/3Hca/2rXhtKl4Y03fCcRkUjTUkkRERGJS879U6h06uQ7TexYvhzOPx+mT4fy5X2n\nEZF90TluIiIikhAyl0e2a+c7SWwpW9aOCGjQwIpbEQkHFW4iIiISd1JToUkTm2krXNh3mtjTqBFs\n3Aj9+vlOIiKRosJNRERE4s4HH8Dxx8P11/tOEpsKFLCv0fPPw5o1vtOISCRoj5uIiIjElU2b4LTT\nbKlklSq+08S2pk1h9Wr46CPfSURkTzrHTUREREKteXObRerVy3eS2Ld1q53t9tFHcPHFvtOISFYq\n3ERERCS0li+H886DhQuhVCnfaeJD375W5E6aBEG2LxFFJK+pq6SIiIiEVosW8PTTKtpy4r77bIZy\nzBjfSUTkYGjGTUREROLC9Olw552wZAkUK+Y7TXz59FN4/XWYNUuzbiKxQjNuIiIiEjrOwbPP2plt\nKtpy7vbb7QiFoUN9JxGR3FLhJiIiIjFv8GDYtQvuv993kviULx+88gq0bAlpab7TiEhuqHATERGR\nmLZjh3WS7NTJChDJneuug8MOg08+8Z1ERHJDe9xEREQkpr3+OkybpmV+kTBxItSvDz/8AAUL+k4j\nkth0HICIiIiExpo1cPrpVrhVqOA7TThccQXUrm0FnIj4o8JNREREQuPJJ6FAAejSxXeS8Jg507pz\nLl0KhQv7TiOSuFS4iYiISCgsWgS1asHixXDUUb7ThMtNN8Hll9uZeCLihwo3ERERCYXrr7dlfY0b\n+04SPvPnwzXXwLJlOl5BxJeIn+MWBEHvIAhWB0GwMMt9rYMgWBkEwdyM27VZ/q1FEAQ/BkGwOAiC\nq3L+XxAREZFEN3asHbT95JO+k4TT2WfbbObbb/tOIiLZdcAZtyAILga2AP2cc2dm3NcK2Oyc67zH\n+1YCBgBVgROAcUAF51z6Hu+nGTcRCaXdu+Hrr2HLFihSxG5Fi/7zdtb7ChTwnVYkNqWlwTnnQOvW\ncNttvtOE15IlULMm/PgjlCjhO41I4snpjNsBLxucc18HQVB2b59rL/fdDAx0zu0GlgdBsAy4AJiR\n3UAiIvHGOZg9G/r3h0GDoGxZOO442LYNtm//55b179u22XlUp54KTzwBDzxg5yuJCHz4oRUSt97q\nO0m4nXYa3HCDnY/Xtq3vNCJyIAcz3tswCIK6wDdAE+fcRqAU/y7SVmIzbyIiobNsGXz8sRVs+fLB\nvfday/JTTz3wxzpns3OzZtlSpdatoW5daNgwex8vEla7dsHLL9sh0UG2x6Elt1q1gvPOg0aN4Jhj\nfKcRkf3JbeH2LvByxtttgU7Aw/t4372uiWzduvX/v52UlERSUlIuo4iIRM+aNTar1r8/LF8OderA\ngAFw/vk5u8gMAihUyJYp1awJv/4K77wD1avb7emnreObLlwl0fTpY+e2XXih7ySJoWxZex1r395m\n3kQk76SkpJCSkpLrj89WV8mMpZLDM/e47evfgiBoDuCca5/xb8lAK+fczD0+RnvcRCSuzJ0LLVvC\nlClw4402u3bFFZHfp7Ztm83iZZ5Z1agR3Hef7YkTCbtdu6B8eRscqV7dd5rE8ccfULkyLFwIJ2id\nlEjURLyr5D4+yfFZ/norkNlx8kugThAEhYIgOBkoD8zKzecQEYkF6enw5ptw9dV27tHKlfDRR9ZG\nOy+aixQtCvXr2wVUly7w1VdQpgw0bw6bN0f+84nEkt69oVIlFW3RVqoUPPwwvPKK7yQisj/Z6So5\nEKgFHA2sBloBSUAVbBnkL8BjzrnVGe//AvAQkAo87ZwbvZfH1IybiMS81autacjGjbYc8uST/eRY\ntsz2/MyZA8OGaQ+chNPOnTbbNniwCjcf1q61ZiWzZ8Mpp/hOI5IYdAC3iEgEJCfDQw/ZrVUrKFjQ\nbx7noEcPa2LSr5/NAIqESY8eNjAxapTvJImrVSvbb9unj+8kIolBhZuIyEHYuRNatIBPP7UlkbHW\nN+nrr6F2bWjcGJo2VfMSCYfM2bZPP4Vq1XynSVwbNtiM/vz5ULq07zQi4ReVPW4iImG0ZIl1svvl\nF5g3L/aKNoCLL4aZM615wz33WDMTkXjXu7c1x1DR5tcRR9gqg86dfScRkb1R4SYiCc856NkTatSA\nRx+Fzz+Ho47ynWrfSpe2mbeCBS3z8uW+E4nk3s6d8OqrtgxY/GvcGPr2hXXrfCcRkT2pcBORhLZh\nA9x1lx2CPWkSNGgQH8sPixSxi6sHHrBGDhMm+E4kkju9esFZZ8EFF/hOImDHAdx6K3Tv7juJiOxJ\ne9xEJGGtWgW1asGVV8Ibb0Dhwr4T5c748XauXIsWdu5bPBSeImCzbeXKwZAhKtxiyeLFcMkltmy8\nWDHfaUTCS3vcRESyYc0auPxyO9y6W7f4LdrA/h/Tp1snuAcfhB07fCcSyZ6ePeHss1W0xZqKFaFm\nTdt7KCKxQzNuIpJw1q+3Yue66+zA2bDMUG3dao0F1qyxluqHHOI7kci+7dhhs21ffAFVq/pOI3ua\nOdOWkS9b5v84FJGw0oybiMh+bNoE11wDl10WrqINbEnTgAFw5JFQrx6kp/tOJLJvvXpBlSoq2mJV\ntWp2NMCgQb6TiEgmzbiJSMLYssUOrq5SxZZHhqloy2rHDtu3V62a7d0TiTWZs21Dh8L55/tOI/sy\nejQ0aQILFkA+DfWLRJxm3ERE9mLbNrjxRjj9dOjaNbxFG9h+vWHDYORI6NLFdxqR/+rZE845R0Vb\nrLvqKlsmOXKk7yQiAppxE5EEsGMH3HwzHHOMtdDPn993ouhYscLOeXvzTbjzTt9pREzmbNuwYXDe\neb7TyIF88okdDfD1176TiISPZtxERLLYtcs22B92GHz4YeIUbQBlysBXX8GTT8Lkyb7TiJgPPoBz\nz1XRFi/uuAP++AOmTPGdREQ04yYioZWaCnXqwO7d8NlnidsZbexYO/ZgwgQ44wzfaSSR7dhhDS++\n/FKFWzzp0QNGjIDhw30nEQkXzbiJiABpadZZcetWGDw4cYs2+OeA8euug99/951GEtn779u+NhVt\n8aVePfjmG/juO99JRBKbZtxEJHTS06F+ffjlFxslLlLEd6LY0L49DBxoyyYPP9x3Gkk0mbNtw4fb\nUkmJL6+9Bj/8AP36+U4iEh45nXFT4SYiodO2rRVs48ZB8eK+08QO5+Cpp2DxYjugu1Ah34kkkbzz\njv3cabldfNq40QrvOXNs/6yIHDwVbiKS0EaNgkcegdmzoVQp32liT1qaNRsoWhQ++khnM0l07N5t\nnSQHDYLq1X2nkdx6/nnYuVPHjIhEigo3EUlYP/8MF15ojUguvth3mti1fTtcfrl9jTp08J1GEkGf\nPvDxxzYLLvHrjz+gcmVYuhSOPtp3GpH4p+YkIpKQtm2D226DF19U0XYgRYrYcrUhQ2wGRCQvpaXZ\n/qiXXvKdRA5WqVI2Y9+tm+8kIolJM24iEvecs65n6em2/C/I9thVYvv2W7j2Wpg5E04+2XcaCatP\nPoGuXe0cMD0349/SpVCjhjV/0h5ikYOjGTcRSTjdu8P8+dZqXBeG2XfeedCsGdxzj+1BEom09HRo\n185m2/TcDIcKFSApCXr29J1EJPFoxk1E4trUqbZEcto063gmOZOeDtdfb+3Z27XznUbCZtgwePll\nOwNMhVt4fPONve4uW6butCIHQzNuIpIw/vwTate2xgcq2nInXz748EO7TZjgO42EiXPwyiu271RF\nW7icfz6ULw+DB/tOIpJYVLiJSFzavRvuussO2r7uOt9p4ttxx1nhVrcurFnjO42ExZgx1jTollt8\nJ5G88Nxz0LGjFegiEh0q3EQkLjVtCiVKQMuWvpOEw5VXwr33woMP6kJMIqNdO3jhBZ0VGFZXX21L\nrXXEg0j06OVUROJO//4wYoQOkI60tm3hr7/g7bd9J5F4N3mynflVu7bvJJJXggCaNIE33vCdRCRx\nqDmJiMSV+fPhiitg/Hg46yzfacLnp5+genVb5nbOOb7TSLy6+mq480545BHfSSQv7dplR4mMHAln\nn+07jUj8yWlzEhVuUZCeDt99Z13vdu+GQw6xLkxZ/9zzvpNOgqOP9p1cJLZs2ABVq9rM0N13+04T\nXgMGQJs2ds6bzmmSnJo9G26/XR0HE0WHDvD999Cvn+8kIvFHhVsMcA6WLLEObRMnQkqK7cW5+GIo\nWhR27rRRqp07//121vt++cVmE26+2W7ly/v+X4n45Zw1OTj5ZHjrLd9pwu/BB20pVO/evpNIvLnl\nFrj8cmjY0HcSiYaNG+GUU2DBAjjxRN9pROKLCjcPnIOff7YiLbNYK1QILrsMLr3UbqVL5+wxd+yw\nxxo2DL78Eo444p8i7oILtK9HEs/770OPHjBjhkbxo2HLFjvbrU0bzW5K9i1YYMskf/4ZihTxnUai\npXFjKFgQXn/ddxKR+BLxwi0Igt7A9cBfzrkzM+47EhgElAGWA3c55zZm/FsL4CEgDWjknBuzl8cM\nReG2aRN07mxttHfv/qdIu+wymxWI1Lk16em29GTYMLutXw833mhF3OWXQ+HCkfk8IrFq6VKoUcMa\nHpx+uu80iWPOHLsInznTRtRFDqROHTjvPGsVL4ljxQob6PnlFzjsMN9pROJHXhRuFwNbgH5ZCrfX\ngbXOudeDIGgGHOGcax4EQSVgAFAVOAEYB1RwzqXv8ZhxXbht2QJdu1rRdv311pb8jDOid8DosmX/\nFHHffWe/IBs3VgEn4bR7txVt9erBk0/6TpN43noLBg6EKVNsRF1kX5YsgZo1bbbt0EN9p5Fou+ce\nK9qbNPGdRCR+5LRwO+CCO+fc18CGPe6+Ceib8XZfIPN4zZuBgc653c655cAy4ILshol127fDm29C\nuXK2HOTrr222rXLl6BVtYJ+/SRObfZg9G2bNgkqVYMgQnb8k4dO2LRx1FDzxhO8kienpp61RUuvW\nvpNIrGvf3va1qWhLTE2a2EDP7t2+k4iEV253Sh3nnFud8fZq4LiMt0sBK7O830ps5i2u7doF77xj\nBdPkydYme+BAqFjRdzI49VT44gvo2dP2olx6Kcyb5zuVSGRMm2Z72/r0ie7giPwjCKBXL/jgA1s6\nKcdykJUAACAASURBVLI3y5fbfmw1JElc551njdQGD/adRHJq+3bfCSS7DrrFRcaax/3N88TtHFBq\nqnVUq1ABhg+HoUOtSIrFs6Muu8wuqurUgWuugUcftYN0ReLV5s1w//3WkKRkSd9pElvJknbI7oMP\n2kCWyJ5ef91+7xxxhO8k4tNzz0HHjlr9E0+cgypVYPFi30kkOwrk8uNWB0FQ0jm3KgiC44HMEuF3\nIGv/xBMz7vuP1lnW3SQlJZGUlJTLKHnj00/hxRfhhBPg449tj02sK1AAGjSw4u3ll235ZPPm0KiR\nuvBJ/Hn6aRuQuOWWA7+v5L3774dPPrEzm1q29J1GYskff9jPhi785JprbN//+PFwxRW+00h2TJpk\n14inneY7SWJISUkhJSUl1x+freMAgiAoCwzfoznJOudchyAImgMl9mhOcgH/NCcpt2cnklhuTrJ5\nszVAmD0bune3C8d4tWSJrTlfsgQ6dbJOlFpuJvFgyBAbdJg7VwdAx5LffrPOcRMn2t5eEYBnn4W0\nNOjSxXcSiQV9+sCgQZCc7DuJZMd990HVqjZYKtGXF10lBwK1gKOx/Wz/A4YBg4GT+O9xAC9gxwGk\nAk8750bv5TFjsnCbO9dmqy6+2H4BFSvmO1FkjB5tXScrVbIXVG0cl1j2xx9wzjnWNbV6dd9pZE/v\nv297aqdNs1l+SWyrV9sRHd99B6VK+U4jsWDnTjsSKTk5NreWyD82bLDv1U8/WRMwiT4dwJ0LzkG3\nbra88O23w3nY7M6dNpM4Y4bt1StXzncikf9KT7elNjVqQKtWvtPI3jhn50ded50tiZLE1rSp/X7p\n2tV3Eokl7dvDDz9A374Hfl/xp3t365D+ySe+kyQuFW45tH49PPww/PqrTe2HuaBxzho9tG4N/frZ\nwboiseTtt2HAADszTLM5seunn6BaNZt1q1DBdxrx5a+/rLvyggVw4om+00gs2bDBul7rZyO2nXuu\nNRbSfkR/In6OW5hNnWpLssqWtQuQMBdtYPvbHn8cPvvMusN16KDOTxI7vv/ezmzr319FW6w79VRr\nUPLwwzZLKonpjTdshYouzGVPRxwBdetqJjaWzZljBXY893JIRAk545aebtP4XbrYXo0bb/QWxZvf\nfoPbboNTTrEjD8Kyn0/i086dNoPTsKEVAxL70tLgkkvgnntsGbYkljVrrAvd/PlQuvSB318Sz/Ll\ndrbbL7/AYYf5TiN7euIJOP54dQn2TUslD2DVKmtrvXOnLclK5JHCHTvs+IC5c23f28kn+04kier5\n5+HHH+Hzz9X5NJ4sXgw1a8I339jKBUkczZvDpk3w7ru+k0gsu/tu61j47LO+k0hW27bZgMu8eRp4\n8U1LJfdj6lRbz3vhhTBhQmIXbQCFC1uXyYcftq/JuHG+E0kimjzZzkp8/30VbfGmYkVrTlG/vpZd\nJ5K1a+GDD6BFC99JJNY1aQJvvQW7d/tOIlkNGWKrXFS0xZ+EKdy+/NIO8u3d27pHag+NCQI7oPuT\nT2wmsnNnXYBJ9GzZAg88AO+9B8cc4zuN5EbTprZPondv30kkWjp3hjvugJNO8p1EYt3559ue2MGD\nfSeRrHr2hEce8Z1CciMhlkr26gUvvWTFW9WqUfu0cWfFCrj1VjjjDPuaFSrkO5GE3eOP25LdPn18\nJ5GDsWCBdSWbOxdOOMF3GslL69ZZJ9Fvv9XyWMmeUaOgWTPbD6lVFf4tXWrnFf/2m67zYoGWSmbh\nHLz6KrzyCkyapKLtQMqUsTbsf/9to6k7d/pOJGE2diyMGGHLaCS+nXWWbXRv0EAz9mH35pvW2EpF\nm2TXNddA/vz2ei/+9e5tHT9VtMWn0M64pafDM89YwTZqFJQqlaefLlR27bJOcdu2WbOIwoV9J5Kw\n2bQJzjzTlmtcdZXvNBIJu3ZZB7kWLez1Q8Jn/XooXx5mz7aOxCLZNXiwDdJNnapZN59277YlzhMn\n2h5l8U8zbthM0d1327T8pEkq2nKqUCEYONDa9950kxVwIpH0zDNw/fUq2sKkUCEbyW3c2FrFS/i8\n9RbcfLOKNsm522+3pjaTJ/tOkthGjrQ9hyra4lfoZtz+/tuWcRx2mLX712xR7qWm2kHdv/8Ow4fr\nrDeJjK++soY4CxZA8eK+00ikNW1qx6707+87iUTShg1QrhzMmmUXfiI51auXzbyNHu07SeK68UYr\noh94wHcSyZTQ57itXg3XXWddjN55x9ZUy8FJS7POQz/9ZOvTDz3UdyKJZ+vW2X6oAQOgVi3faSQv\nbN1qy2DffReuvtp3GomU1q3tQOUPP/QcROLWrl1W9A8dasuqJbp+/91em3/7TQPxsSRhl0r+9BPU\nqAE33AA9eqhoi5T8+W2UrGJFuwjbtMl3IolnTz0Fd92loi3MihWz1+AGDayIk/i3cSN06wYvvug7\nicSzQoXsXLfXXvOdJDH17Qt33qmiLd6FYsZt7lzbL9OypbUXl8hLT4eGDeGbb2yZQ4kSvhNJvPns\nM7vwmzcPihTxnUby2v33w7HHQqdOvpPIwWrbFn78Efr1851E4t3WrbZHctIk7bOKpvR0ayz0ySfq\nsB5rEm6p5Lff2vLI7t2thb3kHees8cDXX1sr9yOP9J1I4sVff9kSyaFDoXp132kkGtassWU5X31l\ny9clPv39ty1vmzrVzm8TOVht28LPP+v8zmiaOBGeflpn6cWihFoqOXeuFW3vvaeiLRqCwM7wuewy\nu61d6zuRxAPn4LHHrNGNirbEccwx0LEj1K9vLaglPnXtasvkVbRJpDz1FHz5Jfz6q+8kiaNnT+tX\noKIt/sXtjNv8+fbLpHt365Aj0eMcvPSSvfCOGwfHHec7kcSy/v2hQwdbZnvIIb7TSDQ5Z6/TV1wB\nzz/vO43k1ObNNts2ebKWtUlkPf887NgBb7/tO0n4bdgAJ59svSCOOsp3GtlTQiyVXLgQrrzSnvB3\n3RXBYJJtzkGrVla8TZoEhx/uO5HEot9/h3POgeRkOPdc32nEh59/hgsugJkz1UY+3rz2mv2+HTDA\ndxIJmz//hDPOgMWLbS+s5J1u3Wyp88CBvpPI3oS+cFu0CC6/HDp3tkO2xR/n/jmPKzlZDSfk35yz\npkEXXGCtxCVxvfGGvUaMHaulOvFi0yZrZpCSApUq+U4jYfT447ZXvl0730nCyzmoUsWumS+/3Hca\n2ZtQF26LF9sPXocOcN99eRBMciw93b4XW7fCkCFQoIDvRBIreva08xRnzoSCBX2nEZ9SU6FaNRvo\nqVfPdxrJjhdesFkRNZCQvJI5G//TT1q1k1e++cZWpi1bBvniuqtFeIW2cFuyxBpivPqqfvHHml27\n4Kab4PjjoXdvjaiL/SKuXt06WVWu7DuNxII5c+Daa23pnZZGxbaVK+Hss+3ojtKlfaeRMLv/flsy\n2by57yTh9PjjcMIJ1pdAYlMoC7cff7SirU0beOihPAwmubZ1qzUgqFnTOslJ4kpLg0susU6vjRv7\nTiOx5Lnn4P/au/d4m6v8j+OvlcqkptSocYt0E4okFAoTQhc0pVRokq5+1aRRmiGRR6WamKSESu5S\nxy0lREkiIbeD0ZByH7lUijjr98dnn3GSdI5z9l7fvff7+Xh4OHsfx/5o9d37+1nrsz5r/XoYNix0\nJHIo7dpZcq2DkiXeli61SqrVq7XdoqDt2gWlS9t2ltKlQ0cjvybljgP44gu7qLt2VdIWZcceC2+/\nDe+8A716hY5GQurVy7pH3ndf6Egkah57DGbPtvcJiabFi2HCBHjoodCRSDqoVMmqM155JXQkqWf4\ncKhVS0lbqon0itvq1VCvHnTuDHfeGf+4JP/WrbNVty5dlGinowULrP37vHlQpkzoaCSKpkyxs92W\nLIHjjgsdjRzoiiusa/P994eORNLFnDlw/fVWXaX90AVj3z5rKtS/v91HS3SlzIrb2rVWHtmpk5K2\nZFKqFEyebPXUY8eGjkYS6ccfrVHNc88paZNf17ChldJ27Ro6EjnQ9OmQmWn7YkQSpWZNOypE7eoL\nzrhxULQo1K0bOhIpaJFccdu40T7Y77pLe2SS1WefWSOC0aM125MuHnjAVlxHjlSDGjm0//7XmtZM\nmADVq4eORsA6BNesadexjtqRRJs61brOLlmi7of55b2Vnz70EFxzTeho5Lck/Yrb1q02I9u6tZK2\nZFatGowaZW1o588PHY3E27RplqS/+KKSNvltxYrZuUK33gq7d4eORsCuX++tZE0k0S67zEqnx40L\nHUny+/BD2LYNmjULHYnEQ6RW3HbutIu3fn07q003gMkvIwPuuQc++MAOc5XUs307VK4MAwbY/jaR\n3PAeWrSwlbfHHw8dTXrbvdv2wwwcaJ+/IiFkZNiRT3Pn6v4vP5o2tffW9u1DRyK5kbQrbrt2wZVX\nWtmMkrbU0aIF9OgBjRrZYa6Sejp0sJk9JW2SF87BSy9Zwv/ZZ6GjSW8vvQTlyytpk7CaNbNzYceP\nDx1J8lq0yM5fbN06dCQSL5FYcdu92y7YU06B115TfXMq6tkTxoyxlbfjjw8djRSUUaPg0UetHLZI\nkdDRSDIaNgyefNI6kRYuHDqa9LNjB5x9tu0xOu+80NFIunvnHdtnuXgxHHlk6GiSjw40Tz5JdwD3\n3r1WU++91djrQk1N3sPdd1u730mT4OijQ0ck+bVuHVxwgZ3fd+GFoaORZOU9NG9u5bY9eoSOJv08\n8og1BNM5WhIF3ltH8ZtugttuCx1NcvnyS/tM/uIL6ygpySGhiZtzbg2wE9gH/OS9r+GcOwkYBZQF\n1gAtvffbD/g5770nKwtuuQU2b7YNqZptTW379sG119ph3a+/rpXVZJaVBY0bwyWX2Jl9IvmxYQNU\nqWKz7dWqhY4mfXz9tf13//xzHdIr0TF3rnVDXLlSlRx5cd99dh/dq1foSCQvEr3HzQP1vPdVvfc1\nYs89DEzx3p8NTIs9/uUPetsbs2YNvPWWkrZ0UKgQDB9uB6trGT+59etnzYQ6dw4diaSCEiWsy+Qt\nt9geF0mMrl3h9tuVtEm01KgBF18M//pX6EiSx9atMGSIJW+S2vK74rYauNB7vzXHc8uBut77Tc65\n4sAM7/05B/yc79TJ8/771kZce57Sy9atUKeOHayuN5nks3y5rbR9/LE6hUrBUclkYi1eDA0a2KrG\nCSeEjkbk5/79b6hVyz5v/vCH0NFEX/fuVio5aFDoSCSvEl0q+R9gB1Yq2d97P8A5t817f2Ls+w74\nJvtxjp/z557rmTFDF2S6+vJLqF3bZtlbtgwdjeTWnj32Ydq+PdxxR+hoJNVs2ADnn28lkxdcEDqa\n1HbFFXZm6v33h45E5ODuvhuOOQaefTZ0JNG2axecdpqd33bOOb/5xyViEp24lfDeb3DOnQxMAf4P\nGJ8zUXPOfeO9P+mAn/MbNniKFz/sl5YU8PnnduMwejTUqxc6GsmN++6zpDsjQ0d2SHwMHWp7NObN\nUxOjeJk+Hdq1g8xMbVOQ6Nq40Tokzp8PZcuGjia6+va1rrBjx4aORA5HXhO3fPVw9N5viP2+xTmX\nAdQANjnninvvNzrnSgCbD/azL73U7X9f16tXj3q6c087VarAyJG24jZtmlpRR93o0TBxop25paRN\n4uWmm+z/tccft/IfKVhZWdCpkx10rKRNoqx4ceuF0KWLNTSTX9q711Ykhw8PHYnk1owZM5gxY8Zh\n//xhr7g554oAhbz33zrnjgXeAx4DGgBbvfdPOeceBop67x8+4Gd9flb6JLWMHGk3ErNmwamnho5G\nDmbFCtuXOHmyStgk/rK7TL77rv5/K2hDh0Lv3ta5T519Jep27rRzBidPtvcE+bkRI+DFF61MUpJT\nwkolnXPlgIzYwyOBYd77J2LHAYwGyvAbxwGIZPvnP21T7UcfwYkn/vafl8T5/nuoWdPKJNu3Dx2N\npIshQ+Dpp1UyWZC2bLHKhvHjrXOfSDJ4/nnb9zppUuhIosV7qFoVeva0PauSnJLuAG6RbA88YDdp\nkyfbhmQJz3to29ZKI197TSWSkjjeQ7Nm1qxEJZMF48YboWRJeOaZ0JGI5N6ePVChAgwcCPXrh44m\nOt57z+6bFi3S6nkyU+ImSSsry/a3/PADjBkDR+ZrB6YUhAED7CydTz6xg9NFEmn9ekvcVDKZfxMm\nwF//ajd5OtRYks3IkbaXa+5cTSBmu+wym1ht0yZ0JJIfiT6AW6TAHHEEDB5ss2u33mqJnISzYAH8\n/e+WRCtpkxBKlrSbtVtusQkdOTw7dsBdd9lEjJI2SUYtW9o9wZgxoSOJhnnz7Ky7Vq1CRyKJphU3\niZxdu+Dyy612u08fza6FsH07VKsGTzyhc/YkLO/t5uSEE6B//9DRJKfsMxf130+S2dSpNgGxbBkc\ndVToaMJq2RIuvthW0SW5qVRSUsL27VbLfvXV8NhjoaNJL95DixZQpoyVSYqE9u23cOGF1hb85ptD\nR5Ncpk+3UqolSyz5FUlmjRpB8+Z2OHe6WrXKkrbVq+G440JHI/mlxE1SxubNcMklNsN2//2ho0kf\nzzxj5SgffqhufhIdixbZno4PPoCKFUNHkxx27bIukn36wJVXho5GJP8WLICmTa1MMF2TljvugFNO\ngR49QkciBUGJm6SUtWsteevWDf7yl9DRpL6ZM+G662wDeJkyoaMR+blBg+zokLlzte8yNzp2hI0b\nYdiw0JGIFJybboLy5aFr19CRJN6CBdC4MSxdCsWKhY5GCoISN0k5K1ZAvXrwwgtwzTWho0ldmzbZ\nvraBA+2DQSRqvLdGJaDjKX7LnDl2nMLixXDyyaGjESk4//kPVK8OmZm28pQu9u2zEsk777QGbpIa\n1FVSUk758nbw5p132uZkKXj79tkZT7feqqRNoss56NfPOqq9+mroaKJr925o1w5691bSJqnn9NNt\n32anTqEjSaz+/aFw4f2TV5KetOImSWPmTFtxmzABLroodDSp5cEHYeFCO/y8UKHQ0YgcWmYmXHop\nTJsGlSuHjiZ6unWD+fNh3DitSkpq+u47qFLFjgtp3jx0NPG3caPtV50xAypVCh2NFCSVSkpKmzTJ\n9rpNnWpvYpJ/zzxjqxczZ8JJJ4WORiR3hg61zfmffgrHHx86muhYvBj+9CebiClVKnQ0IvEzaxZc\ney18/nnql0zeeCOULWtH9EhqUeImKW/UKHjgAesud+aZoaNJboMHw6OPwkcfQenSoaMRyZs77rDD\npUeM0MoSwN69tgfm9tuhffvQ0YjE38MPw/LlkJGRuu8BU6bYNb10KRQpEjoaKWja4yYp7/rrLdlo\n0MBaAsvhmTjRPvTefVdJmySnPn2sedGLL4aOJBr69IHf/x5uuy10JCKJ8dhjdp7Z66+HjiQ+fvzR\nzqzr21dJmxituEnSGjDA9nK8+67KJvNq1iw7ZHviRKhRI3Q0Iodv1SqoVcvKqC+8MHQ04axaZXt/\n58yBM84IHY1I4mSf8ThvnpUTppJu3az8+c03Q0ci8aJSSUkrI0fa4dzjxysBya0lS+xDbsgQaNQo\ndDQi+TdmjHWYmz8fihYNHU3i7dlj13SLFlZGLpJunnrKJnGnTYMjUqSWbMUKqF3b9quqKiZ1KXGT\ntPP229awZNQoqF8/dDTR9uWXUKcO9OoFrVqFjkak4Nx7L3z1Fbz1VurudTkY7631/3//a/t81BVW\n0tG+fdZp9rrrbDI32Xlv20Guuio1/j3y67THTdLOFVfA6NG2923ChNDRRNeWLbbC9re/KWmT1PP0\n07BunZUWpZOePa1UbMQIJW2SvgoVsn1uPXvCsmWho8m/4cPhm2+gQ4fQkUjUaMVNUsbcuXD11Xbo\n7A03hI4mWr77zlqEN2xoH2wiqWjTJqhXz1bg0+Fw3qFD4R//gNmzoUSJ0NGIhPfyy/Zr9mw46qjQ\n0RyebdugYkUYOxZq1gwdjcSbSiUlrS1ZApdfbl0nb789dDTRsGcPXHmlbdp++eX0KiOT9LNuHdSt\na6WT994bOpr4+eADKwubPl0H8opk896qcGrUSN7V97vuss/pfv1CRyKJkNfE7ch4BiOSaOeeazc0\nDRvCzp3w4IOhIworKwvatoVjj7WW6UraJNWVKmUNCurWhd/9LjUncJYvh5YtrTxSSZvIfs7BoEFw\n/vnQtGnyNS375BMYNy41yj0lPpS4Sco580yYOdOStx07oHv39ExYsrJsxWHDBuu2daSudkkTZcvC\n1KnWrOiYY6B169ARFZzNm+2G9MknrZOkiPxciRLw/PPQpo11mk2W88/27oU774Rnn03P7riSOyqV\nlJS1ZYuVTdapY/veUqVFcG58/72ttK1fD++8AyecEDoikcTLzLTkpndvW6FKdrt22V7VRo1sQkpE\nft2NN8LJJ9vB9Mngn/+0z+v33kvPyeZ0pT1uIjls324NS44/Hl57DYoVCx1R/K1dC82aQZUq0L8/\nFC4cOiKRcBYtskSnf3+7LpJVVpbtaStSxLrn6cZO5NC2bYPKle2zP+qr0ytXQq1a1lTlrLNCRyOJ\npOMARHIoWtT2u1SqBFWrwocfho4ovmbNgosustKwV19V0iZSubKd9di+vZUMJ6tOneystoEDlbSJ\n5MaJJ9p+t7ZtYdWq0NH8upUrLbHs1UtJm/w2JW6S8o46Cp56CgYMsLPeune3wzpTzSuvQIsW9kH1\nwAO6uRPJVq2atdZu3Rrefz90NHn3wgswcaIdsK3JGJHca9TIukvWrQuffx46ml9audLKn7t1g1tv\nDR2NJAOVSkpaWb8ebr7Zvh46FEqWDBtPQdi71w7VfvttGD8ezjkndEQi0TRjhpUbjh0LtWuHjiZ3\nJk60zpgffQSnnx46GpHk9MYbdph1RoaVJEZBdtLWvbuStnSmUkmRQyhZEqZMsW5z1aold+kUWA3/\nFVfA0qUwZ46SNpFDqVcPhg2zlelPPw0dzW+bNs0OE8/IUNImkh/XXQeDB0Pz5jB5cuhoYMUKS9p6\n9FDSJnmjxE3STqFC0KULjBpl+146dYKffgodVd6tWGH72SpUgEmTrJ5fRA6tUSMrJ27SxM42zMoK\nHdEv7dlj70tt2sDIkVCzZuiIRJJf48Y2CdKmDYwZEy6OFStsT9vjj9vEjEheKHGTtHXppbBggR10\necklsGZN6Ihyb/Jki7lTJ2t1rjPaRHLvqqvsrMfBg6FBA1i9OnRE+2Vm2oTMihWwcGH0u+GJJJPa\nte3z8957bQIn0ZYvt2u6Z0+45ZbEv74kPyVuktaKFYMJE+yMpxo1rIwqijPw2bZsgQcftDf8t96C\ndu1CRySSnCpUsC6sTZpA9erQr1/Ya997eOklm1C64w7bh3fyyeHiEUlV559v+1179IBnnknc6+ZM\n2tq2TdzrSmpRcxKRmE8/hXvugW+/hYcegptuso6UUbB1q33AvPwytGoFjzySGo1VRKIgM9NKlooU\nsVn4cuUS+/pbttgkzLp1Nnmkvaoi8ff119CwIVxzjZUtxrMTc2amre4/8YSVaopkU3MSkcNUvbo1\n+OjbF4YMgTPPhOefh127wsX0zTfwj3/A2WdbI5IFCyw+JW0iBSd79a1pU3sfeOGFxK2+vfuurQBU\nrGiH7yppE0mM0qXtbNfJk23SNl7X/LJllrQ9+aSSNsm/uKy4OecaA72BQsBA7/1TB3xfK24SeXPn\n2uzY7NlWD3/33XagdyJs3w7PPWc3kM2bW/J22mmJeW2RdLZ8uXV5O/poOxsxXt0cf/wRHn7YSp4H\nD7ZOtyKSeDt32r7XUqXgtdfs2i8oixZZOfZTT+0/ikgkp+Arbs65QkBfoDFQEWjlnKtQ0K8j8TNj\nxozQIURCjRrWgWraNLuZO+MM6NwZNm2K32vu3Gl192eeCWvX2grgwIG/TNo0RtGnMYq2Xxufc86x\nxiVXXWXvAX37FmzXWe+tLLt6dTtXcuFCJW2/RtdQ9KXCGB1/vK18795tq3Dt29vjPXsO7+/76iub\neK1Vy67tp58Ol7SlwvjIz8WjVLIGsMp7v8Z7/xMwEmgWh9eRONGF/nOVKsHrr8O8eZZYVahgZRUT\nJsAXX8C+fYf/d2dlWTfLiRPtiIIzzrBDOT/+GF591R4fjMYo+jRG0Xao8SlUCDp2tPLJN9+Ek06y\nM5e6dLEbuh07cv86e/fCZ59Z99c//xmKF4drr7W/f9Qo+7vl4HQNRV+qjNExx9i1nn0eavfudq22\naQPjxsEPPxz653Mma1WrwpIl0LUrbNwIN96YmH/DwaTK+Mh+8WgiXgr4KsfjrwGdQiNJr1w5K13s\n0sW6v734otWub94M5ctbglex4v5fp5++v02/9za7vmSJHZad/fuyZTbbd+65cN55Vm9fQevTIpFQ\nvjxMn26ly7NnWyL35JM2iXP66dZavE4d+71sWWtu8MMPdvM3c6b9+uQTKFPG/tw119jNXZkyof9l\nInIw5crZpErHjtYsKCPDJl3atoXLL7fJl6ZN4bjjLFkbMwbeeMMmXJs1s2Ttssui09hMUk88Ejdt\nXpOUVrw4dOu2//F331nHqGXL7NegQZaUbdgAZ51lb/DLlkHhwpbcnXuuHajbrp0leDo4WyTaiha1\nfSpNmtjjn36yRkGzZtmNXceOtkpXqpRd++edZ+csdugAI0bAH/4QNn4RybtSpewa7tDBJmjHjbN9\nr+3b20TNunW2B13JmiRSgTcncc5dBHTz3jeOPe4MZOVsUOKcU3InIiIiIiJpLS/NSeKRuB0JrAAu\nA9YDc4FW3vvMAn0hERERERGRNFHgpZLe+73OuQ7AZOw4gEFK2kRERERERA5fXM5xExERERERkYIT\nj+MAfpVzrrFzbrlz7t/OuYcS+dpycM65V5xzm5xzi3M8d5JzbopzbqVz7j3nXIKOnZYDOedOdc5N\nd84tdc4tcc7dG3teYxQRzrnfOefmOOcWOueWOeeeiD2vMYoY51wh59wC59yE2GONUUQ459Y4L3La\nLwAABDpJREFU5xbFxmdu7DmNT4Q454o658Y45zJj73U1NUbR4ZwrH7t+sn/tcM7dqzGKDudc59j9\n3GLn3HDnXOG8jk/CEjcdzB1Zr2JjktPDwBTv/dnAtNhjCeMn4K/e+0rARcA9setGYxQR3vsfgfre\n+/OBykB951wdNEZRdB+wjP3djzVG0eGBet77qt77GrHnND7R0geY5L2vgL3XLUdjFBne+xWx66cq\nUA3YBWSgMYoE59xpQHvgAu/9edh2shvI4/gkcsVNB3NHkPd+JrDtgKevBgbHvh4MNE9oUPI/3vuN\n3vuFsa+/AzKxsxI1RhHivd8V+/Jo7M14GxqjSHHOlQaaAgOB7A5eGqNoObCzmsYnIpxzJwCXeO9f\nAetn4L3fgcYoqhpg99xfoTGKip3YZHyRWCPHIlgTxzyNTyITt4MdzF0qga8vufdH7/2m2NebgD+G\nDEZMbLamKjAHjVGkOOeOcM4txMZiuvd+KRqjqHkO+BuQleM5jVF0eGCqc26ec6597DmNT3SUA7Y4\n5151zs13zg1wzh2LxiiqbgBGxL7WGEWA9/4b4FlgLZawbffeTyGP45PIxE1dUJKQt+41GrvAnHPH\nAW8C93nvv835PY1ReN77rFipZGngUudc/QO+rzEKyDl3JbDZe7+AX67qABqjCKgdK/FqgpWEX5Lz\nmxqf4I4ELgD6ee8vAL7ngJIujVE0OOeOBq4C3jjwexqjcJxzZwD3A6cBJYHjnHM35/wzuRmfRCZu\n64BTczw+FVt1k+jZ5JwrDuCcKwFsDhxPWnPOHYUlbUO892NjT2uMIihWOvQ2tr9AYxQdtYCrnXOr\nsVnoPznnhqAxigzv/YbY71uwfTk10PhEydfA1977T2OPx2CJ3EaNUeQ0AT6LXUug6ygqLgQ+9t5v\n9d7vBd4CLiaP11AiE7d5wFnOudNiswHXA+MT+PqSe+OBtrGv2wJjD/FnJY6ccw4YBCzz3vfO8S2N\nUUQ454pld4Fyzh0DNAQWoDGKDO/9I977U7335bASove9963RGEWCc66Ic+73sa+PBRoBi9H4RIb3\nfiPwlXPu7NhTDYClwAQ0RlHTiv1lkqDrKCqWAxc5546J3ds1wJpl5ekaSug5bs65JkBv9h/M/UTC\nXlwOyjk3AqgLFMNqa7sC44DRQBlgDdDSe789VIzpLNad8ENgEfuXzzsDc9EYRYJz7jxsQ/ERsV9D\nvPdPO+dOQmMUOc65ukBH7/3VGqNocM6Vw1bZwEryhnnvn9D4RItzrgrW3Odo4AvgL9j9nMYoImIT\nH18C5bK3Veg6ig7nXCcsOcsC5gO3Ab8nD+OjA7hFREREREQiLqEHcIuIiIiIiEjeKXETERERERGJ\nOCVuIiIiIiIiEafETUREREREJOKUuImIiIiIiEScEjcREREREZGIU+ImIiIiIiIScUrcRERERERE\nIu7/AQR4aUbBfITVAAAAAElFTkSuQmCC\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,5));\n",
"plt.plot(n, sw_func)\n",
"plt.plot(n, lw_func, color='red')"
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
mbp28/dpp_nets | .ipynb_checkpoints/Evaluation for REINFORCE-checkpoint.ipynb | 1 | 22585 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import argparse\n",
"import os\n",
"import shutil\n",
"import gzip\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.autograd import Variable\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch.autograd import Variable\n",
"\n",
"from dpp_nets.utils.language import Vocabulary, BeerDataset, custom_collate\n",
"from dpp_nets.layers.layers import ChunkTrainerReinforce, ChunkTrainerRelReinforce\n",
"\n",
"from dpp_nets.utils.language import EvalSet"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# Load saved checkpoint\n",
"model = 'shortwords1reg0.1reg_mean30.0lr0.0001reinforce_ckp.pth.tar'\n",
"model_dir = '/Users/Max/checkpoints/beer_reviews/reinforce/' \n",
"model_path = model_dir + model\n",
"model = torch.load(model_path, map_location=lambda storage, loc: storage)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from dpp_nets.utils.language import Vocabulary\n",
"\n",
"embd_path = '/Users/Max/data/beer_reviews/review+wiki.filtered.200.txt.gz'\n",
"word_path = '/Users/Max/data/beer_reviews/reviews.all.train.words.txt.gz'\n",
"\n",
"# Set-up Vocabulary\n",
"vocab = Vocabulary()\n",
"vocab.loadPretrained(embd_path)\n",
"vocab.setStops()\n",
"vocab.loadCorpus(word_path)\n",
"vocab.updateEmbedding()\n",
"vocab.setCuda(False)\n",
"vocab.EmbeddingBag.load_state_dict(model['embedding'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"EMBD_DIM = 200\n",
"KERNEL_DIM = 200\n",
"HIDDEN_DIM = 500\n",
"ENC_DIM = 200\n",
"TARGET_DIM = 3 if model['aspect'] in set(['all', 'short']) else 1\n",
"ALPHA_ITER = 1\n",
"\n",
"if model['mode'] == 'sents':\n",
" trainer = ChunkTrainerReinforce(EMBD_DIM, HIDDEN_DIM, KERNEL_DIM, ENC_DIM, TARGET_DIM, ALPHA_ITER)\n",
"else:\n",
" trainer = ChunkTrainerRelReinforce(EMBD_DIM, HIDDEN_DIM, KERNEL_DIM, ENC_DIM, TARGET_DIM, ALPHA_ITER)\n",
"\n",
"trainer.load_state_dict(model['model'])\n",
"trainer.activation = nn.Sigmoid()\n",
"trainer.reg = model['reg']\n",
"trainer.reg_mean = model['reg_mean']\n",
"\n",
"rat_path = '/Users/Max/data/beer_reviews/annotations.json'\n",
"evalset = EvalSet(rat_path, vocab)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"trainer.kernel_net.layer1.weight"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Plot a table\n",
"print('__________________________Training Table__________________________')\n",
"for k, v in model['train_loss'].items():\n",
" epoch, loss, pred_loss, reg_loss = k, v[0], model['train_pred_loss'][k][0], model['train_reg_loss'][k][0]\n",
" print(str.join(\" | \", ['Epoch: %d' % (epoch), 'Loss: %.5f' % (loss), \n",
" 'Pred Loss: %.5f' % (pred_loss), 'Reg Loss: %.5f' % (reg_loss)]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from dpp_nets.helper.plotting import plot_floats\n",
"\n",
"# Training Plots\n",
"plot_floats(model['train_loss'], xlabel='Epochs', ylabel='MSE + Reg', title='Training MSE + Reg')\n",
"plot_floats(model['train_pred_loss'], xlabel='Epochs', ylabel='MSE', title='Training MSE')\n",
"plot_floats(model['train_reg_loss'], xlabel='Epochs', ylabel='Reg', title='Training Reg')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('_________________________Validation Table_________________________')\n",
"for k, v in model['val_loss'].items():\n",
" epoch, loss, pred_loss, reg_loss = k, v[0], model['val_pred_loss'][k][0], model['val_reg_loss'][k][0]\n",
" print(str.join(\" | \", ['Epoch: %d' % (epoch), 'Loss: %.5f' % (loss), \n",
" 'Pred Loss: %.5f' % (pred_loss), 'Reg Loss: %.5f' % (reg_loss)]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from dpp_nets.helper.plotting import plot_floats\n",
"\n",
"# Training Plots\n",
"plot_floats(model['val_loss'], xlabel='Epochs', ylabel='MSE + Reg', title='Validation MSE + Reg')\n",
"plot_floats(model['val_pred_loss'], xlabel='Epochs', ylabel='MSE', title='Validation MSE')\n",
"plot_floats(model['val_reg_loss'], xlabel='Epochs', ylabel='Reg', title='Validation Reg')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Evaluation on Test Set\n",
"\n",
"loss, pred_loss, reg_loss = evalset.computeLoss(trainer, model['mode'])\n",
"print(str.join(\" | \", ['Test Set:', 'Loss: %.5f' % (loss), \n",
" 'Pred Loss: %.5f' % (pred_loss), 'Reg Loss: %.5f' % (reg_loss)]))\n",
"\n",
"prec, extract = evalset.evaluatePrecision(trainer,model['mode'])\n",
"print(str.join(\" | \", ['Test Set:', 'Precision: %.5f' % (prec), 'Extract: %.5f' % (extract)]))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"index is: 468\n",
"('weak',) set() [('weak',)]\n",
"('beers',) set() [('beers',)]\n",
"('substance',) set() [('substance',)]\n",
"('forgettable',) set() [('forgettable',)]\n",
"('sour',) {'1'} [('sour',)]\n",
"('exciting',) set() [('exciting',)]\n",
"('stout',) {'0'} [('stout',)]\n",
"('saying',) set() [('saying',)]\n",
"('quickly',) {'0'} [('quickly',)]\n",
"('likewise',) {'0'} [('likewise',)]\n",
"Precision is: 0.4\n",
"Extraction Percentage is: 0.08064516129032258\n",
"[(Let me start by saying it's exciting to see beers from Mexico which have color and substance and flavor., set()), (In fact this is the first craft beer I've ever had from Mexico.\t\t, set()), (Poured a meager 1\" head which quickly dissipated to lace., {'0'}), (Dark in color almost like a stout and flavor likewise was similar to a mild stout but with a lighter body., {'0'}), (Smell and taste both had touch of sour almost like a drop of Flemish sour.\t\t, {'1'}), (Anyway, a big step up from forgettable weak Mexican beers, but nowhere close to the craft beers from the States or from European classics.\t\t, set()), (But keep up the good work Cucapa., set())]\n"
]
},
{
"ename": "RuntimeError",
"evalue": "input and target have different number of elements: input[100 x 3] has 300 elements, while target[3] has 3 elements at /Users/soumith/miniconda2/conda-bld/pytorch_1503975723910/work/torch/lib/THNN/generic/MSECriterion.c:12",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-28-b8df56b68075>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Random Samples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mevalset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mode'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/git/dpp_nets/dpp_nets/utils/language.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, trainer, mode, ix)\u001b[0m\n\u001b[1;32m 626\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[0;31m# Prediction and target\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 628\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturnEmbds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreview\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclean\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVariable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 629\u001b[0m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 630\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Coding/anaconda2/envs/torch2/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_pre_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 225\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/git/dpp_nets/dpp_nets/layers/layers.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, words, target)\u001b[0m\n\u001b[1;32m 764\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactivation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 766\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 768\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Coding/anaconda2/envs/torch2/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_pre_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 225\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Coding/anaconda2/envs/torch2/lib/python3.6/site-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[0m_assert_no_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmse_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Coding/anaconda2/envs/torch2/lib/python3.6/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mmse_loss\u001b[0;34m(input, target, size_average)\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 818\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmse_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMSELoss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Coding/anaconda2/envs/torch2/lib/python3.6/site-packages/torch/nn/_functions/thnn/auto.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(ctx, input, target, *args)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m getattr(ctx._backend, update_output.name)(ctx._backend.library_state, input, target,\n\u001b[0;32m---> 47\u001b[0;31m output, *ctx.additional_args)\n\u001b[0m\u001b[1;32m 48\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: input and target have different number of elements: input[100 x 3] has 300 elements, while target[3] has 3 elements at /Users/soumith/miniconda2/conda-bld/pytorch_1503975723910/work/torch/lib/THNN/generic/MSECriterion.c:12"
]
}
],
"source": [
"# Random Samples\n",
"evalset.sample(trainer, model['mode'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"index is: 397\n",
"0 0.549766924786 ('luscious',)\n",
"1 0.519408797876 ('balanced',)\n",
"2 0.50610014369 ('alcohol',)\n",
"3 0.49311295878 ('pear',)\n",
"4 0.461492428317 ('aroma',)\n",
"5 0.394499445425 ('bitter',)\n",
"6 0.342531732584 ('minimal',)\n",
"7 0.323079637132 ('initial',)\n",
"8 0.30356740096 ('rather',)\n",
"9 0.303334538764 ('also',)\n",
"10 0.303199056182 ('orangy',)\n",
"11 0.28635213081 ('modest',)\n",
"12 0.279423096371 ('maple',)\n",
"13 0.262360241968 ('back',)\n",
"14 0.239793818183 ('offset',)\n",
"15 0.231872278559 ('took',)\n",
"16 0.226868038352 ('--',)\n",
"17 0.217652021045 ('medium',)\n",
"18 0.212753540135 (\"'s\",)\n",
"19 0.205490446092 ('potent',)\n",
"20 0.204760849527 ('quickly',)\n",
"21 0.188842681776 ('think',)\n",
"22 0.178785820959 ('poured',)\n",
"23 0.177608366315 ('clear',)\n",
"24 0.173306350039 ('dark',)\n",
"25 0.162810314981 ('fades',)\n",
"26 0.161427160731 ('2002',)\n",
"27 0.161040237487 ('cherry',)\n",
"28 0.16007593745 ('tart',)\n",
"29 0.159982566604 ('2003',)\n",
"30 0.158804968384 ('well',)\n",
"31 0.149028697022 ('malts',)\n",
"32 0.146853994495 ('expected',)\n",
"33 0.14161987744 ('spices',)\n",
"34 0.139594424588 ('reasonably',)\n",
"35 0.137691781302 ('head',)\n",
"36 0.133268548624 ('store',)\n",
"37 0.131682234088 ('taste',)\n",
"38 0.131602793425 ('somewhere',)\n",
"39 0.1310659085 ('kick',)\n",
"40 0.126137507511 ('sweetness',)\n",
"41 0.125647371761 ('sharp',)\n",
"42 0.125638971784 ('section',)\n",
"43 0.122690341967 ('date',)\n",
"44 0.122085377148 ('case',)\n",
"45 0.11598082348 ('start',)\n",
"46 0.115281511761 ('touch',)\n",
"47 0.112379916899 ('gamble',)\n",
"48 0.108956961116 ('thickness',)\n",
"49 0.106414950003 ('gold',)\n",
"50 0.10460946615 ('carbonation',)\n",
"51 0.102683578972 ('reported',)\n",
"52 0.0966297155991 ('mouthfeel',)\n",
"53 0.0956070130292 ('discounted',)\n",
"54 0.0885062963862 ('evident',)\n",
"55 0.0830458013677 ('hops',)\n",
"56 0.080061961501 ('syrupy',)\n",
"57 0.0436853830314 ('finish',)\n"
]
}
],
"source": [
"# Random Marginals\n",
"evalset.computeMarginals(trainer, model['mode'], 397)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.012876863448606085, 0.012892113315031472)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evalset.computeMUEPredLoss(trainer, model['mode'],100)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.014311427982221337, 0.0831022014637619)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evalset.computeMAPPredLoss(trainer, model['mode'])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from torch.autograd import Variable\n",
"review = evalset.vocab.returnEmbds(evalset.words[0].clean.keys()).unsqueeze(0)\n",
"target = Variable(torch.stack([evalset.targets[0] for _ in range(100)]))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Variable containing:\n",
"1.00000e-02 *\n",
" 2.3543\n",
"[torch.FloatTensor of size 1]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.alpha_iter = 100\n",
"trainer.sampler.alpha_iter = 100\n",
"trainer(review, target)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
rdhyee/working-open-data-2014 | notebooks/Day_07_G_Calculating_Diversity.ipynb | 1 | 21805 | {
"metadata": {
"name": "",
"signature": "sha256:20862a8fdae32d268a93a87d575c77ca37600f6fe6eb755d6875ac87687ec3d0"
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Goals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Learn about how to use the Census variables around Hispanic origin to calculate quantities around diversity (remembering the [Racial Dot Map](http://bit.ly/rdotmapintro) as our framing example)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab --no-import-all inline"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from pandas import DataFrame, Series, Index\n",
"import pandas as pd\n",
"\n",
"from itertools import islice"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import census\n",
"import us\n",
"\n",
"import settings"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The census documentation has example URLs but needs your API key to work. In this notebook, we'll use the IPython notebook HTML display mechanism to help out.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"c = census.Census(key=settings.CENSUS_KEY)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# generators for the various census geographic entities of interest\n",
"\n",
"def states(variables='NAME'):\n",
" geo={'for':'state:*'}\n",
" states_fips = set([state.fips for state in us.states.STATES])\n",
" # need to filter out non-states\n",
" for r in c.sf1.get(variables, geo=geo):\n",
" if r['state'] in states_fips:\n",
" yield r\n",
" \n",
"def counties(variables='NAME'):\n",
" \"\"\"ask for all the states in one call\"\"\"\n",
" \n",
" # tabulate a set of fips codes for the states\n",
" states_fips = set([s.fips for s in us.states.STATES])\n",
" \n",
" geo={'for':'county:*',\n",
" 'in':'state:*'} \n",
" for county in c.sf1.get(variables, geo=geo):\n",
" # eliminate counties whose states aren't in a state or DC\n",
" if county['state'] in states_fips:\n",
" yield county\n",
" \n",
"\n",
"def counties2(variables='NAME'):\n",
" \"\"\"generator for all counties\"\"\"\n",
" \n",
" # since we can get all the counties in one call, \n",
" # this function is for demonstrating the use of walking through \n",
" # the states to get at the counties\n",
"\n",
" for state in us.states.STATES:\n",
" geo={'for':'county:*',\n",
" 'in':'state:{fips}'.format(fips=state.fips)}\n",
" for county in c.sf1.get(variables, geo=geo):\n",
" yield county\n",
"\n",
" \n",
"def tracts(variables='NAME'):\n",
" for state in us.states.STATES:\n",
" \n",
" # handy to print out state to monitor progress\n",
" # print state.fips, state\n",
" counties_in_state={'for':'county:*',\n",
" 'in':'state:{fips}'.format(fips=state.fips)}\n",
" \n",
" for county in c.sf1.get('NAME', geo=counties_in_state):\n",
" \n",
" # print county['state'], county['NAME']\n",
" tracts_in_county = {'for':'tract:*',\n",
" 'in': 'state:{s_fips} county:{c_fips}'.format(s_fips=state.fips, \n",
" c_fips=county['county'])}\n",
" \n",
" for tract in c.sf1.get(variables,geo=tracts_in_county):\n",
" yield tract\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def block_groups(variables='NAME'):\n",
" # http://api.census.gov/data/2010/sf1?get=P0010001&for=block+group:*&in=state:02+county:170\n",
" # let's use the county generator\n",
" for county in counties(variables):\n",
" geo = {'for':'block group:*',\n",
" 'in':'state:{state} county:{county}'.format(state=county['state'],\n",
" county=county['county'])\n",
" }\n",
" for block_group in c.sf1.get(variables, geo):\n",
" yield block_group\n",
" \n",
" \n",
"def blocks(variables='NAME'):\n",
" # http://api.census.gov/data/2010/sf1?get=P0010001&for=block:*&in=state:02+county:290+tract:00100\n",
" \n",
" # make use of the tract generator\n",
" for tract in tracts(variables):\n",
" geo={'for':'block:*',\n",
" 'in':'state:{state} county:{county} tract:{tract}'.format(state=tract['state'],\n",
" county=tract['county'],\n",
" tract=tract['tract'])\n",
" }\n",
" for block in c.sf1.get(variables, geo):\n",
" yield block\n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# msa, csas, districts, zip_codes\n",
"\n",
"def msas(variables=\"NAME\"):\n",
" \n",
" for state in us.STATES:\n",
" geo = {'for':'metropolitan statistical area/micropolitan statistical area:*', \n",
" 'in':'state:{state_fips}'.format(state_fips=state.fips)\n",
" }\n",
" \n",
" for msa in c.sf1.get(variables, geo=geo):\n",
" yield msa\n",
"\n",
"def csas(variables=\"NAME\"):\n",
" # http://api.census.gov/data/2010/sf1?get=P0010001&for=combined+statistical+area:*&in=state:24\n",
" for state in us.STATES:\n",
" geo = {'for':'combined statistical area:*', \n",
" 'in':'state:{state_fips}'.format(state_fips=state.fips)\n",
" }\n",
" \n",
" for csa in c.sf1.get(variables, geo=geo):\n",
" yield csa\n",
"\n",
"def districts(variables=\"NAME\"):\n",
" # http://api.census.gov/data/2010/sf1?get=P0010001&for=congressional+district:*&in=state:24\n",
" for state in us.STATES:\n",
" geo = {'for':'congressional district:*', \n",
" 'in':'state:{state_fips}'.format(state_fips=state.fips)\n",
" }\n",
" \n",
" for district in c.sf1.get(variables, geo=geo):\n",
" yield district \n",
" \n",
"def zip_code_tabulation_areas(variables=\"NAME\"):\n",
" # http://api.census.gov/data/2010/sf1?get=P0010001&for=zip+code+tabulation+area:*&in=state:02\n",
" for state in us.STATES:\n",
" geo = {'for':'zip code tabulation area:*', \n",
" 'in':'state:{state_fips}'.format(state_fips=state.fips)\n",
" }\n",
" \n",
" for zip_code_tabulation_area in c.sf1.get(variables, geo=geo):\n",
" yield zip_code_tabulation_area "
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list(islice(msas(), 1))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list(islice(csas(), 1))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"districts_list = list(islice(districts(), 1))\n",
"districts_list"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list(islice(zip_code_tabulation_areas(), 1))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: There are definitely improvements to be made in these generators. One of the most important would be to limit the generators to specific geographies -- typically, we don't want to have all the blocks in the country but the ones in a specific area. A good exercise to rewrite our generators to allow for limited geography."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can compare the total number of tracts we calculate to:\n",
"\n",
"https://www.census.gov/geo/maps-data/data/tallies/tractblock.html\n",
"\n",
"and\n",
"\n",
"https://www.census.gov/geo/maps-data/data/docs/geo_tallies/Tract_Block2010.txt"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Hispanic or Latino Origin and Racial Subcategories"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://www.census.gov/developers/data/sf1.xml\n",
"\n",
"compare to http://www.census.gov/prod/cen2010/briefs/c2010br-02.pdf \n",
"\n",
"I think the P0050001 might be the key category\n",
"\n",
"* P0010001 = P0050001\n",
"* P0050001 = P0050002 + P0050010\n",
"\n",
"P0050002 Not Hispanic or Latino (total) = \n",
"\n",
"* P0050003 Not Hispanic White only \n",
"* P0050004 Not Hispanic Black only\n",
"* P0050006 Not Hispanic Asian only\n",
"* Not Hispanic Other (should also be P0050002 - (P0050003 + P0050004 + P0050006)\n",
" * P0050005 Not Hispanic: American Indian/ American Indian and Alaska Native alone\n",
" * P0050007 Not Hispanic: Native Hawaiian and Other Pacific Islander alone\n",
" * P0050008 Not Hispanic: Some Other Race alone\n",
" * P0050009 Not Hispanic: Two or More Races\n",
"\n",
"* P0050010 Hispanic or Latino\n",
" \n",
"P0050010 = P0050011...P0050017\n",
"\n",
"From [Hispanic and Latino Americans (Wikipedia)](https://en.wikipedia.org/w/index.php?title=Hispanic_and_Latino_Americans&oldid=595018646): \n",
"\n",
"<blockquote>While the two terms are sometimes used interchangeably, Hispanic is a narrower term which mostly refers to persons of Spanish speaking origin or ancestry, while Latino is more frequently used to refer more generally to anyone of Latin American origin or ancestry, including Brazilians.</blockquote>\n",
"\n",
"and\n",
"\n",
"<blockquote>The Census Bureau's 2010 census does provide a definition of the terms Latino or Hispanic and is as follows: \u201cHispanic or Latino\u201d refers to a person of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin regardless of race. It allows respondents to self-define whether they were Latino or Hispanic and then identify their specific country or place of origin.[52] On its website, the Census Bureau defines \"Hispanic\" or \"Latino\" persons as being \"persons who trace their origin [to]... Spanish speaking Central and South America countries, and other Spanish cultures\".</blockquote>\n",
"\n",
"In the [Racial Dot Map](http://bit.ly/rdotmap): \"Whites are coded as blue; African-Americans, green; Asians, red; Hispanics, orange; and all other racial categories are coded as brown.\" \n",
"\n",
"In this notebook, we will relate the Racial Dot Map 5-category scheme to the P005\\* variables."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# let's get the total population -- tabulated in two variables: P0010001, P0050001\n",
"# P0050002 Not Hispanic or Latino (total) \n",
"# P0050010 Hispanic or Latino\n",
"\n",
"r = list(states(('NAME','P0010001','P0050001','P0050002','P0050010')))\n",
"r[:5]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Hispanic/Latino origin vs not-Hispanic/Latino\n",
"# Compare with http://www.census.gov/prod/cen2010/briefs/c2010br-02.pdf Table 1\n",
"# Hispanic/Latino: 50477594\n",
"# non-Hispanic/Latino: 258267944\n",
"\n",
"df=DataFrame(r)\n",
"df[['P0010001', 'P0050001','P0050002','P0050010']] = \\\n",
" df[['P0010001', 'P0050001','P0050002','P0050010']].astype('int')\n",
"df[['P0010001', 'P0050001', 'P0050002', 'P0050010']].sum()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# is the total Hispanic/Latino population and non-Hispanic populations the same as reported in \n",
"# http://www.census.gov/prod/cen2010/briefs/c2010br-02.pdf Table 1\n",
"(df['P0050010'].sum() == 50477594,\n",
" df['P0050002'].sum() == 258267944)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# How about the non-Hispanic/Latino White only category?\n",
"# P0050003\n",
"# total should be 196817552\n",
"\n",
"df = DataFrame(list(states('NAME,P0050003')))\n",
"df['P0050003'] = df['P0050003'].astype('int')\n",
"df.P0050003.sum()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Converting to Racial Dot Map Categories"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SUGGESTED EXERCISE: write a function `convert_to_rdotmap(row)` tha takes an input Python dict that has the keys:\n",
" * NAME\n",
" * P005001, P005002...,P0050016, P0050017 \n",
" \n",
"and that returns a Pandas Series with the following columns:\n",
"\n",
" * Total\n",
" * White\n",
" * Black\n",
" * Asian\n",
" * Hispanic\n",
" * Other\n",
" * Name (note lowercase)\n",
" \n",
"that correspond to those used in the Racial Dot Map.\n",
"\n",
"Also write a function def convert_P005_to_int(df) that converts all the P005\\* columns to `int`"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# USE a little convience function to calculate the variable names to be used\n",
"\n",
"def P005_range(n0,n1): \n",
" return tuple(('P005'+ \"{i:04d}\".format(i=i) for i in xrange(n0,n1)))\n",
"\n",
"P005_vars = P005_range(1,18)\n",
"P005_vars_str = \",\".join(P005_vars)\n",
"P005_vars_with_name = ['NAME'] + list(P005_vars)\n",
"\n",
"P005_vars_with_name"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# HAVE YOU TRIED THE EXERCISE....IF NOT....TRY IT....HERE'S ONE POSSIBLE ANSWER# \n",
"\n",
"# http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/#create\n",
"\n",
"def convert_P005_to_int(df):\n",
" # do conversion in place\n",
" df[list(P005_vars)] = df[list(P005_vars)].astype('int')\n",
" return df\n",
"\n",
"def convert_to_rdotmap(row):\n",
" \"\"\"takes the P005 variables and maps to a series with White, Black, Asian, Hispanic, Other\n",
" Total and Name\"\"\"\n",
" return pd.Series({'Total':row['P0050001'],\n",
" 'White':row['P0050003'],\n",
" 'Black':row['P0050004'],\n",
" 'Asian':row['P0050006'],\n",
" 'Hispanic':row['P0050010'],\n",
" 'Other': row['P0050005'] + row['P0050007'] + row['P0050008'] + row['P0050009'],\n",
" 'Name': row['NAME']\n",
" }, index=['Name', 'Total', 'White', 'Black', 'Hispanic', 'Asian', 'Other'])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"from census import Census\n",
"\n",
"import settings\n",
"from settings import CENSUS_KEY\n",
"\n",
"import time\n",
"from itertools import islice\n",
"\n",
"def P005_range(n0,n1): \n",
" return tuple(('P005'+ \"{i:04d}\".format(i=i) for i in xrange(n0,n1)))\n",
"\n",
"P005_vars = P005_range(1,18)\n",
"P005_vars_str = \",\".join(P005_vars)\n",
"\n",
"\n",
"# http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/#create\n",
"def convert_to_rdotmap(row):\n",
" \"\"\"takes the P005 variables and maps to a series with White, Black, Asian, Hispanic, Other\n",
" Total and Name\"\"\"\n",
" return pd.Series({'Total':row['P0050001'],\n",
" 'White':row['P0050003'],\n",
" 'Black':row['P0050004'],\n",
" 'Asian':row['P0050006'],\n",
" 'Hispanic':row['P0050010'],\n",
" 'Other': row['P0050005'] + row['P0050007'] + row['P0050008'] + row['P0050009'],\n",
" 'Name': row['NAME']\n",
" }, index=['Name', 'Total', 'White', 'Black', 'Hispanic', 'Asian', 'Other'])\n",
"\n",
"\n",
"def normalize(s):\n",
" \"\"\"take a Series and divide each item by the sum so that the new series adds up to 1.0\"\"\"\n",
" total = np.sum(s)\n",
" return s.astype('float') / total\n",
"\n",
"\n",
"def entropy(series):\n",
" \"\"\"Normalized Shannon Index\"\"\"\n",
" # a series in which all the entries are equal should result in normalized entropy of 1.0\n",
" \n",
" # eliminate 0s\n",
" series1 = series[series!=0]\n",
"\n",
" # if len(series) < 2 (i.e., 0 or 1) then return 0\n",
" \n",
" if len(series) > 1:\n",
" # calculate the maximum possible entropy for given length of input series\n",
" max_s = -np.log(1.0/len(series))\n",
" \n",
" total = float(sum(series1))\n",
" p = series1.astype('float')/float(total)\n",
" return sum(-p*np.log(p))/max_s\n",
" else:\n",
" return 0.0\n",
"\n",
" \n",
"def convert_P005_to_int(df):\n",
" # do conversion in place\n",
" df[list(P005_vars)] = df[list(P005_vars)].astype('int')\n",
" return df\n",
" \n",
"\n",
"def diversity(r):\n",
"\n",
" \"\"\"Returns a DataFrame with the following columns\n",
" \"\"\"\n",
" df = DataFrame(r)\n",
" df = convert_P005_to_int(df)\n",
" # df[list(P005_vars)] = df[list(P005_vars)].astype('int')\n",
" df1 = df.apply(convert_to_rdotmap, axis=1)\n",
" \n",
" df1['entropy5'] = df1[['Asian','Black','Hispanic','White','Other']].apply(entropy,axis=1)\n",
" df1['entropy4'] = df1[['Asian','Black','Hispanic','White']].apply(entropy,axis=1)\n",
" return df1\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# states\n",
"\n",
"r=list(states(P005_vars_with_name))\n",
"diversity(r)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# counties\n",
"\n",
"r = list(counties(P005_vars_with_name))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2 = diversity(r)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df2.sort_index(by='entropy5',ascending=False)"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
yl565/statsmodels | examples/notebooks/categorical_interaction_plot.ipynb | 7 | 2008 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot Interaction of Categorical Factors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we will vizualize the interaction between categorical factors. First, we will create some categorical data are initialized. Then plotted using the interaction_plot function which internally recodes the x-factor categories to ingegers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from statsmodels.graphics.factorplots import interaction_plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.seed(12345)\n",
"weight = pd.Series(np.repeat(['low', 'hi', 'low', 'hi'], 15), name='weight')\n",
"nutrition = pd.Series(np.repeat(['lo_carb', 'hi_carb'], 30), name='nutrition')\n",
"days = np.log(np.random.randint(1, 30, size=60))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(figsize=(6, 6))\n",
"fig = interaction_plot(x=weight, trace=nutrition, response=days, \n",
" colors=['red', 'blue'], markers=['D', '^'], ms=10, ax=ax)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
clintonreece/sql-demos | generate.ipynb | 1 | 28134 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import mimesis\n",
"import pandas as pd\n",
"import random\n",
"from secrets import randbelow # requires python 3.6.3\n",
"from sqlalchemy import create_engine, select\n",
"from sqlalchemy import Column, Date, Integer, MetaData, Numeric, Table, Text, Time, String\n",
"from sqlalchemy_utils import UUIDType\n",
"import uuid\n",
"\n",
"NUM_CUSTOMERS = 10\n",
"NUM_PURCHASES = 15 # Should be >= NUM_CUSTOMERS\n",
"NUM_BOOKS = 5"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"add = mimesis.Address('en')\n",
"bus = mimesis.Business('en')\n",
"cod = mimesis.Code('en')\n",
"cry = mimesis.Cryptographic('en')\n",
"dat = mimesis.Datetime('en')\n",
"num = mimesis.Numbers('en')\n",
"per = mimesis.Personal('en')\n",
"\n",
"engine = create_engine(\"postgresql://clinton:reece@localhost:5432/transactions\")\n",
"meta = MetaData()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build Schemas"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"customers_table = Table('customers',\n",
" meta,\n",
" Column('customer_id', UUIDType(binary=False), primary_key=True),\n",
" Column('name', Text, nullable=False),\n",
" Column('email', Text, nullable=False),\n",
" Column('phone', Text, nullable=True),\n",
" Column('street', Text, nullable=False),\n",
" Column('city', Text, nullable=False),\n",
" Column('state', String(length=2), nullable=False))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"books_table = Table('books',\n",
" meta,\n",
" Column('isbn', Text, nullable=False, primary_key=True),\n",
" Column('price', Numeric, nullable=False))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"purchases_table = Table('purchases',\n",
" meta,\n",
" Column('receipt_id', UUIDType(binary=False), index=True),\n",
" Column('customer_id', UUIDType(binary=False)),\n",
" Column('date', Date, nullable=True),\n",
" Column('time', Time, nullable=True),\n",
" Column('isbn', Text, nullable=False),\n",
" Column('quantity', Integer, nullable=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate and Insert Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Customers"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
" <th>name</th>\n",
" <th>email</th>\n",
" <th>phone</th>\n",
" <th>street</th>\n",
" <th>city</th>\n",
" <th>state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ceb388a9-f124-f73d-613f-a6a92a169a8c</td>\n",
" <td>Laraine Rios</td>\n",
" <td>[email protected]</td>\n",
" <td>188.013.8219</td>\n",
" <td>292 Margaret Trace</td>\n",
" <td>Elizabeth City</td>\n",
" <td>NH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0da2d776-273d-c232-b480-64c577e9cdae</td>\n",
" <td>Zachery Rivera</td>\n",
" <td>[email protected]</td>\n",
" <td>(373) 957-4596</td>\n",
" <td>339 San Jacinto Bypass</td>\n",
" <td>Jefferson City</td>\n",
" <td>TN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8e102442-55eb-1d07-68b3-e2ffd5d4af4d</td>\n",
" <td>Karlyn Anthony</td>\n",
" <td>[email protected]</td>\n",
" <td>373-957-4596</td>\n",
" <td>931 Stanton Viaduct</td>\n",
" <td>Southgate</td>\n",
" <td>MT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>d0103c8d-7d1a-ce85-16d8-8358d9017e03</td>\n",
" <td>Scott Floyd</td>\n",
" <td>[email protected]</td>\n",
" <td>+1-(373)-957-4596</td>\n",
" <td>997 Wallen Drung</td>\n",
" <td>Muskego</td>\n",
" <td>IA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>fd7759e2-b3bd-61e3-bf64-53e5ca2758af</td>\n",
" <td>Rigoberto Frazier</td>\n",
" <td>[email protected]</td>\n",
" <td>+1-(373)-957-4596</td>\n",
" <td>63 Cleo Rand Alley</td>\n",
" <td>Elmira</td>\n",
" <td>MO</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ed61fb22-b173-49d8-4bfb-9215e3b4f01d</td>\n",
" <td>Dannielle Espinoza</td>\n",
" <td>[email protected]</td>\n",
" <td>1-373-957-4596</td>\n",
" <td>113 Decatur Garden</td>\n",
" <td>Charlottesville</td>\n",
" <td>AR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8173095d-d036-4fd4-675f-e85af652bfd5</td>\n",
" <td>Jolanda Mccullough</td>\n",
" <td>[email protected]</td>\n",
" <td>+1-(373)-957-4596</td>\n",
" <td>979 Sfgh Access Freeway</td>\n",
" <td>Sedalia</td>\n",
" <td>MS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>6a7f5c71-ec97-ae7f-6cf2-3788b879c8f6</td>\n",
" <td>Abel Shields</td>\n",
" <td>[email protected]</td>\n",
" <td>1-373-957-4596</td>\n",
" <td>699 Jessie East High Street</td>\n",
" <td>Malden</td>\n",
" <td>MI</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>d6116d06-3862-690a-785b-dd6eb86b88b4</td>\n",
" <td>Genaro Beach</td>\n",
" <td>[email protected]</td>\n",
" <td>188-013-8219</td>\n",
" <td>84 Clementina High Street</td>\n",
" <td>Vallejo</td>\n",
" <td>WV</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>c594ba42-8015-9c12-987a-3cf5f4a2cbc2</td>\n",
" <td>Dylan Branch</td>\n",
" <td>[email protected]</td>\n",
" <td>188.013.8219</td>\n",
" <td>1186 Mary Hill</td>\n",
" <td>Davis</td>\n",
" <td>MI</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" customer_id name \\\n",
"0 ceb388a9-f124-f73d-613f-a6a92a169a8c Laraine Rios \n",
"1 0da2d776-273d-c232-b480-64c577e9cdae Zachery Rivera \n",
"2 8e102442-55eb-1d07-68b3-e2ffd5d4af4d Karlyn Anthony \n",
"3 d0103c8d-7d1a-ce85-16d8-8358d9017e03 Scott Floyd \n",
"4 fd7759e2-b3bd-61e3-bf64-53e5ca2758af Rigoberto Frazier \n",
"5 ed61fb22-b173-49d8-4bfb-9215e3b4f01d Dannielle Espinoza \n",
"6 8173095d-d036-4fd4-675f-e85af652bfd5 Jolanda Mccullough \n",
"7 6a7f5c71-ec97-ae7f-6cf2-3788b879c8f6 Abel Shields \n",
"8 d6116d06-3862-690a-785b-dd6eb86b88b4 Genaro Beach \n",
"9 c594ba42-8015-9c12-987a-3cf5f4a2cbc2 Dylan Branch \n",
"\n",
" email phone street \\\n",
"0 [email protected] 188.013.8219 292 Margaret Trace \n",
"1 [email protected] (373) 957-4596 339 San Jacinto Bypass \n",
"2 [email protected] 373-957-4596 931 Stanton Viaduct \n",
"3 [email protected] +1-(373)-957-4596 997 Wallen Drung \n",
"4 [email protected] +1-(373)-957-4596 63 Cleo Rand Alley \n",
"5 [email protected] 1-373-957-4596 113 Decatur Garden \n",
"6 [email protected] +1-(373)-957-4596 979 Sfgh Access Freeway \n",
"7 [email protected] 1-373-957-4596 699 Jessie East High Street \n",
"8 [email protected] 188-013-8219 84 Clementina High Street \n",
"9 [email protected] 188.013.8219 1186 Mary Hill \n",
"\n",
" city state \n",
"0 Elizabeth City NH \n",
"1 Jefferson City TN \n",
"2 Southgate MT \n",
"3 Muskego IA \n",
"4 Elmira MO \n",
"5 Charlottesville AR \n",
"6 Sedalia MS \n",
"7 Malden MI \n",
"8 Vallejo WV \n",
"9 Davis MI "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with engine.connect() as conn:\n",
" # create tables if they don't exist\n",
" meta.create_all(engine, checkfirst=True)\n",
"\n",
" #generate customers table\n",
" for _ in range(0, NUM_CUSTOMERS):\n",
" insert_data = customers_table.insert() \\\n",
" .values(customer_id = cry.uuid(),\n",
" name = per.full_name(),\n",
" email = per.email(),\n",
" phone = per.telephone(),\n",
" street = add.address(),\n",
" city = add.city(),\n",
" state = add.state(abbr=True))\n",
" conn.execute(insert_data)\n",
" \n",
" # create dataframe\n",
" customers_df = pd.read_sql_table(table_name='customers', con=conn)\n",
" \n",
"customers_df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[UUID('ceb388a9-f124-f73d-613f-a6a92a169a8c'),\n",
" UUID('0da2d776-273d-c232-b480-64c577e9cdae'),\n",
" UUID('8e102442-55eb-1d07-68b3-e2ffd5d4af4d'),\n",
" UUID('d0103c8d-7d1a-ce85-16d8-8358d9017e03'),\n",
" UUID('fd7759e2-b3bd-61e3-bf64-53e5ca2758af'),\n",
" UUID('ed61fb22-b173-49d8-4bfb-9215e3b4f01d'),\n",
" UUID('8173095d-d036-4fd4-675f-e85af652bfd5'),\n",
" UUID('6a7f5c71-ec97-ae7f-6cf2-3788b879c8f6'),\n",
" UUID('d6116d06-3862-690a-785b-dd6eb86b88b4'),\n",
" UUID('c594ba42-8015-9c12-987a-3cf5f4a2cbc2')]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select customer IDs\n",
"with engine.connect() as conn:\n",
" customers_select = conn.execute(select([customers_table]))\n",
" customers_list = []\n",
" for c in customers_select:\n",
" customers_list.append(c[0])\n",
" \n",
"customers_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Books"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>isbn</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1-23378-527-4</td>\n",
" <td>40.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1-09229-682-4</td>\n",
" <td>68.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1-32060-596-1</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1-38292-122-5</td>\n",
" <td>14.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1-21650-822-6</td>\n",
" <td>53.50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" isbn price\n",
"0 1-23378-527-4 40.79\n",
"1 1-09229-682-4 68.95\n",
"2 1-32060-596-1 10.00\n",
"3 1-38292-122-5 14.62\n",
"4 1-21650-822-6 53.50"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with engine.connect() as conn:\n",
" # create tables if they don't exist\n",
" meta.create_all(engine, checkfirst=True)\n",
"\n",
" #generate customers table\n",
" for _ in range(0, NUM_BOOKS):\n",
" insert_data = books_table.insert() \\\n",
" .values(isbn = cod.isbn(),\n",
" price = bus.price(minimum=5.00, maximum=100.00).rstrip(' $'))\n",
" conn.execute(insert_data)\n",
" \n",
" # create dataframe\n",
" books_df = pd.read_sql_table(table_name='books', con=conn)\n",
" \n",
"books_df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['1-23378-527-4',\n",
" '1-09229-682-4',\n",
" '1-32060-596-1',\n",
" '1-38292-122-5',\n",
" '1-21650-822-6']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select ISBNs\n",
"with engine.connect() as conn:\n",
" books_select = conn.execute(select([books_table]))\n",
" books_list = []\n",
" for b in books_select:\n",
" books_list.append(b[0])\n",
" \n",
"books_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Purchases"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['0cdf1166-5fa6-0246-33b7-d1149d87a50a',\n",
" '592c2e53-e506-c5a9-eb3e-be8cd9f687f2',\n",
" '71187b0b-c4d2-70ee-0852-51dfc64d7065',\n",
" 'e77d7f7e-2fb8-b56f-d4a9-ed97b550cf5f',\n",
" '7d493d61-72f6-8042-7e50-f32c7fc04b70',\n",
" 'e31e262e-4d88-da66-523f-c0844e504418',\n",
" '08f76f74-cb0e-4e57-6ce2-e2ac7cf34ced',\n",
" '79523cbb-be6f-caba-0fe5-f8d62cbb43d7',\n",
" 'f409efc0-3008-dde5-b68d-fc19d92e0362',\n",
" '87720ce2-5bd3-49c4-d597-87525ca87aad',\n",
" 'd188efe2-7781-ab5b-b169-f593afbba950',\n",
" '41fac398-0838-94b2-fc05-aa31e575163c',\n",
" 'f5d7222a-2ba2-e38c-d699-91d5268f9d98',\n",
" '4b1040d5-f2c5-f462-19a7-a9e038370c75',\n",
" '867873cb-9083-2eab-b88c-85c720fec36a']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# generate receipt IDs\n",
"receipt_id = []\n",
"for _ in range(0, NUM_PURCHASES):\n",
" receipt_id.append(cry.uuid())\n",
"\n",
"receipt_id"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>receipt_id</th>\n",
" <th>customer_id</th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>isbn</th>\n",
" <th>quantity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0cdf1166-5fa6-0246-33b7-d1149d87a50a</td>\n",
" <td>fd7759e2-b3bd-61e3-bf64-53e5ca2758af</td>\n",
" <td>2013-05-25</td>\n",
" <td>18:54:47</td>\n",
" <td>1-21650-822-6</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4b1040d5-f2c5-f462-19a7-a9e038370c75</td>\n",
" <td>8173095d-d036-4fd4-675f-e85af652bfd5</td>\n",
" <td>2011-08-06</td>\n",
" <td>10:07:29</td>\n",
" <td>1-32060-596-1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0cdf1166-5fa6-0246-33b7-d1149d87a50a</td>\n",
" <td>d6116d06-3862-690a-785b-dd6eb86b88b4</td>\n",
" <td>2013-01-07</td>\n",
" <td>10:17:32</td>\n",
" <td>1-32060-596-1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7d493d61-72f6-8042-7e50-f32c7fc04b70</td>\n",
" <td>6a7f5c71-ec97-ae7f-6cf2-3788b879c8f6</td>\n",
" <td>2013-08-09</td>\n",
" <td>03:49:45</td>\n",
" <td>1-38292-122-5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7d493d61-72f6-8042-7e50-f32c7fc04b70</td>\n",
" <td>6a7f5c71-ec97-ae7f-6cf2-3788b879c8f6</td>\n",
" <td>2014-11-11</td>\n",
" <td>15:40:33</td>\n",
" <td>1-32060-596-1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>79523cbb-be6f-caba-0fe5-f8d62cbb43d7</td>\n",
" <td>d0103c8d-7d1a-ce85-16d8-8358d9017e03</td>\n",
" <td>2016-02-18</td>\n",
" <td>14:20:05</td>\n",
" <td>1-21650-822-6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>592c2e53-e506-c5a9-eb3e-be8cd9f687f2</td>\n",
" <td>8e102442-55eb-1d07-68b3-e2ffd5d4af4d</td>\n",
" <td>2014-12-01</td>\n",
" <td>08:26:02</td>\n",
" <td>1-09229-682-4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>e77d7f7e-2fb8-b56f-d4a9-ed97b550cf5f</td>\n",
" <td>8173095d-d036-4fd4-675f-e85af652bfd5</td>\n",
" <td>2014-03-29</td>\n",
" <td>04:25:19</td>\n",
" <td>1-21650-822-6</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>867873cb-9083-2eab-b88c-85c720fec36a</td>\n",
" <td>8173095d-d036-4fd4-675f-e85af652bfd5</td>\n",
" <td>2017-02-09</td>\n",
" <td>21:46:09</td>\n",
" <td>1-23378-527-4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>f409efc0-3008-dde5-b68d-fc19d92e0362</td>\n",
" <td>d0103c8d-7d1a-ce85-16d8-8358d9017e03</td>\n",
" <td>2010-08-18</td>\n",
" <td>18:00:14</td>\n",
" <td>1-09229-682-4</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>e77d7f7e-2fb8-b56f-d4a9-ed97b550cf5f</td>\n",
" <td>8e102442-55eb-1d07-68b3-e2ffd5d4af4d</td>\n",
" <td>2014-11-29</td>\n",
" <td>15:41:13</td>\n",
" <td>1-21650-822-6</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0cdf1166-5fa6-0246-33b7-d1149d87a50a</td>\n",
" <td>d6116d06-3862-690a-785b-dd6eb86b88b4</td>\n",
" <td>2013-06-18</td>\n",
" <td>05:47:29</td>\n",
" <td>1-21650-822-6</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>71187b0b-c4d2-70ee-0852-51dfc64d7065</td>\n",
" <td>ed61fb22-b173-49d8-4bfb-9215e3b4f01d</td>\n",
" <td>2011-08-08</td>\n",
" <td>09:55:53</td>\n",
" <td>1-09229-682-4</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>41fac398-0838-94b2-fc05-aa31e575163c</td>\n",
" <td>d6116d06-3862-690a-785b-dd6eb86b88b4</td>\n",
" <td>2017-05-29</td>\n",
" <td>20:06:39</td>\n",
" <td>1-38292-122-5</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>71187b0b-c4d2-70ee-0852-51dfc64d7065</td>\n",
" <td>0da2d776-273d-c232-b480-64c577e9cdae</td>\n",
" <td>2011-03-12</td>\n",
" <td>21:54:08</td>\n",
" <td>1-21650-822-6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" receipt_id \\\n",
"0 0cdf1166-5fa6-0246-33b7-d1149d87a50a \n",
"1 4b1040d5-f2c5-f462-19a7-a9e038370c75 \n",
"2 0cdf1166-5fa6-0246-33b7-d1149d87a50a \n",
"3 7d493d61-72f6-8042-7e50-f32c7fc04b70 \n",
"4 7d493d61-72f6-8042-7e50-f32c7fc04b70 \n",
"5 79523cbb-be6f-caba-0fe5-f8d62cbb43d7 \n",
"6 592c2e53-e506-c5a9-eb3e-be8cd9f687f2 \n",
"7 e77d7f7e-2fb8-b56f-d4a9-ed97b550cf5f \n",
"8 867873cb-9083-2eab-b88c-85c720fec36a \n",
"9 f409efc0-3008-dde5-b68d-fc19d92e0362 \n",
"10 e77d7f7e-2fb8-b56f-d4a9-ed97b550cf5f \n",
"11 0cdf1166-5fa6-0246-33b7-d1149d87a50a \n",
"12 71187b0b-c4d2-70ee-0852-51dfc64d7065 \n",
"13 41fac398-0838-94b2-fc05-aa31e575163c \n",
"14 71187b0b-c4d2-70ee-0852-51dfc64d7065 \n",
"\n",
" customer_id date time isbn \\\n",
"0 fd7759e2-b3bd-61e3-bf64-53e5ca2758af 2013-05-25 18:54:47 1-21650-822-6 \n",
"1 8173095d-d036-4fd4-675f-e85af652bfd5 2011-08-06 10:07:29 1-32060-596-1 \n",
"2 d6116d06-3862-690a-785b-dd6eb86b88b4 2013-01-07 10:17:32 1-32060-596-1 \n",
"3 6a7f5c71-ec97-ae7f-6cf2-3788b879c8f6 2013-08-09 03:49:45 1-38292-122-5 \n",
"4 6a7f5c71-ec97-ae7f-6cf2-3788b879c8f6 2014-11-11 15:40:33 1-32060-596-1 \n",
"5 d0103c8d-7d1a-ce85-16d8-8358d9017e03 2016-02-18 14:20:05 1-21650-822-6 \n",
"6 8e102442-55eb-1d07-68b3-e2ffd5d4af4d 2014-12-01 08:26:02 1-09229-682-4 \n",
"7 8173095d-d036-4fd4-675f-e85af652bfd5 2014-03-29 04:25:19 1-21650-822-6 \n",
"8 8173095d-d036-4fd4-675f-e85af652bfd5 2017-02-09 21:46:09 1-23378-527-4 \n",
"9 d0103c8d-7d1a-ce85-16d8-8358d9017e03 2010-08-18 18:00:14 1-09229-682-4 \n",
"10 8e102442-55eb-1d07-68b3-e2ffd5d4af4d 2014-11-29 15:41:13 1-21650-822-6 \n",
"11 d6116d06-3862-690a-785b-dd6eb86b88b4 2013-06-18 05:47:29 1-21650-822-6 \n",
"12 ed61fb22-b173-49d8-4bfb-9215e3b4f01d 2011-08-08 09:55:53 1-09229-682-4 \n",
"13 d6116d06-3862-690a-785b-dd6eb86b88b4 2017-05-29 20:06:39 1-38292-122-5 \n",
"14 0da2d776-273d-c232-b480-64c577e9cdae 2011-03-12 21:54:08 1-21650-822-6 \n",
"\n",
" quantity \n",
"0 4 \n",
"1 1 \n",
"2 4 \n",
"3 4 \n",
"4 1 \n",
"5 1 \n",
"6 2 \n",
"7 4 \n",
"8 1 \n",
"9 4 \n",
"10 5 \n",
"11 3 \n",
"12 2 \n",
"13 4 \n",
"14 1 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with engine.connect() as conn:\n",
" # create tables if they don't exist\n",
" meta.create_all(engine, checkfirst=True)\n",
"\n",
" # generate purchases table\n",
" for _ in range(0, NUM_PURCHASES):\n",
" # select random IDs with replacement\n",
" insert_data = purchases_table.insert() \\\n",
" .values(receipt_id = receipt_id[randbelow(NUM_PURCHASES)],\n",
" customer_id = customers_list[randbelow(NUM_CUSTOMERS)],\n",
" date = dat.date(start=2010, end=2017),\n",
" time = dat.time(),\n",
" isbn = list(random.sample(books_list, 1))[0],\n",
" quantity = num.between(1, 5))\n",
" conn.execute(insert_data)\n",
" \n",
" # create dataframe\n",
" purchases_df = pd.read_sql_table(table_name='purchases', con=conn)\n",
" \n",
"purchases_df"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
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"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jpcjr/words_word_ord_or_o | recursively_decomposable_words.ipynb | 1 | 10210 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"Imports that we'll want throughout this notebook\"\"\"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from pprint import pprint"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"Load the words set\"\"\"\n",
"from string import ascii_lowercase\n",
"import re\n",
"\n",
"def get_word(word_candidate):\n",
" \"\"\"\n",
" Return \n",
"\n",
" Namely, a candidate is a word if it comprises letters, and is\n",
" optionally terminated with whitespace.\n",
" \"\"\"\n",
" word_pattern = r'^(?P<word>\\w+)[\\r\\n\\t ]*$'\n",
" m = re.match(word_pattern, word_candidate)\n",
" if m is None:\n",
" return m\n",
" else:\n",
" return m.group('word')\n",
"\n",
"with open('354984si.ngl') as f:\n",
" wordset = set([get_word(line) for line in f if get_word(line)])\n",
"\n",
"# Quirk of Moby Words: remove all one-letter words but 'a' and 'I'\n",
"wordset = set([w for w in wordset if len(w) > 1]) | set(['a', 'i'])\n",
"wordset |= set(['']) # empty string needs to be in there as a base case"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"Find the longest recursively-decomposable subword: bottom-up\n",
"\n",
"Start with [\"a\", \"i\"], add letters in each position.\n",
"\"\"\"\n",
"from string import ascii_lowercase\n",
"from collections import deque\n",
"\n",
"\n",
"def bottom_up(wordset):\n",
" results = {}\n",
" longest_words = ['']\n",
" Q = deque([('', '', '')])\n",
" while len(Q) > 0:\n",
" word, delta, child = Q.pop()\n",
" if word in wordset:\n",
" results[child] = results.get(child, []) + [(delta, word)]\n",
" if len(word) > len(longest_words[0]):\n",
" longest_words = [word]\n",
" elif len(word) == len(longest_words[0]):\n",
" longest_words.append(word)\n",
"\n",
" for i in range(len(word) + 1):\n",
" for c in ascii_lowercase:\n",
" Q.append((''.join([word[:i], c, word[i:]]), c, word))\n",
" return longest_words, results\n",
"\n",
"\n",
"bu_list, bu_results = bottom_up(wordset)\n",
"set(bu_list)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"\n",
"Find the subword graph, from root to longest\n",
"\"\"\"\n",
"from collections import deque\n",
"from graph_tool import Graph, PropertyMap\n",
"\n",
"\n",
"def build_bottomup_subword_graph(bu_results): \n",
" # bu_results: shorter -> [(char, longer)]\n",
" G = Graph(directed=False)\n",
" names = G.new_vertex_property('string')\n",
" deltas = G.new_edge_property('string')\n",
" \n",
" # add '' first to prevent an infinite loop\n",
" v = G.add_vertex()\n",
" names[v] = ''\n",
" w2v = dict([('', v)])\n",
"\n",
" # create nodes for each point\n",
" i = 0\n",
" Q = deque([c for _, c in bu_results['']]) # Q <- [word]\n",
" while len(Q) > 0:\n",
" i += 1\n",
" word = Q.pop()\n",
" if word not in w2v:\n",
" v = G.add_vertex()\n",
" w2v[word] = v\n",
" names[v] = word\n",
" for _, longer in bu_results.get(word, []):\n",
" Q.append(longer)\n",
"\n",
" # add character transitions\n",
" for w, v in w2v.items():\n",
" for delta, longer in bu_results.get(w, []):\n",
" e = G.add_edge(v, w2v[longer])\n",
" deltas[e] = delta\n",
"\n",
" return G, names, deltas, w2v['']\n",
"\n",
"\n",
"G, names, deltas, root = build_bottomup_subword_graph(bu_results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from graph_tool.draw import graph_draw, radial_tree_layout\n",
"\n",
"pos = radial_tree_layout(G, root=root)\n",
"graph_draw(G, pos=pos, vertex_text=names, edge_text=deltas,\n",
" vertex_font_size=16, output_size=(10000, 10000), output='words.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"\n",
"Find the longest recursively-decomposable subwords: top-down\n",
"\n",
"Start with each word, work on decomposition graph. Memoized for speed.\n",
"\"\"\"\n",
"def get_subwords(word):\n",
" return [word[:i] + word[i+1:] for i in range(len(word))]\n",
"\n",
"\n",
"def is_recursively_decomposable(word, wordset, res_cache={}):\n",
" res = res_cache.get(word, None)\n",
" if res is not None:\n",
" return res\n",
" else:\n",
" if len(word) == 0:\n",
" res = True\n",
" parents = []\n",
" else:\n",
" subwords = [(w, is_recursively_decomposable(w, wordset, res_cache)[0])\n",
" for w in get_subwords(word)\n",
" if w in wordset]\n",
" parents = [w for w, d in subwords if d]\n",
" res = len(parents) > 0\n",
" res_cache[word] = (res, parents)\n",
" return (res, parents)\n",
"\n",
"\n",
"def top_down(wordset):\n",
" longest_words = ['']\n",
" result_cache = {}\n",
" for i, w in enumerate(wordset):\n",
" is_rec_decomp, _ = is_recursively_decomposable(w, wordset, result_cache)\n",
" if is_rec_decomp:\n",
" if len(w) > len(longest_words[0]):\n",
" longest_words = [w]\n",
" elif len(w) == len(longest_words[0]):\n",
" longest_words.append(w)\n",
" else:\n",
" pass\n",
" if i % 10000 == 0:\n",
" print(i)\n",
" return (longest_words, result_cache)\n",
"\n",
"\n",
"td_list, td_results = top_down(wordset)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"Build the subword graph, from longestest words to ''\n",
"\"\"\"\n",
"from collections import deque\n",
"from graph_tool import Graph, PropertyMap\n",
"\n",
"\n",
"def build_topdown_subword_graph(word_list, result_cache):\n",
" w2v = {}\n",
" G = Graph()\n",
" P = G.new_vertex_property('string')\n",
" Q = deque([(w, None) for w in word_list]) # Q <- [word, parent_vertex]\n",
" root = None\n",
"\n",
" while len(Q) > 0:\n",
" word, parent = Q.pop()\n",
" if word in w2v:\n",
" v = w2v[word]\n",
" else:\n",
" v = G.add_vertex()\n",
" P[v] = word\n",
" w2v[word] = v\n",
" if word == '':\n",
" root = v\n",
" if parent is not None:\n",
" G.add_edge(parent, v)\n",
" _, children = result_cache[word]\n",
" for child in children:\n",
" Q.append((child, v))\n",
" return G, P, root\n",
"\n",
"\n",
"G, P, root = build_topdown_subword_graph(topdown_list, results)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"\n",
"Visualize derivation graph of longest words -> ''\n",
"\"\"\"\n",
"from graph_tool.draw import graph_draw, radial_tree_layout\n",
"\n",
"pos = radial_tree_layout(G, root=root)\n",
"graph_draw(G, pos=pos, vertex_text=P, vertex_font_size=16, output_size=(5000, 5000), output='words.pdf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\"\"\"Across all \\(w \\in wordset\\), how are word lengths distributed?\"\"\"\n",
"all_word_lengths = [len(w) for w in wordset]\n",
"n, bins, patches = plt.hist(all_word_lengths, max(all_word_lengths)-1, facecolor='red')\n",
"plt.title('Moby Words: Word count vs. Word length')\n",
"plt.savefig('moby_words_hist.png')\n",
"plt.show()\n",
"\n",
"bu_results # shorter -> [(char, longer)]\n",
"\n",
"seen = set([''])\n",
"lengths = []\n",
"Q = deque([w for _, w in bu_results['']])\n",
"while len(Q) > 0:\n",
" w = Q.pop()\n",
" if w not in seen:\n",
" seen.add(w)\n",
" lengths.append(len(w))\n",
" for _, longer in bu_results.get(w, []):\n",
" Q.append(longer)\n",
"\n",
"nc = np.bincount(lengths)\n",
"ac = np.bincount(all_word_lengths)\n",
"\n",
"x, y = zip(*[(i, n / a) for i, (n, a) in enumerate(zip(nc, ac))])\n",
"plt.plot(x, y, 'r-')\n",
"plt.title('Moby Words: Percentage Rec. Decomp. words vs. Length')\n",
"plt.savefig('rec_comp_perc_wc.png')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
zzsza/Kaggle_Expedia-hotel-recommendations | notebook/05. 1차 결과물 제출.ipynb | 1 | 40377 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from __future__ import print_function\n",
"import sklearn\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn import preprocessing\n",
"from datetime import datetime\n",
"import os\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'png'\n",
"pd.set_option(\"max_columns\",50)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 52.4 s\n"
]
}
],
"source": [
"%%time\n",
"train = pd.read_csv(\"../data/train_2013.csv\", index_col=0)\n",
"train = train.reset_index(drop=True)\n",
"np.random.seed(402)\n",
"train = train.ix[np.random.choice(train.index, 50000)]\n",
"train = train.reset_index(drop=True)\n",
"\n",
"test = pd.read_csv(\"../data/test.csv\")\n",
"test_id = test[\"id\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random forest를 통해 유의미한 Feature 8개 설정 ( h2o.ai도 사용함 ) => 그대로 결과물 제출"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# use_col = [\"is_booking\", \"user_id\", \"date_time\",\"user_location_country\",\"orig_destination_distance\", \"srch_co\",\"srch_ci\",\"user_location_region\",\\\n",
"# \"hotel_market\",\"srch_destination_id\",\"hotel_cluster\"]"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'date_time', u'site_name', u'posa_continent', u'user_location_country',\n",
" u'user_location_region', u'user_location_city',\n",
" u'orig_destination_distance', u'user_id', u'is_mobile', u'is_package',\n",
" u'channel', u'srch_ci', u'srch_co', u'srch_adults_cnt',\n",
" u'srch_children_cnt', u'srch_rm_cnt', u'srch_destination_id',\n",
" u'srch_destination_type_id', u'is_booking', u'cnt', u'hotel_continent',\n",
" u'hotel_country', u'hotel_market', u'hotel_cluster'],\n",
" dtype='object')"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.columns"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'id', u'date_time', u'site_name', u'posa_continent',\n",
" u'user_location_country', u'user_location_region',\n",
" u'user_location_city', u'orig_destination_distance', u'user_id',\n",
" u'is_mobile', u'is_package', u'channel', u'srch_ci', u'srch_co',\n",
" u'srch_adults_cnt', u'srch_children_cnt', u'srch_rm_cnt',\n",
" u'srch_destination_id', u'srch_destination_type_id', u'hotel_continent',\n",
" u'hotel_country', u'hotel_market'],\n",
" dtype='object')"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.columns # id가 생기고 hotel_cluster / is_booking / cnt 가 사라짐"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"use_col2 = [\"user_id\", \"date_time\",\"user_location_country\",\"orig_destination_distance\", \"srch_co\",\"srch_ci\",\"user_location_region\",\\\n",
" \"hotel_market\",\"srch_destination_id\"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train_y = train[[\"hotel_cluster\"]]\n",
"train = train[use_col2]\n",
"test = test[use_col2]"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\numpy\\lib\\arraysetops.py:200: FutureWarning: In the future, NAT != NAT will be True rather than False.\n",
" flag = np.concatenate(([True], aux[1:] != aux[:-1]))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 16.3 s\n"
]
}
],
"source": [
"%%time\n",
"le = preprocessing.LabelEncoder()\n",
"# train.fillna(0)\n",
"# test.fillna(0)\n",
"\n",
"train[\"date_time\"] = pd.to_datetime(train[\"date_time\"], errors=\"coerce\")\n",
"train[\"date_time\"] = train[\"date_time\"].dt.date\n",
"train[\"srch_ci\"] = pd.to_datetime(train[\"srch_ci\"], errors=\"coerce\")\n",
"train[\"srch_co\"] = pd.to_datetime(train[\"srch_co\"], errors=\"coerce\")\n",
"\n",
"train[\"date_time\"] = le.fit_transform(train[\"date_time\"])\n",
"train[\"srch_ci\"] = le.fit_transform(train[\"srch_ci\"])\n",
"train[\"srch_co\"] = le.fit_transform(train[\"srch_co\"])\n",
"\n",
"train[\"orig_destination_distance\"].fillna(0, inplace=True)\n",
"\n",
"test[\"date_time\"] = pd.to_datetime(test[\"date_time\"], errors=\"coerce\")\n",
"test[\"date_time\"] = test[\"date_time\"].dt.date\n",
"test[\"srch_ci\"] = pd.to_datetime(test[\"srch_ci\"], errors=\"coerce\")\n",
"test[\"srch_co\"] = pd.to_datetime(test[\"srch_co\"], errors=\"coerce\")\n",
"\n",
"test[\"date_time\"] = le.fit_transform(test[\"date_time\"])\n",
"test[\"srch_ci\"] = le.fit_transform(test[\"srch_ci\"])\n",
"test[\"srch_co\"] = le.fit_transform(test[\"srch_co\"])\n",
"\n",
"test[\"orig_destination_distance\"].fillna(0, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = RandomForestClassifier(n_estimators=10, max_depth=7, n_jobs=-1, random_state=777)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================================================\n",
"# Test shape : (2528243, 9)\n",
"Wall time: 6min 46s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"print('='*50)\n",
"print('# Test shape : {}'.format(test.shape))\n",
"\n",
"model.fit(train,train_y)\n",
"\n",
"preds = model.predict_proba(test)\n",
"preds = np.fliplr(np.argsort(preds, axis=1))\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[64, 5, ..., 25, 11],\n",
" [62, 78, ..., 82, 64],\n",
" ..., \n",
" [91, 48, ..., 41, 32],\n",
" [82, 30, ..., 58, 62]], dtype=int64)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"preds[:,:5]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"result_df = pd.DataFrame([ \" \".join(row) for row in preds[:,:5].astype(str)])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"result_df = result_df.rename(index=str, columns={0:\"hotel_cluster\"})"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"result_df = result_df.reset_index()\n",
"result_df = result_df.rename(index=str,columns={\"index\":\"id\"})"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"result_df1 = pd.read_csv(\"201702061420.csv\", index_col=\"id\").drop([\"Unnamed: 0\"], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"result_df1.to_csv(\"201702061422.csv\")\n"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>hotel_cluster</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2528238</th>\n",
" <td>26 84 73 0 96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2528239</th>\n",
" <td>58 82 78 30 61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2528240</th>\n",
" <td>1 45 79 88 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2528241</th>\n",
" <td>91 48 42 41 32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2528242</th>\n",
" <td>82 30 67 58 62</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" hotel_cluster\n",
"id \n",
"2528238 26 84 73 0 96\n",
"2528239 58 82 78 30 61\n",
"2528240 1 45 79 88 24\n",
"2528241 91 48 42 41 32\n",
"2528242 82 30 67 58 62"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result_df1.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"public score = 0.14201"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(\"=\"*20)\n",
"\n",
"trn_x1 = train\n",
"trn_y1 = train_y\n",
"\n",
"# model = RandomForestClassifier(max_depth=3, n_jobs=-1, random_state=402)\n",
"\n",
"# model.fit(trn_x1,trn_y1)\n",
"\n",
"importances = model.feature_importances_\n",
"\n",
"std = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)\n",
"indices = np.argsort(importances)[::-1]\n",
"\n",
"print(\"Feature ranking:\")\n",
"rank_series = pd.Series([])\n",
"for f in range(trn_x1.shape[1]):\n",
" print(\"%d. feature %d %s (%f)\" % (f + 1, indices[f], trn_x1.columns[indices[f]], importances[indices[f]]))\n",
"\n",
"# rank_series = rank_series.append(pd.Series([trn_x1.columns[indices[f]], importances[indices[f]]]))\n",
"\n",
"# rank_df2.insert(len(rank_df2.columns), column=i ,value=rank_series)\n",
"\n",
"plt.title(\"Feature importances\")\n",
"plt.bar(range(trn_x1.shape[1]), importances[indices], color=\"r\", yerr=std[indices], align=\"center\")\n",
"plt.xticks(range(trn_x1.shape[1]), indices)\n",
"plt.xlim([-1, trn_x1.shape[1]])\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"아래 feature 사용\n",
"\n",
"https://www.kaggle.com/jwegas/expedia-hotel-recommendations/randomforest-test-20160418/run/210428/code"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 43.9 s\n"
]
}
],
"source": [
"%%time\n",
"train = pd.read_csv(\"../data/train_2013.csv\", index_col=0)\n",
"train = train.reset_index(drop=True)\n",
"np.random.seed(402)\n",
"train = train.ix[np.random.choice(train.index, 50000)]\n",
"train = train.reset_index(drop=True)\n",
"\n",
"print(\"resd the train.csv\")\n",
"use_col3 = ['site_name', 'user_location_region', 'is_package', 'srch_adults_cnt', 'srch_children_cnt', 'srch_destination_id', 'hotel_market', 'hotel_country']\n",
"train_y = train[[\"hotel_cluster\"]]\n",
"train = train[use_col3]\n",
"print(\"read the test.csv\")\n",
"test = pd.read_csv(\"../data/test.csv\")\n",
"test = test[use_col3]\n",
"\n",
"print(\"modeling strart\")\n",
"model = RandomForestClassifier(n_estimators=10, max_depth=7, n_jobs=-1, random_state=777)\n",
"print('='*50)\n",
"print('# Test shape : {}'.format(test.shape))\n",
"\n",
"model.fit(train,train_y)\n",
"\n",
"preds = model.predict_proba(test)\n",
"preds = np.fliplr(np.argsort(preds, axis=1))\n",
"\n",
"result_df = pd.DataFrame([ \" \".join(row) for row in preds[:,:5].astype(str)], columns=[\"hotel_cluster\"])\n",
"result_df.index.names = [\"id\"]\n",
"file_name = datetime.now().strftime(\"result_%Y%m%d%H%M%S\") + '.csv'\n",
"result_df.to_csv(os.path.join('../output',file_name), index=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## public score = 0.15316"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature ranking:\n",
"1. feature 6 hotel_market (0.325721)\n",
"2. feature 7 hotel_country (0.310161)\n",
"3. feature 5 srch_destination_id (0.194103)\n",
"4. feature 1 user_location_region (0.050452)\n",
"5. feature 2 is_package (0.040391)\n",
"6. feature 0 site_name (0.033722)\n",
"7. feature 3 srch_adults_cnt (0.024316)\n",
"8. feature 4 srch_children_cnt (0.021133)\n"
]
},
{
"data": {
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TV/PMXAW8kWIxpZuBh4HTZtAfSZI0B2ovy5yZY8Cp5Vfrvp0mHZk5XFG2kvJ2gyRJ6i27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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x15627b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"importances = model.feature_importances_\n",
"\n",
"std = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)\n",
"indices = np.argsort(importances)[::-1]\n",
"\n",
"print(\"Feature ranking:\")\n",
"for f in range(train.shape[1]):\n",
" print(\"%d. feature %d %s (%f)\" % (f + 1, indices[f], train.columns[indices[f]], importances[indices[f]]))\n",
"\n",
"plt.title(\"Feature importances\")\n",
"plt.bar(range(train.shape[1]), importances[indices], color=\"r\", yerr=std[indices], align=\"center\")\n",
"plt.xticks(range(train.shape[1]), indices)\n",
"plt.xlim([-1, train.shape[1]])\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 7.68 s\n"
]
}
],
"source": [
"%%time\n",
"train = pd.read_csv(\"../data/train_2013.csv\", index_col=0)\n",
"train = train.reset_index(drop=True)\n",
"train = train[train[\"is_booking\"] == 1]\n",
"np.random.seed(402)\n",
"train = train.ix[np.random.choice(train.index, 50000)]\n",
"train = train.reset_index(drop=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"read the train.csv\n",
"read the test.csv\n",
"modeling strart\n",
"==================================================\n",
"# Test shape : (2528243, 8)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:21: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 11min 58s\n"
]
}
],
"source": [
"%%time\n",
"train = pd.read_csv(\"../data/train_2013.csv\", index_col=0)\n",
"train = train.reset_index(drop=True)\n",
"train = train[train[\"is_booking\"] == 1]\n",
"np.random.seed(402)\n",
"train = train.ix[np.random.choice(train.index, 50000)]\n",
"train = train.reset_index(drop=True)\n",
"\n",
"print(\"read the train.csv\")\n",
"use_col3 = ['site_name', 'user_location_region', 'is_package', 'srch_adults_cnt', 'srch_children_cnt', 'srch_destination_id', 'hotel_market', 'hotel_country']\n",
"train_y = train[[\"hotel_cluster\"]]\n",
"train = train[use_col3]\n",
"print(\"read the test.csv\")\n",
"test = pd.read_csv(\"../data/test.csv\")\n",
"test = test[use_col3]\n",
"\n",
"print(\"modeling strart\")\n",
"model = RandomForestClassifier(n_estimators=10, max_depth=7, n_jobs=-1, random_state=777)\n",
"print('='*50)\n",
"print('# Test shape : {}'.format(test.shape))\n",
"\n",
"model.fit(train,train_y)\n",
"\n",
"preds = model.predict_proba(test)\n",
"preds = np.fliplr(np.argsort(preds, axis=1))\n",
"\n",
"result_df = pd.DataFrame([ \" \".join(row) for row in preds[:,:5].astype(str)], columns=[\"hotel_cluster\"])\n",
"result_df.index.names = [\"id\"]\n",
"file_name = datetime.now().strftime(\"result_%Y%m%d%H%M%S\") + '.csv'\n",
"print(\"save file\")\n",
"result_df.to_csv(os.path.join('../output',file_name), index=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"\n",
"## result_20170206155850.csv => 0.15218 \n",
"## is_booking == 1 인 친구들 기반"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>site_name</th>\n",
" <th>user_location_region</th>\n",
" <th>is_package</th>\n",
" <th>srch_adults_cnt</th>\n",
" <th>srch_children_cnt</th>\n",
" <th>srch_destination_id</th>\n",
" <th>hotel_market</th>\n",
" <th>hotel_country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>331</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>12696</td>\n",
" <td>122</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>321</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>12189</td>\n",
" <td>637</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>348</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2758</td>\n",
" <td>1916</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>315</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>8267</td>\n",
" <td>675</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>348</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>18741</td>\n",
" <td>462</td>\n",
" <td>50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" site_name user_location_region is_package srch_adults_cnt \\\n",
"0 2 331 0 1 \n",
"1 2 321 0 2 \n",
"2 2 348 0 1 \n",
"3 2 315 0 4 \n",
"4 2 348 0 1 \n",
"\n",
" srch_children_cnt srch_destination_id hotel_market hotel_country \n",
"0 0 12696 122 8 \n",
"1 0 12189 637 50 \n",
"2 0 2758 1916 31 \n",
"3 2 8267 675 50 \n",
"4 0 18741 462 50 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
bbglab/adventofcode | 2018/ferran/day08/memory_maneuver.ipynb | 1 | 5517 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 1"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def parse():\n",
" l = ! cat input.txt | tr '\\n' ';'\n",
" return list(map(int, l[0].rstrip(';').split(';')[0].split(' ')))"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Node:\n",
" \n",
" def __init__(self):\n",
" self.pending = None\n",
" self.meta = None\n",
" self.parent = None\n",
" self.first = True\n",
" \n",
" def add_child(self, node):\n",
" node.parent = self\n",
" self.children.append(node)\n",
" \n",
" def read(self, acc, license):\n",
" if self.first:\n",
" self.pending = license.pop(0)\n",
" self.meta = license.pop(0)\n",
" self.first = False\n",
" while self.pending > 0:\n",
" node = Node()\n",
" acc, license = node.read(acc, license)\n",
" self.pending -= 1\n",
" acc += sum(license[: self.meta])\n",
" license = license[self.meta:]\n",
" return acc, license\n",
"\n",
"def sum_metadata(license):\n",
" root = Node()\n",
" s, unread = root.read(0, license)\n",
" return s"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"138"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"license = [2, 3, 0, 3, 10, 11, 12, 1, 1, 0, 1, 99, 2, 1, 1, 2]\n",
"sum_metadata(license)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solution"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"40984"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = parse()\n",
"sum_metadata(l)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {},
"outputs": [],
"source": [
"class Node:\n",
" \n",
" def __init__(self):\n",
" self.children = []\n",
" self.pending = None\n",
" self.meta = None\n",
" self.parent = None\n",
" self.metadata = None\n",
" self.first = True\n",
" \n",
" def add_child(self, node):\n",
" node.parent = self\n",
" self.children.append(node)\n",
" \n",
" def read(self, license):\n",
" if self.first:\n",
" self.pending = license.pop(0)\n",
" self.meta = license.pop(0)\n",
" self.first = False\n",
" while self.pending > 0:\n",
" node = Node()\n",
" self.add_child(node)\n",
" license = node.read(license)\n",
" self.pending -= 1\n",
" self.metadata = license[: self.meta]\n",
" license = license[self.meta:]\n",
" return license\n",
" \n",
" def nodevalue(self):\n",
" if self.children == []:\n",
" return sum(self.metadata)\n",
" acc = 0\n",
" for i, n in enumerate(self.children):\n",
" acc += self.metadata.count(i+1) * n.nodevalue()\n",
" return acc\n",
"\n",
"def root_value(license):\n",
" root = Node() \n",
" root.read(license)\n",
" return root.nodevalue() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"66"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"license = [2, 3, 0, 3, 10, 11, 12, 1, 1, 0, 1, 99, 2, 1, 1, 2]\n",
"root_value(license)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"37067"
]
},
"execution_count": 234,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = parse()\n",
"root_value(l)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:adventofcode]",
"language": "python",
"name": "conda-env-adventofcode-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
Amarchuk/2FInstability | notebooks/2f/.ipynb_checkpoints/n338-checkpoint.ipynb | 1 | 6448093 | null | gpl-3.0 |
jaybo/Python-Notebooks | TEMCA/BrightfieldAnalyze.ipynb | 1 | 3476 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# analyze the Bright and Dark images\n",
"import cv2 \n",
"import numpy as np\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.colors as mc\n",
"\n",
"def display_surface(img):\n",
" size = 1024 #1920\n",
" w,h = img.shape\n",
" w2 = w / 2\n",
" h2 = h / 2\n",
" img = img[h2 : h2 + size, h2 : h2 + size]\n",
" imin = np.min(img)\n",
" imax = np.max(img)\n",
" cmap = plt.cm.jet\n",
" print imax, imin, cmap.N\n",
" #norm = mc.BoundaryNorm(imax-imin, cmap.N)\n",
"\n",
" fig=plt.figure()\n",
" ax = Axes3D(fig)\n",
" w,h = img.shape\n",
" xx, yy = np.mgrid[0:w, 0:h]\n",
" # plot3D requires a 1D array for x, y, and z\n",
" # ravel() converts the 100x100 array into a 1x10000 array\n",
" #ax.plot_surface(xx,yy,img, cmap=plt.cm.jet)\n",
" ax.plot_surface(xx,yy,img, linewidth=0, cmap=cmap, antialiased=False, shade=False, )\n",
" ax.set_xlabel('X')\n",
" ax.set_ylabel('Y')\n",
" ax.set_zlabel('Z')\n",
" fig.add_axes(ax)\n",
" plt.show()\n",
"\n",
"def display_image(img):\n",
" clicked = False \n",
" def onMouse( event, x, y, flags, param): \n",
" global clicked \n",
" if event == cv2.EVENT_LBUTTONUP: \n",
" clicked = True \n",
"\n",
" cv2.namedWindow('MyWindow') \n",
" cv2.setMouseCallback('MyWindow', onMouse) \n",
" print 'Press any key to stop.' \n",
"\n",
" while cv2. waitKey( 1) == -1 and not clicked: \n",
" cv2. imshow('MyWindow', img) \n",
"\n",
" cv2. destroyWindow('MyWindow')\n",
"\n",
"temca1BDark = r\"C:\\temca\\temca1\\config\\DarkField_48500328.tif\"\n",
"temca1Bright = r\"C:\\temca\\temca1\\config\\BrightField_48500328.tif\"\n",
"temca2Dark = r\"C:\\temca\\temca2\\config\\DarkField_44500428.tif\"\n",
"temca2Bright = r\"C:\\temca\\temca2\\config\\BrightField_44500428.tif\"\n",
"\n",
"bright_file = temca2Bright\n",
"dark_file = temca2Dark\n",
"\n",
"iDark = cv2.imread(dark_file, cv2.IMREAD_UNCHANGED)\n",
"iBright = cv2.imread(bright_file, cv2.IMREAD_UNCHANGED)\n",
"\n",
"iBrightMinusDark = iBright - iDark\n",
"iBrightMinusDarkU8 = np.array(iBrightMinusDark / 256, np.uint8)\n",
"iBrightMinusDarkF32 = np.array(iBrightMinusDark, np.float32)\n",
"\n",
"iCorrected = np.array(iBrightMinusDarkF32, np.uint16)\n",
"\n",
"img = iBrightMinusDarkU8\n",
"#img = iBright / 256\n",
"\n",
"display_surface(img)\n",
"# display_image(img)\n",
"\n",
"\n"
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
chappers/sklearn-recipes | streaming_take2/OGFS-pandas.ipynb | 2 | 18872 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Implementation of the _Online Group Feature Selection_ (OGFS) algorithm.\n",
"\n",
"OGFS uses Lasso, so we will default to Lasso in its filtering with a low tolerance.\n",
"\n",
"**Note**: The output of the algorithm is not to provide a model, but rather the present the group of selected (subset) of features."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sklearn"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.datasets import make_regression, make_classification\n",
"from sklearn.linear_model import SGDRegressor\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import SPEC\n",
"from scipy import stats\n",
"from sklearn.metrics.pairwise import rbf_kernel\n",
"from sklearn.mixture import BayesianGaussianMixture"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def similarity_within_class(X, y):\n",
" return SPEC.similarity_classification(X, y)\n",
"\n",
"def similarity_between_class(X, y):\n",
" \"\"\"\n",
" Calculates betweenclass affinity X (data) y (labels)\n",
" \n",
" note that it only considers the labels\n",
" \"\"\"\n",
" y_series = pd.Series(y)\n",
" y_val = y_series.value_counts(normalize=True)\n",
" n_inv = 1.0/len(set(y))\n",
" \n",
" y_size = len(y)\n",
" sim_matrix = np.zeros((len(y), len(y)))\n",
" for s_i in range(y_size):\n",
" for s_j in range(y_size):\n",
" sim_matrix[s_i, s_j] = n_inv - y_val[y[s_i]] if y[s_i] == y[s_j] else n_inv\n",
" return sim_matrix"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def convert_to_deciles(y, n=10, gmm=False):\n",
" \"\"\"\n",
" By default converts to deciles, can be changed based on choice of n.\n",
" \"\"\"\n",
" if gmm:\n",
" # this is experimental\n",
" bgm = BayesianGaussianMixture(n_components=10)\n",
" bgm.fit(y.reshape(-1, 1))\n",
" return bgm.predict(y.reshape(-1, 1))\n",
" return np.array(pd.cut(y, n, labels=range(n)))"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"X, y = make_regression(n_features=100)\n",
"pdf = pd.DataFrame(X)\n",
"pdf.columns = ['c{}'.format(x) for x in range(X.shape[1])]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(100, 100)"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X1 = pdf[['c{}'.format(x) for x in range(50, 100)]]\n",
"X2 = pdf[['c{}'.format(x) for x in range(50)]]"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def spec_supervised(X, y, is_classification=True):\n",
" if not is_classification:\n",
" y = convert_to_deciles(y, 10, gmm=False)\n",
" W_w = similarity_within_class(X, y)\n",
" W_b = similarity_between_class(X, y)\n",
" s_w = SPEC.spec(**{'X': X, 'y': y, 'style':0, 'mode': 'raw', 'W': W_w})\n",
" s_b = SPEC.spec(**{'X': X, 'y': y, 'style':0, 'mode': 'raw', 'W': W_b})\n",
" return s_b, s_w"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def evaluate_feats1(s_b, s_w, highest_best=True):\n",
" curr_u1 = []\n",
" curr_u2 = []\n",
" my_feats = []\n",
" prev_score = None\n",
" X = s_b/s_w\n",
" eval_order = np.argsort(X).flatten()\n",
" if highest_best:\n",
" eval_order = eval_order[::-1]\n",
" for idx in list(eval_order):\n",
" if prev_score is None:\n",
" curr_u1.append(s_b[idx])\n",
" curr_u2.append(s_w[idx])\n",
" my_feats.append(idx)\n",
" else:\n",
" test_u1 = curr_u1[:]\n",
" test_u2 = curr_u2[:]\n",
" test_u1.append(s_b[idx])\n",
" test_u2.append(s_w[idx])\n",
" score = ((np.sum(test_u1)/np.sum(test_u2)) - prev_score)\n",
" if score > 0.001:\n",
" my_feats.append(idx)\n",
" curr_u1.append(s_b[idx])\n",
" curr_u2.append(s_w[idx])\n",
" prev_score = np.sum(curr_u1)/np.sum(curr_u2)\n",
" return list(my_feats)\n",
"\n",
"def evaluate_feats2(X, alpha=0.05, highest_best=True):\n",
" \"\"\"\n",
" X is the raw scrores\n",
" alpha is the level of significance\n",
" \n",
" This version uses T-test\n",
" \n",
" Returns: set of indices indicating selected features.\n",
" \"\"\"\n",
" eval_order = np.argsort(X)\n",
" if highest_best:\n",
" eval_order = eval_order[::-1]\n",
" selected_feats = []\n",
" selected_idx = []\n",
" for idx in eval_order:\n",
" if len(selected_feats) == 0:\n",
" selected_feats.append(X[idx])\n",
" selected_idx.append(idx)\n",
" continue\n",
" # now continue on and decide what to do\n",
" mu = np.mean(selected_feats)\n",
" sigma = np.std(selected_feats)\n",
" U = len(selected_feats)\n",
" if sigma == 0.0 and U > 1:\n",
" return selected_idx\n",
" elif sigma == 0.0:\n",
" selected_feats.append(X[idx])\n",
" selected_idx.append(idx)\n",
" continue\n",
" \n",
" # otherwise compute score for T test.\n",
" t_stat = (mu - X[idx])/(sigma/np.sqrt(U))\n",
" t_alpha = stats.t.pdf(t_stat, U)\n",
" if t_alpha <= alpha:\n",
" selected_feats.append(X[idx])\n",
" selected_idx.append(idx)\n",
" else:\n",
" return selected_idx\n",
" return selected_idx"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def evaluate_feats(s_b, s_w, alpha=0.05):\n",
" set1 = evaluate_feats1(s_b,s_w)\n",
" set2 = evaluate_feats2(s_b/s_w, alpha)\n",
" return list(set(set1 + set2))"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"X_ = X2.copy()\n",
"X_1 = np.array(X_) \n",
"s_b, s_w = spec_supervised(X_1, y, False)\n",
"aa = evaluate_feats(s_b, s_w)\n",
"col_sel = pdf.columns[aa]"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(aa)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas\n",
"\n",
"class OGFSRegressor(SGDRegressor):\n",
" def __init__(self, loss=\"squared_loss\", penalty=\"l1\", alpha=0.0001,\n",
" l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None,\n",
" shuffle=True, verbose=0, epsilon=0.1,\n",
" random_state=None, learning_rate=\"invscaling\", eta0=0.01,\n",
" power_t=0.25, warm_start=False, average=False, n_iter=None, \n",
" intragroup_alpha=0.05, intergroup_thres=None):\n",
" super(OGFSRegressor, self).__init__(loss=loss, penalty=penalty,\n",
" alpha=alpha, l1_ratio=l1_ratio,\n",
" fit_intercept=fit_intercept,\n",
" max_iter=max_iter, tol=tol,\n",
" shuffle=shuffle,\n",
" verbose=verbose,\n",
" epsilon=epsilon,\n",
" random_state=random_state,\n",
" learning_rate=learning_rate,\n",
" eta0=eta0, power_t=power_t,\n",
" warm_start=warm_start,\n",
" average=average, n_iter=n_iter)\n",
" \"\"\"\n",
" intragroup_alpha : the alpha level of t-test used to determine significance\n",
" intergroup_thres : the threshold for lasso to remove redundancy\n",
" \"\"\"\n",
" self.coef_info = {'cols': [], 'coef':[], 'excluded_cols': []}\n",
" self.seen_cols = []\n",
" self.base_shape = None\n",
" self.intragroup_alpha = intragroup_alpha\n",
" self.intergroup_thres = intergroup_thres if intergroup_thres is not None else epsilon\n",
" \n",
" def add_column_exclusion(self, cols):\n",
" self.coef_info['excluded_cols'] = self.coef_info['excluded_cols'] + cols\n",
" \n",
" def _fit_columns(self, X_, return_x=True, transform_only=False):\n",
" \"\"\"\n",
" Method filter through \"unselected\" columns. The goal of this \n",
" method is to filter any uninformative columns.\n",
" \n",
" This will be selected based on index only?\n",
" \n",
" If return_x is false, it will only return the boolean mask.\n",
" \"\"\"\n",
" X = X_[X_.columns.difference(self.coef_info['excluded_cols'])]\n",
" \n",
" # order the columns correctly...\n",
" col_order = self.coef_info['cols'] + list([x for x in X.columns if x not in self.coef_info['cols']])\n",
" X = X[col_order]\n",
" return X\n",
" \n",
" def _reg_penalty(self, X):\n",
" col_coef = [(col, coef) for col, coef in zip(X.columns.tolist(), self.coef_) if np.abs(coef) >= self.intergroup_thres]\n",
" self.coef_info['cols'] = [x for x, _ in col_coef]\n",
" self.coef_info['coef'] = [x for _, x in col_coef]\n",
" self.coef_info['excluded_cols'] = [x for x in self.seen_cols if x not in self.coef_info['cols']]\n",
" self.coef_ = np.array(self.coef_info['coef']) \n",
" \n",
" def _spectral_sel(self, X_, y):\n",
" \"\"\"\n",
" Partial fit online group feature selection method to \n",
" perform spectral analysis on incoming feature set\n",
" to then expand the coefficient listing\n",
" \"\"\"\n",
" X = np.array(X_) \n",
" s_b, s_w = spec_supervised(X, y, False)\n",
" col_sel = X_.columns[evaluate_feats(s_b, s_w)]\n",
" sel_cols = list(self.coef_info['cols']) + list(col_sel)\n",
" # update removed columns\n",
" self.coef_info['excluded_cols'] = [col for col in self.seen_cols if col not in sel_cols]\n",
" \n",
" \n",
" def fit(self, X, y, coef_init=None, intercept_init=None,\n",
" sample_weight=None):\n",
" X_ = X.copy()\n",
" self.seen_cols = list(set(self.seen_cols + X.columns.tolist()))\n",
" \n",
" # TODO: add the spectral selection here\n",
" self._spectral_sel(X, y)\n",
" X = self._fit_columns(X)\n",
" \n",
" super(OGFSRegressor, self).fit(X, y, coef_init=coef_init, intercept_init=intercept_init,\n",
" sample_weight=sample_weight)\n",
" self._reg_penalty(X)\n",
" return self\n",
" \n",
" def partial_fit(self, X, y, sample_weight=None):\n",
" X_ = X.copy()\n",
" self.seen_cols = list(set(self.seen_cols + X.columns.tolist()))\n",
" X = X[X.columns.difference(self.coef_info['excluded_cols'])]\n",
" \n",
" # TODO: add the spectral selection here\n",
" # it should only consider \"unseen\"\n",
" self._spectral_sel(X[X.columns.difference(self.coef_info['cols'])], y)\n",
" X = self._fit_columns(X)\n",
" \n",
" # now update coefficients\n",
" n_samples, n_features = X.shape\n",
" coef_list = np.zeros(n_features, dtype=np.float64, order=\"C\")\n",
" coef_list[:len(self.coef_info['coef'])] = self.coef_info['coef']\n",
" self.coef_ = coef_list.copy()\n",
" \n",
" super(OGFSRegressor, self).partial_fit(X, y, sample_weight=None) \n",
" self._reg_penalty(X)\n",
" return self\n",
" \n",
" def predict(self, X):\n",
" X = self._fit_columns(X, transform_only=True)\n",
" return super(OGFSRegressor, self).predict(X) "
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OGFSRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,\n",
" fit_intercept=True, intergroup_thres=0.1, intragroup_alpha=0.05,\n",
" l1_ratio=0.15, learning_rate='invscaling', loss='squared_loss',\n",
" max_iter=1000, n_iter=None, penalty='l1', power_t=0.25,\n",
" random_state=None, shuffle=True, tol=None, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = OGFSRegressor(max_iter=1000)\n",
"model.fit(X1, y)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(model.coef_)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OGFSRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,\n",
" fit_intercept=True, intergroup_thres=0.1, intragroup_alpha=0.05,\n",
" l1_ratio=0.15, learning_rate='invscaling', loss='squared_loss',\n",
" max_iter=1000, n_iter=None, penalty='l1', power_t=0.25,\n",
" random_state=None, shuffle=True, tol=None, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.partial_fit(pdf, y)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(model.coef_)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 4.20399211, -23.79420783, 72.65000239, -22.29526175,\n",
" -70.44287091, 77.12159062, 164.70286036, -183.66103474,\n",
" 33.15728032, 118.4157222 , 23.62260588, -173.71254282,\n",
" -43.27094578, 8.01379961, -209.08455797, 48.87267741,\n",
" 126.781505 , 171.25639865, -207.89214382, 8.40472879,\n",
" 61.9156414 , 43.950742 , -122.89385665, -0.94154365,\n",
" 256.39464545, 85.81527225, 36.95240801, -11.01273216,\n",
" -57.32230049, -60.40259386, 37.64562428, 83.77717464,\n",
" 96.42615472, -61.6676576 , 114.18348278, 41.77382645,\n",
" 4.48693582, -42.77718934, -154.2438299 , 32.30700546,\n",
" 54.70853432, -88.39892099, -22.90267357, -126.44500227,\n",
" -187.31247567, 55.43342648, -251.59664339, -98.89955014,\n",
" -143.31289737, 13.48301526, 71.01640266, 112.94841177,\n",
" 80.19293521, 63.41426843, 241.77801832, -155.92303532,\n",
" 89.23871854, -181.68648948, 22.80913335, -22.00219614,\n",
" -26.84660832, -37.60546301, 83.26382754, 108.94775561,\n",
" -22.98192305, 56.99387644, -234.76574219, -88.14442914,\n",
" -225.48702191, 169.76330865, 107.22044938, 178.42748173,\n",
" 161.8334106 , 38.84429757, 3.12073097, -61.76341038,\n",
" -31.45842884, -95.22495136, 54.88343712, 95.07071377,\n",
" 73.45283526, -36.98815433, -96.92816064, 41.49791554,\n",
" -90.98575923, 168.84426647, -98.19327848, -55.8160627 ,\n",
" 71.63863868, -4.49313851, 45.61540172, 109.90642488,\n",
" 20.03334279, -142.14240792, -188.77611288, 69.24620191,\n",
" -8.58291185, 71.61591896, -72.5330467 , -167.38513667])"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.predict(pdf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
pbarbero/TFM | demo/algorithms/djCluster.ipynb | 2 | 36058 | {
"metadata": {
"name": "",
"signature": "sha256:c6313ee964acebd46df70ca064a6009c3dc03e6b88054f1b0f6d8302fa4d3c6b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from position import Position\n",
"from db import connect_db\n",
"from djCluster import DjCluster\n",
"\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime\n",
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['datetime']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cur_sal = connect_db(\"bahia\")\n",
"\n",
"limit = 2000\n",
"cmd = \"SELECT * FROM posicionesgps WHERE latitud<>0 AND longitud<> 0 AND recurso='tetra:12082781' LIMIT {0};\".format(limit)\n",
"cur_sal.execute(cmd)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"2000L"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list_pos = []\n",
"for row in cur_sal.fetchall():\n",
" q = Position(row[0] # id\n",
" , row[2] # resource\n",
" , row[3] # lat\n",
" , row[4] # lon\n",
" , row[5] # speed\n",
" , row[6] # track\n",
" , row[10] # date\n",
" )\n",
" list_pos.append(q)\n",
"print len(list_pos)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2000\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"listPosTyp = []\n",
"lats = []\n",
"longs = []\n",
"\n",
"# Calculamos media lat\n",
"for pos in list_pos:\n",
" lats.append(pos.lat)\n",
"meanLat = np.mean(lats)\n",
"# Calculamos media lon \n",
"for pos in list_pos:\n",
" longs.append(pos.lon)\n",
"meanLon = np.mean(longs)\n",
"# Calculamos desv lat\n",
"devLat = np.std(lats)\n",
"# Calculamos desv lon\n",
"devLon = np.std(longs)\n",
"\n",
"latsTyp = []\n",
"longsTyp = []\n",
"for pos in list_pos:\n",
" q = Position(pos.id\n",
" , pos.resource\n",
" , (pos.lat - meanLat)/devLat\n",
" , (pos.lon - meanLon)/devLon\n",
" , pos.speed\n",
" , pos.track\n",
" , pos.date\n",
" )\n",
" listPosTyp.append(q)\n",
" latsTyp.append(q.lat)\n",
" longsTyp.append(q.lon)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from djCluster import DjCluster"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"[result, noises] = DjCluster(listPosTyp, 0, 0.001, 1, None)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print len(result)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"412\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"resultLat = []\n",
"resultLong = []\n",
"for clus in result:\n",
" resultLat.append(clus.center.lat)\n",
" resultLong.append(clus.center.lon)\n",
" \n",
"noisesLat = []\n",
"noisesLong = []\n",
"for point in noises:\n",
" noisesLat.append(point.lat)\n",
" noisesLong.append(point.lon)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(1, figsize=(15,10))\n",
"plt.subplot(111)\n",
"ax = plt.gca()\n",
"ax.grid(True)\n",
"plt.plot(latsTyp, longsTyp, 'ro', resultLat, resultLong, 'bo', noisesLat, noisesLong, 'bx');\n",
"print len(listPosTyp), len(resultLat), len(noises)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2000 412 0\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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1WrG2dv54bKXT1gvtt+br2RmvHXfT22x9ofTW8a9w8Ia/oJcnmrZWCzFJaoF6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\nREQki/w/7C9g/kFrJpEAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7fa312951710>"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.figure(1, figsize=(15,10))\n",
"plt.subplot(111)\n",
"ax = plt.gca()\n",
"ax.grid(True)\n",
"plt.plot(latsTyp, longsTyp, 'ro');"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-3.0 |
pjabardo/JuBLAS.jl | notebooks/GEMM.ipynb | 1 | 7336 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GEMM"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: replacing module JuBLAS\n"
]
},
{
"data": {
"text/plain": [
"JuBLAS"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"include(\"../src/JuBLAS.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5x5 Array{Float64,2}:\n",
" 0.0 0.0 0.0 0.0 0.0\n",
" 0.0 0.0 0.0 0.0 0.0\n",
" 0.0 0.0 0.0 0.0 0.0\n",
" 0.0 0.0 0.0 0.0 0.0\n",
" 0.0 0.0 0.0 0.0 0.0"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = randn(5,5)\n",
"B = randn(5,5)\n",
"C = zeros(5,5)\n"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n",
"0.0\n",
"0.0\n",
"0.0\n",
"2.2737367544323206e-13\n",
"1.8189894035458565e-12\n",
"0.0\n",
"1.8189894035458565e-12\n",
"0.0\n"
]
}
],
"source": [
"println(maxabs(A*B - JuBLAS.gemm('N', 'N', 1.0, A, B)))\n",
"\n",
"println(maxabs(A'*B - JuBLAS.gemm('T', 'N', 1.0, A, B)))\n",
"\n",
"println(maxabs(A'*B' - JuBLAS.gemm('T', 'T', 1.0, A, B)))\n",
"\n",
"println(maxabs(A*B' - JuBLAS.gemm('N', 'T', 1.0, A, B)))\n",
"\n",
"\n",
"C = 1000*ones(5,5)\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*A*B +C - JuBLAS.gemm!('N', 'N', 2.0, A, B, 1.0, C0)))\n",
"\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*A*B +10*C - JuBLAS.gemm!('N', 'N', 2.0, A, B, 10, C0)))\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*A'*B +10*C - JuBLAS.gemm!('T', 'N', 2.0, A, B, 10, C0)))\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*A*B' +10*C - JuBLAS.gemm!('N', 'T', 2.0, A, B, 10, C0)))\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*A'*B' +10*C - JuBLAS.gemm!('T', 'T', 2.0, A, B, 10, C0)))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"randncmplx (generic function with 1 method)"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"randncmplx(n,m) = randn(n, m) + im*randn(n, m)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.2560739669470201e-15\n",
"1.9860273225978185e-15\n",
"1.7763568394002505e-15\n",
"1.2560739669470201e-15\n",
"8.881784197001252e-16\n",
"9.930136612989092e-16\n",
"9.930136612989092e-16\n",
"2.273806142312601e-13\n",
"2.2737757853755275e-13\n",
"3.552713678800501e-15\n",
"2.273806142312601e-13\n",
"4.54749302446187e-13\n",
"1.7763568394002505e-15\n",
"1.7763568394002505e-15\n"
]
}
],
"source": [
"A = randncmplx(5,5)\n",
"B = randncmplx(5,5)\n",
"\n",
"\n",
"println(maxabs(A*B - JuBLAS.gemm('N', 'N', 1, A, B)))\n",
"println(maxabs(A'*B - JuBLAS.gemm('C', 'N', 1, A, B)))\n",
"println(maxabs(transpose(A)*B - JuBLAS.gemm('T', 'N', 1, A, B)))\n",
"println(maxabs(A*B' - JuBLAS.gemm('N', 'C', 1, A, B)))\n",
"println(maxabs(A*transpose(B) - JuBLAS.gemm('N', 'T', 1, A, B)))\n",
"println(maxabs(transpose(A)*transpose(B) - JuBLAS.gemm('T', 'T', 1, A, B)))\n",
"println(maxabs(ctranspose(A)*ctranspose(B) - JuBLAS.gemm('C', 'C', 1, A, B)))\n",
"\n",
"\n",
"C = 1000*ones(Complex{Float64}, 5, 5)\n",
"C0 = copy(C)\n",
"println(maxabs(2*A*B + 2*C- JuBLAS.gemm!('N', 'N', 2, A, B, 2, C0)))\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*A'*B +2*C - JuBLAS.gemm!('C', 'N', 2, A, B, 2, C0)))\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*transpose(A)*B + 0*C - JuBLAS.gemm!('T', 'N', 2, A, B, 0, C0)))\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*A*B' + 2*C - JuBLAS.gemm!('N', 'C', 2, A, B, 2, C0)))\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*A*transpose(B) + 2*C - JuBLAS.gemm!('N', 'T', 2, A, B, 2, C0)))\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*transpose(A)*transpose(B) + 2*C - JuBLAS.gemm!('T', 'T', 2, A, B, 2, C0)))\n",
"\n",
"C0 = copy(C)\n",
"println(maxabs(2*ctranspose(A)*ctranspose(B) + 2*C - JuBLAS.gemm!('C', 'C', 2, A, B, 2, C0)))"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.273806142312601e-13\n",
"2.2737410912368746e-13\n",
"2.2737410912368746e-13\n",
"8.881784197001252e-16\n",
"8.881784197001252e-16\n",
"2.273892874185396e-13\n",
"4.547508203201813e-13\n"
]
}
],
"source": [
"A = randncmplx(3,5)\n",
"B = randncmplx(5,3)\n",
"\n",
"C = 1000*ones(Complex{Float64}, 3, 3)\n",
"C0 = copy(C)\n",
"println(maxabs(2*A*B + 2*C- JuBLAS.gemm!('N', 'N', 2, A, B, 2, C0)))\n",
"\n",
"C = 1000*ones(Complex{Float64}, 5, 5)\n",
"C0 = copy(C)\n",
"println(maxabs(2*A'*B' + 2*C- JuBLAS.gemm!('C', 'C', 2, A, B, 2, C0)))\n",
"\n",
"C = 1000*ones(Complex{Float64}, 5, 5)\n",
"C0 = copy(C)\n",
"println(maxabs(2*transpose(A)*transpose(B) + 2*C- JuBLAS.gemm!('T', 'T', 2, A, B, 2, C0)))\n",
"\n",
"\n",
"A = randncmplx(3,5)\n",
"B = randncmplx(3,6)\n",
"C = 1000*ones(Complex{Float64}, 5, 6)\n",
"C0 = copy(C)\n",
"println(maxabs(2*A'*B + 2*C- JuBLAS.gemm!('C', 'N', 2, A, B, 2, C0)))\n",
"\n",
"C = 1000*ones(Complex{Float64}, 5, 6)\n",
"C0 = copy(C)\n",
"println(maxabs(2*transpose(A)*B + 2*C- JuBLAS.gemm!('T', 'N', 2, A, B, 2, C0)))\n",
"\n",
"A = randncmplx(3,5)\n",
"B = randncmplx(6,5)\n",
"C = 1000*ones(Complex{Float64}, 3, 6)\n",
"C0 = copy(C)\n",
"println(maxabs(2*A*B' + 2*C- JuBLAS.gemm!('N', 'C', 2, A, B, 2, C0)))\n",
"C0 = copy(C)\n",
"println(maxabs(2*A*transpose(B) + 2*C- JuBLAS.gemm!('N', 'T', 2, A, B, 2, C0)))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.5",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
28ideas/quant-econ | solutions/lqramsey_solutions.ipynb | 1 | 135354 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"quant-econ Solutions: Optimal Taxation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Solutions for http://quant-econ.net/lqramsey.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Exercise 1"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import array\n",
"from lqramsey import *\n",
"\n",
"# == Parameters == #\n",
"beta = 1 / 1.05 \n",
"rho, mg = .95, .35\n",
"A = array([[0, 0, 0, rho, mg*(1-rho)],\n",
" [1, 0, 0, 0, 0],\n",
" [0, 1, 0, 0, 0],\n",
" [0, 0, 1, 0, 0],\n",
" [0, 0, 0, 0, 1]])\n",
"C = np.zeros((5, 1))\n",
"C[0, 0] = np.sqrt(1 - rho**2) * mg / 8\n",
"Sg = array((1, 0, 0, 0, 0)).reshape(1, 5) \n",
"Sd = array((0, 0, 0, 0, 0)).reshape(1, 5) \n",
"Sb = array((0, 0, 0, 0, 2.135)).reshape(1, 5) # Chosen st. (Sc + Sg) * x0 = 1\n",
"Ss = array((0, 0, 0, 0, 0)).reshape(1, 5)\n",
"\n",
"economy = Economy(beta=beta, \n",
" Sg=Sg, \n",
" Sd=Sd, \n",
" Sb=Sb, \n",
" Ss=Ss, \n",
" discrete=False, \n",
" proc=(A, C))\n",
"\n",
"T = 50\n",
"path = compute_paths(T, economy)\n",
"gen_fig_1(path)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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n4cPUjjdvknF9sOswDHAZgJLKEvwQ+YNB0lwXlBXgnxuU5GDRgEX4YuwXaGfe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\nldVoH4vFwqZNmxg5ciTXXnstqampdt9X2JaUlOTuJngsiY0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DunWwaZMqGrBnjypmcfSo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lk37LC6GjIy1PPCG6xYsYI5c+YAMGfOHD7/vHnhjN69ezNq1CgAQkNDGTJkCNnuWrldCG8xYYJa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I/H5g5Ur23EzYGsAm7+npbJJx9Ki+bXnYWn6+9m1EenS0Tuzi4ljvldZWFhKmFzOha3Z6dMJ1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\n1QAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x236a410>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
WNoxchi/Kaukasos | FAI_old/lesson2/L2HW1_LM.ipynb | 1 | 19991 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"FAI1 Practical Deep Learning I | 11 May 2017 | Wayne Nixalo\n",
"\n",
"In this notebook I'll be building a simple linear model in Keras using ```Sequential()```\n",
"\n",
"Tutorial on Linear Model for MNIST: [linky](https://www.kaggle.com/fchollet/simple-deep-mlp-with-keras/code/code)\n",
"\n",
"Keras.io doc on [.fit_generator & Sequential](https://keras.io/models/sequential/#fit_generator)\n",
"\n",
"##### Some Notes:\n",
"It looks like I'll need to use Pandas to work with data in .csv files (MNIST from Kaggle comes that way). For the data sets that come in as folders of .jpegs, I'll use the way shown in class of get_batches & get_data ... but then if that's for a DLNN wouldn't I have to do that for this as well? Will see."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
}
],
"source": [
"# Import relevant libraries\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.optimizers import SGD, RMSprop\n",
"from keras.preprocessing import image\n",
"import numpy as np\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Data functions ~ mostly from utils.py or vgg16.py\n",
"def get_batches(dirname, gen=image.ImageDataGenerator(), shuffle=True, batch_size=4, class_mode='categorical',\n",
" target_size=(224,224)):\n",
" return gen.flow_from_directory(dirname, target_size=target_size,\n",
" class_mode=class_mode, shuffle=shuffle, batch_size=batch_size)\n",
"\n",
"# from keras.utils.np_utils import to_categorical\n",
"# def onehot(x): return to_categorical(labels, num_classes=10)\n",
"\n",
"from keras.utils.np_utils import to_categorical\n",
"def onehot(x): return to_categorical(x)\n",
"\n",
"# from sklearn.preprocessing import OneHotEncoder\n",
"# def onehot(x): return np.array(OneHotEncoder().fit_transform(x.reshape(-1,1)).todense())\n",
" \n",
"# import bcolz\n",
"# def save_data(fname, array): c=bcolz.carray(array, rootdir=fname, mode='w'); c.flush()\n",
"# def load_data(path): return bcolz.open(fname)[:]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Some setup\n",
"path = 'L2HW_data/'\n",
"if not os.path.exists(path): os.mkdir(path)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Getting Data\n",
"\n",
"# val_batches = get_batches(path+'valid/', shuffle=False, batch_size=1)\n",
"# trn_batches = get_batches(path+'train/', shuffle=False, batch_size=1)\n",
"\n",
"# converting classes to OneHot for Keras\n",
"# val_classes = val_batches.classes\n",
"# trn_classes = trn_batches.classes\n",
"# val_labels = onehot(val_classes)\n",
"# trn_labels = onehot(trn_classes)\n",
"\n",
"# See: https://www.kaggle.com/fchollet/simple-deep-mlp-with-keras/code/code\n",
"# for help loading\n",
"# I haven't learned how to batch-load .csv files; I'll blow that bridge \n",
"# when I get to it.\n",
"\n",
"# read data\n",
"import pandas as pd\n",
"trn_data = pd.read_csv(path + 'train.csv')\n",
"trn_labels = trn_data.ix[:,0].values.astype('int32')\n",
"trn_input = (trn_data.ix[:,1:].values).astype('float32')\n",
"test_input = (pd.read_csv(path + 'test.csv').values).astype('float32')\n",
"\n",
"# one-hot encode labels\n",
"trn_labels = onehot(trn_labels)\n",
"\n",
"input_dim = trn_input.shape[1]\n",
"nb_classes = trn_labels.shape[1]\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(42000, 784)\n"
]
}
],
"source": [
"# To show how we'd know what the input dimensions should be without researching MNIST:\n",
"print(trn_input.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That above is 42,0000 images by 784 pixels. So, the usual 28x28 pixel images. We can do the same to take a look at the output, which, not surprisingly, is the 10 possible digits"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(42000, 10)\n"
]
}
],
"source": [
"print(trn_labels.shape)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# I/O Dimensions: determined by data/categories/network\n",
"# Output_Cols = 10\n",
"# input_dim = 784 # for 1st layer only: rest do auto-shape-inference\n",
"\n",
"# Hyperparameters\n",
"LR = 0.1\n",
"optz = SGD(lr=LR)\n",
"# optz = RMSprop(lr=LR)\n",
"lossFn = 'mse'\n",
"# lossFn = 'categorical_cross_entropy'\n",
"metric=['accuracy']\n",
"# metrics=None\n",
"\n",
"LM = Sequential( [Dense(nb_classes, input_shape=(input_dim,))] )\n",
"# LM.compile(optmizer = optz, loss = lossFn, metrics = metric)\n",
"LM.compile(optimizer=SGD(lr=0.1), loss='categorical_crossentropy', metrics=['accuracy'])\n",
"# lm.compile(optimizer=RMSprop(lr=0.1), loss='categorical_crossentropy', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"42000/42000 [==============================] - 4s - loss: 11.4692 - acc: 0.1761 ETA: 3s - los - ETA - ETA: 1s - loss: - ETA: 0s - loss: 11.4513 - acc: - ETA: 0s - los\n",
"Epoch 2/5\n",
"42000/42000 [==============================] - 4s - loss: 11.4692 - acc: 0.1761 ETA: 1s - loss: 1 - ETA: 0s - l\n",
"Epoch 3/5\n",
"42000/42000 [==============================] - 4s - loss: 11.4692 - acc: 0.1761 \n",
"Epoch 4/5\n",
"42000/42000 [==============================] - 4s - loss: 11.4692 - acc: 0.1761 ETA - ETA: 1s - loss: 11.4770 - acc: \n",
"Epoch 5/5\n",
"42000/42000 [==============================] - 4s - loss: 11.4692 - acc: 0.1761 ETA: 0s - loss: 11.4725 - acc: \n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x10c410390>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Train the model on the data\n",
"LM.fit(trn_input, trn_labels, nb_epoch=5, batch_size = 4, verbose=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As I'd expect, a single perceptron layer doing a linear mapping between input to output performs.. poorly. But this is one of the first times I'm hand coding this from scratch, and it's good to be in a place where I can start experimenting without spending the bulk of mental effort over getting the machine to work.\n",
"\n",
"By accidentally running more epochs without re-initializing the model, it seems it plateaus at ```0.1761``` accuracy.\n",
"\n",
"It'll be interesting to see how RMSprop compares to SGD, and mean-squared error vs categorical cross-entropy. More interesting is adding more layers and tweaking their activations and looking at different learning rates: constant or graduated. *Even* more interesting is adding backpropagation and turning it into a neural network.\n",
"\n",
"Below I'm getting the data separated into training and validation sets."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# # Turns out this cell was unnecessary\n",
"# import pandas as pd\n",
"# test_data = pd.read_csv(path + 'test.csv')\n",
"# # wait there are no labels that's the point..\n",
"# # test_labels = test_data.ix[:,0].values.astype('int32')\n",
"# test_input = (test_data.ix[:,1:].values).astype('float32')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(28000, 784)\n",
"(28000,)\n"
]
}
],
"source": [
"# print(test_data.shape)\n",
"# print(test_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unfortunately I don't yet know how to separate a .csv file into a validation set. Wait that sounds easy. Take a random permutation, or for the lazy: the first X amount of inputs and labels from the training set and call that validation. Oh. Okay. Maybe I should do that."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(42000, 785)\n",
"(42000, 784)\n",
"(42000, 10)\n"
]
}
],
"source": [
"# # Wondering if a crash I had earlier was because One-Hotting did something to the labels..\n",
"# trn_data = pd.read_csv(path + 'train.csv')\n",
"# trn_labels = trn_data.ix[:,0].values.astype('int32')\n",
"# trn_input = (trn_data.ix[:,1:].values).astype('float32')\n",
"\n",
"\n",
"# test_data has 42,000 elements. I'll take 2,000 for validation.\n",
"# val_data = trn_data[:2000]\n",
"# trn_2_data = trn_data[2000:]\n",
"\n",
"# val_input = val_data.ix[:,0].values.astype('int32')\n",
"# val_labels = (val_data.ix[:,1:].values).astype('float32')\n",
"\n",
"# trn_2_input = trn_2_data.ix[:,0].values.astype('int32')\n",
"# trn_2_labels = (trn_2_data.ix[:,1:].values).astype('float32')\n",
"\n",
"\n",
"# trying to do this in a way that doesn't kill the kernel\n",
"print(trn_data.shape)\n",
"print(trn_input.shape)\n",
"print(trn_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, so.. training data is the number of images X (number of pixels + label). The label just adds 1 to the vector's length because it's just a decimal {0..9}, giving 42k X 785..\n",
"\n",
"The training input vector has the label removed so its 42k images X 784 pixels..\n",
"\n",
"The trianing labels vector is one-hot encoded to a ten-bit, & of course the 42k images..\n",
"\n",
"So... nothing is saying I can't just cut input & labels and separate those into new training and validation sets. Not sure why I was getting crashes the other way, but we'll see if this works (it should). Ooo, maybe I.. okay maybe I made a mistake with leaving the labels on or something, before."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"((2000, 785), (2000,), (2000, 784))\n",
"((40000,), (40000,), (40000, 784))\n"
]
}
],
"source": [
"# Old\n",
"# print(val_data.shape, val_input.shape, val_labels.shape)\n",
"# print(trn_2_input.shape, trn_2_input.shape, trn_2_labels.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"((2000, 784), (2000, 10))\n",
"((40000, 784), (40000, 10))\n"
]
}
],
"source": [
"val_input = trn_input[:2000]\n",
"val_labels = trn_labels[:2000]\n",
"\n",
"newtrn_input = trn_input[2000:]\n",
"newtrn_labels = trn_labels[2000:]\n",
"\n",
"print(val_input.shape, val_labels.shape)\n",
"print(newtrn_input.shape, newtrn_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, so we got those separated.. let's do the same thing as before: single Linear Model Perceptron Layer, but with a validation set to check against."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The stuff above's One-Hotted.. so don't do it again..\n",
"\n",
"# # I forgot the onehot encode the labels after loading from disk\n",
"# val_labels = onehot(val_labels)\n",
"# trn_2_labels = onehot(trn_2_labels)\n",
"# print(val_labels.shape)\n",
"# print(trn_2_labels.shape)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 40000 samples, validate on 2000 samples\n",
"Epoch 1/5\n",
"40000/40000 [==============================] - 4s - loss: nan - acc: 0.0983 - val_loss: nan - val_acc: 0.0980\n",
"Epoch 2/5\n",
"40000/40000 [==============================] - 4s - loss: nan - acc: 0.0984 - val_loss: nan - val_acc: 0.0980\n",
"Epoch 3/5\n",
"40000/40000 [==============================] - 4s - loss: nan - acc: 0.0984 - val_loss: nan - val_acc: 0.0980\n",
"Epoch 4/5\n",
"40000/40000 [==============================] - 4s - loss: nan - acc: 0.0984 - val_loss: nan - val_acc: 0.0980\n",
"Epoch 5/5\n",
"40000/40000 [==============================] - 5s - loss: nan - acc: 0.0984 - val_loss: nan - val_acc: 0.0980\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x11b16a390>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"LM = Sequential([Dense(nb_classes, input_dim=input_dim)])\n",
"LM.compile(optimizer='sgd', loss='mse', metrics=['accuracy'])\n",
"# LM = Sequential([Dense(nb_classes, activation='sigmoid', input_dim=input_dim)])\n",
"\n",
"LM.fit(newtrn_input, newtrn_labels, nb_epoch=5, batch_size=4,\n",
" validation_data=(val_input, val_labels))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"42000/42000 [==============================] - 4s - loss: 0.0983 - acc: 0.1568 - ETA: 4s - loss: 0.1200 - acc: 0.099 - ETA: 4s - loss: 0.1189 - acc - ETA: 3s - ETA: 0s - loss: 0.0985 - acc: \n",
"Epoch 2/5\n",
"42000/42000 [==============================] - 5s - loss: 0.0933 - acc: 0.1843 - ETA: 4s - loss: 0.0932 - - ETA: 3s - loss: - ETA: 0s - loss: 0.0934 - acc: 0.183 - ETA: 0s - loss: 0.0934 - acc: 0 - ETA: 0s - loss: 0.0934 - \n",
"Epoch 3/5\n",
"42000/42000 [==============================] - 4s - loss: 0.0930 - acc: 0.1920 - ETA: 3s - loss: 0.0927 - acc: 0 - ETA: 3s - loss: 0.0927 - - E - ETA: 0s - loss: 0.0\n",
"Epoch 4/5\n",
"42000/42000 [==============================] - 4s - loss: 0.0928 - acc: 0.1877 - ET - ETA: 0s - loss: 0.0928 \n",
"Epoch 5/5\n",
"42000/42000 [==============================] - 5s - loss: 0.0925 - acc: 0.1981 - ETA: 3s - loss: 0.0929 - - ETA: - ETA: 1s - loss: 0.0926 - a - ETA: 0s - loss: 0.0926 - \n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x118ad3410>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"LM2 = Sequential([Dense(nb_classes, activation='sigmoid', input_dim=input_dim)])\n",
"LM2.compile(optimizer='sgd', loss='mse', metrics=['accuracy'])\n",
"LM2.fit(trn_input, trn_labels, nb_epoch=5, batch_size=4, verbose=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yeah, I'm not seeing a real difference in not/using an activation function for just a single linear layer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Multilayer Perceptron (MLP) can be built by simple adding on layers. The big difference between an MLP and a NN is no backpropagation is going on. A single forward pass is done through the network each epoch, and there isn't any adjustment of weights."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# this notebook will be a bit of a mess; the machine isn't the only one learning\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation, Dropout"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"??Activation"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 40000 samples, validate on 2000 samples\n",
"Epoch 1/5\n",
"40000/40000 [==============================] - 14s - loss: 0.0947 - acc: 0.1021 - val_loss: 0.0900 - val_acc: 0.1085\n",
"Epoch 2/5\n",
"40000/40000 [==============================] - 14s - loss: 0.0933 - acc: 0.1021 - val_loss: 0.0900 - val_acc: 0.1100\n",
"Epoch 3/5\n",
"40000/40000 [==============================] - 13s - loss: 0.0928 - acc: 0.1005 - val_loss: 0.0900 - val_acc: 0.1155\n",
"Epoch 4/5\n",
"40000/40000 [==============================] - 14s - loss: 0.0924 - acc: 0.1002 - val_loss: 0.0900 - val_acc: 0.1655\n",
"Epoch 5/5\n",
"40000/40000 [==============================] - 14s - loss: 0.0919 - acc: 0.1040 - val_loss: 0.0899 - val_acc: 0.1085\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x11be078d0>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# just here so I have them infront of me\n",
"input_dim = input_dim # 784\n",
"nb_classes = nb_classes # 10\n",
"\n",
"MLP = Sequential()\n",
"MLP.add(Dense(112, input_dim=input_dim)) # I'll just set internal output to 4x28=112\n",
"MLP.add(Activation('sigmoid'))\n",
"# I dont know what to set the dropout layer too, but I see it in keras.io as 0.5\n",
"MLP.add(Dropout(0.5))\n",
"\n",
"# and now to add 3 more layers set to sigmoid\n",
"for layer in xrange(3):\n",
" MLP.add(Dense(112))\n",
" MLP.add(Activation('sigmoid'))\n",
" MLP.add(Dropout(0.5))\n",
"\n",
"# and our final layers\n",
"MLP.add(Dense(nb_classes, activation='softmax'))\n",
"\n",
"# this will be a beautiful disaster\n",
"MLP.compile(loss='mse', optimizer='sgd', metrics=['accuracy'])\n",
"\n",
"MLP.fit(newtrn_input, newtrn_labels, nb_epoch=5, batch_size=4,\n",
" validation_data = (val_input, val_labels))"
]
},
{
"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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
scottlittle/solar-sensors | IPnotebooks/less-important-IPNBs/building-data-functions.ipynb | 4 | 565586 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from __future__ import division\n",
"from datetime import datetime,timedelta\n",
"%matplotlib inline\n",
"pd.options.display.max_rows = 99\n",
"pd.options.display.max_columns = 999"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Sensor data!"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def pad(x):\n",
" time = x.astype(str).zfill(4)\n",
" return time[:2] + ':' + time[2:]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>nothing</th>\n",
" <th>Year</th>\n",
" <th>DOY</th>\n",
" <th>CR3000 CF Change [counts]</th>\n",
" <th>CR3000 Zen Angle [degrees]</th>\n",
" <th>Global LI-200 [W/m^2]</th>\n",
" <th>Global CM22 (vent/cor) [W/m^2]</th>\n",
" <th>Global RG780 PSP (vent/cor) [W/m^2]</th>\n",
" <th>Global TSP-1 [W/m^2]</th>\n",
" <th>Global CM6b (cor) [W/m^2]</th>\n",
" <th>Global SP Lite [W/m^2]</th>\n",
" <th>Global SP-110 [W/m^2]</th>\n",
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" <th>Research 2</th>\n",
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" <th>Global 501A [MED/hr]</th>\n",
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" <th>Global 501A [Index]</th>\n",
" <th>Direct NIP #1 [W/m^2]</th>\n",
" <th>Direct NIP #2 [W/m^2]</th>\n",
" <th>Direct LI-201 [W/m^2]</th>\n",
" <th>Direct RG780 NIP [W/m^2]</th>\n",
" <th>Direct CH1 [W/m^2]</th>\n",
" <th>Zebra PSP (cor) [W/m^2]</th>\n",
" <th>Direct CUVA2 [W/m^2]</th>\n",
" <th>Direct CUVB2 [W/m^2]</th>\n",
" <th>500nm TWC Photometer [V]</th>\n",
" <th>Global SPN1 [W/m^2]</th>\n",
" <th>Diffuse SPN1 [W/m^2]</th>\n",
" <th>Data lab Dry Bulb Temp [deg C]</th>\n",
" <th>Data lab RH [%]</th>\n",
" <th>Diffuse PSP (sband/cor) [W/m^2]</th>\n",
" <th>Research F1</th>\n",
" <th>Diffuse 8-48 (vent) [W/m^2]</th>\n",
" <th>Diffuse CM22 (vent/cor) [W/m^2]</th>\n",
" <th>Research F0</th>\n",
" <th>Research 3</th>\n",
" <th>Downwelling IR PIR Vent [W/m^2]</th>\n",
" <th>Downwelling IR CG4 Vent [W/m^2]</th>\n",
" <th>Upwelling IR PIR [W/m^2]</th>\n",
" <th>Instrument Net DW PIR [W/m^2]</th>\n",
" <th>Instrument Net DW CG4 [W/m^2]</th>\n",
" <th>Instrument Net UW PIR [W/m^2]</th>\n",
" <th>Global PSP (cor) [W/m^2]</th>\n",
" <th>Global PSP (vent/cor) [W/m^2]</th>\n",
" <th>Diffuse PSP (vent/cor) [W/m^2]</th>\n",
" <th>Diffuse CUV4 [W/m^2]</th>\n",
" <th>Global CUV4 [W/m^2]</th>\n",
" <th>Avg Wind Speed @ 19ft [m/s]</th>\n",
" <th>Avg Wind Direction @ 19ft [deg from N]</th>\n",
" <th>Peak Wind Speed @ 19ft [m/s]</th>\n",
" <th>Direct MS-56 [W/m^2]</th>\n",
" <th>Research 4</th>\n",
" <th>PIR DW Dome Temp [deg K]</th>\n",
" <th>PIR DW Case Temp [deg K]</th>\n",
" <th>CG4 DW Case Temp [deg K]</th>\n",
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" <th>CR3000 Temp [deg C]</th>\n",
" <th>Deck Dry Bulb Temp [deg C]</th>\n",
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" <th>Global CM22 [mV]</th>\n",
" <th>Global RG780 PSP [mV]</th>\n",
" <th>Global CM6b [mV]</th>\n",
" <th>Zebra PSP [mV]</th>\n",
" <th>Diffuse PSP (sband) [mV]</th>\n",
" <th>Diffuse PSP [mV]</th>\n",
" <th>Diffuse CM22 [mV]</th>\n",
" <th>Global Quantum LI-190 [umol/s/m^2]</th>\n",
" <th>Global Photometric LI-210 [klux]</th>\n",
" <th>Upwelling Shortwave CM3 (CNR1) [W/m^2]</th>\n",
" <th>Upwelling IR CG3 (CNR1) [W/m^2]</th>\n",
" <th>Instrument Net UW CG3 [W/m^2]</th>\n",
" <th>Upwelling Shortwave PSP [W/m^2]</th>\n",
" <th>Upwelling Shortwave LI-200 [W/m^2]</th>\n",
" <th>Upwelling Quantum LI-190 [umol/s/m^2]</th>\n",
" <th>CNR1 Case Temp [deg K]</th>\n",
" <th>Global CM3 (CNR1) [W/m^2]</th>\n",
" <th>Downwelling IR CG3 (CNR1) [W/m^2]</th>\n",
" <th>Instrument Net DW CG3 [W/m^2]</th>\n",
" <th>Snow Depth [cm]</th>\n",
" <th>Precipitation [mm]</th>\n",
" <th>Precipitation (Accumulated) [mm]</th>\n",
" <th>Station Pressure [mBar]</th>\n",
" <th>Global 40-South PSP [W/m^2]</th>\n",
" <th>Global 40-South LI-200 [W/m^2]</th>\n",
" <th>Global Normal CM-21 [W/m^2]</th>\n",
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" <th>Global 90-South LI-200 [W/m^2]</th>\n",
" <th>Global 90-West PSP [W/m^2]</th>\n",
" <th>Global 90-West LI-200 [W/m^2]</th>\n",
" <th>Research RT0</th>\n",
" <th>Research RT1</th>\n",
" <th>Research RT2</th>\n",
" <th>Atmospheric Electric Field [kV/m]</th>\n",
" <th>CR10X Temp (Rad-Twr) [deg C]</th>\n",
" <th>CR10X Battery (Rad-Twr) [VDC]</th>\n",
" <th>LI-2020 Battery [VDC]</th>\n",
" <th>Tower Dry Bulb Temp [deg C]</th>\n",
" <th>Tower RH [%]</th>\n",
" <th>Avg Wind Speed @ 6ft [m/s]</th>\n",
" <th>Avg Wind Direction @ 6ft [deg from N]</th>\n",
" <th>Peak Wind Speed @ 6ft [m/s]</th>\n",
" <th>CR10X Overuns (Rad-Twr) [counts]</th>\n",
" <th>Snow Depth Quality</th>\n",
" <th>SE Dry Bulb Temp [deg C]</th>\n",
" <th>SE RH [%]</th>\n",
" <th>SE-POA Angle [degrees]</th>\n",
" <th>Global SE-POA LI-200 [W/m^2]</th>\n",
" <th>CR10X Overuns (Met-Twr) [counts]</th>\n",
" <th>CR10X Temp (Met-Twr) [deg C]</th>\n",
" <th>CR10X Battery (Met-Twr) [VDC]</th>\n",
" <th>Vertical Wind Shear [1/s]</th>\n",
" <th>Research PVT1</th>\n",
" <th>Research PVT2</th>\n",
" <th>Avg Wind Speed @ 22ft [m/s]</th>\n",
" <th>Avg Wind Direction @ 22ft [deg from N]</th>\n",
" <th>Avg Wind Speed @ 42ft [m/s]</th>\n",
" <th>Avg Wind Direction @ 42ft [deg from N]</th>\n",
" <th>Research PVT0</th>\n",
" <th>Peak Wind Speed @ 22ft [m/s]</th>\n",
" <th>Peak Wind Speed @ 42ft [m/s]</th>\n",
" <th>Delta UT1 [seconds]</th>\n",
" <th>500nm TWC AOD</th>\n",
" <th>Net Radiation Eppley [W/m^2]</th>\n",
" <th>Net Radiation K&Z [W/m^2]</th>\n",
" <th>Atmos Net Infrared PIRs [W/m^2]</th>\n",
" <th>Atmos Net Infrared K&Zs [W/m^2]</th>\n",
" <th>Albedo (PSP)</th>\n",
" <th>Albedo (K&Z)</th>\n",
" <th>Albedo (LI-200)</th>\n",
" <th>Albedo Quantum (LI-190)</th>\n",
" <th>Broadband Turbidity</th>\n",
" <th>500nm Estimated AOD</th>\n",
" <th>Sea-Level Pressure (Est) [mBar]</th>\n",
" <th>Tower Dew Point Temp [deg C]</th>\n",
" <th>Tower Wet Bulb Temp [deg C]</th>\n",
" <th>Tower Wind Chill Temp [deg C]</th>\n",
" <th>Deck Wind Chill Temp [deg C]</th>\n",
" <th>Total Cloud Cover [%]</th>\n",
" <th>Opaque Cloud Cover [%]</th>\n",
" <th>Global Extraterrestrial (calc) [W/m^2]</th>\n",
" <th>Direct Extraterrestrial (calc) [W/m^2]</th>\n",
" <th>Zenith Angle [degrees]</th>\n",
" <th>Azimuth Angle [degrees]</th>\n",
" <th>Airmass</th>\n",
" <th>Delta T [seconds]</th>\n",
" <th>315nm POM-01 Photometer [nA]</th>\n",
" <th>400nm POM-01 Photometer [uA]</th>\n",
" <th>500nm POM-01 Photometer [uA]</th>\n",
" <th>675nm POM-01 Photometer [uA]</th>\n",
" <th>870nm POM-01 Photometer [uA]</th>\n",
" <th>940nm POM-01 Photometer [uA]</th>\n",
" <th>1020nm POM-01 Photometer [uA]</th>\n",
" <th>315nm Obsolete AOD</th>\n",
" <th>400nm Obsolete AOD</th>\n",
" <th>500nm Obsolete AOD</th>\n",
" <th>675nm Obsolete AOD</th>\n",
" <th>870nm Obsolete AOD</th>\n",
" <th>940nm Obsolete AOD</th>\n",
" <th>1020nm Obsolete AOD</th>\n",
" <th>Research F2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-04-01 07:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>135.162783</td>\n",
" <td>-0.057713</td>\n",
" <td>-0.378273</td>\n",
" <td>-1.188463</td>\n",
" <td>3.154796</td>\n",
" <td>-0.592370</td>\n",
" <td>0.000231</td>\n",
" <td>-0.000736</td>\n",
" <td>0.430110</td>\n",
" <td>-0.000369</td>\n",
" <td>-11.826723</td>\n",
" <td>-0.001850</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.001883</td>\n",
" <td>0.000000</td>\n",
" <td>0.289183</td>\n",
" <td>0.003000</td>\n",
" <td>-0.002000</td>\n",
" <td>0.000000</td>\n",
" <td>0.595537</td>\n",
" <td>0.385513</td>\n",
" <td>0.157467</td>\n",
" <td>0.379208</td>\n",
" <td>-0.650401</td>\n",
" <td>1.936944</td>\n",
" <td>0.006800</td>\n",
" <td>-0.000967</td>\n",
" <td>0.000009</td>\n",
" <td>-0.949420</td>\n",
" <td>1.683162</td>\n",
" <td>20.951167</td>\n",
" <td>42.063333</td>\n",
" <td>2.717713</td>\n",
" <td>0.601490</td>\n",
" <td>-0.317465</td>\n",
" <td>-0.291207</td>\n",
" <td>2.853340</td>\n",
" <td>0.000047</td>\n",
" <td>262.818783</td>\n",
" <td>262.898783</td>\n",
" <td>320.319450</td>\n",
" <td>-74.192045</td>\n",
" <td>-60.314888</td>\n",
" <td>-4.835604</td>\n",
" <td>3.043059</td>\n",
" <td>0.654774</td>\n",
" <td>2.864373</td>\n",
" <td>-0.000667</td>\n",
" <td>-0.000350</td>\n",
" <td>0.389917</td>\n",
" <td>43.595000</td>\n",
" <td>0.736667</td>\n",
" <td>-0.225806</td>\n",
" <td>-0.000103</td>\n",
" <td>276.958767</td>\n",
" <td>277.257283</td>\n",
" <td>275.088900</td>\n",
" <td>274.882500</td>\n",
" <td>274.973917</td>\n",
" <td>22.110167</td>\n",
" <td>1.633700</td>\n",
" <td>53.005667</td>\n",
" <td>24.751167</td>\n",
" <td>39.885833</td>\n",
" <td>39.879833</td>\n",
" <td>40.723167</td>\n",
" <td>39.731333</td>\n",
" <td>24.793167</td>\n",
" <td>24.443000</td>\n",
" <td>46.529667</td>\n",
" <td>1.000000</td>\n",
" <td>13.06</td>\n",
" <td>2.682267</td>\n",
" <td>-0.319109</td>\n",
" <td>-0.002149</td>\n",
" <td>-0.029930</td>\n",
" <td>-0.042085</td>\n",
" <td>-0.009491</td>\n",
" <td>-0.053405</td>\n",
" <td>-0.025007</td>\n",
" <td>-0.026520</td>\n",
" <td>-0.021291</td>\n",
" <td>-0.030730</td>\n",
" <td>-0.008213</td>\n",
" <td>-0.091540</td>\n",
" <td>0.000000</td>\n",
" <td>1.729642</td>\n",
" <td>319.237133</td>\n",
" <td>-0.121204</td>\n",
" <td>-0.463229</td>\n",
" <td>-0.019910</td>\n",
" <td>0.000000</td>\n",
" <td>273.298933</td>\n",
" <td>-2.516079</td>\n",
" <td>260.265500</td>\n",
" <td>-56.779863</td>\n",
" <td>-0.070067</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>813.806450</td>\n",
" <td>-2.588342</td>\n",
" <td>-0.005089</td>\n",
" <td>-0.335771</td>\n",
" <td>-1.810622</td>\n",
" <td>-0.042282</td>\n",
" <td>-1.775484</td>\n",
" <td>-0.024794</td>\n",
" <td>-1.648470</td>\n",
" <td>-0.096056</td>\n",
" <td>-1.585964</td>\n",
" <td>-0.057002</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.039413</td>\n",
" <td>5.513950</td>\n",
" <td>13.909667</td>\n",
" <td>19.542500</td>\n",
" <td>1.418617</td>\n",
" <td>56.466667</td>\n",
" <td>0.915633</td>\n",
" <td>83.734167</td>\n",
" <td>1.291667</td>\n",
" <td>0</td>\n",
" <td>194.030000</td>\n",
" <td>1.268400</td>\n",
" <td>55.972500</td>\n",
" <td>-1.959367</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>2.240583</td>\n",
" <td>12.331000</td>\n",
" <td>0.111617</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.663717</td>\n",
" <td>60.967833</td>\n",
" <td>1.344217</td>\n",
" <td>98.071667</td>\n",
" <td>0</td>\n",
" <td>1.161000</td>\n",
" <td>1.807417</td>\n",
" <td>0.1</td>\n",
" <td>0.000000</td>\n",
" <td>-56.382700</td>\n",
" <td>-58.446233</td>\n",
" <td>-57.500667</td>\n",
" <td>-56.338350</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1015.081500</td>\n",
" <td>-5.573400</td>\n",
" <td>-1.508400</td>\n",
" <td>0.904700</td>\n",
" <td>1.435483</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>135.169852</td>\n",
" <td>38.774864</td>\n",
" <td>-1.000000</td>\n",
" <td>67.084</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.717713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 08:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>131.439633</td>\n",
" <td>-0.031592</td>\n",
" <td>-0.402762</td>\n",
" <td>-0.743244</td>\n",
" <td>2.928847</td>\n",
" <td>-0.815485</td>\n",
" <td>0.000628</td>\n",
" <td>-0.001673</td>\n",
" <td>0.561647</td>\n",
" <td>-0.000437</td>\n",
" <td>-11.770308</td>\n",
" <td>-0.001967</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.001833</td>\n",
" <td>0.000000</td>\n",
" <td>0.288450</td>\n",
" <td>0.003000</td>\n",
" <td>-0.002000</td>\n",
" <td>0.000000</td>\n",
" <td>1.806709</td>\n",
" <td>1.678900</td>\n",
" <td>0.145302</td>\n",
" <td>1.415801</td>\n",
" <td>-0.685835</td>\n",
" <td>1.246333</td>\n",
" <td>0.006733</td>\n",
" <td>-0.000967</td>\n",
" <td>0.000010</td>\n",
" <td>-1.218052</td>\n",
" <td>1.483983</td>\n",
" <td>20.964167</td>\n",
" <td>40.469667</td>\n",
" <td>2.225765</td>\n",
" <td>0.874242</td>\n",
" <td>-0.363137</td>\n",
" <td>-0.364490</td>\n",
" <td>1.920637</td>\n",
" <td>0.000049</td>\n",
" <td>262.960450</td>\n",
" <td>262.945300</td>\n",
" <td>315.665783</td>\n",
" <td>-70.923260</td>\n",
" <td>-57.480082</td>\n",
" <td>-6.152236</td>\n",
" <td>2.052663</td>\n",
" <td>0.952191</td>\n",
" <td>2.601132</td>\n",
" <td>-0.000333</td>\n",
" <td>-0.000283</td>\n",
" <td>0.000367</td>\n",
" <td>1.426667</td>\n",
" <td>0.007500</td>\n",
" <td>-0.233050</td>\n",
" <td>-0.000074</td>\n",
" <td>276.357233</td>\n",
" <td>276.636033</td>\n",
" <td>274.470500</td>\n",
" <td>274.201533</td>\n",
" <td>274.289033</td>\n",
" <td>22.078833</td>\n",
" <td>0.879017</td>\n",
" <td>55.708667</td>\n",
" <td>24.793167</td>\n",
" <td>39.920167</td>\n",
" <td>39.912167</td>\n",
" <td>40.734167</td>\n",
" <td>39.734833</td>\n",
" <td>24.806667</td>\n",
" <td>24.452667</td>\n",
" <td>46.534833</td>\n",
" <td>1.000000</td>\n",
" <td>13.06</td>\n",
" <td>2.677150</td>\n",
" <td>-0.305316</td>\n",
" <td>-0.001993</td>\n",
" <td>-0.035680</td>\n",
" <td>-0.038188</td>\n",
" <td>-0.009500</td>\n",
" <td>-0.047759</td>\n",
" <td>-0.026553</td>\n",
" <td>-0.030642</td>\n",
" <td>-0.023404</td>\n",
" <td>-0.030395</td>\n",
" <td>-0.008669</td>\n",
" <td>-0.096568</td>\n",
" <td>0.000000</td>\n",
" <td>2.850575</td>\n",
" <td>314.062800</td>\n",
" <td>0.916466</td>\n",
" <td>-0.145634</td>\n",
" <td>-0.004929</td>\n",
" <td>0.000000</td>\n",
" <td>271.953900</td>\n",
" <td>-1.705619</td>\n",
" <td>260.644217</td>\n",
" <td>-50.391922</td>\n",
" <td>-0.033667</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>812.876883</td>\n",
" <td>-2.885035</td>\n",
" <td>-0.000452</td>\n",
" <td>-0.198589</td>\n",
" <td>-1.553082</td>\n",
" <td>-0.007312</td>\n",
" <td>-1.576223</td>\n",
" <td>-0.004052</td>\n",
" <td>-2.149338</td>\n",
" <td>-0.030246</td>\n",
" <td>-1.183967</td>\n",
" <td>-0.012668</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.035800</td>\n",
" <td>4.528833</td>\n",
" <td>13.940167</td>\n",
" <td>19.482833</td>\n",
" <td>0.846750</td>\n",
" <td>58.938333</td>\n",
" <td>0.034917</td>\n",
" <td>11.559167</td>\n",
" <td>0.079167</td>\n",
" <td>0</td>\n",
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" <td>0.512367</td>\n",
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" <td>1014.151500</td>\n",
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" <td>0.846750</td>\n",
" <td>0.879017</td>\n",
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" <td>2.225765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 09:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>124.468717</td>\n",
" <td>-0.029547</td>\n",
" <td>-0.352172</td>\n",
" <td>-0.110254</td>\n",
" <td>3.044817</td>\n",
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" <td>0.179660</td>\n",
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" <td>0.287633</td>\n",
" <td>0.003000</td>\n",
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" <td>1.294834</td>\n",
" <td>0.859053</td>\n",
" <td>0.151385</td>\n",
" <td>1.576925</td>\n",
" <td>-0.842857</td>\n",
" <td>2.330063</td>\n",
" <td>0.007217</td>\n",
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" <td>1.485424</td>\n",
" <td>21.093667</td>\n",
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" <td>-0.306392</td>\n",
" <td>3.018952</td>\n",
" <td>0.000028</td>\n",
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" <td>308.785900</td>\n",
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" <td>275.869317</td>\n",
" <td>276.178833</td>\n",
" <td>274.051083</td>\n",
" <td>273.566017</td>\n",
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" <td>22.062167</td>\n",
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" <td>3.076337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 10:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>115.330367</td>\n",
" <td>-0.019988</td>\n",
" <td>-0.381466</td>\n",
" <td>-0.386065</td>\n",
" <td>4.172143</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2014-04-01 11:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
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" <td>19.282000</td>\n",
" <td>-0.900517</td>\n",
" <td>66.063333</td>\n",
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" <td>1011.376500</td>\n",
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" <td>104.898891</td>\n",
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" <td>-1.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>3.464543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 12:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>93.591755</td>\n",
" <td>2.596963</td>\n",
" <td>2.510263</td>\n",
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" <td>8.155280</td>\n",
" <td>2.406760</td>\n",
" <td>2.734236</td>\n",
" <td>3.013713</td>\n",
" <td>3.375866</td>\n",
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" <td>0.185850</td>\n",
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" <td>0.001550</td>\n",
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" <td>0.210350</td>\n",
" <td>0.001233</td>\n",
" <td>0.477633</td>\n",
" <td>0.003833</td>\n",
" <td>-0.001667</td>\n",
" <td>0.003617</td>\n",
" <td>1.132023</td>\n",
" <td>2.686147</td>\n",
" <td>0.256138</td>\n",
" <td>1.089744</td>\n",
" <td>-0.401600</td>\n",
" <td>5.294215</td>\n",
" <td>0.007383</td>\n",
" <td>-0.000950</td>\n",
" <td>0.000011</td>\n",
" <td>1.148195</td>\n",
" <td>3.826909</td>\n",
" <td>20.860333</td>\n",
" <td>46.592833</td>\n",
" <td>5.920285</td>\n",
" <td>4.257295</td>\n",
" <td>2.661253</td>\n",
" <td>2.645205</td>\n",
" <td>6.777503</td>\n",
" <td>0.000056</td>\n",
" <td>238.659650</td>\n",
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" <td>7.084622</td>\n",
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" <td>1.421817</td>\n",
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" <td>1.829167</td>\n",
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" <td>274.137583</td>\n",
" <td>274.469483</td>\n",
" <td>272.275250</td>\n",
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" <td>272.221683</td>\n",
" <td>22.023167</td>\n",
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" <td>-1516.450000</td>\n",
" <td>6.419112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 13:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>82.140220</td>\n",
" <td>93.006725</td>\n",
" <td>102.601872</td>\n",
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" <td>4.712717</td>\n",
" <td>0.026550</td>\n",
" <td>4.531800</td>\n",
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" <td>0.106783</td>\n",
" <td>347.214292</td>\n",
" <td>352.459132</td>\n",
" <td>342.261183</td>\n",
" <td>167.151123</td>\n",
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" <td>59.881038</td>\n",
" <td>2.379283</td>\n",
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" <td>47.932593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 14:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>70.739575</td>\n",
" <td>309.078667</td>\n",
" <td>328.021617</td>\n",
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" <td>343.093483</td>\n",
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" <td>15.239833</td>\n",
" <td>0.103183</td>\n",
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" <td>326.506497</td>\n",
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" <td>279.207050</td>\n",
" <td>275.177300</td>\n",
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" <td>276.604950</td>\n",
" <td>21.981000</td>\n",
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" <td>1.392533</td>\n",
" <td>42.181100</td>\n",
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" <td>130.524267</td>\n",
" <td>141.027350</td>\n",
" <td>-128.737883</td>\n",
" <td>-125.192583</td>\n",
" <td>0.205152</td>\n",
" <td>0.191617</td>\n",
" <td>0.274352</td>\n",
" <td>0.130862</td>\n",
" <td>0.074510</td>\n",
" <td>0.105396</td>\n",
" <td>1010.116833</td>\n",
" <td>-4.485350</td>\n",
" <td>-0.628683</td>\n",
" <td>1.486083</td>\n",
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" <td>450.822583</td>\n",
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" <td>70.737625</td>\n",
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" <td>3.093707</td>\n",
" <td>67.084</td>\n",
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" <td>-2782.668883</td>\n",
" <td>-2704.529667</td>\n",
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" <td>-2742.339967</td>\n",
" <td>-2703.006500</td>\n",
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" <td>-2799.467377</td>\n",
" <td>-2799.578437</td>\n",
" <td>58.362060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 15:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>59.675297</td>\n",
" <td>515.724733</td>\n",
" <td>530.869983</td>\n",
" <td>217.584717</td>\n",
" <td>556.887833</td>\n",
" <td>526.208600</td>\n",
" <td>529.064717</td>\n",
" <td>537.373433</td>\n",
" <td>536.185000</td>\n",
" <td>-0.000428</td>\n",
" <td>-11.809245</td>\n",
" <td>26.582333</td>\n",
" <td>0.307633</td>\n",
" <td>0.741867</td>\n",
" <td>0.292183</td>\n",
" <td>26.423833</td>\n",
" <td>0.266900</td>\n",
" <td>25.594500</td>\n",
" <td>0.311583</td>\n",
" <td>0.286367</td>\n",
" <td>1.719000</td>\n",
" <td>850.945667</td>\n",
" <td>854.891467</td>\n",
" <td>785.683133</td>\n",
" <td>335.888800</td>\n",
" <td>847.109450</td>\n",
" <td>316.697337</td>\n",
" <td>27.100500</td>\n",
" <td>0.304583</td>\n",
" <td>2.560210</td>\n",
" <td>545.086617</td>\n",
" <td>86.807618</td>\n",
" <td>20.894333</td>\n",
" <td>45.554833</td>\n",
" <td>78.924567</td>\n",
" <td>526.211737</td>\n",
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" <td>528.009047</td>\n",
" <td>0.000011</td>\n",
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" <td>530.449917</td>\n",
" <td>81.929157</td>\n",
" <td>12.151500</td>\n",
" <td>26.266000</td>\n",
" <td>1.266583</td>\n",
" <td>61.042983</td>\n",
" <td>2.170000</td>\n",
" <td>848.069050</td>\n",
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" <td>280.882033</td>\n",
" <td>281.200233</td>\n",
" <td>276.849150</td>\n",
" <td>279.484283</td>\n",
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" <td>21.998000</td>\n",
" <td>3.580217</td>\n",
" <td>51.199000</td>\n",
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" <td>39.923833</td>\n",
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" <td>-2332.936275</td>\n",
" <td>93.710953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 16:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>49.526853</td>\n",
" <td>641.773733</td>\n",
" <td>650.031067</td>\n",
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" <td>33.930167</td>\n",
" <td>0.487283</td>\n",
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" <td>0.523417</td>\n",
" <td>0.525300</td>\n",
" <td>3.052850</td>\n",
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" <td>809.282616</td>\n",
" <td>737.078850</td>\n",
" <td>309.199107</td>\n",
" <td>801.305856</td>\n",
" <td>473.442217</td>\n",
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" <td>0.408817</td>\n",
" <td>2.480198</td>\n",
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" <td>126.175005</td>\n",
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" <td>22.035667</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2014-04-01 17:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>41.196412</td>\n",
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" <td>587.337800</td>\n",
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" <td>31.362167</td>\n",
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" <td>323.167590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 18:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>36.092960</td>\n",
" <td>392.608133</td>\n",
" <td>390.294017</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2014-04-01 19:00:00</th>\n",
" <td>0</td>\n",
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" <td>1.229968</td>\n",
" <td>67.084</td>\n",
" <td>-1163.123000</td>\n",
" <td>-1160.764310</td>\n",
" <td>-1144.060487</td>\n",
" <td>-1136.988669</td>\n",
" <td>-1145.762176</td>\n",
" <td>-1153.964046</td>\n",
" <td>-1151.096423</td>\n",
" <td>-1162.856527</td>\n",
" <td>-1161.595231</td>\n",
" <td>-1161.616104</td>\n",
" <td>-1161.576798</td>\n",
" <td>-1161.573965</td>\n",
" <td>-1161.243744</td>\n",
" <td>-1161.499249</td>\n",
" <td>341.726487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 20:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>40.033195</td>\n",
" <td>601.494883</td>\n",
" <td>595.654600</td>\n",
" <td>243.419750</td>\n",
" <td>618.469217</td>\n",
" <td>597.310717</td>\n",
" <td>598.020183</td>\n",
" <td>600.819283</td>\n",
" <td>600.274200</td>\n",
" <td>-0.000262</td>\n",
" <td>-11.689897</td>\n",
" <td>33.087333</td>\n",
" <td>0.632500</td>\n",
" <td>1.525567</td>\n",
" <td>0.583383</td>\n",
" <td>32.561333</td>\n",
" <td>0.589000</td>\n",
" <td>32.136500</td>\n",
" <td>0.599100</td>\n",
" <td>0.630850</td>\n",
" <td>3.534767</td>\n",
" <td>193.057962</td>\n",
" <td>194.426235</td>\n",
" <td>171.044951</td>\n",
" <td>71.996035</td>\n",
" <td>183.489713</td>\n",
" <td>528.525317</td>\n",
" <td>7.967533</td>\n",
" <td>0.138067</td>\n",
" <td>0.550390</td>\n",
" <td>619.525750</td>\n",
" <td>455.724017</td>\n",
" <td>20.944500</td>\n",
" <td>43.543167</td>\n",
" <td>365.676333</td>\n",
" <td>592.916997</td>\n",
" <td>440.525833</td>\n",
" <td>437.032483</td>\n",
" <td>594.805643</td>\n",
" <td>-0.000026</td>\n",
" <td>291.985117</td>\n",
" <td>292.420650</td>\n",
" <td>443.846950</td>\n",
" <td>-99.498385</td>\n",
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" <td>57.918085</td>\n",
" <td>588.608683</td>\n",
" <td>593.436017</td>\n",
" <td>427.429517</td>\n",
" <td>23.816167</td>\n",
" <td>33.196833</td>\n",
" <td>1.659667</td>\n",
" <td>82.682067</td>\n",
" <td>2.980833</td>\n",
" <td>184.998111</td>\n",
" <td>-0.000142</td>\n",
" <td>290.030917</td>\n",
" <td>289.761033</td>\n",
" <td>286.071600</td>\n",
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" <td>287.350100</td>\n",
" <td>22.150167</td>\n",
" <td>11.221167</td>\n",
" <td>29.555167</td>\n",
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" <td>39.998667</td>\n",
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" <td>385.646867</td>\n",
" <td>413.929250</td>\n",
" <td>303.129200</td>\n",
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" <td>21.274500</td>\n",
" <td>12.005667</td>\n",
" <td>30.023667</td>\n",
" <td>1.875200</td>\n",
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" <td>2.954167</td>\n",
" <td>0</td>\n",
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" <td>29.749500</td>\n",
" <td>25.418667</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>2.581433</td>\n",
" <td>128.028000</td>\n",
" <td>2.871017</td>\n",
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" <td>0.1</td>\n",
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" <td>0.164057</td>\n",
" <td>0.203528</td>\n",
" <td>0.111183</td>\n",
" <td>2.495319</td>\n",
" <td>2.566424</td>\n",
" <td>1007.416833</td>\n",
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" <td>4.318933</td>\n",
" <td>11.566750</td>\n",
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" <td>63.450000</td>\n",
" <td>1047.284600</td>\n",
" <td>1368.598267</td>\n",
" <td>40.037130</td>\n",
" <td>214.050943</td>\n",
" <td>1.306600</td>\n",
" <td>67.084</td>\n",
" <td>-2096.650390</td>\n",
" <td>-2094.523991</td>\n",
" <td>-2079.235124</td>\n",
" <td>-2072.836061</td>\n",
" <td>-2081.100463</td>\n",
" <td>-2088.243827</td>\n",
" <td>-2085.713050</td>\n",
" <td>-2098.022831</td>\n",
" <td>-2097.333265</td>\n",
" <td>-2097.364420</td>\n",
" <td>-2097.336384</td>\n",
" <td>-2097.300593</td>\n",
" <td>-2097.141142</td>\n",
" <td>-2097.275614</td>\n",
" <td>431.481208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 21:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>47.914428</td>\n",
" <td>363.507883</td>\n",
" <td>363.376033</td>\n",
" <td>142.694905</td>\n",
" <td>377.115700</td>\n",
" <td>366.641850</td>\n",
" <td>368.235667</td>\n",
" <td>372.167783</td>\n",
" <td>366.009117</td>\n",
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" <td>20.830000</td>\n",
" <td>0.338283</td>\n",
" <td>0.815783</td>\n",
" <td>0.320967</td>\n",
" <td>20.473333</td>\n",
" <td>0.306033</td>\n",
" <td>20.317667</td>\n",
" <td>0.333667</td>\n",
" <td>0.330417</td>\n",
" <td>1.890267</td>\n",
" <td>35.017409</td>\n",
" <td>34.888277</td>\n",
" <td>31.224659</td>\n",
" <td>11.725897</td>\n",
" <td>31.498370</td>\n",
" <td>322.930317</td>\n",
" <td>1.448700</td>\n",
" <td>0.020833</td>\n",
" <td>0.090247</td>\n",
" <td>375.314550</td>\n",
" <td>347.777017</td>\n",
" <td>20.875833</td>\n",
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" <td>365.475283</td>\n",
" <td>362.841950</td>\n",
" <td>323.625683</td>\n",
" <td>17.531333</td>\n",
" <td>20.853667</td>\n",
" <td>3.138367</td>\n",
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" <td>287.277000</td>\n",
" <td>22.185167</td>\n",
" <td>11.848500</td>\n",
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" <td>39.976000</td>\n",
" <td>39.963500</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2014-04-01 22:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>57.818428</td>\n",
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" <td>242.999267</td>\n",
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" <td>13.852167</td>\n",
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" <td>3.945833</td>\n",
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" <td>193.935000</td>\n",
" <td>11.267500</td>\n",
" <td>27.691167</td>\n",
" <td>42.700333</td>\n",
" <td>210.204983</td>\n",
" <td>0</td>\n",
" <td>14.452667</td>\n",
" <td>13.386833</td>\n",
" <td>0.017683</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4.406283</td>\n",
" <td>60.130183</td>\n",
" <td>4.513867</td>\n",
" <td>52.302817</td>\n",
" <td>0</td>\n",
" <td>5.235667</td>\n",
" <td>5.294367</td>\n",
" <td>0.1</td>\n",
" <td>3.532886</td>\n",
" <td>108.283133</td>\n",
" <td>104.381000</td>\n",
" <td>-98.841017</td>\n",
" <td>-97.808650</td>\n",
" <td>0.161998</td>\n",
" <td>0.167938</td>\n",
" <td>0.210142</td>\n",
" <td>0.109585</td>\n",
" <td>3.701680</td>\n",
" <td>3.750510</td>\n",
" <td>1007.270833</td>\n",
" <td>-5.201567</td>\n",
" <td>3.894767</td>\n",
" <td>10.524600</td>\n",
" <td>10.252367</td>\n",
" <td>98.700000</td>\n",
" <td>81.333333</td>\n",
" <td>727.758050</td>\n",
" <td>1368.533400</td>\n",
" <td>57.823333</td>\n",
" <td>246.604316</td>\n",
" <td>1.889356</td>\n",
" <td>67.084</td>\n",
" <td>-699.863094</td>\n",
" <td>-699.861344</td>\n",
" <td>-699.757744</td>\n",
" <td>-699.739001</td>\n",
" <td>-699.806552</td>\n",
" <td>-699.853556</td>\n",
" <td>-699.838906</td>\n",
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" <td>-696.092616</td>\n",
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" <td>-695.970001</td>\n",
" <td>-695.867902</td>\n",
" <td>-695.638073</td>\n",
" <td>-695.796191</td>\n",
" <td>250.574370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 23:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>68.759173</td>\n",
" <td>194.051233</td>\n",
" <td>198.755233</td>\n",
" <td>80.235270</td>\n",
" <td>206.265633</td>\n",
" <td>200.441350</td>\n",
" <td>197.755167</td>\n",
" <td>201.774083</td>\n",
" <td>201.052833</td>\n",
" <td>-0.000190</td>\n",
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" <td>10.673867</td>\n",
" <td>0.093900</td>\n",
" <td>0.226550</td>\n",
" <td>0.093050</td>\n",
" <td>10.856133</td>\n",
" <td>0.081100</td>\n",
" <td>10.700267</td>\n",
" <td>0.103200</td>\n",
" <td>0.091533</td>\n",
" <td>0.524800</td>\n",
" <td>19.190415</td>\n",
" <td>19.082951</td>\n",
" <td>16.989494</td>\n",
" <td>7.135526</td>\n",
" <td>15.933266</td>\n",
" <td>174.545950</td>\n",
" <td>0.500083</td>\n",
" <td>0.004333</td>\n",
" <td>0.042144</td>\n",
" <td>209.350983</td>\n",
" <td>203.328083</td>\n",
" <td>20.822500</td>\n",
" <td>39.197833</td>\n",
" <td>164.797133</td>\n",
" <td>205.939388</td>\n",
" <td>191.515450</td>\n",
" <td>190.416300</td>\n",
" <td>213.762947</td>\n",
" <td>-0.000010</td>\n",
" <td>286.947250</td>\n",
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" <td>387.240600</td>\n",
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" <td>204.377633</td>\n",
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" <td>9.357917</td>\n",
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" <td>3.155833</td>\n",
" <td>16.393984</td>\n",
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" <td>288.194733</td>\n",
" <td>288.322917</td>\n",
" <td>285.463950</td>\n",
" <td>285.892283</td>\n",
" <td>285.881983</td>\n",
" <td>22.206000</td>\n",
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" <td>24.855667</td>\n",
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" <td>39.971000</td>\n",
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" <td>1.845632</td>\n",
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" <td>-698.712826</td>\n",
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" <td>-697.815785</td>\n",
" <td>-697.959156</td>\n",
" <td>195.728895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-02 00:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>80.126183</td>\n",
" <td>85.737807</td>\n",
" <td>89.636593</td>\n",
" <td>36.325315</td>\n",
" <td>91.194758</td>\n",
" <td>90.082392</td>\n",
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" <td>90.867118</td>\n",
" <td>90.618232</td>\n",
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" <td>4.842667</td>\n",
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" <td>0.030150</td>\n",
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" <td>0.128600</td>\n",
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" <td>3.209475</td>\n",
" <td>9.208591</td>\n",
" <td>81.577035</td>\n",
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" <td>0.022141</td>\n",
" <td>97.269875</td>\n",
" <td>95.264268</td>\n",
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" <td>22.212833</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2014-04-02 01:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
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" <td>6.805255</td>\n",
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" <td>-2682.950000</td>\n",
" <td>-2682.950000</td>\n",
" <td>-2682.950000</td>\n",
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" <td>11.139272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-02 02:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>102.885935</td>\n",
" <td>-0.015336</td>\n",
" <td>-0.403198</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2014-04-02 03:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2014-04-02 04:00:00</th>\n",
" <td>0</td>\n",
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" <td>280.689533</td>\n",
" <td>281.108533</td>\n",
" <td>278.881583</td>\n",
" <td>278.910550</td>\n",
" <td>279.000150</td>\n",
" <td>22.207833</td>\n",
" <td>5.825450</td>\n",
" <td>49.572500</td>\n",
" <td>24.766667</td>\n",
" <td>39.883833</td>\n",
" <td>39.878667</td>\n",
" <td>40.744667</td>\n",
" <td>39.751333</td>\n",
" <td>24.897667</td>\n",
" <td>24.562167</td>\n",
" <td>46.549833</td>\n",
" <td>1.000000</td>\n",
" <td>13.06</td>\n",
" <td>2.680600</td>\n",
" <td>-0.239878</td>\n",
" <td>-0.001387</td>\n",
" <td>-0.031382</td>\n",
" <td>-0.056696</td>\n",
" <td>-0.013723</td>\n",
" <td>-0.071165</td>\n",
" <td>-0.032484</td>\n",
" <td>-0.027449</td>\n",
" <td>-0.022221</td>\n",
" <td>-0.023712</td>\n",
" <td>-0.011358</td>\n",
" <td>-0.187465</td>\n",
" <td>0.000000</td>\n",
" <td>2.546550</td>\n",
" <td>334.129617</td>\n",
" <td>-4.717630</td>\n",
" <td>-0.207902</td>\n",
" <td>-0.211667</td>\n",
" <td>0.043851</td>\n",
" <td>277.413083</td>\n",
" <td>-2.117827</td>\n",
" <td>251.725367</td>\n",
" <td>-83.944273</td>\n",
" <td>-0.008717</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>808.103733</td>\n",
" <td>-2.516072</td>\n",
" <td>-0.187680</td>\n",
" <td>0.166954</td>\n",
" <td>-1.692143</td>\n",
" <td>-0.248270</td>\n",
" <td>-1.389791</td>\n",
" <td>-0.135996</td>\n",
" <td>-1.502954</td>\n",
" <td>-0.195806</td>\n",
" <td>-1.525610</td>\n",
" <td>-0.112960</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.048133</td>\n",
" <td>9.793333</td>\n",
" <td>13.785667</td>\n",
" <td>19.514333</td>\n",
" <td>5.746117</td>\n",
" <td>52.071500</td>\n",
" <td>1.972300</td>\n",
" <td>262.294333</td>\n",
" <td>2.766667</td>\n",
" <td>0</td>\n",
" <td>193.966667</td>\n",
" <td>5.326883</td>\n",
" <td>52.266833</td>\n",
" <td>-1.907200</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>5.647667</td>\n",
" <td>12.427333</td>\n",
" <td>0.079383</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3.913067</td>\n",
" <td>314.411667</td>\n",
" <td>4.396717</td>\n",
" <td>320.578333</td>\n",
" <td>0</td>\n",
" <td>4.773333</td>\n",
" <td>5.174350</td>\n",
" <td>0.1</td>\n",
" <td>0.000000</td>\n",
" <td>-78.797233</td>\n",
" <td>-82.707500</td>\n",
" <td>-80.105083</td>\n",
" <td>-79.606683</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1009.378667</td>\n",
" <td>-2.937400</td>\n",
" <td>1.735767</td>\n",
" <td>4.427483</td>\n",
" <td>3.317483</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>122.829207</td>\n",
" <td>312.191727</td>\n",
" <td>-1.000000</td>\n",
" <td>67.084</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.617081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-02 05:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>130.164567</td>\n",
" <td>-0.013189</td>\n",
" <td>-0.370537</td>\n",
" <td>-1.349876</td>\n",
" <td>0.698233</td>\n",
" <td>-0.430616</td>\n",
" <td>-0.002282</td>\n",
" <td>-0.000736</td>\n",
" <td>-0.977338</td>\n",
" <td>-0.000372</td>\n",
" <td>-11.802148</td>\n",
" <td>-0.001600</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.000183</td>\n",
" <td>-0.002017</td>\n",
" <td>0.000000</td>\n",
" <td>0.293567</td>\n",
" <td>0.004000</td>\n",
" <td>-0.002000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.953543</td>\n",
" <td>-0.676502</td>\n",
" <td>0.200043</td>\n",
" <td>-0.219586</td>\n",
" <td>-0.628523</td>\n",
" <td>3.443909</td>\n",
" <td>0.006617</td>\n",
" <td>-0.000983</td>\n",
" <td>0.000001</td>\n",
" <td>-0.775378</td>\n",
" <td>1.931920</td>\n",
" <td>20.892000</td>\n",
" <td>43.790500</td>\n",
" <td>4.509819</td>\n",
" <td>1.161582</td>\n",
" <td>-0.080228</td>\n",
" <td>-0.255589</td>\n",
" <td>4.753023</td>\n",
" <td>0.000015</td>\n",
" <td>243.356283</td>\n",
" <td>243.715900</td>\n",
" <td>326.908100</td>\n",
" <td>-109.461383</td>\n",
" <td>-91.605488</td>\n",
" <td>-13.859590</td>\n",
" <td>5.066024</td>\n",
" <td>1.248165</td>\n",
" <td>5.680405</td>\n",
" <td>-0.000700</td>\n",
" <td>-0.000133</td>\n",
" <td>2.744600</td>\n",
" <td>94.192967</td>\n",
" <td>3.675000</td>\n",
" <td>-0.318812</td>\n",
" <td>-0.000093</td>\n",
" <td>279.905433</td>\n",
" <td>280.358367</td>\n",
" <td>277.903900</td>\n",
" <td>278.044967</td>\n",
" <td>278.181117</td>\n",
" <td>22.182167</td>\n",
" <td>4.737133</td>\n",
" <td>53.864833</td>\n",
" <td>24.760833</td>\n",
" <td>39.880667</td>\n",
" <td>39.877167</td>\n",
" <td>40.743000</td>\n",
" <td>39.744667</td>\n",
" <td>24.866000</td>\n",
" <td>24.523333</td>\n",
" <td>46.538500</td>\n",
" <td>1.000000</td>\n",
" <td>13.06</td>\n",
" <td>2.679883</td>\n",
" <td>-0.243243</td>\n",
" <td>-0.003349</td>\n",
" <td>-0.039702</td>\n",
" <td>-0.060532</td>\n",
" <td>-0.012251</td>\n",
" <td>-0.075599</td>\n",
" <td>-0.033050</td>\n",
" <td>-0.034251</td>\n",
" <td>-0.027570</td>\n",
" <td>-0.035151</td>\n",
" <td>-0.010710</td>\n",
" <td>-0.165926</td>\n",
" <td>0.000000</td>\n",
" <td>2.198714</td>\n",
" <td>326.408100</td>\n",
" <td>-5.936293</td>\n",
" <td>-1.350887</td>\n",
" <td>-0.183300</td>\n",
" <td>0.011755</td>\n",
" <td>276.079667</td>\n",
" <td>-2.870887</td>\n",
" <td>240.447433</td>\n",
" <td>-88.654620</td>\n",
" <td>-0.075067</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>807.747267</td>\n",
" <td>-3.762921</td>\n",
" <td>-0.137492</td>\n",
" <td>-0.230232</td>\n",
" <td>-2.863365</td>\n",
" <td>-0.242440</td>\n",
" <td>-2.542086</td>\n",
" <td>-0.154428</td>\n",
" <td>-2.984749</td>\n",
" <td>-0.234892</td>\n",
" <td>-2.329402</td>\n",
" <td>-0.140672</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.049294</td>\n",
" <td>8.691000</td>\n",
" <td>13.818833</td>\n",
" <td>19.435667</td>\n",
" <td>4.455833</td>\n",
" <td>57.420000</td>\n",
" <td>2.139917</td>\n",
" <td>72.586333</td>\n",
" <td>2.866667</td>\n",
" <td>0</td>\n",
" <td>193.970000</td>\n",
" <td>3.715533</td>\n",
" <td>59.080333</td>\n",
" <td>-1.904933</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>4.288700</td>\n",
" <td>12.400500</td>\n",
" <td>0.061767</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3.497567</td>\n",
" <td>153.844450</td>\n",
" <td>3.873733</td>\n",
" <td>145.834100</td>\n",
" <td>0</td>\n",
" <td>4.197000</td>\n",
" <td>4.473200</td>\n",
" <td>0.1</td>\n",
" <td>0.000000</td>\n",
" <td>-80.952767</td>\n",
" <td>-85.261417</td>\n",
" <td>-83.551817</td>\n",
" <td>-82.692200</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1009.022000</td>\n",
" <td>-2.893983</td>\n",
" <td>1.085683</td>\n",
" <td>2.733767</td>\n",
" <td>2.437417</td>\n",
" <td>-1.000000</td>\n",
" <td>-1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>130.174811</td>\n",
" <td>328.439539</td>\n",
" <td>-1.000000</td>\n",
" <td>67.084</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.509819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-02 06:00:00</th>\n",
" <td>0</td>\n",
" <td>2014</td>\n",
" <td>91</td>\n",
" <td>0</td>\n",
" <td>134.449400</td>\n",
" <td>0.002300</td>\n",
" <td>-0.406105</td>\n",
" <td>-1.023715</td>\n",
" <td>1.942328</td>\n",
" <td>-0.497009</td>\n",
" <td>-0.001719</td>\n",
" <td>-0.000602</td>\n",
" <td>-0.380009</td>\n",
" <td>-0.000504</td>\n",
" <td>-11.825365</td>\n",
" <td>-0.001650</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.001983</td>\n",
" <td>0.000000</td>\n",
" <td>0.290683</td>\n",
" <td>0.003567</td>\n",
" <td>-0.002000</td>\n",
" <td>0.000000</td>\n",
" <td>-0.519506</td>\n",
" <td>-0.105700</td>\n",
" <td>0.229780</td>\n",
" <td>0.287535</td>\n",
" <td>-0.503038</td>\n",
" <td>3.483105</td>\n",
" <td>0.006733</td>\n",
" <td>-0.001000</td>\n",
" <td>0.000001</td>\n",
" <td>-1.169034</td>\n",
" <td>1.670404</td>\n",
" <td>20.948333</td>\n",
" <td>41.519833</td>\n",
" <td>4.487795</td>\n",
" <td>1.244385</td>\n",
" <td>0.020740</td>\n",
" <td>-0.268307</td>\n",
" <td>4.492258</td>\n",
" <td>0.000033</td>\n",
" <td>239.513217</td>\n",
" <td>239.634450</td>\n",
" <td>322.068550</td>\n",
" <td>-103.503583</td>\n",
" <td>-86.997172</td>\n",
" <td>-10.297325</td>\n",
" <td>4.792650</td>\n",
" <td>1.345929</td>\n",
" <td>4.727171</td>\n",
" <td>-0.000600</td>\n",
" <td>-0.000217</td>\n",
" <td>2.885667</td>\n",
" <td>59.639667</td>\n",
" <td>3.845833</td>\n",
" <td>-1.015353</td>\n",
" <td>-0.000085</td>\n",
" <td>278.018367</td>\n",
" <td>278.434933</td>\n",
" <td>276.054150</td>\n",
" <td>276.354850</td>\n",
" <td>276.470850</td>\n",
" <td>22.164167</td>\n",
" <td>2.903483</td>\n",
" <td>63.489333</td>\n",
" <td>24.738833</td>\n",
" <td>39.867167</td>\n",
" <td>39.862833</td>\n",
" <td>40.729500</td>\n",
" <td>39.730167</td>\n",
" <td>24.810167</td>\n",
" <td>24.455833</td>\n",
" <td>46.519333</td>\n",
" <td>1.000000</td>\n",
" <td>13.06</td>\n",
" <td>2.679800</td>\n",
" <td>-0.227598</td>\n",
" <td>-0.002934</td>\n",
" <td>-0.037633</td>\n",
" <td>-0.056278</td>\n",
" <td>-0.012217</td>\n",
" <td>-0.069435</td>\n",
" <td>-0.032321</td>\n",
" <td>-0.030505</td>\n",
" <td>-0.024344</td>\n",
" <td>-0.037968</td>\n",
" <td>-0.010413</td>\n",
" <td>-0.113376</td>\n",
" <td>0.000000</td>\n",
" <td>2.472385</td>\n",
" <td>321.358017</td>\n",
" <td>-3.683002</td>\n",
" <td>-0.777386</td>\n",
" <td>-0.118853</td>\n",
" <td>0.002261</td>\n",
" <td>274.534683</td>\n",
" <td>-2.391492</td>\n",
" <td>236.870000</td>\n",
" <td>-85.023690</td>\n",
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" <td>19.616667</td>\n",
" <td>2.843650</td>\n",
" <td>66.763000</td>\n",
" <td>2.377083</td>\n",
" <td>20.884000</td>\n",
" <td>3.058333</td>\n",
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" <td>4.487795</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" nothing Year DOY CR3000 CF Change [counts] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0 2014 91 0 \n",
"2014-04-01 08:00:00 0 2014 91 0 \n",
"2014-04-01 09:00:00 0 2014 91 0 \n",
"2014-04-01 10:00:00 0 2014 91 0 \n",
"2014-04-01 11:00:00 0 2014 91 0 \n",
"2014-04-01 12:00:00 0 2014 91 0 \n",
"2014-04-01 13:00:00 0 2014 91 0 \n",
"2014-04-01 14:00:00 0 2014 91 0 \n",
"2014-04-01 15:00:00 0 2014 91 0 \n",
"2014-04-01 16:00:00 0 2014 91 0 \n",
"2014-04-01 17:00:00 0 2014 91 0 \n",
"2014-04-01 18:00:00 0 2014 91 0 \n",
"2014-04-01 19:00:00 0 2014 91 0 \n",
"2014-04-01 20:00:00 0 2014 91 0 \n",
"2014-04-01 21:00:00 0 2014 91 0 \n",
"2014-04-01 22:00:00 0 2014 91 0 \n",
"2014-04-01 23:00:00 0 2014 91 0 \n",
"2014-04-02 00:00:00 0 2014 91 0 \n",
"2014-04-02 01:00:00 0 2014 91 0 \n",
"2014-04-02 02:00:00 0 2014 91 0 \n",
"2014-04-02 03:00:00 0 2014 91 0 \n",
"2014-04-02 04:00:00 0 2014 91 0 \n",
"2014-04-02 05:00:00 0 2014 91 0 \n",
"2014-04-02 06:00:00 0 2014 91 0 \n",
"\n",
" CR3000 Zen Angle [degrees] Global LI-200 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 135.162783 -0.057713 \n",
"2014-04-01 08:00:00 131.439633 -0.031592 \n",
"2014-04-01 09:00:00 124.468717 -0.029547 \n",
"2014-04-01 10:00:00 115.330367 -0.019988 \n",
"2014-04-01 11:00:00 104.880325 -0.010633 \n",
"2014-04-01 12:00:00 93.591755 2.596963 \n",
"2014-04-01 13:00:00 82.140220 93.006725 \n",
"2014-04-01 14:00:00 70.739575 309.078667 \n",
"2014-04-01 15:00:00 59.675297 515.724733 \n",
"2014-04-01 16:00:00 49.526853 641.773733 \n",
"2014-04-01 17:00:00 41.196412 600.609083 \n",
"2014-04-01 18:00:00 36.092960 392.608133 \n",
"2014-04-01 19:00:00 35.654707 475.058217 \n",
"2014-04-01 20:00:00 40.033195 601.494883 \n",
"2014-04-01 21:00:00 47.914428 363.507883 \n",
"2014-04-01 22:00:00 57.818428 242.212167 \n",
"2014-04-01 23:00:00 68.759173 194.051233 \n",
"2014-04-02 00:00:00 80.126183 85.737807 \n",
"2014-04-02 01:00:00 91.522765 6.741764 \n",
"2014-04-02 02:00:00 102.885935 -0.015336 \n",
"2014-04-02 03:00:00 113.456267 -0.016716 \n",
"2014-04-02 04:00:00 122.816417 -0.019067 \n",
"2014-04-02 05:00:00 130.164567 -0.013189 \n",
"2014-04-02 06:00:00 134.449400 0.002300 \n",
"\n",
" Global CM22 (vent/cor) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.378273 \n",
"2014-04-01 08:00:00 -0.402762 \n",
"2014-04-01 09:00:00 -0.352172 \n",
"2014-04-01 10:00:00 -0.381466 \n",
"2014-04-01 11:00:00 -0.434885 \n",
"2014-04-01 12:00:00 2.510263 \n",
"2014-04-01 13:00:00 102.601872 \n",
"2014-04-01 14:00:00 328.021617 \n",
"2014-04-01 15:00:00 530.869983 \n",
"2014-04-01 16:00:00 650.031067 \n",
"2014-04-01 17:00:00 587.337800 \n",
"2014-04-01 18:00:00 390.294017 \n",
"2014-04-01 19:00:00 465.267183 \n",
"2014-04-01 20:00:00 595.654600 \n",
"2014-04-01 21:00:00 363.376033 \n",
"2014-04-01 22:00:00 242.999267 \n",
"2014-04-01 23:00:00 198.755233 \n",
"2014-04-02 00:00:00 89.636593 \n",
"2014-04-02 01:00:00 6.805255 \n",
"2014-04-02 02:00:00 -0.403198 \n",
"2014-04-02 03:00:00 -0.386799 \n",
"2014-04-02 04:00:00 -0.554220 \n",
"2014-04-02 05:00:00 -0.370537 \n",
"2014-04-02 06:00:00 -0.406105 \n",
"\n",
" Global RG780 PSP (vent/cor) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -1.188463 \n",
"2014-04-01 08:00:00 -0.743244 \n",
"2014-04-01 09:00:00 -0.110254 \n",
"2014-04-01 10:00:00 -0.386065 \n",
"2014-04-01 11:00:00 -0.638284 \n",
"2014-04-01 12:00:00 0.416570 \n",
"2014-04-01 13:00:00 40.586209 \n",
"2014-04-01 14:00:00 133.951197 \n",
"2014-04-01 15:00:00 217.584717 \n",
"2014-04-01 16:00:00 268.250167 \n",
"2014-04-01 17:00:00 237.178222 \n",
"2014-04-01 18:00:00 148.952277 \n",
"2014-04-01 19:00:00 184.811535 \n",
"2014-04-01 20:00:00 243.419750 \n",
"2014-04-01 21:00:00 142.694905 \n",
"2014-04-01 22:00:00 94.553798 \n",
"2014-04-01 23:00:00 80.235270 \n",
"2014-04-02 00:00:00 36.325315 \n",
"2014-04-02 01:00:00 1.219648 \n",
"2014-04-02 02:00:00 -2.009595 \n",
"2014-04-02 03:00:00 -2.613980 \n",
"2014-04-02 04:00:00 -1.355180 \n",
"2014-04-02 05:00:00 -1.349876 \n",
"2014-04-02 06:00:00 -1.023715 \n",
"\n",
" Global TSP-1 [W/m^2] Global CM6b (cor) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 3.154796 -0.592370 \n",
"2014-04-01 08:00:00 2.928847 -0.815485 \n",
"2014-04-01 09:00:00 3.044817 -0.819540 \n",
"2014-04-01 10:00:00 4.172143 -0.834784 \n",
"2014-04-01 11:00:00 5.255002 -0.166084 \n",
"2014-04-01 12:00:00 8.155280 2.406760 \n",
"2014-04-01 13:00:00 112.272325 97.271930 \n",
"2014-04-01 14:00:00 343.093483 320.398183 \n",
"2014-04-01 15:00:00 556.887833 526.208600 \n",
"2014-04-01 16:00:00 678.837533 647.421233 \n",
"2014-04-01 17:00:00 616.113483 587.383550 \n",
"2014-04-01 18:00:00 409.233633 393.452417 \n",
"2014-04-01 19:00:00 489.113217 465.713900 \n",
"2014-04-01 20:00:00 618.469217 597.310717 \n",
"2014-04-01 21:00:00 377.115700 366.641850 \n",
"2014-04-01 22:00:00 252.993033 245.911200 \n",
"2014-04-01 23:00:00 206.265633 200.441350 \n",
"2014-04-02 00:00:00 91.194758 90.082392 \n",
"2014-04-02 01:00:00 5.463964 6.604028 \n",
"2014-04-02 02:00:00 -1.522223 -0.688208 \n",
"2014-04-02 03:00:00 0.373369 -0.193752 \n",
"2014-04-02 04:00:00 2.340684 -0.544696 \n",
"2014-04-02 05:00:00 0.698233 -0.430616 \n",
"2014-04-02 06:00:00 1.942328 -0.497009 \n",
"\n",
" Global SP Lite [W/m^2] Global SP-110 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000231 -0.000736 \n",
"2014-04-01 08:00:00 0.000628 -0.001673 \n",
"2014-04-01 09:00:00 0.000033 -0.002008 \n",
"2014-04-01 10:00:00 0.001786 -0.001071 \n",
"2014-04-01 11:00:00 0.000926 -0.000201 \n",
"2014-04-01 12:00:00 2.734236 3.013713 \n",
"2014-04-01 13:00:00 100.844092 111.313385 \n",
"2014-04-01 14:00:00 321.973167 335.894933 \n",
"2014-04-01 15:00:00 529.064717 537.373433 \n",
"2014-04-01 16:00:00 647.844533 656.507150 \n",
"2014-04-01 17:00:00 596.231583 602.039367 \n",
"2014-04-01 18:00:00 396.336633 401.221767 \n",
"2014-04-01 19:00:00 474.310733 478.947500 \n",
"2014-04-01 20:00:00 598.020183 600.819283 \n",
"2014-04-01 21:00:00 368.235667 372.167783 \n",
"2014-04-01 22:00:00 247.030617 250.872150 \n",
"2014-04-01 23:00:00 197.755167 201.774083 \n",
"2014-04-02 00:00:00 87.262622 90.867118 \n",
"2014-04-02 01:00:00 6.819206 7.291261 \n",
"2014-04-02 02:00:00 0.000231 0.001606 \n",
"2014-04-02 03:00:00 -0.001521 -0.001071 \n",
"2014-04-02 04:00:00 -0.001223 -0.000736 \n",
"2014-04-02 05:00:00 -0.002282 -0.000736 \n",
"2014-04-02 06:00:00 -0.001719 -0.000602 \n",
"\n",
" Global TSP-700 Vent [W/m^2] Research 1 Research 2 \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.430110 -0.000369 -11.826723 \n",
"2014-04-01 08:00:00 0.561647 -0.000437 -11.770308 \n",
"2014-04-01 09:00:00 0.179660 -0.000432 -11.775688 \n",
"2014-04-01 10:00:00 0.454911 -0.000427 -11.796903 \n",
"2014-04-01 11:00:00 0.886524 -0.000473 -11.804185 \n",
"2014-04-01 12:00:00 3.375866 -0.000436 -11.818420 \n",
"2014-04-01 13:00:00 106.246223 -0.000479 -11.780922 \n",
"2014-04-01 14:00:00 331.976933 -0.000448 -11.814145 \n",
"2014-04-01 15:00:00 536.185000 -0.000428 -11.809245 \n",
"2014-04-01 16:00:00 654.817000 -0.000359 -11.689428 \n",
"2014-04-01 17:00:00 592.597183 -0.000371 -11.626752 \n",
"2014-04-01 18:00:00 393.053633 -0.000319 -11.811380 \n",
"2014-04-01 19:00:00 471.199283 -0.000324 -11.728425 \n",
"2014-04-01 20:00:00 600.274200 -0.000262 -11.689897 \n",
"2014-04-01 21:00:00 366.009117 -0.000251 -11.789075 \n",
"2014-04-01 22:00:00 244.916950 -0.000238 -11.805457 \n",
"2014-04-01 23:00:00 201.052833 -0.000190 -11.778812 \n",
"2014-04-02 00:00:00 90.618232 -0.000230 -11.900892 \n",
"2014-04-02 01:00:00 6.583113 -0.000210 -11.879117 \n",
"2014-04-02 02:00:00 -0.841985 -0.000213 -11.859873 \n",
"2014-04-02 03:00:00 -0.667926 -0.000333 -11.786662 \n",
"2014-04-02 04:00:00 0.154693 -0.000348 -11.787415 \n",
"2014-04-02 05:00:00 -0.977338 -0.000372 -11.802148 \n",
"2014-04-02 06:00:00 -0.380009 -0.000504 -11.825365 \n",
"\n",
" Global TUVR [W/m^2] Global 501A [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.001850 0.000000 \n",
"2014-04-01 08:00:00 -0.001967 0.000000 \n",
"2014-04-01 09:00:00 -0.001983 0.000000 \n",
"2014-04-01 10:00:00 -0.001717 0.000000 \n",
"2014-04-01 11:00:00 -0.001833 0.000000 \n",
"2014-04-01 12:00:00 0.185850 0.000633 \n",
"2014-04-01 13:00:00 4.441400 0.019117 \n",
"2014-04-01 14:00:00 14.897333 0.107567 \n",
"2014-04-01 15:00:00 26.582333 0.307633 \n",
"2014-04-01 16:00:00 34.627167 0.546250 \n",
"2014-04-01 17:00:00 32.305833 0.606700 \n",
"2014-04-01 18:00:00 24.237167 0.484350 \n",
"2014-04-01 19:00:00 26.905667 0.533317 \n",
"2014-04-01 20:00:00 33.087333 0.632500 \n",
"2014-04-01 21:00:00 20.830000 0.338283 \n",
"2014-04-01 22:00:00 14.018667 0.179833 \n",
"2014-04-01 23:00:00 10.673867 0.093900 \n",
"2014-04-02 00:00:00 4.560467 0.023050 \n",
"2014-04-02 01:00:00 0.413050 0.001450 \n",
"2014-04-02 02:00:00 -0.001683 0.000000 \n",
"2014-04-02 03:00:00 -0.001683 0.000000 \n",
"2014-04-02 04:00:00 -0.001717 0.000000 \n",
"2014-04-02 05:00:00 -0.001600 0.000000 \n",
"2014-04-02 06:00:00 -0.001650 0.000000 \n",
"\n",
" Global 501A [MED/hr] Global MS210W [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 0.000000 \n",
"2014-04-01 08:00:00 0.000000 0.000000 \n",
"2014-04-01 09:00:00 0.000000 0.000000 \n",
"2014-04-01 10:00:00 0.000000 0.000000 \n",
"2014-04-01 11:00:00 0.000000 0.000000 \n",
"2014-04-01 12:00:00 0.001550 0.000617 \n",
"2014-04-01 13:00:00 0.046017 0.019683 \n",
"2014-04-01 14:00:00 0.259400 0.107017 \n",
"2014-04-01 15:00:00 0.741867 0.292183 \n",
"2014-04-01 16:00:00 1.317567 0.496617 \n",
"2014-04-01 17:00:00 1.463200 0.535383 \n",
"2014-04-01 18:00:00 1.168117 0.431917 \n",
"2014-04-01 19:00:00 1.286250 0.474600 \n",
"2014-04-01 20:00:00 1.525567 0.583383 \n",
"2014-04-01 21:00:00 0.815783 0.320967 \n",
"2014-04-01 22:00:00 0.433617 0.173167 \n",
"2014-04-01 23:00:00 0.226550 0.093050 \n",
"2014-04-02 00:00:00 0.055517 0.023600 \n",
"2014-04-02 01:00:00 0.003517 0.001333 \n",
"2014-04-02 02:00:00 0.000000 -0.000367 \n",
"2014-04-02 03:00:00 0.000000 -0.000033 \n",
"2014-04-02 04:00:00 0.000000 -0.000500 \n",
"2014-04-02 05:00:00 0.000000 -0.000183 \n",
"2014-04-02 06:00:00 0.000000 0.000000 \n",
"\n",
" Global CUVA1 [W/m^2] Global CUVB1 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.001883 0.000000 \n",
"2014-04-01 08:00:00 -0.001833 0.000000 \n",
"2014-04-01 09:00:00 -0.001700 0.000000 \n",
"2014-04-01 10:00:00 -0.002067 0.000000 \n",
"2014-04-01 11:00:00 -0.002250 0.000000 \n",
"2014-04-01 12:00:00 0.210350 0.001233 \n",
"2014-04-01 13:00:00 4.712717 0.026550 \n",
"2014-04-01 14:00:00 15.239833 0.103183 \n",
"2014-04-01 15:00:00 26.423833 0.266900 \n",
"2014-04-01 16:00:00 33.930167 0.487283 \n",
"2014-04-01 17:00:00 31.362167 0.565333 \n",
"2014-04-01 18:00:00 23.060667 0.461367 \n",
"2014-04-01 19:00:00 26.010000 0.504983 \n",
"2014-04-01 20:00:00 32.561333 0.589000 \n",
"2014-04-01 21:00:00 20.473333 0.306033 \n",
"2014-04-01 22:00:00 13.852167 0.153350 \n",
"2014-04-01 23:00:00 10.856133 0.081100 \n",
"2014-04-02 00:00:00 4.842667 0.027767 \n",
"2014-04-02 01:00:00 0.464100 0.002717 \n",
"2014-04-02 02:00:00 -0.001950 0.000000 \n",
"2014-04-02 03:00:00 -0.001967 0.000000 \n",
"2014-04-02 04:00:00 -0.002083 0.000000 \n",
"2014-04-02 05:00:00 -0.002017 0.000000 \n",
"2014-04-02 06:00:00 -0.001983 0.000000 \n",
"\n",
" Global UV-S-A-T [W/m^2] Global UV-S-B-T [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.289183 0.003000 \n",
"2014-04-01 08:00:00 0.288450 0.003000 \n",
"2014-04-01 09:00:00 0.287633 0.003000 \n",
"2014-04-01 10:00:00 0.286733 0.003000 \n",
"2014-04-01 11:00:00 0.286033 0.003000 \n",
"2014-04-01 12:00:00 0.477633 0.003833 \n",
"2014-04-01 13:00:00 4.531800 0.025050 \n",
"2014-04-01 14:00:00 14.355500 0.121033 \n",
"2014-04-01 15:00:00 25.594500 0.311583 \n",
"2014-04-01 16:00:00 33.412833 0.523417 \n",
"2014-04-01 17:00:00 31.022500 0.569500 \n",
"2014-04-01 18:00:00 23.142000 0.459500 \n",
"2014-04-01 19:00:00 25.827000 0.502250 \n",
"2014-04-01 20:00:00 32.136500 0.599100 \n",
"2014-04-01 21:00:00 20.317667 0.333667 \n",
"2014-04-01 22:00:00 13.819833 0.183717 \n",
"2014-04-01 23:00:00 10.700267 0.103200 \n",
"2014-04-02 00:00:00 4.822550 0.030150 \n",
"2014-04-02 01:00:00 0.726650 0.005700 \n",
"2014-04-02 02:00:00 0.300517 0.004000 \n",
"2014-04-02 03:00:00 0.296567 0.004000 \n",
"2014-04-02 04:00:00 0.295717 0.004000 \n",
"2014-04-02 05:00:00 0.293567 0.004000 \n",
"2014-04-02 06:00:00 0.290683 0.003567 \n",
"\n",
" Global UVB-1 [W/m^2] Global 501A [Index] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.002000 0.000000 \n",
"2014-04-01 08:00:00 -0.002000 0.000000 \n",
"2014-04-01 09:00:00 -0.002000 0.000000 \n",
"2014-04-01 10:00:00 -0.002000 0.000000 \n",
"2014-04-01 11:00:00 -0.002000 0.000000 \n",
"2014-04-01 12:00:00 -0.001667 0.003617 \n",
"2014-04-01 13:00:00 0.014133 0.106783 \n",
"2014-04-01 14:00:00 0.095550 0.600933 \n",
"2014-04-01 15:00:00 0.286367 1.719000 \n",
"2014-04-01 16:00:00 0.525300 3.052850 \n",
"2014-04-01 17:00:00 0.592867 3.390300 \n",
"2014-04-01 18:00:00 0.458733 2.706550 \n",
"2014-04-01 19:00:00 0.519067 2.980200 \n",
"2014-04-01 20:00:00 0.630850 3.534767 \n",
"2014-04-01 21:00:00 0.330417 1.890267 \n",
"2014-04-01 22:00:00 0.174900 1.004733 \n",
"2014-04-01 23:00:00 0.091533 0.524800 \n",
"2014-04-02 00:00:00 0.020233 0.128600 \n",
"2014-04-02 01:00:00 -0.000667 0.008200 \n",
"2014-04-02 02:00:00 -0.002000 0.000083 \n",
"2014-04-02 03:00:00 -0.002000 0.000000 \n",
"2014-04-02 04:00:00 -0.002000 0.000000 \n",
"2014-04-02 05:00:00 -0.002000 0.000000 \n",
"2014-04-02 06:00:00 -0.002000 0.000000 \n",
"\n",
" Direct NIP #1 [W/m^2] Direct NIP #2 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.595537 0.385513 \n",
"2014-04-01 08:00:00 1.806709 1.678900 \n",
"2014-04-01 09:00:00 1.294834 0.859053 \n",
"2014-04-01 10:00:00 0.501132 -0.097640 \n",
"2014-04-01 11:00:00 1.338867 1.692903 \n",
"2014-04-01 12:00:00 1.132023 2.686147 \n",
"2014-04-01 13:00:00 347.214292 352.459132 \n",
"2014-04-01 14:00:00 801.450017 805.352850 \n",
"2014-04-01 15:00:00 850.945667 854.891467 \n",
"2014-04-01 16:00:00 806.282114 809.282616 \n",
"2014-04-01 17:00:00 338.970440 340.098909 \n",
"2014-04-01 18:00:00 60.969345 60.800551 \n",
"2014-04-01 19:00:00 148.562719 150.322124 \n",
"2014-04-01 20:00:00 193.057962 194.426235 \n",
"2014-04-01 21:00:00 35.017409 34.888277 \n",
"2014-04-01 22:00:00 4.738511 4.543892 \n",
"2014-04-01 23:00:00 19.190415 19.082951 \n",
"2014-04-02 00:00:00 10.519153 9.459753 \n",
"2014-04-02 01:00:00 -1.409811 -2.061471 \n",
"2014-04-02 02:00:00 -0.961992 -1.393946 \n",
"2014-04-02 03:00:00 -1.429903 -1.744282 \n",
"2014-04-02 04:00:00 -0.196598 -0.036821 \n",
"2014-04-02 05:00:00 -0.953543 -0.676502 \n",
"2014-04-02 06:00:00 -0.519506 -0.105700 \n",
"\n",
" Direct LI-201 [W/m^2] Direct RG780 NIP [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.157467 0.379208 \n",
"2014-04-01 08:00:00 0.145302 1.415801 \n",
"2014-04-01 09:00:00 0.151385 1.576925 \n",
"2014-04-01 10:00:00 0.144626 -0.081305 \n",
"2014-04-01 11:00:00 0.152060 1.417245 \n",
"2014-04-01 12:00:00 0.256138 1.089744 \n",
"2014-04-01 13:00:00 342.261183 167.151123 \n",
"2014-04-01 14:00:00 757.532867 332.148600 \n",
"2014-04-01 15:00:00 785.683133 335.888800 \n",
"2014-04-01 16:00:00 737.078850 309.199107 \n",
"2014-04-01 17:00:00 308.016876 127.330554 \n",
"2014-04-01 18:00:00 55.196354 21.891603 \n",
"2014-04-01 19:00:00 131.815084 55.988718 \n",
"2014-04-01 20:00:00 171.044951 71.996035 \n",
"2014-04-01 21:00:00 31.224659 11.725897 \n",
"2014-04-01 22:00:00 3.649425 1.498908 \n",
"2014-04-01 23:00:00 16.989494 7.135526 \n",
"2014-04-02 00:00:00 11.277067 3.209475 \n",
"2014-04-02 01:00:00 0.259853 -1.003837 \n",
"2014-04-02 02:00:00 0.172335 -0.354242 \n",
"2014-04-02 03:00:00 0.210518 -0.618925 \n",
"2014-04-02 04:00:00 0.136853 0.616039 \n",
"2014-04-02 05:00:00 0.200043 -0.219586 \n",
"2014-04-02 06:00:00 0.229780 0.287535 \n",
"\n",
" Direct CH1 [W/m^2] Zebra PSP (cor) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.650401 1.936944 \n",
"2014-04-01 08:00:00 -0.685835 1.246333 \n",
"2014-04-01 09:00:00 -0.842857 2.330063 \n",
"2014-04-01 10:00:00 -0.771985 2.586846 \n",
"2014-04-01 11:00:00 -0.423089 2.687546 \n",
"2014-04-01 12:00:00 -0.401600 5.294215 \n",
"2014-04-01 13:00:00 340.639881 59.881038 \n",
"2014-04-01 14:00:00 796.333633 125.476875 \n",
"2014-04-01 15:00:00 847.109450 316.697337 \n",
"2014-04-01 16:00:00 801.305856 473.442217 \n",
"2014-04-01 17:00:00 335.042300 486.272650 \n",
"2014-04-01 18:00:00 58.848041 311.073050 \n",
"2014-04-01 19:00:00 143.113575 321.919700 \n",
"2014-04-01 20:00:00 183.489713 528.525317 \n",
"2014-04-01 21:00:00 31.498370 322.930317 \n",
"2014-04-01 22:00:00 2.772224 222.801400 \n",
"2014-04-01 23:00:00 15.933266 174.545950 \n",
"2014-04-02 00:00:00 9.208591 81.577035 \n",
"2014-04-02 01:00:00 -0.709688 9.390413 \n",
"2014-04-02 02:00:00 -0.647776 2.391880 \n",
"2014-04-02 03:00:00 -0.557784 3.004769 \n",
"2014-04-02 04:00:00 -0.292483 3.711211 \n",
"2014-04-02 05:00:00 -0.628523 3.443909 \n",
"2014-04-02 06:00:00 -0.503038 3.483105 \n",
"\n",
" Direct CUVA2 [W/m^2] Direct CUVB2 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.006800 -0.000967 \n",
"2014-04-01 08:00:00 0.006733 -0.000967 \n",
"2014-04-01 09:00:00 0.007217 -0.000983 \n",
"2014-04-01 10:00:00 0.006600 -0.000933 \n",
"2014-04-01 11:00:00 0.006783 -0.000950 \n",
"2014-04-01 12:00:00 0.007383 -0.000950 \n",
"2014-04-01 13:00:00 2.379283 0.019017 \n",
"2014-04-01 14:00:00 17.487667 0.161317 \n",
"2014-04-01 15:00:00 27.100500 0.304583 \n",
"2014-04-01 16:00:00 29.591050 0.408817 \n",
"2014-04-01 17:00:00 13.667283 0.230150 \n",
"2014-04-01 18:00:00 2.636850 0.047050 \n",
"2014-04-01 19:00:00 6.170583 0.110417 \n",
"2014-04-01 20:00:00 7.967533 0.138067 \n",
"2014-04-01 21:00:00 1.448700 0.020833 \n",
"2014-04-01 22:00:00 0.152833 0.001200 \n",
"2014-04-01 23:00:00 0.500083 0.004333 \n",
"2014-04-02 00:00:00 0.155383 0.000750 \n",
"2014-04-02 01:00:00 0.006800 -0.000900 \n",
"2014-04-02 02:00:00 0.006717 -0.000983 \n",
"2014-04-02 03:00:00 0.006850 -0.000983 \n",
"2014-04-02 04:00:00 0.006733 -0.001000 \n",
"2014-04-02 05:00:00 0.006617 -0.000983 \n",
"2014-04-02 06:00:00 0.006733 -0.001000 \n",
"\n",
" 500nm TWC Photometer [V] Global SPN1 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000009 -0.949420 \n",
"2014-04-01 08:00:00 0.000010 -1.218052 \n",
"2014-04-01 09:00:00 0.000009 -1.160809 \n",
"2014-04-01 10:00:00 0.000006 -1.172329 \n",
"2014-04-01 11:00:00 0.000007 -1.687991 \n",
"2014-04-01 12:00:00 0.000011 1.148195 \n",
"2014-04-01 13:00:00 0.732153 92.462173 \n",
"2014-04-01 14:00:00 2.280753 345.793000 \n",
"2014-04-01 15:00:00 2.560210 545.086617 \n",
"2014-04-01 16:00:00 2.480198 657.486000 \n",
"2014-04-01 17:00:00 1.032497 588.442567 \n",
"2014-04-01 18:00:00 0.174497 390.401067 \n",
"2014-04-01 19:00:00 0.435379 466.735983 \n",
"2014-04-01 20:00:00 0.550390 619.525750 \n",
"2014-04-01 21:00:00 0.090247 375.314550 \n",
"2014-04-01 22:00:00 0.006949 250.733800 \n",
"2014-04-01 23:00:00 0.042144 209.350983 \n",
"2014-04-02 00:00:00 0.022141 97.269875 \n",
"2014-04-02 01:00:00 0.000016 8.707721 \n",
"2014-04-02 02:00:00 0.000003 1.099314 \n",
"2014-04-02 03:00:00 0.000003 0.361640 \n",
"2014-04-02 04:00:00 0.000002 -0.647472 \n",
"2014-04-02 05:00:00 0.000001 -0.775378 \n",
"2014-04-02 06:00:00 0.000001 -1.169034 \n",
"\n",
" Diffuse SPN1 [W/m^2] Data lab Dry Bulb Temp [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.683162 20.951167 \n",
"2014-04-01 08:00:00 1.483983 20.964167 \n",
"2014-04-01 09:00:00 1.485424 21.093667 \n",
"2014-04-01 10:00:00 1.432310 20.915167 \n",
"2014-04-01 11:00:00 1.157184 20.871500 \n",
"2014-04-01 12:00:00 3.826909 20.860333 \n",
"2014-04-01 13:00:00 43.406502 21.029333 \n",
"2014-04-01 14:00:00 53.753785 20.905500 \n",
"2014-04-01 15:00:00 86.807618 20.894333 \n",
"2014-04-01 16:00:00 126.175005 20.850833 \n",
"2014-04-01 17:00:00 321.229450 20.810333 \n",
"2014-04-01 18:00:00 330.860900 20.877667 \n",
"2014-04-01 19:00:00 339.347400 20.918333 \n",
"2014-04-01 20:00:00 455.724017 20.944500 \n",
"2014-04-01 21:00:00 347.777017 20.875833 \n",
"2014-04-01 22:00:00 250.874433 20.737667 \n",
"2014-04-01 23:00:00 203.328083 20.822500 \n",
"2014-04-02 00:00:00 95.264268 20.994000 \n",
"2014-04-02 01:00:00 11.389517 20.882000 \n",
"2014-04-02 02:00:00 3.732281 20.906000 \n",
"2014-04-02 03:00:00 3.173785 20.957167 \n",
"2014-04-02 04:00:00 1.971323 20.875667 \n",
"2014-04-02 05:00:00 1.931920 20.892000 \n",
"2014-04-02 06:00:00 1.670404 20.948333 \n",
"\n",
" Data lab RH [%] Diffuse PSP (sband/cor) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 42.063333 2.717713 \n",
"2014-04-01 08:00:00 40.469667 2.225765 \n",
"2014-04-01 09:00:00 40.756833 3.076337 \n",
"2014-04-01 10:00:00 42.506333 3.316883 \n",
"2014-04-01 11:00:00 44.140333 3.464543 \n",
"2014-04-01 12:00:00 46.592833 5.920285 \n",
"2014-04-01 13:00:00 45.595833 40.411885 \n",
"2014-04-01 14:00:00 43.721500 49.154942 \n",
"2014-04-01 15:00:00 45.554833 78.924567 \n",
"2014-04-01 16:00:00 43.122833 107.915268 \n",
"2014-04-01 17:00:00 41.647167 273.298850 \n",
"2014-04-01 18:00:00 42.474500 292.770633 \n",
"2014-04-01 19:00:00 43.230833 295.433583 \n",
"2014-04-01 20:00:00 43.543167 365.676333 \n",
"2014-04-01 21:00:00 39.638500 286.357817 \n",
"2014-04-01 22:00:00 39.816833 211.500000 \n",
"2014-04-01 23:00:00 39.197833 164.797133 \n",
"2014-04-02 00:00:00 38.508833 76.708007 \n",
"2014-04-02 01:00:00 40.205333 10.147155 \n",
"2014-04-02 02:00:00 41.611333 3.387090 \n",
"2014-04-02 03:00:00 44.348333 3.984577 \n",
"2014-04-02 04:00:00 46.322500 4.617081 \n",
"2014-04-02 05:00:00 43.790500 4.509819 \n",
"2014-04-02 06:00:00 41.519833 4.487795 \n",
"\n",
" Research F1 Diffuse 8-48 (vent) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.601490 -0.317465 \n",
"2014-04-01 08:00:00 0.874242 -0.363137 \n",
"2014-04-01 09:00:00 1.940840 -0.398499 \n",
"2014-04-01 10:00:00 1.682720 -0.342303 \n",
"2014-04-01 11:00:00 1.198117 -0.316099 \n",
"2014-04-01 12:00:00 4.257295 2.661253 \n",
"2014-04-01 13:00:00 101.230657 45.821745 \n",
"2014-04-01 14:00:00 322.491048 59.689490 \n",
"2014-04-01 15:00:00 526.211737 97.484863 \n",
"2014-04-01 16:00:00 643.595630 134.607443 \n",
"2014-04-01 17:00:00 584.933762 326.073100 \n",
"2014-04-01 18:00:00 385.500145 335.574650 \n",
"2014-04-01 19:00:00 462.311345 343.026717 \n",
"2014-04-01 20:00:00 592.916997 440.525833 \n",
"2014-04-01 21:00:00 364.821098 337.268850 \n",
"2014-04-01 22:00:00 249.204680 240.666017 \n",
"2014-04-01 23:00:00 205.939388 191.515450 \n",
"2014-04-02 00:00:00 98.498742 87.158580 \n",
"2014-04-02 01:00:00 8.374545 6.855163 \n",
"2014-04-02 02:00:00 0.133935 -0.310105 \n",
"2014-04-02 03:00:00 -0.472417 -0.111524 \n",
"2014-04-02 04:00:00 1.013030 -0.084450 \n",
"2014-04-02 05:00:00 1.161582 -0.080228 \n",
"2014-04-02 06:00:00 1.244385 0.020740 \n",
"\n",
" Diffuse CM22 (vent/cor) [W/m^2] Research F0 Research 3 \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.291207 2.853340 0.000047 \n",
"2014-04-01 08:00:00 -0.364490 1.920637 0.000049 \n",
"2014-04-01 09:00:00 -0.306392 3.018952 0.000028 \n",
"2014-04-01 10:00:00 -0.306752 3.593805 0.000019 \n",
"2014-04-01 11:00:00 -0.286711 3.662333 0.000025 \n",
"2014-04-01 12:00:00 2.645205 6.777503 0.000056 \n",
"2014-04-01 13:00:00 45.404402 99.236167 0.000049 \n",
"2014-04-01 14:00:00 57.423580 326.506497 0.000042 \n",
"2014-04-01 15:00:00 94.525517 528.009047 0.000011 \n",
"2014-04-01 16:00:00 131.225467 645.495980 0.000034 \n",
"2014-04-01 17:00:00 323.491583 580.180125 0.000031 \n",
"2014-04-01 18:00:00 334.776217 384.168353 0.000001 \n",
"2014-04-01 19:00:00 341.179450 465.388892 0.000015 \n",
"2014-04-01 20:00:00 437.032483 594.805643 -0.000026 \n",
"2014-04-01 21:00:00 334.492950 369.484118 -0.000040 \n",
"2014-04-01 22:00:00 238.824200 254.797725 -0.000019 \n",
"2014-04-01 23:00:00 190.416300 213.762947 -0.000010 \n",
"2014-04-02 00:00:00 86.776975 99.414935 -0.000018 \n",
"2014-04-02 01:00:00 6.933463 11.439522 -0.000036 \n",
"2014-04-02 02:00:00 -0.331556 3.485327 -0.000009 \n",
"2014-04-02 03:00:00 -0.245501 3.953577 0.000022 \n",
"2014-04-02 04:00:00 -0.376046 5.059500 0.000003 \n",
"2014-04-02 05:00:00 -0.255589 4.753023 0.000015 \n",
"2014-04-02 06:00:00 -0.268307 4.492258 0.000033 \n",
"\n",
" Downwelling IR PIR Vent [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 262.818783 \n",
"2014-04-01 08:00:00 262.960450 \n",
"2014-04-01 09:00:00 245.923550 \n",
"2014-04-01 10:00:00 242.346150 \n",
"2014-04-01 11:00:00 245.868117 \n",
"2014-04-01 12:00:00 238.659650 \n",
"2014-04-01 13:00:00 229.597533 \n",
"2014-04-01 14:00:00 224.955217 \n",
"2014-04-01 15:00:00 234.388450 \n",
"2014-04-01 16:00:00 246.152250 \n",
"2014-04-01 17:00:00 292.824450 \n",
"2014-04-01 18:00:00 298.899817 \n",
"2014-04-01 19:00:00 303.583233 \n",
"2014-04-01 20:00:00 291.985117 \n",
"2014-04-01 21:00:00 299.761983 \n",
"2014-04-01 22:00:00 294.613217 \n",
"2014-04-01 23:00:00 286.947250 \n",
"2014-04-02 00:00:00 278.139500 \n",
"2014-04-02 01:00:00 277.714883 \n",
"2014-04-02 02:00:00 278.717733 \n",
"2014-04-02 03:00:00 268.230633 \n",
"2014-04-02 04:00:00 254.577117 \n",
"2014-04-02 05:00:00 243.356283 \n",
"2014-04-02 06:00:00 239.513217 \n",
"\n",
" Downwelling IR CG4 Vent [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 262.898783 \n",
"2014-04-01 08:00:00 262.945300 \n",
"2014-04-01 09:00:00 245.702450 \n",
"2014-04-01 10:00:00 242.116367 \n",
"2014-04-01 11:00:00 245.453500 \n",
"2014-04-01 12:00:00 237.971633 \n",
"2014-04-01 13:00:00 228.486367 \n",
"2014-04-01 14:00:00 224.262667 \n",
"2014-04-01 15:00:00 233.429700 \n",
"2014-04-01 16:00:00 245.869450 \n",
"2014-04-01 17:00:00 293.028617 \n",
"2014-04-01 18:00:00 299.628983 \n",
"2014-04-01 19:00:00 303.343100 \n",
"2014-04-01 20:00:00 292.420650 \n",
"2014-04-01 21:00:00 300.913150 \n",
"2014-04-01 22:00:00 295.630233 \n",
"2014-04-01 23:00:00 287.493917 \n",
"2014-04-02 00:00:00 278.628700 \n",
"2014-04-02 01:00:00 278.103617 \n",
"2014-04-02 02:00:00 279.310983 \n",
"2014-04-02 03:00:00 268.633183 \n",
"2014-04-02 04:00:00 254.522933 \n",
"2014-04-02 05:00:00 243.715900 \n",
"2014-04-02 06:00:00 239.634450 \n",
"\n",
" Upwelling IR PIR [W/m^2] Instrument Net DW PIR [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 320.319450 -74.192045 \n",
"2014-04-01 08:00:00 315.665783 -70.923260 \n",
"2014-04-01 09:00:00 308.785900 -85.486930 \n",
"2014-04-01 10:00:00 306.730483 -85.622973 \n",
"2014-04-01 11:00:00 306.906200 -79.167260 \n",
"2014-04-01 12:00:00 306.476100 -84.955217 \n",
"2014-04-01 13:00:00 318.782333 -99.599148 \n",
"2014-04-01 14:00:00 353.693100 -120.744817 \n",
"2014-04-01 15:00:00 391.260367 -120.161483 \n",
"2014-04-01 16:00:00 421.995583 -122.794532 \n",
"2014-04-01 17:00:00 418.786800 -72.566883 \n",
"2014-04-01 18:00:00 398.117400 -70.657958 \n",
"2014-04-01 19:00:00 409.775033 -66.103842 \n",
"2014-04-01 20:00:00 443.846950 -99.498385 \n",
"2014-04-01 21:00:00 410.979267 -94.778737 \n",
"2014-04-01 22:00:00 393.454233 -95.782562 \n",
"2014-04-01 23:00:00 387.240600 -102.884262 \n",
"2014-04-02 00:00:00 371.640067 -112.456033 \n",
"2014-04-02 01:00:00 353.235050 -105.442278 \n",
"2014-04-02 02:00:00 345.583833 -96.406143 \n",
"2014-04-02 03:00:00 339.058783 -93.721045 \n",
"2014-04-02 04:00:00 334.682200 -101.907845 \n",
"2014-04-02 05:00:00 326.908100 -109.461383 \n",
"2014-04-02 06:00:00 322.068550 -103.503583 \n",
"\n",
" Instrument Net DW CG4 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -60.314888 \n",
"2014-04-01 08:00:00 -57.480082 \n",
"2014-04-01 09:00:00 -72.138097 \n",
"2014-04-01 10:00:00 -72.268252 \n",
"2014-04-01 11:00:00 -66.637472 \n",
"2014-04-01 12:00:00 -71.691920 \n",
"2014-04-01 13:00:00 -85.417478 \n",
"2014-04-01 14:00:00 -97.759898 \n",
"2014-04-01 15:00:00 -96.585663 \n",
"2014-04-01 16:00:00 -98.357398 \n",
"2014-04-01 17:00:00 -57.606343 \n",
"2014-04-01 18:00:00 -54.354907 \n",
"2014-04-01 19:00:00 -56.227878 \n",
"2014-04-01 20:00:00 -84.635305 \n",
"2014-04-01 21:00:00 -75.585773 \n",
"2014-04-01 22:00:00 -77.329825 \n",
"2014-04-01 23:00:00 -86.265775 \n",
"2014-04-02 00:00:00 -94.301503 \n",
"2014-04-02 01:00:00 -86.257790 \n",
"2014-04-02 02:00:00 -78.619467 \n",
"2014-04-02 03:00:00 -74.967525 \n",
"2014-04-02 04:00:00 -85.813252 \n",
"2014-04-02 05:00:00 -91.605488 \n",
"2014-04-02 06:00:00 -86.997172 \n",
"\n",
" Instrument Net UW PIR [W/m^2] Global PSP (cor) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -4.835604 3.043059 \n",
"2014-04-01 08:00:00 -6.152236 2.052663 \n",
"2014-04-01 09:00:00 -10.059861 3.217118 \n",
"2014-04-01 10:00:00 -8.868090 3.833190 \n",
"2014-04-01 11:00:00 -6.069392 3.906922 \n",
"2014-04-01 12:00:00 -5.699365 7.084622 \n",
"2014-04-01 13:00:00 3.872481 101.643190 \n",
"2014-04-01 14:00:00 22.595795 333.606400 \n",
"2014-04-01 15:00:00 47.275047 533.686533 \n",
"2014-04-01 16:00:00 61.727173 652.468733 \n",
"2014-04-01 17:00:00 57.839563 586.113950 \n",
"2014-04-01 18:00:00 35.156542 387.947117 \n",
"2014-04-01 19:00:00 47.394700 461.562983 \n",
"2014-04-01 20:00:00 57.918085 588.608683 \n",
"2014-04-01 21:00:00 24.250977 365.475283 \n",
"2014-04-01 22:00:00 13.257701 252.193100 \n",
"2014-04-01 23:00:00 8.216394 209.473783 \n",
"2014-04-02 00:00:00 -7.979606 97.143363 \n",
"2014-04-02 01:00:00 -18.168087 11.524065 \n",
"2014-04-02 02:00:00 -16.840123 3.725237 \n",
"2014-04-02 03:00:00 -11.033229 4.218773 \n",
"2014-04-02 04:00:00 -9.663189 5.388363 \n",
"2014-04-02 05:00:00 -13.859590 5.066024 \n",
"2014-04-02 06:00:00 -10.297325 4.792650 \n",
"\n",
" Global PSP (vent/cor) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.654774 \n",
"2014-04-01 08:00:00 0.952191 \n",
"2014-04-01 09:00:00 2.083704 \n",
"2014-04-01 10:00:00 1.824111 \n",
"2014-04-01 11:00:00 1.300552 \n",
"2014-04-01 12:00:00 4.462973 \n",
"2014-04-01 13:00:00 104.169135 \n",
"2014-04-01 14:00:00 325.576867 \n",
"2014-04-01 15:00:00 530.449917 \n",
"2014-04-01 16:00:00 647.417067 \n",
"2014-04-01 17:00:00 584.753517 \n",
"2014-04-01 18:00:00 385.359033 \n",
"2014-04-01 19:00:00 462.889667 \n",
"2014-04-01 20:00:00 593.436017 \n",
"2014-04-01 21:00:00 362.841950 \n",
"2014-04-01 22:00:00 247.168750 \n",
"2014-04-01 23:00:00 204.377633 \n",
"2014-04-02 00:00:00 96.098902 \n",
"2014-04-02 01:00:00 8.127080 \n",
"2014-04-02 02:00:00 0.144439 \n",
"2014-04-02 03:00:00 -0.502750 \n",
"2014-04-02 04:00:00 1.099886 \n",
"2014-04-02 05:00:00 1.248165 \n",
"2014-04-02 06:00:00 1.345929 \n",
"\n",
" Diffuse PSP (vent/cor) [W/m^2] Diffuse CUV4 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 2.864373 -0.000667 \n",
"2014-04-01 08:00:00 2.601132 -0.000333 \n",
"2014-04-01 09:00:00 3.774704 -0.000400 \n",
"2014-04-01 10:00:00 4.623084 -0.000450 \n",
"2014-04-01 11:00:00 5.745219 -0.000150 \n",
"2014-04-01 12:00:00 8.732907 0.174317 \n",
"2014-04-01 13:00:00 46.372518 3.640083 \n",
"2014-04-01 14:00:00 52.170907 8.460333 \n",
"2014-04-01 15:00:00 81.929157 12.151500 \n",
"2014-04-01 16:00:00 117.244302 14.776667 \n",
"2014-04-01 17:00:00 310.411200 19.437333 \n",
"2014-04-01 18:00:00 325.396683 19.149333 \n",
"2014-04-01 19:00:00 337.312817 19.237000 \n",
"2014-04-01 20:00:00 427.429517 23.816167 \n",
"2014-04-01 21:00:00 323.625683 17.531333 \n",
"2014-04-01 22:00:00 233.858300 12.354333 \n",
"2014-04-01 23:00:00 185.659617 9.357917 \n",
"2014-04-02 00:00:00 83.480288 4.083300 \n",
"2014-04-02 01:00:00 7.476236 0.380400 \n",
"2014-04-02 02:00:00 2.241709 -0.000600 \n",
"2014-04-02 03:00:00 2.593539 -0.000667 \n",
"2014-04-02 04:00:00 6.534989 -0.000117 \n",
"2014-04-02 05:00:00 5.680405 -0.000700 \n",
"2014-04-02 06:00:00 4.727171 -0.000600 \n",
"\n",
" Global CUV4 [W/m^2] Avg Wind Speed @ 19ft [m/s] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.000350 0.389917 \n",
"2014-04-01 08:00:00 -0.000283 0.000367 \n",
"2014-04-01 09:00:00 -0.000483 0.008967 \n",
"2014-04-01 10:00:00 -0.000417 0.105867 \n",
"2014-04-01 11:00:00 -0.000417 0.947533 \n",
"2014-04-01 12:00:00 0.197617 1.421817 \n",
"2014-04-01 13:00:00 4.494100 0.148050 \n",
"2014-04-01 14:00:00 14.784833 1.250367 \n",
"2014-04-01 15:00:00 26.266000 1.266583 \n",
"2014-04-01 16:00:00 34.388333 1.523517 \n",
"2014-04-01 17:00:00 32.129500 2.907300 \n",
"2014-04-01 18:00:00 23.958500 3.658483 \n",
"2014-04-01 19:00:00 26.759500 1.751883 \n",
"2014-04-01 20:00:00 33.196833 1.659667 \n",
"2014-04-01 21:00:00 20.853667 3.138367 \n",
"2014-04-01 22:00:00 14.026000 3.228717 \n",
"2014-04-01 23:00:00 10.750367 2.028450 \n",
"2014-04-02 00:00:00 4.650250 1.938300 \n",
"2014-04-02 01:00:00 0.431567 1.658367 \n",
"2014-04-02 02:00:00 -0.000233 1.694233 \n",
"2014-04-02 03:00:00 -0.000200 3.191833 \n",
"2014-04-02 04:00:00 -0.000233 3.353717 \n",
"2014-04-02 05:00:00 -0.000133 2.744600 \n",
"2014-04-02 06:00:00 -0.000217 2.885667 \n",
"\n",
" Avg Wind Direction @ 19ft [deg from N] \\\n",
"datetime \n",
"2014-04-01 07:00:00 43.595000 \n",
"2014-04-01 08:00:00 1.426667 \n",
"2014-04-01 09:00:00 4.033333 \n",
"2014-04-01 10:00:00 14.920000 \n",
"2014-04-01 11:00:00 177.698333 \n",
"2014-04-01 12:00:00 290.884850 \n",
"2014-04-01 13:00:00 11.116600 \n",
"2014-04-01 14:00:00 36.995650 \n",
"2014-04-01 15:00:00 61.042983 \n",
"2014-04-01 16:00:00 33.183433 \n",
"2014-04-01 17:00:00 37.009333 \n",
"2014-04-01 18:00:00 18.526467 \n",
"2014-04-01 19:00:00 39.059717 \n",
"2014-04-01 20:00:00 82.682067 \n",
"2014-04-01 21:00:00 103.325200 \n",
"2014-04-01 22:00:00 21.010017 \n",
"2014-04-01 23:00:00 83.939467 \n",
"2014-04-02 00:00:00 256.698333 \n",
"2014-04-02 01:00:00 87.886267 \n",
"2014-04-02 02:00:00 26.682000 \n",
"2014-04-02 03:00:00 172.661150 \n",
"2014-04-02 04:00:00 329.161667 \n",
"2014-04-02 05:00:00 94.192967 \n",
"2014-04-02 06:00:00 59.639667 \n",
"\n",
" Peak Wind Speed @ 19ft [m/s] Direct MS-56 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.736667 -0.225806 \n",
"2014-04-01 08:00:00 0.007500 -0.233050 \n",
"2014-04-01 09:00:00 0.031667 -0.576108 \n",
"2014-04-01 10:00:00 0.197500 -0.982323 \n",
"2014-04-01 11:00:00 1.080000 -1.207839 \n",
"2014-04-01 12:00:00 1.829167 -1.179544 \n",
"2014-04-01 13:00:00 0.378333 344.744966 \n",
"2014-04-01 14:00:00 1.971667 801.588200 \n",
"2014-04-01 15:00:00 2.170000 848.069050 \n",
"2014-04-01 16:00:00 2.613333 804.065671 \n",
"2014-04-01 17:00:00 4.012500 336.574740 \n",
"2014-04-01 18:00:00 4.991667 59.389698 \n",
"2014-04-01 19:00:00 2.850000 143.800013 \n",
"2014-04-01 20:00:00 2.980833 184.998111 \n",
"2014-04-01 21:00:00 4.575000 32.223834 \n",
"2014-04-01 22:00:00 4.400000 3.055896 \n",
"2014-04-01 23:00:00 3.155833 16.393984 \n",
"2014-04-02 00:00:00 2.914167 9.882676 \n",
"2014-04-02 01:00:00 2.554167 -0.288003 \n",
"2014-04-02 02:00:00 2.550000 -0.280760 \n",
"2014-04-02 03:00:00 4.283333 -0.284719 \n",
"2014-04-02 04:00:00 4.337500 -0.174424 \n",
"2014-04-02 05:00:00 3.675000 -0.318812 \n",
"2014-04-02 06:00:00 3.845833 -1.015353 \n",
"\n",
" Research 4 PIR DW Dome Temp [deg K] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.000103 276.958767 \n",
"2014-04-01 08:00:00 -0.000074 276.357233 \n",
"2014-04-01 09:00:00 -0.000082 275.869317 \n",
"2014-04-01 10:00:00 -0.000117 275.090333 \n",
"2014-04-01 11:00:00 -0.000120 274.502400 \n",
"2014-04-01 12:00:00 -0.000109 274.137583 \n",
"2014-04-01 13:00:00 -0.000092 275.705000 \n",
"2014-04-01 14:00:00 -0.000109 278.810900 \n",
"2014-04-01 15:00:00 -0.000107 280.882033 \n",
"2014-04-01 16:00:00 -0.000123 283.911750 \n",
"2014-04-01 17:00:00 -0.000114 284.192433 \n",
"2014-04-01 18:00:00 -0.000092 284.666483 \n",
"2014-04-01 19:00:00 -0.000119 285.493300 \n",
"2014-04-01 20:00:00 -0.000142 290.030917 \n",
"2014-04-01 21:00:00 -0.000118 289.284883 \n",
"2014-04-01 22:00:00 -0.000143 288.129400 \n",
"2014-04-01 23:00:00 -0.000125 288.194733 \n",
"2014-04-02 00:00:00 -0.000110 287.813367 \n",
"2014-04-02 01:00:00 -0.000112 285.828717 \n",
"2014-04-02 02:00:00 -0.000134 284.297467 \n",
"2014-04-02 03:00:00 -0.000112 281.604600 \n",
"2014-04-02 04:00:00 -0.000103 280.689533 \n",
"2014-04-02 05:00:00 -0.000093 279.905433 \n",
"2014-04-02 06:00:00 -0.000085 278.018367 \n",
"\n",
" PIR DW Case Temp [deg K] CG4 DW Case Temp [deg K] \\\n",
"datetime \n",
"2014-04-01 07:00:00 277.257283 275.088900 \n",
"2014-04-01 08:00:00 276.636033 274.470500 \n",
"2014-04-01 09:00:00 276.178833 274.051083 \n",
"2014-04-01 10:00:00 275.415333 273.307417 \n",
"2014-04-01 11:00:00 274.803167 272.758750 \n",
"2014-04-01 12:00:00 274.469483 272.275250 \n",
"2014-04-01 13:00:00 275.972517 273.321567 \n",
"2014-04-01 14:00:00 279.207050 275.177300 \n",
"2014-04-01 15:00:00 281.200233 276.849150 \n",
"2014-04-01 16:00:00 284.180033 279.784783 \n",
"2014-04-01 17:00:00 284.085417 280.696600 \n",
"2014-04-01 18:00:00 284.639117 281.336167 \n",
"2014-04-01 19:00:00 285.250600 282.446800 \n",
"2014-04-01 20:00:00 289.761033 286.071600 \n",
"2014-04-01 21:00:00 289.335083 285.885483 \n",
"2014-04-01 22:00:00 288.282400 285.230167 \n",
"2014-04-01 23:00:00 288.322917 285.463950 \n",
"2014-04-02 00:00:00 288.099517 285.379400 \n",
"2014-04-02 01:00:00 286.251267 283.658667 \n",
"2014-04-02 02:00:00 284.702100 282.333733 \n",
"2014-04-02 03:00:00 282.042133 279.443983 \n",
"2014-04-02 04:00:00 281.108533 278.881583 \n",
"2014-04-02 05:00:00 280.358367 277.903900 \n",
"2014-04-02 06:00:00 278.434933 276.054150 \n",
"\n",
" PIR UW Dome Temp [deg K] PIR UW Case Temp [deg K] \\\n",
"datetime \n",
"2014-04-01 07:00:00 274.882500 274.973917 \n",
"2014-04-01 08:00:00 274.201533 274.289033 \n",
"2014-04-01 09:00:00 273.566017 273.663500 \n",
"2014-04-01 10:00:00 272.851117 272.949550 \n",
"2014-04-01 11:00:00 272.312617 272.395700 \n",
"2014-04-01 12:00:00 272.140067 272.221683 \n",
"2014-04-01 13:00:00 273.132683 273.084200 \n",
"2014-04-01 14:00:00 276.747500 276.604950 \n",
"2014-04-01 15:00:00 279.484283 279.256333 \n",
"2014-04-01 16:00:00 282.676917 282.425633 \n",
"2014-04-01 17:00:00 282.721467 282.502983 \n",
"2014-04-01 18:00:00 282.811417 282.731750 \n",
"2014-04-01 19:00:00 283.131317 282.906283 \n",
"2014-04-01 20:00:00 287.598817 287.350100 \n",
"2014-04-01 21:00:00 287.327217 287.277000 \n",
"2014-04-01 22:00:00 286.064417 286.053700 \n",
"2014-04-01 23:00:00 285.892283 285.881983 \n",
"2014-04-02 00:00:00 285.852667 285.924067 \n",
"2014-04-02 01:00:00 284.073567 284.228000 \n",
"2014-04-02 02:00:00 282.374850 282.516933 \n",
"2014-04-02 03:00:00 279.891883 280.030567 \n",
"2014-04-02 04:00:00 278.910550 279.000150 \n",
"2014-04-02 05:00:00 278.044967 278.181117 \n",
"2014-04-02 06:00:00 276.354850 276.470850 \n",
"\n",
" CR3000 Temp [deg C] Deck Dry Bulb Temp [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 22.110167 1.633700 \n",
"2014-04-01 08:00:00 22.078833 0.879017 \n",
"2014-04-01 09:00:00 22.062167 0.130917 \n",
"2014-04-01 10:00:00 22.055500 -0.025983 \n",
"2014-04-01 11:00:00 22.037667 -0.709450 \n",
"2014-04-01 12:00:00 22.023167 -1.038500 \n",
"2014-04-01 13:00:00 22.005667 0.484117 \n",
"2014-04-01 14:00:00 21.981000 2.546667 \n",
"2014-04-01 15:00:00 21.998000 3.580217 \n",
"2014-04-01 16:00:00 22.035667 5.646283 \n",
"2014-04-01 17:00:00 22.083333 6.443900 \n",
"2014-04-01 18:00:00 22.109167 7.130867 \n",
"2014-04-01 19:00:00 22.141167 8.212800 \n",
"2014-04-01 20:00:00 22.150167 11.221167 \n",
"2014-04-01 21:00:00 22.185167 11.848500 \n",
"2014-04-01 22:00:00 22.193833 11.483333 \n",
"2014-04-01 23:00:00 22.206000 11.885333 \n",
"2014-04-02 00:00:00 22.212833 12.160833 \n",
"2014-04-02 01:00:00 22.217167 10.613000 \n",
"2014-04-02 02:00:00 22.201333 9.273033 \n",
"2014-04-02 03:00:00 22.198500 6.200317 \n",
"2014-04-02 04:00:00 22.207833 5.825450 \n",
"2014-04-02 05:00:00 22.182167 4.737133 \n",
"2014-04-02 06:00:00 22.164167 2.903483 \n",
"\n",
" Deck RH [%] 501A Temp [deg C] CUVA1 Temp [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 53.005667 24.751167 39.885833 \n",
"2014-04-01 08:00:00 55.708667 24.793167 39.920167 \n",
"2014-04-01 09:00:00 58.774333 24.794167 39.927000 \n",
"2014-04-01 10:00:00 59.233500 24.743500 39.892333 \n",
"2014-04-01 11:00:00 62.128167 24.716333 39.881333 \n",
"2014-04-01 12:00:00 64.533333 24.697333 39.856333 \n",
"2014-04-01 13:00:00 59.935667 24.777500 39.887500 \n",
"2014-04-01 14:00:00 53.730167 24.805167 39.899167 \n",
"2014-04-01 15:00:00 51.199000 24.853000 39.923833 \n",
"2014-04-01 16:00:00 45.116000 24.874667 39.965167 \n",
"2014-04-01 17:00:00 42.639333 24.843000 39.939333 \n",
"2014-04-01 18:00:00 43.429500 24.827000 39.922667 \n",
"2014-04-01 19:00:00 41.031833 24.871667 39.952333 \n",
"2014-04-01 20:00:00 29.555167 24.887833 39.998667 \n",
"2014-04-01 21:00:00 26.501333 24.868333 39.976000 \n",
"2014-04-01 22:00:00 27.505333 24.863333 39.958500 \n",
"2014-04-01 23:00:00 24.855667 24.869167 39.971000 \n",
"2014-04-02 00:00:00 21.410000 24.859333 39.974500 \n",
"2014-04-02 01:00:00 25.913667 24.839000 39.943500 \n",
"2014-04-02 02:00:00 31.128500 24.835833 39.941500 \n",
"2014-04-02 03:00:00 50.359833 24.783000 39.893500 \n",
"2014-04-02 04:00:00 49.572500 24.766667 39.883833 \n",
"2014-04-02 05:00:00 53.864833 24.760833 39.880667 \n",
"2014-04-02 06:00:00 63.489333 24.738833 39.867167 \n",
"\n",
" CUVB1 Temp [deg C] CUVA2 Temp [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 39.879833 40.723167 \n",
"2014-04-01 08:00:00 39.912167 40.734167 \n",
"2014-04-01 09:00:00 39.923167 40.734500 \n",
"2014-04-01 10:00:00 39.890333 40.718333 \n",
"2014-04-01 11:00:00 39.868333 40.708000 \n",
"2014-04-01 12:00:00 39.848167 40.710333 \n",
"2014-04-01 13:00:00 39.884333 40.735833 \n",
"2014-04-01 14:00:00 39.900667 40.754667 \n",
"2014-04-01 15:00:00 39.919000 40.767167 \n",
"2014-04-01 16:00:00 39.956667 40.788167 \n",
"2014-04-01 17:00:00 39.936500 40.786000 \n",
"2014-04-01 18:00:00 39.915000 40.776167 \n",
"2014-04-01 19:00:00 39.946000 40.796333 \n",
"2014-04-01 20:00:00 39.987333 40.811000 \n",
"2014-04-01 21:00:00 39.963500 40.799000 \n",
"2014-04-01 22:00:00 39.948333 40.798333 \n",
"2014-04-01 23:00:00 39.962667 40.803667 \n",
"2014-04-02 00:00:00 39.961167 40.804833 \n",
"2014-04-02 01:00:00 39.939000 40.786333 \n",
"2014-04-02 02:00:00 39.930667 40.780000 \n",
"2014-04-02 03:00:00 39.883000 40.753000 \n",
"2014-04-02 04:00:00 39.878667 40.744667 \n",
"2014-04-02 05:00:00 39.877167 40.743000 \n",
"2014-04-02 06:00:00 39.862833 40.729500 \n",
"\n",
" CUVB2 Temp [deg C] UVSAT Temp [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 39.731333 24.793167 \n",
"2014-04-01 08:00:00 39.734833 24.806667 \n",
"2014-04-01 09:00:00 39.733833 24.796500 \n",
"2014-04-01 10:00:00 39.720500 24.745333 \n",
"2014-04-01 11:00:00 39.708833 24.727000 \n",
"2014-04-01 12:00:00 39.698000 24.702500 \n",
"2014-04-01 13:00:00 39.725333 24.792333 \n",
"2014-04-01 14:00:00 39.744000 24.895833 \n",
"2014-04-01 15:00:00 39.756667 24.961167 \n",
"2014-04-01 16:00:00 39.782500 25.041667 \n",
"2014-04-01 17:00:00 39.786833 25.018167 \n",
"2014-04-01 18:00:00 39.782667 25.000333 \n",
"2014-04-01 19:00:00 39.809500 25.053333 \n",
"2014-04-01 20:00:00 39.843333 25.191000 \n",
"2014-04-01 21:00:00 39.848833 25.150167 \n",
"2014-04-01 22:00:00 39.837667 25.111167 \n",
"2014-04-01 23:00:00 39.841500 25.125167 \n",
"2014-04-02 00:00:00 39.830833 25.115333 \n",
"2014-04-02 01:00:00 39.806167 25.049500 \n",
"2014-04-02 02:00:00 39.793500 25.011000 \n",
"2014-04-02 03:00:00 39.762167 24.915167 \n",
"2014-04-02 04:00:00 39.751333 24.897667 \n",
"2014-04-02 05:00:00 39.744667 24.866000 \n",
"2014-04-02 06:00:00 39.730167 24.810167 \n",
"\n",
" UVSBT Temp [deg C] UVB-1 Temp [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 24.443000 46.529667 \n",
"2014-04-01 08:00:00 24.452667 46.534833 \n",
"2014-04-01 09:00:00 24.441167 46.535333 \n",
"2014-04-01 10:00:00 24.387167 46.512000 \n",
"2014-04-01 11:00:00 24.361500 46.493667 \n",
"2014-04-01 12:00:00 24.334500 46.478333 \n",
"2014-04-01 13:00:00 24.404500 46.508333 \n",
"2014-04-01 14:00:00 24.523833 46.567833 \n",
"2014-04-01 15:00:00 24.633000 46.608500 \n",
"2014-04-01 16:00:00 24.755667 46.649000 \n",
"2014-04-01 17:00:00 24.716667 46.630000 \n",
"2014-04-01 18:00:00 24.694833 46.621333 \n",
"2014-04-01 19:00:00 24.765167 46.625500 \n",
"2014-04-01 20:00:00 24.882000 46.682667 \n",
"2014-04-01 21:00:00 24.779333 46.661167 \n",
"2014-04-01 22:00:00 24.729833 46.642000 \n",
"2014-04-01 23:00:00 24.747667 46.645000 \n",
"2014-04-02 00:00:00 24.742000 46.639833 \n",
"2014-04-02 01:00:00 24.657167 46.611000 \n",
"2014-04-02 02:00:00 24.619833 46.599333 \n",
"2014-04-02 03:00:00 24.586167 46.564500 \n",
"2014-04-02 04:00:00 24.562167 46.549833 \n",
"2014-04-02 05:00:00 24.523333 46.538500 \n",
"2014-04-02 06:00:00 24.455833 46.519333 \n",
"\n",
" Horiz TP Thermal Corr CR3000 Battery [VDC] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.000000 13.06 \n",
"2014-04-01 08:00:00 1.000000 13.06 \n",
"2014-04-01 09:00:00 1.000000 13.06 \n",
"2014-04-01 10:00:00 1.000000 13.06 \n",
"2014-04-01 11:00:00 1.000000 13.06 \n",
"2014-04-01 12:00:00 1.000000 13.06 \n",
"2014-04-01 13:00:00 1.000000 13.06 \n",
"2014-04-01 14:00:00 1.000000 13.06 \n",
"2014-04-01 15:00:00 0.996667 13.06 \n",
"2014-04-01 16:00:00 1.000000 13.06 \n",
"2014-04-01 17:00:00 1.000000 13.06 \n",
"2014-04-01 18:00:00 1.000000 13.06 \n",
"2014-04-01 19:00:00 1.000000 13.06 \n",
"2014-04-01 20:00:00 1.000000 13.06 \n",
"2014-04-01 21:00:00 1.000000 13.06 \n",
"2014-04-01 22:00:00 1.000000 13.06 \n",
"2014-04-01 23:00:00 1.000000 13.06 \n",
"2014-04-02 00:00:00 1.000000 13.06 \n",
"2014-04-02 01:00:00 1.000000 13.06 \n",
"2014-04-02 02:00:00 1.000000 13.06 \n",
"2014-04-02 03:00:00 1.000000 13.06 \n",
"2014-04-02 04:00:00 1.000000 13.06 \n",
"2014-04-02 05:00:00 1.000000 13.06 \n",
"2014-04-02 06:00:00 1.000000 13.06 \n",
"\n",
" CR3000 Pgm Time [s] Direct Quantum LI-190 [umol/s/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 2.682267 -0.319109 \n",
"2014-04-01 08:00:00 2.677150 -0.305316 \n",
"2014-04-01 09:00:00 2.676967 -0.312716 \n",
"2014-04-01 10:00:00 2.676717 -0.303633 \n",
"2014-04-01 11:00:00 2.678583 -0.301447 \n",
"2014-04-01 12:00:00 2.682483 -0.012279 \n",
"2014-04-01 13:00:00 2.686433 567.697802 \n",
"2014-04-01 14:00:00 2.689567 1515.284833 \n",
"2014-04-01 15:00:00 2.689850 1650.863517 \n",
"2014-04-01 16:00:00 2.690000 1569.591642 \n",
"2014-04-01 17:00:00 2.691000 684.942322 \n",
"2014-04-01 18:00:00 2.690083 137.101354 \n",
"2014-04-01 19:00:00 2.690333 304.726351 \n",
"2014-04-01 20:00:00 2.690600 401.133670 \n",
"2014-04-01 21:00:00 2.690683 86.838647 \n",
"2014-04-01 22:00:00 2.690433 14.768461 \n",
"2014-04-01 23:00:00 2.690083 46.624995 \n",
"2014-04-02 00:00:00 2.690433 26.925951 \n",
"2014-04-02 01:00:00 2.683350 -0.104968 \n",
"2014-04-02 02:00:00 2.678533 -0.235335 \n",
"2014-04-02 03:00:00 2.679467 -0.251485 \n",
"2014-04-02 04:00:00 2.680600 -0.239878 \n",
"2014-04-02 05:00:00 2.679883 -0.243243 \n",
"2014-04-02 06:00:00 2.679800 -0.227598 \n",
"\n",
" Direct TUVR [W/m^2] Global PSP [mV] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.002149 -0.029930 \n",
"2014-04-01 08:00:00 -0.001993 -0.035680 \n",
"2014-04-01 09:00:00 -0.001947 -0.037402 \n",
"2014-04-01 10:00:00 -0.002191 -0.032302 \n",
"2014-04-01 11:00:00 -0.001505 -0.026821 \n",
"2014-04-01 12:00:00 -0.001307 -0.005016 \n",
"2014-04-01 13:00:00 2.011980 0.754383 \n",
"2014-04-01 14:00:00 16.758361 2.667045 \n",
"2014-04-01 15:00:00 27.757582 4.454995 \n",
"2014-04-01 16:00:00 31.233589 5.560369 \n",
"2014-04-01 17:00:00 14.904951 5.111510 \n",
"2014-04-01 18:00:00 2.992394 3.384602 \n",
"2014-04-01 19:00:00 6.823036 4.041615 \n",
"2014-04-01 20:00:00 8.807797 5.115239 \n",
"2014-04-01 21:00:00 1.650372 3.106384 \n",
"2014-04-01 22:00:00 0.196195 2.080768 \n",
"2014-04-01 23:00:00 0.529627 1.664141 \n",
"2014-04-02 00:00:00 0.145580 0.706524 \n",
"2014-04-02 01:00:00 -0.003022 0.016334 \n",
"2014-04-02 02:00:00 -0.003342 -0.040722 \n",
"2014-04-02 03:00:00 -0.004268 -0.034212 \n",
"2014-04-02 04:00:00 -0.001387 -0.031382 \n",
"2014-04-02 05:00:00 -0.003349 -0.039702 \n",
"2014-04-02 06:00:00 -0.002934 -0.037633 \n",
"\n",
" Global PSP Vent [mV] Global CM22 [mV] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.042085 -0.009491 \n",
"2014-04-01 08:00:00 -0.038188 -0.009500 \n",
"2014-04-01 09:00:00 -0.040499 -0.010280 \n",
"2014-04-01 10:00:00 -0.042114 -0.010609 \n",
"2014-04-01 11:00:00 -0.041336 -0.010679 \n",
"2014-04-01 12:00:00 -0.024849 0.020884 \n",
"2014-04-01 13:00:00 0.599119 1.107887 \n",
"2014-04-01 14:00:00 2.077005 3.557645 \n",
"2014-04-01 15:00:00 3.488740 5.763251 \n",
"2014-04-01 16:00:00 4.327001 7.058637 \n",
"2014-04-01 17:00:00 3.963605 6.380711 \n",
"2014-04-01 18:00:00 2.612046 4.238643 \n",
"2014-04-01 19:00:00 3.148414 5.053619 \n",
"2014-04-01 20:00:00 4.011046 6.468666 \n",
"2014-04-01 21:00:00 2.412607 3.944040 \n",
"2014-04-01 22:00:00 1.600630 2.635082 \n",
"2014-04-01 23:00:00 1.294275 2.153223 \n",
"2014-04-02 00:00:00 0.542239 0.966096 \n",
"2014-04-02 01:00:00 -0.014281 0.066246 \n",
"2014-04-02 02:00:00 -0.059134 -0.011431 \n",
"2014-04-02 03:00:00 -0.061177 -0.010918 \n",
"2014-04-02 04:00:00 -0.056696 -0.013723 \n",
"2014-04-02 05:00:00 -0.060532 -0.012251 \n",
"2014-04-02 06:00:00 -0.056278 -0.012217 \n",
"\n",
" Global RG780 PSP [mV] Global CM6b [mV] Zebra PSP [mV] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.053405 -0.025007 -0.026520 \n",
"2014-04-01 08:00:00 -0.047759 -0.026553 -0.030642 \n",
"2014-04-01 09:00:00 -0.051316 -0.031182 -0.030131 \n",
"2014-04-01 10:00:00 -0.053615 -0.031387 -0.027866 \n",
"2014-04-01 11:00:00 -0.051899 -0.022332 -0.023142 \n",
"2014-04-01 12:00:00 -0.046364 0.004150 -0.003870 \n",
"2014-04-01 13:00:00 0.283573 1.034495 0.460871 \n",
"2014-04-01 14:00:00 1.060836 3.464137 1.018831 \n",
"2014-04-01 15:00:00 1.767810 5.709255 2.680700 \n",
"2014-04-01 16:00:00 2.194006 7.030577 4.040896 \n",
"2014-04-01 17:00:00 1.959564 6.388554 4.180706 \n",
"2014-04-01 18:00:00 1.215489 4.274480 2.659819 \n",
"2014-04-01 19:00:00 1.519561 5.062003 2.754959 \n",
"2014-04-01 20:00:00 1.993590 6.488323 4.529211 \n",
"2014-04-01 21:00:00 1.147990 3.975396 2.748291 \n",
"2014-04-01 22:00:00 0.741290 2.658108 1.878346 \n",
"2014-04-01 23:00:00 0.615488 2.159383 1.454184 \n",
"2014-04-02 00:00:00 0.239646 0.953236 0.641552 \n",
"2014-04-02 01:00:00 -0.051497 0.045321 0.019810 \n",
"2014-04-02 02:00:00 -0.073291 -0.031806 -0.035508 \n",
"2014-04-02 03:00:00 -0.076461 -0.025258 -0.028241 \n",
"2014-04-02 04:00:00 -0.071165 -0.032484 -0.027449 \n",
"2014-04-02 05:00:00 -0.075599 -0.033050 -0.034251 \n",
"2014-04-02 06:00:00 -0.069435 -0.032321 -0.030505 \n",
"\n",
" Diffuse PSP (sband) [mV] Diffuse PSP [mV] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.021291 -0.030730 \n",
"2014-04-01 08:00:00 -0.023404 -0.030395 \n",
"2014-04-01 09:00:00 -0.025407 -0.032507 \n",
"2014-04-01 10:00:00 -0.023421 -0.026374 \n",
"2014-04-01 11:00:00 -0.018373 -0.013760 \n",
"2014-04-01 12:00:00 -0.001820 0.003695 \n",
"2014-04-01 13:00:00 0.268491 0.262553 \n",
"2014-04-01 14:00:00 0.327283 0.289775 \n",
"2014-04-01 15:00:00 0.568784 0.503295 \n",
"2014-04-01 16:00:00 0.801945 0.754344 \n",
"2014-04-01 17:00:00 2.172961 2.175312 \n",
"2014-04-01 18:00:00 2.332975 2.285231 \n",
"2014-04-01 19:00:00 2.355458 2.371843 \n",
"2014-04-01 20:00:00 2.904211 2.993655 \n",
"2014-04-01 21:00:00 2.266320 2.255246 \n",
"2014-04-01 22:00:00 1.658923 1.610886 \n",
"2014-04-01 23:00:00 1.275016 1.259082 \n",
"2014-04-02 00:00:00 0.555276 0.519747 \n",
"2014-04-02 01:00:00 0.020573 -0.019441 \n",
"2014-04-02 02:00:00 -0.028799 -0.050518 \n",
"2014-04-02 03:00:00 -0.022008 -0.045695 \n",
"2014-04-02 04:00:00 -0.022221 -0.023712 \n",
"2014-04-02 05:00:00 -0.027570 -0.035151 \n",
"2014-04-02 06:00:00 -0.024344 -0.037968 \n",
"\n",
" Diffuse CM22 [mV] Global Quantum LI-190 [umol/s/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.008213 -0.091540 \n",
"2014-04-01 08:00:00 -0.008669 -0.096568 \n",
"2014-04-01 09:00:00 -0.009433 -0.082690 \n",
"2014-04-01 10:00:00 -0.009448 -0.075907 \n",
"2014-04-01 11:00:00 -0.008742 -0.056713 \n",
"2014-04-01 12:00:00 0.019338 5.550526 \n",
"2014-04-01 13:00:00 0.434281 188.089645 \n",
"2014-04-01 14:00:00 0.550145 633.709817 \n",
"2014-04-01 15:00:00 0.911403 1051.282233 \n",
"2014-04-01 16:00:00 1.268419 1297.439183 \n",
"2014-04-01 17:00:00 3.143509 1190.709833 \n",
"2014-04-01 18:00:00 3.253642 797.031067 \n",
"2014-04-01 19:00:00 3.315795 950.773333 \n",
"2014-04-01 20:00:00 4.246171 1185.995650 \n",
"2014-04-01 21:00:00 3.248949 729.937917 \n",
"2014-04-01 22:00:00 2.317616 487.530383 \n",
"2014-04-01 23:00:00 1.845632 390.101050 \n",
"2014-04-02 00:00:00 0.836147 169.872023 \n",
"2014-04-02 01:00:00 0.059741 12.675858 \n",
"2014-04-02 02:00:00 -0.010274 -0.184172 \n",
"2014-04-02 03:00:00 -0.009102 -0.244994 \n",
"2014-04-02 04:00:00 -0.011358 -0.187465 \n",
"2014-04-02 05:00:00 -0.010710 -0.165926 \n",
"2014-04-02 06:00:00 -0.010413 -0.113376 \n",
"\n",
" Global Photometric LI-210 [klux] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 0.247172 \n",
"2014-04-01 13:00:00 9.071122 \n",
"2014-04-01 14:00:00 30.701753 \n",
"2014-04-01 15:00:00 50.274713 \n",
"2014-04-01 16:00:00 61.267473 \n",
"2014-04-01 17:00:00 56.168585 \n",
"2014-04-01 18:00:00 37.642922 \n",
"2014-04-01 19:00:00 44.663148 \n",
"2014-04-01 20:00:00 54.940933 \n",
"2014-04-01 21:00:00 33.859468 \n",
"2014-04-01 22:00:00 22.579413 \n",
"2014-04-01 23:00:00 17.906647 \n",
"2014-04-02 00:00:00 7.541788 \n",
"2014-04-02 01:00:00 0.535282 \n",
"2014-04-02 02:00:00 0.000000 \n",
"2014-04-02 03:00:00 0.000000 \n",
"2014-04-02 04:00:00 0.000000 \n",
"2014-04-02 05:00:00 0.000000 \n",
"2014-04-02 06:00:00 0.000000 \n",
"\n",
" Upwelling Shortwave CM3 (CNR1) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.729642 \n",
"2014-04-01 08:00:00 2.850575 \n",
"2014-04-01 09:00:00 3.396794 \n",
"2014-04-01 10:00:00 3.101809 \n",
"2014-04-01 11:00:00 2.963834 \n",
"2014-04-01 12:00:00 3.098505 \n",
"2014-04-01 13:00:00 28.161402 \n",
"2014-04-01 14:00:00 61.801647 \n",
"2014-04-01 15:00:00 89.846665 \n",
"2014-04-01 16:00:00 107.812653 \n",
"2014-04-01 17:00:00 96.839458 \n",
"2014-04-01 18:00:00 63.403848 \n",
"2014-04-01 19:00:00 78.699935 \n",
"2014-04-01 20:00:00 98.404693 \n",
"2014-04-01 21:00:00 60.745680 \n",
"2014-04-01 22:00:00 40.809590 \n",
"2014-04-01 23:00:00 34.553562 \n",
"2014-04-02 00:00:00 16.805727 \n",
"2014-04-02 01:00:00 2.671892 \n",
"2014-04-02 02:00:00 1.943042 \n",
"2014-04-02 03:00:00 1.642840 \n",
"2014-04-02 04:00:00 2.546550 \n",
"2014-04-02 05:00:00 2.198714 \n",
"2014-04-02 06:00:00 2.472385 \n",
"\n",
" Upwelling IR CG3 (CNR1) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 319.237133 \n",
"2014-04-01 08:00:00 314.062800 \n",
"2014-04-01 09:00:00 307.210900 \n",
"2014-04-01 10:00:00 305.144350 \n",
"2014-04-01 11:00:00 305.344883 \n",
"2014-04-01 12:00:00 305.310583 \n",
"2014-04-01 13:00:00 315.984333 \n",
"2014-04-01 14:00:00 349.455250 \n",
"2014-04-01 15:00:00 387.098650 \n",
"2014-04-01 16:00:00 418.767817 \n",
"2014-04-01 17:00:00 417.476067 \n",
"2014-04-01 18:00:00 397.375600 \n",
"2014-04-01 19:00:00 409.010367 \n",
"2014-04-01 20:00:00 444.623433 \n",
"2014-04-01 21:00:00 410.776233 \n",
"2014-04-01 22:00:00 393.438883 \n",
"2014-04-01 23:00:00 387.259917 \n",
"2014-04-02 00:00:00 371.794900 \n",
"2014-04-02 01:00:00 352.930683 \n",
"2014-04-02 02:00:00 345.031983 \n",
"2014-04-02 03:00:00 338.422733 \n",
"2014-04-02 04:00:00 334.129617 \n",
"2014-04-02 05:00:00 326.408100 \n",
"2014-04-02 06:00:00 321.358017 \n",
"\n",
" Instrument Net UW CG3 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.121204 \n",
"2014-04-01 08:00:00 0.916466 \n",
"2014-04-01 09:00:00 -0.663851 \n",
"2014-04-01 10:00:00 -2.047806 \n",
"2014-04-01 11:00:00 -0.842510 \n",
"2014-04-01 12:00:00 -1.293181 \n",
"2014-04-01 13:00:00 -1.996640 \n",
"2014-04-01 14:00:00 5.208744 \n",
"2014-04-01 15:00:00 27.285707 \n",
"2014-04-01 16:00:00 44.974790 \n",
"2014-04-01 17:00:00 45.146943 \n",
"2014-04-01 18:00:00 28.991697 \n",
"2014-04-01 19:00:00 36.707345 \n",
"2014-04-01 20:00:00 45.044565 \n",
"2014-04-01 21:00:00 21.288320 \n",
"2014-04-01 22:00:00 12.277554 \n",
"2014-04-01 23:00:00 7.650369 \n",
"2014-04-02 00:00:00 -3.317426 \n",
"2014-04-02 01:00:00 -8.742796 \n",
"2014-04-02 02:00:00 -8.604073 \n",
"2014-04-02 03:00:00 -3.553348 \n",
"2014-04-02 04:00:00 -4.717630 \n",
"2014-04-02 05:00:00 -5.936293 \n",
"2014-04-02 06:00:00 -3.683002 \n",
"\n",
" Upwelling Shortwave PSP [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.463229 \n",
"2014-04-01 08:00:00 -0.145634 \n",
"2014-04-01 09:00:00 -0.236228 \n",
"2014-04-01 10:00:00 -0.150768 \n",
"2014-04-01 11:00:00 0.147563 \n",
"2014-04-01 12:00:00 0.513778 \n",
"2014-04-01 13:00:00 22.955795 \n",
"2014-04-01 14:00:00 66.314738 \n",
"2014-04-01 15:00:00 100.039898 \n",
"2014-04-01 16:00:00 116.806883 \n",
"2014-04-01 17:00:00 101.827673 \n",
"2014-04-01 18:00:00 64.333240 \n",
"2014-04-01 19:00:00 80.867508 \n",
"2014-04-01 20:00:00 102.269063 \n",
"2014-04-01 21:00:00 60.737727 \n",
"2014-04-01 22:00:00 40.044615 \n",
"2014-04-01 23:00:00 33.535415 \n",
"2014-04-02 00:00:00 13.817579 \n",
"2014-04-02 01:00:00 -1.065612 \n",
"2014-04-02 02:00:00 -1.512316 \n",
"2014-04-02 03:00:00 -1.486960 \n",
"2014-04-02 04:00:00 -0.207902 \n",
"2014-04-02 05:00:00 -1.350887 \n",
"2014-04-02 06:00:00 -0.777386 \n",
"\n",
" Upwelling Shortwave LI-200 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.019910 \n",
"2014-04-01 08:00:00 -0.004929 \n",
"2014-04-01 09:00:00 -0.000690 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 0.618686 \n",
"2014-04-01 13:00:00 28.938797 \n",
"2014-04-01 14:00:00 83.986832 \n",
"2014-04-01 15:00:00 122.379018 \n",
"2014-04-01 16:00:00 139.573718 \n",
"2014-04-01 17:00:00 123.786030 \n",
"2014-04-01 18:00:00 82.239790 \n",
"2014-04-01 19:00:00 97.828062 \n",
"2014-04-01 20:00:00 121.953165 \n",
"2014-04-01 21:00:00 76.089023 \n",
"2014-04-01 22:00:00 50.894465 \n",
"2014-04-01 23:00:00 41.546012 \n",
"2014-04-02 00:00:00 18.890884 \n",
"2014-04-02 01:00:00 1.434193 \n",
"2014-04-02 02:00:00 -0.127974 \n",
"2014-04-02 03:00:00 -0.201584 \n",
"2014-04-02 04:00:00 -0.211667 \n",
"2014-04-02 05:00:00 -0.183300 \n",
"2014-04-02 06:00:00 -0.118853 \n",
"\n",
" Upwelling Quantum LI-190 [umol/s/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 0.613792 \n",
"2014-04-01 13:00:00 25.976689 \n",
"2014-04-01 14:00:00 82.415663 \n",
"2014-04-01 15:00:00 125.097783 \n",
"2014-04-01 16:00:00 147.653308 \n",
"2014-04-01 17:00:00 131.315422 \n",
"2014-04-01 18:00:00 87.908562 \n",
"2014-04-01 19:00:00 104.602865 \n",
"2014-04-01 20:00:00 131.859500 \n",
"2014-04-01 21:00:00 81.364652 \n",
"2014-04-01 22:00:00 53.423933 \n",
"2014-04-01 23:00:00 43.549647 \n",
"2014-04-02 00:00:00 18.916307 \n",
"2014-04-02 01:00:00 1.591101 \n",
"2014-04-02 02:00:00 0.133776 \n",
"2014-04-02 03:00:00 0.060115 \n",
"2014-04-02 04:00:00 0.043851 \n",
"2014-04-02 05:00:00 0.011755 \n",
"2014-04-02 06:00:00 0.002261 \n",
"\n",
" CNR1 Case Temp [deg K] Global CM3 (CNR1) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 273.298933 -2.516079 \n",
"2014-04-01 08:00:00 271.953900 -1.705619 \n",
"2014-04-01 09:00:00 270.813217 -1.823460 \n",
"2014-04-01 10:00:00 270.674217 -1.688497 \n",
"2014-04-01 11:00:00 270.443300 -1.392699 \n",
"2014-04-01 12:00:00 270.539067 0.886302 \n",
"2014-04-01 13:00:00 273.001600 94.143645 \n",
"2014-04-01 14:00:00 278.426067 317.681883 \n",
"2014-04-01 15:00:00 281.352617 528.731533 \n",
"2014-04-01 16:00:00 283.925983 652.663017 \n",
"2014-04-01 17:00:00 283.616767 592.490550 \n",
"2014-04-01 18:00:00 283.012350 391.022983 \n",
"2014-04-01 19:00:00 283.680817 468.948850 \n",
"2014-04-01 20:00:00 288.704300 593.252750 \n",
"2014-04-01 21:00:00 287.043200 362.669433 \n",
"2014-04-01 22:00:00 285.565367 241.521917 \n",
"2014-04-01 23:00:00 285.309333 195.617367 \n",
"2014-04-02 00:00:00 284.542167 85.591675 \n",
"2014-04-02 01:00:00 281.998217 3.823558 \n",
"2014-04-02 02:00:00 280.419050 -2.477099 \n",
"2014-04-02 03:00:00 278.040100 -2.631797 \n",
"2014-04-02 04:00:00 277.413083 -2.117827 \n",
"2014-04-02 05:00:00 276.079667 -2.870887 \n",
"2014-04-02 06:00:00 274.534683 -2.391492 \n",
"\n",
" Downwelling IR CG3 (CNR1) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 260.265500 \n",
"2014-04-01 08:00:00 260.644217 \n",
"2014-04-01 09:00:00 242.411517 \n",
"2014-04-01 10:00:00 238.696133 \n",
"2014-04-01 11:00:00 242.657400 \n",
"2014-04-01 12:00:00 235.155167 \n",
"2014-04-01 13:00:00 226.328567 \n",
"2014-04-01 14:00:00 224.722333 \n",
"2014-04-01 15:00:00 237.594950 \n",
"2014-04-01 16:00:00 251.363333 \n",
"2014-04-01 17:00:00 297.758333 \n",
"2014-04-01 18:00:00 300.819233 \n",
"2014-04-01 19:00:00 306.355267 \n",
"2014-04-01 20:00:00 296.273600 \n",
"2014-04-01 21:00:00 301.639150 \n",
"2014-04-01 22:00:00 295.402367 \n",
"2014-04-01 23:00:00 286.890983 \n",
"2014-04-02 00:00:00 276.764917 \n",
"2014-04-02 01:00:00 275.244033 \n",
"2014-04-02 02:00:00 276.348733 \n",
"2014-04-02 03:00:00 265.703383 \n",
"2014-04-02 04:00:00 251.725367 \n",
"2014-04-02 05:00:00 240.447433 \n",
"2014-04-02 06:00:00 236.870000 \n",
"\n",
" Instrument Net DW CG3 [W/m^2] Snow Depth [cm] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -56.779863 -0.070067 \n",
"2014-04-01 08:00:00 -50.391922 -0.033667 \n",
"2014-04-01 09:00:00 -63.037832 -0.077300 \n",
"2014-04-01 10:00:00 -66.027648 -0.087517 \n",
"2014-04-01 11:00:00 -61.178853 -0.016517 \n",
"2014-04-01 12:00:00 -68.868697 -0.012683 \n",
"2014-04-01 13:00:00 -88.385703 -0.081567 \n",
"2014-04-01 14:00:00 -114.877050 -0.053100 \n",
"2014-04-01 15:00:00 -116.501485 0.031833 \n",
"2014-04-01 16:00:00 -115.875683 0.120767 \n",
"2014-04-01 17:00:00 -69.672473 0.167150 \n",
"2014-04-01 18:00:00 -63.582535 0.171017 \n",
"2014-04-01 19:00:00 -61.677207 0.146950 \n",
"2014-04-01 20:00:00 -97.114743 0.032867 \n",
"2014-04-01 21:00:00 -83.265957 0.073517 \n",
"2014-04-01 22:00:00 -81.673698 0.033683 \n",
"2014-04-01 23:00:00 -88.577107 -0.016933 \n",
"2014-04-02 00:00:00 -94.463482 -0.088017 \n",
"2014-04-02 01:00:00 -83.261580 -0.135767 \n",
"2014-04-02 02:00:00 -74.479698 -1.775733 \n",
"2014-04-02 03:00:00 -73.389098 -0.013050 \n",
"2014-04-02 04:00:00 -83.944273 -0.008717 \n",
"2014-04-02 05:00:00 -88.654620 -0.075067 \n",
"2014-04-02 06:00:00 -85.023690 -0.500767 \n",
"\n",
" Precipitation [mm] Precipitation (Accumulated) [mm] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0 0 \n",
"2014-04-01 08:00:00 0 0 \n",
"2014-04-01 09:00:00 0 0 \n",
"2014-04-01 10:00:00 0 0 \n",
"2014-04-01 11:00:00 0 0 \n",
"2014-04-01 12:00:00 0 0 \n",
"2014-04-01 13:00:00 0 0 \n",
"2014-04-01 14:00:00 0 0 \n",
"2014-04-01 15:00:00 0 0 \n",
"2014-04-01 16:00:00 0 0 \n",
"2014-04-01 17:00:00 0 0 \n",
"2014-04-01 18:00:00 0 0 \n",
"2014-04-01 19:00:00 0 0 \n",
"2014-04-01 20:00:00 0 0 \n",
"2014-04-01 21:00:00 0 0 \n",
"2014-04-01 22:00:00 0 0 \n",
"2014-04-01 23:00:00 0 0 \n",
"2014-04-02 00:00:00 0 0 \n",
"2014-04-02 01:00:00 0 0 \n",
"2014-04-02 02:00:00 0 0 \n",
"2014-04-02 03:00:00 0 0 \n",
"2014-04-02 04:00:00 0 0 \n",
"2014-04-02 05:00:00 0 0 \n",
"2014-04-02 06:00:00 0 0 \n",
"\n",
" Station Pressure [mBar] Global 40-South PSP [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 813.806450 -2.588342 \n",
"2014-04-01 08:00:00 812.876883 -2.885035 \n",
"2014-04-01 09:00:00 811.910100 -3.063321 \n",
"2014-04-01 10:00:00 811.026733 -2.637981 \n",
"2014-04-01 11:00:00 810.101383 -1.963475 \n",
"2014-04-01 12:00:00 809.461750 0.295259 \n",
"2014-04-01 13:00:00 809.186183 80.056957 \n",
"2014-04-01 14:00:00 808.839867 339.061650 \n",
"2014-04-01 15:00:00 808.563117 596.762067 \n",
"2014-04-01 16:00:00 807.996967 765.937583 \n",
"2014-04-01 17:00:00 807.772817 639.883667 \n",
"2014-04-01 18:00:00 807.336667 387.418967 \n",
"2014-04-01 19:00:00 806.843033 484.688617 \n",
"2014-04-01 20:00:00 806.142533 632.323467 \n",
"2014-04-01 21:00:00 805.930967 365.271800 \n",
"2014-04-01 22:00:00 805.995050 223.752250 \n",
"2014-04-01 23:00:00 806.107050 178.388233 \n",
"2014-04-02 00:00:00 806.369617 71.292293 \n",
"2014-04-02 01:00:00 806.805967 1.759207 \n",
"2014-04-02 02:00:00 807.582933 -3.719034 \n",
"2014-04-02 03:00:00 808.460783 -3.399521 \n",
"2014-04-02 04:00:00 808.103733 -2.516072 \n",
"2014-04-02 05:00:00 807.747267 -3.762921 \n",
"2014-04-02 06:00:00 807.933250 -3.436008 \n",
"\n",
" Global 40-South LI-200 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.005089 \n",
"2014-04-01 08:00:00 -0.000452 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 2.606458 \n",
"2014-04-01 13:00:00 94.003352 \n",
"2014-04-01 14:00:00 370.202500 \n",
"2014-04-01 15:00:00 637.909267 \n",
"2014-04-01 16:00:00 805.547317 \n",
"2014-04-01 17:00:00 669.281767 \n",
"2014-04-01 18:00:00 402.882183 \n",
"2014-04-01 19:00:00 504.839300 \n",
"2014-04-01 20:00:00 655.893033 \n",
"2014-04-01 21:00:00 379.618550 \n",
"2014-04-01 22:00:00 232.461433 \n",
"2014-04-01 23:00:00 184.311650 \n",
"2014-04-02 00:00:00 80.269932 \n",
"2014-04-02 01:00:00 5.670234 \n",
"2014-04-02 02:00:00 -0.139709 \n",
"2014-04-02 03:00:00 -0.230269 \n",
"2014-04-02 04:00:00 -0.187680 \n",
"2014-04-02 05:00:00 -0.137492 \n",
"2014-04-02 06:00:00 -0.061398 \n",
"\n",
" Global Normal CM-21 [W/m^2] Global 90-North PSP [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.335771 -1.810622 \n",
"2014-04-01 08:00:00 -0.198589 -1.553082 \n",
"2014-04-01 09:00:00 -0.173056 -2.050671 \n",
"2014-04-01 10:00:00 0.137337 -1.435360 \n",
"2014-04-01 11:00:00 -0.658412 -1.275411 \n",
"2014-04-01 12:00:00 3.003503 0.367865 \n",
"2014-04-01 13:00:00 436.032015 42.032792 \n",
"2014-04-01 14:00:00 916.600433 71.748763 \n",
"2014-04-01 15:00:00 993.891783 103.095687 \n",
"2014-04-01 16:00:00 970.682467 117.465237 \n",
"2014-04-01 17:00:00 669.750700 175.716198 \n",
"2014-04-01 18:00:00 397.654900 158.864300 \n",
"2014-04-01 19:00:00 490.193483 165.247700 \n",
"2014-04-01 20:00:00 645.493183 194.722217 \n",
"2014-04-01 21:00:00 363.248683 144.046783 \n",
"2014-04-01 22:00:00 204.914317 107.807715 \n",
"2014-04-01 23:00:00 197.338650 87.301572 \n",
"2014-04-02 00:00:00 100.236498 41.543040 \n",
"2014-04-02 01:00:00 4.737424 0.697371 \n",
"2014-04-02 02:00:00 -1.213126 -2.597951 \n",
"2014-04-02 03:00:00 -0.566150 -2.693104 \n",
"2014-04-02 04:00:00 0.166954 -1.692143 \n",
"2014-04-02 05:00:00 -0.230232 -2.863365 \n",
"2014-04-02 06:00:00 -0.108847 -2.379781 \n",
"\n",
" Global 90-North LI-200 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.042282 \n",
"2014-04-01 08:00:00 -0.007312 \n",
"2014-04-01 09:00:00 -0.001462 \n",
"2014-04-01 10:00:00 -0.000122 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 1.795877 \n",
"2014-04-01 13:00:00 43.033340 \n",
"2014-04-01 14:00:00 69.887100 \n",
"2014-04-01 15:00:00 102.912155 \n",
"2014-04-01 16:00:00 117.983573 \n",
"2014-04-01 17:00:00 187.128367 \n",
"2014-04-01 18:00:00 167.339050 \n",
"2014-04-01 19:00:00 175.751183 \n",
"2014-04-01 20:00:00 205.827733 \n",
"2014-04-01 21:00:00 153.210433 \n",
"2014-04-01 22:00:00 114.794350 \n",
"2014-04-01 23:00:00 91.908037 \n",
"2014-04-02 00:00:00 45.243682 \n",
"2014-04-02 01:00:00 3.966954 \n",
"2014-04-02 02:00:00 -0.043114 \n",
"2014-04-02 03:00:00 -0.161630 \n",
"2014-04-02 04:00:00 -0.248270 \n",
"2014-04-02 05:00:00 -0.242440 \n",
"2014-04-02 06:00:00 -0.169225 \n",
"\n",
" Global 90-East PSP [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -1.775484 \n",
"2014-04-01 08:00:00 -1.576223 \n",
"2014-04-01 09:00:00 -1.515171 \n",
"2014-04-01 10:00:00 -0.968855 \n",
"2014-04-01 11:00:00 -0.675744 \n",
"2014-04-01 12:00:00 3.492503 \n",
"2014-04-01 13:00:00 434.257802 \n",
"2014-04-01 14:00:00 861.250017 \n",
"2014-04-01 15:00:00 815.597267 \n",
"2014-04-01 16:00:00 639.504117 \n",
"2014-04-01 17:00:00 331.558400 \n",
"2014-04-01 18:00:00 204.075150 \n",
"2014-04-01 19:00:00 183.777017 \n",
"2014-04-01 20:00:00 219.477250 \n",
"2014-04-01 21:00:00 169.490850 \n",
"2014-04-01 22:00:00 122.464500 \n",
"2014-04-01 23:00:00 87.128978 \n",
"2014-04-02 00:00:00 37.338450 \n",
"2014-04-02 01:00:00 -0.206762 \n",
"2014-04-02 02:00:00 -2.589093 \n",
"2014-04-02 03:00:00 -2.739786 \n",
"2014-04-02 04:00:00 -1.389791 \n",
"2014-04-02 05:00:00 -2.542086 \n",
"2014-04-02 06:00:00 -2.248630 \n",
"\n",
" Global 90-East LI-200 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.024794 \n",
"2014-04-01 08:00:00 -0.004052 \n",
"2014-04-01 09:00:00 -0.000579 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 4.584086 \n",
"2014-04-01 13:00:00 497.607682 \n",
"2014-04-01 14:00:00 937.376333 \n",
"2014-04-01 15:00:00 870.387750 \n",
"2014-04-01 16:00:00 680.035450 \n",
"2014-04-01 17:00:00 353.196017 \n",
"2014-04-01 18:00:00 220.147583 \n",
"2014-04-01 19:00:00 200.081517 \n",
"2014-04-01 20:00:00 236.965700 \n",
"2014-04-01 21:00:00 185.004350 \n",
"2014-04-01 22:00:00 134.042667 \n",
"2014-04-01 23:00:00 95.156292 \n",
"2014-04-02 00:00:00 42.725397 \n",
"2014-04-02 01:00:00 3.297672 \n",
"2014-04-02 02:00:00 -0.008293 \n",
"2014-04-02 03:00:00 -0.056319 \n",
"2014-04-02 04:00:00 -0.135996 \n",
"2014-04-02 05:00:00 -0.154428 \n",
"2014-04-02 06:00:00 -0.125880 \n",
"\n",
" Global 90-South PSP [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -1.648470 \n",
"2014-04-01 08:00:00 -2.149338 \n",
"2014-04-01 09:00:00 -2.265964 \n",
"2014-04-01 10:00:00 -1.793931 \n",
"2014-04-01 11:00:00 -1.183460 \n",
"2014-04-01 12:00:00 0.572748 \n",
"2014-04-01 13:00:00 52.803713 \n",
"2014-04-01 14:00:00 205.149433 \n",
"2014-04-01 15:00:00 362.298317 \n",
"2014-04-01 16:00:00 464.445650 \n",
"2014-04-01 17:00:00 385.969067 \n",
"2014-04-01 18:00:00 240.684150 \n",
"2014-04-01 19:00:00 293.953900 \n",
"2014-04-01 20:00:00 385.646867 \n",
"2014-04-01 21:00:00 227.304450 \n",
"2014-04-01 22:00:00 135.105433 \n",
"2014-04-01 23:00:00 104.855232 \n",
"2014-04-02 00:00:00 46.138720 \n",
"2014-04-02 01:00:00 -0.051928 \n",
"2014-04-02 02:00:00 -3.272230 \n",
"2014-04-02 03:00:00 -2.808393 \n",
"2014-04-02 04:00:00 -1.502954 \n",
"2014-04-02 05:00:00 -2.984749 \n",
"2014-04-02 06:00:00 -2.594049 \n",
"\n",
" Global 90-South LI-200 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.096056 \n",
"2014-04-01 08:00:00 -0.030246 \n",
"2014-04-01 09:00:00 -0.005098 \n",
"2014-04-01 10:00:00 -0.001246 \n",
"2014-04-01 11:00:00 -0.000227 \n",
"2014-04-01 12:00:00 1.893734 \n",
"2014-04-01 13:00:00 53.805833 \n",
"2014-04-01 14:00:00 231.219733 \n",
"2014-04-01 15:00:00 392.404933 \n",
"2014-04-01 16:00:00 500.355600 \n",
"2014-04-01 17:00:00 415.101483 \n",
"2014-04-01 18:00:00 256.834500 \n",
"2014-04-01 19:00:00 314.272383 \n",
"2014-04-01 20:00:00 413.929250 \n",
"2014-04-01 21:00:00 243.385383 \n",
"2014-04-01 22:00:00 144.548717 \n",
"2014-04-01 23:00:00 111.798048 \n",
"2014-04-02 00:00:00 50.017582 \n",
"2014-04-02 01:00:00 3.449718 \n",
"2014-04-02 02:00:00 -0.013700 \n",
"2014-04-02 03:00:00 -0.086961 \n",
"2014-04-02 04:00:00 -0.195806 \n",
"2014-04-02 05:00:00 -0.234892 \n",
"2014-04-02 06:00:00 -0.216890 \n",
"\n",
" Global 90-West PSP [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -1.585964 \n",
"2014-04-01 08:00:00 -1.183967 \n",
"2014-04-01 09:00:00 -1.832128 \n",
"2014-04-01 10:00:00 -1.512253 \n",
"2014-04-01 11:00:00 -0.946531 \n",
"2014-04-01 12:00:00 0.069106 \n",
"2014-04-01 13:00:00 38.001897 \n",
"2014-04-01 14:00:00 81.025470 \n",
"2014-04-01 15:00:00 109.173517 \n",
"2014-04-01 16:00:00 127.556222 \n",
"2014-04-01 17:00:00 172.731863 \n",
"2014-04-01 18:00:00 163.442583 \n",
"2014-04-01 19:00:00 206.972883 \n",
"2014-04-01 20:00:00 303.129200 \n",
"2014-04-01 21:00:00 213.420400 \n",
"2014-04-01 22:00:00 134.235067 \n",
"2014-04-01 23:00:00 157.102967 \n",
"2014-04-02 00:00:00 88.300293 \n",
"2014-04-02 01:00:00 2.622758 \n",
"2014-04-02 02:00:00 -2.545610 \n",
"2014-04-02 03:00:00 -2.398476 \n",
"2014-04-02 04:00:00 -1.525610 \n",
"2014-04-02 05:00:00 -2.329402 \n",
"2014-04-02 06:00:00 -1.918726 \n",
"\n",
" Global 90-West LI-200 [W/m^2] Research RT0 \\\n",
"datetime \n",
"2014-04-01 07:00:00 -0.057002 0 \n",
"2014-04-01 08:00:00 -0.012668 0 \n",
"2014-04-01 09:00:00 -0.001357 0 \n",
"2014-04-01 10:00:00 -0.000226 0 \n",
"2014-04-01 11:00:00 -0.000113 0 \n",
"2014-04-01 12:00:00 1.176477 0 \n",
"2014-04-01 13:00:00 39.538682 0 \n",
"2014-04-01 14:00:00 84.039700 0 \n",
"2014-04-01 15:00:00 117.157900 0 \n",
"2014-04-01 16:00:00 142.525907 0 \n",
"2014-04-01 17:00:00 191.329117 0 \n",
"2014-04-01 18:00:00 177.901183 0 \n",
"2014-04-01 19:00:00 225.756683 0 \n",
"2014-04-01 20:00:00 329.561833 0 \n",
"2014-04-01 21:00:00 231.896083 0 \n",
"2014-04-01 22:00:00 146.609300 0 \n",
"2014-04-01 23:00:00 168.147800 0 \n",
"2014-04-02 00:00:00 96.777337 0 \n",
"2014-04-02 01:00:00 6.124019 0 \n",
"2014-04-02 02:00:00 -0.001244 0 \n",
"2014-04-02 03:00:00 -0.023289 0 \n",
"2014-04-02 04:00:00 -0.112960 0 \n",
"2014-04-02 05:00:00 -0.140672 0 \n",
"2014-04-02 06:00:00 -0.153906 0 \n",
"\n",
" Research RT1 Research RT2 \\\n",
"datetime \n",
"2014-04-01 07:00:00 0 0 \n",
"2014-04-01 08:00:00 0 0 \n",
"2014-04-01 09:00:00 0 0 \n",
"2014-04-01 10:00:00 0 0 \n",
"2014-04-01 11:00:00 0 0 \n",
"2014-04-01 12:00:00 0 0 \n",
"2014-04-01 13:00:00 0 0 \n",
"2014-04-01 14:00:00 0 0 \n",
"2014-04-01 15:00:00 0 0 \n",
"2014-04-01 16:00:00 0 0 \n",
"2014-04-01 17:00:00 0 0 \n",
"2014-04-01 18:00:00 0 0 \n",
"2014-04-01 19:00:00 0 0 \n",
"2014-04-01 20:00:00 0 0 \n",
"2014-04-01 21:00:00 0 0 \n",
"2014-04-01 22:00:00 0 0 \n",
"2014-04-01 23:00:00 0 0 \n",
"2014-04-02 00:00:00 0 0 \n",
"2014-04-02 01:00:00 0 0 \n",
"2014-04-02 02:00:00 0 0 \n",
"2014-04-02 03:00:00 0 0 \n",
"2014-04-02 04:00:00 0 0 \n",
"2014-04-02 05:00:00 0 0 \n",
"2014-04-02 06:00:00 0 0 \n",
"\n",
" Atmospheric Electric Field [kV/m] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.039413 \n",
"2014-04-01 08:00:00 0.035800 \n",
"2014-04-01 09:00:00 0.037483 \n",
"2014-04-01 10:00:00 0.039260 \n",
"2014-04-01 11:00:00 0.039349 \n",
"2014-04-01 12:00:00 0.041852 \n",
"2014-04-01 13:00:00 0.044829 \n",
"2014-04-01 14:00:00 0.100388 \n",
"2014-04-01 15:00:00 0.108705 \n",
"2014-04-01 16:00:00 0.100525 \n",
"2014-04-01 17:00:00 0.074111 \n",
"2014-04-01 18:00:00 0.069535 \n",
"2014-04-01 19:00:00 0.057281 \n",
"2014-04-01 20:00:00 0.074763 \n",
"2014-04-01 21:00:00 0.064225 \n",
"2014-04-01 22:00:00 0.051050 \n",
"2014-04-01 23:00:00 0.052959 \n",
"2014-04-02 00:00:00 0.048645 \n",
"2014-04-02 01:00:00 0.046647 \n",
"2014-04-02 02:00:00 0.046028 \n",
"2014-04-02 03:00:00 0.047293 \n",
"2014-04-02 04:00:00 0.048133 \n",
"2014-04-02 05:00:00 0.049294 \n",
"2014-04-02 06:00:00 0.044005 \n",
"\n",
" CR10X Temp (Rad-Twr) [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 5.513950 \n",
"2014-04-01 08:00:00 4.528833 \n",
"2014-04-01 09:00:00 3.600100 \n",
"2014-04-01 10:00:00 2.735367 \n",
"2014-04-01 11:00:00 2.080850 \n",
"2014-04-01 12:00:00 1.778250 \n",
"2014-04-01 13:00:00 1.997883 \n",
"2014-04-01 14:00:00 4.958150 \n",
"2014-04-01 15:00:00 8.686750 \n",
"2014-04-01 16:00:00 11.995833 \n",
"2014-04-01 17:00:00 13.400333 \n",
"2014-04-01 18:00:00 13.895667 \n",
"2014-04-01 19:00:00 13.437000 \n",
"2014-04-01 20:00:00 15.014000 \n",
"2014-04-01 21:00:00 16.601333 \n",
"2014-04-01 22:00:00 16.498667 \n",
"2014-04-01 23:00:00 15.932833 \n",
"2014-04-02 00:00:00 15.591167 \n",
"2014-04-02 01:00:00 14.698833 \n",
"2014-04-02 02:00:00 13.158500 \n",
"2014-04-02 03:00:00 11.546500 \n",
"2014-04-02 04:00:00 9.793333 \n",
"2014-04-02 05:00:00 8.691000 \n",
"2014-04-02 06:00:00 7.337517 \n",
"\n",
" CR10X Battery (Rad-Twr) [VDC] LI-2020 Battery [VDC] \\\n",
"datetime \n",
"2014-04-01 07:00:00 13.909667 19.542500 \n",
"2014-04-01 08:00:00 13.940167 19.482833 \n",
"2014-04-01 09:00:00 13.967000 19.421167 \n",
"2014-04-01 10:00:00 13.987500 19.352667 \n",
"2014-04-01 11:00:00 14.007167 19.282000 \n",
"2014-04-01 12:00:00 14.016000 19.223167 \n",
"2014-04-01 13:00:00 13.992167 19.805000 \n",
"2014-04-01 14:00:00 13.885667 21.726333 \n",
"2014-04-01 15:00:00 13.774833 21.657833 \n",
"2014-04-01 16:00:00 13.667333 21.539000 \n",
"2014-04-01 17:00:00 13.654667 21.574333 \n",
"2014-04-01 18:00:00 13.648500 21.560833 \n",
"2014-04-01 19:00:00 13.663833 21.540333 \n",
"2014-04-01 20:00:00 13.608667 21.274500 \n",
"2014-04-01 21:00:00 13.581500 21.351167 \n",
"2014-04-01 22:00:00 13.588333 21.438167 \n",
"2014-04-01 23:00:00 13.600333 21.448333 \n",
"2014-04-02 00:00:00 13.614667 20.920167 \n",
"2014-04-02 01:00:00 13.648000 19.713167 \n",
"2014-04-02 02:00:00 13.688667 19.606000 \n",
"2014-04-02 03:00:00 13.738500 19.556167 \n",
"2014-04-02 04:00:00 13.785667 19.514333 \n",
"2014-04-02 05:00:00 13.818833 19.435667 \n",
"2014-04-02 06:00:00 13.859667 19.616667 \n",
"\n",
" Tower Dry Bulb Temp [deg C] Tower RH [%] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.418617 56.466667 \n",
"2014-04-01 08:00:00 0.846750 58.938333 \n",
"2014-04-01 09:00:00 0.243583 61.269000 \n",
"2014-04-01 10:00:00 -0.509167 64.086667 \n",
"2014-04-01 11:00:00 -0.900517 66.063333 \n",
"2014-04-01 12:00:00 -1.077300 67.921000 \n",
"2014-04-01 13:00:00 0.219750 64.324500 \n",
"2014-04-01 14:00:00 2.392433 57.821667 \n",
"2014-04-01 15:00:00 3.940467 53.820500 \n",
"2014-04-01 16:00:00 6.306550 46.595000 \n",
"2014-04-01 17:00:00 6.894700 44.222333 \n",
"2014-04-01 18:00:00 7.500183 45.003667 \n",
"2014-04-01 19:00:00 8.782650 42.339167 \n",
"2014-04-01 20:00:00 12.005667 30.023667 \n",
"2014-04-01 21:00:00 12.133667 27.731500 \n",
"2014-04-01 22:00:00 11.617167 28.858167 \n",
"2014-04-01 23:00:00 11.953833 26.298667 \n",
"2014-04-02 00:00:00 12.064500 22.512000 \n",
"2014-04-02 01:00:00 10.223833 27.864167 \n",
"2014-04-02 02:00:00 8.892433 33.277000 \n",
"2014-04-02 03:00:00 6.062433 53.083667 \n",
"2014-04-02 04:00:00 5.746117 52.071500 \n",
"2014-04-02 05:00:00 4.455833 57.420000 \n",
"2014-04-02 06:00:00 2.843650 66.763000 \n",
"\n",
" Avg Wind Speed @ 6ft [m/s] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.915633 \n",
"2014-04-01 08:00:00 0.034917 \n",
"2014-04-01 09:00:00 0.033333 \n",
"2014-04-01 10:00:00 0.618083 \n",
"2014-04-01 11:00:00 1.093850 \n",
"2014-04-01 12:00:00 1.245317 \n",
"2014-04-01 13:00:00 0.270683 \n",
"2014-04-01 14:00:00 1.392533 \n",
"2014-04-01 15:00:00 1.572250 \n",
"2014-04-01 16:00:00 2.206000 \n",
"2014-04-01 17:00:00 2.513583 \n",
"2014-04-01 18:00:00 3.245550 \n",
"2014-04-01 19:00:00 2.226333 \n",
"2014-04-01 20:00:00 1.875200 \n",
"2014-04-01 21:00:00 2.946333 \n",
"2014-04-01 22:00:00 2.966017 \n",
"2014-04-01 23:00:00 2.154617 \n",
"2014-04-02 00:00:00 1.830617 \n",
"2014-04-02 01:00:00 1.920650 \n",
"2014-04-02 02:00:00 1.858550 \n",
"2014-04-02 03:00:00 2.335683 \n",
"2014-04-02 04:00:00 1.972300 \n",
"2014-04-02 05:00:00 2.139917 \n",
"2014-04-02 06:00:00 2.377083 \n",
"\n",
" Avg Wind Direction @ 6ft [deg from N] \\\n",
"datetime \n",
"2014-04-01 07:00:00 83.734167 \n",
"2014-04-01 08:00:00 11.559167 \n",
"2014-04-01 09:00:00 7.101667 \n",
"2014-04-01 10:00:00 65.033333 \n",
"2014-04-01 11:00:00 209.057500 \n",
"2014-04-01 12:00:00 258.136667 \n",
"2014-04-01 13:00:00 19.910217 \n",
"2014-04-01 14:00:00 42.181100 \n",
"2014-04-01 15:00:00 44.258417 \n",
"2014-04-01 16:00:00 57.546167 \n",
"2014-04-01 17:00:00 28.515050 \n",
"2014-04-01 18:00:00 25.410167 \n",
"2014-04-01 19:00:00 47.618200 \n",
"2014-04-01 20:00:00 102.789733 \n",
"2014-04-01 21:00:00 95.833483 \n",
"2014-04-01 22:00:00 23.196333 \n",
"2014-04-01 23:00:00 67.460800 \n",
"2014-04-02 00:00:00 260.215000 \n",
"2014-04-02 01:00:00 91.554917 \n",
"2014-04-02 02:00:00 37.905767 \n",
"2014-04-02 03:00:00 96.819167 \n",
"2014-04-02 04:00:00 262.294333 \n",
"2014-04-02 05:00:00 72.586333 \n",
"2014-04-02 06:00:00 20.884000 \n",
"\n",
" Peak Wind Speed @ 6ft [m/s] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.291667 \n",
"2014-04-01 08:00:00 0.079167 \n",
"2014-04-01 09:00:00 0.067500 \n",
"2014-04-01 10:00:00 0.931667 \n",
"2014-04-01 11:00:00 1.339167 \n",
"2014-04-01 12:00:00 1.696667 \n",
"2014-04-01 13:00:00 0.520833 \n",
"2014-04-01 14:00:00 2.054167 \n",
"2014-04-01 15:00:00 2.320833 \n",
"2014-04-01 16:00:00 3.133333 \n",
"2014-04-01 17:00:00 3.437500 \n",
"2014-04-01 18:00:00 4.312500 \n",
"2014-04-01 19:00:00 3.054167 \n",
"2014-04-01 20:00:00 2.954167 \n",
"2014-04-01 21:00:00 4.062500 \n",
"2014-04-01 22:00:00 3.945833 \n",
"2014-04-01 23:00:00 3.063333 \n",
"2014-04-02 00:00:00 2.841667 \n",
"2014-04-02 01:00:00 2.600000 \n",
"2014-04-02 02:00:00 2.595833 \n",
"2014-04-02 03:00:00 3.179167 \n",
"2014-04-02 04:00:00 2.766667 \n",
"2014-04-02 05:00:00 2.866667 \n",
"2014-04-02 06:00:00 3.058333 \n",
"\n",
" CR10X Overuns (Rad-Twr) [counts] Snow Depth Quality \\\n",
"datetime \n",
"2014-04-01 07:00:00 0 194.030000 \n",
"2014-04-01 08:00:00 0 194.028333 \n",
"2014-04-01 09:00:00 0 194.045000 \n",
"2014-04-01 10:00:00 0 194.081667 \n",
"2014-04-01 11:00:00 0 194.056667 \n",
"2014-04-01 12:00:00 0 194.063333 \n",
"2014-04-01 13:00:00 0 194.050000 \n",
"2014-04-01 14:00:00 0 194.033333 \n",
"2014-04-01 15:00:00 0 194.063333 \n",
"2014-04-01 16:00:00 0 194.076667 \n",
"2014-04-01 17:00:00 0 194.070000 \n",
"2014-04-01 18:00:00 0 193.990000 \n",
"2014-04-01 19:00:00 0 194.008333 \n",
"2014-04-01 20:00:00 0 193.998333 \n",
"2014-04-01 21:00:00 0 193.956667 \n",
"2014-04-01 22:00:00 0 193.935000 \n",
"2014-04-01 23:00:00 0 193.938333 \n",
"2014-04-02 00:00:00 0 193.973333 \n",
"2014-04-02 01:00:00 0 194.001667 \n",
"2014-04-02 02:00:00 0 193.938333 \n",
"2014-04-02 03:00:00 0 193.966667 \n",
"2014-04-02 04:00:00 0 193.966667 \n",
"2014-04-02 05:00:00 0 193.970000 \n",
"2014-04-02 06:00:00 0 194.056667 \n",
"\n",
" SE Dry Bulb Temp [deg C] SE RH [%] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.268400 55.972500 \n",
"2014-04-01 08:00:00 -0.139267 61.647833 \n",
"2014-04-01 09:00:00 -1.074167 65.695667 \n",
"2014-04-01 10:00:00 -0.811100 64.732000 \n",
"2014-04-01 11:00:00 -1.382933 67.227833 \n",
"2014-04-01 12:00:00 -1.945300 71.197667 \n",
"2014-04-01 13:00:00 0.054367 64.023333 \n",
"2014-04-01 14:00:00 1.663233 59.425500 \n",
"2014-04-01 15:00:00 2.932200 55.437833 \n",
"2014-04-01 16:00:00 5.284533 46.989833 \n",
"2014-04-01 17:00:00 6.278300 44.261833 \n",
"2014-04-01 18:00:00 7.121117 44.242500 \n",
"2014-04-01 19:00:00 8.268517 41.174333 \n",
"2014-04-01 20:00:00 11.054167 29.749500 \n",
"2014-04-01 21:00:00 11.548833 27.008167 \n",
"2014-04-01 22:00:00 11.267500 27.691167 \n",
"2014-04-01 23:00:00 11.623167 24.986833 \n",
"2014-04-02 00:00:00 11.711167 22.037500 \n",
"2014-04-02 01:00:00 9.749833 27.507500 \n",
"2014-04-02 02:00:00 8.683417 32.271667 \n",
"2014-04-02 03:00:00 5.684150 52.999333 \n",
"2014-04-02 04:00:00 5.326883 52.266833 \n",
"2014-04-02 05:00:00 3.715533 59.080333 \n",
"2014-04-02 06:00:00 2.340950 67.765833 \n",
"\n",
" SE-POA Angle [degrees] Global SE-POA LI-200 [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -1.959367 0.000000 \n",
"2014-04-01 08:00:00 -1.951800 0.000000 \n",
"2014-04-01 09:00:00 -1.945517 0.000000 \n",
"2014-04-01 10:00:00 -1.955683 0.000000 \n",
"2014-04-01 11:00:00 -1.954850 0.000000 \n",
"2014-04-01 12:00:00 -1.953917 2.585240 \n",
"2014-04-01 13:00:00 -10.070417 172.886655 \n",
"2014-04-01 14:00:00 -28.035667 664.295100 \n",
"2014-04-01 15:00:00 -41.448667 898.368650 \n",
"2014-04-01 16:00:00 -40.215500 862.534933 \n",
"2014-04-01 17:00:00 -27.213333 610.574233 \n",
"2014-04-01 18:00:00 -10.010117 383.087250 \n",
"2014-04-01 19:00:00 8.031400 463.262800 \n",
"2014-04-01 20:00:00 25.418667 592.803150 \n",
"2014-04-01 21:00:00 40.099833 334.248383 \n",
"2014-04-01 22:00:00 42.700333 210.204983 \n",
"2014-04-01 23:00:00 28.823000 207.871750 \n",
"2014-04-02 00:00:00 9.817217 91.881112 \n",
"2014-04-02 01:00:00 -1.072900 6.462765 \n",
"2014-04-02 02:00:00 -1.858083 0.000000 \n",
"2014-04-02 03:00:00 -1.889183 0.000000 \n",
"2014-04-02 04:00:00 -1.907200 0.000000 \n",
"2014-04-02 05:00:00 -1.904933 0.000000 \n",
"2014-04-02 06:00:00 -1.905067 0.000000 \n",
"\n",
" CR10X Overuns (Met-Twr) [counts] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0 \n",
"2014-04-01 08:00:00 0 \n",
"2014-04-01 09:00:00 0 \n",
"2014-04-01 10:00:00 0 \n",
"2014-04-01 11:00:00 0 \n",
"2014-04-01 12:00:00 0 \n",
"2014-04-01 13:00:00 0 \n",
"2014-04-01 14:00:00 0 \n",
"2014-04-01 15:00:00 0 \n",
"2014-04-01 16:00:00 0 \n",
"2014-04-01 17:00:00 0 \n",
"2014-04-01 18:00:00 0 \n",
"2014-04-01 19:00:00 0 \n",
"2014-04-01 20:00:00 0 \n",
"2014-04-01 21:00:00 0 \n",
"2014-04-01 22:00:00 0 \n",
"2014-04-01 23:00:00 0 \n",
"2014-04-02 00:00:00 0 \n",
"2014-04-02 01:00:00 0 \n",
"2014-04-02 02:00:00 0 \n",
"2014-04-02 03:00:00 0 \n",
"2014-04-02 04:00:00 0 \n",
"2014-04-02 05:00:00 0 \n",
"2014-04-02 06:00:00 0 \n",
"\n",
" CR10X Temp (Met-Twr) [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 2.240583 \n",
"2014-04-01 08:00:00 0.512367 \n",
"2014-04-01 09:00:00 -1.232333 \n",
"2014-04-01 10:00:00 -2.436800 \n",
"2014-04-01 11:00:00 -2.734733 \n",
"2014-04-01 12:00:00 -2.928333 \n",
"2014-04-01 13:00:00 -2.842733 \n",
"2014-04-01 14:00:00 -0.164850 \n",
"2014-04-01 15:00:00 4.178967 \n",
"2014-04-01 16:00:00 8.397383 \n",
"2014-04-01 17:00:00 10.372333 \n",
"2014-04-01 18:00:00 11.814167 \n",
"2014-04-01 19:00:00 11.274000 \n",
"2014-04-01 20:00:00 14.122167 \n",
"2014-04-01 21:00:00 15.411167 \n",
"2014-04-01 22:00:00 14.452667 \n",
"2014-04-01 23:00:00 13.579000 \n",
"2014-04-02 00:00:00 12.896667 \n",
"2014-04-02 01:00:00 11.071500 \n",
"2014-04-02 02:00:00 9.026833 \n",
"2014-04-02 03:00:00 7.327133 \n",
"2014-04-02 04:00:00 5.647667 \n",
"2014-04-02 05:00:00 4.288700 \n",
"2014-04-02 06:00:00 2.845683 \n",
"\n",
" CR10X Battery (Met-Twr) [VDC] Vertical Wind Shear [1/s] \\\n",
"datetime \n",
"2014-04-01 07:00:00 12.331000 0.111617 \n",
"2014-04-01 08:00:00 12.296333 0.001683 \n",
"2014-04-01 09:00:00 12.258333 0.007117 \n",
"2014-04-01 10:00:00 12.219667 0.129983 \n",
"2014-04-01 11:00:00 12.178000 0.080533 \n",
"2014-04-01 12:00:00 12.149000 0.100067 \n",
"2014-04-01 13:00:00 12.248167 0.036700 \n",
"2014-04-01 14:00:00 13.277833 0.057550 \n",
"2014-04-01 15:00:00 13.441000 0.050500 \n",
"2014-04-01 16:00:00 13.359167 0.041717 \n",
"2014-04-01 17:00:00 13.374500 0.023817 \n",
"2014-04-01 18:00:00 13.384833 0.020683 \n",
"2014-04-01 19:00:00 13.409000 0.049283 \n",
"2014-04-01 20:00:00 13.315500 0.047567 \n",
"2014-04-01 21:00:00 13.333333 0.016950 \n",
"2014-04-01 22:00:00 13.386833 0.017683 \n",
"2014-04-01 23:00:00 13.334167 0.033967 \n",
"2014-04-02 00:00:00 12.716667 0.071500 \n",
"2014-04-02 01:00:00 12.527833 0.072817 \n",
"2014-04-02 02:00:00 12.487500 0.104817 \n",
"2014-04-02 03:00:00 12.456667 0.066450 \n",
"2014-04-02 04:00:00 12.427333 0.079383 \n",
"2014-04-02 05:00:00 12.400500 0.061767 \n",
"2014-04-02 06:00:00 12.369333 0.078617 \n",
"\n",
" Research PVT1 Research PVT2 \\\n",
"datetime \n",
"2014-04-01 07:00:00 0 0 \n",
"2014-04-01 08:00:00 0 0 \n",
"2014-04-01 09:00:00 0 0 \n",
"2014-04-01 10:00:00 0 0 \n",
"2014-04-01 11:00:00 0 0 \n",
"2014-04-01 12:00:00 0 0 \n",
"2014-04-01 13:00:00 0 0 \n",
"2014-04-01 14:00:00 0 0 \n",
"2014-04-01 15:00:00 0 0 \n",
"2014-04-01 16:00:00 0 0 \n",
"2014-04-01 17:00:00 0 0 \n",
"2014-04-01 18:00:00 0 0 \n",
"2014-04-01 19:00:00 0 0 \n",
"2014-04-01 20:00:00 0 0 \n",
"2014-04-01 21:00:00 0 0 \n",
"2014-04-01 22:00:00 0 0 \n",
"2014-04-01 23:00:00 0 0 \n",
"2014-04-02 00:00:00 0 0 \n",
"2014-04-02 01:00:00 0 0 \n",
"2014-04-02 02:00:00 0 0 \n",
"2014-04-02 03:00:00 0 0 \n",
"2014-04-02 04:00:00 0 0 \n",
"2014-04-02 05:00:00 0 0 \n",
"2014-04-02 06:00:00 0 0 \n",
"\n",
" Avg Wind Speed @ 22ft [m/s] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.663717 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.004633 \n",
"2014-04-01 10:00:00 0.149333 \n",
"2014-04-01 11:00:00 0.937950 \n",
"2014-04-01 12:00:00 1.240150 \n",
"2014-04-01 13:00:00 0.122317 \n",
"2014-04-01 14:00:00 1.539617 \n",
"2014-04-01 15:00:00 1.642017 \n",
"2014-04-01 16:00:00 2.133583 \n",
"2014-04-01 17:00:00 3.506000 \n",
"2014-04-01 18:00:00 4.633633 \n",
"2014-04-01 19:00:00 2.408117 \n",
"2014-04-01 20:00:00 2.581433 \n",
"2014-04-01 21:00:00 4.434650 \n",
"2014-04-01 22:00:00 4.406283 \n",
"2014-04-01 23:00:00 3.066167 \n",
"2014-04-02 00:00:00 1.918633 \n",
"2014-04-02 01:00:00 2.375183 \n",
"2014-04-02 02:00:00 2.310983 \n",
"2014-04-02 03:00:00 3.999750 \n",
"2014-04-02 04:00:00 3.913067 \n",
"2014-04-02 05:00:00 3.497567 \n",
"2014-04-02 06:00:00 3.257550 \n",
"\n",
" Avg Wind Direction @ 22ft [deg from N] \\\n",
"datetime \n",
"2014-04-01 07:00:00 60.967833 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 2.063333 \n",
"2014-04-01 10:00:00 33.441667 \n",
"2014-04-01 11:00:00 184.561667 \n",
"2014-04-01 12:00:00 301.673333 \n",
"2014-04-01 13:00:00 31.796833 \n",
"2014-04-01 14:00:00 60.287883 \n",
"2014-04-01 15:00:00 71.602650 \n",
"2014-04-01 16:00:00 54.016583 \n",
"2014-04-01 17:00:00 187.110450 \n",
"2014-04-01 18:00:00 127.754417 \n",
"2014-04-01 19:00:00 50.870383 \n",
"2014-04-01 20:00:00 128.028000 \n",
"2014-04-01 21:00:00 105.566250 \n",
"2014-04-01 22:00:00 60.130183 \n",
"2014-04-01 23:00:00 97.822583 \n",
"2014-04-02 00:00:00 257.541833 \n",
"2014-04-02 01:00:00 134.916533 \n",
"2014-04-02 02:00:00 60.761233 \n",
"2014-04-02 03:00:00 258.636500 \n",
"2014-04-02 04:00:00 314.411667 \n",
"2014-04-02 05:00:00 153.844450 \n",
"2014-04-02 06:00:00 345.755000 \n",
"\n",
" Avg Wind Speed @ 42ft [m/s] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.344217 \n",
"2014-04-01 08:00:00 0.010333 \n",
"2014-04-01 09:00:00 0.048050 \n",
"2014-04-01 10:00:00 0.941950 \n",
"2014-04-01 11:00:00 1.428850 \n",
"2014-04-01 12:00:00 1.850000 \n",
"2014-04-01 13:00:00 0.346067 \n",
"2014-04-01 14:00:00 1.890467 \n",
"2014-04-01 15:00:00 1.950183 \n",
"2014-04-01 16:00:00 2.387783 \n",
"2014-04-01 17:00:00 3.651133 \n",
"2014-04-01 18:00:00 4.759567 \n",
"2014-04-01 19:00:00 2.708083 \n",
"2014-04-01 20:00:00 2.871017 \n",
"2014-04-01 21:00:00 4.538017 \n",
"2014-04-01 22:00:00 4.513867 \n",
"2014-04-01 23:00:00 3.273100 \n",
"2014-04-02 00:00:00 2.354333 \n",
"2014-04-02 01:00:00 2.819267 \n",
"2014-04-02 02:00:00 2.950150 \n",
"2014-04-02 03:00:00 4.405133 \n",
"2014-04-02 04:00:00 4.396717 \n",
"2014-04-02 05:00:00 3.873733 \n",
"2014-04-02 06:00:00 3.736617 \n",
"\n",
" Avg Wind Direction @ 42ft [deg from N] Research PVT0 \\\n",
"datetime \n",
"2014-04-01 07:00:00 98.071667 0 \n",
"2014-04-01 08:00:00 5.288333 0 \n",
"2014-04-01 09:00:00 10.791667 0 \n",
"2014-04-01 10:00:00 92.212333 0 \n",
"2014-04-01 11:00:00 201.020000 0 \n",
"2014-04-01 12:00:00 304.600000 0 \n",
"2014-04-01 13:00:00 36.672833 0 \n",
"2014-04-01 14:00:00 48.295950 0 \n",
"2014-04-01 15:00:00 70.062600 0 \n",
"2014-04-01 16:00:00 36.414783 0 \n",
"2014-04-01 17:00:00 166.824983 0 \n",
"2014-04-01 18:00:00 83.680167 0 \n",
"2014-04-01 19:00:00 48.660800 0 \n",
"2014-04-01 20:00:00 118.245733 0 \n",
"2014-04-01 21:00:00 103.151017 0 \n",
"2014-04-01 22:00:00 52.302817 0 \n",
"2014-04-01 23:00:00 108.162367 0 \n",
"2014-04-02 00:00:00 270.686667 0 \n",
"2014-04-02 01:00:00 115.923133 0 \n",
"2014-04-02 02:00:00 45.738617 0 \n",
"2014-04-02 03:00:00 215.904250 0 \n",
"2014-04-02 04:00:00 320.578333 0 \n",
"2014-04-02 05:00:00 145.834100 0 \n",
"2014-04-02 06:00:00 339.097083 0 \n",
"\n",
" Peak Wind Speed @ 22ft [m/s] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.161000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.024667 \n",
"2014-04-01 10:00:00 0.392667 \n",
"2014-04-01 11:00:00 1.174667 \n",
"2014-04-01 12:00:00 1.772000 \n",
"2014-04-01 13:00:00 0.201667 \n",
"2014-04-01 14:00:00 2.179333 \n",
"2014-04-01 15:00:00 2.496667 \n",
"2014-04-01 16:00:00 3.090000 \n",
"2014-04-01 17:00:00 4.513667 \n",
"2014-04-01 18:00:00 5.729667 \n",
"2014-04-01 19:00:00 3.342000 \n",
"2014-04-01 20:00:00 3.804333 \n",
"2014-04-01 21:00:00 5.577667 \n",
"2014-04-01 22:00:00 5.235667 \n",
"2014-04-01 23:00:00 4.059000 \n",
"2014-04-02 00:00:00 3.090000 \n",
"2014-04-02 01:00:00 3.183667 \n",
"2014-04-02 02:00:00 3.151000 \n",
"2014-04-02 03:00:00 4.919000 \n",
"2014-04-02 04:00:00 4.773333 \n",
"2014-04-02 05:00:00 4.197000 \n",
"2014-04-02 06:00:00 4.070333 \n",
"\n",
" Peak Wind Speed @ 42ft [m/s] Delta UT1 [seconds] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.807417 0.1 \n",
"2014-04-01 08:00:00 0.063217 0.1 \n",
"2014-04-01 09:00:00 0.126433 0.1 \n",
"2014-04-01 10:00:00 1.479050 0.1 \n",
"2014-04-01 11:00:00 1.674800 0.1 \n",
"2014-04-01 12:00:00 2.306583 0.1 \n",
"2014-04-01 13:00:00 0.600550 0.1 \n",
"2014-04-01 14:00:00 2.420283 0.1 \n",
"2014-04-01 15:00:00 2.641350 0.1 \n",
"2014-04-01 16:00:00 3.266717 0.1 \n",
"2014-04-01 17:00:00 4.504783 0.1 \n",
"2014-04-01 18:00:00 5.711267 0.1 \n",
"2014-04-01 19:00:00 3.468850 0.1 \n",
"2014-04-01 20:00:00 3.847850 0.1 \n",
"2014-04-01 21:00:00 5.521800 0.1 \n",
"2014-04-01 22:00:00 5.294367 0.1 \n",
"2014-04-01 23:00:00 4.195250 0.1 \n",
"2014-04-02 00:00:00 3.304617 0.1 \n",
"2014-04-02 01:00:00 3.506750 0.1 \n",
"2014-04-02 02:00:00 3.702567 0.1 \n",
"2014-04-02 03:00:00 5.281733 0.1 \n",
"2014-04-02 04:00:00 5.174350 0.1 \n",
"2014-04-02 05:00:00 4.473200 0.1 \n",
"2014-04-02 06:00:00 4.498467 0.1 \n",
"\n",
" 500nm TWC AOD Net Radiation Eppley [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 -56.382700 \n",
"2014-04-01 08:00:00 0.000000 -51.607417 \n",
"2014-04-01 09:00:00 0.000000 -60.542450 \n",
"2014-04-01 10:00:00 0.000000 -62.409433 \n",
"2014-04-01 11:00:00 0.000000 -59.885050 \n",
"2014-04-01 12:00:00 0.075842 -63.867217 \n",
"2014-04-01 13:00:00 0.163861 -7.971467 \n",
"2014-04-01 14:00:00 0.071731 130.524267 \n",
"2014-04-01 15:00:00 0.128819 273.538150 \n",
"2014-04-01 16:00:00 0.454258 354.766867 \n",
"2014-04-01 17:00:00 2.466706 356.963467 \n",
"2014-04-01 18:00:00 5.338806 221.808200 \n",
"2014-04-01 19:00:00 4.561166 275.830383 \n",
"2014-04-01 20:00:00 2.378717 339.305167 \n",
"2014-04-01 21:00:00 3.456625 190.886933 \n",
"2014-04-01 22:00:00 3.532886 108.283133 \n",
"2014-04-01 23:00:00 1.647150 70.548917 \n",
"2014-04-02 00:00:00 0.953873 -11.219233 \n",
"2014-04-02 01:00:00 0.199782 -66.327450 \n",
"2014-04-02 02:00:00 0.000000 -65.209350 \n",
"2014-04-02 03:00:00 0.000000 -69.843967 \n",
"2014-04-02 04:00:00 0.000000 -78.797233 \n",
"2014-04-02 05:00:00 0.000000 -80.952767 \n",
"2014-04-02 06:00:00 0.000000 -80.432117 \n",
"\n",
" Net Radiation K&Z [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -58.446233 \n",
"2014-04-01 08:00:00 -54.370833 \n",
"2014-04-01 09:00:00 -65.257433 \n",
"2014-04-01 10:00:00 -66.511267 \n",
"2014-04-01 11:00:00 -63.290050 \n",
"2014-04-01 12:00:00 -67.927183 \n",
"2014-04-01 13:00:00 -13.057517 \n",
"2014-04-01 14:00:00 141.027350 \n",
"2014-04-01 15:00:00 287.354400 \n",
"2014-04-01 16:00:00 369.320050 \n",
"2014-04-01 17:00:00 366.050883 \n",
"2014-04-01 18:00:00 229.143550 \n",
"2014-04-01 19:00:00 280.900017 \n",
"2014-04-01 20:00:00 345.047150 \n",
"2014-04-01 21:00:00 192.767317 \n",
"2014-04-01 22:00:00 104.381000 \n",
"2014-04-01 23:00:00 64.435733 \n",
"2014-04-02 00:00:00 -20.335383 \n",
"2014-04-02 01:00:00 -70.693717 \n",
"2014-04-02 02:00:00 -68.067267 \n",
"2014-04-02 03:00:00 -71.819183 \n",
"2014-04-02 04:00:00 -82.707500 \n",
"2014-04-02 05:00:00 -85.261417 \n",
"2014-04-02 06:00:00 -84.602117 \n",
"\n",
" Atmos Net Infrared PIRs [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -57.500667 \n",
"2014-04-01 08:00:00 -52.705333 \n",
"2014-04-01 09:00:00 -62.862350 \n",
"2014-04-01 10:00:00 -64.384333 \n",
"2014-04-01 11:00:00 -61.038083 \n",
"2014-04-01 12:00:00 -67.816450 \n",
"2014-04-01 13:00:00 -89.184800 \n",
"2014-04-01 14:00:00 -128.737883 \n",
"2014-04-01 15:00:00 -156.871917 \n",
"2014-04-01 16:00:00 -175.843333 \n",
"2014-04-01 17:00:00 -125.962350 \n",
"2014-04-01 18:00:00 -99.217583 \n",
"2014-04-01 19:00:00 -106.191800 \n",
"2014-04-01 20:00:00 -151.861833 \n",
"2014-04-01 21:00:00 -111.217283 \n",
"2014-04-01 22:00:00 -98.841017 \n",
"2014-04-01 23:00:00 -100.293350 \n",
"2014-04-02 00:00:00 -93.500567 \n",
"2014-04-02 01:00:00 -75.520167 \n",
"2014-04-02 02:00:00 -66.866100 \n",
"2014-04-02 03:00:00 -70.828150 \n",
"2014-04-02 04:00:00 -80.105083 \n",
"2014-04-02 05:00:00 -83.551817 \n",
"2014-04-02 06:00:00 -82.555333 \n",
"\n",
" Atmos Net Infrared K&Zs [W/m^2] Albedo (PSP) \\\n",
"datetime \n",
"2014-04-01 07:00:00 -56.338350 0.000000 \n",
"2014-04-01 08:00:00 -51.117500 0.000000 \n",
"2014-04-01 09:00:00 -61.508450 0.000000 \n",
"2014-04-01 10:00:00 -63.027983 0.000000 \n",
"2014-04-01 11:00:00 -59.891383 0.000000 \n",
"2014-04-01 12:00:00 -67.338950 0.012152 \n",
"2014-04-01 13:00:00 -87.497967 0.212958 \n",
"2014-04-01 14:00:00 -125.192583 0.205152 \n",
"2014-04-01 15:00:00 -153.668950 0.188565 \n",
"2014-04-01 16:00:00 -172.898367 0.179115 \n",
"2014-04-01 17:00:00 -124.447450 0.167550 \n",
"2014-04-01 18:00:00 -97.746617 0.164533 \n",
"2014-04-01 19:00:00 -105.667267 0.169747 \n",
"2014-04-01 20:00:00 -152.202783 0.170435 \n",
"2014-04-01 21:00:00 -109.863083 0.166758 \n",
"2014-04-01 22:00:00 -97.808650 0.161998 \n",
"2014-04-01 23:00:00 -99.766000 0.163427 \n",
"2014-04-02 00:00:00 -93.166200 0.138023 \n",
"2014-04-02 01:00:00 -74.827067 0.012957 \n",
"2014-04-02 02:00:00 -65.721000 0.000000 \n",
"2014-04-02 03:00:00 -69.789550 0.000000 \n",
"2014-04-02 04:00:00 -79.606683 0.000000 \n",
"2014-04-02 05:00:00 -82.692200 0.000000 \n",
"2014-04-02 06:00:00 -81.723567 0.000000 \n",
"\n",
" Albedo (K&Z) Albedo (LI-200) Albedo Quantum (LI-190) \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 0.000000 0.000000 \n",
"2014-04-01 08:00:00 0.000000 0.000000 0.000000 \n",
"2014-04-01 09:00:00 0.000000 0.000000 0.000000 \n",
"2014-04-01 10:00:00 0.000000 0.000000 0.000000 \n",
"2014-04-01 11:00:00 0.000000 0.000000 0.000000 \n",
"2014-04-01 12:00:00 0.024998 0.016347 0.008382 \n",
"2014-04-01 13:00:00 0.284012 0.329208 0.137852 \n",
"2014-04-01 14:00:00 0.191617 0.274352 0.130862 \n",
"2014-04-01 15:00:00 0.169273 0.238087 0.119153 \n",
"2014-04-01 16:00:00 0.164687 0.217523 0.113685 \n",
"2014-04-01 17:00:00 0.159578 0.209398 0.110487 \n",
"2014-04-01 18:00:00 0.160205 0.210423 0.110322 \n",
"2014-04-01 19:00:00 0.166275 0.207718 0.109820 \n",
"2014-04-01 20:00:00 0.164057 0.203528 0.111183 \n",
"2014-04-01 21:00:00 0.166793 0.209388 0.111415 \n",
"2014-04-01 22:00:00 0.167938 0.210142 0.109585 \n",
"2014-04-01 23:00:00 0.173833 0.214397 0.111637 \n",
"2014-04-02 00:00:00 0.190088 0.220093 0.111783 \n",
"2014-04-02 01:00:00 0.059510 0.053470 0.029322 \n",
"2014-04-02 02:00:00 0.000000 0.000000 0.000000 \n",
"2014-04-02 03:00:00 0.000000 0.000000 0.000000 \n",
"2014-04-02 04:00:00 0.000000 0.000000 0.000000 \n",
"2014-04-02 05:00:00 0.000000 0.000000 0.000000 \n",
"2014-04-02 06:00:00 0.000000 0.000000 0.000000 \n",
"\n",
" Broadband Turbidity 500nm Estimated AOD \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 0.000000 \n",
"2014-04-01 08:00:00 0.000000 0.000000 \n",
"2014-04-01 09:00:00 0.000000 0.000000 \n",
"2014-04-01 10:00:00 0.000000 0.000000 \n",
"2014-04-01 11:00:00 0.000000 0.000000 \n",
"2014-04-01 12:00:00 0.128939 0.129717 \n",
"2014-04-01 13:00:00 0.164464 0.177748 \n",
"2014-04-01 14:00:00 0.074510 0.105396 \n",
"2014-04-01 15:00:00 0.122041 0.169344 \n",
"2014-04-01 16:00:00 0.433366 0.494410 \n",
"2014-04-01 17:00:00 2.930044 3.000778 \n",
"2014-04-01 18:00:00 5.827364 5.904716 \n",
"2014-04-01 19:00:00 3.764116 3.842199 \n",
"2014-04-01 20:00:00 2.495319 2.566424 \n",
"2014-04-01 21:00:00 4.056570 4.117653 \n",
"2014-04-01 22:00:00 3.701680 3.750510 \n",
"2014-04-01 23:00:00 1.604344 1.637040 \n",
"2014-04-02 00:00:00 1.196089 1.211443 \n",
"2014-04-02 01:00:00 0.371752 0.373545 \n",
"2014-04-02 02:00:00 0.000000 0.000000 \n",
"2014-04-02 03:00:00 0.000000 0.000000 \n",
"2014-04-02 04:00:00 0.000000 0.000000 \n",
"2014-04-02 05:00:00 0.000000 0.000000 \n",
"2014-04-02 06:00:00 0.000000 0.000000 \n",
"\n",
" Sea-Level Pressure (Est) [mBar] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1015.081500 \n",
"2014-04-01 08:00:00 1014.151500 \n",
"2014-04-01 09:00:00 1013.186000 \n",
"2014-04-01 10:00:00 1012.301167 \n",
"2014-04-01 11:00:00 1011.376500 \n",
"2014-04-01 12:00:00 1010.736833 \n",
"2014-04-01 13:00:00 1010.460667 \n",
"2014-04-01 14:00:00 1010.116833 \n",
"2014-04-01 15:00:00 1009.838333 \n",
"2014-04-01 16:00:00 1009.272667 \n",
"2014-04-01 17:00:00 1009.048833 \n",
"2014-04-01 18:00:00 1008.612333 \n",
"2014-04-01 19:00:00 1008.118000 \n",
"2014-04-01 20:00:00 1007.416833 \n",
"2014-04-01 21:00:00 1007.207000 \n",
"2014-04-01 22:00:00 1007.270833 \n",
"2014-04-01 23:00:00 1007.381167 \n",
"2014-04-02 00:00:00 1007.645333 \n",
"2014-04-02 01:00:00 1008.081000 \n",
"2014-04-02 02:00:00 1008.858500 \n",
"2014-04-02 03:00:00 1009.735333 \n",
"2014-04-02 04:00:00 1009.378667 \n",
"2014-04-02 05:00:00 1009.022000 \n",
"2014-04-02 06:00:00 1009.207833 \n",
"\n",
" Tower Dew Point Temp [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -5.573400 \n",
"2014-04-01 08:00:00 -5.553383 \n",
"2014-04-01 09:00:00 -5.616467 \n",
"2014-04-01 10:00:00 -5.783517 \n",
"2014-04-01 11:00:00 -5.806067 \n",
"2014-04-01 12:00:00 -5.654750 \n",
"2014-04-01 13:00:00 -5.119867 \n",
"2014-04-01 14:00:00 -4.485350 \n",
"2014-04-01 15:00:00 -4.053717 \n",
"2014-04-01 16:00:00 -3.791083 \n",
"2014-04-01 17:00:00 -3.954733 \n",
"2014-04-01 18:00:00 -3.233217 \n",
"2014-04-01 19:00:00 -2.957200 \n",
"2014-04-01 20:00:00 -4.485067 \n",
"2014-04-01 21:00:00 -5.305983 \n",
"2014-04-01 22:00:00 -5.201567 \n",
"2014-04-01 23:00:00 -6.058317 \n",
"2014-04-02 00:00:00 -7.743450 \n",
"2014-04-02 01:00:00 -6.722750 \n",
"2014-04-02 02:00:00 -5.818283 \n",
"2014-04-02 03:00:00 -2.459483 \n",
"2014-04-02 04:00:00 -2.937400 \n",
"2014-04-02 05:00:00 -2.893983 \n",
"2014-04-02 06:00:00 -2.401950 \n",
"\n",
" Tower Wet Bulb Temp [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -1.508400 \n",
"2014-04-01 08:00:00 -1.818383 \n",
"2014-04-01 09:00:00 -2.178133 \n",
"2014-04-01 10:00:00 -2.660183 \n",
"2014-04-01 11:00:00 -2.894400 \n",
"2014-04-01 12:00:00 -2.938083 \n",
"2014-04-01 13:00:00 -2.028200 \n",
"2014-04-01 14:00:00 -0.628683 \n",
"2014-04-01 15:00:00 0.351283 \n",
"2014-04-01 16:00:00 1.702250 \n",
"2014-04-01 17:00:00 1.944767 \n",
"2014-04-01 18:00:00 2.517950 \n",
"2014-04-01 19:00:00 3.266800 \n",
"2014-04-01 20:00:00 4.318933 \n",
"2014-04-01 21:00:00 4.123017 \n",
"2014-04-01 22:00:00 3.894767 \n",
"2014-04-01 23:00:00 3.814017 \n",
"2014-04-02 00:00:00 3.407550 \n",
"2014-04-02 01:00:00 2.760250 \n",
"2014-04-02 02:00:00 2.371550 \n",
"2014-04-02 03:00:00 2.086850 \n",
"2014-04-02 04:00:00 1.735767 \n",
"2014-04-02 05:00:00 1.085683 \n",
"2014-04-02 06:00:00 0.426383 \n",
"\n",
" Tower Wind Chill Temp [deg C] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.904700 \n",
"2014-04-01 08:00:00 0.846750 \n",
"2014-04-01 09:00:00 0.243583 \n",
"2014-04-01 10:00:00 -0.611467 \n",
"2014-04-01 11:00:00 -1.779967 \n",
"2014-04-01 12:00:00 -1.992100 \n",
"2014-04-01 13:00:00 0.219750 \n",
"2014-04-01 14:00:00 1.486083 \n",
"2014-04-01 15:00:00 2.809950 \n",
"2014-04-01 16:00:00 4.792067 \n",
"2014-04-01 17:00:00 5.213350 \n",
"2014-04-01 18:00:00 5.400417 \n",
"2014-04-01 19:00:00 7.642767 \n",
"2014-04-01 20:00:00 11.566750 \n",
"2014-04-01 21:00:00 11.191483 \n",
"2014-04-01 22:00:00 10.524600 \n",
"2014-04-01 23:00:00 11.294167 \n",
"2014-04-02 00:00:00 11.681733 \n",
"2014-04-02 01:00:00 9.566017 \n",
"2014-04-02 02:00:00 8.037683 \n",
"2014-04-02 03:00:00 4.554150 \n",
"2014-04-02 04:00:00 4.427483 \n",
"2014-04-02 05:00:00 2.733767 \n",
"2014-04-02 06:00:00 0.579450 \n",
"\n",
" Deck Wind Chill Temp [deg C] Total Cloud Cover [%] \\\n",
"datetime \n",
"2014-04-01 07:00:00 1.435483 -1.000000 \n",
"2014-04-01 08:00:00 0.879017 -1.000000 \n",
"2014-04-01 09:00:00 0.130917 -1.000000 \n",
"2014-04-01 10:00:00 -0.051400 -1.000000 \n",
"2014-04-01 11:00:00 -1.609733 -1.000000 \n",
"2014-04-01 12:00:00 -2.386817 -1.000000 \n",
"2014-04-01 13:00:00 0.484117 4.716667 \n",
"2014-04-01 14:00:00 1.756200 1.366667 \n",
"2014-04-01 15:00:00 2.694483 8.533333 \n",
"2014-04-01 16:00:00 4.778117 14.616667 \n",
"2014-04-01 17:00:00 4.421450 80.933333 \n",
"2014-04-01 18:00:00 4.725917 91.200000 \n",
"2014-04-01 19:00:00 7.436367 95.483333 \n",
"2014-04-01 20:00:00 10.827350 97.050000 \n",
"2014-04-01 21:00:00 10.721050 99.066667 \n",
"2014-04-01 22:00:00 10.252367 98.700000 \n",
"2014-04-01 23:00:00 11.334317 98.750000 \n",
"2014-04-02 00:00:00 11.713883 81.466667 \n",
"2014-04-02 01:00:00 10.148950 0.483333 \n",
"2014-04-02 02:00:00 8.475917 -1.000000 \n",
"2014-04-02 03:00:00 3.928200 -1.000000 \n",
"2014-04-02 04:00:00 3.317483 -1.000000 \n",
"2014-04-02 05:00:00 2.437417 -1.000000 \n",
"2014-04-02 06:00:00 0.167250 -1.000000 \n",
"\n",
" Opaque Cloud Cover [%] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -1.000000 \n",
"2014-04-01 08:00:00 -1.000000 \n",
"2014-04-01 09:00:00 -1.000000 \n",
"2014-04-01 10:00:00 -1.000000 \n",
"2014-04-01 11:00:00 -1.000000 \n",
"2014-04-01 12:00:00 -1.000000 \n",
"2014-04-01 13:00:00 1.633333 \n",
"2014-04-01 14:00:00 1.033333 \n",
"2014-04-01 15:00:00 1.833333 \n",
"2014-04-01 16:00:00 9.116667 \n",
"2014-04-01 17:00:00 58.766667 \n",
"2014-04-01 18:00:00 54.283333 \n",
"2014-04-01 19:00:00 62.333333 \n",
"2014-04-01 20:00:00 63.450000 \n",
"2014-04-01 21:00:00 76.750000 \n",
"2014-04-01 22:00:00 81.333333 \n",
"2014-04-01 23:00:00 67.850000 \n",
"2014-04-02 00:00:00 30.516667 \n",
"2014-04-02 01:00:00 -0.400000 \n",
"2014-04-02 02:00:00 -1.000000 \n",
"2014-04-02 03:00:00 -1.000000 \n",
"2014-04-02 04:00:00 -1.000000 \n",
"2014-04-02 05:00:00 -1.000000 \n",
"2014-04-02 06:00:00 -1.000000 \n",
"\n",
" Global Extraterrestrial (calc) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 5.542967 \n",
"2014-04-01 13:00:00 186.917950 \n",
"2014-04-01 14:00:00 450.822583 \n",
"2014-04-01 15:00:00 690.108133 \n",
"2014-04-01 16:00:00 887.439733 \n",
"2014-04-01 17:00:00 1029.239867 \n",
"2014-04-01 18:00:00 1105.820100 \n",
"2014-04-01 19:00:00 1111.967300 \n",
"2014-04-01 20:00:00 1047.284600 \n",
"2014-04-01 21:00:00 916.216717 \n",
"2014-04-01 22:00:00 727.758050 \n",
"2014-04-01 23:00:00 494.891900 \n",
"2014-04-02 00:00:00 234.166417 \n",
"2014-04-02 01:00:00 19.090317 \n",
"2014-04-02 02:00:00 0.000000 \n",
"2014-04-02 03:00:00 0.000000 \n",
"2014-04-02 04:00:00 0.000000 \n",
"2014-04-02 05:00:00 0.000000 \n",
"2014-04-02 06:00:00 0.000000 \n",
"\n",
" Direct Extraterrestrial (calc) [W/m^2] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 296.583117 \n",
"2014-04-01 13:00:00 1368.825533 \n",
"2014-04-01 14:00:00 1368.793017 \n",
"2014-04-01 15:00:00 1368.760550 \n",
"2014-04-01 16:00:00 1368.728050 \n",
"2014-04-01 17:00:00 1368.695667 \n",
"2014-04-01 18:00:00 1368.663200 \n",
"2014-04-01 19:00:00 1368.630700 \n",
"2014-04-01 20:00:00 1368.598267 \n",
"2014-04-01 21:00:00 1368.565800 \n",
"2014-04-01 22:00:00 1368.533400 \n",
"2014-04-01 23:00:00 1368.501000 \n",
"2014-04-02 00:00:00 1368.468500 \n",
"2014-04-02 01:00:00 524.571033 \n",
"2014-04-02 02:00:00 0.000000 \n",
"2014-04-02 03:00:00 0.000000 \n",
"2014-04-02 04:00:00 0.000000 \n",
"2014-04-02 05:00:00 0.000000 \n",
"2014-04-02 06:00:00 0.000000 \n",
"\n",
" Zenith Angle [degrees] Azimuth Angle [degrees] \\\n",
"datetime \n",
"2014-04-01 07:00:00 135.169852 38.774864 \n",
"2014-04-01 08:00:00 131.446205 28.696564 \n",
"2014-04-01 09:00:00 124.476137 45.530383 \n",
"2014-04-01 10:00:00 115.340581 59.243411 \n",
"2014-04-01 11:00:00 104.898891 70.686948 \n",
"2014-04-01 12:00:00 93.644466 80.800323 \n",
"2014-04-01 13:00:00 82.138483 90.413872 \n",
"2014-04-01 14:00:00 70.737625 100.321120 \n",
"2014-04-01 15:00:00 59.673836 111.437533 \n",
"2014-04-01 16:00:00 49.525984 124.991157 \n",
"2014-04-01 17:00:00 41.196335 142.561463 \n",
"2014-04-01 18:00:00 36.094108 165.156999 \n",
"2014-04-01 19:00:00 35.657372 190.676434 \n",
"2014-04-01 20:00:00 40.037130 214.050943 \n",
"2014-04-01 21:00:00 47.919115 232.468461 \n",
"2014-04-01 22:00:00 57.823333 246.604316 \n",
"2014-04-01 23:00:00 68.763959 258.053650 \n",
"2014-04-02 00:00:00 80.130844 268.114101 \n",
"2014-04-02 01:00:00 91.582280 277.742003 \n",
"2014-04-02 02:00:00 102.914436 287.739028 \n",
"2014-04-02 03:00:00 113.473283 298.917847 \n",
"2014-04-02 04:00:00 122.829207 312.191727 \n",
"2014-04-02 05:00:00 130.174811 328.439539 \n",
"2014-04-02 06:00:00 134.457681 347.828980 \n",
"\n",
" Airmass Delta T [seconds] \\\n",
"datetime \n",
"2014-04-01 07:00:00 -1.000000 67.084 \n",
"2014-04-01 08:00:00 -1.000000 67.084 \n",
"2014-04-01 09:00:00 -1.000000 67.084 \n",
"2014-04-01 10:00:00 -1.000000 67.084 \n",
"2014-04-01 11:00:00 -1.000000 67.084 \n",
"2014-04-01 12:00:00 12.648892 67.084 \n",
"2014-04-01 13:00:00 8.195024 67.084 \n",
"2014-04-01 14:00:00 3.093707 67.084 \n",
"2014-04-01 15:00:00 1.994829 67.084 \n",
"2014-04-01 16:00:00 1.544909 67.084 \n",
"2014-04-01 17:00:00 1.329824 67.084 \n",
"2014-04-01 18:00:00 1.236892 67.084 \n",
"2014-04-01 19:00:00 1.229968 67.084 \n",
"2014-04-01 20:00:00 1.306600 67.084 \n",
"2014-04-01 21:00:00 1.495610 67.084 \n",
"2014-04-01 22:00:00 1.889356 67.084 \n",
"2014-04-01 23:00:00 2.806854 67.084 \n",
"2014-04-02 00:00:00 6.290051 67.084 \n",
"2014-04-02 01:00:00 15.896664 67.084 \n",
"2014-04-02 02:00:00 -1.000000 67.084 \n",
"2014-04-02 03:00:00 -1.000000 67.084 \n",
"2014-04-02 04:00:00 -1.000000 67.084 \n",
"2014-04-02 05:00:00 -1.000000 67.084 \n",
"2014-04-02 06:00:00 -1.000000 67.084 \n",
"\n",
" 315nm POM-01 Photometer [nA] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 -1516.450000 \n",
"2014-04-01 13:00:00 -3266.078876 \n",
"2014-04-01 14:00:00 -2797.566223 \n",
"2014-04-01 15:00:00 -2326.015468 \n",
"2014-04-01 16:00:00 -2089.157512 \n",
"2014-04-01 17:00:00 -1510.874753 \n",
"2014-04-01 18:00:00 -1398.622142 \n",
"2014-04-01 19:00:00 -1163.123000 \n",
"2014-04-01 20:00:00 -2096.650390 \n",
"2014-04-01 21:00:00 -1282.657693 \n",
"2014-04-01 22:00:00 -699.863094 \n",
"2014-04-01 23:00:00 -699.810996 \n",
"2014-04-02 00:00:00 -1399.792280 \n",
"2014-04-02 01:00:00 -2682.950000 \n",
"2014-04-02 02:00:00 0.000000 \n",
"2014-04-02 03:00:00 0.000000 \n",
"2014-04-02 04:00:00 0.000000 \n",
"2014-04-02 05:00:00 0.000000 \n",
"2014-04-02 06:00:00 0.000000 \n",
"\n",
" 400nm POM-01 Photometer [uA] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 -1516.450000 \n",
"2014-04-01 13:00:00 -3262.250977 \n",
"2014-04-01 14:00:00 -2782.668883 \n",
"2014-04-01 15:00:00 -2307.797400 \n",
"2014-04-01 16:00:00 -2072.584433 \n",
"2014-04-01 17:00:00 -1505.370356 \n",
"2014-04-01 18:00:00 -1397.888477 \n",
"2014-04-01 19:00:00 -1160.764310 \n",
"2014-04-01 20:00:00 -2094.523991 \n",
"2014-04-01 21:00:00 -1282.412708 \n",
"2014-04-01 22:00:00 -699.861344 \n",
"2014-04-01 23:00:00 -699.625638 \n",
"2014-04-02 00:00:00 -1399.693749 \n",
"2014-04-02 01:00:00 -2682.950000 \n",
"2014-04-02 02:00:00 0.000000 \n",
"2014-04-02 03:00:00 0.000000 \n",
"2014-04-02 04:00:00 0.000000 \n",
"2014-04-02 05:00:00 0.000000 \n",
"2014-04-02 06:00:00 0.000000 \n",
"\n",
" 500nm POM-01 Photometer [uA] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 -1516.450000 \n",
"2014-04-01 13:00:00 -3230.531833 \n",
"2014-04-01 14:00:00 -2704.529667 \n",
"2014-04-01 15:00:00 -2215.352750 \n",
"2014-04-01 16:00:00 -1981.180770 \n",
"2014-04-01 17:00:00 -1470.139893 \n",
"2014-04-01 18:00:00 -1392.244784 \n",
"2014-04-01 19:00:00 -1144.060487 \n",
"2014-04-01 20:00:00 -2079.235124 \n",
"2014-04-01 21:00:00 -1280.324395 \n",
"2014-04-01 22:00:00 -699.757744 \n",
"2014-04-01 23:00:00 -698.712826 \n",
"2014-04-02 00:00:00 -1399.128473 \n",
"2014-04-02 01:00:00 -2682.950000 \n",
"2014-04-02 02:00:00 0.000000 \n",
"2014-04-02 03:00:00 0.000000 \n",
"2014-04-02 04:00:00 0.000000 \n",
"2014-04-02 05:00:00 0.000000 \n",
"2014-04-02 06:00:00 0.000000 \n",
"\n",
" 675nm POM-01 Photometer [uA] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 -1516.450000 \n",
"2014-04-01 13:00:00 -3190.539538 \n",
"2014-04-01 14:00:00 -2643.539500 \n",
"2014-04-01 15:00:00 -2157.977917 \n",
"2014-04-01 16:00:00 -1929.279015 \n",
"2014-04-01 17:00:00 -1451.619768 \n",
"2014-04-01 18:00:00 -1389.808390 \n",
"2014-04-01 19:00:00 -1136.988669 \n",
"2014-04-01 20:00:00 -2072.836061 \n",
"2014-04-01 21:00:00 -1279.671170 \n",
"2014-04-01 22:00:00 -699.739001 \n",
"2014-04-01 23:00:00 -698.318502 \n",
"2014-04-02 00:00:00 -1398.730745 \n",
"2014-04-02 01:00:00 -2682.950000 \n",
"2014-04-02 02:00:00 0.000000 \n",
"2014-04-02 03:00:00 0.000000 \n",
"2014-04-02 04:00:00 0.000000 \n",
"2014-04-02 05:00:00 0.000000 \n",
"2014-04-02 06:00:00 0.000000 \n",
"\n",
" 870nm POM-01 Photometer [uA] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 -1516.450000 \n",
"2014-04-01 13:00:00 -3198.412110 \n",
"2014-04-01 14:00:00 -2673.809333 \n",
"2014-04-01 15:00:00 -2198.435917 \n",
"2014-04-01 16:00:00 -1971.052973 \n",
"2014-04-01 17:00:00 -1468.423441 \n",
"2014-04-01 18:00:00 -1392.727708 \n",
"2014-04-01 19:00:00 -1145.762176 \n",
"2014-04-01 20:00:00 -2081.100463 \n",
"2014-04-01 21:00:00 -1280.920596 \n",
"2014-04-01 22:00:00 -699.806552 \n",
"2014-04-01 23:00:00 -698.895102 \n",
"2014-04-02 00:00:00 -1399.054748 \n",
"2014-04-02 01:00:00 -2682.950000 \n",
"2014-04-02 02:00:00 0.000000 \n",
"2014-04-02 03:00:00 0.000000 \n",
"2014-04-02 04:00:00 0.000000 \n",
"2014-04-02 05:00:00 0.000000 \n",
"2014-04-02 06:00:00 0.000000 \n",
"\n",
" 940nm POM-01 Photometer [uA] \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 \n",
"2014-04-01 08:00:00 0.000000 \n",
"2014-04-01 09:00:00 0.000000 \n",
"2014-04-01 10:00:00 0.000000 \n",
"2014-04-01 11:00:00 0.000000 \n",
"2014-04-01 12:00:00 -1516.450000 \n",
"2014-04-01 13:00:00 -3245.836487 \n",
"2014-04-01 14:00:00 -2742.339967 \n",
"2014-04-01 15:00:00 -2259.567083 \n",
"2014-04-01 16:00:00 -2024.488623 \n",
"2014-04-01 17:00:00 -1487.837439 \n",
"2014-04-01 18:00:00 -1395.636410 \n",
"2014-04-01 19:00:00 -1153.964046 \n",
"2014-04-01 20:00:00 -2088.243827 \n",
"2014-04-01 21:00:00 -1281.851177 \n",
"2014-04-01 22:00:00 -699.853556 \n",
"2014-04-01 23:00:00 -699.441123 \n",
"2014-04-02 00:00:00 -1399.541195 \n",
"2014-04-02 01:00:00 -2682.950000 \n",
"2014-04-02 02:00:00 0.000000 \n",
"2014-04-02 03:00:00 0.000000 \n",
"2014-04-02 04:00:00 0.000000 \n",
"2014-04-02 05:00:00 0.000000 \n",
"2014-04-02 06:00:00 0.000000 \n",
"\n",
" 1020nm POM-01 Photometer [uA] 315nm Obsolete AOD \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 0.000000 \n",
"2014-04-01 08:00:00 0.000000 0.000000 \n",
"2014-04-01 09:00:00 0.000000 0.000000 \n",
"2014-04-01 10:00:00 0.000000 0.000000 \n",
"2014-04-01 11:00:00 0.000000 0.000000 \n",
"2014-04-01 12:00:00 -1516.450000 -1516.450000 \n",
"2014-04-01 13:00:00 -3214.386885 -3266.196517 \n",
"2014-04-01 14:00:00 -2703.006500 -2799.557365 \n",
"2014-04-01 15:00:00 -2229.510583 -2332.943257 \n",
"2014-04-01 16:00:00 -2000.014520 -2099.433496 \n",
"2014-04-01 17:00:00 -1479.697544 -1514.482244 \n",
"2014-04-01 18:00:00 -1394.586051 -1395.749888 \n",
"2014-04-01 19:00:00 -1151.096423 -1162.856527 \n",
"2014-04-01 20:00:00 -2085.713050 -2098.022831 \n",
"2014-04-01 21:00:00 -1281.551221 -1280.485584 \n",
"2014-04-01 22:00:00 -699.838906 -697.006581 \n",
"2014-04-01 23:00:00 -699.218973 -698.505590 \n",
"2014-04-02 00:00:00 -1399.278592 -1399.193231 \n",
"2014-04-02 01:00:00 -2682.950000 -2682.950000 \n",
"2014-04-02 02:00:00 0.000000 0.000000 \n",
"2014-04-02 03:00:00 0.000000 0.000000 \n",
"2014-04-02 04:00:00 0.000000 0.000000 \n",
"2014-04-02 05:00:00 0.000000 0.000000 \n",
"2014-04-02 06:00:00 0.000000 0.000000 \n",
"\n",
" 400nm Obsolete AOD 500nm Obsolete AOD \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 0.000000 \n",
"2014-04-01 08:00:00 0.000000 0.000000 \n",
"2014-04-01 09:00:00 0.000000 0.000000 \n",
"2014-04-01 10:00:00 0.000000 0.000000 \n",
"2014-04-01 11:00:00 0.000000 0.000000 \n",
"2014-04-01 12:00:00 -1516.450000 -1516.450000 \n",
"2014-04-01 13:00:00 -3266.085689 -3266.109060 \n",
"2014-04-01 14:00:00 -2799.515812 -2799.557142 \n",
"2014-04-01 15:00:00 -2332.845700 -2332.908597 \n",
"2014-04-01 16:00:00 -2099.261140 -2099.339196 \n",
"2014-04-01 17:00:00 -1513.834141 -1513.897901 \n",
"2014-04-01 18:00:00 -1394.013087 -1394.017157 \n",
"2014-04-01 19:00:00 -1161.595231 -1161.616104 \n",
"2014-04-01 20:00:00 -2097.333265 -2097.364420 \n",
"2014-04-01 21:00:00 -1279.431655 -1279.432265 \n",
"2014-04-01 22:00:00 -696.092616 -696.072059 \n",
"2014-04-01 23:00:00 -698.131368 -698.125295 \n",
"2014-04-02 00:00:00 -1398.891252 -1398.880228 \n",
"2014-04-02 01:00:00 -2682.950000 -2682.950000 \n",
"2014-04-02 02:00:00 0.000000 0.000000 \n",
"2014-04-02 03:00:00 0.000000 0.000000 \n",
"2014-04-02 04:00:00 0.000000 0.000000 \n",
"2014-04-02 05:00:00 0.000000 0.000000 \n",
"2014-04-02 06:00:00 0.000000 0.000000 \n",
"\n",
" 675nm Obsolete AOD 870nm Obsolete AOD \\\n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 0.000000 \n",
"2014-04-01 08:00:00 0.000000 0.000000 \n",
"2014-04-01 09:00:00 0.000000 0.000000 \n",
"2014-04-01 10:00:00 0.000000 0.000000 \n",
"2014-04-01 11:00:00 0.000000 0.000000 \n",
"2014-04-01 12:00:00 -1516.450000 -1516.450000 \n",
"2014-04-01 13:00:00 -3266.118844 -3266.122694 \n",
"2014-04-01 14:00:00 -2799.574647 -2799.584262 \n",
"2014-04-01 15:00:00 -2332.933071 -2332.944231 \n",
"2014-04-01 16:00:00 -2099.365255 -2099.371276 \n",
"2014-04-01 17:00:00 -1513.896135 -1513.885202 \n",
"2014-04-01 18:00:00 -1393.994070 -1394.044946 \n",
"2014-04-01 19:00:00 -1161.576798 -1161.573965 \n",
"2014-04-01 20:00:00 -2097.336384 -2097.300593 \n",
"2014-04-01 21:00:00 -1279.394041 -1279.357092 \n",
"2014-04-01 22:00:00 -695.970001 -695.867902 \n",
"2014-04-01 23:00:00 -698.081250 -698.013523 \n",
"2014-04-02 00:00:00 -1398.858095 -1398.831842 \n",
"2014-04-02 01:00:00 -2682.950000 -2682.950000 \n",
"2014-04-02 02:00:00 0.000000 0.000000 \n",
"2014-04-02 03:00:00 0.000000 0.000000 \n",
"2014-04-02 04:00:00 0.000000 0.000000 \n",
"2014-04-02 05:00:00 0.000000 0.000000 \n",
"2014-04-02 06:00:00 0.000000 0.000000 \n",
"\n",
" 940nm Obsolete AOD 1020nm Obsolete AOD Research F2 \n",
"datetime \n",
"2014-04-01 07:00:00 0.000000 0.000000 2.717713 \n",
"2014-04-01 08:00:00 0.000000 0.000000 2.225765 \n",
"2014-04-01 09:00:00 0.000000 0.000000 3.076337 \n",
"2014-04-01 10:00:00 0.000000 0.000000 3.316883 \n",
"2014-04-01 11:00:00 0.000000 0.000000 3.464543 \n",
"2014-04-01 12:00:00 -1516.450000 -1516.450000 6.419112 \n",
"2014-04-01 13:00:00 -3266.029577 -3266.116859 47.932593 \n",
"2014-04-01 14:00:00 -2799.467377 -2799.578437 58.362060 \n",
"2014-04-01 15:00:00 -2332.805651 -2332.936275 93.710953 \n",
"2014-04-01 16:00:00 -2099.216038 -2099.360080 127.898515 \n",
"2014-04-01 17:00:00 -1513.660427 -1513.852988 323.167590 \n",
"2014-04-01 18:00:00 -1393.696674 -1394.002571 335.654353 \n",
"2014-04-01 19:00:00 -1161.243744 -1161.499249 341.726487 \n",
"2014-04-01 20:00:00 -2097.141142 -2097.275614 431.481208 \n",
"2014-04-01 21:00:00 -1279.152994 -1279.328659 337.405500 \n",
"2014-04-01 22:00:00 -695.638073 -695.796191 250.574370 \n",
"2014-04-01 23:00:00 -697.815785 -697.959156 195.728895 \n",
"2014-04-02 00:00:00 -1398.694442 -1398.810783 91.098543 \n",
"2014-04-02 01:00:00 -2682.950000 -2682.950000 11.139272 \n",
"2014-04-02 02:00:00 0.000000 0.000000 3.387090 \n",
"2014-04-02 03:00:00 0.000000 0.000000 3.984577 \n",
"2014-04-02 04:00:00 0.000000 0.000000 4.617081 \n",
"2014-04-02 05:00:00 0.000000 0.000000 4.509819 \n",
"2014-04-02 06:00:00 0.000000 0.000000 4.487795 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#contains headers for all data (I edited \"nothing\" column in header file b/c it's just 0's)\n",
"\n",
"filefolder = 'data/sensor_data/colorado6months/'\n",
"desired_file = '20140401.csv'\n",
"\n",
"def return_sensor_data(desired_file, filefolder):\n",
" '''Input: desired file and folder\n",
" Output: sensor data pandas dataframe\n",
" Usage: filefolder = 'data/sensor_data/colorado6months/'\n",
" desired_file = '20140401.csv'\n",
" return_sensor_data(myfile, filefolder)'''\n",
" \n",
" df_header = pd.read_csv(filefolder + 'header.csv')\n",
" headers = df_header.columns\n",
" df_sensor = pd.read_csv(filefolder + desired_file,header=None) #sensors\n",
" df_sensor.columns = headers\n",
" df_sensor['MST'] = df_sensor['MST'].map(pad)\n",
" #make a datetime index:\n",
" df_sensor['datetime'] = pd.to_datetime(df_sensor['Year'].astype(str)+\n",
" df_sensor['DOY'].astype(str)+\n",
" df_sensor['MST'].astype(str), \n",
" format='%Y%j%H:%M')\n",
" #convert to UTC time, website shows that they disregard daylight savings time\n",
" df_sensor['datetime'] = df_sensor['datetime'] + pd.Timedelta(hours=7) \n",
" df_sensor.set_index(['datetime'],inplace=True) #set created column as index ...\n",
" df_sensor = df_sensor.resample('H')# ... so that we can resample it (hourly)\n",
" return df_sensor \n",
"\n",
"return_sensor_data(desired_file, filefolder)\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##PV Output Data!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <thead>\n",
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" <th>Power</th>\n",
" </tr>\n",
" <tr>\n",
" <th>datetime</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-04-01 06:00:00</th>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
" <th>2014-04-01 11:00:00</th>\n",
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" <tr>\n",
" <th>2014-04-01 13:00:00</th>\n",
" <td>669.416667</td>\n",
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" <tr>\n",
" <th>2014-04-01 14:00:00</th>\n",
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" <tr>\n",
" <th>2014-04-01 15:00:00</th>\n",
" <td>6951.916667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 16:00:00</th>\n",
" <td>9926.916667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 17:00:00</th>\n",
" <td>3256.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 18:00:00</th>\n",
" <td>5681.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 19:00:00</th>\n",
" <td>10425.083333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 20:00:00</th>\n",
" <td>5369.083333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 21:00:00</th>\n",
" <td>6518.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 22:00:00</th>\n",
" <td>3878.416667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-01 23:00:00</th>\n",
" <td>2280.083333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-02 00:00:00</th>\n",
" <td>1003.083333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-04-02 01:00:00</th>\n",
" <td>71.250000</td>\n",
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"text/plain": [
" Power\n",
"datetime \n",
"2014-04-01 06:00:00 0.000000\n",
"2014-04-01 07:00:00 0.000000\n",
"2014-04-01 08:00:00 0.000000\n",
"2014-04-01 09:00:00 0.000000\n",
"2014-04-01 10:00:00 0.000000\n",
"2014-04-01 11:00:00 0.000000\n",
"2014-04-01 12:00:00 8.333333\n",
"2014-04-01 13:00:00 669.416667\n",
"2014-04-01 14:00:00 3238.666667\n",
"2014-04-01 15:00:00 6951.916667\n",
"2014-04-01 16:00:00 9926.916667\n",
"2014-04-01 17:00:00 3256.666667\n",
"2014-04-01 18:00:00 5681.250000\n",
"2014-04-01 19:00:00 10425.083333\n",
"2014-04-01 20:00:00 5369.083333\n",
"2014-04-01 21:00:00 6518.333333\n",
"2014-04-01 22:00:00 3878.416667\n",
"2014-04-01 23:00:00 2280.083333\n",
"2014-04-02 00:00:00 1003.083333\n",
"2014-04-02 01:00:00 71.250000\n",
"2014-04-02 02:00:00 0.000000\n",
"2014-04-02 03:00:00 0.000000\n",
"2014-04-02 04:00:00 0.000000\n",
"2014-04-02 05:00:00 0.000000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filefolder = 'data/pvoutput/pvoutput6months/'\n",
"desired_file = '20140401.csv'\n",
"\n",
"def return_pvoutput_data(desired_file, filefolder):\n",
" '''Input: Desired file and folder\n",
" Output: PV Output dataframe\n",
" Usage: filefolder = 'data/pvoutput/pvoutput6months/'\n",
" desired_file = '20140401.csv'\n",
" return_pvoutput_data(desired_file, filefolder)'''\n",
" df_output = pd.read_csv(filefolder + desired_file) #pvoutput\n",
" #df_output['datetime'] = df_output['datetime'] + pd.Timedelta(hours=6) #convert to utc time\n",
" df_output['datetime'] = df_output['datetime'].apply(pd.to_datetime)\n",
" df_output['datetime'] = df_output['datetime'] + pd.Timedelta(hours=6) #convert to utc time\n",
" df_output.set_index(['datetime'],inplace=True) #set created column as index ...\n",
" df_output = df_output.resample('H') # ...so that we can resample it (hourly)\n",
" return df_output[['Power']]\n",
"\n",
"return_pvoutput_data(desired_file, filefolder)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"##Moooor Satellite Data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%run data_helper_functions.py\n",
"from data_helper_functions import plot_satellite_image, return_satellite_data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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MjiVsAFGvXj3Z1w4AhUuLPD09eWtI4t69exgzZgxvDcn0799fiPnq48ePy3Yf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KUrQDcR8t8EJiPXaJmGnCnV9Nl/AbkA3HzkBXSvAk8hfz8mAR8A45Bv9g8gr8R/DFgJTEX+zsw2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\nlIqZFhBKKaWUitn/B2c7A9kuVwEmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10782e6d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"filename = 'data/satellite/colorado/summer6months/data/goes15.2014.091.110018.BAND_06.nc'\n",
"plot_satellite_image(filename)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x109256750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lons, lats, data = return_satellite_data(filename)\n",
"\n",
"plt.figure(figsize=(8, 8))\n",
"imgplot = plt.imshow(data)\n",
"imgplot.set_interpolation('none')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Find files based on channel and datetime for satellite data"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from datetime import datetime\n",
"from data_helper_functions import plot_satellite_image, \\\n",
" return_satellite_data, find_closest_date, find_file_details, \\\n",
" find_closest_date, find_desired_file"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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mZHhBH0ymmnIUu/YFwZQLNqSADQ4AgC/QlPpYVnb0HDEfG3jQGYUYZvsrS1kIIYSoTxRw\nhRBCiAxQwBVCCCEyQAFXCCGEyAAFXCGEECID1hpwzWy4mVWa2ds1bG3MbJSZTTez58zMbywphBBC\nCADfoCzIzPYAsATAnSml7aptVwNYkFK62szOBbBpSum8r+kSWpNj7xSc8DVi/0WgYY3N3+FlAWi2\niW+PGsJ3CHxFvnnPy0dSyUvXft+1F585iWpYqnzZ8j2p5oimD7v2KL2eNVYHgJmP+yn+6EIlwHBi\nP45LGvX0O8mvmsgb+lsVv577HfQv175FUMPyfipy7eOnB7UtZcQelbaMIHZeNYGgVzz3/TXQnEns\nQXUdriL26LH6lS052NCDqJyJDVDYOdAc4j+oAd1fp5IxswZSX6N8f0BASYeXqOYwPObao+b8bEgB\nGygCADPQg/oYB+Ep6mPDEL4ISvxYSU7roO4tOh4jKjNi+7A8KCVaVzpiE/zZjq59WVBK6VUAH33N\nPARrXiJGADh0vVYphBBCbOTU9jvcDimlyuqfKxG/9xNCCCEaPNHUym9ESimZmf853melNc5UAjQu\nWd/TCSGEEBsMc8umobIs12pwxlo+nq5twK00s44ppblm1gnAPPevmpfW8vBCCCHEhk/Hkl7oWJL7\njrojNsEbl/g5M0DtP1J+AsDQ6p+HAuTbfyGEEEIA+GZlQfcBeB1ALzP70MxOAHAlgH3NbDqAvap/\nF0IIIQShbqcFlZBjfx4I2VSga4PRKL26+vagcuOkW/z6iIerDqeaRdcEdS9sOss+XFLc2y//OQhP\nU83dVf44lTPzrqEaVjJwDa0DAXYFL48owBLX/iYGUE3z5E9nqcAWVDPpFlKjFU2bGsSv54493nft\n38OrVNMGi1z738edQTV9+r/p2if/MKhTYeVwv+ESHM0f69E97nDtz63cj2oW3UOu7xuCNfiXAi2T\nAxCXOlUQe1RKyAjuPTZhKKiuQatDeK3Txwv8VgR9u7CaJWAPvOLao0k9zDcDPamGTbxpDv+eBIDp\npIwH4K8nTbGcatikrqgE6kN0c+0rg+k+84L83UriaxHsA1sf29NuaI6HbaCmBQkhhBD1iQKuEEII\nkQEKuEIIIUQGKOAKIYQQGaCAK4QQQmRA3WYpX0iO/UAgZBnMLAsY4I3S3TyxatiQAp5ohy1ffof6\nlqXmrn3uK1vyA17mmwc89zKVsAy9XphONYvhZ1BWRKmkwWXx7gX9fMfVfMPbLJ/l2hedEmR+DyPZ\ng61bcE2QAWu/8h/UhaddSDUsS7EreNb8cbPu8x1nBRcx6eHe6A5/gAMA9OjwHvX1Ix39Hxw31LUD\nAP7IXRQ2JyzKRD4g8LEkU37r8WER/qyKHGf55sLD51PJkrnBg6rwr/0j92ZTKYDOmO3a22IhP02Y\n/u2zAG1d+xx0pppl4PfY7Cpf1zSPl590I/dLlHFcRfoyzVjKhzEsmboZ9dGM+uC2LOzjXw9LZvrn\nKW4MlPcwZSkLIYQQ9YkCrhBCCJEBCrhCCCFEBijgCiGEEBmggCuEEEJkgAKuEEIIkQF1Wha076rH\nXd+okUO48CJifzBY556kHCVqXj6V2EcHmru5q/ux/gE/GL01F93vm4+77jYquecDv6yjY3fe8Lxp\n+sJfW99gbZdyF5jso0DTktijOVOs6uVPgYbPcABG+mbblF9bzab7wwsOb/UI1dx9z0m+YzKVAGOJ\n/eBAwwZ9AMDz5DGdP4Zr2g307T8PznMnsR8XaIoC383EvmOgYb7zA82BxB6VLAVr2Kb3eNe+LaZQ\nDSv/WY4mVPM6dnXt5eP68sW1JtfCxKBu8mPuQndij0o3O5I15K/kmrGN1/08rPQn0kUa5qvwzcVt\ngfKzVRYkhBBC1CsKuEIIIUQGKOAKIYQQGaCAK4QQQmSAAq4QQgiRAXWapfxe6uT6PqUpq8CY5GdK\nfmpcc/bUG1z7Db1+RjWn7nW77yjjWYWwzwLf9q75kKqHqGRh8htgvzY9SK9mgx8O5xKcQuzBw8Ev\nAh9LLCwKNH7COtAr0JCswkaH8ob+qz7jGZ44m2Q9BomSmETsFW9xTb5/LdBG/wDPgA0uhTa/8QdC\nAECvRv4wizeO2ZMfkF1b/lyOHIMDH4MvG2C37D2BppLY9w80HxD7qVzS8ZD/UB8bRDD+5d35AR8j\ndjLIAgAwjtj3DjRd19EO8IEwALCA2HvW4nhRxjE7nj/TIEdF4GPrjo7HZpSQOQ3F7YHyy5WlLIQQ\nQtQrCrhCCCFEBijgCiGEEBmggCuEEEJkgAKuEEIIkQFrDbhmNtzMKs3s7Rq2UjObaWYTqv9FHUiF\nEEKIBs9ay4LMbA/kWjjfmVLartp2MYBPU0rXBrr083S965uMPvR8rUlO/PG4i2o6kZzz63E61byd\n/NKN/4zrTTW0wTwA/N432zK+v+OX+lMAfpcuo5qRA4/wHRdTCfBTYg9mF6Aw8D27jDgquKbDtr69\nsjxYQ7FvX8LODwAtuCsRnb3ANX3J9ICJrF4AQCGptyBVSQAAdic9EWgODXz9iX07v2E+AKBrW98e\nlYjQhvBBeV1Uj9JtE99eEByOldFE5R5sCSWBZlPuGnDOy659zIzvcdEzZHgAGWoCIC5hY7CX2+ge\nrwh8rKF/sD+0fCwaHMAgJTkA+MATgD/eaB+Yb6JvLu4ElN+0HmVBKaVX4c+BCUZNCCGEEKIm6/Md\n7q/NbJKZDTOzqKRfCCGEaPBEH7pE3ATgD9U/X4rcFNITv/5HY0uf/vLnziU90bmEfEQohBBCfBeZ\nXwYsKAMALAy+aQJqGXBTSvNW/2xmtwF40vu7nUrZlGchhBBiI2Czktw/AG07AYvGXkL/tFYfKZtZ\nzSbJhwF4m/2tEEIIIb5ZlvJ9yOWYtUOuTfjFyOXy7QAgAXgfwMkppcqv6dIuyc/+3B/P0vMtTX46\n4p9/S9KAAWCab27zJO+SfmLeMNfeB5OppiyVUN/waaTr+UlUAmvp7/3OT71CNWOeIel+P+HnoQ3h\ng+TcMFuTzWOoqEUG7MwgS3kQ+Qri38F5sIi7+vnHO3GcP/wCAM5LV7j24t2CDvwsk5RkNgKArfKv\nhb+vHEo1d9mP+QEJo+f7w0EAYMVeJEO4IjhgEbFH2aJRpm1tsnDZfAA2oAAALif2mwNNlK3NBihE\nWbisiCLKyPZftuLzsGqEaHhBRAWxdww0RcQe7SnLro4eazQMgX3cy1/y+TXUyjcXFwHlZTxLea0f\nKaeUjnHMw9emE0IIIcQa1GlKCCGEyAAFXCGEECIDFHCFEEKIDFDAFUIIITJAAVcIIYTIgLWWBdX6\nwGbp3lWHuL6jT+Qd2fcc/pRrb5VYh3LgicOP9jX38ZzzY5ve69qbpuVUc/0F51MfriH2FSu4hnWz\nbxSUyjxBSmUCCRusEJZgtAx83Yl97Cdc05WUnJBqKgDAeN98ygN0ZgaGBN3+93/ZL7dKF/AltH3J\nL/+5rskZVHMF/Ovk3nQs1fTf913fsQdf2/jSbahvBbm2OmIO1byb/MEdB01+kS9i+3HMwTU7BFMc\nWNXSO1xCS0GiYSPHE3t0T0QlJ3nEHnUe8ucdAHPX0q7Io08tanyikpwFwYCQrmRACJ9Jw0uGoofK\namii5yEqR2MNiKPnnJXy/cA3F3cCyv+2HsMLhBBCCLH+KOAKIYQQGaCAK4QQQmSAAq4QQgiRAQq4\nQgghRAYo4AohhBAZUKdlQauudjOjccJZN1Ldnf/4hWs//cgrqWZs2oksgq/vtVmk3uKMIOc8cB0w\n4lHX/sykw7iIVToFU2XwW2KPJozQ6RpvcU1+UNbB9mFJcC11JE/GZlxiP/GP9+I5rAYDKOkcrOGX\nxP5fLknbknX35Od55GBf88PH+Hmu+sFprv284/7CRYO467MT/TUUTK3iolN8czo9uJE6EHuwbJwc\n+JYSe49Aw+6XBwINu/c+DzRRaRKZHkPPA/AyFTbdB+D3XmGgYY8pep3ZJ/CxUp5oig8rvakINGwf\noucoKnVilVPRCB+23+S5K+4MlN+isiAhhBCiXlHAFUIIITJAAVcIIYTIAAVcIYQQIgMUcIUQQogM\nqNMs5UZlfkbk5YN/Q3XXpLNc+/xem/OTRQ2rKWSoQEHQWD3KHmxO9vE10oEfAPJ39O1RM+3EBgRU\nBqKexP5ZoPGb9oc0I4MVAKCdb7at+fV3/Yt+NnLflTtTTRHep76FZBHbtSGDAwA03su3f/Igv07u\nb3SUa//FU3dSzc7f9wcrvPnP71ENhrDBAQDQxrWmvbfkkufJtbU1GTwBANOm+Pbe23INa2QP0Hke\nuz81ikq+SE1c+5gBg/l5WIZwbTNg1zGbFQDP6o0a+rPjRedha4tez6LXVJbVG+3dVGKP1l2b54id\nB+Avg1GGN3stLvLNxZ2A8puUpSyEEELUKwq4QgghRAYo4AohhBAZoIArhBBCZEAYcM2sm5m9ZGbv\nmNlkMzut2t7GzEaZ2XQze87Moq++hRBCiAbP2t7hrgBwRkqpN3KdW081s20AnAdgVEppKwAvVP8u\nhBBCCMI6lQWZ2WMAbqj+NzilVGlmHQGUpZS2/trfpl3SC+5xOmM2PccKknP+z7kHU03rdn6H8EUP\ndaEaXEbsk4OG/mge+FgX99FcYtu45vNW3kolV+b9znccEpQzPf6kb+/K9xQzy7mPlROZX4oCAC0/\n8Us33itgufrAZqP8uomT9v0r1QwvPJX6LlxyoWu/rFFfqoEd6dv/G9QmbO6XsCRWSwAAxdu55l2m\nllHJG41K+PGKyMCBQ7kEzxB7bUpOokb2UVkHKznZNNC8RuwrglK5rcn9Gq2bzEgJiUr8osEGDLY/\n0XlY0/7oeYj2oQ+xL+SS4hGTXHv5X4J7bxqxj+WS8DGxPYqGFzAfGfxQXAyUl38LZUFmVgSgH3IR\npENKafXVXAkebYQQQgiBbxhwzawQwMMATk8pfVrTl3Jvkeume4YQQgixkRC9mQYAmFlj5ILtXSml\n1RM9K82sY0pprpl1AjDP035YOuLLnzcp6YtWJTt8C0sWQgghNhTKqv8BC4OP1YG1BFwzMwDDAExJ\nKV1fw/UEgKEArqr+rztau1vp0G+0XCGEEOK7SUn1P6BtW2DRokvoX67tHe5uAI4H8JaZTai2nQ/g\nSgAPmtmJACoAkMwSIYQQQgB1PLyAZSkvQwuq65Y+dO3/zPsRPxlpjI8CLkFF4GNEGZ5Xk308lmSL\nAjQT0K7hz0mHA/3m/HM/6MbPk+8PkcCVLMUUwI3BdfEL/zHZYq559N4DXPu2ec9RzRhiP3Yk39Mx\n+/vZvgAwKP9F35F4djVaknP5swZy9CNZ1LY91/xfiW//5dNUYkHX9ZVP+8fLO2AYXwPIY7UiLtmf\nDAiIsmajTFJ2L0ewbN/oGyz2ViPKzo3enrDBBtE+sOzvSDO5Fhp2m7NsYyB+jljWM8sWB3iBR9NA\nwwYoREMXouuHPUfRUAr2WMkAjuL2QPnlGl4ghBBC1CsKuEIIIUQGKOAKIYQQGaCAK4QQQmSAAq4Q\nQgiRAQq4QgghRAbUaVkQzvCP3feaf1PdpFMH+Y4fBifbl7X3CIYNtCOlSVHK+czAx2YHRNvLUu9n\nlQUif+AB8oN21itXEMfrXPMIKfcAgBG+2Yw/2LmPtHLtF65iUySAW4tO8x3+DAIAwCW/5L7f/8P/\n/8u8w0nZFACDX6a2cmAR1eSN/oR4WE0HgsEP73LN5UP44Yb5z0Wa8Qg/3rWH+/aRXFKr0hZSUgEA\nWEDsUbkOu2ejcg82MyPSREMc2PqiUiJ2//NqL76GaBACa/ZfFGii/a4g9mjdbB9qM2wgqGYM94Ht\nXVRKxM5F1l3cCSi/SWVBQgghRL2igCuEEEJkgAKuEEIIkQEKuEIIIUQGKOAKIYQQGVC3WcpHkGNX\nBsLdiH1aoDmW2PmUJGB3Yo+y8+4OfIwoA45la0bZkMxXEWgml/v2YK7CNqs+5U7ytE49th+VXPpA\nnmv/3Q9WUY2tICd6Nnhi00XU9ab5a+h3M///zrzr/Azmosk8e/iDIj9t9k8Vv6Kas4pucO2nfHAd\n1RyKx6lv/6tfdu0/O/tGqhl+4am+I8okZbDG8wAwKfDtTezRPcEySaNMaXafR1UK0WNa62TxdVhD\nlMXNMoFZIqa3AAAMbUlEQVSnBpoiYo8ysmtDVMXBhgDUJhs62uvoeOx6YGuLIGtQlrIQQgixAaCA\nK4QQQmSAAq4QQgiRAQq4QgghRAYo4AohhBAZoIArhBBCZEDdlgWRxu/ID/KwV75GHKRpPwDYZ749\nsab9ALAJsc8KNJGPDENAMATA3nLNv135JJVc25Z07l9MSn8AoLjYNW8zbTyVnIKbqO+0obf6jjl8\nCbY3aaZ/bVCbdC+x3x9cs8MW8TWwXH5jwwYAwB8KUfUI76A+/hD/Wn0du1LNaZv4e2q78Mf6o2fv\npL4Hew31HTtRCRr/1d+HFZexewXAB77ZzuTrvmTXc6jvopF/8h3j+BIwkNh/UgtNVLLYO/CxyyEq\nU2GN9qMyldqUQNWmJCcq8anNAAVWzhStm52HDbgA4sfEytuCmSLhgAeH4i5A+QiVBQkhhBD1igKu\nEEIIkQEKuEIIIUQGKOAKIYQQGRAGXDPrZmYvmdk7ZjbZzE6rtpea2Uwzm1D974BsliuEEEJ8N1lb\ny+0VAM5IKU00s0IA48xsFHLt669NKV1b5ysUQgghNgLWqSzIzB4DcANyM32WpJSuCf42oQ85dt/g\nJPcwR5SvX0HsUbkHy0dvGWi6BD4yruP7B3LJU1N8+63bUon93t/Td2cWUc02Qytc+zN38ZKleWhP\nfc8k/wONyehDNZPuHOQ7gukspwzw/3/uxmFnUc2IE4+kvhM+uMt3nNeYL+IB32wW3DdkG2wq16z4\npb+GXa59kWrG5HemPkN31373yqOo5riej/qOf1AJwIZKBROB7E6+D4PGveTaf5Oup5qjXnnCdwQD\nr6zQX8PK2fw9SN4Ef3IUAGBHYn+YS+jbndpMwykKNLUpr4nWwMp/omlKrDRph0DD1n1HoOHVenwK\nU22mM/3bN39rZUFmVgSgX41T/drMJpnZMDOLhmcJIYQQDZ5vFHCrP05+CMDpKaUlAG4CsAVy/38y\nBwB9pyuEEEKIbzA22cwaI/fByN0ppccAIKU0r4b/NgB+a6TK0jU/F5QAhSXrsVQhhBBiA2NRGfBR\nGQBg4fz4T8OAa2YGYBiAKSmt+RLFzDqllFY38jsMwNvuATqUfqP1CiGEEN9J2pTk/gFo2wVYNOkS\n+qdre4e7G4DjAbxlZhOqbRcAOMbMdkAuW/l9ACev34qFEEKIjZs6HV4wtsrPtu1/0LtU98hTflbv\nEb1G8pPNWOaaD1j5DJU8s+NhvmPiv/h50CbwMchgBQAAyY615lzS0x9E0G3adCr5MG8r/zSH8uf+\ngYeHUN8RfclzEWUIdvLN6Xo+vGCXLfwM3TeHfY+fZwZ3pf3IufYM7gHzr9WOK/mDnZvHsln5EADD\npr6jEcl+B/CXlX+jvtFpgGu/N98fxgAACf19x5lBN33/ckThcfyztaNakNRvAH/ARa69a9FCqnnm\nAz/b/ue4hWrOSVe59l8PHUY1fxrxa+orND8l+v7Es8JfuuX7vmMuldAs5Z9dfSOVDP/Dqb5jn+A8\n/LLj64sGB7AM5mCYBvVF2dVsQAHAs6uj/WZvSckaitsD5X/Q8AIhhBCiXlHAFUIIITJAAVcIIYTI\nAAVcIYQQIgMUcIUQQogMUMAVQgghMqBOy4LmJb/z88LUluputxNce/PEy2suOeNK3/FXXkoAtoae\nXIJLAx+rEgma8xd28UsnlmyyPDgR2YfGpD4DAFawDuq83MNsRbAGf/CDPc3Ldary83zH2fwsq/r6\n/z+Y93LQRL4pd2Gpb7b9+T1w683Hu/b/19i3A8BmK3u79m7pv1QzfuDuviMoc7DmfN1pLOncnyr4\nAVnZ2z5BWRC7VKP7KCgFaX6cf89+dh9/zbAv/H3IP45PLygo9C+Gvk341IXW+Ij6nhh8tGtPx/Oy\nN/Qg9mDgQfGN/vreG7891ezc/xXXPmYKH16Ccu7C5cROqo8AAOzpeyHQsH2YGWgOD3xstsrkWmhI\nCVRxO6D8PJUFCSGEEPWKAq4QQgiRAQq4QgghRAYo4AohhBAZoIArhBBCZECdZilXLfAz9PLb+1mu\nAHB71TGufQDGUM0huTG9/8MV6QKqOSLvH8QTDBvoxzMlcTWx71fJNSwTeFKQFcoahPtbkIM1+x4W\nZSIH+9CHNOF/h0sM5DrrGyyBZAimxUHm59jgeH7SPLADl9i//HWTJMQc5Ck3G0Ult6+8zbVvnXgX\n+UF5/nCHHMG1ymhN7MHlSBvCdww046JFkMqCrsHjIbdyYW8+QGFJj81ce5MpH1PNs233p76/JX+w\nwdOf+MNYAOCqVue69ucSP89k8zPgbw4GtlWgyLX/auENVLNiPh+0ATbfoR2X0EzghwINCxN+QniO\naLABuyYnBppZxE6yoTW8QAghhNgAUMAVQgghMiCzgFv2Wt18dP2dIkWzdhsOKZXV9xI2CFLiTRYa\nDLoWAADlZbPrewkbBlVl9b2COiWzgPuyYg2A1+t7ARsIZfW9gA0DBVzoWshRXjanvpewYbCqrL5X\nUKfoI2UhhBAiA/Lr9Oh5/df83Gg2kNcZANC/P/l7AG2whWtvhmVU0xvNXXtr2qwU6N+fZZmSnr8A\n0Iu70JKdaM0Wz57dCJ0719hytgTWlxkAS/ZFl0DTgq0t0ET/L8a2NehjXPOhzp4NdO5c/ctWwRKK\nfHPiLXJ5b1+A74N/yQEA7BOyhugbEvJcmH31Ipk9qyk6d8nZ2HXfgl4kQP/+wbVaG9g13D7QsKz5\nKEm6xt595VoAQO+/aA3keW3RiL+8LSOthxvn8eu+MHgB2JL0od6hEX+ONsPmX/5cgPe+/L0HTRcH\n2ItDy+BGaofOrr1f8FhXRj3J2WtNtGx2nfT76q+zPwQ6d1u9CKIpCs4TrYFdk9FrBtN0882bt4nb\nUNdpWVCdHFgIIYTYgGFlQXUWcIUQQgixBn2HK4QQQmSAAq4QQgiRAXUecM3sADObamblZub3MtsI\nMbPhZlZpZm/XsLUxs1FmNt3MnjOz6Cv+7zxm1s3MXjKzd8xsspmdVm1vaPvQzMxGm9lEM5tiZldU\n2xvUPqzGzPLMbIKZPVn9e4PaBzOrMLO3qvdgTLWtQe0BAJhZazN7yMzerb4vBm7s+1CnAdfM8gDc\nAOAAANsCOMbMtqnLc25A3I7c467JeQBGpZS2AvBC9e8bMysAnJFS6g1gEIBTq5//BrUPKaXPAeyZ\nUtoBwPYA9jSz3dHA9qEGpwOYgjX5yg1tHxKAkpRSv5TSgGpbQ9sDAPgLgKdSStsgd19MxUa+D3X9\nDncAgPdSShUppRUA7gdwSB2fc4MgpfQqgI++Zh4CYET1zyMAHJrpojImpTQ3pTSx+uclAN5Frqig\nQe0DAKSUVte1NUGu9uUjNMB9MLOuAA4CcBvWVIs1uH3A/xYFNqg9MLNWAPZIKQ0HgJTSypTSx9jI\n96GuA24XAB/W+H0m4orRjZ0OKaXVs2QqAXSoz8VkiZkVIVd1NxoNcB/MrJGZTUTu8b6UUnoHDXAf\nAFwH4GwAq2rYGto+JADPm9lYMzup2tbQ9mALAPPN7HYzG29mt5pZATbyfajrgKuaI0LK1WM1iP0x\ns0IADwM4PaWvtq1oKPuQUlpV/ZFyVwDfM7M9v+bf6PfBzH4AYF5KaQJI25eGsA8Adksp9QNwIHJf\ns+xR09lA9iAfudY7/5dS6g9gKb728fHGuA91HXBn4as9Oboh9y63oVJpZh0BwMw6AZhXz+upc8ys\nMXLB9q6Uvhxc3OD2YTXVH5uNBLAjGt4+7ApgiJm9D+A+AHuZ2V1oYPuQUppT/d/5AB5F7qu3BrUH\nyMWBmSmlN6t/fwi5ADx3Y96Hug64YwEUm1mRmTUBcBSAJ+r4nBsyTwAYWv3zUMRj47/zmJkhN656\nSkrp+hquhrYP7VZnW5pZcwD7ApiABrYPKaULUkrdUkpbIDdG/MWU0o/RgPbBzFpYdY/P6o9Q9wPw\nNhrQHgC5/A4AH5rZ6p6U+wB4B8CT2Ij3oc47TZnZgQCuRy5RZFhK6Yo6PeEGgpndB2AwgHbIfRdx\nEYDHATwIYHMAFQCOTCktrq811jXVmbivAHgLaz4aOh/AGDSsfdgOuQSQRtX/7kop/cnM2qAB7UNN\nzGwwgDNTSkMa0j6Y2RbIvasFch+r3pNSuqIh7cFqzKwvcslzTQDMAHACcnFio90HtXYUQgghMkCd\npoQQQogMUMAVQgghMkABVwghhMgABVwhhBAiAxRwhRBCiAxQwBVCCCEyQAFXCCGEyAAFXCGEECID\n/j9dgB/zv2VPIwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107764bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"filefolder = \"data/satellite/colorado/summer6months/data/\"\n",
"desired_datetime = datetime(2014, 5, 5, 22) #year, month, day [, hour, minute, second]\n",
"desired_channel = 'BAND_01' #channel\n",
"\n",
"sat_image_file = filefolder+find_desired_file(desired_datetime, desired_channel, filefolder)\n",
"\n",
"lons, lats, data = return_satellite_data(sat_image_file)\n",
"plt.figure(figsize=(8, 8))\n",
"imgplot = plt.imshow(data)\n",
"imgplot.set_interpolation('none')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Get correct file for Sensor Data and PV Output!\n",
"Now we have to do something similar to what we did for satellite data"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'20140505.csv'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"from datetime import datetime\n",
"\n",
"def find_file_from_date(desired_datetime, filefolder, filetype = 'csv'):\n",
" '''Input: datetime, folder, filetype; Output: file'''\n",
" data_files = os.listdir(filefolder)\n",
"\n",
" list_of_files = [] #contains all the files\n",
" list_of_files_details = [] #contains information collected from filename\n",
" for myfile in data_files:\n",
" if (myfile[-2:] == filetype or myfile[-3:] == filetype): #only get netCDF by default\n",
" list_of_files.append(myfile)\n",
" list_of_files_details.append(myfile.split('.'))\n",
"\n",
" for i,val in enumerate(list_of_files_details):\n",
" try: \n",
" if datetime.strptime(list_of_files_details[i][0], '%Y%m%d') == desired_datetime:\n",
" return list_of_files[i]\n",
" except:\n",
" pass\n",
" \n",
"filefolder = \"data/pvoutput/pvoutput6months/\"\n",
"desired_datetime = datetime(2014, 5, 5) #year, month, day [, hour, minute, second]\n",
"\n",
"find_file_from_date(desired_datetime, filefolder)"
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
pachi/eppy | eppy/readhtml.ipynb | 2 | 86680 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from bs4 import BeautifulSoup, NavigableString, Tag\n",
"fname = \"resources/outputfiles/V_7_2/5ZoneCAVtoVAVWarmestTempFlowTable_ABUPS.html\"\n",
"soup = BeautifulSoup(open(fname, 'r'))\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"btables = soup.find_all(['p', 'table', 'hr'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"atables = soup.hr.find_all(['p', 'table', 'hr'])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i, t in enumerate(btables):\n",
" if t.name == 'hr':\n",
" for j in t:\n",
"# print j.name\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ctables = soup.hr.find_all()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for child in soup.body:\n",
" print child.name\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NavigableString' object has no attribute 'name'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-d2a9d01e62cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msoup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/santosh/.virtualenvs/eplus/lib/python2.7/site-packages/bs4/element.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m 665\u001b[0m raise AttributeError(\n\u001b[1;32m 666\u001b[0m \"'%s' object has no attribute '%s'\" % (\n\u001b[0;32m--> 667\u001b[0;31m self.__class__.__name__, attr))\n\u001b[0m\u001b[1;32m 668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0moutput_ready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"minimal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NavigableString' object has no attribute 'name'"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(soup)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 7,
"text": [
"4"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in soup:"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "unexpected EOF while parsing (<ipython-input-8-3856909d8bca>, line 1)",
"output_type": "pyerr",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-8-3856909d8bca>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m for i in soup:\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"soup.find_all('p')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 9,
"text": [
"[<p><a href=\"#toc\" style=\"float: right\">Table of Contents</a></p>,\n",
" <p>Program Version:<b>EnergyPlus-Windows-OMP-32 7.2.0.006, YMD=2013.01.28 16:38</b></p>,\n",
" <p>Tabular Output Report in Format: <b>HTML</b></p>,\n",
" <p>Building: <b>Building</b></p>,\n",
" <p>Environment: <b>San Francisco Intl Ap CA USA TMY3 WMO#=724940</b></p>,\n",
" <p>Simulation Timestamp: <b>2013-01-28\r\n",
" 16:38:08</b></p>,\n",
" <p><a href=\"#toc\" style=\"float: right\">Table of Contents</a></p>,\n",
" <p>Report:<b> Annual Building Utility Performance Summary</b></p>,\n",
" <p>For:<b> Entire Facility</b></p>,\n",
" <p>Timestamp: <b>2013-01-28\r\n",
" 16:38:08</b></p>,\n",
" <p><b>Table of Contents</b></p>]"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print soup.next"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p = soup.find_all('p')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p[0].children.next()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<a href=\"#toc\" style=\"float: right\">Table of Contents</a>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hr = soup.hr.children"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hr.next()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 14,
"text": [
"u'\\n'"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in hr:\n",
" print i.name"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NavigableString' object has no attribute 'name'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-6ec4fc9b09b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/santosh/.virtualenvs/eplus/lib/python2.7/site-packages/bs4/element.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m 665\u001b[0m raise AttributeError(\n\u001b[1;32m 666\u001b[0m \"'%s' object has no attribute '%s'\" % (\n\u001b[0;32m--> 667\u001b[0;31m self.__class__.__name__, attr))\n\u001b[0m\u001b[1;32m 668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0moutput_ready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"minimal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NavigableString' object has no attribute 'name'"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"p\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hr = soup.hr.children"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"br = soup.hr.br"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in soup.hr.descendants:\n",
" if i.name not in ( 'tr', 'td', None, 'br'):\n",
" if i.name == 'p':\n",
" print i"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NavigableString' object has no attribute 'name'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-cdd2bde62ff2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msoup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescendants\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0;34m'tr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'td'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'br'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'p'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/santosh/.virtualenvs/eplus/lib/python2.7/site-packages/bs4/element.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m 665\u001b[0m raise AttributeError(\n\u001b[1;32m 666\u001b[0m \"'%s' object has no attribute '%s'\" % (\n\u001b[0;32m--> 667\u001b[0;31m self.__class__.__name__, attr))\n\u001b[0m\u001b[1;32m 668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0moutput_ready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"minimal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NavigableString' object has no attribute 'name'"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pr = soup.table.next_siblings"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in pr:\n",
" print i.name"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NavigableString' object has no attribute 'name'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-20-9407ae26a156>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/santosh/.virtualenvs/eplus/lib/python2.7/site-packages/bs4/element.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m 665\u001b[0m raise AttributeError(\n\u001b[1;32m 666\u001b[0m \"'%s' object has no attribute '%s'\" % (\n\u001b[0;32m--> 667\u001b[0;31m self.__class__.__name__, attr))\n\u001b[0m\u001b[1;32m 668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0moutput_ready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"minimal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NavigableString' object has no attribute 'name'"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print pr.previous_sibling"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'generator' object has no attribute 'previous_sibling'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-9735192ee4f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprevious_sibling\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'generator' object has no attribute 'previous_sibling'"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pr = soup.table.previous_elements"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in pr:\n",
" try:\n",
" name = i.name\n",
" except AttributeError, e:\n",
" continue \n",
" if i.name not in ('br', None):\n",
" if i.name == 'hr':\n",
" break\n",
" print i"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<b>Site and Source Energy</b>\n",
"<b></b>\n",
"<b>Values gathered over 8760.00 hours</b>\n",
"<b>2013-01-28\r\n",
" 16:38:08</b>\n",
"<p>Timestamp: <b>2013-01-28\r\n",
" 16:38:08</b></p>\n",
"<b> Entire Facility</b>\n",
"<p>For:<b> Entire Facility</b></p>\n",
"<b> Annual Building Utility Performance Summary</b>\n",
"<p>Report:<b> Annual Building Utility Performance Summary</b></p>\n",
"<a name=\"AnnualBuildingUtilityPerformanceSummary::EntireFacility\"></a>\n",
"<a href=\"#toc\" style=\"float: right\">Table of Contents</a>\n",
"<p><a href=\"#toc\" style=\"float: right\">Table of Contents</a></p>\n"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
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"input": [
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],
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"metadata": {},
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"prompt_number": 24
},
{
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"input": [
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" print el.name"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'unicode' object has no attribute 'name'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-25-e40149827360>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msoup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'table'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'unicode' object has no attribute 'name'"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
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"input": [
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],
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"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"e = soup.table.next_elements"
],
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"prompt_number": 27
},
{
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" try:\n",
" name = i.name\n",
" return True\n",
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" return False \n",
"\n",
"\n",
"pr = soup.table.previous_elements\n",
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" if not has_name(i):\n",
" continue\n",
" if i.name not in ('br', None):\n",
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" break\n",
" print i.name\n",
"e = soup.table.next_elements\n",
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" continue\n",
" if j.name == 'table':\n",
" print j.name\n",
" pr = j.previous_elements\n",
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" if not has_name(i):\n",
" continue\n",
" if i.name not in ('br', None):\n",
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" break\n",
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],
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{
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" continue\n",
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" beforetable.append(i.name)\n",
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" tabletup.append(beforetable)\n",
" tabletup.append(j.name)\n",
" if tabletup:\n",
" all.append(tabletup)\n",
" \n",
" "
],
"language": "python",
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{
"output_type": "stream",
"stream": "stdout",
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{
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" try:\n",
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" if not has_name(i):\n",
" continue\n",
" if i.name not in ('br', None):\n",
" if i.name in ('table', 'hr', 'tr', 'td'):\n",
" break\n",
" print i.name\n",
" beforetable.append(i)\n",
" beforetable.reverse()\n",
" tabletup.append(beforetable)\n",
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"language": "python",
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{
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"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Table of Contents\n",
"Report: Annual Building Utility Performance Summary\n",
"For: Entire Facility\n",
"Timestamp: 2013-01-28\r\n",
" 16:38:08\n"
]
}
],
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{
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{
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" continue\n",
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" tabletup.append(beforetable)\n",
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{
"output_type": "stream",
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"text": [
"Table of Contents\n",
"\n",
"Report: Annual Building Utility Performance Summary\n",
"For: Entire Facility\n",
"Timestamp: 2013-01-28\r\n",
" 16:38:08\n",
"Values gathered over 8760.00 hours\n",
"\n",
"Site and Source Energy\n",
"- Table -----\n",
"Site to Source Energy Conversion Factors\n",
"- Table -----\n",
"Building Area\n",
"- Table -----\n",
"End Uses\n",
"- Table -----\n",
"Note: Natural gas appears to be the principal heating source based on energy usage.\n",
"End Uses By Subcategory\n",
"- Table -----\n",
"Normalized Metrics\n",
"Utility Use Per Conditioned Floor Area\n",
"- Table -----\n",
"Utility Use Per Total Floor Area\n",
"- Table -----\n",
"Electric Loads Satisfied\n",
"- Table -----\n",
"On-Site Thermal Sources\n",
"- Table -----\n",
"Water Source Summary\n",
"- Table -----\n",
"Comfort and Setpoint Not Met Summary\n",
"- Table -----\n",
"- Table -----\n"
]
}
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{
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"<tr><td></td>\n",
"<td align=\"right\">ELECTRICITYNET:FACILITY [J]</td>\n",
"<td align=\"right\">ELECTRICITYNET:FACILITY {Maximum}[W]</td>\n",
"<td align=\"right\">ELECTRICITYNET:FACILITY {TIMESTAMP}</td>\n",
"<td align=\"right\">INTERIORLIGHTS:ELECTRICITY {AT MAX/MIN} [W]</td>\n",
"<td align=\"right\">INTERIOREQUIPMENT:ELECTRICITY {AT MAX/MIN} [W]</td>\n",
"<td align=\"right\">FANS:ELECTRICITY {AT MAX/MIN} [W]</td>\n",
"<td align=\"right\">HEATING:ELECTRICITY {AT MAX/MIN} [W]</td>\n",
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"<td align=\"right\">GENERATORS:ELECTRICITY [Invalid/Undefined]</td>\n",
"<td align=\"right\">ELECTRICITYPRODUCED:FACILITY [Invalid/Undefined]</td>\n",
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"<td align=\"right\">January</td>\n",
"<td align=\"right\">0.171128E+12</td>\n",
"<td align=\"right\"> 157049.426</td>\n",
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"<td align=\"right\">February</td>\n",
"<td align=\"right\">0.160835E+12</td>\n",
"<td align=\"right\"> 193039.840</td>\n",
"<td align=\"right\">03-FEB-13:00</td>\n",
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"<td align=\"right\">0.212043E+12</td>\n",
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"<tr>\n",
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"<td align=\"right\">0.181469E+12</td>\n",
"<td align=\"right\"> 201248.208</td>\n",
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"<td align=\"right\"> 13769.302</td>\n",
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"<td align=\"right\"> 61507.074</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 17.607</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
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"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
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"<tr>\n",
"<td align=\"right\">May</td>\n",
"<td align=\"right\">0.203926E+12</td>\n",
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"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">July</td>\n",
"<td align=\"right\">0.230901E+12</td>\n",
"<td align=\"right\"> 235258.279</td>\n",
"<td align=\"right\">12-JUL-15:15</td>\n",
"<td align=\"right\"> 71973.843</td>\n",
"<td align=\"right\"> 53980.383</td>\n",
"<td align=\"right\"> 14525.904</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 94771.056</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 7.093</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">August</td>\n",
"<td align=\"right\">0.255759E+12</td>\n",
"<td align=\"right\"> 228777.354</td>\n",
"<td align=\"right\">30-AUG-14:00</td>\n",
"<td align=\"right\"> 71973.843</td>\n",
"<td align=\"right\"> 53980.383</td>\n",
"<td align=\"right\"> 15823.009</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 86987.338</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 12.781</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">September</td>\n",
"<td align=\"right\">0.245803E+12</td>\n",
"<td align=\"right\"> 246369.600</td>\n",
"<td align=\"right\">29-SEP-14:30</td>\n",
"<td align=\"right\"> 71973.843</td>\n",
"<td align=\"right\"> 53980.383</td>\n",
"<td align=\"right\"> 21705.841</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 98703.816</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 5.717</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">October</td>\n",
"<td align=\"right\">0.215025E+12</td>\n",
"<td align=\"right\"> 216010.129</td>\n",
"<td align=\"right\">10-OCT-14:00</td>\n",
"<td align=\"right\"> 71973.843</td>\n",
"<td align=\"right\"> 53980.383</td>\n",
"<td align=\"right\"> 18295.672</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 71743.938</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 16.292</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">November</td>\n",
"<td align=\"right\">0.179267E+12</td>\n",
"<td align=\"right\"> 197527.989</td>\n",
"<td align=\"right\">07-NOV-13:00</td>\n",
"<td align=\"right\"> 71973.843</td>\n",
"<td align=\"right\"> 53980.383</td>\n",
"<td align=\"right\"> 14517.081</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 57006.712</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 49.971</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">December</td>\n",
"<td align=\"right\">0.172032E+12</td>\n",
"<td align=\"right\"> 191824.399</td>\n",
"<td align=\"right\">13-DEC-13:30</td>\n",
"<td align=\"right\"> 71973.843</td>\n",
"<td align=\"right\"> 53980.383</td>\n",
"<td align=\"right\"> 12761.796</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 53013.048</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 95.330</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Annual Sum or Average</td>\n",
"<td align=\"right\">0.246113E+13</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Minimum of Months</td>\n",
"<td align=\"right\">0.160835E+12</td>\n",
"<td align=\"right\"> 157049.426</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\"> 71973.843</td>\n",
"<td align=\"right\"> 53980.383</td>\n",
"<td align=\"right\"> 11549.438</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 19536.328</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 5.717</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Maximum of Months</td>\n",
"<td align=\"right\">0.255759E+12</td>\n",
"<td align=\"right\"> 246369.600</td>\n",
"<td align=\"right\">\u00a0</td>\n",
"<td align=\"right\"> 71973.843</td>\n",
"<td align=\"right\"> 53980.383</td>\n",
"<td align=\"right\"> 21705.841</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 98703.816</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 121.467</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"<td align=\"right\"> 0.000</td>\n",
"</tr>\n",
"</table>)"
],
"output_type": "pyout",
"prompt_number": 106,
"text": [
"<IPython.core.display.HTML at 0x108ad5650>"
]
}
],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 104,
"text": [
"2"
]
}
],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pr = soup.table.previous_elements\n",
"for i in pr:\n",
" if i.name not in ('br', None):\n",
" if i.name == 'hr':\n",
" break\n",
" print i.get_text()\n",
"e = soup.table.next_elements\n",
"for j in e:\n",
" if j.name == 'table':\n",
" print j.name\n",
" pr = j.previous_elements\n",
" for i in pr:\n",
" if i.name not in ('br', None):\n",
" if i.name in ('table', 'hr', 'tr', 'td'):\n",
" break\n",
" print i.get_text()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NavigableString' object has no attribute 'name'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-69-aa34f52be7f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msoup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprevious_elements\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'br'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'hr'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/santosh/.virtualenvs/eplus/lib/python2.7/site-packages/bs4/element.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m 665\u001b[0m raise AttributeError(\n\u001b[1;32m 666\u001b[0m \"'%s' object has no attribute '%s'\" % (\n\u001b[0;32m--> 667\u001b[0;31m self.__class__.__name__, attr))\n\u001b[0m\u001b[1;32m 668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0moutput_ready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"minimal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NavigableString' object has no attribute 'name'"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pr = soup.table.previous_elements\n",
"for i in pr:\n",
" if i.name not in ('br', None):\n",
" if i.name == 'hr':\n",
" break\n",
" if i.name == 'p':\n",
" print i.get_text()\n",
"e = soup.table.next_elements\n",
"for j in e:\n",
" if j.name == 'table':\n",
" print j.name\n",
" pr = j.previous_elements\n",
" for i in pr:\n",
" if i.name not in ('br', None):\n",
" if i.name in ('table', 'hr', 'tr', 'td'):\n",
" break\n",
" print i.get_text()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NavigableString' object has no attribute 'name'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-30-faac7f6a7b01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msoup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprevious_elements\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'br'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'hr'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/santosh/.virtualenvs/eplus/lib/python2.7/site-packages/bs4/element.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m 665\u001b[0m raise AttributeError(\n\u001b[1;32m 666\u001b[0m \"'%s' object has no attribute '%s'\" % (\n\u001b[0;32m--> 667\u001b[0;31m self.__class__.__name__, attr))\n\u001b[0m\u001b[1;32m 668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0moutput_ready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"minimal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NavigableString' object has no attribute 'name'"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"soup.table.previousSibling?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import HTML"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print str(soup.table)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<table border=\"1\" cellpadding=\"4\" cellspacing=\"0\">\n",
"<tr><td></td>\n",
"<td align=\"right\">Total Energy [kWh]</td>\n",
"<td align=\"right\">Energy Per Total Building Area [kWh/m2]</td>\n",
"<td align=\"right\">Energy Per Conditioned Building Area [kWh/m2]</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Total Site Energy</td>\n",
"<td align=\"right\"> 47694.47</td>\n",
"<td align=\"right\"> 51.44</td>\n",
"<td align=\"right\"> 51.44</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Net Site Energy</td>\n",
"<td align=\"right\"> 47694.47</td>\n",
"<td align=\"right\"> 51.44</td>\n",
"<td align=\"right\"> 51.44</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Total Source Energy</td>\n",
"<td align=\"right\"> 140159.10</td>\n",
"<td align=\"right\"> 151.16</td>\n",
"<td align=\"right\"> 151.16</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Net Source Energy</td>\n",
"<td align=\"right\"> 140159.10</td>\n",
"<td align=\"right\"> 151.16</td>\n",
"<td align=\"right\"> 151.16</td>\n",
"</tr>\n",
"</table>\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"HTML(str(soup.table))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table border=\"1\" cellpadding=\"4\" cellspacing=\"0\">\n",
"<tr><td></td>\n",
"<td align=\"right\">Total Energy [kWh]</td>\n",
"<td align=\"right\">Energy Per Total Building Area [kWh/m2]</td>\n",
"<td align=\"right\">Energy Per Conditioned Building Area [kWh/m2]</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Total Site Energy</td>\n",
"<td align=\"right\"> 47694.47</td>\n",
"<td align=\"right\"> 51.44</td>\n",
"<td align=\"right\"> 51.44</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Net Site Energy</td>\n",
"<td align=\"right\"> 47694.47</td>\n",
"<td align=\"right\"> 51.44</td>\n",
"<td align=\"right\"> 51.44</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Total Source Energy</td>\n",
"<td align=\"right\"> 140159.10</td>\n",
"<td align=\"right\"> 151.16</td>\n",
"<td align=\"right\"> 151.16</td>\n",
"</tr>\n",
"<tr>\n",
"<td align=\"right\">Net Source Energy</td>\n",
"<td align=\"right\"> 140159.10</td>\n",
"<td align=\"right\"> 151.16</td>\n",
"<td align=\"right\"> 151.16</td>\n",
"</tr>\n",
"</table>"
],
"output_type": "pyout",
"prompt_number": 34,
"text": [
"<IPython.core.display.HTML at 0x107ebf850>"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for t in soup.table.next_elements:\n",
" if t.name == 'table':\n",
" HTML(str(t))\n",
" break"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NavigableString' object has no attribute 'name'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-35-ec63312f6647>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msoup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext_elements\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'table'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/santosh/.virtualenvs/eplus/lib/python2.7/site-packages/bs4/element.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m 665\u001b[0m raise AttributeError(\n\u001b[1;32m 666\u001b[0m \"'%s' object has no attribute '%s'\" % (\n\u001b[0;32m--> 667\u001b[0;31m self.__class__.__name__, attr))\n\u001b[0m\u001b[1;32m 668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0moutput_ready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"minimal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NavigableString' object has no attribute 'name'"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"HTML(str(t))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n"
],
"output_type": "pyout",
"prompt_number": 36,
"text": [
"<IPython.core.display.HTML at 0x107e76790>"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ts = soup.table.next_elements"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l = [str(i) for i in ts if i.name == 'table']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NavigableString' object has no attribute 'name'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-38-c033ef8b668c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mts\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'table'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/santosh/.virtualenvs/eplus/lib/python2.7/site-packages/bs4/element.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr)\u001b[0m\n\u001b[1;32m 665\u001b[0m raise AttributeError(\n\u001b[1;32m 666\u001b[0m \"'%s' object has no attribute '%s'\" % (\n\u001b[0;32m--> 667\u001b[0;31m self.__class__.__name__, attr))\n\u001b[0m\u001b[1;32m 668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 669\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0moutput_ready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"minimal\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NavigableString' object has no attribute 'name'"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"HTML(l[-1])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'l' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-39-c31a7a062543>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'l' is not defined"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
haru110jp/StochEvolution | kmr_compute_stationary_ex.ipynb | 1 | 4654 | {
"metadata": {
"name": "",
"signature": "sha256:4962fb533ac9517c2da8205dfa31a340c3e4f11ee0dc38ca335f64257c07fec7"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from kmr_stationary import kmr_compute_stationary"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = kmr_compute_stationary(n=4, payoffs=[[4,0], [3,2]], epsilon=0.01) # payoffs = stug hunt game\n",
"\"\"\"\n",
"This shows\n",
"1 states\n",
"2 transition matrix\n",
"3 stationary distribution\n",
"\"\"\"\n",
"print a"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['10000', '01000', '00100', '00010', '00001']\n",
"[[ 0.995 0.005 0. 0. 0. ]\n",
" [ 0.24875 0.7475 0.00375 0. 0. ]\n",
" [ 0. 0.4975 0.5 0.0025 0. ]\n",
" [ 0. 0. 0.00375 0.7475 0.24875]\n",
" [ 0. 0. 0. 0.005 0.995 ]]\n",
"[ 9.75249963e-01 1.96030143e-02 1.47761415e-04 9.85076097e-05\n",
" 4.90075358e-03]\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = kmr_compute_stationary(n=8, payoffs=[[6,0,0], [5,7,5], [0,5,8]], epsilon=0.01) # payoffs = Young's game\n",
"\"\"\"\n",
"This function shows\n",
"1 states\n",
"2 transition matrix\n",
"3 stationary distribution\n",
"\"\"\"\n",
"print b"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['1100000000', '0110000000', '1010000000', '0101000000', '0011000000', '1001000000', '0100100000', '0010100000', '0001100000', '1000100000', '0100010000', '0010010000', '0001010000', '0000110000', '1000010000', '0100001000', '0010001000', '0001001000', '0000101000', '0000011000', '1000001000', '0100000100', '0010000100', '0001000100', '0000100100', '0000010100', '0000001100', '1000000100', '0100000010', '0010000010', '0001000010', '0000100010', '0000010010', '0000001010', '0000000110', '1000000010', '0100000001', '0010000001', '0001000001', '0000100001', '0000010001', '0000001001', '0000000101', '0000000011', '1000000001']\n",
"[[ 9.93333333e-01 3.33333333e-03 3.33333333e-03 ..., 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00]\n",
" [ 1.24166667e-01 8.69583333e-01 4.16666667e-04 ..., 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00]\n",
" [ 1.24166667e-01 4.16666667e-04 8.69583333e-01 ..., 0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00]\n",
" ..., \n",
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 8.69583333e-01\n",
" 1.24166667e-01 0.00000000e+00]\n",
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 3.33333333e-03\n",
" 9.93333333e-01 0.00000000e+00]\n",
" [ 0.00000000e+00 0.00000000e+00 0.00000000e+00 ..., 0.00000000e+00\n",
" 0.00000000e+00 9.93333333e-01]]\n",
"[ 9.23036581e-03 2.47753131e-04 2.47836979e-04 5.82842144e-06\n",
" 2.88439112e-06 2.92856587e-06 6.01803471e-08 6.49545325e-08\n",
" 7.26451041e-09 3.11008561e-08 2.02729359e-09 3.01941578e-09\n",
" 5.33792126e-09 1.74265198e-11 8.36641561e-09 3.54658088e-08\n",
" 4.77738280e-09 3.57317768e-09 2.35669913e-11 3.90239548e-14\n",
" 1.97603340e-06 5.96434107e-06 5.50332154e-08 2.39395704e-09\n",
" 2.19452057e-11 7.17088860e-14 8.67269588e-17 2.95949280e-04\n",
" 5.91933399e-04 5.96347518e-06 3.44449597e-08 1.26285020e-10\n",
" 2.96831938e-13 4.30642300e-16 2.60564185e-15 2.51990257e-02\n",
" 2.51990552e-02 2.95964860e-04 1.98672133e-06 8.33810009e-09\n",
" 2.24080174e-11 3.76357781e-14 2.84480778e-15 1.01514624e-13\n",
" 9.38664257e-01]\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
SJSlavin/phys202-2015-work | assignments/assignment12/FittingModelsEx02.ipynb | 1 | 30665 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Fitting Models Exercise 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.optimize as opt"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Fitting a decaying oscillation"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"For this problem you are given a raw dataset in the file `decay_osc.npz`. This file contains three arrays:\n",
"\n",
"* `tdata`: an array of time values\n",
"* `ydata`: an array of y values\n",
"* `dy`: the absolute uncertainties (standard deviations) in y\n",
"\n",
"Your job is to fit the following model to this data:\n",
"\n",
"$$ y(t) = A e^{-\\lambda t} \\cos{\\omega t + \\delta} $$\n",
"\n",
"First, import the data using NumPy and make an appropriately styled error bar plot of the raw data."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"data": {
"text/plain": [
"<Container object of 3 artists>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f13bf50ad68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# YOUR CODE HERE\n",
"data = np.load(\"decay_osc.npz\")\n",
"t = data[\"tdata\"]\n",
"y = data[\"ydata\"]\n",
"dy = data[\"dy\"]\n",
"\n",
"plt.errorbar(t, y, dy, fmt=\".b\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "8fe685c8222cc4b0b71fde4d0409d50f",
"grade": true,
"grade_id": "fittingmodelsex02a",
"points": 5
}
},
"outputs": [],
"source": [
"assert True # leave this to grade the data import and raw data plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Now, using `curve_fit` to fit this model and determine the estimates and uncertainties for the parameters:\n",
"\n",
"* Print the parameters estimates and uncertainties.\n",
"* Plot the raw and best fit model.\n",
"* You will likely have to pass an initial guess to `curve_fit` to get a good fit.\n",
"* Treat the uncertainties in $y$ as *absolute errors* by passing `absolute_sigma=True`. "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A = -4.89598759534 +- 0.00371734185465\n",
"lambda = 0.09366414726 +- 7.69678908713e-06\n",
"omega = -1.00111421585 +- 6.16411622718e-07\n",
"sigma = 0.0266129863316 +- 0.000188457359338\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f13bf1cc2b0>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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omt5o2E5RKATTp+f/XOAc7N8fli1DjXvfzrk5cqRJAHPmFFntThQvrmvJjh0m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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f13bf379ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# YOUR CODE HERE\n",
"def model(t, A, lambd, omega, sigma):\n",
" return A*np.exp(-lambd * t) * np.cos(omega*t) + sigma\n",
"\n",
"theta_best, theta_cov = opt.curve_fit(model, t, y, sigma=dy)\n",
"\n",
"print(\"A = \", theta_best[0], \" +- \", theta_cov[0,0])\n",
"print(\"lambda = \", theta_best[1], \" +- \", theta_cov[1,1])\n",
"print(\"omega = \", theta_best[2], \" +- \", theta_cov[2,2])\n",
"print(\"sigma = \", theta_best[3], \" +- \", theta_cov[3,3])\n",
"\n",
"fitline = model(t, theta_best[0], theta_best[1], theta_best[2], theta_best[3])\n",
"\n",
"plt.errorbar(t, y, dy, fmt=\".b\")\n",
"plt.plot(t, fitline, color=\"r\")\n",
"plt.xlabel(\"t\")\n",
"plt.ylabel(\"y\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "abacc1ad72e3412252e4ed47c8f65897",
"grade": true,
"grade_id": "fittingmodelsex02b",
"points": 5
}
},
"outputs": [],
"source": [
"assert True # leave this cell for grading the fit; should include a plot and printout of the parameters+errors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jlaura/isis3 | isis/notebooks/crop_crism_trdr.ipynb | 1 | 17615 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pvl\n",
"import struct\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import datetime\n",
"import os.path\n",
"import binascii"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"crism_file = '/home/arsanders/testData/crism/tsts/trdr/input/frt0001e5c3_07_if124s_trr3.lbl'\n",
"image_file = crism_file"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"header = pvl.load(crism_file)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PVLModule([\n",
" ('PDS_VERSION_ID', 'PDS3')\n",
" ('LABEL_REVISION_NOTE',\n",
" '2004-11-22, S. Slavney (GEO); 2005-12-20, H. Taylor (JHU/APL); 2006-04-05, '\n",
" 'S. Murchie (JHU/APL); 2006-09-18, P. Cavender (JHU/APL); 2007-02-19, P. '\n",
" 'Cavender (JHU/APL); Version 2, new stray light subtraction 2010-06-01, D. '\n",
" 'Humm (JHU/APL); Version 3, shutter mirror correction 2010-10-12, C. Hash '\n",
" '(ACT); Version 3, Added data filter control parameters')\n",
" ('DATA_SET_ID', 'MRO-M-CRISM-3-RDR-TARGETED-V1.0')\n",
" ('PRODUCT_ID', 'FRT0001E5C3_07_IF124S_TRR3')\n",
" ('INSTRUMENT_HOST_NAME', 'MARS RECONNAISSANCE ORBITER')\n",
" ('SPACECRAFT_ID', 'MRO')\n",
" ('INSTRUMENT_NAME', 'COMPACT RECONNAISSANCE IMAGING SPECTROMETER FOR MARS')\n",
" ('INSTRUMENT_ID', 'CRISM')\n",
" ('TARGET_NAME', 'MARS')\n",
" ('PRODUCT_TYPE', 'TARGETED_RDR')\n",
" ('PRODUCT_CREATION_TIME',\n",
" datetime.datetime(2011, 6, 8, 10, 52, 30, tzinfo=datetime.timezone.utc))\n",
" ('START_TIME',\n",
" datetime.datetime(2011, 6, 2, 4, 3, 9, 29000, tzinfo=datetime.timezone.utc))\n",
" ('STOP_TIME',\n",
" datetime.datetime(2011, 6, 2, 4, 5, 0, 763000, tzinfo=datetime.timezone.utc))\n",
" ('SPACECRAFT_CLOCK_START_COUNT', '10/0991454621.14521')\n",
" ('SPACECRAFT_CLOCK_STOP_COUNT', '10/0991454732.62633')\n",
" ('ORBIT_NUMBER', 'NULL')\n",
" ('OBSERVATION_TYPE', 'FRT')\n",
" ('OBSERVATION_ID', '16#0001E5C3#')\n",
" ('MRO:OBSERVATION_NUMBER', 7)\n",
" ('MRO:ACTIVITY_ID', 'IF124')\n",
" ('MRO:SENSOR_ID', 'S')\n",
" ('MRO:DETECTOR_TEMPERATURE', -53.663)\n",
" ('MRO:OPTICAL_BENCH_TEMPERATURE', -42.348)\n",
" ('MRO:SPECTROMETER_HOUSING_TEMP', -65.178)\n",
" ('MRO:SPHERE_TEMPERATURE', -42.484)\n",
" ('MRO:FPE_TEMPERATURE', 5.269)\n",
" ('PRODUCT_VERSION_ID', '3')\n",
" ('SOURCE_PRODUCT_ID',\n",
" frozenset({'CDR400991452611_SP0042501S_3',\n",
" 'CDR400991452639_BP0042500S_3',\n",
" 'CDR400991452639_BP1018400S_3',\n",
" 'CDR400991454611_BI1018400S_3',\n",
" 'CDR400991454611_UB1018400S_3',\n",
" 'CDR400991454737_BI1018400S_3',\n",
" 'CDR400991454737_UB1018400S_3',\n",
" 'CDR410000000000_SH0000001S_4',\n",
" 'CDR410000000000_SS0000001S_2',\n",
" 'CDR450924300802_DM0000000S_3',\n",
" 'CDR450924300802_NU1000001S_3',\n",
" 'CDR450924300802_SF0000000S_2',\n",
" 'CDR450924300802_TD0000000S_2',\n",
" 'CDR6_0_0991440032_ST_J_0',\n",
" 'CDR6_1_0000000000_AS_S_0',\n",
" 'CDR6_1_0000000000_DB_S_0',\n",
" 'CDR6_1_0000000000_EB_S_0',\n",
" 'CDR6_1_0000000000_GH_S_1',\n",
" 'CDR6_1_0000000000_HD_J_1',\n",
" 'CDR6_1_0000000000_HK_J_1',\n",
" 'CDR6_1_0000000000_HV_J_1',\n",
" 'CDR6_1_0000000000_LC_S_1',\n",
" 'CDR6_1_0000000000_LI_J_0',\n",
" 'CDR6_1_0000000000_VL_S_0',\n",
" 'CDR6_2_0835294537_PP_S_0',\n",
" 'FRT0001E5C3_07_SC124S_EDR0'}))\n",
" ('MRO:INVALID_PIXEL_LOCATION', frozenset())\n",
" ('PRODUCER_INSTITUTION_NAME',\n",
" 'JOHNS HOPKINS UNIVERSITY APPLIED PHYSICS LABORATORY')\n",
" ('SOFTWARE_NAME', 'crism_imagecal')\n",
" ('SOFTWARE_VERSION_ID', '2.5.3')\n",
" ('TARGET_CENTER_DISTANCE', Quantity(value='NULL', units='KM'))\n",
" ('SOLAR_DISTANCE', Quantity(value=213788591.232902, units='KM'))\n",
" ('SOLAR_LONGITUDE', 303.590051)\n",
" ('SHUTTER_MODE_ID', 'OPEN')\n",
" ('LIGHT_SOURCE_NAME', 'NONE')\n",
" ('MRO:CALIBRATION_LAMP_STATUS', 'OFF')\n",
" ('MRO:CALIBRATION_LAMP_LEVEL', 'N/A')\n",
" ('PIXEL_AVERAGING_WIDTH', 1)\n",
" ('MRO:INSTRUMENT_POINTING_MODE', 'DYNAMIC POINTING')\n",
" ('SCAN_MODE_ID', 'SHORT')\n",
" ('MRO:FRAME_RATE', Quantity(value=3.75, units='HZ'))\n",
" ('MRO:EXPOSURE_PARAMETER', 184)\n",
" ('SAMPLING_MODE_ID', 'HYPERSPEC')\n",
" ('COMPRESSION_TYPE', 'NONE')\n",
" ('MRO:WAVELENGTH_FILTER', '0')\n",
" ('MRO:WAVELENGTH_FILE_NAME', 'CDR450924300802_WA0000000S_2.IMG')\n",
" ('MRO:PIXEL_PROC_FILE_NAME', 'CDR6_2_0835294537_PP_S_0.TAB')\n",
" ('MRO:INV_LOOKUP_TABLE_FILE_NAME', 'CDR6_1_0000000000_LI_J_0.TAB')\n",
" ('MRO:ATMO_CORRECTION_FLAG', 'OFF')\n",
" ('MRO:THERMAL_CORRECTION_MODE', 'OFF')\n",
" ('MRO:PHOTOCLIN_CORRECTION_FLAG', 'OFF')\n",
" ('MRO:SPATIAL_RESAMPLING_FLAG', 'OFF')\n",
" ('MRO:SPATIAL_RESAMPLING_FILE', 'N/A')\n",
" ('MRO:SPATIAL_RESCALING_FLAG', 'OFF')\n",
" ('MRO:SPATIAL_RESCALING_FILE', 'N/A')\n",
" ('MRO:SPECTRAL_RESAMPLING_FLAG', 'OFF')\n",
" ('MRO:SPECTRAL_RESAMPLING_FILE', 'N/A')\n",
" ('MRO:HDF_SOFTWARE_NAME', 'crismhdf')\n",
" ('MRO:HDF_SOFTWARE_VERSION_ID', '1.0.5')\n",
" ('MRO:IF_MIN_VALUE', 0.0)\n",
" ('MRO:IF_MAX_VALUE', 1.0)\n",
" ('MRO:TRACE_MIN_VALUE', 0.01)\n",
" ('MRO:TRACE_MAX_VALUE', 100.0)\n",
" ('MRO:REFZ_MEDIAN_BOX_WIDTH', 15)\n",
" ('MRO:REFZ_SMOOTH_BOX_WIDTH', 5)\n",
" ('MRO:FRAM_STAT_MEDIAN_BOX_WIDTH', 5)\n",
" ('MRO:FRAM_STAT_MIN_DEVIATION', 0.005)\n",
" ('MRO:FRAM_STAT_MEDIAN_CONF_LVL', 0.999999)\n",
" ('MRO:FRAM_STAT_IQR_CONF_LVL', 0.999999)\n",
" ('MRO:RSC_REF_XY_MEDIAN_WIDTH', 15)\n",
" ('MRO:RSC_REF_XY_SMOOTH_WIDTH', 5)\n",
" ('MRO:RSC_REF_YZ_MEDIAN_WIDTH', 15)\n",
" ('MRO:RSC_REF_YZ_SMOOTH_WIDTH', 5)\n",
" ('MRO:RSC_RATIO_XY_MEDIAN_WIDTH', 25)\n",
" ('MRO:RSC_RATIO_XY_SMOOTH_WIDTH', 13)\n",
" ('MRO:RSC_RES_XY_PLY_ORDER', 5)\n",
" ('MRO:RSC_RES_XY_PLY_EXTND_WIDTH', 38)\n",
" ('MRO:LOG_XFORM_NEG_CLIP_VALUE', 'N/A')\n",
" ('MRO:IKF_NUM_REGIONS', 'N/A')\n",
" ('MRO:IKF_START_CHANNEL', ['N/A', 'N/A'])\n",
" ('MRO:IKF_STOP_CHANNEL', ['N/A', 'N/A'])\n",
" ('MRO:IKF_CONFIDENCE_LEVEL', ['N/A', 'N/A'])\n",
" ('MRO:IKF_WEIGHTING_STDDEV', ['N/A', 'N/A'])\n",
" ('MRO:IKF_KERNEL_SIZE_X', ['N/A', 'N/A'])\n",
" ('MRO:IKF_KERNEL_SIZE_Y', ['N/A', 'N/A'])\n",
" ('MRO:IKF_KERNEL_SIZE_Z', ['N/A', 'N/A'])\n",
" ('MRO:IKF_MODEL_ORDER_X', ['N/A', 'N/A'])\n",
" ('MRO:IKF_MODEL_ORDER_Y', ['N/A', 'N/A'])\n",
" ('MRO:IKF_MODEL_ORDER_Z', ['N/A', 'N/A'])\n",
" ('FILE',\n",
" {'FILE_RECORDS': 44941,\n",
" 'IMAGE': {'BANDS': 107,\n",
" 'BAND_STORAGE_TYPE': 'LINE_INTERLEAVED',\n",
" 'LINES': 420,\n",
" 'LINE_SAMPLES': 640,\n",
" 'SAMPLE_BITS': 32,\n",
" 'SAMPLE_TYPE': 'PC_REAL',\n",
" 'UNIT': 'I_OVER_F'},\n",
" 'RECORD_BYTES': 2560,\n",
" 'RECORD_TYPE': 'FIXED_LENGTH',\n",
" 'ROWNUM_TABLE': {'COLUMN': {'BIT_MASK': 511,\n",
" 'BYTES': 2,\n",
" 'COLUMN_NUMBER': 1,\n",
" 'DATA_TYPE': 'MSB_UNSIGNED_INTEGER',\n",
" 'DESCRIPTION': 'Detector row number from which '\n",
" 'the data was taken.',\n",
" 'NAME': 'DETECTOR_ROW_NUMBER',\n",
" 'START_BYTE': 1},\n",
" 'COLUMNS': 1,\n",
" 'DESCRIPTION': 'The detector is subsampled in the spectral '\n",
" 'direction by selecting specific rows to be '\n",
" 'downlinked. This table provides a list of '\n",
" 'the rows selected for all frames in this '\n",
" 'multidimensional image cube.',\n",
" 'INTERCHANGE_FORMAT': 'BINARY',\n",
" 'NAME': 'SELECTED ROWS FROM DETECTOR',\n",
" 'ROWS': 107,\n",
" 'ROW_BYTES': 2},\n",
" '^IMAGE': 'FRT0001E5C3_07_IF124S_TRR3.IMG',\n",
" '^ROWNUM_TABLE': ['FRT0001E5C3_07_IF124S_TRR3.IMG', 44941]})\n",
" ('FILE',\n",
" {'FILE_RECORDS': 420,\n",
" 'RECORD_BYTES': 1221,\n",
" 'RECORD_TYPE': 'FIXED_LENGTH',\n",
" 'TRDR_HK_TABLE': {'COLUMNS': 233,\n",
" 'INTERCHANGE_FORMAT': 'ASCII',\n",
" 'NAME': 'TARGETED RDR HOUSEKEEPING TABLE',\n",
" 'ROWS': 420,\n",
" 'ROW_BYTES': 1221,\n",
" '^STRUCTURE': 'TRDRHK.FMT'},\n",
" '^TRDR_HK_TABLE': 'FRT0001E5C3_07_RA124S_HKP3.TAB'})\n",
"])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"header"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(crism_file, 'rb') as f:\n",
" image_file = os.path.dirname(crism_file) + \"/\" + header['FILE'][\"^IMAGE\"].lower()\n",
" with open(image_file, 'rb') as im_f:\n",
" b_image_data = im_f.read()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_lines = 1\n",
"line_length = header['FILE']['IMAGE']['LINE_SAMPLES'] * (header['FILE']['IMAGE']['SAMPLE_BITS']//8)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def read_crism_trdr(b_image_data, line_length, n_lines, n_bands):\n",
" image_data = []\n",
" for j in range(n_lines*n_bands):\n",
" image_sample = np.frombuffer(b_image_data[j*line_length:(j+1)*line_length],\n",
" dtype=np.float32, count=int(line_length/4))\n",
" image_data.append(image_sample)\n",
" return np.array(image_data)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#\n",
"n_output_bands = 107\n",
"n_bands = header['FILE']['IMAGE']['BANDS']\n",
"image_data = read_crism_trdr(b_image_data, line_length, n_lines, n_bands)\n",
"\n",
"cropped_image_data = image_data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2ab7ccf77f10>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(cropped_image_data)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"image_fn, image_ext = os.path.splitext(image_file)\n",
"mini_image_fn = image_fn + '_cropped' + image_ext\n",
"mini_image_bn = os.path.basename(mini_image_fn)\n",
"\n",
"# Overwrite the number of lines in the label\n",
"header['FILE']['IMAGE']['LINES'] = n_lines\n",
"header['FILE']['IMAGE']['BANDS'] = n_output_bands\n",
"header['FILE']['^IMAGE'] = mini_image_bn\n",
"header['FILE']['FILE_RECORDS'] = n_lines * n_output_bands + 1\n",
"header['FILE']['^ROWNUM_TABLE'][0] = mini_image_bn\n",
"header['FILE']['^ROWNUM_TABLE'][1] = n_lines * n_output_bands + 1\n",
"header['FILE']['ROWNUM_TABLE']['ROWS'] = n_output_bands\n",
"# Access the second instance of \"FILE\", which can't be accessed by name\n",
"header[-1][1]['TRDR_HK_TABLE'] = 0\n",
"header[-1][1]['^TRDR_HK_TABLE'] = 0"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6596"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_fn, label_ext = os.path.splitext(crism_file)\n",
"out_label = label_fn + '_cropped' + label_ext\n",
"\n",
"grammar = pvl.grammar.ISISGrammar()\n",
"grammar.comments+=((\"#\", \"\\n\"), )\n",
"encoder = pvl.encoder.ISISEncoder()\n",
"pvl.dump(header, out_label, encoder=encoder, grammar=grammar)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(mini_image_fn, 'wb+') as f:\n",
" b_reduced_image_data = cropped_image_data.tobytes()\n",
" f.seek(0, 2)\n",
" f.write(b_reduced_image_data)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python autocnet",
"language": "python",
"name": "autocnet"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| unlicense |
MIT-LCP/mimic-workshop | temp/03-example-patient-ich.ipynb | 1 | 25562 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring the trajectory of a single patient"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Python libraries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We first need to import some tools for working with data in Python. \n",
"- NumPy is for working with numbers\n",
"- Pandas is for analysing data\n",
"- MatPlotLib is for making plots\n",
"- Sqlite3 to connect to the database"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import sqlite3\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Connect to the database"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- We can use the sqlite3 library to connect to the MIMIC database\n",
"- Once the connection is established, we'll run a simple SQL query."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Connect to the MIMIC database\n",
"conn = sqlite3.connect('data/mimicdata.sqlite')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create our test query\n",
"test_query = \"\"\"\n",
"SELECT subject_id, hadm_id, admittime, dischtime, admission_type, diagnosis\n",
"FROM admissions\n",
"LIMIT 10;\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Run the query and assign the results to a variable\n",
"test = pd.read_sql_query(test_query,conn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Display the first few rows\n",
"test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load the chartevents data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- The chartevents table contains data charted at the patient bedside. It includes variables such as heart rate, respiratory rate, temperature, and so on.\n",
"- We'll begin by loading the chartevents data for a single patient."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"query = \"\"\"\n",
"SELECT de.icustay_id\n",
" , (strftime('%s',de.charttime)-strftime('%s',ie.intime))/60.0/60.0 as HOURS\n",
" , di.label\n",
" , de.value\n",
" , de.valuenum\n",
" , de.uom\n",
"FROM chartevents de\n",
"INNER join d_items di\n",
"ON de.itemid = di.itemid\n",
"INNER join icustays ie\n",
"ON de.icustay_id = ie.icustay_id\n",
"WHERE de.subject_id = 40084\n",
"ORDER BY charttime;\n",
"\"\"\"\n",
"\n",
"ce = pd.read_sql_query(query,conn)\n",
"\n",
"\n",
"# OPTION 2: load chartevents from a CSV file\n",
"# ce = pd.read_csv('data/example_chartevents.csv', index_col='HOURSSINCEADMISSION')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Preview the data\n",
"# Use 'head' to limit the number of rows returned\n",
"ce.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Review the patient's heart rate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- We can select individual columns using the column name. \n",
"- For example, if we want to select just the label column, we write **```ce.LABEL```** or alternatively **```ce['LABEL']```**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Select a single column\n",
"ce['LABEL'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- In a similar way, we can select rows from data using indexes. \n",
"- For example, to select rows where the label is equal to 'Heart Rate', we would create an index using **```[ce.LABEL=='Heart Rate']```** "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Select just the heart rate rows using an index\n",
"ce[ce.LABEL=='Heart Rate'].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot 1: How did the patients heart rate change over time?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Using the methods described above to select our data of interest, we can create our x and y axis values to create a time series plot of heart rate."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Which time stamps have a corresponding heart rate measurement?\n",
"print ce.index[ce.LABEL=='Heart Rate']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# Set x equal to the times\n",
"x_hr = ce.HOURS[ce.LABEL=='Heart Rate']\n",
"\n",
"# Set y equal to the heart rates\n",
"y_hr = ce.VALUENUM[ce.LABEL=='Heart Rate']\n",
"\n",
"# Plot time against heart rate\n",
"plt.figure(figsize=(14, 6))\n",
"plt.plot(x_hr,y_hr)\n",
"\n",
"\n",
"plt.xlabel('Time',fontsize=16)\n",
"plt.ylabel('Heart rate',fontsize=16)\n",
"plt.title('Heart rate over time from admission to the intensive care unit')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ce['LABEL'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Task 1\n",
"\n",
"* What is happening to this patient's heart rate?\n",
"* Plot respiratory rate over time for the patient.\n",
"* Is there anything unusual about the patient's respiratory rate?\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Exercise 1 here\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# What is happening to this patient's heart rate?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Set x equal to the times\n",
"x_hr = ce.HOURS[ce.LABEL=='Heart Rate']\n",
"\n",
"# Set y equal to the heart rates\n",
"y_hr = ce.VALUENUM[ce.LABEL=='Heart Rate']\n",
"\n",
"# Plot time against heart rate\n",
"plt.figure(figsize=(14, 6))\n",
"plt.plot(x_hr,y_hr)\n",
"\n",
"# Get some information regarding arctic sun\n",
"plt.plot(ce.HOURS[ce.LABEL=='Arctic Sun/Alsius Set Temp'], \n",
" ce.VALUENUM[ce.LABEL=='Arctic Sun/Alsius Set Temp'],\n",
" 'k+--',markersize=8)\n",
"plt.plot(ce.HOURS[ce.LABEL=='Arctic Sun Water Temp'], \n",
" ce.VALUENUM[ce.LABEL=='Arctic Sun Water Temp'],\n",
" 'r+--',markersize=8)\n",
"plt.plot(ce.HOURS[ce.LABEL=='Arctic Sun/Alsius Temp #1 C'], \n",
" ce.VALUENUM[ce.LABEL=='Arctic Sun/Alsius Temp #1 C'],\n",
" 'b+--',markersize=8)\n",
"plt.plot(ce.HOURS[ce.LABEL=='Arctic Sun/Alsius Temp #2 C'], \n",
" ce.VALUENUM[ce.LABEL=='Arctic Sun/Alsius Temp #2 C'],\n",
" 'g+--',markersize=8)\n",
"\n",
"plt.xlabel('Time',fontsize=16)\n",
"plt.ylabel('Heart rate',fontsize=16)\n",
"\n",
"plt.xlabel('Time (hours)',fontsize=16)\n",
"plt.ylabel('Heart rate / temperature',fontsize=16)\n",
"plt.title('Heart rate over time')\n",
"plt.ylim(0,80)\n",
"plt.xlim(0,48)\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot 2: Did the patient's vital signs breach any alarm thresholds?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Alarm systems in the intensive care unit are commonly based on high and low thresholds defined by the carer.\n",
"- False alarms are often a problem and so thresholds may be set arbitrarily to reduce alarms.\n",
"- As a result, alarm settings carry limited information."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=(14, 6))\n",
"\n",
"plt.plot(ce.HOURS[ce.LABEL=='Respiratory Rate'], \n",
" ce.VALUENUM[ce.LABEL=='Respiratory Rate'],\n",
" 'k+', markersize=10, linewidth=4)\n",
"\n",
"plt.plot(ce.HOURS[ce.LABEL=='Resp Alarm - High'], \n",
" ce.VALUENUM[ce.LABEL=='Resp Alarm - High'],\n",
" 'm--')\n",
"\n",
"plt.plot(ce.HOURS[ce.LABEL=='Resp Alarm - Low'], \n",
" ce.VALUENUM[ce.LABEL=='Resp Alarm - Low'],\n",
" 'm--')\n",
"\n",
"plt.xlabel('Time',fontsize=16)\n",
"plt.ylabel('Respiratory rate',fontsize=16)\n",
"plt.title('Respiratory rate over time from admission, with upper and lower alarm thresholds')\n",
"plt.ylim(0,55)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Task 2\n",
"\n",
"- Based on the data, does it look like the alarms would have triggered for this patient?\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot 3: What is patient's level of consciousness?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Glasgow Coma Scale (GCS) is a measure of consciousness.\n",
"- It is commonly used for monitoring patients in the intensive care unit. \n",
"- It consists of three components: eye response; verbal response; motor response."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Display the first few rows of the GCS eye response data\n",
"ce[ce.LABEL=='GCS - Eye Opening'].head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Prepare the size of the figure\n",
"plt.figure(figsize=(18, 10))\n",
"\n",
"# Set x equal to the times\n",
"x_hr = ce.HOURS[ce.LABEL=='Heart Rate']\n",
"\n",
"# Set y equal to the heart rates\n",
"y_hr = ce.VALUENUM[ce.LABEL=='Heart Rate']\n",
"\n",
"\n",
"plt.plot(x_hr,y_hr)\n",
"\n",
"plt.plot(ce.HOURS[ce.LABEL=='Respiratory Rate'], \n",
" ce.VALUENUM[ce.LABEL=='Respiratory Rate'],\n",
" 'k', markersize=6)\n",
"\n",
"# Add a text label to the y-axis\n",
"plt.text(-5,155,'GCS - Eye Opening',fontsize=14)\n",
"plt.text(-5,150,'GCS - Motor Response',fontsize=14)\n",
"plt.text(-5,145,'GCS - Verbal Response',fontsize=14) \n",
"\n",
"# Iterate over list of GCS labels, plotting around 1 in 10 to avoid overlap\n",
"for i, txt in enumerate(ce.VALUE[ce.LABEL=='GCS - Eye Opening'].values):\n",
" if np.mod(i,6)==0 and i < 65:\n",
" plt.annotate(txt, (ce.HOURS[ce.LABEL=='GCS - Eye Opening'].values[i],155),fontsize=14)\n",
" \n",
"for i, txt in enumerate(ce.VALUE[ce.LABEL=='GCS - Motor Response'].values):\n",
" if np.mod(i,6)==0 and i < 65:\n",
" plt.annotate(txt, (ce.HOURS[ce.LABEL=='GCS - Motor Response'].values[i],150),fontsize=14)\n",
"\n",
"for i, txt in enumerate(ce.VALUE[ce.LABEL=='GCS - Verbal Response'].values):\n",
" if np.mod(i,6)==0 and i < 65:\n",
" plt.annotate(txt, (ce.HOURS[ce.LABEL=='GCS - Verbal Response'].values[i],145),fontsize=14)\n",
"\n",
"plt.title('Vital signs and Glasgow Coma Scale over time from admission',fontsize=16)\n",
"\n",
"plt.xlabel('Time (hours)',fontsize=16)\n",
"plt.ylabel('Heart rate or GCS',fontsize=16)\n",
"plt.ylim(10,165)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Task 3\n",
"\n",
"- How is the patient's consciousness changing over time?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stop here..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot 2: What other data do we have on the patient?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Using Pandas 'read_csv function' again, we'll now load the outputevents data - this table contains all information about patient outputs (urine output, drains, dialysis)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# OPTION 1: load outputs from the patient\n",
"query = \"\"\"\n",
"select de.icustay_id\n",
" , (strftime('%s',de.charttime)-strftime('%s',ie.intime))/60.0/60.0 as HOURS\n",
" , di.label\n",
" , de.value\n",
" , de.valueuom\n",
"from outputevents de \n",
"inner join icustays ie\n",
" on de.icustay_id = ie.icustay_id\n",
"inner join d_items di\n",
" on de.itemid = di.itemid\n",
"where de.subject_id = 40084\n",
"order by charttime;\n",
"\"\"\"\n",
"\n",
"oe = pd.read_sql_query(query,conn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"oe.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=(14, 10))\n",
"\n",
"plt.figure(figsize=(14, 6))\n",
"plt.title('Fluid output over time')\n",
"\n",
"plt.plot(oe.HOURS, \n",
" oe.VALUE.cumsum()/1000, \n",
" 'ro', markersize=8, label='Output volume, L')\n",
"\n",
"plt.xlim(0,72)\n",
"plt.ylim(0,10)\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To provide necessary context to this plot, it would help to include patient input data. This provides the necessary context to determine a patient's fluid balance - a key indicator in patient health."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# OPTION 1: load inputs given to the patient (usually intravenously) using the database connection\n",
"query = \"\"\"\n",
"select de.icustay_id\n",
" , (strftime('%s',de.starttime)-strftime('%s',ie.intime))/60.0/60.0 as HOURS_START\n",
" , (strftime('%s',de.endtime)-strftime('%s',ie.intime))/60.0/60.0 as HOURS_END\n",
" , de.linkorderid\n",
" , di.label\n",
" , de.amount\n",
" , de.amountuom\n",
" , de.rate\n",
" , de.rateuom\n",
"from inputevents_mv de \n",
"inner join icustays ie\n",
" on de.icustay_id = ie.icustay_id\n",
"inner join d_items di\n",
" on de.itemid = di.itemid\n",
"where de.subject_id = 40084\n",
"order by endtime;\n",
"\"\"\"\n",
"\n",
"ie = pd.read_sql_query(query,conn)\n",
"\n",
"# # OPTION 2: load ioevents using the CSV file with endtime as the index\n",
"# ioe = pd.read_csv('inputevents.csv'\n",
"# ,header=None\n",
"# ,names=['subject_id','itemid','label','starttime','endtime','amount','amountuom','rate','rateuom']\n",
"# ,parse_dates=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ie.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the column headers are different: we have \"HOURS_START\" and \"HOURS_END\". This is because inputs are administered over a fixed period of time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ie['LABEL'].unique()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=(14, 10))\n",
"\n",
"# Plot the cumulative input against the cumulative output\n",
"plt.plot(ie.HOURS_END[ie.AMOUNTUOM=='mL'], \n",
" ie.AMOUNT[ie.AMOUNTUOM=='mL'].cumsum()/1000, \n",
" 'go', markersize=8, label='Intake volume, L')\n",
"\n",
"plt.plot(oe.HOURS, \n",
" oe.VALUE.cumsum()/1000, \n",
" 'ro', markersize=8, label='Output volume, L')\n",
"\n",
"plt.title('Fluid balance over time',fontsize=16)\n",
"plt.xlabel('Hours',fontsize=16)\n",
"plt.ylabel('Volume (litres)',fontsize=16)\n",
"# plt.ylim(0,38)\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"As the plot shows, the patient's intake tends to be above their output (as one would expect!) - but there are periods where they are almost one to one. One of the biggest challenges of working with ICU data is that context is everything - let's look at a treatment (lasix) that we know will affect this graph."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=(14, 10))\n",
"\n",
"# Plot the cumulative input against the cumulative output\n",
"plt.plot(ie.HOURS_END[ie.AMOUNTUOM=='mL'], \n",
" ie.AMOUNT[ie.AMOUNTUOM=='mL'].cumsum()/1000, \n",
" 'go', markersize=8, label='Intake volume, L')\n",
"\n",
"plt.plot(oe.HOURS, \n",
" oe.VALUE.cumsum()/1000, \n",
" 'ro', markersize=8, label='Output volume, L')\n",
"\n",
"# example on getting two columns from a dataframe: ie[['HOURS_START','HOURS_END']].head()\n",
"\n",
"for i, idx in enumerate(ie.index[ie.LABEL=='Furosemide (Lasix)']):\n",
" plt.plot([ie.HOURS_START[ie.LABEL=='Furosemide (Lasix)'][idx],\n",
" ie.HOURS_END[ie.LABEL=='Furosemide (Lasix)'][idx]],\n",
" [ie.RATE[ie.LABEL=='Furosemide (Lasix)'][idx],\n",
" ie.RATE[ie.LABEL=='Furosemide (Lasix)'][idx]],\n",
" 'b-',linewidth=4)\n",
" \n",
"\n",
"plt.title('Fluid balance over time',fontsize=16)\n",
"plt.xlabel('Hours',fontsize=16)\n",
"plt.ylabel('Volume (litres)',fontsize=16)\n",
"# plt.ylim(0,38)\n",
"plt.legend()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ie['LABEL'].unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 2\n",
"\n",
"* Plot the alarms for the mean arterial pressure ('```Arterial Blood Pressure mean```')\n",
"* HINT: you can use ```ce.LABEL.unique()``` to find a list of variable names\n",
"* Were the alarm thresholds breached?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Exercise 2 here\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot 3: Were the patient's other vital signs stable?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=(14, 10))\n",
"\n",
"plt.plot(ce.index[ce.LABEL=='Heart Rate'], \n",
" ce.VALUENUM[ce.LABEL=='Heart Rate'],\n",
" 'rx', markersize=8, label='HR')\n",
"\n",
"plt.plot(ce.index[ce.LABEL=='O2 saturation pulseoxymetry'], \n",
" ce.VALUENUM[ce.LABEL=='O2 saturation pulseoxymetry'], \n",
" 'g.', markersize=8, label='O2')\n",
"\n",
"plt.plot(ce.index[ce.LABEL=='Arterial Blood Pressure mean'], \n",
" ce.VALUENUM[ce.LABEL=='Arterial Blood Pressure mean'], \n",
" 'bv', markersize=8, label='MAP')\n",
"\n",
"plt.plot(ce.index[ce.LABEL=='Respiratory Rate'], \n",
" ce.VALUENUM[ce.LABEL=='Respiratory Rate'], \n",
" 'k+', markersize=8, label='RR')\n",
"\n",
"plt.title('Vital signs over time from admission')\n",
"plt.ylim(0,130)\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot 5: Laboratory measurements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using Pandas 'read_csv function' again, we'll now load the labevents data.\n",
"This data corresponds to measurements made in a laboratory - usually on a sample of patient blood. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# OPTION 1: load labevents data using the database connection\n",
"query = \"\"\"\n",
"SELECT de.subject_id\n",
" , de.charttime\n",
" , di.label, de.value, de.valuenum\n",
" , de.uom\n",
"FROM labevents de\n",
"INNER JOIN d_labitems di\n",
" ON de.itemid = di.itemid\n",
"where de.subject_id = 40084\n",
"\"\"\"\n",
"\n",
"le = pd.read_sql_query(query,conn)\n",
"\n",
"# OPTION 2: load labevents from the CSV file\n",
"# le = pd.read_csv('data/example_labevents.csv', index_col='HOURSSINCEADMISSION')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# preview the labevents data\n",
"le.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# preview the ioevents data\n",
"le[le.LABEL=='HEMOGLOBIN']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=(14, 10))\n",
"\n",
"plt.plot(le.index[le.LABEL=='HEMATOCRIT'], \n",
" le.VALUENUM[le.LABEL=='HEMATOCRIT'], \n",
" 'go', markersize=6, label='Haematocrit')\n",
"\n",
"plt.plot(le.index[le.LABEL=='HEMOGLOBIN'], \n",
" le.VALUENUM[le.LABEL=='HEMOGLOBIN'], \n",
" 'bv', markersize=8, label='Hemoglobin')\n",
"\n",
"plt.title('Laboratory measurements over time from admission')\n",
"plt.ylim(0,38)\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot 5: intravenous medications"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Using the Pandas 'read_csv function' again, we'll now load the the ioevents dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# load ioevents\n",
"ioe = pd.read_csv('data/example_ioevents.csv',index_col='HOURSSINCEADMISSION_START')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ioe.head()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=(14, 10))\n",
"\n",
"plt.plot(ie.CHARTTIME[ie.LABEL=='Midazolam (Versed)'], \n",
" ie.RATE[ie.LABEL=='Midazolam (Versed)'], \n",
" 'go', markersize=6, label='Midazolam (Versed)')\n",
"\n",
"plt.plot(ie.CHARTTIME[ie.LABEL=='Propofol'], \n",
" ie.RATE[ie.LABEL=='Propofol'], \n",
" 'bv', markersize=8, label='Propofol')\n",
"\n",
"plt.plot(ie.CHARTTIME[ie.LABEL=='Fentanyl'], \n",
" ie.RATE[ie.LABEL=='Fentanyl'], \n",
" 'k+', markersize=8, label='Fentanyl')\n",
"\n",
"plt.title('Inputs over time from admission')\n",
"plt.ylim(0,380)\n",
"plt.legend()"
]
}
],
"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",
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},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
antoniomezzacapo/qiskit-tutorial | community/teach_me_qiskit_2018/cryptography/Cryptography.ipynb | 1 | 53738 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../../../images/qiskit-heading.gif\" alt=\"Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook\" width=\"500 px\" align=\"left\">"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quantum Cryptography\n",
"In this notebook we are going to introduce the theory of **Quantum Cryptography** and one of its possible applications.\n",
"\n",
"If you are new to this topic, I suggest you to read the [Introduction notebook](https://github.com/rugantio/Quantum_crypto/blob/master/Introduction.ipynb), where you can find basic required notions of cryptography and quantum computers. If you feel confident about your background, or you just want to take a peek, you can follow up.\n",
"\n",
"This notebook consists in a possible implementation of the **BB84** cryptographic protocol on a quantum computer, reproducing **Quantum Key Distribution** and eavesdropper detection. It makes use of IBM's QISKit, a python library that can manipulate quantum circuits, either via a simulation or a real execution on IBM's backend."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Contributors\n",
"Costantino Carugno"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Requirements\n",
"Throughout the notebook we will make use of:\n",
"- QISKit 0.6 (with IBMQ access for remote backend) \n",
"- Standard python scientific libs stack: numpy etc.\n",
"- Knowledge of basic Quantum Mechanics is assumed\n",
"- Notions of traditional Cryptography are helpful although not strictly necessary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quantum Key Distribution\n",
"In 1984, building on the work of *Wiesner*, *Charles Bennett*, an IBM's researcher, and *Gilles Brassard*, of the Université de Montréal, developed the first **quantum cryptographic protocol**, which goes under the codename of **BB84**. \n",
"\n",
"Suppose that *Alice* and *Bob* are connected via a **quantum channel** that they can use to exchange qubits. This channel is not used directly to send a private message, but only to exchange random qubits that after processing will compose the encryption key. \n",
"\n",
"If key sharing is completed successfully, this key can be used in the well known way as a **one-time pad** (OTP) to produce a safely encrypted message to be delivered over a **classical channel** using symmetrical cryptography. The key should be completely random, as long as the message, and discarded after use; the procedure can be repeated for every message that needs to be delivered. \n",
"\n",
"More specifically *Alice* produces an **initial key**, selecting a sequence of **random bits**, '$0$' and '$1$', and picking a sequence of **polarization eigenstates**, with respect to a randomly chosen basis between: **rectilinear** $\\{\\lvert 0 \\rangle,\\ \\lvert 1 \\rangle\\}$ and **diagonal** $\\{\\lvert \\nearrow \\rangle,\\ \\lvert \\searrow \\rangle\\}$.\n",
"\n",
"*Alice* encodes the classical bits of the key one by one in a **qubit**, by preparing each qubit in an eigenstate of the basis chosen, so that only by measuring the qubits in the **right basis** one can retrieve with **certainty** the right classical bit, just as it happens with quantum money. In the meantime *Alice* keeps a note (in a **table**) of the basis that she has picked for every single qubit she has encoded.\n",
"\n",
"Now, using the quantum channel, she sends the stream of qubits to *Bob*, who is **unaware** of the basis used by *Alice* for the encoding. *Bob* receives these qubits prepared in a certain polarization eigenstate but, due to the **no-cloning theorem**, he is unable to recognize which basis *Alice* used, because he cannot distinguish **non-orthogonal states** with a single measurement. Nonetheless he proceeds anyway with measuring each photon's polarization using a basis chosen randomly (between rectilinear and diagonal), and he keeps a note of the measurement result and the associated basis that he used in a report **table**. \n",
"\n",
"Statistically, *Bob* will pick the **right basis**, the same that *Alice* picked, about **$1/2$** of the times, and the wrong basis about **$1/2$** of the times. When he measures using the right basis he correctly retrieves the information bit of the key, but when he picks the wrong basis the information bit is not certain, since with respect to this basis, the qubit is in a **superposition** of the eigenstates of the right bases, and it can collapse in either two of them with equal probability of **$1/2.$**\n",
"\n",
"For this reason *Alice* and *Bob* decide to **sift** their key, which in practical terms means that they discard from the key all the bits obtained via measurements made in the wrong basis, since they are not reliable. The price for this action is that the key will lose about **$1/2$** of its length, but the payoff is that they don't need to unveil their measurements, they just need to compare their tables, where they recorded the basis chosen, and they do that **after** the measurement has occurred.\n",
"\n",
"So they open the **classical channel** and only now *Alice* tells (publicly) *Bob* which basis she used to encode the key; they **compare** the **tables** and discard the bits obtained measuring qubits in different basis. What they obtain is a perfectly correlate **sifted key**, the same for both of them, ready for use. This key can be employed as a one-time pad and once is used up completely, the procedure can be repeated again to produce a new random key. \n",
"\n",
"What happens if we now introduce an **eavesdropper** in the communication? Suppose that *Eve* is able to intercept the qubits that Alice sends to Bob, and that she can also tap the classical communication channel. When she gets hold of the qubits she still doesn't know which basis *Alice* used, just like *Bob*. She is forced to make a guess, and she will pick the wrong basis **$1/2$** of the times. If she measures in the wrong basis she has **$1/2$** probability to make the qubit collapse in the wrong eigenstate, so that on the whole she will have altered about **$1/4$** of the original qubits. This is the main difference with classical crypto: thanks to quantum mechanics observing implies measuring, and if this is not done accordingly, it changes the actual state (key).\n",
"\n",
"*Eve* produces a **candidate key** and passes on these (now altered) qubits to Bob who proceeds himself with his measurements. *Bob* constructs his table list of random basis and also obtains his candidate key, which will of course be different from *Eve*'s. When* Alice* broadcasts his basis table on the classical channel and *Bob* sift his key accordingly, he will obtain a key different from *Alice*'s, unusable, since even in the same basis choice qubits will be different about **$1/4$** of the times. If *Alice* try to encrypt a message, symmetrical cryptography would fail and both *Alice* and *Bob* will know that communication has been compromised. \n",
"\n",
"If *Alice* and *Bob* never compare their measurement and they only compare basis tables they have no way of knowing that the state has been altered, until the encrypted message is produced, sent and decryption fails. However they can decide to initiate **key sharing** by also comparing their measurement on a certain number of qubits, and, only when they are convinced that the channel is free of interference, they proceed with the actual key sharing. Of course the part of the key that represents the unveiled measurement has to be discarded from it. In real world application is comprises about $1/3$ of the whole key.\n",
"\n",
"In this notebook I will be demonstrating exactly this behavior, how initial key sharing can be used to detect an eavesdropper. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## QKD proof of concept on a quantum computer\n",
"Quantum Key Distribution requires special apparatus made for key sharing. Having at our disposal IBM's quantum computer, here we present a proof-of-concept of how the process can be realized, using real quantum measuring devices. \n",
"\n",
"The key sharing part will be simulated using different quantum circuits one for each party (*Alice*, *Bob*, *Eve*) in the exchange, since we don't have a real quantum channel. We present first the simple case in which only *Alice* and *Bob* are present, and we later proceed to introduce *Eve* and demonstrate how she can be caught.\n",
"\n",
"First we check for and import the required libraries: "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import numpy for random number generation\n",
"import numpy as np\n",
"\n",
"# importing Qiskit\n",
"from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister, execute, Aer\n",
"\n",
"# Import basic plotting tools\n",
"from qiskit.tools.visualization import plot_histogram"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we do some preliminary settings to better manipulate quantum circuits and we set the number of available (qu)bits to 16"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Creating registers with n qubits\n",
"n = 16 # for a local backend n can go as up as 23, after that it raises a Memory Error\n",
"qr = QuantumRegister(n, name='qr')\n",
"cr = ClassicalRegister(n, name='cr')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We create Alice's quantum circuit, made of $n$ qubits (and $n$ bits in a classical register, for measuring). We use $randint$ from numpy to generate a random number in the available range which will be our key and then we write the resulted number in binary and we memorize the key in a proper variable"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Quantum circuit for alice state\n",
"alice = QuantumCircuit(qr, cr, name='Alice')\n",
"\n",
"# Generate a random number in the range of available qubits [0,65536))\n",
"alice_key = np.random.randint(0, high=2**n)\n",
"\n",
"# Cast key to binary for encoding\n",
"# range: key[0]-key[15] with key[15] least significant figure\n",
"alice_key = np.binary_repr(alice_key, n) # n is the width"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parse the generated key and we encode it in Alice's circuit, initializing her qubits to the computational basis: $\\{\\lvert 0 \\rangle,\\ \\lvert 1 \\rangle\\}$, according to the value bit. Then we apply a rotation to about half of these qubits, so that about $1/2$ of them will now be in one of the eigenstates of the diagonal basis: $\\{\\lvert \\nearrow \\rangle,\\ \\lvert \\searrow \\rangle\\}$. We record the basis choice in a list (table) that will later be used for key verification."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Encode key as alice qubits \n",
"# IBM's qubits are all set to |0> initially\n",
"for index, digit in enumerate(alice_key):\n",
" if digit == '1':\n",
" alice.x(qr[index]) # if key has a '1', change state to |1>\n",
" \n",
"# Switch randomly about half qubits to diagonal basis\n",
"alice_table = [] # Create empty basis table\n",
"for index in range(len(qr)): # BUG: enumerate(q) raises an out of range error\n",
" if 0.5 < np.random.random(): # With 50% chance...\n",
" alice.h(qr[index]) # ...change to diagonal basis\n",
" alice_table.append('X') # character for diagonal basis\n",
" else:\n",
" alice_table.append('Z') # character for computational basis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How can we send this state to Bob? As said, we don't have another quantum computer, but we can create another quantum circuit, which we call $bob$, and initialize Bob's initial state to Alice's output state. To accomplish this task we define a helper function, *SendState*, that retrieves the qasm code of a given quantum circuit, $qc1$, does some filtering to extract the quantum gates applied, and produces new instructions that uses to initialize another circuit, $qc2$. This trick works because QISKit maintains a python dictionary of quantum circuits with their relative qasm instructions."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# get_qasm method needs the str label\n",
"# alternatively we can use circuits[0] but since dicts are not ordered\n",
"# it is not a good idea to put them in a func\n",
"# circuits = list(qp.get_circuit_names())\n",
"\n",
"def SendState(qc1, qc2, qc1_name):\n",
" ''' This function takes the output of a circuit qc1 (made up only of x and \n",
" h gates and initializes another circuit qc2 with the same state\n",
" ''' \n",
" \n",
" # Quantum state is retrieved from qasm code of qc1\n",
" qs = qc1.qasm().split(sep=';')[4:-1]\n",
"\n",
" # Process the code to get the instructions\n",
" for index, instruction in enumerate(qs):\n",
" qs[index] = instruction.lstrip()\n",
"\n",
" # Parse the instructions and apply to new circuit\n",
" for instruction in qs:\n",
" if instruction[0] == 'x':\n",
" old_qr = int(instruction[5:-1])\n",
" qc2.x(qr[old_qr])\n",
" elif instruction[0] == 'h':\n",
" old_qr = int(instruction[5:-1])\n",
" qc2.h(qr[old_qr])\n",
" elif instruction[0] == 'm': # exclude measuring:\n",
" pass\n",
" else:\n",
" raise Exception('Unable to parse instruction')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can create Bob's circuit and \"send\" Alice's qubits to Bob. We pretend that this state is unknown to Bob so that he doesn't know which basis to use and decides randomly that $1/2$ of the qubits are to be measured in the rectilinear basis and the other $1/2$ in the diagonal basis; we then record Bob's choice in his table list variable "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"bob = QuantumCircuit(qr, cr, name='Bob')\n",
"\n",
"SendState(alice, bob, 'Alice') \n",
"\n",
"# Bob doesn't know which basis to use\n",
"bob_table = []\n",
"for index in range(len(qr)): \n",
" if 0.5 < np.random.random(): # With 50% chance...\n",
" bob.h(qr[index]) # ...change to diagonal basis\n",
" bob_table.append('X')\n",
" else:\n",
" bob_table.append('Z')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Bob can now go ahead and measure all his qubits and store the measurement in the classical register. We build and run the circuit on the local backend, but, if a token is provided and enough credits are available, it can also be executed on the remote backend with 16 qubits, ibmqx5. Note that is very important that $shots=1$, since we have to pretend that Bob has only one measurement chance, otherwise he could statistically infer the basis used (you can try). "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Measure all qubits\n",
"for index in range(len(qr)): \n",
" bob.measure(qr[index], cr[index])\n",
" \n",
"# Execute the quantum circuit \n",
"backend = Aer.get_backend('qasm_simulator') \n",
"result = execute(bob, backend=backend, shots=1).result()\n",
"plot_histogram(result.get_counts(bob))\n",
"\n",
"# Result of the measure is Bob's key candidate\n",
"bob_key = list(result.get_counts(bob))[0]\n",
"bob_key = bob_key[::-1] # key is reversed so that first qubit is the first element of the list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The histogram is not highly informative of course, but we can see that the measure has been performed correctly. Alice and Bob can switch over to the classical channel, compare their basis table lists, and discard qubits measured using different basis."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Same choice for qubit: 0, basis: X\n",
"Same choice for qubit: 1, basis: Z\n",
"Same choice for qubit: 2, basis: X\n",
"Different choice for qubit: 3, Alice has X, Bob has Z\n",
"Same choice for qubit: 4, basis: X\n",
"Different choice for qubit: 5, Alice has X, Bob has Z\n",
"Different choice for qubit: 6, Alice has X, Bob has Z\n",
"Different choice for qubit: 7, Alice has Z, Bob has X\n",
"Same choice for qubit: 8, basis: Z\n",
"Same choice for qubit: 9, basis: X\n",
"Same choice for qubit: 10, basis: Z\n",
"Same choice for qubit: 11, basis: Z\n",
"Different choice for qubit: 12, Alice has Z, Bob has X\n",
"Same choice for qubit: 13, basis: X\n",
"Different choice for qubit: 14, Alice has X, Bob has Z\n",
"Same choice for qubit: 15, basis: X\n"
]
}
],
"source": [
"keep = []\n",
"discard = []\n",
"for qubit, basis in enumerate(zip(alice_table, bob_table)):\n",
" if basis[0] == basis[1]:\n",
" print(\"Same choice for qubit: {}, basis: {}\" .format(qubit, basis[0])) \n",
" keep.append(qubit)\n",
" else:\n",
" print(\"Different choice for qubit: {}, Alice has {}, Bob has {}\" .format(qubit, basis[0], basis[1]))\n",
" discard.append(qubit)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We know that Bob will pick the wrong basis for $1/2$ of the qubits, so we can check that this theoretical probability is indeed replicated. We also know that although Bob picks the wrong basis, he can still end up with right eigenstate, and that he will do so about $1/2$ of the times, getting right $3/4$ of the qubits. We can check when Alice's and Bob's measurements coincide due to pure chance, although noting that this step is never performed in the actual key sharing step, but only in the inital sharing to test for eavesdropper."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentage of qubits to be discarded according to table comparison: 0.625\n",
"Measurement convergence by additional chance: 0.875\n"
]
}
],
"source": [
"acc = 0\n",
"for bit in zip(alice_key, bob_key):\n",
" if bit[0] == bit[1]:\n",
" acc += 1\n",
"\n",
"print('Percentage of qubits to be discarded according to table comparison: ', len(keep)/n)\n",
"print('Measurement convergence by additional chance: ', acc/n) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now before sifting the keys we perform a check on a certain number of the qubits, comparing their value to see if they have been altered. Since we have only 16 qubits, which is a really low number, we check all of them. Although the procedure is limited to exchange 16 qubits at a time it can be repeated as many times as needed."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percentage of similarity between the keys: 1.0\n"
]
}
],
"source": [
"new_alice_key = [alice_key[qubit] for qubit in keep]\n",
"new_bob_key = [bob_key[qubit] for qubit in keep]\n",
"\n",
"acc = 0\n",
"for bit in zip(new_alice_key, new_bob_key):\n",
" if bit[0] == bit[1]:\n",
" acc += 1 \n",
" \n",
"print('Percentage of similarity between the keys: ', acc/len(new_alice_key)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the qubits measured are the same can accept the new sifted keys. The new sifted keys are printed to stdout, of course this step is just to verify the rightness of the protocol, when the procedure is repeated, each party is not supposed to know the other's sifted key. \n",
"\n",
"Note that, in the real world, quantum channel are subject to information loss since detectors are not perfectly efficient and some photons are going to be lost along the way. Thus, the similarity between the keys will hardly be $1.0$, but surely not as low as $0.75$ which we know is the case in which it has been eavesdropped. As a percentage cut-off we can pick $0.9$ and perform a check before calling the exchange successfull or invalid. You can try to insert a parameter that represents this loss as exercise. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Key exchange has been successfull\n",
"New Alice's key: ['0', '0', '0', '0', '1', '0', '1', '1', '1', '0']\n",
"New Bob's key: ['0', '0', '0', '0', '1', '0', '1', '1', '1', '0']\n"
]
}
],
"source": [
"if (acc//len(new_alice_key) == 1):\n",
" print(\"Key exchange has been successfull\")\n",
" print(\"New Alice's key: \", new_alice_key)\n",
" print(\"New Bob's key: \", new_bob_key)\n",
"else:\n",
" print(\"Key exchange has been tampered! Check for eavesdropper or try again\")\n",
" print(\"New Alice's key is invalid: \", new_alice_key)\n",
" print(\"New Bob's key is invalid: \", new_bob_key)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Everything overlaps perfectly, that is indeed almost trivial. It's time to introduce Eve, the eavesdropper, and see what happens. We create Eve's circuit and we initiliaze it to Alice's state. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"eve = QuantumCircuit(qr, cr, name='Eve')\n",
"SendState(alice, eve, 'Alice') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just like Bob, Eve doesn't know which basis to use and she picks them randomly while recording her choice in a (table) list"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"eve_table = []\n",
"for index in range(len(qr)): \n",
" if 0.5 < np.random.random(): \n",
" eve.h(qr[index]) \n",
" eve_table.append('X')\n",
" else:\n",
" eve_table.append('Z')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"She measures according to her basis choice and she generates her candidate key"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"for index in range(len(qr)): \n",
" eve.measure(qr[index], cr[index])\n",
" \n",
"# Execute (build and run) the quantum circuit \n",
"backend = Aer.get_backend('qasm_simulator') \n",
"result = execute(eve, backend=backend, shots=1).result()\n",
"\n",
"# Result of the measure is Eve's key\n",
"eve_key = list(result.get_counts(eve))[0]\n",
"eve_key = eve_key[::-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Up to now, Eve did exactly what Bob did in the previous example. From this point on, however, things are a bit tricky. Eve has measured the state causing qubits to collapse in different eigenstates. This property is not easy to implement in QISKit because measurement results are stored in classical registered, while the qubits themselves are \"unchanged\". Therefore we need to update Eve's qubits to the new altered states starting from the results of the measures (Eve's key), reversing the instructions that Eve has executed, and apply them to qubits when necessary, which means when the basis choice was different.\n",
"\n",
"You can try figure out yourself how a state is changed after a measurement, but remember that unitary operators in general don't commute."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Same choice for qubit: 0, basis: X\n",
"Same choice for qubit: 1, basis: Z\n",
"Same choice for qubit: 2, basis: X\n",
"Same choice for qubit: 3, basis: X\n",
"Different choice for qubit: 4, Alice has X, Eve has Z\n",
"Different choice for qubit: 5, Alice has X, Eve has Z\n",
"Different choice for qubit: 6, Alice has X, Eve has Z\n",
"Different choice for qubit: 7, Alice has Z, Eve has X\n",
"Same choice for qubit: 8, basis: Z\n",
"Different choice for qubit: 9, Alice has X, Eve has Z\n",
"Different choice for qubit: 10, Alice has Z, Eve has X\n",
"Different choice for qubit: 11, Alice has Z, Eve has X\n",
"Same choice for qubit: 12, basis: Z\n",
"Different choice for qubit: 13, Alice has X, Eve has Z\n",
"Different choice for qubit: 14, Alice has X, Eve has Z\n",
"Same choice for qubit: 15, basis: X\n"
]
}
],
"source": [
"# Update states to new eigenstates (of wrongly chosen basis)\n",
"for qubit, basis in enumerate(zip(alice_table, eve_table)):\n",
" if basis[0] == basis[1]:\n",
" print(\"Same choice for qubit: {}, basis: {}\" .format(qubit, basis[0]))\n",
" else:\n",
" print(\"Different choice for qubit: {}, Alice has {}, Eve has {}\" .format(qubit, basis[0], basis[1]))\n",
" if eve_key[qubit] == alice_key[qubit]:\n",
" eve.h(qr[qubit])\n",
" else:\n",
" if basis[0] == 'X' and basis[1] == 'Z':\n",
" eve.h(qr[qubit])\n",
" eve.x(qr[qubit])\n",
" else:\n",
" eve.x(qr[qubit])\n",
" eve.h(qr[qubit])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Eve's altered state is now sent to Bob that performs the usual routine"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"SendState(eve, bob, 'Eve')\n",
" \n",
"bob_table = []\n",
"for index in range(len(qr)): \n",
" if 0.5 < np.random.random(): \n",
" bob.h(qr[index]) \n",
" bob_table.append('X')\n",
" else:\n",
" bob_table.append('Z')\n",
" \n",
"for index in range(len(qr)): \n",
" bob.measure(qr[index], cr[index])\n",
" \n",
"result = execute(bob, backend=backend, shots=1).result()\n",
"plot_histogram(result.get_counts(bob))\n",
"\n",
"bob_key = list(result.get_counts(bob))[0]\n",
"bob_key = bob_key[::-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After the measure Alice and Bob share the basis table lists and perform the usual checks"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Different choice for qubit: 0, Alice has X, Bob has Z\n",
"Same choice for qubit: 1, basis: Z\n",
"Same choice for qubit: 2, basis: X\n",
"Different choice for qubit: 3, Alice has X, Bob has Z\n",
"Same choice for qubit: 4, basis: X\n",
"Different choice for qubit: 5, Alice has X, Bob has Z\n",
"Same choice for qubit: 6, basis: X\n",
"Same choice for qubit: 7, basis: Z\n",
"Same choice for qubit: 8, basis: Z\n",
"Different choice for qubit: 9, Alice has X, Bob has Z\n",
"Different choice for qubit: 10, Alice has Z, Bob has X\n",
"Different choice for qubit: 11, Alice has Z, Bob has X\n",
"Same choice for qubit: 12, basis: Z\n",
"Different choice for qubit: 13, Alice has X, Bob has Z\n",
"Different choice for qubit: 14, Alice has X, Bob has Z\n",
"Same choice for qubit: 15, basis: X\n",
"\n",
"Percentage of qubits to be discarded according to table comparison: 0.5\n",
"Measurement convergence by additional chance: 0.4375\n",
"\n",
"Percentage of similarity between the keys: 0.375\n",
"\n",
"Key exchange has been tampered! Check for eavesdropper or try again\n",
"New Alice's key is invalid: ['0', '0', '0', '1', '0', '1', '1', '0']\n",
"New Bob's key is invalid: ['0', '0', '1', '0', '1', '0', '1', '1']\n"
]
}
],
"source": [
"keep = []\n",
"discard = []\n",
"for qubit, basis in enumerate(zip(alice_table, bob_table)):\n",
" if basis[0] == basis[1]:\n",
" print(\"Same choice for qubit: {}, basis: {}\" .format(qubit, basis[0])) \n",
" keep.append(qubit)\n",
" else:\n",
" print(\"Different choice for qubit: {}, Alice has {}, Bob has {}\" .format(qubit, basis[0], basis[1]))\n",
" discard.append(qubit)\n",
" \n",
"acc = 0\n",
"for bit in zip(alice_key, bob_key):\n",
" if bit[0] == bit[1]:\n",
" acc += 1\n",
"\n",
"print('\\nPercentage of qubits to be discarded according to table comparison: ', len(keep)/n)\n",
"print('Measurement convergence by additional chance: ', acc/n) \n",
"\n",
"new_alice_key = [alice_key[qubit] for qubit in keep]\n",
"new_bob_key = [bob_key[qubit] for qubit in keep]\n",
"\n",
"acc = 0\n",
"for bit in zip(new_alice_key, new_bob_key):\n",
" if bit[0] == bit[1]:\n",
" acc += 1 \n",
" \n",
"print('\\nPercentage of similarity between the keys: ', acc/len(new_alice_key)) \n",
"\n",
"if (acc//len(new_alice_key) == 1):\n",
" print(\"\\nKey exchange has been successfull\")\n",
" print(\"New Alice's key: \", new_alice_key)\n",
" print(\"New Bob's key: \", new_bob_key)\n",
"else:\n",
" print(\"\\nKey exchange has been tampered! Check for eavesdropper or try again\")\n",
" print(\"New Alice's key is invalid: \", new_alice_key)\n",
" print(\"New Bob's key is invalid: \", new_bob_key)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see when Alice and Bob reveal their key to each other they notice a discordance: Eve has been caught! To really get the percentages right, you can try repeating the experiment multiple times or you can write a higher routine that iterates the key sharing; in either case you will se that they converge to the expected values."
]
}
],
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| apache-2.0 |
tomekkorbak/lstm-for-aspect-based-sentiment-analysis | Presentation.ipynb | 1 | 43698 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Głęboka sieć neuronowa w aspektowej analizie wydźwięku\n",
"\n",
"## Tomek Korbak\n",
"\n",
"#### 24 maja 2016\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Problem\n",
"\n",
"Wytrenować klasyfikator, który dostając na wejściu zdanie języka polskiego, zwróci jego wydźwięk, to znaczy słowo wyrażające opinię."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/tomek/.virtualenvs/deeplearning/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"gpu0\n"
]
}
],
"source": [
"import json\n",
"from itertools import chain\n",
"from pprint import pprint\n",
"from time import time\n",
"import os\n",
"\n",
"import numpy as np\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"from gensim.models import Word2Vec\n",
"from gensim.corpora.dictionary import Dictionary\n",
"\n",
"\n",
"os.environ['THEANO_FLAGS'] = \"device=gpu1\" \n",
"import theano\n",
"# theano.config.device = 'gpu' # Compute using GPU\n",
"# theano.config.floatX = 'float32'\n",
"\n",
"from keras.preprocessing import sequence\n",
"from keras.models import Sequential, Model\n",
"from keras.layers import Input\n",
"from keras.layers.embeddings import Embedding\n",
"from keras.layers.recurrent import LSTM\n",
"from keras.layers.core import Dense, Dropout\n",
"from keras.layers.wrappers import TimeDistributed\n",
"from keras.utils.visualize_util import plot\n",
"\n",
"np.random.seed(1337)\n",
"\n",
"print theano.config.device"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"def indices_to_one_hot_encodings(index, vector_length):\n",
" return [[1, 0] if i == index else [0, 1] for i in xrange(vector_length)]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3906\n"
]
},
{
"data": {
"text/plain": [
"1431"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load and process treebank data\n",
"\n",
"treebank_file1 = open('json/OPTA-treebank-0.1.json')\n",
"treebank_file2 = open('skladnica_output.json')\n",
"treebank = chain(list(json.load(treebank_file1)), list(json.load(treebank_file2)))\n",
"\n",
"X = []\n",
"y = []\n",
"labels = []\n",
"for entry in treebank:\n",
" tree = entry['parsedSent']\n",
" words = []\n",
" sentiment = None\n",
" for index, node in enumerate(tree):\n",
" word = node.split('\\t')[1].lower()\n",
" words.append(word)\n",
" if node.split('\\t')[10] == 'S':\n",
" sentiment = index\n",
" if sentiment:\n",
" labels.append(words[sentiment])\n",
" X.append(words)\n",
" y.append(indices_to_one_hot_encodings(sentiment, len(words)))\n",
"\n",
"dataset_length = len(X)\n",
"slicing_point = int(dataset_length*0.9)\n",
"\n",
"X_train_raw = X[:slicing_point]\n",
"y_train_raw = y[:slicing_point]\n",
"X_test_raw = X[slicing_point+1:]\n",
"y_test_raw = y[slicing_point+1:]\n",
"\n",
"treebank_vocabulary = set(chain(*X))\n",
"print len(treebank_vocabulary)\n",
"\n",
"X_train = X_train_raw \n",
"y_train = labels\n",
"\n",
"len(X_train) + len(X_test_raw)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"raczej nie dla młodych chłopców . \n",
"młodych \n",
"\n",
"spokojnie mogą konkurować z nowymi zapachami . \n",
"konkurować \n",
"\n",
"mają one wyjątkowy zapach , który długo utrzymuje się na skórze . \n",
"wyjątkowy \n",
"\n",
"idealnie pasuje na każdą sylwetkę . \n",
"pasuje \n",
"\n",
": ) i macie rację-to dostojny zapach , nie dla chłystków w dresach . \n",
"dostojny \n",
"\n"
]
}
],
"source": [
"# Przykłady z danych treningowych:\n",
"\n",
"for index in [2, 44, 111, 384, 69]:\n",
" print ' '.join(X_train[index]), '\\n', y_train[index], '\\n'"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Dane pochodzą z ręcznie tagowanego treebanku (korpusu anotowanego składniowo) opracowanego przez Zespół Inżynierii Lingwistycznej IPI PAN na bazie Narodowego Korpusu Języka Polskiego (Wawer, 2015).\n",
"\n",
"Treebank liczy około 1431 zdania. (To dość mało)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"w2v_model = Word2Vec.load('w2v_allwiki_nkjp300_200.model')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0, u'zapachnie'), (1, u'PORADNI'), (2, u'Fitelberga'), (3, u'komedianta'), (4, u'Zaprzesta\\u0107'), (5, u'Nampo'), (6, u'Schloendorff'), (7, u'zn\\u0119kanym'), (8, u'synkopy'), (9, u'unifikacji')]\n"
]
}
],
"source": [
"# Import w2v's dictionary to a bag-of-words model\n",
"w2v_vocabulary = Dictionary()\n",
"w2v_vocabulary.doc2bow(w2v_model.vocab.keys(), allow_update=True)\n",
"print w2v_vocabulary.items()[:10]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# Initialize dicts for representing w2v's dictionary as indices and 200-dim vectors\n",
"w2indx = {v: k+1 for k, v in w2v_vocabulary.items()}\n",
"w2vec = {word: w2v_model[word] for word in w2indx.keys()}"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"w2v_vocabulary_size = len(w2indx) + 1\n",
"w2v_vocabulary_dimension = len(w2vec.values()[0])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[51615, 277138, 416148, 422622, 318134, 584324, 176240, 503788, 0]\n"
]
}
],
"source": [
"def map_treebank_words_to_w2v_indices(treebank_data, w2indx):\n",
" treebank_data_vec = []\n",
" for sentence in treebank_data:\n",
" vectorized_sentence = []\n",
" for word in sentence:\n",
" try:\n",
" vectorized_sentence.append(w2indx[word])\n",
" except KeyError: # words absent in w2v model will be indexed as 0s\n",
" vectorized_sentence.append(0)\n",
" treebank_data_vec.append(vectorized_sentence)\n",
" return treebank_data_vec \n",
"\n",
"X_train = map_treebank_words_to_w2v_indices(X_train_raw, w2indx)\n",
"X_test = map_treebank_words_to_w2v_indices(X_test_raw, w2indx)\n",
"\n",
"print X_test[4]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# Define numpy weights matrix for embedding layer\n",
"embedding_weights = np.zeros((w2v_vocabulary_size , w2v_vocabulary_dimension))\n",
"for word, index in w2indx.items():\n",
" embedding_weights[index, :] = w2vec[word]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"39"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# max sentence length\n",
"max(\n",
" len(max(X_train, key=lambda sentence: len(sentence))),\n",
" len(max(X_test, key=lambda sentence: len(sentence)))\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# Normalize sequences length to 40 (will be extended with 0s)\n",
"sentence_length = 40\n",
"X_train = sequence.pad_sequences(X_train, maxlen=sentence_length)\n",
"X_test = sequence.pad_sequences(X_test, maxlen=sentence_length)\n",
"\n",
"y_train = sequence.pad_sequences(y_train_raw, maxlen=sentence_length, value=[0, 1])\n",
"y_test = sequence.pad_sequences(y_test_raw, maxlen=sentence_length, value=[0, 1])\n",
"\n",
"# print X_train[2]\n",
"# print y_train[2]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"inputs = Input(shape=(sentence_length,), dtype='int32')\n",
"\n",
"x = Embedding(\n",
" input_dim=w2v_vocabulary_size, \n",
" output_dim=w2v_vocabulary_dimension,\n",
" input_length=sentence_length,\n",
" mask_zero=True,\n",
" weights=[embedding_weights]\n",
")(inputs)\n",
"\n",
"lstm_out = LSTM(200, return_sequences=True)(x)\n",
"\n",
"regularized_data = Dropout(0.3)(lstm_out)\n",
"\n",
"predictions = TimeDistributed(Dense(2, activation='sigmoid'))(regularized_data)\n",
"\n",
"model = Model(input=inputs, output=predictions)\n",
"\n",
"\n",
"model.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#Architektura sieci\n",
"\n",
"Zasadniczą rolę odegrają dwie warstwy, same będące pełnoprawnymi sieciami neuronowymi:\n",
"* Warstwa embedding, mapująca słowa na wektory liczb zmiennoprzecinkowych w sposób spełniający pewne kryteria\n",
"* Warstwa LSTM, sieć rekurencyjna szczególnie dobrze radzącą sobie z przetwarzaniem szeregów czasowych"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"input_1 (InputLayer) (None, 40) 0 \n",
"____________________________________________________________________________________________________\n",
"embedding_1 (Embedding) (None, 40, 200) 141387200 input_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"lstm_1 (LSTM) (None, 40, 200) 320800 embedding_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_1 (Dropout) (None, 40, 200) 0 lstm_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"timedistributed_1 (TimeDistributed)(None, 40, 2) 402 dropout_1[0][0] \n",
"====================================================================================================\n",
"Total params: 141708402\n",
"____________________________________________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
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},
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import SVG\n",
"from keras.utils.visualize_util import model_to_dot\n",
"\n",
"SVG(model_to_dot(model).create(prog='dot', format='svg'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Word embeddings\n",
"\n",
"Word embedding to rodzaj statystycznego modelu językowego, który reprezentuje słowa (rzadziej złożone frazy lub całe dokumenty) jako punkty w *n*-wymiarowej przestrzeni liniowej.\n",
"\n",
"Bardzo pożądaną cechą tego mapowania jest to, że relacje geometryczne między punktami tej przestrzeni *odwzorowują* relacje semantyczne między kodowanymi słowami.\n",
"\n",
"Model taki trenuje się na bardzo dużych korpusach, każąc mu rozpoznawać wzorce współwystępowania słów. Najczęściej używanym algorytmem jest Word2Vec, opracowany przez Google (Mikolov et al., 2013a)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Skorzystaliśmy z gotowego embeddingu opracowanego przez IPI PAN, wytrenowanego (z użyciem Word2Vec) na całej polskojęzycznej Wikipedii oraz trzystumilionowym zbalansowanym podkorpusie NKJP."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.21601944, 0.44051808, 0.79559946, -1.68282688, -1.74369335,\n",
" -1.28059053, -0.39722326, 2.53322577, -1.58840847, 2.89625692,\n",
" 0.21176136, -1.24656022, -1.851632 , -0.6131658 , 0.09280811,\n",
" 0.2779803 , 1.01950109, -3.0857935 , 1.35601997, 1.5254544 ,\n",
" -0.05327027, -0.82323092, -2.36823678, -0.4112888 , -1.64042878,\n",
" -0.00641074, 0.90136075, -1.92337966, -3.58218908, -0.88566846,\n",
" 0.32599321, -2.20273089, -0.59569108, -1.85917747, 1.84256315,\n",
" 1.71745014, -0.37838364, 1.85855174, 1.9673841 , -0.79426467,\n",
" -0.73808187, 0.3366071 , -2.58625793, -1.25763309, 0.27206179,\n",
" -2.31144023, -0.47351044, -1.59982193, -2.06607461, -0.86752278,\n",
" 1.72592294, -0.69465017, -2.29019308, 1.78491342, 2.56894565,\n",
" -0.80506438, -0.9288221 , 1.59838867, -0.23708618, -1.67918718,\n",
" -0.03720945, -1.16133761, 1.24743545, 1.21191287, -2.56924391,\n",
" 1.95127881, -1.05310512, -1.2471137 , -0.15193334, 2.89646387,\n",
" 0.19207561, -0.3380039 , 1.75885475, 1.19938064, -0.52413052,\n",
" 0.25685585, 1.414047 , -0.81787252, 0.01545324, 2.43729377,\n",
" -1.24133492, 1.43378913, -1.10528064, 2.2033422 , 0.42157343,\n",
" 1.33486164, -1.91032958, -0.05642552, -2.79640102, -0.65357786,\n",
" 0.9766531 , -0.69810498, 0.7656948 , 0.81560051, 1.44289052,\n",
" -3.42624354, 1.7126863 , -0.82548726, 1.64029348, 0.73431754,\n",
" 2.40092421, 1.27722132, 2.08962965, 1.30449235, 0.0638469 ,\n",
" 0.72862577, 0.67618513, -1.45907044, 1.22684562, -0.60784018,\n",
" -1.47931552, -0.08033065, -1.74318349, 0.77499628, -2.85728574,\n",
" -0.23431365, -1.01691294, -0.77199751, -1.15593803, -0.54051322,\n",
" 2.62005734, -0.69065034, -0.89262605, 1.89626825, -3.08517241,\n",
" 1.38132167, -1.99941468, -1.14268947, -0.59968561, -1.28886127,\n",
" 0.42360941, 1.24560022, 0.90514952, -0.10391094, -0.26320365,\n",
" 0.35489824, -1.05735612, -0.76474804, 0.41786137, -1.42204642,\n",
" 2.17182851, -1.68198073, -0.42641062, 0.39401928, 1.54483426,\n",
" -0.05141195, 1.17525744, -1.66848934, 2.78684568, 1.33698678,\n",
" -1.61166167, 1.05483115, 0.11988362, 1.7951647 , -1.31195021,\n",
" 0.15935197, -0.54916567, 0.92104959, 0.52489078, -0.98027211,\n",
" 2.92191052, -1.14910531, 1.53513885, 0.85645872, -2.45151067,\n",
" -2.02326226, -1.0662744 , -1.68197167, -2.98466253, -1.63391221,\n",
" -0.55829096, -1.35489964, 1.69099152, -0.0917596 , 0.61268401,\n",
" -0.42509523, -1.53889883, -0.77681881, 1.03906298, -0.68360895,\n",
" -0.75959706, -1.59627187, 2.31796312, 0.44461161, 1.00981271,\n",
" 0.80096054, 2.06762266, 1.13360274, 0.12779287, -1.55116296,\n",
" -2.96953368, 0.04946666, -0.88799638, -2.01712489, -1.00320017,\n",
" -2.56315923, -0.38519019, -0.59912634, 0.61479992, -0.94088185], dtype=float32)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# w modelu, który wykorzystaliśmy, słowa są reprezentowane jako\n",
"# 200-elementowe wektory 32-bitowych liczb zmiennoprzecinkowych\n",
"w2v_model['filozofia']"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(200,)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w2v_model['filozofia'].shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.49721665657867431"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w2v_model.similarity(u'filozofia', u'inżynieria')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.7536983594530452"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w2v_model.similarity(u'filozofia', u'nauka')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.71805379109199341"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w2v_model.similarity(u'filozofia', u'literatura')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'Derrida'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# wskaż słowo niepasujące do pozostałych\n",
"w2v_model.doesnt_match(['Kant', 'Leibniz', 'Derrida', 'Wittgenstein'])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'kr\\xf3lowa', 0.7674408555030823),\n",
" (u'cesarzowa', 0.669882595539093),\n",
" (u'ksi\\u0119\\u017cniczka', 0.667689323425293),\n",
" (u'ksi\\u0119\\u017cna', 0.6258757710456848),\n",
" (u'caryca', 0.6103663444519043),\n",
" (u'dama', 0.6032381057739258),\n",
" (u'imperatorowa', 0.6007956862449646),\n",
" (u'dynastia', 0.6004920601844788),\n",
" (u'hrabina', 0.573236882686615),\n",
" (u'elekcja', 0.5710059404373169)]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Kobieta + król - mężczyzna = królowa\n",
"# Medialny przykład z (Mikolov et al., 2013b)\n",
"w2v_model.most_similar(positive=[u'kobieta', u'król'], negative=[u'mężczyzna'])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'Kulturalna', 0.5529330968856812),\n",
" (u'Warszawa', 0.539720356464386),\n",
" ('Berlin', 0.52457195520401),\n",
" (u'Almanach', 0.5210937261581421),\n",
" (u'Ekspres', 0.5202639698982239),\n",
" (u'Literacki', 0.5191857814788818),\n",
" (u'Wroc\\u0142aw', 0.5109399557113647),\n",
" (u'Krak\\xf3w', 0.5104151964187622),\n",
" (u'Londyn', 0.5102135539054871),\n",
" (u'Energoprojekt', 0.5069946050643921)]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Paryż - Francja + Polska = Warszawa\n",
"w2v_model.most_similar(positive=[u'Paryż', u'Polska'], negative=[u'Francja'])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'malarstwo', 0.3539747893810272),\n",
" (u'literatur\\u0119', 0.35328927636146545),\n",
" (u'humanistyczne', 0.3381263017654419),\n",
" (u'buddyzm', 0.3335428237915039),\n",
" (u'sport', 0.3307268023490906),\n",
" (u'nauki', 0.32819390296936035),\n",
" (u'astronomi\\u0105', 0.3244932293891907),\n",
" (u'filozoficzne', 0.32311704754829407),\n",
" (u'krajoznawstwo', 0.3222176730632782),\n",
" (u'tw\\xf3rczo\\u015b\\u0107', 0.3211820423603058)]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# filozofia - logika = literatura\n",
"w2v_model.most_similar(positive=[u'filozofia',], negative=[u'logika'])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[(u'wiedza', 0.49253422021865845),\n",
" (u'praca', 0.4822917580604553),\n",
" (u'nauka', 0.4789964258670807),\n",
" (u'praktyka', 0.4766387343406677),\n",
" (u'koncepcja', 0.47612860798835754),\n",
" (u'teoria', 0.4726237952709198),\n",
" (u'pisanina', 0.46762341260910034),\n",
" (u'wizja', 0.46703991293907166),\n",
" (u'problematyka', 0.4616868495941162),\n",
" (u'inicjatywa', 0.45774880051612854)]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# filozofia - postmodernizm = wiedza\n",
"w2v_model.most_similar(positive=[u'filozofia',], negative=[u'postmodernizm'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Różnica wektorów „Paryż” i „Francja” reprezentuje pojęcie STOLICA?\n",
"<img src=\"capitals.png\">\n",
"\n",
"<center>Rzut (przez analizę składowych głównych) 1000-wymiarowego word embeddingu dla języka angielskiego. Przedruk z (Mikolov et al., 2013).</center>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Warstwa LSTM\n",
"\n",
"Long Short-Term Memory to bardzo popularna architektura rekurencyjnych sieci neuronowych, często używana do etykietowania lub predykcji szeregów czasowych (Hochreiter i Schmidhuber, 1997).\n",
"\n",
"LSTM, dzięki połączeniom rekurencyjnym, utrzymuje coś w rodzaju *pamięci roboczej*, którą w każdej iteracji może aktualizować.\n",
"\n",
"Zdolność do zapamiętywania odległych zależności (*long-term dependecies*), takich jak związek zgody, czyni ją najpopularniejszą architekturą do przetwarzania języka naturalnego."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Przetwarzanie danych sekwencyjnych przez rekurencyjną sieć neuronową\n",
"\n",
"Przy *n*-tej iteracji sieć dostaje na wejście *n*-ty element ciągu uczącego oraz pewien wektor zapamiętany z (*n-1*)-tej iteracji.\n",
"\n",
"<img src=\"RNN-unrolled.png\"> <br />\n",
"\n",
"<center><small>Przedruk z http://colah.github.io/posts/2015-08-Understanding-LSTMs/</small></center>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Przy każdej iteracji sieć decyduje, która informacje usunąć z pamięci roboczej, a które od niej dodać. Reguły aktualizacji pamięci roboczej (jako macierz wag połączeń) także podlegają uczeniu.\n",
"\n",
"<img src=\"LSTM3-chain.png\"> <br />\n",
"\n",
"<center><small>Przedruk z http://colah.github.io/posts/2015-08-Understanding-LSTMs/</small></center>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Przepływ danych przez sieć\n",
"\n",
"<img align=\"right\" src=\"model.png\">\n",
"\n",
"Przykład | Opis\n",
"--- | ---\n",
"```'Kotek'``` | token\n",
" 89762 | indeks tokenu w modelu w2v\n",
"```array([ 0.21601944, ..., dtype=float32)``` | 200-elementowy wektor\n",
" ...kolejne wektory... | dalsze etapy przetwarzania\n",
" ```[0.9111, 0.0999]``` | zero-jedynkowy rozkład prawdopodobieństwa przynależności do klasy wydźwięk lub nie-wydźwięk"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 1288 samples, validate on 143 samples\n",
"Epoch 1/5\n",
"310s - loss: 0.2090 - acc: 0.9761 - val_loss: 0.2701 - val_acc: 0.9680\n",
"Epoch 2/5\n",
"329s - loss: 0.0860 - acc: 0.9872 - val_loss: 0.2604 - val_acc: 0.9699\n",
"Epoch 3/5\n",
"344s - loss: 0.0412 - acc: 0.9905 - val_loss: 0.2790 - val_acc: 0.9678\n",
"Epoch 4/5\n",
"358s - loss: 0.0233 - acc: 0.9917 - val_loss: 0.2874 - val_acc: 0.9657\n",
"Epoch 5/5\n",
"378s - loss: 0.0171 - acc: 0.9921 - val_loss: 0.3155 - val_acc: 0.9652\n"
]
}
],
"source": [
"batch_size = 5\n",
"n_epoch = 5\n",
"\n",
"\n",
"hist = model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=n_epoch, \n",
" validation_data=(X_test, y_test), verbose=2)\n",
"\n",
"# epochs = 10\n",
"\n",
"# for i in range(epochs):\n",
"# print('Epoch', i, '/', epochs)\n",
"# model.fit"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"# Uczenie\n",
"\n",
"<img src=\"plot.png\">"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"# plt.rcParams['figure.figsize'] = (10,10)\n",
"\n",
"# axes = plt.gca()\n",
"# x_min = hist.epoch[0]\n",
"# x_max = hist.epoch[-1]+1\n",
"# axes.set_xlim([x_min,x_max])\n",
"\n",
"# plt.scatter(hist.epoch, hist.history['acc'], color='r')\n",
"# plt.plot(hist.history['acc'], color='r', label=u'Trafność mierzona na zbiorze treningowym')\n",
"# plt.scatter(hist.epoch, hist.history['val_acc'], color='c')\n",
"# plt.plot(hist.history['val_acc'], color='c', label=u'Trafność mierzona na zbiorze walidacyjnym')\n",
"# plt.xlabel('epoki')\n",
"# plt.ylabel(u'Trafność')\n",
"# plt.title(u'Trafność w kolejnych epokach')\n",
"# plt.legend()\n",
"# plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Ocena trafności\n",
"\n",
"Oceny trafności dokonano na 143-zdaniowym podzbiorze treebanku, niewykorzystanym podczas treningu.\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test accuracy: 0.965209802548\n"
]
}
],
"source": [
"# Ułamek poprawnie sklasyfikowanych tokenów\n",
"score, acc = model.evaluate(X_test, y_test, batch_size=batch_size, verbose=0)\n",
"print 'Test accuracy:', acc"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"143/143 [==============================] - 0s \n"
]
}
],
"source": [
"predictions = model.predict(X_test, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"def change_encoding_word(word):\n",
" return 1 if list(np.rint(word)) == [1, 0] else 0\n",
"\n",
"def change_encoding(one_hot_encoded_sentence):\n",
" # Switch from ndarray([[0.88, 0.11], [0.34, 0.98]]) encoding to [1, 0] encoding \n",
" # and finally index number\n",
" normalized_sentence = []\n",
" for word in one_hot_encoded_sentence:\n",
" normalized_sentence.append(change_encoding_word(word))\n",
" return normalized_sentence\n",
"\n",
"def accurately_evaluated_samples():\n",
" total_accuracy = 0\n",
" for n, sentence in enumerate(predictions):\n",
" index_of_sentiment = np.argmax(change_encoding(sentence))\n",
"# print change_encoding_word(y_test[n][index_of_sentiment])\n",
" total_accuracy += change_encoding_word(y_test[n][index_of_sentiment])\n",
" return total_accuracy\n",
" \n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Wartość bardzo przeszacowana ze względu na nierównomierną częśtość występowania klas (1:39 dla wydźwięku vs nie-wydźwięku)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Bardziej adekwatna metryka"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0.34965034965034963"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Ułamek tokenów-wydźwięków, które poprawnie rozpoznano jako wydźwięki\n",
"float(accurately_evaluated_samples())/y_test.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Nie wygląda imponująco, ale...\n",
"* Przy zgadywaniu trafność wynosiłaby 0,0625% (= $1/40^2$),\n",
"* Maksymalny możliwy wynik oscyluje wokół 80%, bo taka jest średnia zgodność ludzkich anotacji (Ogneva, 2012)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Plany na przyszłość\n",
"\n",
"* Zwiększenie trafności przewidywań\n",
" * Dodanie drugiej (i kolejnych) warstwy LSTM powinno umożliwić budowanie przez sieć bardziej złożonych hierarchicznych reprezentacji zależności w zdaniach\n",
" * Wydłużenie treningu sieci przy wykonywaniu obliczeń przez GPU lub szybszą maszynę\n",
" \n",
"* Rozszerzenie problemu o inne trenowanie innych klasyfikatorów:\n",
" * Ekstrakcja obiektów, których aspektom przypisywany jest wydźwięk\n",
" * Klasyfikowanie wydźwięku (pozytywny lub negatywny) dla pary `<obiekt, wydźwięk>`"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Kod źródłowy\n",
"\n",
"Repozytorium jest dostępne pod adresem: https://github.com/tomekkorbak/lstm-for-aspect-based-sentiment-analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# Użyte oprogramowanie\n",
"\n",
"* Keras (wysokopoziomowy wrapper na Theano)\n",
"* Theano (implementacja sieci i jej treningu)\n",
"* Scikit-learn (walidacja)\n",
"* Gensim (model językowy Word2Vec)\n",
"* Numpy (pomocnicze obliczenia numeryczne)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Bibliografia\n",
"1. Hochreiter, S. i Schmidhuber, J, (1997). Long Short-Term Memory, „Neural Computation”, 9 (8), ss. 1735-1780.\n",
"2. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., i Dean, J. (2013a). Distributed Representations of Words and Phrases and their Compositionality. „Advances in Neural Information Processing Systems 26”, ss. 3111-3119.\n",
"3. Mikolov, T., Chen, K., Corrado, G. i Dean, J. (2013b). Efficient estimation of word representations in vector space. \n",
"4. Ogneva, M. (2012). How Companies Can Use Sentiment Analysis to Improve Their Business\n",
"5. Wawer, A. (2015). Towards Domain-Independent Opinion Target Extraction. „IEEE 15th International Conference on Data Mining Workshops”, ss. 1326-1331.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# <center>Dziękuję za uwagę</center>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"{'acc': [0.97610637001832079,\n",
" 0.98715061578691377,\n",
" 0.99050852938653522,\n",
" 0.9916537196277091,\n",
" 0.99210014624625265],\n",
" 'loss': [0.20902245508807693,\n",
" 0.08603155619380938,\n",
" 0.041203499367635593,\n",
" 0.023266617574053673,\n",
" 0.017146764686461306],\n",
" 'val_acc': [0.96800700267711715,\n",
" 0.96993008193436203,\n",
" 0.96783217975309677,\n",
" 0.96573427965591008,\n",
" 0.96520980254753486],\n",
" 'val_loss': [0.2701167392355579,\n",
" 0.26044547015970404,\n",
" 0.2789940552694814,\n",
" 0.28738793543168717,\n",
" 0.31553373897409104]}"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hist.history"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"143/143 [==============================] - 0s \n",
"('Test score:', 0.31553373897409104)\n",
"('Test accuracy:', 0.96520980254753486)\n"
]
}
],
"source": [
"score, acc = model.evaluate(X_test, y_test,\n",
" batch_size=batch_size)\n",
"print('Test score:', score)\n",
"print('Test accuracy:', acc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
gregcaporaso/sketchbook | 2014.09.23-bl9-to-biom.ipynb | 1 | 7330 | {
"metadata": {
"name": "",
"signature": "sha256:3ecf03a6a1eefc6bfc00a4b9de2ae582045e641b28526ffa9f22070a239df9a1"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is some really ugly code illustrating how you could generate a biom table from a blast9 file. We're doing this for an experiment with RapSearch2 for read mapping, and are using this for testing purposes only. We'll need to get better support for this before using for real."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# This cell is not necessary when running the steps below on an existing file\n",
"\n",
"s = \"\"\"# Fields: Query Subject identity aln-len mismatch gap-openings q.start q.end s.start s.end log(e-value) bit-score\n",
"4472165.3.31762_6165\t11846454\t50\t24\t12\t0\t12\t83\t967\t990\t0.405978\t27.72\n",
"4472165.3.31762_6165\t11846454\t90.9091\t11\t1\t0\t54\t86\t197\t207\t0.405978\t28.11\n",
"4472165.3.31762_6165\t11815348\t57.6923\t26\t11\t0\t12\t89\t55\t80\t0.86\t35.42\n",
"4472165.3.31762_6165\t12307352\t57.6923\t26\t11\t0\t12\t89\t1005\t1030\t0.86\t35.42\n",
"4472165.3.31762_6165\t12304087\t57.6923\t26\t11\t0\t12\t89\t999\t1024\t0.86\t35.42\n",
"4472165.3.31762_6165\t12022820\t57.6923\t26\t11\t0\t12\t89\t1721\t1746\t0.97\t35.04\n",
"4472165.3.31762_6165\t3545817\t57.6923\t26\t11\t0\t12\t89\t418\t443\t0.97\t35.04\n",
"4472165.3.31762_6165\t11801263\t57.6923\t26\t11\t0\t12\t89\t54\t79\t0.97\t35.04\n",
"4472165.3.31762_10621\t6133288\t45.8333\t24\t13\t0\t75\t4\t35\t58\t0.86\t35.42\n",
"4472165.3.31762_10621\t12247112\t45.8333\t24\t13\t0\t75\t4\t1318\t1341\t0.86\t35.42\n",
"4472165.3.31762_10621\t4381619\t45.8333\t24\t13\t0\t75\t4\t423\t446\t0.98\t35.04\n",
"4472165.4.31762_6165\t11846454\t50\t24\t12\t0\t12\t83\t967\t990\t0.405978\t27.72\n",
"4472165.4.31762_6165\t11846454\t90.9091\t11\t1\t0\t54\t86\t197\t207\t0.405978\t28.11\n",
"4472165.4.31762_6165\t11815348\t57.6923\t26\t11\t0\t12\t89\t55\t80\t0.86\t35.42\n",
"4472165.4.31762_6165\t12307352\t57.6923\t26\t11\t0\t12\t89\t1005\t1030\t0.86\t35.42\n",
"4472165.4.31762_6165\t12304087\t57.6923\t26\t11\t0\t12\t89\t999\t1024\t0.86\t35.42\n",
"4472165.4.31762_6165\t12022820\t57.6923\t26\t11\t0\t12\t89\t1721\t1746\t0.97\t35.04\n",
"4472165.4.31762_6165\t3545817\t57.6923\t26\t11\t0\t12\t89\t418\t443\t0.97\t35.04\n",
"4472165.4.31762_6165\t11801263\t57.6923\t26\t11\t0\t12\t89\t54\t79\t0.97\t35.04\n",
"4472165.4.31762_10621\t6133288\t45.8333\t24\t13\t0\t75\t4\t35\t58\t0.86\t35.42\n",
"4472165.4.31762_10621\t12247112\t45.8333\t24\t13\t0\t75\t4\t1318\t1341\t0.86\t35.42\n",
"4472165.4.31762_10621\t4381619\t45.8333\t24\t13\t0\t75\t4\t423\t446\t0.98\t35.04\"\"\"\n",
"\n",
"import tempfile\n",
"\n",
"tf = tempfile.NamedTemporaryFile(delete=False)\n",
"tf.write(s)\n",
"tf.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Update the minimum percent id and the MaxEvalue here - note that RapSearch2 is outputting \n",
"# log(e-value). The MaxEvalue used here should be the log(e-value).\n",
"\n",
"from brokit.blat import MinimalBlatParser9\n",
"\n",
"max_log_evalue = 1.0\n",
"minimum_pct_id = 0.55\n",
"\n",
"def bl9_to_observation_map(raw_output_fp, output_observation_map_fp, max_log_evalue, minimum_pct_id):\n",
" \"\"\" Generate observation map from .bl9 file\n",
" \"\"\"\n",
" result = {}\n",
" pct_id_field = 2\n",
" evalue_field = 10\n",
" output_observation_map_f = open(output_observation_map_fp, 'w')\n",
" last_observed_query_id = None\n",
" for summary, blat_results in MinimalBlatParser9(open(raw_output_fp, 'U'), include_column_names=False):\n",
" for e in blat_results:\n",
" if (float(e[evalue_field]) <= max_log_evalue and\n",
" float(e[pct_id_field]) / 100. >= minimum_pct_id):\n",
" query_id = e[0]\n",
" subject_id = e[1]\n",
" if query_id == last_observed_query_id:\n",
" # we've observed a duplicate hit, ignore this one\n",
" continue\n",
" else:\n",
" last_observed_query_id = query_id\n",
" try:\n",
" result[subject_id].append(query_id)\n",
" except KeyError:\n",
" result[subject_id] = [query_id]\n",
" for e in result.items():\n",
" output_observation_map_f.write(\n",
" '%s\\t%s\\n' %\n",
" (e[0], '\\t'.join(e[1])))\n",
" output_observation_map_f.close()\n",
" return result"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result = bl9_to_observation_map(tf.name, 'observation-map.txt', max_log_evalue, minimum_pct_id)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cat observation-map.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"11846454\t4472165.3.31762_6165\t4472165.4.31762_6165\r\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!make_otu_table.py -i observation-map.txt -o table.biom"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!biom summarize-table -i table.biom"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Num samples: 2\r\n",
"Num observations: 1\r\n",
"Total count: 2\r\n",
"Table density (fraction of non-zero values): 1.000\r\n",
"\r\n",
"Counts/sample summary:\r\n",
" Min: 1.0\r\n",
" Max: 1.0\r\n",
" Median: 1.000\r\n",
" Mean: 1.000\r\n",
" Std. dev.: 0.000\r\n",
" Sample Metadata Categories: \r\n",
" Observation Metadata Categories: \r\n",
"\r\n",
"Counts/sample detail:\r\n",
" 4472165.3.31762: 1.0\r\n",
" 4472165.4.31762: 1.0\r\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
}
],
"metadata": {}
}
]
} | bsd-3-clause |
Phylex/Vakuumtechnik | programme/Auswertung Aufgabe 4.ipynb | 1 | 22132 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy import stats\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as mpatches\n",
"import numpy as np\n",
"import auswertung as au"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"au.import_experiment_data('../Daten/2017-05-08-1414-Aufgabe 4')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['Druck IM', 'Bar', 'IM', -5, 0.0], ['Zeit', 's', 'uhr', 0, 0.0]]\n"
]
}
],
"source": [
"DruckT1 = au.messungen[0]\n",
"p0 = au.messungen[0][0]\n",
"V = 10.1\n",
"Zeit = au.messungen[1]\n",
"print(au.messgroessen)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: divide by zero encountered in double_scalars\n",
" app.launch_new_instance()\n",
"/usr/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in double_scalars\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"# calculate the neccesary data\n",
"logpdurchp0 = [np.log(elem/p0) for elem in DruckT1]\n",
"Sp = [-V*elem*(1/Zeit[i]) for i,elem in enumerate(logpdurchp0)]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# create LaTeX table\n",
"tabellendaten = au.pivot_table(au.messungen)\n",
"au.Create_Messdaten_tabellen('../Daten/Aufgabe4Tabelle',au.messgroessen, tabellendaten)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.rc('text', usetex=True)\n",
"plt.rc('font', family='serif')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f8b6e6c80f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# render figure\n",
"figure = plt.figure()\n",
"axis_1 = figure.add_subplot(111)\n",
"xlabel_1 = axis_1.set_xlabel(r'Druck [$10^{-5}$ Bar]')\n",
"ylabel_1 = axis_1.set_ylabel('S(p) [l/s]')\n",
"T1kurve = axis_1.plot(DruckT1, Sp, 'bx', label='S(p)')\n",
"plt.legend()\n",
"\n",
"plt.savefig('../Daten/grap_aufgabe4.pdf')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f8b6e3a9cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure = plt.figure()\n",
"axis_1= figure.add_subplot(111)\n",
"xlabel_1 = axis_1.set_xlabel('ln(Druck)')\n",
"ylabel_1 = axis_1.set_ylabel('S(p) [l/s]')\n",
"SpLogp = axis_1.plot(np.log10(DruckT1), Sp, color='cyan', marker='x', linestyle ='', label='S(p)')\n",
"plt.legend()\n",
"\n",
"plt.savefig('../Daten/grap_aufgabe_ln_4.pdf')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1.31657251 0.59 ]\n",
" [ 1.386953 0.13 ]\n",
" [ 1.28148139 0.05 ]\n",
" [ 1.12837189 0.03 ]]\n"
]
}
],
"source": [
"mean_range = np.array([ x for x in zip(Sp,DruckT1) if 1.5 > x[0] > 1])\n",
"print(mean_range)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.27834469657\n",
"Mean Performance of Pump = 4.60204090766 m^3/h\n"
]
}
],
"source": [
"mean_performance = np.mean(au.pivot_table(mean_range)[0])\n",
"print(mean_performance)\n",
"print('Mean Performance of Pump = '+str(mean_performance*3.6)+' m^3/h')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
georgetown-analytics/yelp-classification | machine_learning/JSON_cleanup.ipynb | 1 | 7520 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### JSON Cleanup Process"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h4> Main JSON Files: </h4>\n",
" \n",
"1. yelp_academic_dataset_business\n",
"2. yelp_academic_dataset_review\n",
"3. yelp_academic_dataset_customer\n",
"\n",
"<h4> For each business entry: </h4>\n",
"<ul>\n",
" <li> Keep if business is restaurant </li>\n",
" <li> Keep if location is in state list </li>\n",
" <li> Output list of valid businesses </li>\n",
" <li> Output csv file for all busineses and business attributes </li>\n",
"</ul>\n",
"\n",
"<h4> For each review entry: </h4>\n",
"<ul>\n",
" <li> Keep only businesses in valid business list </li>\n",
" <li> Create a dictionary with each business as a key </li>\n",
" <li> For each key, create a list of unique customers in that business </li>\n",
" <li> Find the unique list of customers for all businesses </li>\n",
" <li> Output csv file for all reviews and review attributes </li>\n",
"</ul>\n",
"\n",
"<h4> For each customer entry: </h4>\n",
"<ul>\n",
" <li> Keep if customer in the unique list of customers for all businesses </li>\n",
" <li> Output csv file of all customers and customer attributes </li>\n",
" </ul>"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import json\n",
"\n",
"def clean_states(filename, states):\n",
" records = []\n",
" with open(filename, 'r') as fd:\n",
" for line in fd:\n",
" j_content = json.loads(line)\n",
" if j_content['state'] in states:\n",
" records.append(j_content)\n",
" \n",
" return records\n",
"\n",
"def clean_restaurants(json_list):\n",
" records = []\n",
" for json in json_list:\n",
" try:\n",
" if 'Restaurants' in json['categories'] or 'Restaurant' in json['categories']:\n",
" records.append(json)\n",
" except TypeError:\n",
" pass\n",
" \n",
" return records\n",
"\n",
"def return_ids(data_list, id_type):\n",
" id_list = []\n",
" for data_point in data_list:\n",
" id_list.append(data_point[id_type])\n",
" \n",
" return id_list\n",
"\n",
"def filter_reviews(filename, business_list):\n",
" records = []\n",
" with open(filename, 'r') as fd:\n",
" for line in fd:\n",
" j_content = json.loads(line)\n",
" if j_content['business_id'] in business_list:\n",
" records.append(j_content)\n",
" \n",
" return records\n",
"\n",
"def filter_customers(filename):\n",
" records = []\n",
" \n",
" with open(filename, 'r') as fd:\n",
" for line in fd:\n",
" j_content = json.loads(line)\n",
" for record in j_content:\n",
" records.append(record['user_id'])\n",
" \n",
" customer_file = '/Volumes/Data/yelp_dataset/yelp_academic_dataset_user.json'\n",
" customer_records = []\n",
" \n",
" with open(customer_file, 'r') as fd:\n",
" for line in fd:\n",
" j_content = json.loads(line)\n",
" if j_content['user_id'] in set(records):\n",
" customer_records.append(j_content)\n",
"\n",
" return customer_records\n",
"\n",
"def output_data(json_data, filename):\n",
" output_directory = \"/Volumes/Data/yelp_dataset/cleaned_data/\"\n",
" filename = output_directory + filename\n",
" with open(filename, 'w') as outfile:\n",
" json.dump(json_data, outfile)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Declare data directory and input JSON file\n",
"data_dir = \"/Volumes/data/yelp_dataset/\"\n",
"business_jsonFile = data_dir + 'yelp_academic_dataset_business.json'\n",
"\n",
"#Declare the list of states to be kept\n",
"states = ['AZ', 'IL', 'WI', 'OH', 'NC', 'NV']\n",
"state_data = clean_states(business_jsonFile, states)\n",
"\n",
"#Keep only restaurants\n",
"restaurant_data = clean_restaurants(state_data)\n",
"\n",
"#Output cleaned up JSON file\n",
"business_output = \"cleaned_business_data.json\"\n",
"output_data(restaurant_data, business_output)\n",
"\n",
"#Create a list of unique business IDs\n",
"business_list = list(set(return_ids(restaurant_data, 'business_id')))\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reviews_jsonFile = data_dir + 'yelp_academic_dataset_review.json'\n",
"review_data = filter_reviews(reviews_jsonFile, business_list)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reviews_output = \"cleaned_review_data.json\"\n",
"output_data(review_data, reviews_output)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reviews_file = \"/Users/robertsonwang/Desktop/Python/Yelp/cleaned_review_data.json\"\n",
"customers_list = filter_customers(reviews_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Merge the state data key from the business JSON into the review JSON"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"business_json = json.load(open(\"/Users/robertsonwang/Desktop/Python/Yelp/Yelp_scrapper/cleaned_business_data.json\"))\n",
"\n",
"filtered_biz = {}\n",
"for line in business_json:\n",
" filtered_biz[line['business_id']] = line['state']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reviews_json = json.load(open(\"/Users/robertsonwang/Desktop/Python/Yelp/cleaned_review_data.json\"))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for review in reviews_json:\n",
" review['state'] = filtered_biz[review['business_id']]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open('cleaned_reviews_states', 'w') as outfile:\n",
" json.dump(reviews_json, outfile)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
jinntrance/MOOC | coursera/ml-classification/assignments/module-9-precision-recall-assignment-blank.ipynb | 1 | 101306 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring precision and recall\n",
"\n",
"The goal of this second notebook is to understand precision-recall in the context of classifiers.\n",
"\n",
" * Use Amazon review data in its entirety.\n",
" * Train a logistic regression model.\n",
" * Explore various evaluation metrics: accuracy, confusion matrix, precision, recall.\n",
" * Explore how various metrics can be combined to produce a cost of making an error.\n",
" * Explore precision and recall curves.\n",
" \n",
"Because we are using the full Amazon review dataset (not a subset of words or reviews), in this assignment we return to using GraphLab Create for its efficiency. As usual, let's start by **firing up GraphLab Create**.\n",
"\n",
"Make sure you have the latest version of GraphLab Create (1.8.3 or later). If you don't find the decision tree module, then you would need to upgrade graphlab-create using\n",
"\n",
"```\n",
" pip install graphlab-create --upgrade\n",
"```\n",
"See [this page](https://dato.com/download/) for detailed instructions on upgrading."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"A newer version of GraphLab Create (v1.9) is available! Your current version is v1.8.5.\n",
"\n",
"You can use pip to upgrade the graphlab-create package. For more information see https://dato.com/products/create/upgrade.\n"
]
}
],
"source": [
"import graphlab\n",
"from __future__ import division\n",
"import numpy as np\n",
"graphlab.canvas.set_target('ipynb')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load amazon review dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-05-14 19:52:52,565 [INFO] graphlab.cython.cy_server, 176: GraphLab Create v1.8.5 started. Logging: /tmp/graphlab_server_1463226762.log\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This non-commercial license of GraphLab Create is assigned to [email protected] and will expire on September 27, 2016. For commercial licensing options, visit https://dato.com/buy/.\n"
]
}
],
"source": [
"products = graphlab.SFrame('amazon_baby.gl/')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Extract word counts and sentiments"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As in the first assignment of this course, we compute the word counts for individual words and extract positive and negative sentiments from ratings. To summarize, we perform the following:\n",
"\n",
"1. Remove punctuation.\n",
"2. Remove reviews with \"neutral\" sentiment (rating 3).\n",
"3. Set reviews with rating 4 or more to be positive and those with 2 or less to be negative."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def remove_punctuation(text):\n",
" import string\n",
" return text.translate(None, string.punctuation) \n",
"\n",
"# Remove punctuation.\n",
"review_clean = products['review'].apply(remove_punctuation)\n",
"\n",
"# Count words\n",
"products['word_count'] = graphlab.text_analytics.count_words(review_clean)\n",
"\n",
"# Drop neutral sentiment reviews.\n",
"products = products[products['rating'] != 3]\n",
"\n",
"# Positive sentiment to +1 and negative sentiment to -1\n",
"products['sentiment'] = products['rating'].apply(lambda rating : +1 if rating > 3 else -1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's remember what the dataset looks like by taking a quick peek:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sentiment</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Wipe Pouch</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">it came early and was not<br>disappointed. i love ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3, 'love': 1,<br>'it': 3, 'highly': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'quilt': 1,<br>'it': 1, 'comfortable': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. I ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3, 'ingenious':<br>1, 'love': 2, 'what': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'all': 2,<br>'help': 1, 'cried': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">When the Binky Fairy came<br>to our house, we didn't ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'this': 2,<br>'her': 1, 'help': 2, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A Tale of Baby's Days<br>with Peter Rabbit ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lovely book, it's bound<br>tightly so you may no ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'shop': 1, 'noble': 1,<br>'is': 1, 'it': 1, 'as': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Perfect for new parents.<br>We were able to keep ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'all': 1,<br>'right': 1, 'had': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A friend of mine pinned<br>this product on Pinte ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1, 'fantastic':<br>1, 'help': 1, 'give': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This has been an easy way<br>for my nanny to record ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 1, 'standarad':<br>1, 'another': 1, 'when': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I love this journal and<br>our nanny uses it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 2, 'nannys': 1,<br>'just': 1, 'food': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
"</table>\n",
"[166752 rows x 5 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\tword_count\tdict\n",
"\tsentiment\tint\n",
"\n",
"Rows: 166752\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n",
"| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n",
"| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n",
"| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n",
"| Baby Tracker® - Daily ... | Perfect for new parents. W... | 5.0 |\n",
"| Baby Tracker® - Daily ... | A friend of mine pinned th... | 5.0 |\n",
"| Baby Tracker® - Daily ... | This has been an easy way ... | 4.0 |\n",
"| Baby Tracker® - Daily ... | I love this journal and ou... | 4.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"+-------------------------------+-----------+\n",
"| word_count | sentiment |\n",
"+-------------------------------+-----------+\n",
"| {'and': 3, 'love': 1, 'it'... | 1 |\n",
"| {'and': 2, 'quilt': 1, 'it... | 1 |\n",
"| {'and': 3, 'ingenious': 1,... | 1 |\n",
"| {'and': 2, 'all': 2, 'help... | 1 |\n",
"| {'and': 2, 'this': 2, 'her... | 1 |\n",
"| {'shop': 1, 'noble': 1, 'i... | 1 |\n",
"| {'and': 2, 'all': 1, 'righ... | 1 |\n",
"| {'and': 1, 'fantastic': 1,... | 1 |\n",
"| {'all': 1, 'standarad': 1,... | 1 |\n",
"| {'all': 2, 'nannys': 1, 'j... | 1 |\n",
"+-------------------------------+-----------+\n",
"[166752 rows x 5 columns]\n",
"Note: Only the head of the SFrame is printed.\n",
"You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Split data into training and test sets\n",
"\n",
"We split the data into a 80-20 split where 80% is in the training set and 20% is in the test set."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data, test_data = products.random_split(.8, seed=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train a logistic regression classifier\n",
"\n",
"We will now train a logistic regression classifier with **sentiment** as the target and **word_count** as the features. We will set `validation_set=None` to make sure everyone gets exactly the same results. \n",
"\n",
"Remember, even though we now know how to implement logistic regression, we will use GraphLab Create for its efficiency at processing this Amazon dataset in its entirety. The focus of this assignment is instead on the topic of precision and recall."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<pre>Logistic regression:</pre>"
],
"text/plain": [
"Logistic regression:"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of examples : 133416</pre>"
],
"text/plain": [
"Number of examples : 133416"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of classes : 2</pre>"
],
"text/plain": [
"Number of classes : 2"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of feature columns : 1</pre>"
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"Number of feature columns : 1"
]
},
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},
{
"data": {
"text/html": [
"<pre>Number of unpacked features : 121712</pre>"
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"Number of unpacked features : 121712"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of coefficients : 121713</pre>"
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"text/plain": [
"Number of coefficients : 121713"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Starting L-BFGS</pre>"
],
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"Starting L-BFGS"
]
},
"metadata": {},
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{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
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"--------------------------------------------------------"
]
},
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{
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"text/html": [
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"+-----------+----------+-----------+--------------+-------------------+"
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},
"metadata": {},
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},
{
"data": {
"text/html": [
"<pre>| Iteration | Passes | Step size | Elapsed Time | Training-accuracy |</pre>"
],
"text/plain": [
"| Iteration | Passes | Step size | Elapsed Time | Training-accuracy |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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"text/plain": [
"+-----------+----------+-----------+--------------+-------------------+"
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},
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},
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"text/html": [
"<pre>This model may not be ideal. To improve it, consider doing one of the following:\n",
"(a) Increasing the regularization.\n",
"(b) Standardizing the input data.\n",
"(c) Removing highly correlated features.\n",
"(d) Removing `inf` and `NaN` values in the training data.</pre>"
],
"text/plain": [
"This model may not be ideal. To improve it, consider doing one of the following:\n",
"(a) Increasing the regularization.\n",
"(b) Standardizing the input data.\n",
"(c) Removing highly correlated features.\n",
"(d) Removing `inf` and `NaN` values in the training data."
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = graphlab.logistic_classifier.create(train_data, target='sentiment',\n",
" features=['word_count'],\n",
" validation_set=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will explore the advanced model evaluation concepts that were discussed in the lectures.\n",
"\n",
"## Accuracy\n",
"\n",
"One performance metric we will use for our more advanced exploration is accuracy, which we have seen many times in past assignments. Recall that the accuracy is given by\n",
"\n",
"$$\n",
"\\mbox{accuracy} = \\frac{\\mbox{# correctly classified data points}}{\\mbox{# total data points}}\n",
"$$\n",
"\n",
"To obtain the accuracy of our trained models using GraphLab Create, simply pass the option `metric='accuracy'` to the `evaluate` function. We compute the **accuracy** of our logistic regression model on the **test_data** as follows:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy: 0.914536837053\n"
]
}
],
"source": [
"accuracy= model.evaluate(test_data, metric='accuracy')['accuracy']\n",
"print \"Test Accuracy: %s\" % accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Baseline: Majority class prediction\n",
"\n",
"Recall from an earlier assignment that we used the **majority class classifier** as a baseline (i.e reference) model for a point of comparison with a more sophisticated classifier. The majority classifier model predicts the majority class for all data points. \n",
"\n",
"Typically, a good model should beat the majority class classifier. Since the majority class in this dataset is the positive class (i.e., there are more positive than negative reviews), the accuracy of the majority class classifier can be computed as follows:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Baseline accuracy (majority class classifier): 0.842782577394\n"
]
}
],
"source": [
"baseline = len(test_data[test_data['sentiment'] == 1])/len(test_data)\n",
"print \"Baseline accuracy (majority class classifier): %s\" % baseline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Quiz Question:** Using accuracy as the evaluation metric, was our **logistic regression model** better than the baseline (majority class classifier)?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Confusion Matrix\n",
"\n",
"The accuracy, while convenient, does not tell the whole story. For a fuller picture, we turn to the **confusion matrix**. In the case of binary classification, the confusion matrix is a 2-by-2 matrix laying out correct and incorrect predictions made in each label as follows:\n",
"```\n",
" +---------------------------------------------+\n",
" | Predicted label |\n",
" +----------------------+----------------------+\n",
" | (+1) | (-1) |\n",
"+-------+-----+----------------------+----------------------+\n",
"| True |(+1) | # of true positives | # of false negatives |\n",
"| label +-----+----------------------+----------------------+\n",
"| |(-1) | # of false positives | # of true negatives |\n",
"+-------+-----+----------------------+----------------------+\n",
"```\n",
"To print out the confusion matrix for a classifier, use `metric='confusion_matrix'`:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">target_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">count</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1406</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3798</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1443</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">26689</td>\n",
" </tr>\n",
"</table>\n",
"[4 rows x 3 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\ttarget_label\tint\n",
"\tpredicted_label\tint\n",
"\tcount\tint\n",
"\n",
"Rows: 4\n",
"\n",
"Data:\n",
"+--------------+-----------------+-------+\n",
"| target_label | predicted_label | count |\n",
"+--------------+-----------------+-------+\n",
"| 1 | -1 | 1406 |\n",
"| -1 | -1 | 3798 |\n",
"| -1 | 1 | 1443 |\n",
"| 1 | 1 | 26689 |\n",
"+--------------+-----------------+-------+\n",
"[4 rows x 3 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"confusion_matrix = model.evaluate(test_data, metric='confusion_matrix')['confusion_matrix']\n",
"confusion_matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: How many predicted values in the **test set** are **false positives**?"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"false_pos = 1443"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Computing the cost of mistakes\n",
"\n",
"\n",
"Put yourself in the shoes of a manufacturer that sells a baby product on Amazon.com and you want to monitor your product's reviews in order to respond to complaints. Even a few negative reviews may generate a lot of bad publicity about the product. So you don't want to miss any reviews with negative sentiments --- you'd rather put up with false alarms about potentially negative reviews instead of missing negative reviews entirely. In other words, **false positives cost more than false negatives**. (It may be the other way around for other scenarios, but let's stick with the manufacturer's scenario for now.)\n",
"\n",
"Suppose you know the costs involved in each kind of mistake: \n",
"1. \\$100 for each false positive.\n",
"2. \\$1 for each false negative.\n",
"3. Correctly classified reviews incur no cost.\n",
"\n",
"**Quiz Question**: Given the stipulation, what is the cost associated with the logistic regression classifier's performance on the **test set**?"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"145706"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cost = 1443*100+1406 \n",
"cost "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Precision and Recall"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You may not have exact dollar amounts for each kind of mistake. Instead, you may simply prefer to reduce the percentage of false positives to be less than, say, 3.5% of all positive predictions. This is where **precision** comes in:\n",
"\n",
"$$\n",
"[\\text{precision}] = \\frac{[\\text{# positive data points with positive predicitions}]}{\\text{[# all data points with positive predictions]}} = \\frac{[\\text{# true positives}]}{[\\text{# true positives}] + [\\text{# false positives}]}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So to keep the percentage of false positives below 3.5% of positive predictions, we must raise the precision to 96.5% or higher. \n",
"\n",
"**First**, let us compute the precision of the logistic regression classifier on the **test_data**."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision on test data: 0.948706099815\n"
]
}
],
"source": [
"precision = model.evaluate(test_data, metric='precision')['precision']\n",
"print \"Precision on test data: %s\" % precision"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: Out of all reviews in the **test set** that are predicted to be positive, what fraction of them are **false positives**? (Round to the second decimal place e.g. 0.25)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.05129390018484292"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1- precision"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question:** Based on what we learned in lecture, if we wanted to reduce this fraction of false positives to be below 3.5%, we would: (see the quiz)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A complementary metric is **recall**, which measures the ratio between the number of true positives and that of (ground-truth) positive reviews:\n",
"\n",
"$$\n",
"[\\text{recall}] = \\frac{[\\text{# positive data points with positive predicitions}]}{\\text{[# all positive data points]}} = \\frac{[\\text{# true positives}]}{[\\text{# true positives}] + [\\text{# false negatives}]}\n",
"$$\n",
"\n",
"Let us compute the recall on the **test_data**."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recall on test data: 0.949955508098\n"
]
}
],
"source": [
"recall = model.evaluate(test_data, metric='recall')['recall']\n",
"print \"Recall on test data: %s\" % recall"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: What fraction of the positive reviews in the **test_set** were correctly predicted as positive by the classifier?\n",
"\n",
"**Quiz Question**: What is the recall value for a classifier that predicts **+1** for all data points in the **test_data**?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Precision-recall tradeoff\n",
"\n",
"In this part, we will explore the trade-off between precision and recall discussed in the lecture. We first examine what happens when we use a different threshold value for making class predictions. We then explore a range of threshold values and plot the associated precision-recall curve. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Varying the threshold\n",
"\n",
"False positives are costly in our example, so we may want to be more conservative about making positive predictions. To achieve this, instead of thresholding class probabilities at 0.5, we can choose a higher threshold. \n",
"\n",
"Write a function called `apply_threshold` that accepts two things\n",
"* `probabilities` (an SArray of probability values)\n",
"* `threshold` (a float between 0 and 1).\n",
"\n",
"The function should return an array, where each element is set to +1 or -1 depending whether the corresponding probability exceeds `threshold`."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def apply_threshold(probabilities, threshold):\n",
" ### YOUR CODE GOES HERE\n",
" # +1 if >= threshold and -1 otherwise.\n",
" return graphlab.SArray([1 if p >= threshold else -1 for p in probabilities])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run prediction with `output_type='probability'` to get the list of probability values. Then use thresholds set at 0.5 (default) and 0.9 to make predictions from these probability values."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"probabilities = model.predict(test_data, output_type='probability')\n",
"predictions_with_default_threshold = apply_threshold(probabilities, 0.5)\n",
"predictions_with_high_threshold = apply_threshold(probabilities, 0.9)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of positive predicted reviews (threshold = 0.5): 28132\n"
]
}
],
"source": [
"print \"Number of positive predicted reviews (threshold = 0.5): %s\" % (predictions_with_default_threshold == 1).sum()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of positive predicted reviews (threshold = 0.9): 25630\n"
]
}
],
"source": [
"print \"Number of positive predicted reviews (threshold = 0.9): %s\" % (predictions_with_high_threshold == 1).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: What happens to the number of positive predicted reviews as the threshold increased from 0.5 to 0.9?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploring the associated precision and recall as the threshold varies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By changing the probability threshold, it is possible to influence precision and recall. We can explore this as follows:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Threshold = 0.5\n",
"precision_with_default_threshold = graphlab.evaluation.precision(test_data['sentiment'],\n",
" predictions_with_default_threshold)\n",
"\n",
"recall_with_default_threshold = graphlab.evaluation.recall(test_data['sentiment'],\n",
" predictions_with_default_threshold)\n",
"\n",
"# Threshold = 0.9\n",
"precision_with_high_threshold = graphlab.evaluation.precision(test_data['sentiment'],\n",
" predictions_with_high_threshold)\n",
"recall_with_high_threshold = graphlab.evaluation.recall(test_data['sentiment'],\n",
" predictions_with_high_threshold)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision (threshold = 0.5): 0.948706099815\n",
"Recall (threshold = 0.5) : 0.949955508098\n"
]
}
],
"source": [
"print \"Precision (threshold = 0.5): %s\" % precision_with_default_threshold\n",
"print \"Recall (threshold = 0.5) : %s\" % recall_with_default_threshold"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision (threshold = 0.9): 0.969527896996\n",
"Recall (threshold = 0.9) : 0.884463427656\n"
]
}
],
"source": [
"print \"Precision (threshold = 0.9): %s\" % precision_with_high_threshold\n",
"print \"Recall (threshold = 0.9) : %s\" % recall_with_high_threshold"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question (variant 1)**: Does the **precision** increase with a higher threshold?\n",
"\n",
"**Quiz Question (variant 2)**: Does the **recall** increase with a higher threshold?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Precision-recall curve\n",
"\n",
"Now, we will explore various different values of tresholds, compute the precision and recall scores, and then plot the precision-recall curve."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0.5 0.50505051 0.51010101 0.51515152 0.52020202 0.52525253\n",
" 0.53030303 0.53535354 0.54040404 0.54545455 0.55050505 0.55555556\n",
" 0.56060606 0.56565657 0.57070707 0.57575758 0.58080808 0.58585859\n",
" 0.59090909 0.5959596 0.6010101 0.60606061 0.61111111 0.61616162\n",
" 0.62121212 0.62626263 0.63131313 0.63636364 0.64141414 0.64646465\n",
" 0.65151515 0.65656566 0.66161616 0.66666667 0.67171717 0.67676768\n",
" 0.68181818 0.68686869 0.69191919 0.6969697 0.7020202 0.70707071\n",
" 0.71212121 0.71717172 0.72222222 0.72727273 0.73232323 0.73737374\n",
" 0.74242424 0.74747475 0.75252525 0.75757576 0.76262626 0.76767677\n",
" 0.77272727 0.77777778 0.78282828 0.78787879 0.79292929 0.7979798\n",
" 0.8030303 0.80808081 0.81313131 0.81818182 0.82323232 0.82828283\n",
" 0.83333333 0.83838384 0.84343434 0.84848485 0.85353535 0.85858586\n",
" 0.86363636 0.86868687 0.87373737 0.87878788 0.88383838 0.88888889\n",
" 0.89393939 0.8989899 0.9040404 0.90909091 0.91414141 0.91919192\n",
" 0.92424242 0.92929293 0.93434343 0.93939394 0.94444444 0.94949495\n",
" 0.95454545 0.95959596 0.96464646 0.96969697 0.97474747 0.97979798\n",
" 0.98484848 0.98989899 0.99494949 1. ]\n"
]
}
],
"source": [
"threshold_values = np.linspace(0.5, 1, num=100)\n",
"print threshold_values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each of the values of threshold, we compute the precision and recall scores."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"precision_all = []\n",
"recall_all = []\n",
"\n",
"probabilities = model.predict(test_data, output_type='probability')\n",
"for threshold in threshold_values:\n",
" predictions = apply_threshold(probabilities, threshold)\n",
" \n",
" precision = graphlab.evaluation.precision(test_data['sentiment'], predictions)\n",
" recall = graphlab.evaluation.recall(test_data['sentiment'], predictions)\n",
" \n",
" precision_all.append(precision)\n",
" recall_all.append(recall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's plot the precision-recall curve to visualize the precision-recall tradeoff as we vary the threshold."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YP42yGRVMmjOFirnTKJk+idJpkyiZNoni8tK0xywiMpEooeagqhPmQJ/Ts6+L3n3d9O7r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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11996e810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"def plot_pr_curve(precision, recall, title):\n",
" plt.rcParams['figure.figsize'] = 7, 5\n",
" plt.locator_params(axis = 'x', nbins = 5)\n",
" plt.plot(precision, recall, 'b-', linewidth=4.0, color = '#B0017F')\n",
" plt.title(title)\n",
" plt.xlabel('Precision')\n",
" plt.ylabel('Recall')\n",
" plt.rcParams.update({'font.size': 16})\n",
" \n",
"plot_pr_curve(precision_all, recall_all, 'Precision recall curve (all)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: Among all the threshold values tried, what is the **smallest** threshold value that achieves a precision of 96.5% or better? Round your answer to 3 decimal places."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.838383838384\n"
]
}
],
"source": [
"za = zip(threshold_values, precision_all)\n",
"for t, p in za:\n",
" if p >= 0.965:\n",
" print t\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: Using `threshold` = 0.98, how many **false negatives** do we get on the **test_data**? (**Hint**: You may use the `graphlab.evaluation.confusion_matrix` function implemented in GraphLab Create.)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">target_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_label</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">count</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">487</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22269</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5826</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4754</td>\n",
" </tr>\n",
"</table>\n",
"[4 rows x 3 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\ttarget_label\tint\n",
"\tpredicted_label\tint\n",
"\tcount\tint\n",
"\n",
"Rows: 4\n",
"\n",
"Data:\n",
"+--------------+-----------------+-------+\n",
"| target_label | predicted_label | count |\n",
"+--------------+-----------------+-------+\n",
"| -1 | 1 | 487 |\n",
"| 1 | 1 | 22269 |\n",
"| 1 | -1 | 5826 |\n",
"| -1 | -1 | 4754 |\n",
"+--------------+-----------------+-------+\n",
"[4 rows x 3 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions = apply_threshold(probabilities, 0.98)\n",
"graphlab.evaluation.confusion_matrix(test_data['sentiment'],\n",
" predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"This is the number of false negatives (i.e the number of reviews to look at when not needed) that we have to deal with using this classifier."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluating specific search terms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So far, we looked at the number of false positives for the **entire test set**. In this section, let's select reviews using a specific search term and optimize the precision on these reviews only. After all, a manufacturer would be interested in tuning the false positive rate just for their products (the reviews they want to read) rather than that of the entire set of products on Amazon.\n",
"\n",
"## Precision-Recall on all baby related items\n",
"\n",
"From the **test set**, select all the reviews for all products with the word 'baby' in them."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"baby_reviews = test_data[test_data['name'].apply(lambda x: 'baby' in x.lower())]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's predict the probability of classifying these reviews as positive:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"probabilities = model.predict(baby_reviews, output_type='probability')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot the precision-recall curve for the **baby_reviews** dataset.\n",
"\n",
"**First**, let's consider the following `threshold_values` ranging from 0.5 to 1:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"threshold_values = np.linspace(0.5, 1, num=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Second**, as we did above, let's compute precision and recall for each value in `threshold_values` on the **baby_reviews** dataset. Complete the code block below."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"precision_all = []\n",
"recall_all = []\n",
"\n",
"for threshold in threshold_values:\n",
" \n",
" # Make predictions. Use the `apply_threshold` function \n",
" ## YOUR CODE HERE \n",
" predictions = apply_threshold(probabilities, threshold)\n",
"\n",
" # Calculate the precision.\n",
" # YOUR CODE HERE\n",
" precision = graphlab.evaluation.precision(baby_reviews['sentiment'], predictions)\n",
" \n",
" # YOUR CODE HERE\n",
" recall = graphlab.evaluation.recall(baby_reviews['sentiment'], predictions)\n",
" \n",
" # Append the precision and recall scores.\n",
" precision_all.append(precision)\n",
" recall_all.append(recall)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question**: Among all the threshold values tried, what is the **smallest** threshold value that achieves a precision of 96.5% or better for the reviews of data in **baby_reviews**? Round your answer to 3 decimal places."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.863636363636\n"
]
}
],
"source": [
"za = zip(threshold_values, precision_all)\n",
"for t, p in za:\n",
" if p >= 0.965:\n",
" print t\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Quiz Question:** Is this threshold value smaller or larger than the threshold used for the entire dataset to achieve the same specified precision of 96.5%?\n",
"\n",
"**Finally**, let's plot the precision recall curve."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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24Ee4qpq95md2y2XszVMYfPFE0jLSA8hQEkEFNUYqqCLSmB0LNvPlT19l0/+W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\nTrM3hrBUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119990fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_pr_curve(precision_all, recall_all, \"Precision-Recall (Baby)\")"
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
jdvelasq/machine-learning | old/ML-R-10-modelado-de-topicos.ipynb | 1 | 71533 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Aprendizaje de maquinas -- R -- Modelado de tópicos\n",
"Notas de clase sobre aprendizaje de maquinas usando R"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Juan David Velásquez Henao** \n",
"[email protected] \n",
"Universidad Nacional de Colombia, Sede Medellín \n",
"Facultad de Minas \n",
"Medellín, Colombia \n",
"\n",
"[Licencia]\n",
"\n",
"[Readme]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Software utilizado**.\n",
"\n",
"> Este es un documento interactivo escrito como un notebook de [Jupyter](http://jupyter.org), en el cual se presenta un tutorial sobre regresión logistica usando **R** en el contexto de aprendizaje de maquinas. Los notebooks de Jupyter permiten incoporar simultáneamente código, texto, gráficos y ecuaciones. El código presentado en este notebook puede ejecutarse en los sistemas operativos Linux y OS X.\n",
"\n",
"> Haga click [aquí](https://github.com/jdvelasq/guias-de-instalacion) para obtener instrucciones detalladas sobre como instalar Jupyter en Windows y Mac OS X.\n",
"\n",
"> Haga clic [aquí] para ver la última versión de este documento en nbviewer.\n",
"\n",
"> Descargue la última versión de este documento a su disco duro; luego, carguelo y ejecutelo en línea en [Try Jupyter!](https://try.jupyter.org)\n",
"\n",
"> Haga clic [aquí](https://github.com/jdvelasq/ETVL-R/blob/master/ETVL-R-5-visualizacion-1-base.ipynb) para ver el tutorial de visualización y gráficas."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Contenido"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
">* [Introducción](#Introducción)\n",
"* [LDA](#LDA)\n",
" * [Especificación y estimación del modelo](#Especificación-y-estimación-del-modelo)\n",
"* [Aplicación 1](#Aplicación-1)\n",
"* [Aplicación 2](#Aplicación-2)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Bibliografía**.\n",
"\n",
"> \n",
"\n",
"**Material complementario.**\n",
"> Webinar RStudio [Getting your data into R](https://www.rstudio.com/resources/webinars/getting-your-data-into-r/) \n",
"\n",
"---\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introducción\n",
"[Contenido](#Contenido)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"El modelado de tópicos es un área relativamente reciente que se originó en los campos de procesamiento del lenguaje natural y la recuperación de información, pero tambien ha sido aplicado en una serie de otros dominios. Muchos problemas de clasificación, como el análisis del sentimientos, implican asignar una sola clase a una observación particular por otra parte, en el modelado de tópicos la idea es asignar una mezcla de diferentes clases a una observación.\n",
"\n",
"Básicamente en este tipo de problema, se tiene un conjunto de características y un conjunto de variables ocultas o latentes que generan estas características. Crucialmente, cada observación en el conjunto datos contiene características que se han generado a partir de una mezcla de un subconjunto de estas variables ocultas. Por ejemplo, un ensayo, un sitio web o un artículo de prensa puede tener un tema central como la política, pero también puede incluir uno o más elementos de otros temas, como los derechos humanos, la historia o la economía.\n",
"\n",
"El modelado de tópicos es utilizado de diferentes maneras en la minería de texto. Una posible aplicación es agrupar documentos similares, basados en su tema más predominante, que finalmente, se puede ver como una forma de agrupación. Donde se estudia la composición del tema, las palabras más frecuentes, así como el tamaño relativo de los grupos que se obtienen, y finalmente se resume la información sobre una colección particular de documentos. \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LDA\n",
"[Contenido](#Contenido)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hay dos métodos de aprendizaje automático con las iniciales LDA:\n",
" 1. Asignación de Dirichlet latente, que es un método de modelado\n",
" 2. Análisis discriminante lineal, que es un método de clasificación\n",
" \n",
"No tienen relación, excepto por el hecho de que las iniciales LDA pueden referirse a cualquiera.\n",
"\n",
"LDA pertenece a una clase de modelos que son llamados modelos generativos ya que tienen una especie de fábula, \n",
"que explica cómo se generaron los datos. Esta historia es generativa a una simplificación de la realidad, por supuesto,\n",
"para hacer más fácil el aprendizaje de la máquina. En primer lugar crear temas mediante la asignación de los pesos \n",
"de probabilidad a las palabras. cada tema será asignar diferentes pesos a diferentes palabras.\n",
"\n",
"El método usado para ajustar los modelos es el VEM (Variational Expectation-Maximization). Tanto R como Python utilizan este método en sus algoritmos."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Especificación y estimación del modelo\n",
"[Contenido](#Contenido)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se tiene un número de tópicos $k$ fijado a-priori. El modelo LDA asume que para un documento $w=(w_1,...,w_N)$ de un corpus $D$ que contiene $N$ palabras de un vocabulario con $V$ términos, $w_i \\in \\{1,...,V\\}$ para todo $i=1,...,N$.\n",
"\n",
"1. El término de distribución $\\beta$ es determinado para cada tópico por:$\\beta \\sim Dirichlet(\\delta)$\n",
"2. Las proporciones $\\theta$ del tópico de distibución para el documento $w$ son determinadas por: $\\theta \\sim Dirichlet(\\alpha)$\n",
"3. Para cada una de la $N$ palabras $w_i$:\n",
" + Escoja un tópico $z_i \\sim Multinomial(\\theta)$\n",
" + Escoja una palabra $w_i$ desde una distribución de probabilidad multinomial condicionada en el tópico $z_i: p(w_i|z_i , \\beta)$\n",
"\n",
"$\\beta$ es el término de distribución de tópicos y contiene la probabilidad de que una palabra ocurra en un tópico dado.\n",
"\n",
"<p>Para estimar el modelo LDA se realiza el proceso de estimación de máxima verosimilitud (MLE - Maximum Likelihood Estimation). La suma del logaritmo de las similitudes de todos los documentos se maximiza con respecto a los parámetros $\\alpha$ y $\\beta$. En este caso $\\beta$ es el parámetro de interés. Para un documento $w \\in D$, la estimación está dada por:\n",
"\n",
"<img src=\"images/topicmodel.png\" width=500>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Aplicación 1\n",
"[Contenido](#Contenido)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Los datos corresponden a la Primera Conferencia de Recuperación de Texto (TREC-1) 1992. Es un objeto de la clase `DocumentTermMatrix` proporcionado por el paquete `tm`. Es una matriz que consta de documento-término y contiene 10473 términos en 2246 documentos."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> [` LDA{topicmodels}`](https://cran.r-project.org/web/packages/topicmodels/topicmodels.pdf) "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
": package 'topicmodels' was built under R version 3.3.2"
]
}
],
"source": [
"## Instale y cargue las siguientes librerías\n",
"library(topicmodels)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Lectura de los datos\n",
"data(\"AssociatedPress\", # Nombre de la base de datos \n",
" package = \"topicmodels\") # Paquete donde extrae los datos\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"[1] 2246"
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"metadata": {},
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],
"source": [
"## Modelo de temas utilizando LDA\n",
"LDA <- LDA(AssociatedPress, \n",
" k = 5)\n",
"\n",
"AssociatedPress$nrow # Número de documentos "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<ol class=list-inline>\n",
"\t<li>5</li>\n",
"\t<li>3</li>\n",
"\t<li>5</li>\n",
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"1. 5\n",
"2. 3\n",
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"6. 1\n",
"\n",
"\n"
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"source": [
"## Temas a los que pertenece los doc.\n",
"head(topics(LDA))\n",
"tail(topics(LDA))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
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CYII=",
"text/plain": [
"Plot with title \"Histogram of topics(LDA)\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Histograma de frecuencia\n",
"options(repr.plot.width=10, repr.plot.height=4)\n",
"\n",
"hist(topics(LDA), # Datos\n",
" breaks=5, # Particiones\n",
" xlab=\"# Temas\", # Nombre del eje x \n",
" ylab=\"# Documentos\", # Nombre del eje y\n",
" col=\"green\") # Color de las barras "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El $\\alpha$ del modelo está definido como 1 dividido el número de tópicos. Se puede configurar el parametro de la siguiente manera: "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Asignación de parametro alpha\n",
"LDA2 <- LDA(AssociatedPress, # Datos\n",
" control = list(alpha = 1), # Alpha\n",
" k = 5)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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gAA4AaubyozGAzKI+XQXNFU5siKBjZl55hiwmq/3+92u8cvBgAAoO6unzLa6/XK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\nAiEAAECkBEIAAIBI/f83dN2IhfSk5AAAAABJRU5ErkJggg==",
"text/plain": [
"Plot with title \"Histogram of topics(LDA2)\""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Histograma de frecuencia LDA2\n",
"options(repr.plot.width=10, repr.plot.height=4)\n",
"\n",
"hist(topics(LDA2), # Datos\n",
" breaks=5, # Particiones\n",
" xlab=\"# Temas\", # Nombre del eje x \n",
" ylab=\"# Documentos\", # Nombre del eje y\n",
" col=\"purple\") # Color de las barras "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<dl class=dl-horizontal>\n",
"\t<dt>Topic 1</dt>\n",
"\t\t<dd>'bush'</dd>\n",
"\t<dt>Topic 2</dt>\n",
"\t\t<dd>'percent'</dd>\n",
"\t<dt>Topic 3</dt>\n",
"\t\t<dd>'government'</dd>\n",
"\t<dt>Topic 4</dt>\n",
"\t\t<dd>'air'</dd>\n",
"\t<dt>Topic 5</dt>\n",
"\t\t<dd>'i'</dd>\n",
"</dl>\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[Topic 1] 'bush'\n",
"\\item[Topic 2] 'percent'\n",
"\\item[Topic 3] 'government'\n",
"\\item[Topic 4] 'air'\n",
"\\item[Topic 5] 'i'\n",
"\\end{description*}\n"
],
"text/markdown": [
"Topic 1\n",
": 'bush'Topic 2\n",
": 'percent'Topic 3\n",
": 'government'Topic 4\n",
": 'air'Topic 5\n",
": 'i'\n",
"\n"
],
"text/plain": [
" Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 \n",
" \"bush\" \"percent\" \"government\" \"air\" \"i\" "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<dl class=dl-horizontal>\n",
"\t<dt>Topic 1</dt>\n",
"\t\t<dd>'million'</dd>\n",
"\t<dt>Topic 2</dt>\n",
"\t\t<dd>'soviet'</dd>\n",
"\t<dt>Topic 3</dt>\n",
"\t\t<dd>'i'</dd>\n",
"\t<dt>Topic 4</dt>\n",
"\t\t<dd>'percent'</dd>\n",
"\t<dt>Topic 5</dt>\n",
"\t\t<dd>'bush'</dd>\n",
"</dl>\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[Topic 1] 'million'\n",
"\\item[Topic 2] 'soviet'\n",
"\\item[Topic 3] 'i'\n",
"\\item[Topic 4] 'percent'\n",
"\\item[Topic 5] 'bush'\n",
"\\end{description*}\n"
],
"text/markdown": [
"Topic 1\n",
": 'million'Topic 2\n",
": 'soviet'Topic 3\n",
": 'i'Topic 4\n",
": 'percent'Topic 5\n",
": 'bush'\n",
"\n"
],
"text/plain": [
" Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 \n",
"\"million\" \"soviet\" \"i\" \"percent\" \"bush\" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Tópicos\n",
"\n",
"terms(LDA)\n",
"terms(LDA2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Aplicación 2\n",
"[Contenido](#Contenido)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"> [`tm {tm}`](https://cran.r-project.org/web/packages/tm/tm.pdf) \n",
"\n",
"\n",
"> [`SnowballC {SnowballC}`](https://cran.r-project.org/web/packages/SnowballC/SnowballC.pdf) \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El comienzo del ejerecicio es para realizar el preprocesamiento de los datos, posteriormente se realizará el modelado de tópicos."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
": package 'tm' was built under R version 3.3.2Loading required package: NLP\n",
"Warning message:\n",
": package 'NLP' was built under R version 3.3.2Warning message:\n",
": package 'SnowballC' was built under R version 3.3.2"
]
}
],
"source": [
"## Instale y cargue las siguientes librerias\n",
"\n",
"library(tm)\n",
"library(SnowballC)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Direccionar el directorio a la carpeta\n",
"setwd(\"topicmodel/\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Obtener lista de archivos con extensión .txt\n",
"filenames <- list.files(getwd(),pattern=\"*.txt\")\n",
"\n",
"## Leer los archivos en un vector de caracteres\n",
"files <- lapply(filenames,readLines)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> [`Corpus {tm}`](https://www.rdocumentation.org/packages/tm/versions/0.3-1/topics/Corpus) "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"c(\"Big Data metaphors we live by\", \"\", \"\", \"When Big Data metaphors erase human sensemaking, and the ways in which values are baked into categories, algorithms and visualizations, we have indeed lost the plot, not found it
",
" \", \"\", \"Quoted from my essay on metaphors for Big Data, co-written with Simon Buckingham Shum:\", \"\")\n"
]
}
],
"source": [
"## Creación 'Corpus' del vector de caracteres\n",
"docs <- Corpus(VectorSource(files))\n",
"\n",
"## Mostar archivo 2 (bigdata)\n",
"writeLines(as.character(docs[[2]]))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Transformar contenido para poner todo en minuscula\n",
"\n",
"docs <-tm_map(docs, # Datos formato 'Corpus'\n",
" content_transformer(tolower)) # Transformación \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Retirar simbolos que puedan afectar el estudio\n",
"\n",
"toSpace <- content_transformer(function(x, pattern){\n",
" return (gsub(pattern,\" \", x))\n",
" }\n",
" )\n",
"\n",
"docs <- tm_map(docs, toSpace, \"-\")\n",
"docs <- tm_map(docs, toSpace, \"’\")\n",
"docs <- tm_map(docs, toSpace, \"‘\")\n",
"docs <- tm_map(docs, toSpace, \"•\")\n",
"docs <- tm_map(docs, toSpace, \"“\")\n",
"docs <- tm_map(docs, toSpace, \"”\")\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cbig data metaphors live big data metaphors erase human sensemaking ways values baked categories algorithms visualizations indeed lost plot found
",
" quoted essay metaphors big data co written simon buckingham shum \n"
]
}
],
"source": [
"## Retirar puntuación\n",
"docs <- tm_map(docs, removePunctuation)\n",
"\n",
"## Remover números\n",
"docs <- tm_map(docs, removeNumbers)\n",
"\n",
"## Remover palabras \n",
"docs <- tm_map(docs, removeWords, stopwords(\"english\"))\n",
"\n",
"## Remover espacios en blanco\n",
"docs <- tm_map(docs, stripWhitespace)\n",
"\n",
"## Mostar archivo 2 (bigdata) con las transformaciones realizadas\n",
"writeLines(as.character(docs[[2]]))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Guardar documento\n",
"docs <- tm_map(docs,stemDocument)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Arreglar errores de sintaxis en ingles y generales\n",
"\n",
"docs <- tm_map(docs, content_transformer(gsub),\n",
" pattern = \"organiz\", replacement = \"organ\")\n",
"\n",
"docs <- tm_map(docs, content_transformer(gsub),\n",
" pattern = \"organis\", replacement = \"organ\")\n",
"\n",
"docs <- tm_map(docs, content_transformer(gsub),\n",
" pattern = \"andgovern\", replacement = \"govern\")\n",
"\n",
"docs <- tm_map(docs, content_transformer(gsub),\n",
" pattern = \"inenterpris\", replacement = \"enterpris\")\n",
"\n",
"docs <- tm_map(docs, content_transformer(gsub),\n",
" pattern = \"team-\", replacement = \"team\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cbig data metaphor live big data metaphor eras human sensemak valu bake categori algorithm visual inde lost plot
",
" quot essay metaphor big data co written simon buckingham shum\n"
]
}
],
"source": [
"## Definir conjunto de palabras para removerlas\n",
"\n",
"myStopwords <- c(\"can\", \"say\",\"one\",\"way\",\"use\",\n",
" \"also\",\"howev\",\"tell\",\"will\",\n",
" \"much\",\"need\",\"take\",\"tend\",\n",
" \"even\",\"like\",\"particular\",\n",
" \"rather\",\"said\",\"get\",\"well\",\n",
" \"make\",\"ask\",\"come\",\"end\",\n",
" \"first\",\"two\",\"help\",\"often\",\n",
" \"may\",\"might\",\"see\",\"someth\",\n",
" \"thing\",\"point\",\"post\",\"look\",\n",
" \"right\",\"now\",\"think\",\"‘ve \",\n",
" \"‘re \",\"anoth\",\"put\",\"set\",\n",
" \"new\",\"good\",\"want\",\"sure\",\n",
" \"kind\",\"larg\",\"yes,\",\"day\",\n",
" \"etc\",\"quit\",\"sinc\",\"attempt\",\n",
" \"lack\",\"seen\",\"awar\",\"littl\",\n",
" \"ever\",\"moreov\",\"though\",\"found\",\n",
" \"abl\",\"enough\",\"far\",\"earli\",\n",
" \"away\",\"achiev\",\"draw\",\"last\",\n",
" \"never\",\"brief\",\"bit\",\"entir\",\n",
" \"brief\",\"great\",\"lot\")\n",
"\n",
"## Remover palabras\n",
"docs <- tm_map(docs, removeWords, myStopwords)\n",
"\n",
"## Mostar archivo 2 (bigdata) con las transformaciones realizadas\n",
"writeLines(as.character(docs[[2]]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"> [` DocumentTermMatrix{tm}`](https://cran.r-project.org/web/packages/tm/tm.pdf) "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"3821"
],
"text/latex": [
"3821"
],
"text/markdown": [
"3821"
],
"text/plain": [
"[1] 3821"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Crear documento tipo matriz de conjunto de datos\n",
"dtm <- DocumentTermMatrix(docs)\n",
"\n",
"## Nombre de filas como nombre de archivos\n",
"rownames(dtm) <- filenames\n",
"\n",
"## Sumar sobre columnas\n",
"freq <- colSums(as.matrix(dtm))\n",
"\n",
"## Longitud = numero total de terminos\n",
"length(freq)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<dl class=dl-horizontal>\n",
"\t<dt>organ</dt>\n",
"\t\t<dd>277</dd>\n",
"\t<dt>manag</dt>\n",
"\t\t<dd>231</dd>\n",
"\t<dt>work</dt>\n",
"\t\t<dd>211</dd>\n",
"\t<dt>system</dt>\n",
"\t\t<dd>192</dd>\n",
"\t<dt>project</dt>\n",
"\t\t<dd>188</dd>\n",
"\t<dt>problem</dt>\n",
"\t\t<dd>173</dd>\n",
"</dl>\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[organ] 277\n",
"\\item[manag] 231\n",
"\\item[work] 211\n",
"\\item[system] 192\n",
"\\item[project] 188\n",
"\\item[problem] 173\n",
"\\end{description*}\n"
],
"text/markdown": [
"organ\n",
": 277manag\n",
": 231work\n",
": 211system\n",
": 192project\n",
": 188problem\n",
": 173\n",
"\n"
],
"text/plain": [
" organ manag work system project problem \n",
" 277 231 211 192 188 173 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<dl class=dl-horizontal>\n",
"\t<dt>therebi</dt>\n",
"\t\t<dd>1</dd>\n",
"\t<dt>timeorgan</dt>\n",
"\t\t<dd>1</dd>\n",
"\t<dt>twilling</dt>\n",
"\t\t<dd>1</dd>\n",
"\t<dt>uncommit</dt>\n",
"\t\t<dd>1</dd>\n",
"\t<dt>unionist</dt>\n",
"\t\t<dd>1</dd>\n",
"\t<dt>workday</dt>\n",
"\t\t<dd>1</dd>\n",
"</dl>\n"
],
"text/latex": [
"\\begin{description*}\n",
"\\item[therebi] 1\n",
"\\item[timeorgan] 1\n",
"\\item[twilling] 1\n",
"\\item[uncommit] 1\n",
"\\item[unionist] 1\n",
"\\item[workday] 1\n",
"\\end{description*}\n"
],
"text/markdown": [
"therebi\n",
": 1timeorgan\n",
": 1twilling\n",
": 1uncommit\n",
": 1unionist\n",
": 1workday\n",
": 1\n",
"\n"
],
"text/plain": [
" therebi timeorgan twilling uncommit unionist workday \n",
" 1 1 1 1 1 1 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Ordenar de manera descendiente\n",
"ord <- order(freq,decreasing=TRUE)\n",
"\n",
"## Lista de los terminos\n",
"head(freq[ord]) # Primeros terminos\n",
"tail(freq[ord]) # últimos terminos\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Guardar terminos en un archivo CSV\n",
"write.csv(freq[ord],\"word_freq.csv\") # Puede ver los datos en la carpeta de directorio que definio al comienzo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Modelado"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message:\n",
": package 'topicmodels' was built under R version 3.3.2"
]
}
],
"source": [
"## Instale y cargue las siguientes librerias\n",
"\n",
"library(topicmodels)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Definición de parametros\n",
"\n",
"burnin <- 4000\n",
"iter <- 2000\n",
"thin <- 500\n",
"seed <-list(2003,5,63,100001,765)\n",
"nstart <- 5\n",
"\n",
"\n",
"## Número de tópicos\n",
"k <- 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"> [` LDA {topicmodels}`](https://cran.r-project.org/web/packages/topicmodels/topicmodels.pdf) "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Modelado\n",
"\n",
"ldaOut <-LDA(dtm, # data\n",
" k, # Número de tópicos \n",
" method=\"Gibbs\", # Método de ajuste\n",
" control=list(nstart=nstart, # Control de parametros\n",
" seed = seed,\n",
" best=T,\n",
" burnin = burnin, \n",
" iter = iter,\n",
" thin=thin))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Tópicos asociados a cada artchivo\n",
"ldaOut.topics <- as.matrix(topics(ldaOut)) \n",
"\n",
"## Guardar salida en archivo CSV\n",
"write.csv(ldaOut.topics,\n",
" file=paste(\"LDAGibbs\",k,\"DocsToTopics.csv\"))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<tbody>\n",
"\t<tr><th scope=row>BeyondEntitiesAndRelationships.txt</th><td>1</td></tr>\n",
"\t<tr><th scope=row>bigdata.txt</th><td>1</td></tr>\n",
"\t<tr><th scope=row>ConditionsOverCauses.txt</th><td>4</td></tr>\n",
"\t<tr><th scope=row>EmergentDesignInEnterpriseIT.txt</th><td>4</td></tr>\n",
"\t<tr><th scope=row>FromInformationToKnowledge.txt</th><td>3</td></tr>\n",
"\t<tr><th scope=row>FromTheCoalface.txt</th><td>5</td></tr>\n",
"\t<tr><th scope=row>HeraclitusAndParmenides.txt</th><td>2</td></tr>\n",
"\t<tr><th scope=row>IroniesOfEnterpriseIT.txt</th><td>5</td></tr>\n",
"\t<tr><th scope=row>MakingSenseOfOrganizationalChange.txt</th><td>2</td></tr>\n",
"\t<tr><th scope=row>MakingSenseOfSensemaking.txt</th><td>3</td></tr>\n",
"\t<tr><th scope=row>ObjectivityAndTheEthicalDimensionOfDecisionMaking.txt</th><td>2</td></tr>\n",
"\t<tr><th scope=row>OnTheInherentAmbiguitiesOfManagingProjects.txt</th><td>3</td></tr>\n",
"\t<tr><th scope=row>OrganisationalSurprise.txt</th><td>4</td></tr>\n",
"\t<tr><th scope=row>ProfessionalsOrPoliticians.txt</th><td>5</td></tr>\n",
"\t<tr><th scope=row>RitualsInInformationSystemDesign.txt</th><td>1</td></tr>\n",
"\t<tr><th scope=row>RoutinesAndReality.txt</th><td>4</td></tr>\n",
"\t<tr><th scope=row>ScapegoatsAndSystems.txt</th><td>4</td></tr>\n",
"\t<tr><th scope=row>SherlockHolmesFailedProjects.txt</th><td>2</td></tr>\n",
"\t<tr><th scope=row>sherlockHolmesMgmtFetis.txt</th><td>2</td></tr>\n",
"\t<tr><th scope=row>SixHeresiesForBI.txt</th><td>1</td></tr>\n",
"\t<tr><th scope=row>SixHeresiesForEnterpriseArchitecture.txt</th><td>5</td></tr>\n",
"\t<tr><th scope=row>TheArchitectAndTheApparition.txt</th><td>5</td></tr>\n",
"\t<tr><th scope=row>TheCloudAndTheGrass.txt</th><td>3</td></tr>\n",
"\t<tr><th scope=row>TheConsultantsDilemma.txt</th><td>5</td></tr>\n",
"\t<tr><th scope=row>TheDangerWithin.txt</th><td>4</td></tr>\n",
"\t<tr><th scope=row>TheDilemmasOfEnterpriseIT.txt</th><td>5</td></tr>\n",
"\t<tr><th scope=row>TheEssenceOfEntrepreneurship.txt</th><td>1</td></tr>\n",
"\t<tr><th scope=row>ThreeTypesOfUncertainty.txt</th><td>2</td></tr>\n",
"\t<tr><th scope=row>TOGAFOrNotTOGAF.txt</th><td>5</td></tr>\n",
"\t<tr><th scope=row>UnderstandingFlexibility.txt</th><td>5</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{r|l}\n",
"\tBeyondEntitiesAndRelationships.txt & 1\\\\\n",
"\tbigdata.txt & 1\\\\\n",
"\tConditionsOverCauses.txt & 4\\\\\n",
"\tEmergentDesignInEnterpriseIT.txt & 4\\\\\n",
"\tFromInformationToKnowledge.txt & 3\\\\\n",
"\tFromTheCoalface.txt & 5\\\\\n",
"\tHeraclitusAndParmenides.txt & 2\\\\\n",
"\tIroniesOfEnterpriseIT.txt & 5\\\\\n",
"\tMakingSenseOfOrganizationalChange.txt & 2\\\\\n",
"\tMakingSenseOfSensemaking.txt & 3\\\\\n",
"\tObjectivityAndTheEthicalDimensionOfDecisionMaking.txt & 2\\\\\n",
"\tOnTheInherentAmbiguitiesOfManagingProjects.txt & 3\\\\\n",
"\tOrganisationalSurprise.txt & 4\\\\\n",
"\tProfessionalsOrPoliticians.txt & 5\\\\\n",
"\tRitualsInInformationSystemDesign.txt & 1\\\\\n",
"\tRoutinesAndReality.txt & 4\\\\\n",
"\tScapegoatsAndSystems.txt & 4\\\\\n",
"\tSherlockHolmesFailedProjects.txt & 2\\\\\n",
"\tsherlockHolmesMgmtFetis.txt & 2\\\\\n",
"\tSixHeresiesForBI.txt & 1\\\\\n",
"\tSixHeresiesForEnterpriseArchitecture.txt & 5\\\\\n",
"\tTheArchitectAndTheApparition.txt & 5\\\\\n",
"\tTheCloudAndTheGrass.txt & 3\\\\\n",
"\tTheConsultantsDilemma.txt & 5\\\\\n",
"\tTheDangerWithin.txt & 4\\\\\n",
"\tTheDilemmasOfEnterpriseIT.txt & 5\\\\\n",
"\tTheEssenceOfEntrepreneurship.txt & 1\\\\\n",
"\tThreeTypesOfUncertainty.txt & 2\\\\\n",
"\tTOGAFOrNotTOGAF.txt & 5\\\\\n",
"\tUnderstandingFlexibility.txt & 5\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"1. 1\n",
"2. 1\n",
"3. 4\n",
"4. 4\n",
"5. 3\n",
"6. 5\n",
"7. 2\n",
"8. 5\n",
"9. 2\n",
"10. 3\n",
"11. 2\n",
"12. 3\n",
"13. 4\n",
"14. 5\n",
"15. 1\n",
"16. 4\n",
"17. 4\n",
"18. 2\n",
"19. 2\n",
"20. 1\n",
"21. 5\n",
"22. 5\n",
"23. 3\n",
"24. 5\n",
"25. 4\n",
"26. 5\n",
"27. 1\n",
"28. 2\n",
"29. 5\n",
"30. 5\n",
"\n",
"\n"
],
"text/plain": [
" [,1]\n",
"BeyondEntitiesAndRelationships.txt 1 \n",
"bigdata.txt 1 \n",
"ConditionsOverCauses.txt 4 \n",
"EmergentDesignInEnterpriseIT.txt 4 \n",
"FromInformationToKnowledge.txt 3 \n",
"FromTheCoalface.txt 5 \n",
"HeraclitusAndParmenides.txt 2 \n",
"IroniesOfEnterpriseIT.txt 5 \n",
"MakingSenseOfOrganizationalChange.txt 2 \n",
"MakingSenseOfSensemaking.txt 3 \n",
"ObjectivityAndTheEthicalDimensionOfDecisionMaking.txt 2 \n",
"OnTheInherentAmbiguitiesOfManagingProjects.txt 3 \n",
"OrganisationalSurprise.txt 4 \n",
"ProfessionalsOrPoliticians.txt 5 \n",
"RitualsInInformationSystemDesign.txt 1 \n",
"RoutinesAndReality.txt 4 \n",
"ScapegoatsAndSystems.txt 4 \n",
"SherlockHolmesFailedProjects.txt 2 \n",
"sherlockHolmesMgmtFetis.txt 2 \n",
"SixHeresiesForBI.txt 1 \n",
"SixHeresiesForEnterpriseArchitecture.txt 5 \n",
"TheArchitectAndTheApparition.txt 5 \n",
"TheCloudAndTheGrass.txt 3 \n",
"TheConsultantsDilemma.txt 5 \n",
"TheDangerWithin.txt 4 \n",
"TheDilemmasOfEnterpriseIT.txt 5 \n",
"TheEssenceOfEntrepreneurship.txt 1 \n",
"ThreeTypesOfUncertainty.txt 2 \n",
"TOGAFOrNotTOGAF.txt 5 \n",
"UnderstandingFlexibility.txt 5 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ldaOut.topics"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 6 primeros terminos por cada tópico\n",
"ldaOut.terms <- as.matrix(terms(ldaOut,6))\n",
"write.csv(ldaOut.terms,\n",
" file=paste(\"LDAGibbs\",k,\"TopicsToTerms.csv\"))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table>\n",
"<thead><tr><th scope=col>Topic 1</th><th scope=col>Topic 2</th><th scope=col>Topic 3</th><th scope=col>Topic 4</th><th scope=col>Topic 5</th></tr></thead>\n",
"<tbody>\n",
"\t<tr><td>data </td><td>chang </td><td>question </td><td>system </td><td>enterpris </td></tr>\n",
"\t<tr><td>model </td><td>decis </td><td>map </td><td>project </td><td>organ </td></tr>\n",
"\t<tr><td>differ </td><td>problem </td><td>time </td><td>manag </td><td>consult </td></tr>\n",
"\t<tr><td>view </td><td>organ </td><td>ibi </td><td>organ </td><td>work </td></tr>\n",
"\t<tr><td>busi </td><td>consequ </td><td>issu </td><td>process </td><td>flexibl </td></tr>\n",
"\t<tr><td>practic </td><td>uncertainti</td><td>exampl </td><td>design </td><td>manag </td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"\\begin{tabular}{lllll}\n",
" Topic 1 & Topic 2 & Topic 3 & Topic 4 & Topic 5\\\\\n",
"\\hline\n",
"\t data & chang & question & system & enterpris \\\\\n",
"\t model & decis & map & project & organ \\\\\n",
"\t differ & problem & time & manag & consult \\\\\n",
"\t view & organ & ibi & organ & work \\\\\n",
"\t busi & consequ & issu & process & flexibl \\\\\n",
"\t practic & uncertainti & exampl & design & manag \\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"1. 'data'\n",
"2. 'model'\n",
"3. 'differ'\n",
"4. 'view'\n",
"5. 'busi'\n",
"6. 'practic'\n",
"7. 'chang'\n",
"8. 'decis'\n",
"9. 'problem'\n",
"10. 'organ'\n",
"11. 'consequ'\n",
"12. 'uncertainti'\n",
"13. 'question'\n",
"14. 'map'\n",
"15. 'time'\n",
"16. 'ibi'\n",
"17. 'issu'\n",
"18. 'exampl'\n",
"19. 'system'\n",
"20. 'project'\n",
"21. 'manag'\n",
"22. 'organ'\n",
"23. 'process'\n",
"24. 'design'\n",
"25. 'enterpris'\n",
"26. 'organ'\n",
"27. 'consult'\n",
"28. 'work'\n",
"29. 'flexibl'\n",
"30. 'manag'\n",
"\n",
"\n"
],
"text/plain": [
" Topic 1 Topic 2 Topic 3 Topic 4 Topic 5 \n",
"[1,] data chang question system enterpris\n",
"[2,] model decis map project organ \n",
"[3,] differ problem time manag consult \n",
"[4,] view organ ibi organ work \n",
"[5,] busi consequ issu process flexibl \n",
"[6,] practic uncertainti exampl design manag "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ldaOut.terms"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Ejercicio.--** El siguiente conjunto de datos contiene 229 archivos de texto donde cada uno tiene el resumen de un artículo cientifico. Realice un modelado de tópicos para este conjunto de datos utilizando las técnicas presentadas en este notebook.\n",
"\n",
"\n",
"[Datos](https://drive.google.com/file/d/0B4psHlllKLPUQjNyUlFWUjZubmM/view?usp=sharing)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Contenido](#Contenido)\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r",
"version": "3.3.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
QuantConnect/Lean | Tests/Research/RegressionTemplates/BasicTemplateResearchPython.ipynb | 3 | 3883 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Welcome to The QuantConnect Research Page\n",
"#### Refer to this page for documentation https://www.quantconnect.com/docs/research/overview#\n",
"#### Contribute to this template file https://github.com/QuantConnect/Lean/blob/master/Research/BasicQuantBookTemplate.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## QuantBook Basics\n",
"\n",
"### Start QuantBook\n",
"- Add the references and imports\n",
"- Create a QuantBook instance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load in our startup script, required to set runtime for PythonNet\n",
"%run ./start.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create an instance\n",
"qb = QuantBook()\n",
"\n",
"# Select asset data\n",
"spy = qb.AddEquity(\"SPY\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Historical Data Requests\n",
"\n",
"We can use the QuantConnect API to make Historical Data Requests. The data will be presented as multi-index pandas.DataFrame where the first index is the Symbol.\n",
"\n",
"For more information, please follow the [link](https://www.quantconnect.com/docs#Historical-Data-Historical-Data-Requests)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"startDate = DateTime(2021,1,1)\n",
"endDate = DateTime(2021,12,31)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Gets historical data from the subscribed assets, the last 360 datapoints with daily resolution\n",
"h1 = qb.History(qb.Securities.Keys, startDate, endDate, Resolution.Daily)\n",
"\n",
"if h1.shape[0] < 1:\n",
" raise Exception(\"History request resulted in no data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Indicators\n",
"\n",
"We can easily get the indicator of a given symbol with QuantBook. \n",
"\n",
"For all indicators, please checkout QuantConnect Indicators [Reference Table](https://www.quantconnect.com/docs#Indicators-Reference-Table)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Example with BB, it is a datapoint indicator\n",
"# Define the indicator\n",
"bb = BollingerBands(30, 2)\n",
"\n",
"# Gets historical data of indicator\n",
"bbdf = qb.Indicator(bb, \"SPY\", startDate, endDate, Resolution.Daily)\n",
"\n",
"# drop undesired fields\n",
"bbdf = bbdf.drop('standarddeviation', 1)\n",
"\n",
"if bbdf.shape[0] < 1:\n",
" raise Exception(\"Bollinger Bands resulted in no data\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
birdsarah/bokeh-miscellany | dask_time_out_example.ipynb | 1 | 10543 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bird/miniconda3/envs/ovscrptd/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
" data = yaml.load(f.read()) or {}\n",
"/home/bird/miniconda3/envs/ovscrptd/lib/python3.6/site-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n",
" defaults = yaml.load(f)\n"
]
},
{
"data": {
"text/html": [
"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Client</h3>\n",
"<ul>\n",
" <li><b>Scheduler: </b>tcp://127.0.0.1:33705\n",
" <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3>Cluster</h3>\n",
"<ul>\n",
" <li><b>Workers: </b>4</li>\n",
" <li><b>Cores: </b>12</li>\n",
" <li><b>Memory: </b>33.35 GB</li>\n",
"</ul>\n",
"</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<Client: scheduler='tcp://127.0.0.1:33705' processes=4 cores=12>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client\n",
"\n",
"client = Client(\"tcp://127.0.0.1:33705\")\n",
"client"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import dask\n",
"import dask.dataframe as dd\n",
"\n",
"from random import randint\n",
"from time import sleep"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# This generally seems terrible pretty sure I shouldn't be using \"sleep\" for this demonstration\n",
"\n",
"def short_running_func():\n",
" print('short') # If using dask these print statements are likely in your console\n",
" sleep(0.1)\n",
" return 'short'\n",
"\n",
"def time_out_func():\n",
" print('long') # If using dask these print statements are likely in your console\n",
" result = ''\n",
" try:\n",
" sleep(0.5)\n",
" raise TimeoutError()\n",
" result = 'long'\n",
" except TimeoutError as e:\n",
" result = 'timeout'\n",
" return result\n",
" \n",
"def process_column(value):\n",
" result = None\n",
" \n",
" # Randomly pick short or long to demonstrate\n",
" pick = randint(0, 1) \n",
" if pick == 0:\n",
" result = short_running_func()\n",
" else:\n",
" result = time_out_func()\n",
"\n",
" return result \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bird/miniconda3/envs/ovscrptd/lib/python3.6/site-packages/dask/dataframe/utils.py:391: FutureWarning: Creating a DatetimeIndex by passing range endpoints is deprecated. Use `pandas.date_range` instead.\n",
" tz=idx.tz, name=idx.name)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
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" <th>name</th>\n",
" <th>x</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>2000-01-01 00:00:00</td>\n",
" <td>1009</td>\n",
" <td>Bob</td>\n",
" <td>-0.656071</td>\n",
" <td>-0.505284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2000-01-01 00:00:01</td>\n",
" <td>1086</td>\n",
" <td>Oliver</td>\n",
" <td>0.286447</td>\n",
" <td>0.681899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2000-01-01 00:00:02</td>\n",
" <td>1010</td>\n",
" <td>Dan</td>\n",
" <td>-0.290703</td>\n",
" <td>-0.728478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2000-01-01 00:00:03</td>\n",
" <td>1018</td>\n",
" <td>Frank</td>\n",
" <td>0.973330</td>\n",
" <td>0.356958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2000-01-01 00:00:04</td>\n",
" <td>998</td>\n",
" <td>Oliver</td>\n",
" <td>-0.849845</td>\n",
" <td>0.802483</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp id name x y\n",
"0 2000-01-01 00:00:00 1009 Bob -0.656071 -0.505284\n",
"1 2000-01-01 00:00:01 1086 Oliver 0.286447 0.681899\n",
"2 2000-01-01 00:00:02 1010 Dan -0.290703 -0.728478\n",
"3 2000-01-01 00:00:03 1018 Frank 0.973330 0.356958\n",
"4 2000-01-01 00:00:04 998 Oliver -0.849845 0.802483"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Keep it small for now\n",
"df = dask.datasets.timeseries().reset_index()\n",
"df = df.loc[0:100,:]\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 74.6 ms, sys: 26.5 ms, total: 101 ms\n",
"Wall time: 28.2 s\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>timestamp</th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>process</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2000-01-01 00:00:00</td>\n",
" <td>1009</td>\n",
" <td>Bob</td>\n",
" <td>-0.656071</td>\n",
" <td>-0.505284</td>\n",
" <td>short</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2000-01-01 00:00:01</td>\n",
" <td>1086</td>\n",
" <td>Oliver</td>\n",
" <td>0.286447</td>\n",
" <td>0.681899</td>\n",
" <td>timeout</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2000-01-01 00:00:02</td>\n",
" <td>1010</td>\n",
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" <td>-0.290703</td>\n",
" <td>-0.728478</td>\n",
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" <th>3</th>\n",
" <td>2000-01-01 00:00:03</td>\n",
" <td>1018</td>\n",
" <td>Frank</td>\n",
" <td>0.973330</td>\n",
" <td>0.356958</td>\n",
" <td>short</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2000-01-01 00:00:04</td>\n",
" <td>998</td>\n",
" <td>Oliver</td>\n",
" <td>-0.849845</td>\n",
" <td>0.802483</td>\n",
" <td>timeout</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp id name x y process\n",
"0 2000-01-01 00:00:00 1009 Bob -0.656071 -0.505284 short\n",
"1 2000-01-01 00:00:01 1086 Oliver 0.286447 0.681899 timeout\n",
"2 2000-01-01 00:00:02 1010 Dan -0.290703 -0.728478 timeout\n",
"3 2000-01-01 00:00:03 1018 Frank 0.973330 0.356958 short\n",
"4 2000-01-01 00:00:04 998 Oliver -0.849845 0.802483 timeout"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['process'] = df['name'].apply(process_column, meta='O')\n",
"%time df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
locuslab/dreaml | examples/MNIST.ipynb | 1 | 10740 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This jupyter notebook contains a working example of running stochastic gradient descent on MNIST using dreaml. It performs PCA and genereates random kitchen sink features while running mini-batched stochastic gradient descent. You can adjust the hyperparameters (i.e. step size) or generate more features and observe the resulting performance. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named dreaml",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-ec6306561b8a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtime\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msleep\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mdreaml\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mdm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mdreaml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mserver\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstart\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mdreaml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSoftmax\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mImportError\u001b[0m: No module named dreaml"
]
}
],
"source": [
"# Import libraries\n",
"import cPickle, gzip\n",
"import numpy as np\n",
"from time import sleep\n",
"import dreaml as dm\n",
"from dreaml.server import start\n",
"from dreaml.loss import Softmax\n",
"import dreaml.transformations as trans"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load data from files\n",
"f = gzip.open('mnist.pkl.gz', 'rb')\n",
"train_set, valid_set, test_set = cPickle.load(f)\n",
"f.close()\n",
"n_train=1000\n",
"n_test=100\n",
"X_train = train_set[0][0:n_train,:]\n",
"y_train = train_set[1][0:n_train,None]\n",
"X_test = valid_set[0][0:n_train,:]\n",
"y_test = valid_set[1][0:n_train,None]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we initialize the dataframe and start the web frontend for visualization. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = dm.DataFrame()\n",
"start(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we load the data into the dataframe. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[\"data/train/\", \"input/raw/\"] = dm.DataFrame.from_matrix(X_train)\n",
"df[\"data/train/\", \"input/label/\"] = dm.DataFrame.from_matrix(y_train)\n",
"df[\"data/test/\", \"input/raw/\"] = dm.DataFrame.from_matrix(X_test)\n",
"df[\"data/test/\", \"input/label/\"] = dm.DataFrame.from_matrix(y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we run PCA on the input. These are placed within the features folder. Note that the PCA transformation creates the PCA basis vectors as part of the subroutine. These are stored in an automatically generated directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[\"data/\", \"features/pca/\"] = trans.PCA(df[\"data/train/\", \"input/raw/\"], \n",
" df[\"data/\",\"input/raw/\"],\n",
" num_bases=50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the PCA features, we also generate an initial set of 1000 kitchen sink features. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[\"data/\", \"features/ks1/\"] = trans.KitchenSinks(df[\"data/\",\"features/pca/\"],\n",
" num_features=1000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we start the stochastic gradient descent process using Softmax loss. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[\"weights/\", \"features/\"] = trans.SGD(Softmax,\n",
" np.zeros((50,1000)),\n",
" df[\"data/train/\", \"features/\"],\n",
" df[\"data/train/\",\"input/label/\"],\n",
" batch_size=50,\n",
" reg=0.01,\n",
" step_size=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's compute some metrics on each datapoint to evaluate the progress of our model. In this case, softmax loss and multi-classification error."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[\"data/\",\"metrics/\"] = trans.Metrics([Softmax.f_vec, Softmax.err],\n",
" df[\"weights/\", \"features/\"],\n",
" df[\"data/\", \"features/\"],\n",
" df[\"data/\", \"input/label/\"],\n",
" reg=0.01,\n",
" metrics_names=[\"SoftmaxLoss\",\n",
" \"MulticlassError\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We can plot a sequence of evaluations of arbitrary functions f that return a pair of lists (ys,xs) of numbers to plots. In this case, we compute the average softmax loss of all the training examples over the number of iterations, and the [trainerr,testerr] also over the number of iterations. These plots show up on the web frontend. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"def softmax_average():\n",
" metrics = df[\"data/\",\"metrics/\"].get_matrix()\n",
" n = df[\"data/train/\",\"metrics/\"].shape()[0]\n",
" niters = df[\"weights/\", \"features/\"].T().niters\n",
" return ([np.mean(metrics[0:n,0])],[niters])\n",
"\n",
"def traintest_average():\n",
" metrics = df[\"data/\",\"metrics/\"].get_matrix()\n",
" n = df[\"data/train/\",\"metrics/\"].shape()[0]\n",
" niters = df[\"weights/\", \"features/\"].T().niters\n",
" return ([np.mean(metrics[0:n,1]),np.mean(metrics[n+1:,1])],[niters,niters])\n",
"\n",
"df[\"plot/\",\"loss/\"] = dm.Plotter(softmax_average,\n",
" \"objective loss\",\n",
" legend=[\"softmax\"])\n",
"\n",
"df[\"plot/\",\"err/\"] = dm.Plotter(traintest_average,\n",
" \"train and test err\",\n",
" legend=[\"train\",\"test\"],\n",
" colors=[\"blue\",\"green\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have our plots set up, we can make changes to the model and see how dreaml reactively implements these changes in real-time!\n",
"\n",
"Example 1: You might have noticed that the training and testing error is all over the place. The following code retrieves the SGD transformation and changes the value of the step size being taken."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df[\"weights/\", \"features/\"].T().step_size = 1e-2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example 2: we can continue to generate more random kitchen sink features. Try it and see how it affects the model's performance. Note that all existing transformations see the change and restart accordingly, if needed. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df[\"data/\", \"features/ks2/\"] = trans.KitchenSinks(df[\"data/\",\"features/pca/\"],\n",
" num_features=1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df[\"data/\", \"features/ks3/\"] = trans.KitchenSinks(df[\"data/\",\"features/pca/\"],\n",
" num_features=1000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df[\"data/\", \"features/ks4/\"] = trans.KitchenSinks(df[\"data/\",\"features/pca/\"],\n",
" num_features=1000)"
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
lee212/simpleazure | ipynb/classic/Tutorial (classic) - Deploying Windows and Linux VMs.ipynb | 1 | 12177 | {
"metadata": {
"name": "Tutorial - Deploying Windows and Linux VMs"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "# Deploying Windows and Linux VMs\n\nThis tutorial shows how to create a number of VMs with mixed OS."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Specify the number of VMs\n\nWe expect to have 5 mixed VMs including 2 Windows Server VMs and 3 Ubuntu VMs. \nNUM_W indicates the number of Windows VMs, \nNUM_L indicates the number of Linux VMs below. \n\n*If you made changes, plese make sure you run the selected IPython Notebook cell by 'ctrl + Enter'*"
},
{
"cell_type": "code",
"collapsed": false,
"input": "NUM_W = 2\nNUM_L = 3\n\nNUM = NUM_W + NUM_L",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Initialize azure SDK\n\nIt is about setting credentials and obtaining access, etc.\nFor the rest of the parts, there is not much thing that you have to change but it might be important to understand how it works."
},
{
"cell_type": "code",
"collapsed": false,
"input": "from azure import *",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": "import os\nimport json\n\nclass Credentials(object):\n '''\n Azure credentials needed to run Azure.\n '''\n def __init__(self):\n configFilename = os.environ[\"HOME\"] + \"/.azure/config.json\"\n tmpName = os.path.join(os.getcwd(), configFilename)\n \n if not os.path.exists(tmpName):\n errMsg = \"Cannot run Azure when the expected config file containing Azure credentials, '%s', does not exist!\" % (tmpName)\n raise EnvironmentError(errMsg)\n\n with open(tmpName, \"r\") as f:\n self.ns = json.load(f)\n self.config_path = os.path.dirname(tmpName)\n\n def getManagementCertFile(self):\n try:\n return self.ns[u'managementcertfile'] \n except:\n return self.config_path + \"/managementCertificate.pem\"\n def getSubscriptionId(self):\n return self.ns[u'subscriptionid'] \n\n def getSubscription(self):\n return self.ns[u'subscription'] \n \n def getServiceBusKey(self):\n return self.ns[u'servicebuskey'] \n\n def getServiceBusNamespace(self):\n return self.ns[u'servicebusns']\n\n def getStorageServicesKey(self):\n return self.ns[u'storageserviceskey']\n\n def getStorageServicesName(self):\n return self.ns[u'storageservicesname']\n\n def getLinuxOSVHD(self):\n return self.ns[u'linuxosvhd']\n\n def getProxyHost(self):\n return self.ns[u'proxyhost']\n\n def getProxyPort(self):\n return self.ns[u'proxyport']",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": "cert = Credentials()\n\nsubscription_id = cert.getSubscription()\ncertificate_path = cert.getManagementCertFile()",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": "from azure.servicemanagement import *\n\nsms = ServiceManagementService(subscription_id, certificate_path)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": "name = 'myvm-cluster-'\n# In my case, I need to use 'Central US' location instead of 'West US' due to the location constraint of my subscription.\nlocation = \"Central US\"",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Get image names for Windows and Linux OS\nlinux_image_name will be set for an image name of latest Ubuntu 12.04 \nwindow_image_name will be set for an image name of Windows Server 2008 R2 SP1."
},
{
"cell_type": "code",
"collapsed": false,
"input": "result = sms.list_os_images()\nfor image in result:\n if image.os == \"Linux\":\n if image.category == \"Canonical\":\n try:\n if image.label.index(\"12.04\"):\n linux_image_name = image.name\n except:\n pass\n elif image.os == \"Windows\":\n if image.category == \"Microsoft Windows Server Group\":\n try:\n if image.label.index(\"2008 R2 SP1\"):\n window_image_name = image.name\n except:\n pass",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": "result = sms.list_storage_accounts()\nfor account in result:\n storage_account = account.service_name",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": "container = \"cluster\"\nblob_l = \"ubuntu-12-04.vhd\"\nblob_w = \"Win2K8R2SP1.vhd\"",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": "windows_blob_url = \"blob.core.windows.net\"\n#media_link = \"http://\" + storage_account + \".\" + windows_blob_url + \"/\" + container + \"/\" + blob",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": "#os_hd = OSVirtualHardDisk(image_name, media_link)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": "linux_user_id = 'azureuser'\nlinux_user_passwd = 'mypassword1234@'\nlinux_config = LinuxConfigurationSet(name, linux_user_id, linux_user_passwd, False)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": "window_user_passwd = linux_user_passwd\ntzone = 'Pacific Standard Time'\nwindow_config = WindowsConfigurationSet(computer_name=name, \n admin_password=window_user_passwd, \n reset_password_on_first_logon=False, \n enable_automatic_updates=False, \n time_zone=tzone)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": "azure_config = os.environ[\"HOME\"] + '/.azure'\nthumbprint_path = azure_config + '/.ssh/thumbprint'\nauthorized_keys = \"/home/\" + linux_user_id + \"/.ssh/authorized_keys\"",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": "try:\n thumbprint=open(thumbprint_path, 'r').readline().split('\\n')[0]\nexcept:\n thumbprint=None",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": "publickey = PublicKey(thumbprint, authorized_keys)\n#keypair = KeyPair(thumbprint, key_pair_path)\n\nlinux_config.ssh.public_keys.public_keys.append(publickey)\n#linux_config.ssh.key_pairs.key_pairs.append(keypair)",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 72
},
{
"cell_type": "markdown",
"metadata": {},
"source": "## Configure certificate for Windows"
},
{
"cell_type": "code",
"collapsed": false,
"input": "window_config.domain_join = None\nwindow_config.stored_certificate_settings.stored_certificate_settings.append(CertificateSetting(thumbprint=thumbprint, store_name='My', store_location='LocalMachine'))",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": "network = ConfigurationSet()\nnetwork.configuration_set_type = 'NetworkConfiguration'\nnetwork.input_endpoints.input_endpoints.append(ConfigurationSetInputEndpoint('ssh', 'tcp', '22', '22'))",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": "import base64\ncert_data_path = azure_config + \"/.ssh/myCert.pfx\"\nwith open(cert_data_path, \"rb\") as bfile:\n cert_data = base64.b64encode(bfile.read())",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": "cert_format = 'pfx'\ncert_password = ''",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 76
},
{
"cell_type": "code",
"collapsed": false,
"input": "from time import sleep",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": "%time\nresults = []\nresults_cert = []\nlinux_cnt = 0\nfor num in range(NUM):\n new_name = name + str(num)\n if linux_cnt < NUM_L:\n image_name = linux_image_name\n blob = blob_l\n sys_config = linux_config\n linux_cnt += 1\n continue\n else:\n image_name = window_image_name\n blob = blob_w \n sys_config = window_config\n media_link = \"http://\" + storage_account + \".\" + windows_blob_url + \"/\" + container + \"/0-\" + new_name + \"-\" + blob\n res = sms.create_hosted_service(service_name=new_name, label=new_name, location=location)\n sleep(5)\n os_hd = OSVirtualHardDisk(image_name, media_link)\n result_cert = sms.add_service_certificate(service_name=new_name,\n data=cert_data,\n certificate_format=cert_format,\n password=cert_password)\n print new_name\n try:\n print vars(result_cert)\n except:\n print result_cert\n sleep(5)\n result = sms.create_virtual_machine_deployment(service_name=new_name,\n deployment_name=new_name,\n deployment_slot='production',\n label=new_name,\n role_name=new_name,\n system_config=sys_config,\n os_virtual_hard_disk=os_hd,\n network_config=network,\n role_size='Small')\n results.append(result)\n results_cert.append(result_cert)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s\nWall time: 0.00 s\nmyvm-cluster-3"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n{'request_id': '5b46797d3fdf467eb25be36b309a7a7a'}\nmyvm-cluster-4"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n{'request_id': '34917e9844bb42feb3e63492ca2dc474'}\n"
}
],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": "for result in results:\n request_id = result.request_id\n status = sms.get_operation_status(request_id)\n try:\n print vars(status.error)\n except:\n print vars(status)",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-3.0 |
jamondouglas/lamdaconf2015 | speakers/acfoltzer/CryptolWorkshop.ipynb | 16 | 60881 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Intro to Cryptol and High Assurance Crypto Engineering\n",
"\n",
"## LambdaConf 2015\n",
"\n",
"### Adam Foltzer, Research Engineer at Galois, Inc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resources for the Workshop\n",
"\n",
"- Cryptol: <http://www.cryptol.net/downloads.html>\n",
"- Programming Cryptol book: <http://www.cryptol.net/files/ProgrammingCryptol.pdf>\n",
"- Workshop materials: <https://github.com/degoes-consulting/lambdaconf-2015/tree/master/speakers/acfoltzer>\n",
"- ICryptol Notebook (for the adventurous): <https://github.com/GaloisInc/ICryptol>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Goals\n",
"\n",
"- Understand the purpose of the Cryptol language and its role in high-assurance engineering\n",
"- Create and modify cryptographic programs in Cryptol\n",
"- Use the interactive Cryptol interpreter to develop, test, and prove properties about Cryptol programs\n",
"- Learn how to participate in the Cryptol open source community\n",
"- Anything else? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Outline\n",
"\n",
"- Introduce Cryptol _(~15 min)_\n",
"- Hands-on lab for Chapter 2 _(~10 min)_\n",
" - Get familiar with the Cryptol interpreter and basic expressions\n",
"- Learn more Cryptol via examples and exercises _(~20 min)_\n",
"- Hands-on lab for Chapter 3 _(~15 min)_\n",
" - Classical cryptosystems: Caesar, Vigenère, Scytale\n",
"\n",
"\n",
"- Break _(~10min)_\n",
"\n",
"\n",
"- Introduce property-driven development _(~15min)_\n",
"- Property-driven development exercises _(~20min)_\n",
" - Hands-on lab for Chapter 5\n",
" - Use :check, :sat, and :prove\n",
"- ZUC cipher demo and closing discussion _(~15min)_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Formal Methods\n",
"\n",
"- How can software and systems be made robust (safe, secure, correct) in a cost-effective manner?\n",
"- How can one obtain high assurance that a design has been faithfully implemented?\n",
"- How can we ensure that other people’s systems are secure?\n",
"- How can we compose a secure solution from black-box components?\n",
"\n",
"\n",
"- _Formal Methods_ are verification techniques that work by building a mathematical model of an artifact and proving properties about it\n",
"- Formal methods are _complementary_ to testing\n",
" - Testing techniques generate weak evidence about the real artifact **Worry: Have I tested enough?**\n",
" - Formal methods generate strong evidence about a model of the artifact **Worry: Is the model faithful enough?**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Our Take\n",
"\n",
"- Let the software itself be trustworthy\n",
" - Software artifacts to speak for themselves\n",
" - Reduce reliance on the process that created them\n",
"- Use mathematical models to enable tractable analysis\n",
" - Executable models and formal methods\n",
" - A model is an abstraction that allows thought at a higher level\n",
"- Follow open standards\n",
" - Build individual components with high internal integrity\n",
" - Maximize interoperability"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cryptol: Applying Formal Methods to Cryptography\n",
"\n",
"- Cryptography lacks clear reference implementations:\n",
"\n",
"<img src=\"files/images/crypto.png\" alt=\"A C implementation of a crypto function, along with its mathematical specification\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"- Cryptol is a domain-specific language for specifying cryptographic algorithms:\n",
"- Size-polymorphic, and statically-typed with type inference\n",
"- Lightweight Haskell-style module system\n",
"- Interpreter with a read-eval-print loop (REPL)\n",
"- Transparent integration with SAT and SMT solvers for proving properties expressed in Cryptol"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cryptol Specifications\n",
"\n",
"- File of mathematical definitions\n",
" - Two kinds of definitions: values (`x`) and functions (`F`)\n",
" - Definitions may be accompanied by a type declarations (a signature)\n",
"- Definitions are computationally neutral\n",
" - Cryptol tools provide the computational content (interpreters, compilers, code generators, verifiers) \n",
"- Domain-specific data and control abstractions\n",
" - Sequences (as in the definition of `x`)\n",
" - Recurrence relations rather than loops\n",
"- Algorithms parameterized on size\n",
" - Size constraints are explicit in many specs\n",
" - Number of iterations may depend on size\n",
" - A sized type system captures and maintains size constraints"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Cryptol REPL Commands\n",
"\n",
"_N.B., these first few don't work in ICryptol notebooks_\n",
"\n",
"- Load a module or a file\n",
"\n",
"```\n",
":m AES\n",
":l AES.cry\n",
"```\n",
"\n",
"- Reload the current file\n",
"\n",
"```\n",
":r\n",
"```\n",
"\n",
"- Edit the current file\n",
"\n",
"```\n",
":e\n",
"```\n",
"\n",
"- Quit the interpreter\n",
"\n",
"```\n",
":q\n",
"```\n",
"\n",
"- Tab-complete identifiers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Browse current definitions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Type Synonyms\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"=============\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" type Bool = Bit\n",
" type Char = [8]\n",
" type String n = [n][8]\n",
" type Word n = [n]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Symbols\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"=======\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" drop : {front, back, elem} (fin front) => [front +\n",
" back]elem -> [back]elem\n",
" groupBy : {each, parts, elem} (fin each) => [parts *\n",
" each]elem -> [parts][each]elem\n",
" splitBy : {parts, each, elem} (fin each) => [each *\n",
" parts]elem -> [parts][each]elem\n",
" tail : {a, b} [1 + a]b -> [a]b\n",
" take : {front, back, elem} (fin front) => [front +\n",
" back]elem -> [front]elem\n",
" undefined : {a} a\n",
" width : {bits, len, elem} (fin len, fin bits,\n",
" bits >= width len) => [len]elem -> [bits]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":browse"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Get the type of an expression"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 + 2 : {a} (fin a, a >= 2) => [a]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(+) : {a} (Arith a) => a -> a -> a\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"width : {bits, len, elem} (fin len, fin bits,\n",
" bits >= width len) => [len]elem -> [bits]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":type 1+2\n",
":type (+)\n",
":type width"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Set the base for output"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 4\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0o12\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":set base=8\n",
"10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Show 8-bit sequences as ASCII"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"Hello\"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":set base=16\n",
":set ascii=on\n",
"[0x48, 0x65, 0x6c, 0x6c, 0x6f] : [5][8]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Show all available commands"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" :t, :type check the type of an expression\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :b, :browse display the current environment\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :?, :help display a brief description about a built-in operator\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :s, :set set an environmental option (:set on its own displays current values)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :check use random testing to check that the argument always returns true (if no argument, check all properties)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :exhaust use exhaustive testing to prove that the argument always returns true (if no argument, check all properties)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :prove use an external solver to prove that the argument always returns true (if no argument, check all properties)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :sat use a solver to find a satisfying assignment for which the argument returns true (if no argument, find an assignment for all properties)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :debug_specialize do type specialization on a closed expression\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :q, :quit exit the REPL\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :l, :load load a module\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :r, :reload reload the currently loaded module\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :e, :edit edit the currently loaded module\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :! execute a command in the shell\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :cd set the current working directory\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
" :m, :module load a module\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":help"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Show available user settings"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"ascii = on\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"base = 16\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"debug = off\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"infLength = 5\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"iteSolver = off\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"mono-binds = on\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"prover = cvc4\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"satNum = 1\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"smtfile = -\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"tc-debug = 0\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"tc-solver = cvc4 --lang=smt2 --incremental --rewrite-divk\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"tests = 100\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"warnDefaulting = off\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"warnShadowing = off\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Cryptol Expressions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bits"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p = True\n",
"q = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Homogeneous sequences"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs : [7]Bit\n",
"xs = [False, True, False, True, False, False, True]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs @ 0"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xs ! 0"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 4\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[0x1, 0x2, 0x3, 0x4, 0x5, 0x3, 0x6, 0x8]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"[1 .. 5] # [3, 6, 8]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 2\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[0x2, 0x3, 0x0, 0x0]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"[0, 1, 2, 3] << 2"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 2\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[0x2, 0x3, 0x0, 0x1]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"[0, 1, 2, 3] <<< 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nested sequences"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ys : [2][4][4]Bit\n",
"ys = [[1, 2, 3, 4], [5, 6, 7, 8]]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0x1, 0x2, 0x3, 0x4]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ys @ 0"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[[0x1, 0x2, 0x3, 0x4], [0x5, 0x6, 0x7, 0x8]]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ys @@ [0,1]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 2\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0x2\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"width ys"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sequence comprehensions"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 4\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[0x5, 0x7, 0x9, 0xb, 0xf]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"[ 2*x + 3 | x <- [1, 2, 3, 4] ] # [15]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Words\n",
"\n",
"- Words are sequences of bits\n",
"- Arithmetic is modulo word size"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x, y, z : [8]\n",
"x = 123\n",
"y = 0xF4\n",
"z = 0b11110100"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 1\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0x0\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"1 + 1"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0x2\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(1 : [2]) + 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Heterogeneous Tuples"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"t = (13, \"hello\", True)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"hello\"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"t.1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Records"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"type Point3D = { x : [16], y : [16], z : [16] }\n",
"p1 = { x = 22, y = 35, z = 18 } : Point3D"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0x0016\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p1.x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Boolean operators\n",
"\n",
"- Defined on bits as well as pointwise on structures"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"// Boolean operators defined on bits as well as pointwise on structures\n",
"b = True || False\n",
"w = 0xFFFF && 1\n",
"ws = [0x0000, 0xFFFF] ^ [0x1A1A, 0x0F0F]\n",
"p2 = ~p1"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(True,\n",
" 0x0001,\n",
" [0x1a1a, 0xf0f0],\n",
" {x = 0xffe9, y = 0xffdc, z = 0xffed})\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(b, w, ws, p2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### If-then-else"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0xbeef\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"if 0xFF < 42 then 0xdead else 0xbeef"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Local Bindings\n",
"\n",
"- `where` clauses bind values within definitions"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"isValid x = withinRange && isEven where\n",
" withinRange = x > 5 && x < 10\n",
" isEven = (x && 1) == 0 "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions\n",
"\n",
"- Functions are _mathematical functions_, not procedures that return values\n",
"- Functions can have multiple arguments and return multiple results in a tuple"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"XYandXplusY : [8] -> [8] -> ([8], [8])\n",
"XYandXplusY x y = (xy, x + y) \n",
" where xy = x * y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Functions can be anonymous with lambda"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<function>\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(\\(x, y) -> 2 + x*y) : ([8], [8]) -> [8]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"- Chapter 2: Crash Course\n",
" - Practice with basic language features\n",
" - More exercises than we have time for; skim and refer to later"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sequence Comprehensions\n",
"\n",
"_(Exercises 1:7-43)_\n",
"\n",
"- The most-used control structure in Cryptol\n",
"\n",
"#### Cartesian comprehensions\n",
"\n",
"\n",
"\n",
"#### Parallel comprehensions\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Types\n",
"\n",
"_(Exercises 1:44-47)_\n",
"\n",
"- All expressions have strong static types\n",
"- Type inference adds flexibility\n",
"- Monomorphic types:\n",
"```\n",
"(2 >= 3) : Bit\n",
"[0x02, 0x14, 0x05, 0x30] : [4][8]Bit \n",
"(3,5,True) : ([8],[32],Bool)\n",
"F : ([16],[16]) -> [16] \n",
"```\n",
"- Polymorphic types (a family of types):\n",
"```\n",
"[2, 4, 5, 3] : {a} [4][a]Bit\n",
"tail : {a, b} [1 + a]b -> [a]b \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modules\n",
"\n",
"- A module `Foo` is defined in `Foo.cry`\n",
"\n",
"```\n",
"module Foo where\n",
"\n",
"import Bar\n",
"\n",
"x = Bar.baz + 1\n",
"```\n",
"\n",
"- `import` statements go before declarations\n",
"- Files with no module declaration are implicitly `Main`\n",
"- Modules not currently supported in the ICryptol notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recurrences\n",
"\n",
"- Shift circuits described in code\n",
"- Stream definitions can be recursive and define infinite-length streams"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nats = [0] # [ y + 1 | y <- nats ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Not limited to one initial value and one recursive reference"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"as = [0x3F, 0xE2, 0x65, 0xCA] # new\n",
"new = [ a ^ b ^ c | a <- as\n",
" | b <- drop`{1}as\n",
" | c <- drop`{3}as ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Functions\n",
"\n",
"- Some built-in primitives\n",
"- Some functions defined in `Cryptol.cry` prelude"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 4\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[0x2, 0x3]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"take`{2}[2 .. 10]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 4\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[0x4, 0x5, 0x6, 0x7, 0x8, 0x9, 0xa]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"drop`{2}[2 .. 10]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"groupBy : {each, parts, elem} (fin each) => [parts *\n",
" each]elem -> [parts][each]elem\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":type groupBy"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 7\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[[0x01, 0x02], [0x03, 0x04], [0x05, 0x06], [0x07, 0x08],\n",
" [0x09, 0x0a], [0x0b, 0x0c], [0x0d, 0x0e], [0x0f, 0x10],\n",
" [0x11, 0x12], [0x13, 0x14], [0x15, 0x16], [0x17, 0x18],\n",
" [0x19, 0x1a], [0x1b, 0x1c], [0x1d, 0x1e], [0x1f, 0x20],\n",
" [0x21, 0x22], [0x23, 0x24], [0x25, 0x26], [0x27, 0x28],\n",
" [0x29, 0x2a], [0x2b, 0x2c], [0x2d, 0x2e], [0x2f, 0x30],\n",
" [0x31, 0x32], [0x33, 0x34], [0x35, 0x36], [0x37, 0x38],\n",
" [0x39, 0x3a], [0x3b, 0x3c], [0x3d, 0x3e], [0x3f, 0x40],\n",
" [0x41, 0x42], [0x43, 0x44], [0x45, 0x46], [0x47, 0x48],\n",
" [0x49, 0x4a], [0x4b, 0x4c], [0x4d, 0x4e], [0x4f, 0x50],\n",
" [0x51, 0x52], [0x53, 0x54], [0x55, 0x56], [0x57, 0x58],\n",
" [0x59, 0x5a], [0x5b, 0x5c], [0x5d, 0x5e], [0x5f, 0x60],\n",
" [0x61, 0x62], [0x63, 0x64]]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":set ascii=off\n",
"groupBy`{2}[1 .. 100]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 7\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"[[0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07, 0x08, 0x09, 0x0a, 0x0b,\n",
" 0x0c, 0x0d, 0x0e, 0x0f, 0x10, 0x11, 0x12, 0x13, 0x14, 0x15, 0x16,\n",
" 0x17, 0x18, 0x19, 0x1a, 0x1b, 0x1c, 0x1d, 0x1e, 0x1f, 0x20, 0x21,\n",
" 0x22, 0x23, 0x24, 0x25, 0x26, 0x27, 0x28, 0x29, 0x2a, 0x2b, 0x2c,\n",
" 0x2d, 0x2e, 0x2f, 0x30, 0x31, 0x32],\n",
" [0x33, 0x34, 0x35, 0x36, 0x37, 0x38, 0x39, 0x3a, 0x3b, 0x3c, 0x3d,\n",
" 0x3e, 0x3f, 0x40, 0x41, 0x42, 0x43, 0x44, 0x45, 0x46, 0x47, 0x48,\n",
" 0x49, 0x4a, 0x4b, 0x4c, 0x4d, 0x4e, 0x4f, 0x50, 0x51, 0x52, 0x53,\n",
" 0x54, 0x55, 0x56, 0x57, 0x58, 0x59, 0x5a, 0x5b, 0x5c, 0x5d, 0x5e,\n",
" 0x5f, 0x60, 0x61, 0x62, 0x63, 0x64]]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(split [1 .. 100]) : [2]_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zero\n",
"\n",
"- Cryptol's `zero` is very flexible; it can have any type"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[[{x = 0x0000, y = 0x0000, z = 0x0000},\n",
" {x = 0x0000, y = 0x0000, z = 0x0000}],\n",
" [{x = 0x0000, y = 0x0000, z = 0x0000},\n",
" {x = 0x0000, y = 0x0000, z = 0x0000}]]\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"zero : [2][2]Point3D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Negating `zero` is handy for getting a value of all `True`s"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{x = 0xffff, y = 0xffff, z = 0xffff}\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"~zero : Point3D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ROT13\n",
"\n",
"\n",
"\n",
"- Substitution cipher\n",
" - Each letter in the plaintext is replaced by a corresponding letter in the ciphertext\n",
"- `ROT13(ROT13(x)) == x`\n",
"- Hello World of cryptography (and Cryptol)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ROT13 : {n} [n][8] -> [n][8]\n",
"ROT13 msg = [ shift x | x <- msg ]\n",
" where map = ['A' .. 'Z'] <<< 13\n",
" shift c = map @ (c - 'A') "
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"map = ['A' .. 'Z'] <<< 13"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 7\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"\"NOPQRA\"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":set ascii=on\n",
"map @@ [0,1,2,3,4,13]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shift c = map @ (c - 'A')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 7\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'P'\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shift 'C'"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"('C' - 'A') == 2"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 7\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'P'\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"map @ 2"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Assuming a = 7\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"\"URYYB\"\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"[ shift x | x <- \"HELLO\" ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"- With `ROT13` defined in `ROT13.cry` (in this repo)\n",
"\n",
"```\n",
"Cryptol> :l ROT13.cry\n",
"Loading module Cryptol\n",
"Loading module Main\n",
"Main> :set ascii=on\n",
"Main> ROT13(\"HELLOWORLD\")\n",
"“URYYBJBEYQ\"\n",
"Main> ROT13(ROT13(\"HELLOWORLD\"))\n",
"\"HELLOWORLD\"\n",
"```\n",
"\n",
"- Chapter 3: Classic Ciphers\n",
" - Substitution ciphers: Caesar, Vigenère\n",
" - Try to make it through Exercises 1-10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Property-Driven Development\n",
"\n",
"Properties can express:\n",
"\n",
"- Correctness properties of a specification for validation\n",
"- Equivalence of a high-level specification and an \"implementation-specification\"\n",
"- Design principles that guide the development of a derived specification\n",
"- The correctness of a compilation path\n",
"- Equivalence of an implementation outside Cryptol and a specification\n",
"\n",
"### Design-Refinement Correctness\n",
"\n",
"\n",
"\n",
"### Properties in Cryptol\n",
"\n",
"- Cryptol values of type `Bit`: good for test vectors"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"property two_plus_two = 2 + 2 == 4\n",
"property ROT13_hello = ROT13(\"HELLO\") == \"URYYB\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Cryptol functions returning type `Bit`: good for broader statements"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"property plus_id_l x = 0 + x == x\n",
"property plus_assoc x y z = x + (y + z) == (x + y) + z"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Arguments to properties can be any type\n",
"\n",
"### Randomized Testing\n",
"\n",
"- `:check` evaluates a property with random values (like QuickCheck)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Using random testing.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"testing..."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"passed 100 tests.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Coverage: 39.06% (100 of 256 values)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":check \\(x : [8]) -> x + 1 != x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- `:check` either takes an expression, or checks all properties in the file\n",
"- Intended as fast and easy checking as you program"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Proving Properties\n",
"\n",
"- `:check` does not give proof\n",
"- `:prove` has the same syntax, but proves properties for all values"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Q.E.D.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":prove \\(x : [8]) -> x + 1 != x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- If a property is falsifiable, `:check` and `:prove` give counterexamples"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Using exhaustive testing.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"FAILED for the following inputs:\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0x7\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":check \\x -> x != 0x7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Depending on the size of the input, `:prove` can be much more effective"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"haystack x = x != 0xdeadbeef"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Using random testing.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"testing..."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"passed 100 tests.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Coverage: 0.00% (100 of 2^^32 values)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":check haystack"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"haystack 0xdeadbeef = False\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":prove haystack"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monomorphic Properties\n",
"\n",
"- Cryptol cannot currently reason about polymorphic functions"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"property plus_id_l x = 0 + x == x"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Not a monomorphic type:\n",
"{a} (fin a) => [a] -> Bit\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":prove plus_id_l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Instead, we provide a monomorphic type signature"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Q.E.D.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":prove plus_id_l : [32] -> Bit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Increase assurance by making similar properties at multiple types"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plus_id_l_1 : [1] -> Bit\n",
"plus_id_l_8 : [8] -> Bit\n",
"plus_id_l_32 : [32] -> Bit\n",
"plus_id_l_128 : [128] -> Bit\n",
"property plus_id_l_1 x = plus_id_l x\n",
"property plus_id_l_8 x = plus_id_l x\n",
"property plus_id_l_32 x = plus_id_l x\n",
"property plus_id_l_128 x = plus_id_l x"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
":prove ROT13_hello\n",
"\t"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Q.E.D.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
":prove plus_assoc\n",
"\t"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Not a monomorphic type:\n",
"{a} (Cmp a, Arith a) => a -> a -> a -> Bit\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
":prove plus_id_l\n",
"\t"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Not a monomorphic type:\n",
"{a} (fin a) => [a] -> Bit\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
":prove plus_id_l_1\n",
"\t"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Q.E.D.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
":prove plus_id_l_128\n",
"\t"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Q.E.D.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
":prove plus_id_l_32\n",
"\t"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Q.E.D.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
":prove plus_id_l_8\n",
"\t"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Q.E.D.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
":prove two_plus_two\n",
"\t"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Q.E.D.\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":prove"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Satisfiability\n",
"\n",
"- `:prove`: is a property true for all inputs?\n",
"- `:sat`: is there any input that makes this property true?\n",
"\n",
"#### Example\n",
"- Cryptol does not come with matrix math routines, but we can implement them"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mmult : {a, b, c, w} (fin a, fin b, fin w)\n",
" => [a][b][w] -> [b][c][w] -> [a][c][w]\n",
"mmult xss yss = [ [ sum (col * row) | col <- transpose yss ] \n",
" | row <- xss ]\n",
"\n",
"sum : {a,n} (Arith a, fin n) => [n]a -> a\n",
"sum xs = sums!0\n",
" where sums = [zero] # [ x + y | x <- xs | y <- sums ]\n",
"\n",
"// 3x3 identity matrix\n",
"mi = [[1,0,0],\n",
" [0,1,0],\n",
" [0,0,1]] "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Now we can use `:sat` to invert a matrix"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ma : [3][3][72]\n",
"ma = [[4,2,3],\n",
" [8,5,2],\n",
" [5,8,9]]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(\\x -> mmult ma x == mi) [[0xbbe3d1070bbe3d1071,\n",
" 0xf1e88385df1e88385e, 0x19d5b98a919d5b98a9],\n",
" [0x919d5b98a919d5b98a, 0xceadcc548ceadcc549, 0xda6c0964fda6c09650],\n",
" [0xa46756e62a46756e63, 0x33ab7315233ab73152,\n",
" 0xf69b02593f69b02594]] = True\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
":sat \\x -> mmult ma x == mi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"- Chapter 5: High-Assurance Programming\n",
" - Example properties and intro to random testing, automated proving, and satisfiability checking\n",
" - Try to look at Exercises 1-9, 12, 14, 15, 17-19"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ZUC Demo\n",
"\n",
"- ZUC: stream cipher in GSM cell phone standards\n",
"- ZUC 1.4 had a bug; we can find it with `:prove`\n",
"- Detailed (Cryptol 1) writeup at <https://galois.com/blog/2011/06/zuc-in-cryptol/>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Open Source\n",
"\n",
"- Homepage <www.cryptol.net>\n",
"- GitHub <www.github.com/GaloisInc/cryptol>\n",
"- Now on Hackage, Homebrew, Nixpkgs\n",
"- Mailing list <http://community.galois.com/mailman/listinfo/cryptol-users>\n",
"- Community Contributions\n",
" - /examples/contrib\n",
" - 13 pull requests and counting from folks outside Galois "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Haskell",
"language": "haskell",
"name": "haskell"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| artistic-2.0 |
mne-tools/mne-tools.github.io | 0.18/_downloads/3c22b754d3ee35b041302de37d5f9515/plot_decoding_spatio_temporal_source.ipynb | 1 | 6555 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Decoding source space data\n\n\nDecoding to MEG data in source space on the left cortical surface. Here\nunivariate feature selection is employed for speed purposes to confine the\nclassification to a small number of potentially relevant features. The\nclassifier then is trained to selected features of epochs in source space.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# sphinx_gallery_thumbnail_number = 2\n\n# Author: Denis A. Engemann <[email protected]>\n# Alexandre Gramfort <[email protected]>\n# Jean-Remi King <[email protected]>\n# Eric Larson <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.feature_selection import SelectKBest, f_classif\nfrom sklearn.linear_model import LogisticRegression\n\nimport mne\nfrom mne.minimum_norm import apply_inverse_epochs, read_inverse_operator\nfrom mne.decoding import (cross_val_multiscore, LinearModel, SlidingEstimator,\n get_coef)\n\nprint(__doc__)\n\ndata_path = mne.datasets.sample.data_path()\nfname_fwd = data_path + 'MEG/sample/sample_audvis-meg-oct-6-fwd.fif'\nfname_evoked = data_path + '/MEG/sample/sample_audvis-ave.fif'\nsubjects_dir = data_path + '/subjects'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set parameters\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\nfname_cov = data_path + '/MEG/sample/sample_audvis-cov.fif'\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\n\ntmin, tmax = -0.2, 0.8\nevent_id = dict(aud_r=2, vis_r=4) # load contra-lateral conditions\n\n# Setup for reading the raw data\nraw = mne.io.read_raw_fif(raw_fname, preload=True)\nraw.filter(None, 10., fir_design='firwin')\nevents = mne.read_events(event_fname)\n\n# Set up pick list: MEG - bad channels (modify to your needs)\nraw.info['bads'] += ['MEG 2443'] # mark bads\npicks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True,\n exclude='bads')\n\n# Read epochs\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,\n picks=picks, baseline=(None, 0), preload=True,\n reject=dict(grad=4000e-13, eog=150e-6),\n decim=5) # decimate to save memory and increase speed"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute inverse solution\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"snr = 3.0\nnoise_cov = mne.read_cov(fname_cov)\ninverse_operator = read_inverse_operator(fname_inv)\n\nstcs = apply_inverse_epochs(epochs, inverse_operator,\n lambda2=1.0 / snr ** 2, verbose=False,\n method=\"dSPM\", pick_ori=\"normal\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Decoding in sensor space using a logistic regression\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Retrieve source space data into an array\nX = np.array([stc.lh_data for stc in stcs]) # only keep left hemisphere\ny = epochs.events[:, 2]\n\n# prepare a series of classifier applied at each time sample\nclf = make_pipeline(StandardScaler(), # z-score normalization\n SelectKBest(f_classif, k=500), # select features for speed\n LinearModel(LogisticRegression(C=1, solver='liblinear')))\ntime_decod = SlidingEstimator(clf, scoring='roc_auc')\n\n# Run cross-validated decoding analyses:\nscores = cross_val_multiscore(time_decod, X, y, cv=5, n_jobs=1)\n\n# Plot average decoding scores of 5 splits\nfig, ax = plt.subplots(1)\nax.plot(epochs.times, scores.mean(0), label='score')\nax.axhline(.5, color='k', linestyle='--', label='chance')\nax.axvline(0, color='k')\nplt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To investigate weights, we need to retrieve the patterns of a fitted model\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# The fitting needs not be cross validated because the weights are based on\n# the training sets\ntime_decod.fit(X, y)\n\n# Retrieve patterns after inversing the z-score normalization step:\npatterns = get_coef(time_decod, 'patterns_', inverse_transform=True)\n\nstc = stcs[0] # for convenience, lookup parameters from first stc\nvertices = [stc.lh_vertno, np.array([], int)] # empty array for right hemi\nstc_feat = mne.SourceEstimate(np.abs(patterns), vertices=vertices,\n tmin=stc.tmin, tstep=stc.tstep, subject='sample')\n\nbrain = stc_feat.plot(views=['lat'], transparent=True,\n initial_time=0.1, time_unit='s',\n subjects_dir=subjects_dir)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
net-titech/CREST-Deep-M | notebooks/JPEG Compression.ipynb | 1 | 5417 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Converting Parameters to JPEG"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.misc import imsave, toimage\n",
"from os import listdir\n",
"from os.path import basename, splitext\n",
"import glob\n",
"import numpy as np\n",
"\n",
"npy_path = '../compressed-models/alexnet/npy/'\n",
"jpg_path = '../compressed-models/alexnet/jpegs/'\n",
"gif_path = '../compressed-models/alexnet/gifs/'\n",
"png_path = '../compressed-models/alexnet/pngs/'\n",
"txt_path = '../compressed-models/alexnet/txts/'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"npy_list = glob.glob(npy_path + '*.npy')\n",
"min_max = {}\n",
"\n",
"for file in npy_list:\n",
" f = np.load(file)\n",
" x = f.shape[0]\n",
" y = np.prod(f.shape[1:])\n",
" f_reshape = f.reshape(x, y)\n",
" #f_normalized = np.round((f_reshape + 1) / 2. * 255.) \n",
" filename = splitext(basename(file))[0]\n",
" #toimage(jpg_path + filename + '.jpg', f_resha)\n",
" min_max[filename] = (f_reshape.min(), f_reshape.max())\n",
" np.savetxt(txt_path + filename + '.txt', f_reshape)\n",
" \n",
"#np.save(jpg_path + 'range.npy', min_max)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load all npys and convert them to JPEG"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"npy_list = glob.glob(npy_path + '*.npy')\n",
"min_max = {}\n",
"\n",
"for file in npy_list:\n",
" f = np.load(file)\n",
" x = f.shape[0]\n",
" y = np.prod(f.shape[1:])\n",
" f_reshape = f.reshape(x, y)\n",
" #f_normalized = np.round((f_reshape + 1) / 2. * 255.) \n",
" filename = splitext(basename(file))[0]\n",
" #toimage(jpg_path + filename + '.jpg', f_resha)\n",
" min_max[filename] = (f_reshape.min(), f_reshape.max())\n",
" imsave(jpg_path + filename + '.jpg', f_reshape)\n",
" \n",
"np.save(jpg_path + 'range.npy', min_max)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load all npys and convert them to GIF"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"npy_list = glob.glob(npy_path + '*.npy')\n",
"min_max = {}\n",
"\n",
"for file in npy_list:\n",
" f = np.load(file)\n",
" x = f.shape[0]\n",
" y = np.prod(f.shape[1:])\n",
" f_reshape = f.reshape(x, y)\n",
" #f_normalized = np.round((f_reshape + 1) / 2. * 255.) \n",
" filename = splitext(basename(file))[0]\n",
" #toimage(jpg_path + filename + '.jpg', f_resha)\n",
" min_max[filename] = (f_reshape.min(), f_reshape.max())\n",
" imsave(gif_path + filename + '.gif', f_reshape)\n",
" \n",
"np.save(gif_path + 'range.npy', min_max)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load all npys and convert them to PNG"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"npy_list = glob.glob(npy_path + '*.npy')\n",
"min_max = {}\n",
"\n",
"for file in npy_list:\n",
" f = np.load(file)\n",
" x = f.shape[0]\n",
" y = np.prod(f.shape[1:])\n",
" f_reshape = f.reshape(x, y)\n",
" #f_normalized = np.round((f_reshape + 1) / 2. * 255.) \n",
" filename = splitext(basename(file))[0]\n",
" #toimage(jpg_path + filename + '.jpg', f_resha)\n",
" min_max[filename] = (f_reshape.min(), f_reshape.max())\n",
" imsave(png_path + filename + '.png', f_reshape)\n",
" \n",
"np.save(png_path + 'range.npy', min_max)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Reference Sizes:\n",
"\n",
"conv1 (96, 3, 11, 11) (96,) \n",
"conv2 (256, 48, 5, 5) (256,) \n",
"conv3 (384, 256, 3, 3) (384,) \n",
"conv4 (384, 192, 3, 3) (384,) \n",
"conv5 (256, 192, 3, 3) (256,) \n",
"fc6\t(4096, 9216) (4096,) \n",
"fc7\t(4096, 4096) (4096,) \n",
"fc8\t(1000, 4096) (1000,)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Sanity Check"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from scipy.misc import imread\n",
"\n",
"f = imread(jpg_path + 'conv1.jpg')\n",
"min_max = np.load('range.npy')\n",
"#f_normalized = (f / 255. * 2.) - 1\n",
"print f[0]\n",
"print min_max\n",
"\n"
]
}
],
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
cmorgan/toyplot | docs/matrix-visualization.ipynb | 1 | 1939256 | null | bsd-3-clause |
seanpue/al340 | lessons/textanalysis/.ipynb_checkpoints/testing code - ignore-checkpoint.ipynb | 1 | 13347 | {
"metadata": {
"name": "",
"signature": "sha256:baecb6354529d3b52625f3cc677d0bad6e1a71660fb0737e9ba623e79e60c710"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import nltk\n",
"sentence = \"Hello my name is joe.\"\n",
"tokens = nltk.word_tokenize(sentence)\n",
"tokens"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"['Hello', 'my', 'name', 'is', 'joe', '.']"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tagged = nltk.pos_tag(tokens)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tagged"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"[('Hello', 'NNP'),\n",
" ('my', 'PRP$'),\n",
" ('name', 'NN'),\n",
" ('is', 'VBZ'),\n",
" ('joe', 'NN'),\n",
" ('.', '.')]"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"entities = nltk.chunk.ne_chunk(tagged)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(entities)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"nltk.tree.Tree"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['info', 'linalg', 'random', 'fft', 'power']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" from nltk.corpus import treebank"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t = treebank.parsed_sents('wsj_0001.mrg')[0]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t.draw()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"from pylab import *\n",
"\n",
"\n",
"x = linspace(0, 5, 10)\n",
"y = x ** 2\n",
"figure()\n",
"plot(x, y, 'r')\n",
"xlabel('x')\n",
"ylabel('y')\n",
"title('title')\n",
"show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Q1R47A5ieUtoV+J/K15Jq6c034dvfLjZb3XVXsdtWLammoZ9SugP422oPHwaMq9wfBxxR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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x1044a2110>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
xianjunzhengbackup/code | data science/machine_learning_for_the_web/chapter_3/Regression_problem.ipynb | 1 | 9294 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn import cross_validation\n",
"from sklearn import svm\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.linear_model import Lasso\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.metrics import mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"linear regression\n",
"mean R2: 0.72 (+/- 0.15)\n",
"MSE: 23.5515499366\n",
"ridge regression\n",
"mean R2: 0.72 (+/- 0.16)\n",
"MSE: 23.7397585761\n",
"lasso regression\n",
"mean R2: 0.71 (+/- 0.17)\n",
"MSE: 24.734860679\n",
"decision tree regression\n",
"mean R2: 0.75 (+/- 0.24)\n",
"MSE: 19.8023913043\n",
"random forest regression\n",
"mean R2: 0.87 (+/- 0.12)\n",
"MSE: 10.9910313913\n",
"linear support vector machine\n",
"mean R2: 0.70 (+/- 0.25)\n",
"MSE: 25.833801836\n",
"support vector machine rbf\n",
"mean R2: -0.01 (+/- 0.11)\n",
"MSE: 83.8283880541\n",
"knn\n",
"mean R2: 0.54 (+/- 0.23)\n",
"MSE: 37.8792632411\n"
]
}
],
"source": [
"df = pd.read_csv('housing.csv',sep=',',header=None)\n",
"#shuffle the data\n",
"df = df.iloc[np.random.permutation(len(df))]\n",
"X= df[df.columns[:-1]].values\n",
"Y = df[df.columns[-1]].values\n",
"\n",
"cv = 10\n",
"print 'linear regression'\n",
"lin = LinearRegression()\n",
"scores = cross_validation.cross_val_score(lin, X, Y, cv=cv)\n",
"print(\"mean R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(lin, X,Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'ridge regression'\n",
"ridge = Ridge(alpha=1.0)\n",
"scores = cross_validation.cross_val_score(ridge, X, Y, cv=cv)\n",
"print(\"mean R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(ridge, X,Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'lasso regression'\n",
"lasso = Lasso(alpha=0.1)\n",
"scores = cross_validation.cross_val_score(lasso, X, Y, cv=cv)\n",
"print(\"mean R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(lasso, X,Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'decision tree regression'\n",
"tree = DecisionTreeRegressor(random_state=0)\n",
"scores = cross_validation.cross_val_score(tree, X, Y, cv=cv)\n",
"print(\"mean R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(tree, X,Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'random forest regression'\n",
"forest = RandomForestRegressor(n_estimators=50, max_depth=None,min_samples_split=1, \n",
" random_state=0)\n",
"scores = cross_validation.cross_val_score(forest, X, Y, cv=cv)\n",
"print(\"mean R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(forest, X,Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"#svm\n",
"print 'linear support vector machine'\n",
"svm_lin = svm.SVR(epsilon=0.2,kernel='linear',C=1)\n",
"scores = cross_validation.cross_val_score(svm_lin, X, Y, cv=cv)\n",
"print(\"mean R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(svm_lin, X,Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'support vector machine rbf'\n",
"clf = svm.SVR(epsilon=0.2,kernel='rbf',C=1.)\n",
"scores = cross_validation.cross_val_score(clf, X, Y, cv=cv)\n",
"print(\"mean R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(clf, X,Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'knn'\n",
"knn = KNeighborsRegressor()\n",
"scores = cross_validation.cross_val_score(knn, X, Y, cv=cv)\n",
"print(\"mean R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(knn, X,Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"feature selection on linear regression\n",
"R2: 0.61 (+/- 0.31)\n",
"MSE: 33.182126206\n",
"feature selection ridge regression\n",
"R2: 0.61 (+/- 0.32)\n",
"MSE: 33.2543979822\n",
"feature selection on lasso regression\n",
"R2: 0.68 (+/- 0.20)\n",
"MSE: 27.4174043724\n",
"feature selection on decision tree\n",
"R2: 0.70 (+/- 0.35)\n",
"MSE: 24.1185968379\n",
"feature selection on random forest\n",
"R2: 0.84 (+/- 0.14)\n",
"MSE: 13.6755712332\n",
"feature selection on linear support vector machine\n",
"R2: 0.60 (+/- 0.33)\n",
"MSE: 25.833801836\n"
]
}
],
"source": [
"from sklearn.feature_selection import RFE\n",
"best_features=4\n",
"print 'feature selection on linear regression'\n",
"rfe_lin = RFE(lin,best_features).fit(X,Y)\n",
"mask = np.array(rfe_lin.support_)\n",
"scores = cross_validation.cross_val_score(lin, X[:,mask], Y, cv=cv)\n",
"print(\"R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(lin, X[:,mask],Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'feature selection ridge regression'\n",
"rfe_ridge = RFE(ridge,best_features).fit(X,Y)\n",
"mask = np.array(rfe_ridge.support_)\n",
"scores = cross_validation.cross_val_score(ridge, X[:,mask], Y, cv=cv)\n",
"print(\"R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(ridge, X[:,mask],Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'feature selection on lasso regression'\n",
"rfe_lasso = RFE(lasso,best_features).fit(X,Y)\n",
"mask = np.array(rfe_lasso.support_)\n",
"scores = cross_validation.cross_val_score(lasso, X[:,mask], Y, cv=cv)\n",
"print(\"R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(lasso, X[:,mask],Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'feature selection on decision tree'\n",
"rfe_tree = RFE(tree,best_features).fit(X,Y)\n",
"mask = np.array(rfe_tree.support_)\n",
"scores = cross_validation.cross_val_score(tree, X[:,mask], Y, cv=cv)\n",
"print(\"R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(tree, X[:,mask],Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'feature selection on random forest'\n",
"rfe_forest = RFE(forest,best_features).fit(X,Y)\n",
"mask = np.array(rfe_forest.support_)\n",
"scores = cross_validation.cross_val_score(forest, X[:,mask], Y, cv=cv)\n",
"print(\"R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(forest, X[:,mask],Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)\n",
"\n",
"print 'feature selection on linear support vector machine'\n",
"rfe_svm = RFE(svm_lin,best_features).fit(X,Y)\n",
"mask = np.array(rfe_svm.support_)\n",
"scores = cross_validation.cross_val_score(svm_lin, X[:,mask], Y, cv=cv)\n",
"print(\"R2: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
"predicted = cross_validation.cross_val_predict(svm_lin, X,Y, cv=cv)\n",
"print 'MSE:',mean_squared_error(Y,predicted)"
]
},
{
"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
}
| mit |
esumitra/minecraft-programming | notebooks/Adventure-1B.ipynb | 1 | 5309 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Circles and Spheres\n",
"\n",
"\n",
"\n",
"In our first class we learned about functions. A function is a task to do something. Some of the functions we used are **mc.postToChat** to post a message to Minecraft and **time.sleep** to pause our program execution for 1 second. In today's adventure, we learn more about functions. We will use new functions to build circles and spheres in Minecraft.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions and arguments\n",
"In our previous class we were introduced to functions. Sometimes you need more than just a task name to complete a task. \n",
"\n",
"E.g., \"Practice you writing!\", is not enough, you want to know for how long. \"Practice your writing for 15 minutes!\" is more helpful.\n",
"\n",
"If we had a function for the \"practice writing\" task, we would write is as,\n",
"\n",
"```python\n",
"practiceWriting(15)\n",
"```\n",
"\n",
"the additional pieces information we pass to a function are called its arguments. A function can have zero or more arguments just like a real tasks need zero or more pieces of information to complete them. Both the functions that you used so far needed arguments. For the function examples below,\n",
"\n",
"```python\n",
"mc.postToChat(\"Hi Kids\")\n",
"time.sleep(1)\n",
"```\n",
"\n",
"The **mc.postToChat** function needs the argument _\"Hi Kids\"_ which is the message to post to the Minecraft chat window. The **time.sleep** function needs the argument _1_ which is the number of seconds to pause the program from running."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building functions\n",
"\n",
"The first building function **drawings.drawMyCircle** uses two arguments - the radius of the circle to build and the block id to use for building the circle. \n",
"\n",
"```python\n",
"drawings.drawMyCircle(radius,blockId)\n",
"# e.g., drawings.drawMyCircle(5,block.STONE.id)\n",
"```\n",
"\n",
"The second building function **drawings.drawMySphere** uses two arguments - the radius of the sphere to build and the block id to use for building the sphere. \n",
"\n",
"```python\n",
"drawings.drawMySphere(radius,blockId)\n",
"# e.g., drawings.drawMySphere(5,block.STONE.id)\n",
"```\n",
"\n",
"In the following tasks, you will use these functions with different arguments to build circles and spheres in Minecraft. \n",
"\n",
"**Note:** Before you start your tasks, **start Minecraft** and run the program cell below to make the building functions available to your programs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('/home/pi/minecraft-programming')\n",
"import mcpi.block as block\n",
"import time\n",
"import drawings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 1\n",
"Use the building functions to build three circles with radii 5, 9 and 13 in the same position (center)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Task 1 program\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 2\n",
"Use the building functions to build three spheres of radius 5, 9 and 13 in three different positions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Task 2 program\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 3\n",
"\n",
"Write a program to build a sphere of radius 5 at the center, a circle of radius 8 and a circle of radius 13 at Steve's current position"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Task 3 program\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 4\n",
"Use a new function **drawings.drawMyHollowSphere**. The function takes two arguments. First the **radius** of the hollow sphere to build and second, the **blockId** to use for building the hollow sphere."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Task 4 program\n"
]
}
],
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
liu-annie/CaI | Graphs_2.ipynb | 1 | 882548 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: Qt4Agg\n"
]
}
],
"source": [
"drive_path = 'c:/'\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"import sys\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib\n",
"from scipy.stats import ks_2samp\n",
"from scipy.stats import anderson_ksamp\n",
"from scipy.stats import kruskal\n",
"from scipy.stats import variation\n",
"from scipy import signal as sps\n",
"import seaborn as sns\n",
"import glob\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"complete=pd.DataFrame([])\n",
"date='160620_3'\n",
"os.chdir('C:\\\\Users\\\\Annie\\\\Documents\\\\Data\\\\Ca_Imaging\\\\GoodFiles\\\\%s'%date)\n",
"for filename in glob.glob('*dt.txt'):\n",
" f=pd.read_csv(filename,nrows=175)\n",
" df=f[[col for col in f.columns if 'G PMT' in col]]\n",
" complete=pd.concat([complete,df],axis=1)\n",
"# peak=[]\n",
"# for col in df.columns:\n",
"# a=df[col]\n",
"# firsta=1;\n",
"# firstb=24;\n",
"# #Figures out if there is a min or max and sees if it passes threshold (3SD)\n",
"# if np.absolute(min(a[26:80]))>np.absolute(max(a[26:80])) and np.absolute(min(a[26:80]))>=3*np.std(df[col][firsta:firstb]):\n",
"# b=min(a[26:80])\n",
"# peak.append(b)\n",
"# elif np.absolute(max(a[26:80]))>np.absolute(min(a[26:80]))and np.absolute(max(a[26:80]))>=3*np.std(df[col][firsta:firstb]):\n",
"# b=max(a[26:80])\n",
"# peak.append(b)\n",
"# else:\n",
"# b=0\n",
"# peak.append(b)\n",
"# peaks=pd.DataFrame(peak).T\n",
"# peaks.columns=df.columns\n",
"# peaks=pd.concat([pd.DataFrame({'Trial':[int(filename.split('dt')[0])]}),peaks],axis=1)\n",
"# peakdf=peakdf.append(peaks,ignore_index=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# PLOT TRACES"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"sns.set(palette=\"muted\",color_codes=True);\n",
"sns.set_context(\"poster\",font_scale=1.3);\n",
"plt.figure(figsize=(8,7))\n",
"plt.plot(complete);\n",
"sns.despine()\n",
"plt.ylabel('DF/F');\n",
"plt.title('Traces from one imaging session');\n",
"plt.xlabel('Frame');\n",
"plt.tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Separate by group"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Group</th>\n",
" <th>Mouse</th>\n",
" <th>MS01</th>\n",
" <th>THA</th>\n",
" <th>Blank</th>\n",
" <th>MS10</th>\n",
" <th>AP</th>\n",
" <th>MS05</th>\n",
" <th>IAA05</th>\n",
" <th>PA</th>\n",
" <th>IAA01</th>\n",
" <th>IAA10</th>\n",
" <th>Hexanal10</th>\n",
" <th>Hexanone</th>\n",
" <th>Hexanal01</th>\n",
" <th>Hexanal05</th>\n",
" <th>EB</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Control</td>\n",
" <td>160321_1</td>\n",
" <td>0.015671</td>\n",
" <td>0.067358</td>\n",
" <td>NaN</td>\n",
" <td>0.150474</td>\n",
" <td>0.036738</td>\n",
" <td>0.169456</td>\n",
" <td>0.034294</td>\n",
" <td>0.037389</td>\n",
" <td>0.188597</td>\n",
" <td>0.099708</td>\n",
" <td>0.069948</td>\n",
" <td>0.093353</td>\n",
" <td>0.062999</td>\n",
" <td>0.124211</td>\n",
" <td>-0.061840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Control</td>\n",
" <td>160321_1</td>\n",
" <td>-0.034636</td>\n",
" <td>0.095266</td>\n",
" <td>NaN</td>\n",
" <td>0.074001</td>\n",
" <td>0.073576</td>\n",
" <td>0.139423</td>\n",
" <td>0.001814</td>\n",
" <td>-0.002271</td>\n",
" <td>0.188666</td>\n",
" <td>0.041676</td>\n",
" <td>0.036447</td>\n",
" <td>-0.026087</td>\n",
" <td>0.014205</td>\n",
" <td>0.101018</td>\n",
" <td>0.056893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Control</td>\n",
" <td>160321_1</td>\n",
" <td>-0.044179</td>\n",
" <td>0.146676</td>\n",
" <td>NaN</td>\n",
" <td>0.260675</td>\n",
" <td>0.058214</td>\n",
" <td>0.095906</td>\n",
" <td>0.127803</td>\n",
" <td>0.221412</td>\n",
" <td>0.477065</td>\n",
" <td>0.287615</td>\n",
" <td>0.263380</td>\n",
" <td>0.022945</td>\n",
" <td>0.129928</td>\n",
" <td>0.293199</td>\n",
" <td>0.153817</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Control</td>\n",
" <td>160321_1</td>\n",
" <td>0.146939</td>\n",
" <td>0.094015</td>\n",
" <td>NaN</td>\n",
" <td>0.071141</td>\n",
" <td>0.110235</td>\n",
" <td>0.157300</td>\n",
" <td>0.104363</td>\n",
" <td>0.051386</td>\n",
" <td>0.248613</td>\n",
" <td>0.034975</td>\n",
" <td>0.037260</td>\n",
" <td>-0.057530</td>\n",
" <td>0.104142</td>\n",
" <td>0.198974</td>\n",
" <td>0.082859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Control</td>\n",
" <td>160321_1</td>\n",
" <td>0.268299</td>\n",
" <td>0.069755</td>\n",
" <td>NaN</td>\n",
" <td>0.220858</td>\n",
" <td>0.116399</td>\n",
" <td>0.233311</td>\n",
" <td>0.240154</td>\n",
" <td>0.203381</td>\n",
" <td>0.393188</td>\n",
" <td>0.309290</td>\n",
" <td>0.145721</td>\n",
" <td>-0.147715</td>\n",
" <td>0.315627</td>\n",
" <td>0.538062</td>\n",
" <td>0.694684</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Group Mouse MS01 THA Blank MS10 AP MS05 \\\n",
"0 Control 160321_1 0.015671 0.067358 NaN 0.150474 0.036738 0.169456 \n",
"1 Control 160321_1 -0.034636 0.095266 NaN 0.074001 0.073576 0.139423 \n",
"2 Control 160321_1 -0.044179 0.146676 NaN 0.260675 0.058214 0.095906 \n",
"3 Control 160321_1 0.146939 0.094015 NaN 0.071141 0.110235 0.157300 \n",
"4 Control 160321_1 0.268299 0.069755 NaN 0.220858 0.116399 0.233311 \n",
"\n",
" IAA05 PA IAA01 IAA10 Hexanal10 Hexanone Hexanal01 \\\n",
"0 0.034294 0.037389 0.188597 0.099708 0.069948 0.093353 0.062999 \n",
"1 0.001814 -0.002271 0.188666 0.041676 0.036447 -0.026087 0.014205 \n",
"2 0.127803 0.221412 0.477065 0.287615 0.263380 0.022945 0.129928 \n",
"3 0.104363 0.051386 0.248613 0.034975 0.037260 -0.057530 0.104142 \n",
"4 0.240154 0.203381 0.393188 0.309290 0.145721 -0.147715 0.315627 \n",
"\n",
" Hexanal05 EB \n",
"0 0.124211 -0.061840 \n",
"1 0.101018 0.056893 \n",
"2 0.293199 0.153817 \n",
"3 0.198974 0.082859 \n",
"4 0.538062 0.694684 "
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filename='C:\\Users\\Annie\\Documents\\Data\\Ca_Imaging\\GoodFiles\\\\fullpeak.csv'\n",
"comp=pd.read_csv(filename)\n",
"comp_sorted=comp.reindex_axis(comp.mean().sort_values().index, axis=1)\n",
"comp_labels=pd.DataFrame(comp.Mouse)\n",
"comp_group=pd.DataFrame(comp.Group)\n",
"tmp=[comp_group,comp_labels,comp_sorted]\n",
"composite_full=pd.concat(tmp,axis=1)\n",
"cfull=pd.melt(composite_full,['Group','Mouse'],var_name=\"Odor\")\n",
"composite_full.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df=composite_full\n",
"# df.columns=['Group','Mouse',1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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z+tn+eTx2GlYX8+6rpzl5ZIiX/uMYd39pGVvvrsnp7SCXcqP//L7I5fYtkc7w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rJfK5+DoeL7O10+TbeIeh021wHfY0CHiicmE+cQxtVKr7S0bx3hH41xrsKqCqPqutAFuSoo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M1LE1cBvb9/qpcjdfjtMWCVBwah/5TdUEIxEOJPahJLk/3vCZsdsxfcoY37eMTw7ksuNI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\ngASyEEIIEQckkIUQQog48P8Sck0rvMMC+AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d41860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for x in df.index:\n",
" plt.plot(df.iloc[x,2:]);"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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qPV1WK+ZTlXz1+TEOt7nIMtUw8et36C0pxt7aitzXF49J2Xyc0beg0OMrfkPI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oRBBg75OHuZET8JU3GJeqvHrpNLIU4F99ehyrIhJPnMMErp+t4to4igtI34LXZTtH7tvAyVUe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\nkoI5u4+1S0UaW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QV4IzG0PSkq5hjLy30QRsgtB2c0y+LIMHa8W7KneBFa5NOrV6DZwqrW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inatU3frc0xvt4sG2A+xKDaNIyvteg+sYXD59krPvKHcYFIKo4bkGIf9+fA0dpJe/hxYZ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"text/plain": [
"<matplotlib.figure.Figure at 0x1268b898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for x in df[df.Group=='Control'].index:\n",
" plt.plot(df.iloc[x,2:]);\n",
" plt.xlabel('Odor')\n",
" plt.ylabel('Peak DF/F')\n",
" plt.title('Control')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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S1tm1jYZgHACpYQr+niCpjmpC6NgfmcuMzG7cfj1N+kH0HiPJPUGac2ejI0S/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\n0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7eQSHH/4AzueIoV2SauK3Hw6P4hDrdOcY/pJPbOUUjLOHMraL08m6HMOApBweWl27ii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\nvsJAaQcEZRu7GlBvqhR6goIhaQzZ1DMaTnweTZuGoI/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MQcCQtcLycJ51MUSo4TArJZ5Q\n2EDyoTGjGVX4lLjNWHGTXjJNJ5mmHU1RD0Vp9S1E3abvhWkFEYK/Q53NhHvgfgfoFSQuYGsWut4j\n3k9ioGNH66hTt2jG6/QVH8tT8HoRhq0MoyJEsxdlpxmj2ovctw+NgVXkYBNE4S5wSySuBI8AAg8t\nsDG8PmGvQ9Kus7+1giptXn0oTiGr8fxqhMTDSf7iygJ7rThKpkBswSKmHiEw0wCMbd2mlcxQqyrU\nVwcU6Yih8tuPzbLtunSLr/P43DZOH4Kf78LJKfRJH1WRuAG0fR3T9tDW25xpTXPJm8XvC37/+VkW\nTy4CcLXc5McbFVoyQHE9jp5/m8StVc6mH2A9lOdk8yb9UJgboRmG4x1+l7fxrjmsH3yI0aObTCT+\nA4D3HwjpBoMlphi4jglFYHuCRkklGfRoaVEqMk6xG6XUjmJ7Gn1Po9iOEMj7KWdV+vgoIAQqAbri\ng+ogVYtAC5BKHIgTTWvo+ShqPHQ3f+5bHnbdprvdxm8NCu/i9DnQ2eRwfYU9M8/1+Aw7oSFQBZG0\nzrG9ayQbVbZHZ7idmiZZLpJy2viGQZCM48dDWBEdGY1ALIIIfbwAK7A9vO7AItTrunhdl+hUnNBQ\nhMeU8xxhmUIzwm0rwlqsiib7HBFRFsJgaAGup7BbyPMLtUQp5PBw4wFiWVgtTmHrBnEqdJIpurEE\ngfj4pEbIAAMXW3z82AQBcbokRZskbZKiTYo2cdnB8GxcV8N2VSxvsPU8FctTcAKB6w/q91VFYmoe\n2bBkNJcgGjL4wYtpTFdHyXbYXUzjhoYw232e/NmfM1bfvas0+OJDaVZnBop0uh1iZvs4j916nZ/P\nPsVaI0U20iPltbntDKMpAV+bv87BqTpCE1jFgJf1J8jPHOars0Povyb1N/gNmP+Douv6/OVaAaXQ\n553zO9iOjyICnk6u8MTxAmu1PLxXYWxnfXBMuXl+FjnCrpFE02DY3uKR7iUMpUMzbHIlO0V4QmLZ\nOpbw0U2LyYTNmAZTukbqThVaR0b4vv8sHWKc3n2XI1fPEJRs9IfTeO/WkX2fV5/5OqGujlU1kRr0\nsiESTRvfCpif3WJh/zpSCt67up8bxSzpdIu+UNjpxuha6n3gJQSMJ12mXA+3O6ACR4bLHJjfIB7v\n3XdN/ADq7Si1cpqNrQkcxyCVbHHi6BKRiMWGHfBi16UuXFQxRjTyWZCC2PsVPrXyOiPtNc6PPsX6\nhM6phVvcVDrcchV0fZ6ksYirDFbhIdciVugw72xQ3Mrh+xrJoELEXebn4VOIQGNaSiJCYftO/zmA\npsPDyjoP3TxDKCoReQOsAGn5yL4Pff9Ozl6jE0vSjkXoxKK0Y8NUIzl6lsQrtakSpaeE8YWG/ISZ\nt8L9IG8i0YQEJaDvK/QRWCKgJ8U9qp8BpR+5A9gmg8psJEgZoAY2pt8n7HeJ+W2Sdp2sVSFpd9B/\nhe1ux9D4m+fi9EMq33y/T+TxMf7d5aNU7Bi58RjadASpG4ggYG7lCovXLvLuw8+zvaXQ6g5SPgI4\nti9Lt29zKH2RE+MlnKaP3OyhHIyja4MWqrcsh2uOd68y2Vc5VvYQK2O8YxxFlQEvqBt85uvPkpyf\nxQskZ0oNXtmpYgeSRLPGp974KXqlxSujn8b3uiTpciU8RzbS4/fMdzAulrk69BS9eZhYXMZUuKvT\nX/YD1FaY6UaISK0EzQ44Aa1QgtXJRW6PLFDvaljtgKDjojXbZM02c/EaWkhQ9aJsNJMU21E+ZGhU\nEZAKW3QdA8sb5KGTIYvnF1aJpau80ehy4kyDuViYv4w/yWo9TTLc5/ihqzTDkpL+NIGSve+eDLrk\nFZymhRAKemJABQc9h4ml6zxx+VUMx2Jp/ygx20SgcXP+KNuz+3no7KscvnKWzewULyVP8kjxEoe6\nG/da0AQooyGUuSjMxdDiKo7UqPhJil6Kvh6nLpPUSNIl+rHnZZQSv6W+cnel6zgaxWqGnfYwpX4G\n2zCJ5G2UZEBHRGkTxeGT8/vS8RB9n4RrMxyuMhorEr26R+TSDqgWtSdmmRqXdESUgpNk00tQFTE8\nNYavxvCV0Mc+U5EBcTqklHtA/+HXGL2PMSyup+I6OrarUe+GuLS5n9rBcbyoTqTcIH2tx4mtl0j1\n9v4f9t7rSa40Te/7fcef9KYqy6IcquCBBtqbmZ5pMzszS2p2yRDXzMWGFOKdKBeSqBtFSP+AQhG6\nZYQupCW5G+Qazuz0uJ4e277RMA1TAArlfWVW2pPHf58ushpAm6HIaO5c9RtxIisRlZUH3znne93z\nPg8Gkn/5zRJ7ThU920VIjRN3v86rXx/hX/xkh3bgcGK4gReZbLXzvDK3zDPjO1hZkL2Epb1Rfn7s\nZb5xrMbXxsqfaYX8fduXzvwL2Eq7w7+4uwsIpBfQulsn9noIM6SsN3h2Ypmd6hA7XZvC4RapCOk7\nGj3XJLY+u4wmcMzQmTZ1pg2dEeNhRBumis3EYaOb5777VWLb4fjV63z1ve+DAuPZMtGih++5XD79\nKlGQRzzSOpQoQpEwNLlHoCm22zk22/nPZBmaklSiNmNhg1rYBBQflE7TNvOAYqyUMBEoROAACtuK\nsOyI0ydWGB5qPRhj83yLtbsjbOyNkygbISQjxw6Ym96k5Pa4ESX80o8ItQky7jfRVIp7cxOzociO\n7hONrbBmOJjmKSxzHoSJQDIS77Hw/vsYhmBPnaDby6HrCbXOPW5rBvfd40xogmGpOECwdcShrQnF\nJX2Lry39GmdER1wqYU5/MgN+sFaRRAUS5aeIfnLk5AfvlZ9CkJKEECqX2CnRK9XYsUu0mz5RN2Tb\nHqNl5oiF+G0JPQCGljJa8KjYIYlvkIsDhtNDCl6LfLeJ5QdoUmElMUaafO7fULpAWjpSH4iSIBVa\nIiFJ2C8b/O3LJaSmMW89yQv6Ov/qo/Mkw0PkxjMo00AkkuLOIS//6l/jBj4/feoPWKy7REogMh2M\n8h6qMYWIDP7JY4ucqDUJvAGoyrAFPSl5K4hhFV54+wDDsjh48gzvz2RZD9YJ6B6V00vs7z9BpFk8\n3lpkgV3q51+hdnKO0eEci0nIlcMOIJhaWeTJt19nebjAh5VxarsGN5zjFJyA7+bfpfj2GvdGznE4\ncZznnrqBlgpu/LxM+WCNkc7+UYZlcHd4gXsXHqc9M42/79O9sc+JzD7z+QbK0jlQBdZaJfZ6D52a\nLlLK2Q6zw21yOry/PkYvshnOeXz71DJ3Dyq8uzaOQnB+bJ+XTyzzofQ4XOrx+IHG1bGz3Ng+TmbU\nZWxOp2cNNG4NJDVTkjb6fK3wS15TL9ETOVjaZWzlHvsnT5JMDB2Nakqi/h0C9Rt0MUrG/RaabjJ/\n/QO+8vaP8QpVvn/8K6x2ByRBpu2xML7F6UqH+WKMezSeFacavdDE0BLyzsNgTynoxClNzQRRJBBl\n6qrEYVygku4RYuORxxdZfN0l0OzP5U3XVEJO9CgLjzw99E5Ec7eMtbbFlj7BXL7LwtwGo7UBliKI\nDH4eeJTeO+TSHZ++maft1rBzMcWyj51LwBB0pU09dNjD4TBbJCjnCbMuoSiAKCPtLNL8vGw/xZF9\nstKjILsU6VESHap6l5LRY0eM8OP4KyS6ydlrb3PqyhXem/wO2ajJMxvf5+qCzS8vVRBGgh7ZpFbI\nE8FXePujDKnSeHFunc1mjpGM4qvTi2TyCiUV/r2EH5ZfoT00yR/NjTLrJvzg3l9zGHn800v/NdqX\n3OxfzH4XzvwvP3yTX7deRz2YZv3/16G2Q0XWT3FDSZjmyBWKjFo6x7MR45kQ7aj8pqQiSTTWtsdZ\na07Sllk0PaI+O0KStRha2uYbv/l/6YsqzelJ2mGFtqiS6A/LVh0UdT3Fk4JQCT7dDzZlTDnuMuEf\nMO3vDEr5cZdUaDScEp45mLceCtvUrRLvlM9xcFSKdW3BRKQoHwUDJdtnrnKVkXMxQgwqmLoGO90c\nv7x2goyXRUdQR3FgRBwrt5grH9Ao77Ooj2M7LyGIWEh/xu2kiDBPoetDADihx2hjnSeKdym6EYt3\nZ9jYGgMEw2KbA1lDoXPoahSjmJ7UWTti+wLFKbHHt9d+gTtroV8qolcHa7QZwq16EduKyDoBGSsm\no0FGCDKawBUC/T8gulaxfJDVK1+Cn5IGEtmXdH2TfVFgRa+wHldwXcHZyRZTlR5F6RG818ZcamKo\nz1fyikyLXr5EaNsYSUK+28QOAgSQCGjnddp5nVbu6DWv084bdDIaShv0P551JxiXLj9qPYkxnEdo\nAiPscfqjq0SNIn2jwnj7DoHm89PcOaTQqA3p+NkVgijEaBzju5cWOVbqkUiBoSn8WOeteoaDvT1e\nurpP0fvU+Vs67vkTiFefY9vwWGpusHxwwM7Ns/hRgTF1wDPWu+yN6GwW8sQGkMmhnKcR+ghamnDu\n5rtU9j7g7ccKZD+a4hYnyFoRf1T+gJG3llDTOYyyTv+Gjx0PrvaWPcS14gK38zMUzw8xORIy2lzF\nqh+yI4ustUrsf8p5T6hDprrbeBMulcf2Oa8Z/PTOcW7uDqNrgt9/9hjnJiJah8v4/T06vYi318bZ\n6eSxjYRXT6xSG9nmJ36JfDhPp7yAFBpKKey+x3PZG8zr65gixY91vNDEyQr+Xf8lunaRJ70G354Z\nYdUo8Tc3t2naikD9LVJ1BufI9EX5RAAAIABJREFUNJO3R/nO1b9Dajq3L4ywfLZAJg1Z0Fzm8iGW\nPlj/RAoEnxR6+ngipR8Z/GZlglrW4+Roi0Otwpaqsa1G2Kf6GX4DgSRLn4LwKNAjLzyyqkdO+VT0\nDn/VabIjU76yc4wnag3socGXttoD/oRScdDUanZdvK1dfuIK9vMapfvnGNrUCWVMLWoy6e8zHtSx\n1Sfn+/99FtgurdIQrdIw7VKFbrFCt1ikly+Rfs4kgJYmA8pXKXn+V68xsrrFcuU8zpnzbK73mPR+\nwRsv9IgsjeLhFIWdEZYMiWwPY+kJ/+j8XTqrKWdnW2SrA9xDuhNwvzHFLxa+Tt6x+M9rPfYab/Lj\nzXWmrl4AmeXP/vm3MH/LZMJ/avvSmX8B+/5H7/Kjg786emej61U0mcWsK8KegZ9YqMjidHuLhVMa\n706+yjBtvrr9a4p0MMdMMDX6OLRVjg1/iG4/R9pP6WsZuoUS/cxgthepMIIEs5uQ2+ySaR0QqQK+\nGIxXxSgqSpARggDFMgPtbwBNSCaKXUbzHk6vT+XONnPdTewjYphE02i7JnoK+SB88EgrAYFho8cJ\nG5kRLBkTaDZvV86zfQQ6s02NsVgxdPQZUerwlQuLFN2QbmiStwcP6OZagdu354lEDjv2OLv3ywfC\nK+2KwU9fepre0EsPF1dJjq3e5eTtK4xvriCUYv3UJdbEaeLYImd1ObH9JvUJm/cKrzJykBAAaygG\nV15xTB7ynf1fUjppoJ8voGUNpFIsBop31ibodYo45QNEZ4jDdgk/NhC2j8h00DNdNLdN0W1iZOUD\nB58RgyMnbFzDIYtGTklckWBrCf8hmgoqTEk+aJFeH3AKKMB3sxwOjbI7NkWnVKGXL2FEEWMbdyjv\n3yXWfQ7zBvWCTStv0M0KQld9LsuGBtgCXCE440yyz0X29AH4Lgl7jG++g80E/VyBV177N3xUe5me\nU2WivchY8zp7VhVTRtwszLNZG+e/euYa+SOCj0gq3j3I8+biCULf4cXGFZ5r3QRDw3xlCFG1SK+2\nSRe7A+aSrI7xWBH9bAFhDZDi37u5wM3dYfJ2yB9fvM1kqfdwbRQsqWnekRfxyJClz7PaVebFGu99\nWOaH9TPYRsqfTlxm9I07oCASBivZMXaHaowNBxSdiJ3iKHXPZa2Z5+AR522IlGGtTzaVzDSXudi4\niWdZXPuDk5wptzjYH+PHd+YIYoPjEwX+i2+fZmLo4eeHh/MsrdS5fHefX11dZseT2KMFcuMuHI0g\nibjFcbnG/UWXnbpLLefxh+fvUrQCbD3lY3Czpxy+L79BS+V4Ip/ylFWisR/xq90PuFt4BxHOguxS\ninb54x+3MFLFzW8OcWw6w8inhFUeXb/9XobtVh6/n+XU7AEVq0tHZmjIEg2tzLYaYU8NkX78pCtF\nIemS9zx0P0ILA2TBwbMt9m+0iRIdOXaPyvA+f2jOcOAHXN4tszF6k+l7xxjfr9Jxh6lW2pw9vUQ+\nN5CojRON1dUJij+/yvceG2PHHEU1xpCpCSjQk4ejlEqRUwllFVFWPkWjR9XpkDMDTFJkDMQSFStU\nrNCSFCOJMeMYI4kwkhgjjtHTBN/N0i1W6BQrdIplOsUqneIgCXn++uuUpgPeXP86aeogkPRyDdZP\nXCE1Eob3ZpFrJ9lGIRGUXZ/vPn6LUpLByNYRpkD5KZ2rIW/OfYv16gRjhsdX5E95y2/Sul+munUJ\npRkIJfmn/9OLGOaXEqhfyH4npDFb23z/x3/N9bk+qawjMLDtJ7DNc5wp5ch2E957b5HRfJNjQ31K\nZUXfyNFRWTrk6KQ5umRJH9W7VQrDTzF7EZmmh9MJEIFCRoIegi6KPhw58IGVgRkEBoIGkjUNbCNl\nPG3y3MQac6d8SCTxGwfIJY9U19CzA+Y12XvwSCMRBJaN7SbULcFuyaCb0Ti+GVLqmQglaZMh0Eyk\n0HivfI6VzDgAlg6jUlBTYJgxZ8/d4VitST8yCDxBpRyTpnD3vXGW28cRKKbSTcb0FugGKXB5dpid\nkWMs3LnN2Y8uk+n3CIXBYu00/dET9L08up4yJ28yvPMRxtePsdQ+xfrOMFtH2tIAFdnlD7pvMnFG\nop/MIUyNUCquBinvrU4gOjVOz7V5cXgL82geOJGC9W6JlXqRtUaezdbDFoShhYxba5TMHcKcx+aE\nhvycypkQGRytTF7LUwtSpustjh0ekCFEuDrC1VFeSnK1hQwku+NTLC+cZ2VmltAMkbKD5u9j+gcg\nPAIrINA/v9qjQgcZZSByMawYN9Mja/q4eg5dm0SJSQJ9GF8bOKKoFXA6vcu56Aqvl/4Q0w/51vf+\nHDsM+LuRF9GzU9iazkTrNifr75IInd3JceZeBL1kopTigyCmZU1wfOQCu/sbjP2btxird6BoYn17\nhB2zzHZSJFY+ot9h9N4BtXtt9ESRWILGmRyd0wXsfJ6lrSnevzOCEIpXa7d42l4HBO28zmHZIDAs\nNtQFNvVTSKFT62zzxN1f0ywW+HcbZzE0yR+NX2H63jL90SIb+THW5BBrrSIH3kPna4qUSafJZLHH\nUEFipzrxZsr4nStkE4+r4zX6L2c4bzj88PYCy40yhoAnxrO8cKZGtVamMpzDdgbP6PBwnvWdFm9s\nrfLW3gFKDPrhMpEEex7H1RqnR67zRuBhHVaQ9Xk2WxUsPeUPzt3j7Gj9E9fRkw5/J1+mSZGz4i7n\n+zf5l1EbTySM3r5IpbLJ0+8tUuilLL9Y5sz58ic+H0Ww2Smx2iyw28kwkgtQRshINqIyrLEramyr\nGrtqiJSHDG65yMNtB2j7Cf1GQitRKBXRFCYpArNkE3dDSKEyc5OL1R5+Y5KPtoYRscWw41EJXYTS\nqQ0f0u/b9Lwsr3ztXeqah9VzGSoPqFkP+zZvrUxy9aCIyDXRKnuIQguhRyAdiIqkvRJJM4fsFR6M\nXGq2jlW0MEs2VtHGyJkIobBFiEOIS4hNhCljDBkjPUHUtAgOTKwwwooCxvL7jFf2yDtdfiCfZnoq\n5qJ+h4/8hO23nqdeOGRr7joKxdjiODutE/jG4PtnK03++OIikaZR0GOUUqQ3u1zvznDt0ouEhssC\ny1SSN/m1lzJ77TxafDSxIiPu6Ab/x//80oNq69+3fenMv4AFScpf/PomxVvvcHW+QMupY2sOJWOI\njFFBYuORweezQA6UwvUDMh0P9kLy3T5aKEiUhRI6CoUPdBhIkfb4JKBaVym2SJnUDIrSQArQ810m\nu3cZ210nH3cxvzmCPukiWzHtn/YID2OKae8BDakCfN3BcwStcsrekMnO0IBBKjE+eQNWmwkX7vU5\ntRpiJYqu4dDWc0ihcbl0mjvZKRACS1OMqIFTn5ve4vSJVYRQXN8ZZjTXY6Tgs39Q4uq1k8SpTdHf\n5dzur3DSh0C6br7I+shJ7mdnsFGYfRelNGqFPRaWfsNqWefG9PMUdqrUlcbWUaMjIwO+nV7m9Nke\nxuxgQ2+nkveDhA83RnGak1yc7fF8bQXjU0Q4qVIkDJDjuhAEic7qYZGlepmlepmW//AaDmc8poot\nisUeyvRpi4ie0+NApASfAaLp6KKEk2QZqQfYXsTuWJleFiQ9pOwckZF+2gS2sMloDlnNpqCbFDSd\nsiYoa5KcCLAJ8MmyR40dVWNHDdN7BNSkqRS/HtBb61IZ/pA/mWpTNsFLHeTPtjHvtfhB7XluFOYx\ngBMIMgjcZJtn9t9A7w+kceNpl1+eOIYx8xJzlXHGupuYf/7n0PbRZjKELx/jB/Us94MYUotkbxoV\nDEqtdhryePsOT7Vuk5EhidC5nxlnozSDPzrF3Z5BkkrOFHd5tneTvPRxtJjQ0JC9mKjvcvnSS6zP\nngKlOKXdp7xyn+8tD1Dx5UxA3XuIfTA0Sbak4drwtSs/Y6q3g34kj0pGA0NDdlKkULz+wjSPnVbs\nbk/xxr1pYqkz4oScskMMpROlJiEWkTIxsgq7pNEfKbBr2SihoZQknx5y8f46dxfb3MzNkqJTcEK+\ncfI+m7ltPurqROvnSVtDKGA6IzmXa1LI9Zmb2cQwUrzU5jVe5pASI/IO21u3mdqdR+uUmYxucnL9\nfa6dy/KLC1ledEwKwmQ10egEAz6EcVMwYSmwK+wy8iDzTh5Rh3OiLnrHg0MfoxeSOAaHXWgHIBON\nSSukKrN0dY3AUERRiDBDhktdGt0izW6OIQRDgI0AFFNDG8zPrZOYDr9680koHrJU2qPdqCF7Jco5\nj5Oz99GLddaTlLp82IoRwkHXqqSyhVKfZJ8ktVD9AmmnRNo7IkNKBrgbLdtGFBpo+SZaroWup5hi\nMN1hCTCEhpA6SeCS9h1EbKJJAwW0xjoUs/8Zf6T/iAJd/tV2yFY2RSQGydIlok71AXf1V2bXeXlh\nHakUhiaQByGtD0J+fu5V6qPTKKFzXnzIVrxCY0NnevkSChOUIk76BGYGWxP8s//xxS9V076o/S6c\n+Z3tJv/6/iqxnXkgN/moCSR51aMQ9tDbEc1dC9kTOFGKq9SA4/MRC5WkAbTFIPN+tAvpEFM2Q0pF\nCCZrWLGieMdDJQaZuM2F7TfIHgm8yBEX61s1jJxOsuyR/GwfosFlSxxBPa+zUzHZHTLZqZp0cw9r\nw0JBWWgMGRpTpkZeE7wbxGwfKbxpqWJyP+GJWz5TewESqFslQs3iemGem/k5pNAwkYxoGidyPZ68\neJuMG7LeyHNzr8pzsztk9JQrH52kXq+Somg6ktyoi1kscPPeIcUgZE6HNDZxHZ9T8grZu/f41fnz\nJOEp/Mhl/UhEQ1cpL+q3ef6xfcwjxrCtOOW9IOL2Tg2nPs2Txw54fmIX4yhKTpTi7cBiSxWY0n0u\nGD45fbBGrVSyGqfspRJNs6hqJmbgsLVXYadRYKuXJ5GDNTO0lJlKm/mhJvNDLTJOn7pM2Uh1VhKb\nepoSyh6fh6cQCLKaRVVTVHRBVVOUdY2yplHQPtuvT5WgTpmttMZ6WqMuhkm0h/04FSfE7ZCgGRO3\nQuJuBEqRmb4GtTqaSHjBsnnWNRCaYHPN5i82T8DsEjhdRupjjO3MEAYus9MbzKvrdK62yB0OakB7\nQwbasM3wHQ8kRE+W+OEph1X5Of1+JTBVnmxaw4yz5ELB/OoGsysruEGAEuBPleF8kbweIXZ95E6A\n3AkgfmSLKRjoXxtmb3KGN+XjNClhJiHG4X22brtIKRgrtBhzuii7yPrceUphi5dWfoTUDbRYIn1J\n0heEPsSGzX6pwP7ZCiUjy3JzGF/ZGAaUjADdEkSmTaTbnztSBZBPe0xHW0wEOzhpQKg0eomBF2ss\n10vca1WQSnBiuMG5+bv8RnYw/AKtpfP0+zkqmT4vLiwxVmgzkhGEsaTV03jd+n26dpHslkd5sYWm\nYhQGF+TPEa8I/toP8JXi91yHM5ZBS1TZUiNHmffwJ5x3mRZxss1evEWa7qDUZwNGPTYp1SepHBzD\nPgq+JIqOJvGcgP2+TRGNYQT5j/s5QjI3tMT87DZ6SWOjmefnN+fZ7WcJkGi5Nlqhjlk4hHzrgUC8\nhsYxQ2PW1JjUbQ77YwQe3N/Ps9zKkDh9tGwbI9dCz7WRxif75yKykV6RxCsckSYVILEwHA8918Yo\nNDHyDZTt8+/rvJ80X0baeZzgNT5KYlRoE9x5EoL80Xkq/uGJJR6f3RvcxqEkefeQ68kYl5/5JtLO\no6uA8fg1FvtNphYvkumNgBBoMiJAxzrqtUUZg//mnz3/JQDui9rvwpmvvvkaf2VWMYQkR4+s38c8\nDAlbAq+rCCMTLckj1Cd7JhJFIAS+krQAj4Gi06MLK3SBVXFwhl3sosmwV2d4b4vhvS20lmC1cBGp\nGUy0bjPfuIymJCDRz+SxXhxQuybvNNm6D1vDDl6xx27NYr+okz5S9nEQTJoa47rOuKExaujYQpAq\nRScR2JEikx2glt8PEq6FMeHRmeqpYqQRc2olYHo7QkQ2LTPHYm6Ga4UFEs3AQDKmK149vcT0xAF+\nZPJ6/zEKqsnT+VX2d2rcWpxDSp19JHvASTPBii2EkMzWVpm++R4bRo4bY8+CX2UdRRtAKS7a67x6\ncZNcWaGU4k6U8H4Ys9GooDZP8FS1y0snV/gY/BpKxdthjj39HC1tBvUxC5tSGPTI0WVU61E5GnmR\nssVa3GY1ipluTBOtLBAlBqnTR9BgvZ+noRUfrKdrRoxVDpmp1VmotqlYEqE07qZl7qUZYqWY1bss\n6G0KGkh0ImUSYNFXJj4afSnohIKGb9JMivhaCWUX0dw84hGe59RPiFohUTskaoWkXox9xFw3OAQF\nQBbqrC9cRRAjdbh0EPNUdYhyNSVQcCMyKfsmx0t9wtDk7fcv4HlZjOkdWrNNqlstHruxTX5j0NtW\nwPpcnh88liF21cfLx0SSY9SWFK2Eoj4IBAuaIPfIZib9lPRKi/R29zNMbQD9TIZGqcqOXeUwX+KF\nSwcM53x2uxn++uoJRu2Y1tkFIttF73ro9TZV5dEvl2mXhxAyRSUpyjAQ/5GbqJYmWGEwOKIQKwqw\n4hBbRFhajK3HjPW3Ge7uIVIFR4f6+Odk8FqXOX5sP8GaqGHqKV+ZXaNTW+F+PyR/f5yN7mO4ach3\ndn/Nid8DfdLlyltzbPjHOLg0RFywmL63yKW3f83l8W+R2AFfe+EKO1qe18MqQh/D1kdJxUNmsTJt\nxsUe42IgfkI84DGQmkLPFOjpgrVuk3pHkHbK2N0SVqdEHNr0gUa2R1uPiXt5XGlQRVBlIGYDkGgx\nx47tcHJ0jfWwzJ39Cov7FUIjQis00IsN9PwhfKz6pkDTq5jGMXRtHNkvo+92Oc4WlWyXfmIQxDqO\nJSiaBeK0ynpPcXe/T5wYYIYYuQ7Dwx6WU6cjDgnsT90voU3qFZH9gXOXXhFXQSEbMZTtcXpsn7Fy\niwSFn2gQW6zsTPB+oYGyd5BenmTpMbQ4Qyw1HBTfffIGU9VBUpTe6dK52ueHF0x2Jk6QzXwDW20T\ntn5E0isxc+9xhBpk40KlKG0gw3wI6OVDvvPkBmce/28Rn5Po/X3Yl878C9jN13/CjVsd+kGGMLVR\nn4nkFVJTxJogRBAkkpBPZtyPmuEaA+1kLaESNii12uT7HawweFAab7k1DnIzGGnIfON9at1VTJWA\nLtC/XsU8VSBOJO80fK4YGr72MCMUwIiuMW7ojOsao7qOndo4IsKwBCtxwvvBoLT5xLt1ZjcGaFox\nbKGfLaAv5FCmYDlOeM9L2VCDMixqkGU6QcKx3YgTKzF2O8uye4zLxVOEuoWmJAtFj39w8TYFN+LW\nwQzv5y7wmH2XWW+Da9dP0u3lHpxrtdzkZHIZ/foB784+icdxdoC9ozLYlHXIty6uMF72iRPF1Sjm\ngzim1cmjthZ4rtrn+dltbCOl5zkoK+DDtMqBOE9dHzCclVWLoXAfzdHpkKdNjj6fHVcTSmKGMVpP\nYfkRjrvDhn2fyTser1xZJtIs3imd5Xp1mkRZID+WaxxkKmbxgHy5TjmvYQhJqCCIoR8YBIFFGtrI\n0IUkh2FXMLNFrKKLmbc+Ic4R92Lijo/q9NFaAa6n4aA9cN66poiKkrBoEBYckkKW6u0ebiMkzus8\nvvQ3/PopyeaoRSWY5L8/f5pe/R0+HqDreTq5bEoQ5Xj/yjN0WjFnL2jMlH5D+MN11E6AdAaMeUaq\nCA3B4rxL/tI8SVTi2maRINEpOhEFJyTvhDiqS659SKbRIXPQJ3MYf6LNExtgHe3/2/kMb+ae4X52\njOfO3uaV8TaGBpurIY3LfVTfYbk4T+o6lEdM7p96DPWow1YKmUhknCJjhUoklX6T8e4eIg04mHWo\n6Tq3Nodo90xIUtJk8HsyTnGTkErUYSRuMJo2qSRdCqFHNuyjK0mgZzBliP5bZvofNSngVv44b9Se\npI9NLedxaeEOl60D9KZBfe0p0iDPpUKbP3j+Bu1Olg/fLqKbK6xc/CZ+YYy51j2SSGdfGycqm8hH\nAjmZthgNPSqdPtWwyczINvmcTyoFaVdhGCki80na11RC3cuw3c6x085z0Mlx2MtgaYq5SpO5YgNX\nV2hiMAmhDIHM6Fh6D0PF+LFJP9YQZoQwI3QzQglJGFn0Qxs/yOCHGfqRSz+08UIDLzDwQpP080Am\nn7K8HWNJDS3WSRhUJx+tJ2TMACfTwco0kdkufq5LYn2y4qBCG+mVBs69X8DwswxlQoayAZ5nsTlx\nBy3bIe0MsdCbYKsxaKHVnIA/e+YqOWfABhm/tstmZ5LF8WfZmF2mVV7GFjOE6QpTSxcptEYfUuIJ\ngQQ6pmKrWMeZ2SPQtgGN//Nr/zum/iUA7gvZ7wTN/pfvsrni/71/z2cs7ZNhGbPYI6yEJJWIx6sG\nw4bObpLyN15ARyqyQjBhaIxoJmaYJ+3maftZvEyZkW6T0dsfYL6cZ6pscTuKeaOh89UPGlS9DOsz\n89w9cYJ+rorRX6O6fYPprVVmChalmSz6iENHSq52Q65HCd5Rj90NNIzUJDAUw82QC9dTGuk4l4un\n8QwXDcmFsTpfW1hD+RbLHzr0qzqT0yn+Xp6DeoW50RWGPrzOqpxjpfw4B8JgQ0lSISiaPr93ZpUz\nIw36PryTRFxPI4LAJdla4Ewm5VvzW2StBKXg7vIxrrdP0Z/L4ucGlKg1Wcfa32PG2KKaCbhfL9EL\nLSZLXaaHuoRGhpbK0UiLbPdGacoCiWsgrc8pu0pJrtOi0jwg02mzJktsWyapbJJ6Jsor8gByboQI\n20dFDsTOAOBTsgeSnCULM/dIyVxJ0n4f1etidiJyDSh4Ovoj8PU4oxMVLMKiRViySbLGZ+eBpWL0\nyj5mK6EQHIC/yr1vCA60fU6aBt/J2kSJgXM0myw0h9r8n9E6WONH32vS7zvMdq8xt3eF1Zkcrz3l\noEt45n7A+bsBej9BIriTm+K90hn6usOkv8+xYJ9Jf5+h+KGQUCI0dpwym4Uyh9Uaychx9KxDobnC\n/NJ1xvf2wdVQr47jTll4scYP/D4rj8zYO6Ek35OIwCEX2viFE3gzT0CSIN67SbHephj3KUcdFvqb\n5NKAxfk8+gvDrK/P8f7GOKCohi0KiUeg2/R1m1C3iIVBKh4KlHxstpLMKkVeM5EqxUs8umlALDRS\nDVI3RLoBWD4VJ6CUjwidmLoe0VWQXZqn0ZkG4LHxHdTkXe7JELl9nHBnlj85f4dTYw3+zgu4FSX8\noTXETfsr7DH84BwML2ZS32U+s8642MfAp5uC7OUoZiMsM6abZHi3fZ40LlJY3EAdtDksjhBqGWQq\nEIgBJgRFPhNQGz5kZKhOpdLh85LHOBV0Q4tOYD947QQWnXDw2g0H/67Ubwd4Za1BYFewjwI8OyLv\nDIRebD2h0XfZ62bZ62bZ7Wbx40/ymBtITCVJ0YjEw/FamwHwt2SGyGybINshyLbxM20SK/rkIxA6\nKK+IyLbR7IDypouuvsLWvo5M4fnZdV5dWEcTIFsR3X/bIE00liafZM+eRZPQy9c5GF1meukS2lE2\njhBIJWnnffYn14nzGyAG96omCkxaZ/hfXvjHv3Vt/lPbl878C9jl31zh+7e6RL4iCSVSPlwaoRSa\nUqRCe7g3aCmW2WYoajHb6VDq9zHkoMPz8a8oFBKNnuHScEtsZas0TBdlpAgzQpkhXmUXLd9EmBHT\nhs53sg4ZTXCtJ/jpVhH8AnavhJXYNDWLwB/8datscTbZ5MLKm1w7qTFyNs9zrsV6pLF8zUXak2xO\nzdPPFQbnoiS6ipAfMzElPtJbJY3uMZ3sc87WmCrbCEOwFKdcaQesHQWhTiCZ3wgYaqZ4rk62axC0\nx/mwcJK2mUegOD9+wDOTO+wujZG7u0nN3sIZgsNVl8Xqs+xbedaR+AhMLeGrxzd5bnqLTgd+TcRd\nQmRsEm8f54Rm8c2FLUruIFIPfVgKprmeO0dbDHjAc4ceU8Ftak6DUyNN5G6ArIfox3MIV6fV0Fk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bPtHu7pczHSPj8bXuRetoQuQvxhiZNWVKFDGbuUoQSV0zYnnE+2WE+3CfsjbLfEdgrssESe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\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ZoaIG2B31KV3Rmcjs5qbUbkbXXNQTr9M4cxqkj9IbwbhlEGXYIlAvKS3ZtkGhmKGwlcGT\n/QyO9TGyO4wKV97ik/s4yPzjQqPmMDcVEvvqYnkncM8wVfYd6uPgsX5SmevLlH61cAsFNv/66zTe\nfCMkaiGQroswTZLH7yL9wIOYA4M/72a+LyonnmXj//sT1H1p9AfDid1CTefEzC7ObHQRzWwQ7DmF\ntTlEZf4mfCT7Uw3u2VWjJ7uJ6pZx/mIRdIX0f/spYrlDWKk9KOr7E10gAxy/Tctr0fLsztai6bZY\nb26yWl9jpb5OySlf8TmBoNvKdlIP8zspiDmr62cSKrmae+/0dp2/nlkjZWj82wNDJPTrZ5L2bsjl\nEpyaL/CN2XUKtkveMvjN8d6rmowsTb6EV/0xihJQsY9wPtlG+ecfY5g3ceL4A/i6waFMnM+P9BC7\nhpbFD4IbZP4LiJ+V8GQQUHjtDIs/fgZ94RyWW0MkNLRPdKOOR0FCLHWYzMinqQceXz/7Dc5uXyAb\nyXCwaz83ZfYwsGZjv/wK9VMnwfRRhqNoE1lEjxZWWAOCQLC1naK4laG4nSWZHWBkdzeju7tIpt97\ndfLLROaXw7FdFma2iUUNegYS6O9gzvtFhZSS+slXKX77m2iGRvwTnyR5192o0V+ciYoMAhb+1z+g\nvb5B/n/8r6m2zyDtsHhHxdF4ZWGAU4UkSRUO9W1zdKiMoXQK/Agd/3Wf9jPTKJ99EOfe27GvIGb7\nCpJueTa2b9N0W9i+je057ylsApAyEpcJ/oTk3RfLY6jXLmjwau+9n65u86OVLQZjJv/NvkH0jylq\n+2dBICUna03+cXIVX8Jd+TQPD3b9TG0urs5QWvoWpuFQqo2i7j3Gy4/9OTeddXnh3n/BZu8QcU3l\nC2N5JtIfjy473CDzX0hcC8KrlJqc/uGr1F97le7aAqm8jXZPF0rGgDaYzhjpfZ/GTcVQFlaovfwS\ntVMnIO2jDFmoYwlE4tKN0GhYbBYzFIpZGq1uhsZyjOzuYmgse1U+4F9WMr+IG+d3/aL6/HOs/9kf\nk37wIXq+9F/gNNdYWP4Jan2OtxZxa0uY8yTn2m0WGw6//Y9hCdU/e7QL932KiggEEc0kokawtAiW\nZnX2Vz6OahY90W764r3E9Y+eFK722kkp+dbcBq9t1TiUjfPF8d7r0lW0WG/x3cUCyw2HhK7yL8fy\n7El9uP+zUd1i6fRfErUqVOtZevZ/gSff/EdG/+kVVsZv5eTtn0RVVf6XY7vf03x/LXGDzH8BcS0H\nzHrV5tRLS8yeuEB3bY7R8Q1ihzWErhBs2Phv2hANV+BKXwShdiox+SqFYnrHfG7FuxnZ3cXo7i56\n+t5uPv95nNv1iBvnd/1Ceh5zv//v8et1xv/DV1ET4eBot+u8Nvsdguos5cBjxoMCOoYWxdIiTLxR\nZOL5eVbumqB8781vIee3knUEUzU/tqCoq8HPcu28IOBPL6ywULd5oD/Lpwa6PqLWXT2qbY8fLBc5\ntRWe0+19GR7KZ66Z+dt1baZO/DVxa5mWbZEe/A2W/HW2/vzPyVYttvM5Hvof/j2KekOb/UPhF3VA\n+SD4KAbMZqPNGyeWOH1ylYi/xc0TU6SHrhT8qNYSbG6mKWxlqNRS9A9lGdndxciu9zeff1D8IpPB\nB8GN87u+UfrJjyn87V+R/ReP0v3or13xnhd45HJJSluXAs4Ct83c//TvkI7D2H/4Kmrs4zOrXmv8\nrNeu4fp87dwiJcfjt8Z7Odx1bRTbfla4QcBz62V+urZNO5D0R00+N5zjtvGea943gyBg6uQ/Y6mv\n43oqSuxhUmO7ePabf4i6WeKR3/s/rttCK788jr4buALRmMHxT+7iljuGefOVZU680k18rUi+Z4tK\nNU5xK4OqxRjZ1cVtn+xicDRz3adQ3cANXC1S99zL1nf/kfLjPyH78GdQIpfKDmuKhvaWgbn67DP4\nlQqZR37lF5rIPwxiusrv7Onn/z23zLfnNsiYGsPxazO5vxpIKTlXbvD9pSLbjktUU/nscBfHupMf\nmalbURQmbn2U+bM9KP7jKPb32Th9G5/78h+EhaCuQ7fDRdwYvX/JEbF0brtnjMO3D3HmIGZlbAAA\nDT5JREFUtVWW5rbpHUty/KFuevoS13XnvIEb+LBQTJPMgw+x9Y/fofL0U2Q+/fC7His9j+3Hvo8w\nDDKffuRjbOX1h7xl8qVdvfzF5Cpfn1rj3x4YImN+PAVGADZaDt9bLDJdbaIIuDuf5oH+LJb28ayK\nRw/cycZiN9W1bxMzTnDm+SL77/gimv7x/QdXi+vP0XMDHwkMU+PI8WE+/6VbuOPe8Z3iIjdwA7/s\nSN//IMI0Kf34B+9ZK6H64gt421uhCE4y+a7H/eeCvakYnx3O0fB8/mJqFdt/l1K+1xAtz+efFwr8\n4elFpqtN9iSj/Hc3jfDZ4dzHRuQXkR/eQ37vv6LRTJKKzXHhpT8h+ICS3j8P3CDzG7iBG/ilhhqP\nk7r3k3ilEtUXX3jHY2QQsP3Yd0FVyTz8mY+5hdcv7synOd6TYqPV5u9m1gk+ohCrQEpe2qzw1TcX\neGGzTNrU+e09ffzu3n56rGuv8f9BkczkGL/1X1NtDKMIm+s5xOyGmf0GbuAGfumReehhyk/8hO0f\nfI/kXXe/Taa59srLuBsbpO69Dz378elv/yLgs8M5tmyXC5Umjy0V+ezwtS02MlcLU83Wmg6GEiq4\n3Z1Po10nee6maXHwE7/7827G++L6+Ldu4AZu4AY+QujZLMk77sRdX6d+6rUr3pNBwPb3vguKQuYz\nn/05tfD6hSoEX9rVS0/E4LmNMi9tVq7J95Ydl7+ZXuOPzy+z1nQ40pXg9w6Ncl9f9roh8l8k3PjH\nbuAGbuA/C2Qe+RUASo997wpzaeP1U7RXlkncfgdGrufn1bzrGhFN5Xf29vP/t3f3QVXVCRjHvxeu\nAgKCSqQSvqzk2/pCgslOQ6vO0qi1TZo1KKA5NY3SCymTN5VQc8JxmqAacQvdqcAXmlFmBHO3SVup\nmBrM0AHUZn1ZUcRQoETR4HLv/mHdMdu0zaOHc+/z+e++HM7z48J97jn3nPPrYfen7EQjR8633Xih\nX9HhcrG7vom8mhNUt1zgruAAFoyI5rE/9KWnF11J8XZTmYuITwjo35/ge8Zx+fgxLn1zGLhy+lPT\nh2UA9J72kJnxurzeAd1IjemHzWZj85EGzl5qv/FCV3G73VQ3t5JXfYLdp5sJ9Pdj5uA7mT8imuiQ\nwBv/ALkulbmI+IzeP26dN//jQwC+23+AH/5znJC4eAL6R5kZzRIGhQYxY1AklztdFP77NG3O33aE\ne0PbD2z4pp4tR8/Q2uHk/r69WDR6EONu4Tnjvkb7NETEZwQNiSFo2HDaamu4XHeC77ZtA6D3g381\nOZl13BPRk7OX29nT0MKmIw3MGxqF/Vcu93yxo5Nd9U1Unv0eNzA8LJhpAyKICDTvCHVvpTIXEZ/S\ne+qD1H9zmDN/X097/SmCR48hcMBAs2NZyl+i+nD2cge1LRfYfqKRGYMif3bdis4fTzXbXd/EpU4X\nEYHdeGjAHQy9yQlR5NepzEXEp/T44ygCogfww8k6QFvlv4efzcZjg+/kux862HfuPJGB3Uns1wuA\nI+fb2FF3lsZL7QT4+zEtOoI/RYbj/zsna5LfRmUuIj7FZrPRe+qDNBT8jbDRowiKudvsSJbU3d+P\ntLv7s+7gSf556hzd/G0c+b6Ng99dxAbER/Tkgbv6ENJNNXM76LcsIj4nJH48kZcuEZ04AevOCWe+\nnt3tzLm7H+8cPkXpibMADAwJ5KEBdxAVrCPUbyeVuYj4HJufH+F/nkjgHaG0WniK166gf3Ags4f0\n418NzSREhjG2tyZwMoPKXEREbsqw8GCGhevgNjPpPHMRERGLU5mLiIhYnMpcRETE4lTmIiIiFqcy\nFxERsTiVuYiIiMWpzEVERCxOZS4iImJxKnMRERGLU5mLiIhYnMpcRETE4lTmIiIiFqcyFxERsTjT\nyvzjjz8mMzPTrNWLiIh4DVOmQH311VepqKhgxIgRZqxeRETEq5iyZT5u3DhWrFhhxqpFRES8zi3d\nMt+6dSvvv//+z+5bvXo1U6dOpbKy8lauWkRExGfY3G6324wVV1ZW8sEHH/D666+bsXoRERGvoaPZ\nRURELE5lLiIiYnGm7WYXERERY2jLXERExOJU5iIiIhanMhcREbG4Llvmbreb5cuXk5yczJw5czh5\n8qTZkQzldDpZvHgxKSkpPP7443zyySdmR7olmpqamDhxIsePHzc7iuEKCgpITk7m0UcfZdu2bWbH\nMYzT6SQzM5Pk5GRSU1O96rU7cOAAaWlpANTV1TF79mxSU1NZuXKlycmMcfX4Dh06REpKCnPmzOGp\np56iubnZ5HQ37+rx/aSsrIzk5GSTEhnr6vE1NzeTnp5OWloas2fPvmEHdtky37VrF+3t7RQXF5OZ\nmcnq1avNjmSo0tJSevXqxaZNm1i/fj2rVq0yO5LhnE4ny5cvJzAw0OwohqusrKSqqori4mKKiopo\naGgwO5JhysvLcblcFBcXk56eTl5entmRDLFhwwaysrLo6OgArlzAatGiRWzcuBGXy8WuXbtMTnhz\nrh1fTk4O2dnZFBYWkpSUREFBgckJb8614wM4ePCg13yQvnZ8r732Gg8//DBFRUVkZGRw7Nix6y7f\nZct83759JCYmAjB27FhqampMTmSsqVOnkpGRAYDL5cJuN+Uy+bfUmjVrmDVrFpGRkWZHMdznn3/O\n0KFDSU9PZ8GCBUyaNMnsSIYZNGgQnZ2duN1uWltb6datm9mRDDFw4EDy8/M9t2tra4mPjwfg/vvv\n54svvjArmiGuHV9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"text/plain": [
"<matplotlib.figure.Figure at 0x14fa4f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for x in df[df.Group=='Hexanal'].index:\n",
" plt.plot(df.iloc[x,2:]);\n",
" plt.xlabel('Odor')\n",
" plt.ylabel('Peak DF/F')\n",
" plt.title('Hexanal')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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qtQzGKyheevVZJ3tZVtiyrZ72fi+XrszlgX+6jM9nDXOR6yDrVhWTm2E+42R/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\n3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nuhH5VKwY5rahEVIvHZGWZ//AMEpZLCv/4m1rIihJpGIj1dhLu6QJZIB4I4fv0ACrOZ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\n4oGv4W0MomwMc3uuwt27rtB4qcRXW0dYjfSwGwVVQGFvhtyVbYQ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ZJtDer9SllChth6Dl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\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rD6P6Ej+9+RD5RoW58Sm+k9zPR+e/idVs89CxJOe2GQw3B/nIYBdL9/EwcCUL31UQLR+p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\nOCmD+ub8UG+raWr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OJxvrbWxtsOHw+lvzg6P8rfnhMRHoNKE5cdSZblhMOOcnxfBNbTPL\nyur4ZU7ycR/vU1U+Kqtj4wAMe5DAF0KITvyt/BjWVFtZtLOcals7ZW0dAETotPw0JZZzEqKIl9b8\nGeHiQQmUtjnY2thKbpSZCQlRR33c4WGfajZwywALe5DAF0KII5yfFMu3tc18V9WEApwVZT7Ymg9H\nK635M4pOozA7J4W/7Srno7I6BoUbjxgoKWHvJ4EvhBBdhOu1XJebjE2BIUYDsQZ9sEsSxxFn1HN1\nloX3impYUlTN7SPSA4MmfarK/yutY1PDwA57ABlGKoQQRzE8JoKfD06RsO8nRsdFcm5iFDUOFyvL\nGwAJ+66khS+EECIk/CwjkfK2Dr6vbyE7ysSB6iYJ+8NIC18IIURI0Gs0zM5NQa9R+HdRDd8caCRt\nAI7GPxYJfCGEECHDYgpjZqYFFciMMnPL0LRuz7cfKqRLXwghREg5OyGKFLOBYYPisDXZg13OGUNa\n+EIIIUJOqtlwUjPvDSTy0xBCCCEGAAl8IYQQYgCQwBdCCCEGAAl8IYQQYgCQwBdCCCEGAAl8IYQQ\nYgCQwBdCCCEGgD6feMfpdDJ//nwaGxuJiIjgz3/+M7GxsZ0e88QTT7BlyxbCw8MBePnll4mIiOjr\nUoUQQoiQ0eeB/9577zFkyBDuuusuVq5cycsvv8zDDz/c6TGFhYW88cYbxMTE9HV5QgghREjq8y79\nzZs3M3XqVACmTp3Kt99+2+l+VVUpKytj4cKF5Ofn88EHH/R1iUIIIUTI6dUW/vvvv88///nPTrcl\nJCQEuufDw8Npa2vrdH97eztz587l5ptvxuPxcOONNzJ69GiGDBnSm6UKIYQQIU1RVVXtyze8++67\nufXWWxk9ejRtbW3k5+ezfPnywP0+nw+HwxE4f//MM88wdOhQZs6c2ZdlCiGEECGlz7v0J0yYwJo1\nawBYs2YN55xzTqf7S0pKyM/PR1VV3G43mzdvZuTIkX1dphBCCBFS+ryF39HRwYIFC6ivrycsLIxn\nn32W+Ph43n77bTIzM5k+fTpvvvkmK1euRK/Xc+WVV3Ldddf1ZYlCCCFEyOnzwBdCCCFE35OJd4QQ\nQogBQAJfCCGEGAAk8IUQQogBoF8HvqqqPProo8yePZsbb7yRioqKYJfUYzweDw888ABz5szh2muv\n5Ysvvgh2Sb2isbGRadOmUVJSEuxSetyrr77K7Nmzueaaa0JuAimPx8P999/P7NmzueGGG0Lq89u+\nfTtz584FoLy8nOuvv54bbriBxx57LMiVdd/h27Z7927mzJnDjTfeyK9//WuampqCXF33Hb59hyxf\nvpzZs2cHqaKedfj2NTU1cccddzB37lyuv/76k8q/fh34q1evxuVysWTJEu6//36eeuqpYJfUY5Yt\nW0ZsbCyLFy/mtdde4/HHHw92ST3O4/Hw6KOPYjQag11Kj9uwYQNbt25lyZIlLFq0iOrq6mCX1KPW\nrFmDz+djyZIl3HHHHTz//PPBLqlHvP766zzyyCO43W4AnnrqKX7729/y7rvv4vP5WL16dZArPH1d\nt+3JJ59k4cKFvPPOO8yYMYNXX301yBV2T9ftA9i1a1fIHGx33b5nnnmGmTNnsmjRIu655x6Ki4tP\n+Br9OvA3b95MXl4eAGPHjqWgoCDIFfWcyy67jHvuuQfwT0ak0/X5sge97umnnyY/Px+LxRLsUnrc\nN998w5AhQ7jjjju4/fbbmT59erBL6lFZWVl4vV5UVaW1tRW9Xh/sknpEZmYmL730UuD7wsLCwFwh\nR5sKvD/pum3PP/88Q4cOBfwH3waDIVil9Yiu22e1WvnLX/5yxFot/VXX7duyZQs1NTXcfPPNrFix\ngkmTJp3wNfp14Le1tREZGRn4XqfT4fP5glhRzzGZTJjNZtra2rjnnnu47777gl1Sj1q6dCnx8fFM\nmTKFULwy1Gq1UlBQwF//+lf++Mc/cv/99we7pB4VHh7OgQMHuPTSS1m4cOER3aj91YwZM9BqtYHv\nD//dDA8Pp7W1NRhl9Yiu25aQkAD4g+Nf//oXv/rVr4JUWc84fPt8Ph+PPPIIv//97zGZTCGxj+n6\n+VVWVhITE8Nbb71FcnLySfXQ9OvAj4iIwG63B773+XxoNP16kzqprq7mpptu4qqrruLyyy8Pdjk9\naunSpaxbt465c+eyZ88eFixYQGNjY7DL6jExMTHk5eWh0+nIzs7GYDCExDnSQ95++23y8vJYtWoV\ny5YtY8GCBbhcrmCX1eMO35/Y7XaioqKCWE3PW7lyJY899hivvvrqEcuU92eFhYWUl5cHDraLiopC\n6pQv+Pcxh3oOL7jgAgoLC0/4nH6djodP07tt27aQWmCnoaGBefPmMX/+fK666qpgl9Pj3n33XRYt\nWsSiRYsYNmwYTz/9NPHx8cEuq8ecffbZfP311wDU1tbS0dERUjvU6OjowCJYkZGReDyekOldO9yI\nESPYuHEjAGvXruXss88OckU956OPPmLx4sUsWrSItLS0YJfTY1RVZfTo0Sxfvpx33nmH5557jsGD\nB/Pggw8Gu7QedfbZZwfyb+PGjQwePPiEz+nXJ4ZnzJjBunXrAiMwQ+kI7pVXXsFms/Hyyy/z0ksv\noSgKr7/+OmFhYcEurccpihLsEnrctGnT2LRpE7NmzQpcTRJK23nTTTfx0EMPMWfOnMCI/VAcfLlg\nwQL+8Ic/4Ha7yc3N5dJLLw12ST3C5/Px5JNPkpqayp133omiKJx77rncddddwS6t20Lp/9nxLFiw\ngEceeYT33nuPyMhInn322RM+R6bWFUIIIQaAft2lL4QQQoiTI4EvhBBCDAAS+EIIIcQAIIEvhBBC\nDAAS+EIIIcQAIIEvhBBCDAAS+EKII7S3t/OnP/2Jiy++mCuvvJIbbrjhqPPIV1ZWcsEFFwShQiHE\nqerXE+8IIXrHbbfdxogRI1i5ciU6nY7du3dz66238txzzzFx4sTA41RVHTATnQjR30ngCyE62bBh\nA9XV1bzzzjuB24YPH87tt9/OSy+9xIIFCwIrkB1abQ2gsbGRhx9+mKqqKnQ6Hffddx95eXm8+OKL\nbNu2jZqaGubMmUN+fn6fb5MQQrr0hRBd7Ny5k1GjRh1x+8SJE9m5cycLFizggQceYOnSpaSnpwfu\nf/zxx5k8eTLLli3jhRde4KGHHgosGORyuVixYoWEvRBBJIEvhOhEURS8Xu8Rt7vdbrxeL3V1dUye\nPBmAq6++OnD/d999x6xZswBIT09n3LhxbN++HYCxY8f2QeVCiOORwBdCdDJmzBhPQl1nAAABB0lE\nQVQKCgqOCP2tW7cyZsyYTmuLH2vtePAv0HLoNQwGQy9WLIQ4GRL4QohOzjnnHAYPHsyTTz6Jx+MB\noKCggH/84x/cfffdpKWlBZblXL58eeB5kydP5v333wegoqKCrVu3Mm7cuL7fACHEUclqeUKII7hc\nLp577jm++uordDod0dHR/OY3v2HSpEns37+fBx98EK/Xy7hx41izZg2ff/45dXV1LFy4kMrKSjQa\nDffeey/Tp0/nxRdfBAiJpVeF6M8k8IUQQogBQLr0hRBCiAFAAl8IIYQYACTwhRBCiAFAAl8IIYQY\nACTwhRBCiAFAAl8IIYQYACTwhRBCiAFAAl8IIYQYAP4/zO+Ki7YW4RoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x13a50a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for x in df[df.Group=='Mint'].index:\n",
" plt.plot(df.iloc[x,2:]);\n",
" plt.xlabel('Odor')\n",
" plt.ylabel('Peak DF/F')\n",
" plt.title('Mint')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rs = np.random.RandomState(4)\n",
"pos = rs.randint(-1, 2, (20, 5)).cumsum(axis=1)\n",
"pos -= pos[:, 0, np.newaxis]\n",
"step = np.tile(range(5), 20)\n",
"walk = np.repeat(range(20), 5)\n",
"df = pd.DataFrame(np.c_[pos.flat, step, walk],\n",
" columns=[\"position\", \"step\", \"walk\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
h2oai/h2o-3 | h2o-py/demos/rulefit_demo.ipynb | 1 | 31470 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Rulefit demo - Titanic Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## H2O Rulefit algorithm\n",
"\n",
"Rulefit algorithm combines tree ensembles and linear models to take advantage of both methods: a tree ensemble accuracy and a linear model interpretability. The general algorithm fits a tree ensebmle to the data, builds a rule ensemble by traversing each tree, evaluates the rules on the data to build a rule feature set and fits a sparse linear model (LASSO) to the rule feature set joined with the original feature set.\n",
"\n",
"For more information, refer to: http://statweb.stanford.edu/~jhf/ftp/RuleFit.pdf by Jerome H. Friedman and Bogden E. Popescu.\n",
"\n",
"## Demo example\n",
"\n",
"We will train a rulefit model to predict the rules defining whether or not someone will survive:\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checking whether there is an H2O instance running at http://localhost:54321 . connected.\n"
]
},
{
"data": {
"text/html": [
"<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O_cluster_uptime:</td>\n",
"<td>4 mins 19 secs</td></tr>\n",
"<tr><td>H2O_cluster_timezone:</td>\n",
"<td>Europe/Prague</td></tr>\n",
"<tr><td>H2O_data_parsing_timezone:</td>\n",
"<td>UTC</td></tr>\n",
"<tr><td>H2O_cluster_version:</td>\n",
"<td>3.34.0.99999</td></tr>\n",
"<tr><td>H2O_cluster_version_age:</td>\n",
"<td>17 minutes </td></tr>\n",
"<tr><td>H2O_cluster_name:</td>\n",
"<td>zuzanaolajcova</td></tr>\n",
"<tr><td>H2O_cluster_total_nodes:</td>\n",
"<td>1</td></tr>\n",
"<tr><td>H2O_cluster_free_memory:</td>\n",
"<td>3.546 Gb</td></tr>\n",
"<tr><td>H2O_cluster_total_cores:</td>\n",
"<td>12</td></tr>\n",
"<tr><td>H2O_cluster_allowed_cores:</td>\n",
"<td>12</td></tr>\n",
"<tr><td>H2O_cluster_status:</td>\n",
"<td>locked, healthy</td></tr>\n",
"<tr><td>H2O_connection_url:</td>\n",
"<td>http://localhost:54321</td></tr>\n",
"<tr><td>H2O_connection_proxy:</td>\n",
"<td>{\"http\": null, \"https\": null}</td></tr>\n",
"<tr><td>H2O_internal_security:</td>\n",
"<td>False</td></tr>\n",
"<tr><td>H2O_API_Extensions:</td>\n",
"<td>Algos, AutoML, Core V3, TargetEncoder, Core V4</td></tr>\n",
"<tr><td>Python_version:</td>\n",
"<td>3.8.1 final</td></tr></table></div>"
],
"text/plain": [
"-------------------------- ----------------------------------------------\n",
"H2O_cluster_uptime: 4 mins 19 secs\n",
"H2O_cluster_timezone: Europe/Prague\n",
"H2O_data_parsing_timezone: UTC\n",
"H2O_cluster_version: 3.34.0.99999\n",
"H2O_cluster_version_age: 17 minutes\n",
"H2O_cluster_name: zuzanaolajcova\n",
"H2O_cluster_total_nodes: 1\n",
"H2O_cluster_free_memory: 3.546 Gb\n",
"H2O_cluster_total_cores: 12\n",
"H2O_cluster_allowed_cores: 12\n",
"H2O_cluster_status: locked, healthy\n",
"H2O_connection_url: http://localhost:54321\n",
"H2O_connection_proxy: {\"http\": null, \"https\": null}\n",
"H2O_internal_security: False\n",
"H2O_API_Extensions: Algos, AutoML, Core V3, TargetEncoder, Core V4\n",
"Python_version: 3.8.1 final\n",
"-------------------------- ----------------------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import h2o\n",
"from h2o.estimators import H2ORuleFitEstimator, H2ORandomForestEstimator\n",
"\n",
"# init h2o cluster\n",
"h2o.init()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
]
}
],
"source": [
"df = h2o.import_file(\"https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv\",\n",
" col_types={'pclass': \"enum\", 'survived': \"enum\"})\n",
"x = [\"age\", \"sibsp\", \"parch\", \"sex\", \"pclass\"]\n",
"\n",
"# Split the dataset into train and test\n",
"train, test = df.split_frame(ratios=[.8], seed=1234)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the `algorithm` parameter, a user can set whether algorithm will use DRF or GBM to fit a tree enseble. \n",
"\n",
"Using the `min_rule_length` and `max_rule_length` parameters, a user can set interval of tree enseble depths to be fitted. The bigger this interval is, the more tree ensembles will be fitted (1 per each depth) and the bigger the rule feature set will be.\n",
"\n",
"Using the `max_num_rules` parameter, the maximum number of rules to return can be set.\n",
"\n",
"Using the `model_type` parameter, the type of base learners in the enseble can be set.\n",
"\n",
"Using the `rule_generation_ntrees` parameter, the number of trees for tree enseble can be set."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"rulefit Model Build progress: |██████████████████████████████████████████████████| (done) 100%\n",
"Model Details\n",
"=============\n",
"H2ORuleFitEstimator : RuleFit\n",
"Model Key: RuleFit_model_python_1636562504000_1\n",
"\n",
"\n",
"Rulefit Model Summary: \n"
]
},
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>family</th>\n",
" <th>link</th>\n",
" <th>regularization</th>\n",
" <th>number_of_predictors_total</th>\n",
" <th>number_of_active_predictors</th>\n",
" <th>number_of_iterations</th>\n",
" <th>rule_ensemble_size</th>\n",
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" <th>max_depth</th>\n",
" <th>mean_depth</th>\n",
" <th>min_leaves</th>\n",
" <th>max_leaves</th>\n",
" <th>mean_leaves</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td></td>\n",
" <td>binomial</td>\n",
" <td>logit</td>\n",
" <td>Lasso (lambda = 0.01292 )</td>\n",
" <td>20784</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>20776.0</td>\n",
" <td>500.0</td>\n",
" <td>500.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>5.5</td>\n",
" <td>0.0</td>\n",
" <td>135.0</td>\n",
" <td>41.552</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" family link regularization number_of_predictors_total \\\n",
"0 binomial logit Lasso (lambda = 0.01292 ) 20784 \n",
"\n",
" number_of_active_predictors number_of_iterations rule_ensemble_size \\\n",
"0 8 3 20776.0 \n",
"\n",
" number_of_trees number_of_internal_trees min_depth max_depth \\\n",
"0 500.0 500.0 0.0 10.0 \n",
"\n",
" mean_depth min_leaves max_leaves mean_leaves \n",
"0 5.5 0.0 135.0 41.552 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"ModelMetricsBinomialGLM: rulefit\n",
"** Reported on train data. **\n",
"\n",
"MSE: 0.14668202166384883\n",
"RMSE: 0.3829908897922362\n",
"LogLoss: 0.4616331658988569\n",
"Null degrees of freedom: 1053\n",
"Residual degrees of freedom: 1045\n",
"Null deviance: 1405.0919048764067\n",
"Residual deviance: 973.1227137147903\n",
"AIC: 991.1227137147903\n",
"AUC: 0.8361042692939246\n",
"AUCPR: 0.7904193564939762\n",
"Gini: 0.6722085385878491\n",
"\n",
"Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.44132286664639514: \n"
]
},
{
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>Error</th>\n",
" <th>Rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>526.0</td>\n",
" <td>122.0</td>\n",
" <td>0.1883</td>\n",
" <td>(122.0/648.0)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>106.0</td>\n",
" <td>300.0</td>\n",
" <td>0.2611</td>\n",
" <td>(106.0/406.0)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Total</td>\n",
" <td>632.0</td>\n",
" <td>422.0</td>\n",
" <td>0.2163</td>\n",
" <td>(228.0/1054.0)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 Error Rate\n",
"0 0 526.0 122.0 0.1883 (122.0/648.0)\n",
"1 1 106.0 300.0 0.2611 (106.0/406.0)\n",
"2 Total 632.0 422.0 0.2163 (228.0/1054.0)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Maximum Metrics: Maximum metrics at their respective thresholds\n"
]
},
{
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>metric</th>\n",
" <th>threshold</th>\n",
" <th>value</th>\n",
" <th>idx</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>max f1</td>\n",
" <td>0.441323</td>\n",
" <td>0.724638</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>max f2</td>\n",
" <td>0.160033</td>\n",
" <td>0.783832</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>max f0point5</td>\n",
" <td>0.809013</td>\n",
" <td>0.774478</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>max accuracy</td>\n",
" <td>0.523805</td>\n",
" <td>0.790323</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>max precision</td>\n",
" <td>0.809013</td>\n",
" <td>0.919048</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>max recall</td>\n",
" <td>0.156308</td>\n",
" <td>1.000000</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>max specificity</td>\n",
" <td>0.855041</td>\n",
" <td>0.973765</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>max absolute_mcc</td>\n",
" <td>0.523805</td>\n",
" <td>0.550968</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>max min_per_class_accuracy</td>\n",
" <td>0.441323</td>\n",
" <td>0.738916</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>max mean_per_class_accuracy</td>\n",
" <td>0.441323</td>\n",
" <td>0.775322</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>max tns</td>\n",
" <td>0.855041</td>\n",
" <td>631.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>max fns</td>\n",
" <td>0.855041</td>\n",
" <td>217.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>max fps</td>\n",
" <td>0.156308</td>\n",
" <td>648.000000</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>max tps</td>\n",
" <td>0.156308</td>\n",
" <td>406.000000</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>max tnr</td>\n",
" <td>0.855041</td>\n",
" <td>0.973765</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>max fnr</td>\n",
" <td>0.855041</td>\n",
" <td>0.534483</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>max fpr</td>\n",
" <td>0.156308</td>\n",
" <td>1.000000</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>max tpr</td>\n",
" <td>0.156308</td>\n",
" <td>1.000000</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" metric threshold value idx\n",
"0 max f1 0.441323 0.724638 3.0\n",
"1 max f2 0.160033 0.783832 7.0\n",
"2 max f0point5 0.809013 0.774478 1.0\n",
"3 max accuracy 0.523805 0.790323 2.0\n",
"4 max precision 0.809013 0.919048 1.0\n",
"5 max recall 0.156308 1.000000 8.0\n",
"6 max specificity 0.855041 0.973765 0.0\n",
"7 max absolute_mcc 0.523805 0.550968 2.0\n",
"8 max min_per_class_accuracy 0.441323 0.738916 3.0\n",
"9 max mean_per_class_accuracy 0.441323 0.775322 3.0\n",
"10 max tns 0.855041 631.000000 0.0\n",
"11 max fns 0.855041 217.000000 0.0\n",
"12 max fps 0.156308 648.000000 8.0\n",
"13 max tps 0.156308 406.000000 8.0\n",
"14 max tnr 0.855041 0.973765 0.0\n",
"15 max fnr 0.855041 0.534483 0.0\n",
"16 max fpr 0.156308 1.000000 8.0\n",
"17 max tpr 0.156308 1.000000 8.0"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Gains/Lift Table: Avg response rate: 38.52 %, avg score: 38.52 %\n"
]
},
{
"data": {
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" <th></th>\n",
" <th>group</th>\n",
" <th>cumulative_data_fraction</th>\n",
" <th>lower_threshold</th>\n",
" <th>lift</th>\n",
" <th>cumulative_lift</th>\n",
" <th>response_rate</th>\n",
" <th>score</th>\n",
" <th>cumulative_response_rate</th>\n",
" <th>cumulative_score</th>\n",
" <th>capture_rate</th>\n",
" <th>cumulative_capture_rate</th>\n",
" <th>gain</th>\n",
" <th>cumulative_gain</th>\n",
" <th>kolmogorov_smirnov</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0.195446</td>\n",
" <td>0.855041</td>\n",
" <td>2.381821</td>\n",
" <td>2.381821</td>\n",
" <td>0.917476</td>\n",
" <td>0.855041</td>\n",
" <td>0.917476</td>\n",
" <td>0.855041</td>\n",
" <td>0.465517</td>\n",
" <td>0.465517</td>\n",
" <td>138.182123</td>\n",
" <td>138.182123</td>\n",
" <td>0.439283</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.348197</td>\n",
" <td>0.523805</td>\n",
" <td>1.402839</td>\n",
" <td>1.952350</td>\n",
" <td>0.540373</td>\n",
" <td>0.530891</td>\n",
" <td>0.752044</td>\n",
" <td>0.712839</td>\n",
" <td>0.214286</td>\n",
" <td>0.679803</td>\n",
" <td>40.283940</td>\n",
" <td>95.234963</td>\n",
" <td>0.539371</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.400380</td>\n",
" <td>0.414525</td>\n",
" <td>1.132826</td>\n",
" <td>1.845540</td>\n",
" <td>0.436364</td>\n",
" <td>0.441323</td>\n",
" <td>0.710900</td>\n",
" <td>0.677452</td>\n",
" <td>0.059113</td>\n",
" <td>0.738916</td>\n",
" <td>13.282579</td>\n",
" <td>84.553965</td>\n",
" <td>0.550645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0.528463</td>\n",
" <td>0.307335</td>\n",
" <td>0.788433</td>\n",
" <td>1.589329</td>\n",
" <td>0.303704</td>\n",
" <td>0.307335</td>\n",
" <td>0.612208</td>\n",
" <td>0.587746</td>\n",
" <td>0.100985</td>\n",
" <td>0.839901</td>\n",
" <td>-21.156723</td>\n",
" <td>58.932883</td>\n",
" <td>0.506568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1.000000</td>\n",
" <td>0.156308</td>\n",
" <td>0.339525</td>\n",
" <td>1.000000</td>\n",
" <td>0.130785</td>\n",
" <td>0.158208</td>\n",
" <td>0.385199</td>\n",
" <td>0.385203</td>\n",
" <td>0.160099</td>\n",
" <td>1.000000</td>\n",
" <td>-66.047517</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" group cumulative_data_fraction lower_threshold lift \\\n",
"0 1 0.195446 0.855041 2.381821 \n",
"1 2 0.348197 0.523805 1.402839 \n",
"2 3 0.400380 0.414525 1.132826 \n",
"3 4 0.528463 0.307335 0.788433 \n",
"4 5 1.000000 0.156308 0.339525 \n",
"\n",
" cumulative_lift response_rate score cumulative_response_rate \\\n",
"0 2.381821 0.917476 0.855041 0.917476 \n",
"1 1.952350 0.540373 0.530891 0.752044 \n",
"2 1.845540 0.436364 0.441323 0.710900 \n",
"3 1.589329 0.303704 0.307335 0.612208 \n",
"4 1.000000 0.130785 0.158208 0.385199 \n",
"\n",
" cumulative_score capture_rate cumulative_capture_rate gain \\\n",
"0 0.855041 0.465517 0.465517 138.182123 \n",
"1 0.712839 0.214286 0.679803 40.283940 \n",
"2 0.677452 0.059113 0.738916 13.282579 \n",
"3 0.587746 0.100985 0.839901 -21.156723 \n",
"4 0.385203 0.160099 1.000000 -66.047517 \n",
"\n",
" cumulative_gain kolmogorov_smirnov \n",
"0 138.182123 0.439283 \n",
"1 95.234963 0.539371 \n",
"2 84.553965 0.550645 \n",
"3 58.932883 0.506568 \n",
"4 0.000000 0.000000 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/plain": []
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rfit = H2ORuleFitEstimator(algorithm=\"drf\", \n",
" min_rule_length=1, \n",
" max_rule_length=10, \n",
" max_num_rules=100, \n",
" model_type=\"rules_and_linear\",\n",
" rule_generation_ntrees=50,\n",
" seed=1234)\n",
"rfit.train(training_frame=train, x=x, y=\"survived\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The output for the Rulefit model includes:\n",
" - model parameters\n",
" - rule importences in tabular form\n",
" - training and validation metrics of the underlying linear model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Rule Importance: \n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>variable</th>\n",
" <th>coefficient</th>\n",
" <th>rule</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td></td>\n",
" <td>M2T21N13</td>\n",
" <td>1.298409e+00</td>\n",
" <td>(sex in {female}) & (sibsp < 3.5 or sibsp is NA) & (pclass in {1, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td></td>\n",
" <td>M2T23N21</td>\n",
" <td>-8.453746e-01</td>\n",
" <td>(sex in {male} or sex is NA) & (pclass in {2, 3} or pclass is NA) ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td></td>\n",
" <td>M1T0N7</td>\n",
" <td>3.809983e-01</td>\n",
" <td>(pclass in {1, 2}) & (sex in {female})</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td></td>\n",
" <td>M1T28N10</td>\n",
" <td>-3.448192e-01</td>\n",
" <td>(sex in {male} or sex is NA) & (age >= 13.496771812438965 or age i...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td></td>\n",
" <td>M1T23N7</td>\n",
" <td>3.310857e-01</td>\n",
" <td>(sex in {female}) & (sibsp < 2.5 or sibsp is NA)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td></td>\n",
" <td>M1T37N10</td>\n",
" <td>-2.319945e-01</td>\n",
" <td>(sex in {male} or sex is NA) & (age >= 14.977890968322754 or age i...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td></td>\n",
" <td>M4T3N45</td>\n",
" <td>-2.797404e-02</td>\n",
" <td>(sex in {male} or sex is NA) & (pclass in {2, 3} or pclass is NA) ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td></td>\n",
" <td>M1T1N7</td>\n",
" <td>2.887806e-14</td>\n",
" <td>(pclass in {1, 2}) & (sex in {female})</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" variable coefficient \\\n",
"0 M2T21N13 1.298409e+00 \n",
"1 M2T23N21 -8.453746e-01 \n",
"2 M1T0N7 3.809983e-01 \n",
"3 M1T28N10 -3.448192e-01 \n",
"4 M1T23N7 3.310857e-01 \n",
"5 M1T37N10 -2.319945e-01 \n",
"6 M4T3N45 -2.797404e-02 \n",
"7 M1T1N7 2.887806e-14 \n",
"\n",
" rule \n",
"0 (sex in {female}) & (sibsp < 3.5 or sibsp is NA) & (pclass in {1, ... \n",
"1 (sex in {male} or sex is NA) & (pclass in {2, 3} or pclass is NA) ... \n",
"2 (pclass in {1, 2}) & (sex in {female}) \n",
"3 (sex in {male} or sex is NA) & (age >= 13.496771812438965 or age i... \n",
"4 (sex in {female}) & (sibsp < 2.5 or sibsp is NA) \n",
"5 (sex in {male} or sex is NA) & (age >= 14.977890968322754 or age i... \n",
"6 (sex in {male} or sex is NA) & (pclass in {2, 3} or pclass is NA) ... \n",
"7 (pclass in {1, 2}) & (sex in {female}) "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import display\n",
"display(rfit.rule_importance())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are several rules that can be recapped as:\n",
"\n",
"### Higgest Likelihood of Survival:\n",
"1. women in class 1 or 2 with 3 siblings/spouses aboard or less\n",
"2. women in class 1 or 2\n",
"3. women with 2 siblings/spouses aboard or less\n",
"\n",
"### Lowest Likelihood of Survival:\n",
"1. male in class 2 or 3 of age >= 9.4\n",
"2. male of age >= 13.4\n",
"3. male of age >= 14.8\n",
"4. male in class 2 or 3 with no parents/children aboard of age between 14 to 61\n",
"\n",
"Note: The rules are additive. That means that if a passenger is described by multiple rules, their probability is added together from those rules."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "h2o3pyenv",
"language": "python",
"name": "h2o3pyenv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
marxav/hello-world | evolution_strategy.ipynb | 1 | 12367 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# From https://deeplearningcourses.com/c/cutting-edge-artificial-intelligence\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def evolution_strategy(\n",
" f,\n",
" population_size,\n",
" sigma,\n",
" lr,\n",
" initial_params,\n",
" num_iters):\n",
"\n",
" # assume initial params is a 1-D array\n",
" num_params = len(initial_params)\n",
" reward_per_iteration = np.zeros(num_iters)\n",
"\n",
" params = initial_params\n",
" #print(params)\n",
" #print()\n",
" for t in range(num_iters):\n",
" N = np.random.randn(population_size, num_params)\n",
" #print(N)\n",
" R = np.zeros(population_size) # stores the reward\n",
"\n",
" # loop through each \"offspring\"\n",
" for j in range(population_size):\n",
" params_try = params + sigma*N[j]\n",
" R[j] = f(params_try)\n",
"\n",
" m = R.mean()\n",
" A = (R - m) / R.std()\n",
" #print(R)\n",
" #print(m)\n",
" #print(R.std())\n",
" #print(A)\n",
" \n",
" reward_per_iteration[t] = m\n",
" params = params + lr/(population_size*sigma) * np.dot(N.T, A)\n",
" #print(params)\n",
" #print()\n",
" return params, reward_per_iteration"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def reward_function(params):\n",
" x0 = params[0]\n",
" x1 = params[1]\n",
" x2 = params[2]\n",
" return -(x0**2 + 0.1*(x1 - 10)**2 + 0.5*(x2 + 20)**2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final params: [ -0.0214839 9.98001188 -19.98064004]\n"
]
}
],
"source": [
"if __name__ == '__main__':\n",
" best_params, rewards = evolution_strategy(\n",
" f=reward_function,\n",
" population_size=2,\n",
" sigma=0.1,\n",
" lr=1e-3,\n",
" initial_params=np.random.randn(3),\n",
" num_iters=10000,\n",
" )\n",
"\n",
" # plot the rewards per iteration\n",
" plt.plot(rewards)\n",
" plt.show()\n",
"\n",
" # final params\n",
"print(\"Final params:\", best_params)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
qrsforever/workspace | python/learn/matplot/Global_Config.ipynb | 1 | 4516 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 全局配置\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# rc: run configure"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 如何配置"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 全局配置文件\n",
"print(matplotlib.matplotlib_fname())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 修改方式一 \n",
"matplotlib.rcParams['lines.linewidth'] = 2 \n",
"matplotlib.rcParams['lines.color'] = 'r' \n",
"\n",
"# 修改方式二 \n",
"matplotlib.rc('lines', linewidth=4, color='g') \n",
"\n",
"# 恢复默认参数 \n",
"matplotlib.rcdefaults()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 显示所有默认配置\n",
"print(matplotlib.rc_params())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 设置显示样式"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(plt.style.available)\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 常用参数设置"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 中文 (不起作用, /usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/matplotlibrc)\n",
"# https://yun.baidu.com/disk/home?#/all?vmode=list&path=%2F%E6%88%91%E7%9A%84%E8%B5%84%E6%BA%90%2F%E5%AD%97%E4%BD%93\n",
"plt.rcParams['font.sans-serif'] = 'SimHei'\n",
"# 负号正常显示\n",
"plt.rcParams['axes.unicode_minus'] = False\n",
"# 图像显示大小\n",
"plt.rcParams['figure.figsize'] = (6.0, 6.0)\n",
"# 使用数学公式 (depend: sudo apt install texlive-full)\n",
"plt.rcParams['text.usetex'] = True\n",
"plt.rcParams['text.latex.preamble'] = [r'\\usepackage{amsmath}']\n",
"# 更多preamble, \\newcommand自定义命令\n",
"plt.rcParams['text.latex.preamble'] = [\n",
" r'\\usepackage{amsmath}',\n",
" r'\\usepackage{helvet}',\n",
" r'\\usepackage{sansmath}',\n",
" r'\\sansmath',\n",
" r'\\renewcommand{\\familydefault}{\\sfdefault}',\n",
" r'\\usepackage[T1]{fontenc}',\n",
" r'\\usepackage{graphicx}',\n",
" r'\\usepackage{relsize}',\n",
" r'\\newcommand{\\bigpi}{\\scalebox{5}{\\ensuremath{\\pi}}}'\n",
"]\n",
"\n",
"# 坐标label字体大小\n",
"plt.rcParams['axes.labelsize'] = 16\n",
"\n",
"# 图像 插补方式: 紧邻\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"# color map\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 其他参数设置"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 保存图像像素\n",
"plt.rcParams['savefig.dpi'] = 300\n",
"# 图像分辨率\n",
"plt.rcParams['figure.dpi'] = 300\n",
"# 设置线条样式\n",
"plt.rcParams['lines.linestyle'] = '-.'\n",
"# 设置线条宽度\n",
"plt.rcParams['lines.linewidth'] = 3\n",
"# 设置线条标识\n",
"plt.rcParams['lines.marker'] = 'o'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 坐标设置"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 不显示坐标轴\n",
"plt.axis('off')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
rsignell-usgs/ipython-notebooks | files/CSW Testing Geonetwork.ipynb | 2 | 15616 | {
"metadata": {
"name": "CSW Testing Geonetwork"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Exploring CSW access in Python"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"HTML('<iframe src=http://cmgds.marine.usgs.gov/geonetwork/srv/en/main.home width=900 height=280></iframe>')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<iframe src=http://cmgds.marine.usgs.gov/geonetwork/srv/en/main.home width=900 height=280></iframe>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"<IPython.core.display.HTML at 0x36adf10>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from owslib.csw import CatalogueServiceWeb\n",
"from owslib import fes"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# connect to CSW, explore it's properties\n",
"#endpoint = 'http://www.ngdc.noaa.gov/geoportal/csw' # NGDC Geoportal\n",
"#endpoint = 'http://www.nodc.noaa.gov/geoportal/csw' # NODC Geoportal: granule level\n",
"#endpoint = 'http://data.nodc.noaa.gov/geoportal/csw' # NODC Geoportal: collection level\n",
" \n",
"#endpoint = 'http://geodiscover.cgdi.ca/wes/serviceManagerCSW/csw' # NRCAN CUSTOM\n",
"#endpoint = 'http://geoport.whoi.edu/gi-cat/services/cswiso' # USGS Woods Hole GI_CAT\n",
"#endpoint = 'http://cida.usgs.gov/gdp/geonetwork/srv/en/csw' # USGS CIDA Geonetwork\n",
"endpoint = 'http://cmgds.marine.usgs.gov/geonetwork/srv/en/csw' # USGS Coastal and Marine Program\n",
"csw = CatalogueServiceWeb(endpoint)\n",
"csw.version"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"'2.0.2'"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"[op.name for op in csw.operations]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"['GetCapabilities',\n",
" 'DescribeRecord',\n",
" 'GetDomain',\n",
" 'GetRecords',\n",
" 'GetRecordById',\n",
" 'Transaction']"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bbox=[-141,42,-52,84]\n",
"#bbox=[-71.5, 39.5, -63.0, 46]\n",
"csw.getrecords(keywords=['sea_water_temperature'],bbox=bbox,maxrecords=20)\n",
"#csw.getrecords(keywords=['sea_water_temperature'],maxrecords=20)\n",
"csw.results"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "DeprecationWarning",
"evalue": "Please use the updated 'getrecords2' method instead of 'getrecords'. \n The 'getrecords' method will be upgraded to use the 'getrecords2' parameters\n in a future version of OWSLib.",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mDeprecationWarning\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-7-db5a2ca3f992>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mbbox\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m141\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m52\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m84\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m#bbox=[-71.5, 39.5, -63.0, 46]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mcsw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetrecords\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeywords\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'sea_water_temperature'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmaxrecords\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;31m#csw.getrecords(keywords=['sea_water_temperature'],maxrecords=20)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mcsw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/local/python27_epd/lib/python2.7/site-packages/owslib/csw.py\u001b[0m in \u001b[0;36mgetrecords\u001b[1;34m(self, qtype, keywords, typenames, propertyname, bbox, esn, sortby, outputschema, format, startposition, maxrecords, cql, xml, resulttype)\u001b[0m\n\u001b[0;32m 186\u001b[0m raise DeprecationWarning(\"\"\"Please use the updated 'getrecords2' method instead of 'getrecords'. \n\u001b[0;32m 187\u001b[0m \u001b[0mThe\u001b[0m \u001b[1;34m'getrecords'\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mupgraded\u001b[0m \u001b[0mto\u001b[0m \u001b[0muse\u001b[0m \u001b[0mthe\u001b[0m \u001b[1;34m'getrecords2'\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m in a future version of OWSLib.\"\"\")\n\u001b[0m\u001b[0;32m 189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 190\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mxml\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mDeprecationWarning\u001b[0m: Please use the updated 'getrecords2' method instead of 'getrecords'. \n The 'getrecords' method will be upgraded to use the 'getrecords2' parameters\n in a future version of OWSLib."
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for rec in iteritems(csw.records):\n",
" print rec.abstract"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'iteritems' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-65-fe2b7cabe14a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mrec\u001b[0m \u001b[1;32min\u001b[0m \u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcsw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mrec\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mabstract\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'iteritems' is not defined"
]
}
],
"prompt_number": 65
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a=csw.records['data/oceansites/DATA/STATION-M/OS_STATION-M-1_194810_D_CTD.nc']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print a.xml"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<csw:SummaryRecord xmlns:csw=\"http://www.opengis.net/cat/csw/2.0.2\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:dcmiBox=\"http://dublincore.org/documents/2000/07/11/dcmi-box/\" xmlns:dct=\"http://purl.org/dc/terms/\" xmlns:gml=\"http://www.opengis.net/gml\" xmlns:ows=\"http://www.opengis.net/ows\" xmlns:xsd=\"http://www.w3.org/2001/XMLSchema\">\n",
"<dc:identifier scheme=\"urn:x-esri:specification:ServiceType:ArcIMS:Metadata:FileID\">data/oceansites/DATA/STATION-M/OS_STATION-M-1_194810_D_CTD.nc</dc:identifier>\n",
"<dc:identifier scheme=\"urn:x-esri:specification:ServiceType:ArcIMS:Metadata:DocID\">{1DB52543-50EF-471E-BBFE-A5A87C42EC42}</dc:identifier>\n",
"<dc:title>OceanSITES STATION-M in-situ data</dc:title>\n",
"<dc:type scheme=\"urn:x-esri:specification:ServiceType:ArcIMS:Metadata:ContentType\">downloadableData</dc:type>\n",
"<dc:type scheme=\"urn:x-esri:specification:ServiceType:ArcIMS:Metadata:ContentType\">liveData</dc:type>\n",
"<dc:subject>sea_water_temperature</dc:subject>\n",
"<dc:subject>sea_water_salinity</dc:subject>\n",
"<dc:subject>depth</dc:subject>\n",
"<dc:subject>time</dc:subject>\n",
"<dc:subject>depth</dc:subject>\n",
"<dc:subject>latitude</dc:subject>\n",
"<dc:subject>longitude</dc:subject>\n",
"<dc:subject>climatologyMeteorologyAtmosphere</dc:subject>\n",
"<dct:modified>2013-03-16T02:45:29-06:00</dct:modified>\n",
"<dct:abstract>EuroSITES European Ocean Observatory NetworkEU Framework 7 collaborative project contract FP7-ENV-2007-1-202955</dct:abstract>\n",
"<dct:abstract>EuroSITES European Ocean Observatory NetworkEU Framework 7 collaborative project contract FP7-ENV-2007-1-202955</dct:abstract>\n",
"<dct:references scheme=\"urn:x-esri:specification:ServiceType:ArcIMS:Metadata:Onlink\">http://dods.ndbc.noaa.gov/thredds/dodsC/data/oceansites/DATA/STATION-M/OS_STATION-M-1_194810_D_CTD.nc.html</dct:references>\n",
"<dct:references scheme=\"urn:x-esri:specification:ServiceType:ArcIMS:Metadata:Document\">http://www.ngdc.noaa.gov/geoportal/csw?getxml=%7B1DB52543-50EF-471E-BBFE-A5A87C42EC42%7D</dct:references>\n",
"<ows:WGS84BoundingBox>\n",
"<ows:LowerCorner>-358.2666666507721 66.0</ows:LowerCorner>\n",
"<ows:UpperCorner>2.049999952316284 66.16666412353516</ows:UpperCorner>\n",
"</ows:WGS84BoundingBox>\n",
"<ows:BoundingBox>\n",
"<ows:LowerCorner>-358.2666666507721 66.0</ows:LowerCorner>\n",
"<ows:UpperCorner>2.049999952316284 66.16666412353516</ows:UpperCorner>\n",
"</ows:BoundingBox>\n",
"<dc:date>2009-11-01Z</dc:date>\n",
"</csw:SummaryRecord>\n",
"\n"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get supported result types\n",
"csw.getdomain('GetRecords.resultType')\n",
"csw.results"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ExceptionReport",
"evalue": "'Not a valid request: GetDomain Valid requests are: GetCapabilities GetRecords GetRecordsSimple DescribeRecord GetRecordById Transaction Harvest GetResource'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mExceptionReport\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-9-0d430bef627f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# get supported result types\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcsw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetdomain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'GetRecords.resultType'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mcsw\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/rsignell/epd-7.2-1/lib/python2.7/site-packages/owslib/csw.py\u001b[0m in \u001b[0;36mgetdomain\u001b[1;34m(self, dname, dtype)\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxml2string\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0metree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtostring\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 154\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invoke\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 157\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexceptionreport\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/rsignell/epd-7.2-1/lib/python2.7/site-packages/owslib/csw.py\u001b[0m in \u001b[0;36m_invoke\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 494\u001b[0m \u001b[0mval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mutil\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnspath_eval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ows:Exception'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnamespaces\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 495\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mval\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 496\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mows\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExceptionReport\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_exml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mowscommon\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnamespace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 497\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 498\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexceptionreport\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mExceptionReport\u001b[0m: 'Not a valid request: GetDomain Valid requests are: GetCapabilities GetRecords GetRecordsSimple DescribeRecord GetRecordById Transaction Harvest GetResource'"
]
}
],
"prompt_number": 9
}
],
"metadata": {}
}
]
} | unlicense |
sanger-pathogens/pathogen-informatics-training | Notebooks/DEAGO/go-term-enrichment.ipynb | 1 | 10094 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Running a gene ontology (GO) term enrichment analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"Once you have your list of differentially expressed (DE) genes, the next step is often to ask whether the DE genes associated with a particular biological process or function. To do this, we can perform a gene ontology (GO) term enrichment analysis. A gene ontology (GO) is a controlled vocabulary which describes gene products from in any organism (i.e. species agnostic). These descriptions are broken down into three categories:\n",
"\n",
" * **molecular function (MF)**\n",
" _what the gene product does_\n",
" \n",
" * **biological process (BP)**\n",
" _why the gene product does this_\n",
" \n",
" * **cell component (CC)**\n",
" _where the gene product acts_\n",
" \n",
"DEAGO uses **[topGO](https://bioconductor.org/packages/release/bioc/html/topGO.html)** to compare a target gene list (e.g. your DE genes) to the background (all genes) and identifies GO terms which are significantly enriched or over-represented in your gene list.\n",
"\n",
"The objectives of this part of the tutorial are:\n",
"\n",
" * run a GO analysis with DEAGO\n",
" * interpret the output GO report from DEAGO\n",
" \n",
" \n",
"### Input files\n",
"\n",
"We will need to give DEAGO three bits of information:\n",
"\n",
" * *the name/location of the directory containing our gene count files (counts)*\n",
" \n",
" \n",
" * *the name/location of our sample/condition mapping file (targets.txt)*\n",
" \n",
" \n",
" * *the name/location of our formatted annotation file (ensembl_mm10_deago_formatted.tsv)*\n",
" \n",
"\n",
"### Running a GO analysis with DEAGO\n",
"\n",
"To simplest command to run a GO analysis with DEAGO the command would be:\n",
"\n",
"```\n",
"deago -c <counts_directory> -t <targets file> -a <annotation file> --go\n",
"```\n",
"\n",
"To indicate that we want to run a GO analysis, we use the `--go` option. Notice here that we **must** provide a formatted annotation file using the `-a` option as we need to know the GO terms assoicated with each gene for the analysis. \n",
"\n",
"However, as before, our count files were generated by featureCounts for this tutorial, so we need to also tell DEAGO the count format with the `--count_type` option:\n",
"\n",
"```\n",
"deago -c <counts_directory> -t <targets file> -a <annotation file> --go \\\n",
" --count_type featurecounts\n",
"```\n",
"\n",
"We're also using the `--control` option, as before, which tells DEAGO the condition you want to use as your reference or control, in this case **WT_Ctrl**. \n",
"\n",
"```\n",
"deago -c <counts_directory> -t <targets file> -a <annotation file> --go\\\n",
" --count_type featurecounts --control <control>\n",
"```\n",
"\n",
"### Output files\n",
"\n",
"Once your GO analysis has finished, you should see several new files and directories:\n",
"\n",
" * **`deago.config`** \n",
" _config file with key/value parameters defining the analysis_ \n",
"\n",
"\n",
" * **`deago.rlog`** \n",
" _log of the R output generated when converting the R markdown to HTML_ \n",
"\n",
"\n",
" * **`deago_markdown.Rmd`** \n",
" _R markdown used to run the analysis_ \n",
"\n",
"\n",
" * **`deago_markdown.html`** \n",
" _HTML report generated from the R markdown_ \n",
" \n",
" \n",
" * **`results_<timestammp>`** \n",
" _directory containing unfiltered DE analysis results and normalised counts for all genes, one file per contrast_\n",
" \n",
"#### Results directory\n",
"\n",
"DEAGO also writes the GO term enrichment analysis results tables containing the top 30 significantly enriched GO terms to individual files, split by GO term level (MF and BP), in your timestamped results directory. If possible, there will be three GO tables for each GO term level containing the results for analyses using all genes, up-regulated genes only and down-regulated genes\n",
"\n",
" * [contrast]_BP.tsv\n",
" * [contrast]_BP_up.tsv\n",
" * [contrast]_BP_down.tsv\n",
" * [contrast]_MF.tsv\n",
" * [contrast]_MF_up.tsv\n",
" * [contrast]_MF_down.tsv\n",
"\n",
"### GO analysis report\n",
"\n",
"The output file we're interested in is deago_markdown.html which is your GO analysis report. Go ahead and open it in a web browser (e.g. Chrome, Firefox, IE, Safari...). You can do this by going to \"File -> Open\" in the top navigation or (if you have Firefox installed, use the command:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"firefox deago_markdown.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You'll already be familiar with the **`QC plots`** and **`Pairwise contrasts`** sections. Click on **`Pairwise contrasts`** and then go to the contrast subsection for **`wt_il22_vs_ko_il22`**. You should now see six new subsections, *6.7.2 - 6.7.7*, which contain your GO term enrichment analysis results for that contrast.\n",
"\n",
"The first three subsections have the prefix **`GO term enrichment - BP`** and contain the results for the _Biological Processes (BP)_. The remaining three subsections have the prefix **`GO term enrichment - MF`** and contain the results for the _Molecular Functions (MF)_.\n",
"\n",
"For each GO level (BP or MF) there is a subsection containing the results from GO term enrichment analyses which were run using all DE genes, up-regulated genes only and down-regulated genes only.\n",
"\n",
"Let's take a look at the BP (upregulated genes only) table for the contrast wt_il22_vs_ko_il22 (subsection 6.7.3). All of the results tables are interactive. Despite performing a single-factor analyis, we can still find the top GO description for up-regulated genes _inflammatory response_ in our results table (GO:0006954)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The paper also mentions _Prdm1_ which is upregulated by IL-22RA1 signalling and is a susceptibility gene in to inflammatory bowel disease. Let's see whether it's associated with any of the enriched GO terms. Type \"prdm1\" in the top right search box to search the whole table."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that one GO term matches: GO:0031663 (lipopolysaccharide-mediated signaling pathway).\n",
"\n",
"Scrolling to the right, we can see how the search found this match. For each GO term, the DE genes which are associated with that term are also shown in the table."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 5\n",
"\n",
"**First, let's make sure we're in the data directory.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cd data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Each DEAGO analysis should be self-contained, so let's create a new directory for our GO analysis.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mkdir go_analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cd go_analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Now, let's get our GO report.**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"deago --build_config -c ../counts -t ../targets.txt \\\n",
" --count_type featurecounts \\\n",
" -a ../ensembl_mm10_deago_formatted.tsv \\\n",
" --control WT_Ctrl -go"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Questions\n",
"\n",
"In [Figure 5C](https://www.ncbi.nlm.nih.gov/pubmed/25263220) (below), the authors have highlighted four genes: ***Fut2***, ***Sec1***, ***Fut8*** and ***B4galt1*** which are associated with glycosylation. \n",
"\n",
" \n",
"\n",
"**Answer the following question for all four genes:**\n",
"\n",
"**Q1: Which biological processes is this upregulated gene associated with?** "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What's next?\n",
"\n",
"If you want a recap of input file preparation, head back to [running a differential expression (DE) analysis](differential-expression.ipynb).\n",
"\n",
"Otherwise, let's continue on to [troubleshooting](troubleshooting.ipynb)."
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Bash",
"language": "bash",
"name": "bash"
},
"language_info": {
"codemirror_mode": "shell",
"file_extension": ".sh",
"mimetype": "text/x-sh",
"name": "bash"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
munhyunsu/Hobby | NumpyPractice/Basic.ipynb | 1 | 1129 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"'''An example\n",
"'''\n",
"import numpy as np\n",
"\n",
"a = np.arange(15).reshape(3, 5)\n",
"print(a)\n",
"print(a.shape)\n",
"print(a.ndim)\n",
"print(a.dtype.name)\n",
"print(a.itemsize)\n",
"print(a.size)\n",
"print(type(a))\n",
"\n",
"b = np.array([6, 7, 8])\n",
"print(b)\n",
"print(type(b))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"'''Array Creation\n",
"'''"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
guyk1971/deep-learning | seq2seq/sequence_to_sequence_implementation.ipynb | 1 | 38473 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"# Character Sequence to Sequence \n",
"In this notebook, we'll build a model that takes in a sequence of letters, and outputs a sorted version of that sequence. We'll do that using what we've learned so far about Sequence to Sequence models. This notebook was updated to work with TensorFlow 1.1 and builds on the work of Dave Currie. Check out Dave's post [Text Summarization with Amazon Reviews](https://medium.com/towards-data-science/text-summarization-with-amazon-reviews-41801c2210b).\n",
"\n",
"<img src=\"images/sequence-to-sequence.jpg\"/>\n",
"\n",
"\n",
"## Dataset \n",
"\n",
"The dataset lives in the /data/ folder. At the moment, it is made up of the following files:\n",
" * **letters_source.txt**: The list of input letter sequences. Each sequence is its own line. \n",
" * **letters_target.txt**: The list of target sequences we'll use in the training process. Each sequence here is a response to the input sequence in letters_source.txt with the same line number."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import time\n",
"\n",
"import helper\n",
"\n",
"source_path = 'data/letters_source.txt'\n",
"target_path = 'data/letters_target.txt'\n",
"\n",
"source_sentences = helper.load_data(source_path)\n",
"target_sentences = helper.load_data(target_path)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's start by examining the current state of the dataset. `source_sentences` contains the entire input sequence file as text delimited by newline symbols."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"source_sentences[:50].split('\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"`target_sentences` contains the entire output sequence file as text delimited by newline symbols. Each line corresponds to the line from `source_sentences`. `target_sentences` contains a sorted characters of the line."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"target_sentences[:50].split('\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Preprocess\n",
"To do anything useful with it, we'll need to turn the each string into a list of characters: \n",
"\n",
"<img src=\"images/source_and_target_arrays.png\"/>\n",
"\n",
"Then convert the characters to their int values as declared in our vocabulary:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def extract_character_vocab(data):\n",
" special_words = ['<PAD>', '<UNK>', '<GO>', '<EOS>']\n",
"\n",
" set_words = set([character for line in data.split('\\n') for character in line])\n",
" int_to_vocab = {word_i: word for word_i, word in enumerate(special_words + list(set_words))}\n",
" vocab_to_int = {word: word_i for word_i, word in int_to_vocab.items()}\n",
"\n",
" return int_to_vocab, vocab_to_int\n",
"\n",
"# Build int2letter and letter2int dicts\n",
"source_int_to_letter, source_letter_to_int = extract_character_vocab(source_sentences)\n",
"target_int_to_letter, target_letter_to_int = extract_character_vocab(target_sentences)\n",
"\n",
"# Convert characters to ids\n",
"source_letter_ids = [[source_letter_to_int.get(letter, source_letter_to_int['<UNK>']) for letter in line] for line in source_sentences.split('\\n')]\n",
"target_letter_ids = [[target_letter_to_int.get(letter, target_letter_to_int['<UNK>']) for letter in line] + [target_letter_to_int['<EOS>']] for line in target_sentences.split('\\n')] \n",
"\n",
"print(\"Example source sequence\")\n",
"print(source_letter_ids[:3])\n",
"print(\"\\n\")\n",
"print(\"Example target sequence\")\n",
"print(target_letter_ids[:3])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The last step in the preprocessing stage is to determine the the longest sequence size in the dataset we'll be using, then pad all the sequences to that length."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sequence Length\n",
"7\n",
"\n",
"\n",
"Input sequence example\n",
"[[14, 4, 5, 26, 26, 0, 0], [22, 6, 17, 0, 0, 0, 0], [28, 14, 11, 27, 19, 0, 0]]\n",
"\n",
"\n",
"Target sequence example\n",
"[[5, 14, 26, 26, 4, 0, 0], [22, 6, 17, 0, 0, 0, 0], [14, 19, 28, 27, 11, 0, 0]]\n"
]
}
],
"source": [
"def pad_id_sequences(source_ids, source_letter_to_int, target_ids, target_letter_to_int, sequence_length):\n",
" new_source_ids = [sentence + [source_letter_to_int['<pad>']] * (sequence_length - len(sentence)) \\\n",
" for sentence in source_ids]\n",
" new_target_ids = [sentence + [target_letter_to_int['<pad>']] * (sequence_length - len(sentence)) \\\n",
" for sentence in target_ids]\n",
"\n",
" return new_source_ids, new_target_ids\n",
"\n",
"\n",
"\n",
"# Pad all sequences up to sequence length\n",
"source_ids, target_ids = pad_id_sequences(source_letter_ids, source_letter_to_int, \n",
" target_letter_ids, target_letter_to_int, sequence_length)\n",
"\n",
"print(\"Sequence Length\")\n",
"print(sequence_length)\n",
"print(\"\\n\")\n",
"print(\"Input sequence example\")\n",
"print(source_ids[:3])\n",
"print(\"\\n\")\n",
"print(\"Target sequence example\")\n",
"print(target_ids[:3])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"This is the final shape we need them to be in. We can now proceed to building the model."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Model\n",
"#### Check the Version of TensorFlow\n",
"This will check to make sure you have the correct version of TensorFlow"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from distutils.version import LooseVersion\n",
"import tensorflow as tf\n",
"from tensorflow.python.layers.core import Dense\n",
"\n",
"\n",
"# Check TensorFlow Version\n",
"assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer'\n",
"print('TensorFlow Version: {}'.format(tf.__version__))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Hyperparameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Number of Epochs\n",
"epochs = 60\n",
"# Batch Size\n",
"batch_size = 128\n",
"# RNN Size\n",
"rnn_size = 50\n",
"# Number of Layers\n",
"num_layers = 2\n",
"# Embedding Size\n",
"encoding_embedding_size = 15\n",
"decoding_embedding_size = 15\n",
"# Learning Rate\n",
"learning_rate = 0.001"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Input"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_model_inputs():\n",
" input_data = tf.placeholder(tf.int32, [None, None], name='input')\n",
" targets = tf.placeholder(tf.int32, [None, None], name='targets')\n",
" lr = tf.placeholder(tf.float32, name='learning_rate')\n",
"\n",
" target_sequence_length = tf.placeholder(tf.int32, (None,), name='target_sequence_length')\n",
" max_target_sequence_length = tf.reduce_max(target_sequence_length, name='max_target_len')\n",
" source_sequence_length = tf.placeholder(tf.int32, (None,), name='source_sequence_length')\n",
" \n",
" return input_data, targets, lr, target_sequence_length, max_target_sequence_length, source_sequence_length\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Sequence to Sequence Model\n",
"\n",
"We can now start defining the functions that will build the seq2seq model. We are building it from the bottom up with the following components:\n",
"\n",
" 2.1 Encoder\n",
" - Embedding\n",
" - Encoder cell\n",
" 2.2 Decoder\n",
" 1- Process decoder inputs\n",
" 2- Set up the decoder\n",
" - Embedding\n",
" - Decoder cell\n",
" - Dense output layer\n",
" - Training decoder\n",
" - Inference decoder\n",
" 2.3 Seq2seq model connecting the encoder and decoder\n",
" 2.4 Build the training graph hooking up the model with the \n",
" optimizer\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### 2.1 Encoder\n",
"\n",
"The first bit of the model we'll build is the encoder. Here, we'll embed the input data, construct our encoder, then pass the embedded data to the encoder.\n",
"\n",
"- Embed the input data using [`tf.contrib.layers.embed_sequence`](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence)\n",
"<img src=\"images/embed_sequence.png\" />\n",
"\n",
"- Pass the embedded input into a stack of RNNs. Save the RNN state and ignore the output.\n",
"<img src=\"images/encoder.png\" />"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"def encoding_layer(input_data, rnn_size, num_layers,\n",
" source_sequence_length, source_vocab_size, \n",
" encoding_embedding_size):\n",
"\n",
"\n",
" # Encoder embedding\n",
" enc_embed_input = tf.contrib.layers.embed_sequence(input_data, source_vocab_size, encoding_embedding_size)\n",
"\n",
" # RNN cell\n",
" def make_cell(rnn_size):\n",
" enc_cell = tf.contrib.rnn.LSTMCell(rnn_size,\n",
" initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))\n",
" return enc_cell\n",
"\n",
" enc_cell = tf.contrib.rnn.MultiRNNCell([make_cell(rnn_size) for _ in range(num_layers)])\n",
" \n",
" enc_output, enc_state = tf.nn.dynamic_rnn(enc_cell, enc_embed_input, sequence_length=source_sequence_length, dtype=tf.float32)\n",
" \n",
" return enc_output, enc_state"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 2.2 Decoder\n",
"\n",
"The decoder is probably the most involved part of this model. The following steps are needed to create it:\n",
"\n",
" 1- Process decoder inputs\n",
" 2- Set up the decoder components\n",
" - Embedding\n",
" - Decoder cell\n",
" - Dense output layer\n",
" - Training decoder\n",
" - Inference decoder\n",
"\n",
"\n",
"### Process Decoder Input\n",
"\n",
"\n",
"In the training process, the target sequences will be used in two different places:\n",
"\n",
" 1. Using them to calculate the loss\n",
" 2. Feeding them to the decoder during training to make the model more robust.\n",
"\n",
"Now we need to address the second point. Let's assume our targets look like this in their letter/word form (we're doing this for readibility. At this point in the code, these sequences would be in int form):\n",
"\n",
"\n",
"<img src=\"images/targets_1.png\"/>\n",
"\n",
"We need to do a simple transformation on the tensor before feeding it to the decoder:\n",
"\n",
"1- We will feed an item of the sequence to the decoder at each time step. Think about the last timestep -- where the decoder outputs the final word in its output. The input to that step is the item before last from the target sequence. The decoder has no use for the last item in the target sequence in this scenario. So we'll need to remove the last item. \n",
"\n",
"We do that using tensorflow's tf.strided_slice() method. We hand it the tensor, and the index of where to start and where to end the cutting.\n",
"\n",
"<img src=\"images/strided_slice_1.png\"/>\n",
"\n",
"2- The first item in each sequence we feed to the decoder has to be GO symbol. So We'll add that to the beginning.\n",
"\n",
"\n",
"<img src=\"images/targets_add_go.png\"/>\n",
"\n",
"\n",
"Now the tensor is ready to be fed to the decoder. It looks like this (if we convert from ints to letters/symbols):\n",
"\n",
"<img src=\"images/targets_after_processing_1.png\"/>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Process the input we'll feed to the decoder\n",
"def process_decoder_input(target_data, vocab_to_int, batch_size):\n",
" '''Remove the last word id from each batch and concat the <GO> to the begining of each batch'''\n",
" ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n",
" dec_input = tf.concat([tf.fill([batch_size, 1], vocab_to_int['<GO>']), ending], 1)\n",
"\n",
" return dec_input"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"\n",
"### Set up the decoder components\n",
"\n",
" - Embedding\n",
" - Decoder cell\n",
" - Dense output layer\n",
" - Training decoder\n",
" - Inference decoder\n",
"\n",
"#### 1- Embedding\n",
"Now that we have prepared the inputs to the training decoder, we need to embed them so they can be ready to be passed to the decoder. \n",
"\n",
"We'll create an embedding matrix like the following then have tf.nn.embedding_lookup convert our input to its embedded equivalent:\n",
"<img src=\"images/embeddings.png\" />\n",
"\n",
"#### 2- Decoder Cell\n",
"Then we declare our decoder cell. Just like the encoder, we'll use an tf.contrib.rnn.LSTMCell here as well.\n",
"\n",
"We need to declare a decoder for the training process, and a decoder for the inference/prediction process. These two decoders will share their parameters (so that all the weights and biases that are set during the training phase can be used when we deploy the model).\n",
"\n",
"First, we'll need to define the type of cell we'll be using for our decoder RNNs. We opted for LSTM.\n",
"\n",
"#### 3- Dense output layer\n",
"Before we move to declaring our decoders, we'll need to create the output layer, which will be a tensorflow.python.layers.core.Dense layer that translates the outputs of the decoder to logits that tell us which element of the decoder vocabulary the decoder is choosing to output at each time step.\n",
"\n",
"#### 4- Training decoder\n",
"Essentially, we'll be creating two decoders which share their parameters. One for training and one for inference. The two are similar in that both created using tf.contrib.seq2seq.**BasicDecoder** and tf.contrib.seq2seq.**dynamic_decode**. They differ, however, in that we feed the the target sequences as inputs to the training decoder at each time step to make it more robust.\n",
"\n",
"We can think of the training decoder as looking like this (except that it works with sequences in batches):\n",
"<img src=\"images/sequence-to-sequence-training-decoder.png\"/>\n",
"\n",
"The training decoder **does not** feed the output of each time step to the next. Rather, the inputs to the decoder time steps are the target sequence from the training dataset (the orange letters).\n",
"\n",
"#### 5- Inference decoder\n",
"The inference decoder is the one we'll use when we deploy our model to the wild.\n",
"\n",
"<img src=\"images/sequence-to-sequence-inference-decoder.png\"/>\n",
"\n",
"We'll hand our encoder hidden state to both the training and inference decoders and have it process its output. TensorFlow handles most of the logic for us. We just have to use the appropriate methods from tf.contrib.seq2seq and supply them with the appropriate inputs.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def decoding_layer(target_letter_to_int, decoding_embedding_size, num_layers, rnn_size,\n",
" target_sequence_length, max_target_sequence_length, enc_state, dec_input):\n",
" # 1. Decoder Embedding\n",
" target_vocab_size = len(target_letter_to_int)\n",
" dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size]))\n",
" dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n",
"\n",
" # 2. Construct the decoder cell\n",
" def make_cell(rnn_size):\n",
" dec_cell = tf.contrib.rnn.LSTMCell(rnn_size,\n",
" initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))\n",
" return dec_cell\n",
"\n",
" dec_cell = tf.contrib.rnn.MultiRNNCell([make_cell(rnn_size) for _ in range(num_layers)])\n",
" \n",
" # 3. Dense layer to translate the decoder's output at each time \n",
" # step into a choice from the target vocabulary\n",
" output_layer = Dense(target_vocab_size,\n",
" kernel_initializer = tf.truncated_normal_initializer(mean = 0.0, stddev=0.1))\n",
"\n",
"\n",
" # 4. Set up a training decoder and an inference decoder\n",
" # Training Decoder\n",
" with tf.variable_scope(\"decode\"):\n",
"\n",
" # Helper for the training process. Used by BasicDecoder to read inputs.\n",
" training_helper = tf.contrib.seq2seq.TrainingHelper(inputs=dec_embed_input,\n",
" sequence_length=target_sequence_length,\n",
" time_major=False)\n",
" \n",
" \n",
" # Basic decoder\n",
" training_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell,\n",
" training_helper,\n",
" enc_state,\n",
" output_layer) \n",
" \n",
" # Perform dynamic decoding using the decoder\n",
" training_decoder_output = tf.contrib.seq2seq.dynamic_decode(training_decoder,\n",
" impute_finished=True,\n",
" maximum_iterations=max_target_sequence_length)[0]\n",
" # 5. Inference Decoder\n",
" # Reuses the same parameters trained by the training process\n",
" with tf.variable_scope(\"decode\", reuse=True):\n",
" start_tokens = tf.tile(tf.constant([target_letter_to_int['<GO>']], dtype=tf.int32), [batch_size], name='start_tokens')\n",
"\n",
" # Helper for the inference process.\n",
" inference_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings,\n",
" start_tokens,\n",
" target_letter_to_int['<EOS>'])\n",
"\n",
" # Basic decoder\n",
" inference_decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell,\n",
" inference_helper,\n",
" enc_state,\n",
" output_layer)\n",
" \n",
" # Perform dynamic decoding using the decoder\n",
" inference_decoder_output = tf.contrib.seq2seq.dynamic_decode(inference_decoder,\n",
" impute_finished=True,\n",
" maximum_iterations=max_target_sequence_length)[0]\n",
" \n",
"\n",
" \n",
" return training_decoder_output, inference_decoder_output"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 2.3 Seq2seq model \n",
"Let's now go a step above, and hook up the encoder and decoder using the methods we just declared"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"def seq2seq_model(input_data, targets, lr, target_sequence_length, \n",
" max_target_sequence_length, source_sequence_length,\n",
" source_vocab_size, target_vocab_size,\n",
" enc_embedding_size, dec_embedding_size, \n",
" rnn_size, num_layers):\n",
" \n",
" # Pass the input data through the encoder. We'll ignore the encoder output, but use the state\n",
" _, enc_state = encoding_layer(input_data, \n",
" rnn_size, \n",
" num_layers, \n",
" source_sequence_length,\n",
" source_vocab_size, \n",
" encoding_embedding_size)\n",
" \n",
" \n",
" # Prepare the target sequences we'll feed to the decoder in training mode\n",
" dec_input = process_decoder_input(targets, target_letter_to_int, batch_size)\n",
" \n",
" # Pass encoder state and decoder inputs to the decoders\n",
" training_decoder_output, inference_decoder_output = decoding_layer(target_letter_to_int, \n",
" decoding_embedding_size, \n",
" num_layers, \n",
" rnn_size,\n",
" target_sequence_length,\n",
" max_target_sequence_length,\n",
" enc_state, \n",
" dec_input) \n",
" \n",
" return training_decoder_output, inference_decoder_output\n",
" \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Model outputs *training_decoder_output* and *inference_decoder_output* both contain a 'rnn_output' logits tensor that looks like this:\n",
"\n",
"<img src=\"images/logits.png\"/>\n",
"\n",
"The logits we get from the training tensor we'll pass to tf.contrib.seq2seq.**sequence_loss()** to calculate the loss and ultimately the gradient.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Build the graph\n",
"train_graph = tf.Graph()\n",
"# Set the graph to default to ensure that it is ready for training\n",
"with train_graph.as_default():\n",
" \n",
" # Load the model inputs \n",
" input_data, targets, lr, target_sequence_length, max_target_sequence_length, source_sequence_length = get_model_inputs()\n",
" \n",
" # Create the training and inference logits\n",
" training_decoder_output, inference_decoder_output = seq2seq_model(input_data, \n",
" targets, \n",
" lr, \n",
" target_sequence_length, \n",
" max_target_sequence_length, \n",
" source_sequence_length,\n",
" len(source_letter_to_int),\n",
" len(target_letter_to_int),\n",
" encoding_embedding_size, \n",
" decoding_embedding_size, \n",
" rnn_size, \n",
" num_layers) \n",
" \n",
" # Create tensors for the training logits and inference logits\n",
" training_logits = tf.identity(training_decoder_output.rnn_output, 'logits')\n",
" inference_logits = tf.identity(inference_decoder_output.sample_id, name='predictions')\n",
" \n",
" # Create the weights for sequence_loss\n",
" masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks')\n",
"\n",
" with tf.name_scope(\"optimization\"):\n",
" \n",
" # Loss function\n",
" cost = tf.contrib.seq2seq.sequence_loss(\n",
" training_logits,\n",
" targets,\n",
" masks)\n",
"\n",
" # Optimizer\n",
" optimizer = tf.train.AdamOptimizer(lr)\n",
"\n",
" # Gradient Clipping\n",
" gradients = optimizer.compute_gradients(cost)\n",
" capped_gradients = [(tf.clip_by_value(grad, -5., 5.), var) for grad, var in gradients if grad is not None]\n",
" train_op = optimizer.apply_gradients(capped_gradients)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Get Batches\n",
"\n",
"There's little processing involved when we retreive the batches. This is a simple example assuming batch_size = 2\n",
"\n",
"Source sequences (it's actually in int form, we're showing the characters for clarity):\n",
"\n",
"<img src=\"images/source_batch.png\" />\n",
"\n",
"Target sequences (also in int, but showing letters for clarity):\n",
"\n",
"<img src=\"images/target_batch.png\" />"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def pad_sentence_batch(sentence_batch, pad_int):\n",
" \"\"\"Pad sentences with <PAD> so that each sentence of a batch has the same length\"\"\"\n",
" max_sentence = max([len(sentence) for sentence in sentence_batch])\n",
" return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def get_batches(targets, sources, batch_size, source_pad_int, target_pad_int):\n",
" \"\"\"Batch targets, sources, and the lengths of their sentences together\"\"\"\n",
" for batch_i in range(0, len(sources)//batch_size):\n",
" start_i = batch_i * batch_size\n",
" sources_batch = sources[start_i:start_i + batch_size]\n",
" targets_batch = targets[start_i:start_i + batch_size]\n",
" pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int))\n",
" pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int))\n",
" \n",
" # Need the lengths for the _lengths parameters\n",
" pad_targets_lengths = []\n",
" for target in pad_targets_batch:\n",
" pad_targets_lengths.append(len(target))\n",
" \n",
" pad_source_lengths = []\n",
" for source in pad_sources_batch:\n",
" pad_source_lengths.append(len(source))\n",
" \n",
" yield pad_targets_batch, pad_sources_batch, pad_targets_lengths, pad_source_lengths"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"source": [
"## Train\n",
"We're now ready to train our model. If you run into OOM (out of memory) issues during training, try to decrease the batch_size."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'source_ids' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-29d5072a99c2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtrain_source\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msource_ids\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mtrain_target\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarget_ids\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'source_ids' is not defined"
]
}
],
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Split data to training and validation sets\n",
"train_source = source_letter_ids[batch_size:]\n",
"train_target = target_letter_ids[batch_size:]\n",
"valid_source = source_letter_ids[:batch_size]\n",
"valid_target = target_letter_ids[:batch_size]\n",
"(valid_targets_batch, valid_sources_batch, valid_targets_lengths, valid_sources_lengths) = next(get_batches(valid_target, valid_source, batch_size,\n",
" source_letter_to_int['<PAD>'],\n",
" target_letter_to_int['<PAD>']))\n",
"\n",
"display_step = 20 # Check training loss after every 20 batches\n",
"\n",
"checkpoint = \"best_model.ckpt\" \n",
"with tf.Session(graph=train_graph) as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" for epoch_i in range(1, epochs+1):\n",
" for batch_i, (targets_batch, sources_batch, targets_lengths, sources_lengths) in enumerate(\n",
" get_batches(train_target, train_source, batch_size,\n",
" source_letter_to_int['<PAD>'],\n",
" target_letter_to_int['<PAD>'])):\n",
" \n",
" # Training step\n",
" _, loss = sess.run(\n",
" [train_op, cost],\n",
" {input_data: sources_batch,\n",
" targets: targets_batch,\n",
" lr: learning_rate,\n",
" target_sequence_length: targets_lengths,\n",
" source_sequence_length: sources_lengths})\n",
"\n",
" # Debug message updating us on the status of the training\n",
" if batch_i % display_step == 0 and batch_i > 0:\n",
" \n",
" # Calculate validation cost\n",
" validation_loss = sess.run(\n",
" [cost],\n",
" {input_data: valid_sources_batch,\n",
" targets: valid_targets_batch,\n",
" lr: learning_rate,\n",
" target_sequence_length: valid_targets_lengths,\n",
" source_sequence_length: valid_sources_lengths})\n",
" \n",
" print('Epoch {:>3}/{} Batch {:>4}/{} - Loss: {:>6.3f} - Validation loss: {:>6.3f}'\n",
" .format(epoch_i,\n",
" epochs, \n",
" batch_i, \n",
" len(train_source) // batch_size, \n",
" loss, \n",
" validation_loss[0]))\n",
"\n",
" \n",
" \n",
" # Save Model\n",
" saver = tf.train.Saver()\n",
" saver.save(sess, checkpoint)\n",
" print('Model Trained and Saved')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Prediction"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def source_to_seq(text):\n",
" '''Prepare the text for the model'''\n",
" sequence_length = 7\n",
" return [source_letter_to_int.get(word, source_letter_to_int['<UNK>']) for word in text]+ [source_letter_to_int['<PAD>']]*(sequence_length-len(text))\n"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from ./best_model.ckpt\n",
"Original Text: hello\n",
"\n",
"Source\n",
" Word Ids: [23, 8, 12, 12, 16, 0, 0]\n",
" Input Words: h e l l o <PAD> <PAD>\n",
"\n",
"Target\n",
" Word Ids: [9, 23, 12, 12, 16, 3]\n",
" Response Words: e h l l o <EOS>\n"
]
}
],
"source": [
"\n",
"\n",
"input_sentence = 'hello'\n",
"text = source_to_seq(input_sentence)\n",
"\n",
"checkpoint = \"./best_model.ckpt\"\n",
"\n",
"loaded_graph = tf.Graph()\n",
"with tf.Session(graph=loaded_graph) as sess:\n",
" # Load saved model\n",
" loader = tf.train.import_meta_graph(checkpoint + '.meta')\n",
" loader.restore(sess, checkpoint)\n",
"\n",
" input_data = loaded_graph.get_tensor_by_name('input:0')\n",
" logits = loaded_graph.get_tensor_by_name('predictions:0')\n",
" source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0')\n",
" target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0')\n",
" \n",
" #Multiply by batch_size to match the model's input parameters\n",
" answer_logits = sess.run(logits, {input_data: [text]*batch_size, \n",
" target_sequence_length: [len(text)]*batch_size, \n",
" source_sequence_length: [len(text)]*batch_size})[0] \n",
"\n",
"\n",
"pad = source_letter_to_int[\"<PAD>\"] \n",
"\n",
"print('Original Text:', input_sentence)\n",
"\n",
"print('\\nSource')\n",
"print(' Word Ids: {}'.format([i for i in text]))\n",
"print(' Input Words: {}'.format(\" \".join([source_int_to_letter[i] for i in text])))\n",
"\n",
"print('\\nTarget')\n",
"print(' Word Ids: {}'.format([i for i in answer_logits if i != pad]))\n",
"print(' Response Words: {}'.format(\" \".join([target_int_to_letter[i] for i in answer_logits if i != pad])))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
CompPhysics/MachineLearning | doc/Programs/JupyterFiles/Examples/Intro to ML Examples/Make Moons.ipynb | 2 | 89780 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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Tw2DOQMqYIpjvWIBVq2fDaj0a7a4SEVEUMCeIiMgXZgRRbGMRR4Uqtq9FYlY+4gcP8/r9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\nJ3Dx5IXezydu/TXe/0cJcyKAdW0tkk4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XH/qVopV3IqJI+8ejj+PauY95zYkNBwUcePKp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\nnEhOFHFSXNtfvDem5cr6Tb6uGZWhK4/J2GeRrD7dXPrZsTk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SseUkbNnn2m00hNBQWPv14nPfgrFXzzSfwbFyeNGdl68eq/qSEzR35P5t7P+zJzVNH7EL1xwa22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\ntt2FKTpj8gjdOedcfbK+gz5Z30F3v95P3e4eqe8LU+u/QAAvLO4m74kD7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"text/plain": [
"<matplotlib.figure.Figure at 0x11091f438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"accuracy on test set: 0.880000\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import mglearn\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.datasets import make_moons\n",
"import numpy as np\n",
"X, y = make_moons(n_samples=100, noise=0.25, random_state=3)\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)\n",
"forest = RandomForestClassifier(n_estimators=5, random_state=2)\n",
"forest.fit(X_train, y_train)\n",
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n",
" oob_score=False, random_state=2, verbose=0, warm_start=False)\n",
"\n",
"fig, axes = plt.subplots(2, 3, figsize=(20, 10))\n",
"for i, (ax, tree) in enumerate(zip(axes.ravel(), forest.estimators_)):\n",
" ax.set_title(\"tree %d\" % i)\n",
" mglearn.plots.plot_tree_partition(X_train, y_train, tree, ax=ax)\n",
"mglearn.plots.plot_2d_separator(forest, X_train, fill=True, ax=axes[-1, -1], alpha=.4)\n",
"axes[-1, -1].set_title(\"random forest\")\n",
"plt.scatter(X_train[:, 0], X_train[:, 1], c=np.array(['r', 'b'])[y_train], s=60)\n",
"plt.show()\n",
"\n",
"forest = RandomForestClassifier(n_estimators=100, random_state=0)\n",
"forest.fit(X_train, y_train)\n",
"print(\"accuracy on test set: %f\" % forest.score(X_test, y_test))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neural_network import MLPClassifier\n",
"mlp = MLPClassifier(hidden_layer_sizes=[100], activation='relu', random_state=0, learning_rate='constant')\n",
"mlp.fit(X_train, y_train)\n",
"mglearn.plots.plot_2d_separator(mlp, X_train, fill=True, alpha=.3)\n",
"plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=60, cmap=mglearn.cm2)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| cc0-1.0 |
mrustl/flopy | examples/Notebooks/flopy3_external_file_handling.ipynb | 1 | 64217 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FloPy\n",
"\n",
"### Quick demo on how FloPy handles external files for arrays"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os \n",
"import shutil\n",
"import flopy\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# make a model\n",
"nlay,nrow,ncol = 10,20,5\n",
"model_ws = os.path.join(\"data\",\"external_demo\")\n",
"\n",
"if os.path.exists(model_ws):\n",
" shutil.rmtree(model_ws)\n",
"\n",
"# the place for all of your hand made and costly model inputs \n",
"array_dir = os.path.join(\"data\",\"array_dir\")\n",
"if os.path.exists(array_dir):\n",
" shutil.rmtree(array_dir) \n",
"os.mkdir(array_dir) \n",
" \n",
"ml = flopy.modflow.Modflow(model_ws=model_ws)\n",
"dis = flopy.modflow.ModflowDis(ml,nlay=nlay,nrow=nrow,ncol=ncol,steady=False,nper=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"make an ``hk`` and ```vka``` array. We'll save ```hk``` to files - pretent that you spent months making this important model property. Then make an ```lpf```"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hk = np.zeros((nlay,nrow,ncol)) + 5.0\n",
"vka = np.zeros_like(hk)\n",
"fnames = []\n",
"for i,h in enumerate(hk):\n",
" fname = os.path.join(array_dir,\"hk_{0}.ref\".format(i+1))\n",
" fnames.append(fname)\n",
" np.savetxt(fname,h)\n",
" vka[i] = i+1\n",
"lpf = flopy.modflow.ModflowLpf(ml,hk=fnames,vka=vka)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also have some recharge with mixed args as well. Pretend the recharge in the second stress period is very important and precise"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"warmup_recharge = np.ones((nrow,ncol))\n",
"important_recharge = np.random.random((nrow,ncol))\n",
"fname = os.path.join(array_dir,\"important_recharge.ref\")\n",
"np.savetxt(fname,important_recharge)\n",
"rch = flopy.modflow.ModflowRch(ml,rech={0:warmup_recharge,1:fname})"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ml.write_input()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the files that were created"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model_ws: data/external_demo\n",
"hk_1.ref\n",
"hk_10.ref\n",
"hk_2.ref\n",
"hk_3.ref\n",
"hk_4.ref\n",
"hk_5.ref\n",
"hk_6.ref\n",
"hk_7.ref\n",
"hk_8.ref\n",
"hk_9.ref\n",
"important_recharge.ref\n",
"modflowtest.dis\n",
"modflowtest.lpf\n",
"modflowtest.nam\n",
"modflowtest.rch\n"
]
}
],
"source": [
"print(\"model_ws:\",ml.model_ws)\n",
"print('\\n'.join(os.listdir(ml.model_ws)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that a copy of the ``hk`` files as well as the important recharge file were made in the ```model_ws```.Let's looks at the ```lpf``` file"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['# LPF for MODFLOW, generated by Flopy.\\n',\n",
" ' 53 -1E+30 0 \\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" 'OPEN/CLOSE hk_1.ref 1 (FREE) -1 hk layer 1 \\n',\n",
" 'INTERNAL 1 (5E15.6) -1 #vka layer 1 \\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"open(os.path.join(ml.model_ws,ml.name+\".lpf\"),'r').readlines()[:20]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the ```open/close``` approach was used - this is because ``ml.array_free_format`` is ``True``. Notice that ```vka``` is written internally"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ml.array_free_format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now change ```model_ws```"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data/external_demo\n",
"\n",
"changing model workspace...\n",
" data/new_external_demo_dir\n"
]
}
],
"source": [
"print(ml.model_ws)\n",
"ml.model_ws = os.path.join(\"data\",\"new_external_demo_dir\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now when we call ``write_input()``, a copy of external files are made in the current ```model_ws```"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ml.write_input()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hk_1.ref\n",
"hk_10.ref\n",
"hk_2.ref\n",
"hk_3.ref\n",
"hk_4.ref\n",
"hk_5.ref\n",
"hk_6.ref\n",
"hk_7.ref\n",
"hk_8.ref\n",
"hk_9.ref\n",
"important_recharge.ref\n"
]
}
],
"source": [
"# list the files in model_ws that have 'hk' in the name\n",
"print('\\n'.join([name for name in os.listdir(ml.model_ws) if \"hk\" in name or \"impor\" in name]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we see that the external files were copied to the new ```model_ws```\n",
"\n",
"### Using ```external_path```\n",
"\n",
"It is sometimes useful when first building a model to write the model arrays as external files for processing and parameter estimation. The ```model``` attribute ```external_path``` triggers this behavior"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# make a model - same code as before except for the model constructor\n",
"nlay,nrow,ncol = 10,20,5\n",
"model_ws = os.path.join(\"data\",\"external_demo\")\n",
"\n",
"if os.path.exists(model_ws):\n",
" shutil.rmtree(model_ws)\n",
"\n",
"# the place for all of your hand made and costly model inputs \n",
"array_dir = os.path.join(\"data\",\"array_dir\")\n",
"if os.path.exists(array_dir):\n",
" shutil.rmtree(array_dir) \n",
"os.mkdir(array_dir) \n",
"\n",
"# lets make an external path relative to the model_ws\n",
"ml = flopy.modflow.Modflow(model_ws=model_ws, external_path=\"ref\")\n",
"dis = flopy.modflow.ModflowDis(ml,nlay=nlay,nrow=nrow,ncol=ncol,steady=False,nper=2)\n",
"\n",
"hk = np.zeros((nlay,nrow,ncol)) + 5.0\n",
"vka = np.zeros_like(hk)\n",
"fnames = []\n",
"for i,h in enumerate(hk):\n",
" fname = os.path.join(array_dir,\"hk_{0}.ref\".format(i+1))\n",
" fnames.append(fname)\n",
" np.savetxt(fname,h)\n",
" vka[i] = i+1\n",
"lpf = flopy.modflow.ModflowLpf(ml,hk=fnames,vka=vka) \n",
"\n",
"warmup_recharge = np.ones((nrow,ncol))\n",
"important_recharge = np.random.random((nrow,ncol))\n",
"fname = os.path.join(array_dir,\"important_recharge.ref\")\n",
"np.savetxt(fname,important_recharge)\n",
"rch = flopy.modflow.ModflowRch(ml,rech={0:warmup_recharge,1:fname})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the model constructor created both ```model_ws``` and ```external_path``` which is _relative to the model_ws_"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['ref']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir(ml.model_ws)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, when we call ```write_input()```, any array properties that were specified as ```np.ndarray``` will be written externally. If a scalar was passed as the argument, the value remains internal to the model input files"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Util2d:delr: resetting 'how' to external\n",
"Util2d:delc: resetting 'how' to external\n",
"Util2d:model_top: resetting 'how' to external\n",
"Util2d:botm layer 1: resetting 'how' to external\n",
"Util2d:botm layer 2: resetting 'how' to external\n",
"Util2d:botm layer 3: resetting 'how' to external\n",
"Util2d:botm layer 4: resetting 'how' to external\n",
"Util2d:botm layer 5: resetting 'how' to external\n",
"Util2d:botm layer 6: resetting 'how' to external\n",
"Util2d:botm layer 7: resetting 'how' to external\n",
"Util2d:botm layer 8: resetting 'how' to external\n",
"Util2d:botm layer 9: resetting 'how' to external\n",
"Util2d:botm layer 10: resetting 'how' to external\n",
"Util2d:ss layer 1: resetting 'how' to external\n",
"Util2d:ss layer 2: resetting 'how' to external\n",
"Util2d:ss layer 3: resetting 'how' to external\n",
"Util2d:ss layer 4: resetting 'how' to external\n",
"Util2d:ss layer 5: resetting 'how' to external\n",
"Util2d:ss layer 6: resetting 'how' to external\n",
"Util2d:ss layer 7: resetting 'how' to external\n",
"Util2d:ss layer 8: resetting 'how' to external\n",
"Util2d:ss layer 9: resetting 'how' to external\n",
"Util2d:ss layer 10: resetting 'how' to external\n"
]
},
{
"data": {
"text/plain": [
"['# LPF for MODFLOW, generated by Flopy.\\n',\n",
" ' 53 -1E+30 0 \\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" 'OPEN/CLOSE ref/hk_1.ref 1 (FREE) -1 hk layer 1 \\n',\n",
" 'OPEN/CLOSE ref/vka_layer_1.ref 1 (5E15.6) -1 vka layer 1 \\n',\n",
" 'OPEN/CLOSE ref/ss_layer_1.ref 1 (5E15.6) -1 ss layer 1 \\n',\n",
" 'OPEN/CLOSE ref/hk_2.ref 1 (FREE) -1 hk layer 2 \\n',\n",
" 'OPEN/CLOSE ref/vka_layer_2.ref 1 (5E15.6) -1 vka layer 2 \\n',\n",
" 'OPEN/CLOSE ref/ss_layer_2.ref 1 (5E15.6) -1 ss layer 2 \\n',\n",
" 'OPEN/CLOSE ref/hk_3.ref 1 (FREE) -1 hk layer 3 \\n',\n",
" 'OPEN/CLOSE ref/vka_layer_3.ref 1 (5E15.6) -1 vka layer 3 \\n',\n",
" 'OPEN/CLOSE ref/ss_layer_3.ref 1 (5E15.6) -1 ss layer 3 \\n',\n",
" 'OPEN/CLOSE ref/hk_4.ref 1 (FREE) -1 hk layer 4 \\n',\n",
" 'OPEN/CLOSE ref/vka_layer_4.ref 1 (5E15.6) -1 vka layer 4 \\n',\n",
" 'OPEN/CLOSE ref/ss_layer_4.ref 1 (5E15.6) -1 ss layer 4 \\n',\n",
" 'OPEN/CLOSE ref/hk_5.ref 1 (FREE) -1 hk layer 5 \\n']"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ml.write_input()\n",
"open(os.path.join(ml.model_ws,ml.name+\".lpf\"),'r').readlines()[:20]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, ```vka``` was also written externally, but not the storage properties.Let's verify the contents of the external path directory. We see our hard-fought ```hk``` and ```important_recharge``` arrays, as well as the ``vka`` arrays."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Util2d:delr: resetting 'how' to external\n",
"Util2d:delc: resetting 'how' to external\n",
"Util2d:model_top: resetting 'how' to external\n",
"Util2d:botm layer 1: resetting 'how' to external\n",
"Util2d:botm layer 2: resetting 'how' to external\n",
"Util2d:botm layer 3: resetting 'how' to external\n",
"Util2d:botm layer 4: resetting 'how' to external\n",
"Util2d:botm layer 5: resetting 'how' to external\n",
"Util2d:botm layer 6: resetting 'how' to external\n",
"Util2d:botm layer 7: resetting 'how' to external\n",
"Util2d:botm layer 8: resetting 'how' to external\n",
"Util2d:botm layer 9: resetting 'how' to external\n",
"Util2d:botm layer 10: resetting 'how' to external\n",
"Util2d:ss layer 1: resetting 'how' to external\n",
"Util2d:ss layer 2: resetting 'how' to external\n",
"Util2d:ss layer 3: resetting 'how' to external\n",
"Util2d:ss layer 4: resetting 'how' to external\n",
"Util2d:ss layer 5: resetting 'how' to external\n",
"Util2d:ss layer 6: resetting 'how' to external\n",
"Util2d:ss layer 7: resetting 'how' to external\n",
"Util2d:ss layer 8: resetting 'how' to external\n",
"Util2d:ss layer 9: resetting 'how' to external\n",
"Util2d:ss layer 10: resetting 'how' to external\n"
]
},
{
"data": {
"text/plain": [
"['# LPF for MODFLOW, generated by Flopy.\\n',\n",
" ' 53 -1E+30 0 \\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" 'OPEN/CLOSE ref/hk_1.ref 1 (FREE) -1 hk layer 1 \\n',\n",
" 'OPEN/CLOSE ref/vka_layer_1.ref 1 (5E15.6) -1 vka layer 1 \\n',\n",
" 'OPEN/CLOSE ref/ss_layer_1.ref 1 (5E15.6) -1 ss layer 1 \\n',\n",
" 'OPEN/CLOSE ref/hk_2.ref 1 (FREE) -1 hk layer 2 \\n',\n",
" 'OPEN/CLOSE ref/vka_layer_2.ref 1 (5E15.6) -1 vka layer 2 \\n',\n",
" 'OPEN/CLOSE ref/ss_layer_2.ref 1 (5E15.6) -1 ss layer 2 \\n',\n",
" 'OPEN/CLOSE ref/hk_3.ref 1 (FREE) -1 hk layer 3 \\n',\n",
" 'OPEN/CLOSE ref/vka_layer_3.ref 1 (5E15.6) -1 vka layer 3 \\n',\n",
" 'OPEN/CLOSE ref/ss_layer_3.ref 1 (5E15.6) -1 ss layer 3 \\n',\n",
" 'OPEN/CLOSE ref/hk_4.ref 1 (FREE) -1 hk layer 4 \\n',\n",
" 'OPEN/CLOSE ref/vka_layer_4.ref 1 (5E15.6) -1 vka layer 4 \\n',\n",
" 'OPEN/CLOSE ref/ss_layer_4.ref 1 (5E15.6) -1 ss layer 4 \\n',\n",
" 'OPEN/CLOSE ref/hk_5.ref 1 (FREE) -1 hk layer 5 \\n']"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ml.lpf.ss.how = \"internal\"\n",
"ml.write_input()\n",
"open(os.path.join(ml.model_ws,ml.name+\".lpf\"),'r').readlines()[:20]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"botm_layer_1.ref\n",
"botm_layer_10.ref\n",
"botm_layer_2.ref\n",
"botm_layer_3.ref\n",
"botm_layer_4.ref\n",
"botm_layer_5.ref\n",
"botm_layer_6.ref\n",
"botm_layer_7.ref\n",
"botm_layer_8.ref\n",
"botm_layer_9.ref\n",
"delc.ref\n",
"delr.ref\n",
"hk_1.ref\n",
"hk_10.ref\n",
"hk_2.ref\n",
"hk_3.ref\n",
"hk_4.ref\n",
"hk_5.ref\n",
"hk_6.ref\n",
"hk_7.ref\n",
"hk_8.ref\n",
"hk_9.ref\n",
"important_recharge.ref\n",
"model_top.ref\n",
"rech_0.ref\n",
"ss_layer_1.ref\n",
"ss_layer_10.ref\n",
"ss_layer_2.ref\n",
"ss_layer_3.ref\n",
"ss_layer_4.ref\n",
"ss_layer_5.ref\n",
"ss_layer_6.ref\n",
"ss_layer_7.ref\n",
"ss_layer_8.ref\n",
"ss_layer_9.ref\n",
"vka_layer_1.ref\n",
"vka_layer_10.ref\n",
"vka_layer_2.ref\n",
"vka_layer_3.ref\n",
"vka_layer_4.ref\n",
"vka_layer_5.ref\n",
"vka_layer_6.ref\n",
"vka_layer_7.ref\n",
"vka_layer_8.ref\n",
"vka_layer_9.ref\n"
]
}
],
"source": [
"print('\\n'.join(os.listdir(os.path.join(ml.model_ws,ml.external_path))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fixed format\n",
"\n",
"All of this behavior also works for fixed-format type models (really, really old models - I mean OLD!)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Util2d:delr: resetting 'how' to external\n",
"Util2d:delc: resetting 'how' to external\n",
"Util2d:model_top: resetting 'how' to external\n",
"Util2d:botm layer 1: resetting 'how' to external\n",
"Util2d:botm layer 2: resetting 'how' to external\n",
"Util2d:botm layer 3: resetting 'how' to external\n",
"Util2d:botm layer 4: resetting 'how' to external\n",
"Util2d:botm layer 5: resetting 'how' to external\n",
"Util2d:botm layer 6: resetting 'how' to external\n",
"Util2d:botm layer 7: resetting 'how' to external\n",
"Util2d:botm layer 8: resetting 'how' to external\n",
"Util2d:botm layer 9: resetting 'how' to external\n",
"Util2d:botm layer 10: resetting 'how' to external\n",
"Util2d hk layer 1: can't be free format...resetting\n",
"Util2d:ss layer 1: resetting 'how' to external\n",
"Util2d hk layer 2: can't be free format...resetting\n",
"Util2d:ss layer 2: resetting 'how' to external\n",
"Util2d hk layer 3: can't be free format...resetting\n",
"Util2d:ss layer 3: resetting 'how' to external\n",
"Util2d hk layer 4: can't be free format...resetting\n",
"Util2d:ss layer 4: resetting 'how' to external\n",
"Util2d hk layer 5: can't be free format...resetting\n",
"Util2d:ss layer 5: resetting 'how' to external\n",
"Util2d hk layer 6: can't be free format...resetting\n",
"Util2d:ss layer 6: resetting 'how' to external\n",
"Util2d hk layer 7: can't be free format...resetting\n",
"Util2d:ss layer 7: resetting 'how' to external\n",
"Util2d hk layer 8: can't be free format...resetting\n",
"Util2d:ss layer 8: resetting 'how' to external\n",
"Util2d hk layer 9: can't be free format...resetting\n",
"Util2d:ss layer 9: resetting 'how' to external\n",
"Util2d hk layer 10: can't be free format...resetting\n",
"Util2d:ss layer 10: resetting 'how' to external\n",
"Util2d rech_2: can't be free format...resetting\n"
]
}
],
"source": [
"# make a model - same code as before except for the model constructor\n",
"nlay,nrow,ncol = 10,20,5\n",
"model_ws = os.path.join(\"data\",\"external_demo\")\n",
"\n",
"if os.path.exists(model_ws):\n",
" shutil.rmtree(model_ws)\n",
"\n",
"# the place for all of your hand made and costly model inputs \n",
"array_dir = os.path.join(\"data\",\"array_dir\")\n",
"if os.path.exists(array_dir):\n",
" shutil.rmtree(array_dir) \n",
"os.mkdir(array_dir) \n",
"\n",
"# lets make an external path relative to the model_ws\n",
"ml = flopy.modflow.Modflow(model_ws=model_ws, external_path=\"ref\")\n",
"\n",
"# explicitly reset the free_format flag BEFORE ANY PACKAGES ARE MADE!!!\n",
"ml.array_free_format = False\n",
"\n",
"dis = flopy.modflow.ModflowDis(ml,nlay=nlay,nrow=nrow,ncol=ncol,steady=False,nper=2)\n",
"\n",
"hk = np.zeros((nlay,nrow,ncol)) + 5.0\n",
"vka = np.zeros_like(hk)\n",
"fnames = []\n",
"for i,h in enumerate(hk):\n",
" fname = os.path.join(array_dir,\"hk_{0}.ref\".format(i+1))\n",
" fnames.append(fname)\n",
" np.savetxt(fname,h)\n",
" vka[i] = i+1\n",
"lpf = flopy.modflow.ModflowLpf(ml,hk=fnames,vka=vka)\n",
"ml.lpf.ss.how = \"internal\"\n",
"warmup_recharge = np.ones((nrow,ncol))\n",
"important_recharge = np.random.random((nrow,ncol))\n",
"fname = os.path.join(array_dir,\"important_recharge.ref\")\n",
"np.savetxt(fname,important_recharge)\n",
"rch = flopy.modflow.ModflowRch(ml,rech={0:warmup_recharge,1:fname})\n",
"\n",
"ml.write_input()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that now the external arrays are being handled through the name file. Let's look at the name file"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['# Name file for MODFLOW-2005, generated by Flopy.\\n',\n",
" '#xul:0; yul:20; rotation:0; proj4_str:+init=EPSG:4326; units:meters; lenuni:2 ;start_datetime:1-1-1970\\n',\n",
" 'LIST 2 modflowtest.list\\n',\n",
" 'DIS 11 modflowtest.dis \\n',\n",
" 'LPF 15 modflowtest.lpf \\n',\n",
" 'RCH 19 modflowtest.rch \\n',\n",
" 'DATA 1001 ref/delr.ref\\n',\n",
" 'DATA 1002 ref/delc.ref\\n',\n",
" 'DATA 1003 ref/model_top.ref\\n',\n",
" 'DATA 1004 ref/botm_layer_1.ref\\n',\n",
" 'DATA 1005 ref/botm_layer_2.ref\\n',\n",
" 'DATA 1006 ref/botm_layer_3.ref\\n',\n",
" 'DATA 1007 ref/botm_layer_4.ref\\n',\n",
" 'DATA 1008 ref/botm_layer_5.ref\\n',\n",
" 'DATA 1009 ref/botm_layer_6.ref\\n',\n",
" 'DATA 1010 ref/botm_layer_7.ref\\n',\n",
" 'DATA 1011 ref/botm_layer_8.ref\\n',\n",
" 'DATA 1012 ref/botm_layer_9.ref\\n',\n",
" 'DATA 1013 ref/botm_layer_10.ref\\n',\n",
" 'DATA 1014 ref/hk_1.ref\\n',\n",
" 'DATA 1015 ref/vka_layer_1.ref\\n',\n",
" 'DATA 1016 ref/ss_layer_1.ref\\n',\n",
" 'DATA 1017 ref/hk_2.ref\\n',\n",
" 'DATA 1018 ref/vka_layer_2.ref\\n',\n",
" 'DATA 1019 ref/ss_layer_2.ref\\n',\n",
" 'DATA 1020 ref/hk_3.ref\\n',\n",
" 'DATA 1021 ref/vka_layer_3.ref\\n',\n",
" 'DATA 1022 ref/ss_layer_3.ref\\n',\n",
" 'DATA 1023 ref/hk_4.ref\\n',\n",
" 'DATA 1024 ref/vka_layer_4.ref\\n',\n",
" 'DATA 1025 ref/ss_layer_4.ref\\n',\n",
" 'DATA 1026 ref/hk_5.ref\\n',\n",
" 'DATA 1027 ref/vka_layer_5.ref\\n',\n",
" 'DATA 1028 ref/ss_layer_5.ref\\n',\n",
" 'DATA 1029 ref/hk_6.ref\\n',\n",
" 'DATA 1030 ref/vka_layer_6.ref\\n',\n",
" 'DATA 1031 ref/ss_layer_6.ref\\n',\n",
" 'DATA 1032 ref/hk_7.ref\\n',\n",
" 'DATA 1033 ref/vka_layer_7.ref\\n',\n",
" 'DATA 1034 ref/ss_layer_7.ref\\n',\n",
" 'DATA 1035 ref/hk_8.ref\\n',\n",
" 'DATA 1036 ref/vka_layer_8.ref\\n',\n",
" 'DATA 1037 ref/ss_layer_8.ref\\n',\n",
" 'DATA 1038 ref/hk_9.ref\\n',\n",
" 'DATA 1039 ref/vka_layer_9.ref\\n',\n",
" 'DATA 1040 ref/ss_layer_9.ref\\n',\n",
" 'DATA 1041 ref/hk_10.ref\\n',\n",
" 'DATA 1042 ref/vka_layer_10.ref\\n',\n",
" 'DATA 1043 ref/ss_layer_10.ref\\n',\n",
" 'DATA 1044 ref/rech_0.ref\\n',\n",
" 'DATA 1045 ref/important_recharge.ref\\n']"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"open(os.path.join(ml.model_ws,ml.name+\".nam\"),'r').readlines()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### \"free\" and \"binary\" format"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Util2d:delr: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/delr.ref\n",
"Util2d:delc: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/delc.ref\n",
"Util2d:model_top: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/model_top.ref\n",
"Util2d:botm layer 1: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_1.ref\n",
"Util2d:botm layer 2: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_2.ref\n",
"Util2d:botm layer 3: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_3.ref\n",
"Util2d:botm layer 4: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_4.ref\n",
"Util2d:botm layer 5: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_5.ref\n",
"Util2d:botm layer 6: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_6.ref\n",
"Util2d:botm layer 7: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_7.ref\n",
"Util2d:botm layer 8: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_8.ref\n",
"Util2d:botm layer 9: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_9.ref\n",
"Util2d:botm layer 10: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_10.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_1.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_1.ref\n",
"Util2d:ss layer 1: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_1.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_2.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_2.ref\n",
"Util2d:ss layer 2: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_2.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_3.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_3.ref\n",
"Util2d:ss layer 3: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_3.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_4.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_4.ref\n",
"Util2d:ss layer 4: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_4.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_5.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_5.ref\n",
"Util2d:ss layer 5: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_5.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_6.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_6.ref\n",
"Util2d:ss layer 6: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_6.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_7.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_7.ref\n",
"Util2d:ss layer 7: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_7.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_8.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_8.ref\n",
"Util2d:ss layer 8: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_8.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_9.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_9.ref\n",
"Util2d:ss layer 9: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_9.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_10.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_10.ref\n",
"Util2d:ss layer 10: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_10.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/rech_0.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/important_recharge.ref\n"
]
}
],
"source": [
"ml.dis.botm[0].format.binary = True\n",
"ml.write_input()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['# Name file for MODFLOW-2005, generated by Flopy.\\n',\n",
" '#xul:0; yul:20; rotation:0; proj4_str:+init=EPSG:4326; units:meters; lenuni:2 ;start_datetime:1-1-1970\\n',\n",
" 'LIST 2 modflowtest.list\\n',\n",
" 'DIS 11 modflowtest.dis \\n',\n",
" 'LPF 15 modflowtest.lpf \\n',\n",
" 'RCH 19 modflowtest.rch \\n',\n",
" 'DATA 1046 ref/delr.ref\\n',\n",
" 'DATA 1047 ref/delc.ref\\n',\n",
" 'DATA 1048 ref/model_top.ref\\n',\n",
" 'DATA(BINARY) 1049 ref/botm_layer_1.ref REPLACE\\n',\n",
" 'DATA 1050 ref/botm_layer_2.ref\\n',\n",
" 'DATA 1051 ref/botm_layer_3.ref\\n',\n",
" 'DATA 1052 ref/botm_layer_4.ref\\n',\n",
" 'DATA 1053 ref/botm_layer_5.ref\\n',\n",
" 'DATA 1054 ref/botm_layer_6.ref\\n',\n",
" 'DATA 1055 ref/botm_layer_7.ref\\n',\n",
" 'DATA 1056 ref/botm_layer_8.ref\\n',\n",
" 'DATA 1057 ref/botm_layer_9.ref\\n',\n",
" 'DATA 1058 ref/botm_layer_10.ref\\n',\n",
" 'DATA 1059 ref/hk_1.ref\\n',\n",
" 'DATA 1060 ref/vka_layer_1.ref\\n',\n",
" 'DATA 1061 ref/ss_layer_1.ref\\n',\n",
" 'DATA 1062 ref/hk_2.ref\\n',\n",
" 'DATA 1063 ref/vka_layer_2.ref\\n',\n",
" 'DATA 1064 ref/ss_layer_2.ref\\n',\n",
" 'DATA 1065 ref/hk_3.ref\\n',\n",
" 'DATA 1066 ref/vka_layer_3.ref\\n',\n",
" 'DATA 1067 ref/ss_layer_3.ref\\n',\n",
" 'DATA 1068 ref/hk_4.ref\\n',\n",
" 'DATA 1069 ref/vka_layer_4.ref\\n',\n",
" 'DATA 1070 ref/ss_layer_4.ref\\n',\n",
" 'DATA 1071 ref/hk_5.ref\\n',\n",
" 'DATA 1072 ref/vka_layer_5.ref\\n',\n",
" 'DATA 1073 ref/ss_layer_5.ref\\n',\n",
" 'DATA 1074 ref/hk_6.ref\\n',\n",
" 'DATA 1075 ref/vka_layer_6.ref\\n',\n",
" 'DATA 1076 ref/ss_layer_6.ref\\n',\n",
" 'DATA 1077 ref/hk_7.ref\\n',\n",
" 'DATA 1078 ref/vka_layer_7.ref\\n',\n",
" 'DATA 1079 ref/ss_layer_7.ref\\n',\n",
" 'DATA 1080 ref/hk_8.ref\\n',\n",
" 'DATA 1081 ref/vka_layer_8.ref\\n',\n",
" 'DATA 1082 ref/ss_layer_8.ref\\n',\n",
" 'DATA 1083 ref/hk_9.ref\\n',\n",
" 'DATA 1084 ref/vka_layer_9.ref\\n',\n",
" 'DATA 1085 ref/ss_layer_9.ref\\n',\n",
" 'DATA 1086 ref/hk_10.ref\\n',\n",
" 'DATA 1087 ref/vka_layer_10.ref\\n',\n",
" 'DATA 1088 ref/ss_layer_10.ref\\n',\n",
" 'DATA 1089 ref/rech_0.ref\\n',\n",
" 'DATA 1090 ref/important_recharge.ref\\n']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"open(os.path.join(ml.model_ws,ml.name+\".nam\"),'r').readlines()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['# Discretization file for MODFLOW, generated by Flopy.\\n',\n",
" ' 10 20 5 2 4 2\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 1046 1 (5E15.6) -1 #delr\\n',\n",
" ' 1047 1 (20E15.6) -1 #delc\\n',\n",
" ' 1048 1 (5E15.6) -1 #model_top\\n',\n",
" ' -1049 1 (BINARY) -1 #botm layer 1\\n',\n",
" ' 1050 1 (5E15.6) -1 #botm layer 2\\n',\n",
" ' 1051 1 (5E15.6) -1 #botm layer 3\\n',\n",
" ' 1052 1 (5E15.6) -1 #botm layer 4\\n',\n",
" ' 1053 1 (5E15.6) -1 #botm layer 5\\n',\n",
" ' 1054 1 (5E15.6) -1 #botm layer 6\\n',\n",
" ' 1055 1 (5E15.6) -1 #botm layer 7\\n',\n",
" ' 1056 1 (5E15.6) -1 #botm layer 8\\n',\n",
" ' 1057 1 (5E15.6) -1 #botm layer 9\\n',\n",
" ' 1058 1 (5E15.6) -1 #botm layer 10\\n',\n",
" ' 1.000000 1 1.000000 TR \\n',\n",
" ' 1.000000 1 1.000000 TR \\n']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"open(os.path.join(ml.model_ws,ml.name+\".dis\"),'r').readlines()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The ```.how``` attribute\n",
"```Util2d``` includes a ```.how``` attribute that gives finer grained control of how arrays will written"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'external'"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ml.lpf.hk[0].how"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will raise an error since our model does not support free format..."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Util2d:delr: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/delr.ref\n",
"Util2d:delc: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/delc.ref\n",
"Util2d:model_top: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/model_top.ref\n",
"Util2d:botm layer 1: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_1.ref\n",
"Util2d:botm layer 2: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_2.ref\n",
"Util2d:botm layer 3: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_3.ref\n",
"Util2d:botm layer 4: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_4.ref\n",
"Util2d:botm layer 5: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_5.ref\n",
"Util2d:botm layer 6: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_6.ref\n",
"Util2d:botm layer 7: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_7.ref\n",
"Util2d:botm layer 8: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_8.ref\n",
"Util2d:botm layer 9: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_9.ref\n",
"Util2d:botm layer 10: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_10.ref\n"
]
},
{
"ename": "AssertionError",
"evalue": "Util2d error: 'how' is openclose,but model doesn't support free fmt",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-23-6654880ec161>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlpf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"openclose\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlpf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/Users/jdhughes/Documents/Development/flopy_git/flopy/mbase.py\u001b[0m in \u001b[0;36mwrite_input\u001b[0;34m(self, SelPackList, check)\u001b[0m\n\u001b[1;32m 570\u001b[0m \u001b[0;31m# or default for package level check would have to be False\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 571\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheck\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 573\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/jdhughes/Documents/Development/flopy_git/flopy/modflow/mflpf.py\u001b[0m in \u001b[0;36mwrite_file\u001b[0;34m(self, check)\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0mtransient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_package\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'DIS'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteady\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnlay\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhk\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_file_entry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 257\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchani\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 258\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhani\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_file_entry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Users/jdhughes/Documents/Development/flopy_git/flopy/utils/util_array.py\u001b[0m in \u001b[0;36mget_file_entry\u001b[0;34m(self, how)\u001b[0m\n\u001b[1;32m 2061\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhow\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"openclose\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2062\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray_free_format\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Util2d error: 'how' is openclose,\"\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2063\u001b[0;31m \u001b[0;34m\"but model doesn't support free fmt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2064\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2065\u001b[0m \u001b[0;31m# write a file if needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: Util2d error: 'how' is openclose,but model doesn't support free fmt"
]
}
],
"source": [
"ml.lpf.hk[0].how = \"openclose\"\n",
"ml.lpf.hk[0].how\n",
"ml.write_input()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So let's reset hk layer 1 back to external..."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'external'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ml.lpf.hk[0].how = \"external\"\n",
"ml.lpf.hk[0].how"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ml.dis.top.how = \"external\""
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Util2d:delr: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/delr.ref\n",
"Util2d:delc: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/delc.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/model_top.ref\n",
"Util2d:botm layer 1: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_1.ref\n",
"Util2d:botm layer 2: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_2.ref\n",
"Util2d:botm layer 3: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_3.ref\n",
"Util2d:botm layer 4: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_4.ref\n",
"Util2d:botm layer 5: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_5.ref\n",
"Util2d:botm layer 6: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_6.ref\n",
"Util2d:botm layer 7: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_7.ref\n",
"Util2d:botm layer 8: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_8.ref\n",
"Util2d:botm layer 9: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_9.ref\n",
"Util2d:botm layer 10: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/botm_layer_10.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_1.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_1.ref\n",
"Util2d:ss layer 1: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_1.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_2.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_2.ref\n",
"Util2d:ss layer 2: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_2.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_3.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_3.ref\n",
"Util2d:ss layer 3: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_3.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_4.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_4.ref\n",
"Util2d:ss layer 4: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_4.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_5.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_5.ref\n",
"Util2d:ss layer 5: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_5.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_6.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_6.ref\n",
"Util2d:ss layer 6: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_6.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_7.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_7.ref\n",
"Util2d:ss layer 7: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_7.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_8.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_8.ref\n",
"Util2d:ss layer 8: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_8.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_9.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_9.ref\n",
"Util2d:ss layer 9: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_9.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/hk_10.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/vka_layer_10.ref\n",
"Util2d:ss layer 10: resetting 'how' to external\n",
"BaseModel.add_external() warning: replacing existing filename ref/ss_layer_10.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/rech_0.ref\n",
"BaseModel.add_external() warning: replacing existing filename ref/important_recharge.ref\n"
]
}
],
"source": [
"ml.write_input()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['# Discretization file for MODFLOW, generated by Flopy.\\n',\n",
" ' 10 20 5 2 4 2\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 1104 1 (5E15.6) -1 #delr\\n',\n",
" ' 1105 1 (20E15.6) -1 #delc\\n',\n",
" ' 1106 1 (5E15.6) -1 #model_top\\n',\n",
" ' -1107 1 (BINARY) -1 #botm layer 1\\n',\n",
" ' 1108 1 (5E15.6) -1 #botm layer 2\\n',\n",
" ' 1109 1 (5E15.6) -1 #botm layer 3\\n',\n",
" ' 1110 1 (5E15.6) -1 #botm layer 4\\n',\n",
" ' 1111 1 (5E15.6) -1 #botm layer 5\\n',\n",
" ' 1112 1 (5E15.6) -1 #botm layer 6\\n',\n",
" ' 1113 1 (5E15.6) -1 #botm layer 7\\n',\n",
" ' 1114 1 (5E15.6) -1 #botm layer 8\\n',\n",
" ' 1115 1 (5E15.6) -1 #botm layer 9\\n',\n",
" ' 1116 1 (5E15.6) -1 #botm layer 10\\n',\n",
" ' 1.000000 1 1.000000 TR \\n',\n",
" ' 1.000000 1 1.000000 TR \\n']"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"open(os.path.join(ml.model_ws,ml.name+\".dis\"),'r').readlines()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['# LPF for MODFLOW, generated by Flopy.\\n',\n",
" ' 53 -1E+30 0 \\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00 1.000000E+00\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 0 0 0 0 0 0 0 0 0 0\\n',\n",
" ' 1117 1 (5E15.6) -1 #hk layer 1\\n',\n",
" ' 1118 1 (5E15.6) -1 #vka layer 1\\n',\n",
" ' 1119 1 (5E15.6) -1 #ss layer 1\\n',\n",
" ' 1120 1 (5E15.6) -1 #hk layer 2\\n',\n",
" ' 1121 1 (5E15.6) -1 #vka layer 2\\n',\n",
" ' 1122 1 (5E15.6) -1 #ss layer 2\\n',\n",
" ' 1123 1 (5E15.6) -1 #hk layer 3\\n',\n",
" ' 1124 1 (5E15.6) -1 #vka layer 3\\n',\n",
" ' 1125 1 (5E15.6) -1 #ss layer 3\\n',\n",
" ' 1126 1 (5E15.6) -1 #hk layer 4\\n',\n",
" ' 1127 1 (5E15.6) -1 #vka layer 4\\n',\n",
" ' 1128 1 (5E15.6) -1 #ss layer 4\\n',\n",
" ' 1129 1 (5E15.6) -1 #hk layer 5\\n',\n",
" ' 1130 1 (5E15.6) -1 #vka layer 5\\n',\n",
" ' 1131 1 (5E15.6) -1 #ss layer 5\\n',\n",
" ' 1132 1 (5E15.6) -1 #hk layer 6\\n',\n",
" ' 1133 1 (5E15.6) -1 #vka layer 6\\n',\n",
" ' 1134 1 (5E15.6) -1 #ss layer 6\\n',\n",
" ' 1135 1 (5E15.6) -1 #hk layer 7\\n',\n",
" ' 1136 1 (5E15.6) -1 #vka layer 7\\n',\n",
" ' 1137 1 (5E15.6) -1 #ss layer 7\\n',\n",
" ' 1138 1 (5E15.6) -1 #hk layer 8\\n',\n",
" ' 1139 1 (5E15.6) -1 #vka layer 8\\n',\n",
" ' 1140 1 (5E15.6) -1 #ss layer 8\\n',\n",
" ' 1141 1 (5E15.6) -1 #hk layer 9\\n',\n",
" ' 1142 1 (5E15.6) -1 #vka layer 9\\n',\n",
" ' 1143 1 (5E15.6) -1 #ss layer 9\\n',\n",
" ' 1144 1 (5E15.6) -1 #hk layer 10\\n',\n",
" ' 1145 1 (5E15.6) -1 #vka layer 10\\n',\n",
" ' 1146 1 (5E15.6) -1 #ss layer 10\\n']"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"open(os.path.join(ml.model_ws,ml.name+\".lpf\"),'r').readlines()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['# Name file for MODFLOW-2005, generated by Flopy.\\n',\n",
" '#xul:0; yul:20; rotation:0; proj4_str:+init=EPSG:4326; units:meters; lenuni:2 ;start_datetime:1-1-1970\\n',\n",
" 'LIST 2 modflowtest.list\\n',\n",
" 'DIS 11 modflowtest.dis \\n',\n",
" 'LPF 15 modflowtest.lpf \\n',\n",
" 'RCH 19 modflowtest.rch \\n',\n",
" 'DATA 1104 ref/delr.ref\\n',\n",
" 'DATA 1105 ref/delc.ref\\n',\n",
" 'DATA 1106 ref/model_top.ref\\n',\n",
" 'DATA(BINARY) 1107 ref/botm_layer_1.ref REPLACE\\n',\n",
" 'DATA 1108 ref/botm_layer_2.ref\\n',\n",
" 'DATA 1109 ref/botm_layer_3.ref\\n',\n",
" 'DATA 1110 ref/botm_layer_4.ref\\n',\n",
" 'DATA 1111 ref/botm_layer_5.ref\\n',\n",
" 'DATA 1112 ref/botm_layer_6.ref\\n',\n",
" 'DATA 1113 ref/botm_layer_7.ref\\n',\n",
" 'DATA 1114 ref/botm_layer_8.ref\\n',\n",
" 'DATA 1115 ref/botm_layer_9.ref\\n',\n",
" 'DATA 1116 ref/botm_layer_10.ref\\n',\n",
" 'DATA 1117 ref/hk_1.ref\\n',\n",
" 'DATA 1118 ref/vka_layer_1.ref\\n',\n",
" 'DATA 1119 ref/ss_layer_1.ref\\n',\n",
" 'DATA 1120 ref/hk_2.ref\\n',\n",
" 'DATA 1121 ref/vka_layer_2.ref\\n',\n",
" 'DATA 1122 ref/ss_layer_2.ref\\n',\n",
" 'DATA 1123 ref/hk_3.ref\\n',\n",
" 'DATA 1124 ref/vka_layer_3.ref\\n',\n",
" 'DATA 1125 ref/ss_layer_3.ref\\n',\n",
" 'DATA 1126 ref/hk_4.ref\\n',\n",
" 'DATA 1127 ref/vka_layer_4.ref\\n',\n",
" 'DATA 1128 ref/ss_layer_4.ref\\n',\n",
" 'DATA 1129 ref/hk_5.ref\\n',\n",
" 'DATA 1130 ref/vka_layer_5.ref\\n',\n",
" 'DATA 1131 ref/ss_layer_5.ref\\n',\n",
" 'DATA 1132 ref/hk_6.ref\\n',\n",
" 'DATA 1133 ref/vka_layer_6.ref\\n',\n",
" 'DATA 1134 ref/ss_layer_6.ref\\n',\n",
" 'DATA 1135 ref/hk_7.ref\\n',\n",
" 'DATA 1136 ref/vka_layer_7.ref\\n',\n",
" 'DATA 1137 ref/ss_layer_7.ref\\n',\n",
" 'DATA 1138 ref/hk_8.ref\\n',\n",
" 'DATA 1139 ref/vka_layer_8.ref\\n',\n",
" 'DATA 1140 ref/ss_layer_8.ref\\n',\n",
" 'DATA 1141 ref/hk_9.ref\\n',\n",
" 'DATA 1142 ref/vka_layer_9.ref\\n',\n",
" 'DATA 1143 ref/ss_layer_9.ref\\n',\n",
" 'DATA 1144 ref/hk_10.ref\\n',\n",
" 'DATA 1145 ref/vka_layer_10.ref\\n',\n",
" 'DATA 1146 ref/ss_layer_10.ref\\n',\n",
" 'DATA 1147 ref/rech_0.ref\\n',\n",
" 'DATA 1148 ref/important_recharge.ref\\n']"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"open(os.path.join(ml.model_ws,ml.name+\".nam\"),'r').readlines()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
gray-stanton/tensorflow-char-rnn | test.ipynb | 1 | 42832 | {
"cells": [
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import model\n",
"import tf_parser\n",
"import importlib\n",
"import tensorflow as tf\n",
"import random\n",
"import numpy as np\n",
"import pickle\n",
"import char_rnn"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<module 'char_rnn' from '/home/gray/scripts/tensorflow-char-rnn/char_rnn.py'>"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"importlib.reload(model)\n",
"importlib.reload(char_rnn)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.softmax_sample(np.array([0.3, 0.5, 0.3]), temperature = 1)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_dir = '/home/gray/scripts/test/'\n",
"model_name = 'grays_model'"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_params = {\n",
" 'seq-length' : 50,\n",
" 'char-map-size' : 101,\n",
" 'label-length' : 50,\n",
" 'hidden-layer-sizes' : [100, 100],\n",
" 'hidden-layer-details' : [{'activation' : tf.nn.relu}, {'activation' : tf.nn.relu}],\n",
" 'minimizer-options' : {'learning-rate' : 0.1}\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "New model directory not empty: /home/gray/scripts/test/",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-f58e63a6bc02>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/home/gray/scripts/tensorflow-char-rnn/model.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(model_dir, params, model_name)\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'New model directory not empty: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'my_model'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: New model directory not empty: /home/gray/scripts/test/"
]
}
],
"source": [
"model.create(test_dir, test_params, model_name = model_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def character_generator ():\n",
" i = 1 \n",
" for _ in range(0, 1000):\n",
" x = [[ (i + t )% 101 for t in range(0,51)], [(i + t + 45) % 101 for t in range(0, 51)]]\n",
" i += i + random.randint(1, 5)\n",
" \n",
" yield((np.array([s[0:50]for s in x], dtype = np.uint8), np.array([s[1:51]for s in x], dtype = np.uint8)))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for k, _ in character_generator():\n",
" print(k)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fake_char_map2 = {'a' : 1, 'b' : 2} \n",
"def new_character_generator():\n",
" for _ in range(0, 2000):\n",
" def gen_miniseq():\n",
" seq = [random.choice([1, 2]) for _ in range(0, 4) ]\n",
" for i in range(4, 51):\n",
" if seq[-1] == 1 and seq[-4] == 1:\n",
" newchar = np.random.choice([1, 2 ], p = [0.9, 0.1])\n",
" if seq[-1] == 1 and seq[-4] == 2:\n",
" newchar = np.random.choice([1, 2], p = [0.6, 0.4])\n",
" if seq[-1] == 2 and seq[-4] == 2:\n",
" newchar = np.random.choice([1, 2], p = [0.2, 0.8])\n",
" if seq[-1] == 2 and seq[-4] == 1 :\n",
" newchar = np.random.choice([1, 2], p = [0.3, 0.7])\n",
" seq.append(newchar)\n",
" return seq\n",
" seqs = [gen_miniseq() for _ in range(0, 26)]\n",
" labels = [s[1:51] for s in seqs]\n",
" chars = [s[0:50] for s in seqs]\n",
" yield (np.array(chars, dtype = np.uint8), np.array(labels, dtype = np.uint8))\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for c, l in new_character_generator():\n",
" print(c[0, :])\n",
" print(l[0, :])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.train(test_dir, {'report-freq' : 100, 'state-init' : 'ZERO', 'shapes' : [100, 100]}, character_generator(), model_name )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fake_char_map = {str(i) : i for i in range(0, 101)}\n",
"fake_char_map[' '] = 0\n",
"fake_inv_map = {i : str(i) for i in range(0, 101)}\n",
"fake_config = {'seq-length' : 50, 'char-map' : fake_char_map, 'inv-char-map' : fake_inv_map, 'state-init' : 'ZERO',\n",
" 'temperature' : 1, 'shapes' : [100, 100]}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.generate(test_dir, seed_text = '56789', gen_length=200, generate_config = fake_config, model_name=model_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"new_test_dir = '/home/gray/scripts/test2/'\n",
"new_test_params = {\n",
" 'seq-length' : 50,\n",
" 'char-map-size' : 3,\n",
" 'label-length' : 50,\n",
" 'hidden-layer-sizes' : [50, 50],\n",
" 'hidden-layer-details' : [{'activation' : tf.nn.relu}, {'activation' : tf.nn.relu}],\n",
" 'minimizer-options' : {'learning-rate' : 0.1}\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.create(new_test_dir, new_test_params, model_name = 'abab_model')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.train(\n",
" new_test_dir,\n",
" {'report-freq' : 5, 'state-init' : 'ZERO', 'shapes' : [50, 50]},\n",
" new_character_generator(), 'abab_model')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fake_inv_map2 = {1 : 'a', 2 : 'b', 0 : ' '}\n",
"fake_char_map2 = {'a' : 1, 'b' : 2, ' ' : 0} \n",
"fake_config2 = {'seq-length' : 50, 'char-map' : fake_char_map2, 'inv-char-map' : fake_inv_map2, 'state-init' : 'ZERO',\n",
" 'temperature' : 0.1, 'shapes' : [50, 50]}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from /home/gray/scripts/test2/abab_model-2000\n",
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"bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbaa\n",
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"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\n",
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]
}
],
"source": [
"model.generate(\n",
" new_test_dir, \n",
" seed_text = 'bababababababab',\n",
" gen_length=200, \n",
" generate_config=fake_config2,\n",
" model_name='abab_model')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(\"/home/gray/scripts/shakespeare/shakespeare.char_map\", 'rb') as char_f:\n",
" char_map = pickle.load(char_f)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(' ', 0), ('!', 1), ('$', 2), ('&', 3), (\"'\", 4), (',', 5), ('-', 6), ('.', 7), ('3', 8), (':', 9), (';', 10), ('?', 11), ('A', 12), ('B', 13), ('C', 14), ('D', 15), ('E', 16), ('F', 17), ('G', 18), ('H', 19), ('I', 20), ('J', 21), ('K', 22), ('L', 23), ('M', 24), ('N', 25), ('O', 26), ('P', 27), ('Q', 28), ('R', 29), ('S', 30), ('T', 31), ('U', 32), ('V', 33), ('W', 34), ('X', 35), ('Y', 36), ('Z', 37), ('a', 38), ('b', 39), ('c', 40), ('d', 41), ('e', 42), ('f', 43), ('g', 44), ('h', 45), ('i', 46), ('j', 47), ('k', 48), ('l', 49), ('m', 50), ('n', 51), ('o', 52), ('p', 53), ('q', 54), ('r', 55), ('s', 56), ('t', 57), ('u', 58), ('v', 59), ('w', 60), ('x', 61), ('y', 62), ('z', 63)]\n"
]
}
],
"source": [
"print([(chr(c), v) for c, v in char_map.items()])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"shk = np.load('/home/gray/scripts/shakespeare2/shakespeare2.npy')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([17, 46, 55, 56, 57, 0, 14, 46, 57, 46, 63, 42, 51, 9, 13, 42, 43,\n",
" 52, 55, 42, 0, 60, 42, 0, 53, 55, 52, 40, 42, 42, 41, 0, 38, 51,\n",
" 62, 0, 43, 58, 55, 57, 45, 42, 55, 5, 0, 45, 42, 38, 55, 0, 50,\n",
" 42, 0, 56, 53, 42, 38, 48, 7, 12, 49, 49, 9, 30, 53, 42, 38, 48,\n",
" 5, 0, 56, 53, 42, 38, 48, 7, 17, 46, 55, 56, 57, 0, 14, 46, 57,\n",
" 46, 63, 42, 51, 9, 36, 52, 58, 0, 38, 55, 42, 0, 38, 49], dtype=uint8)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shk[0:100]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First Citizen:\r\n",
"Before we proceed any further, hear me speak.\r\n",
"\r\n",
"All:\r\n",
"Speak, speak.\r\n",
"\r\n",
"First Citizen:\r\n",
"You are all resolved rather to die than to famish?\r\n",
"\r\n",
"All:\r\n",
"cat: write error: Broken pipe\r\n"
]
}
],
"source": [
"! cat /home/gray/scripts/shakespeare2/tinyshakespeare.txt | head"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"it = char_rnn._make_batch_iterator(shk, 50, 32, 2000, 1)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "IndexError",
"evalue": "index 1075394 is out of bounds for axis 1 with size 1075394",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-72-2e6b650a0aa3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/gray/scripts/tensorflow-char-rnn/char_rnn.py\u001b[0m in \u001b[0;36m_make_batch_iterator\u001b[0;34m(arr, length, batch_size, niter, nepoch)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mniter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 59\u001b[0m \u001b[0mstart_points\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstart_points\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/home/gray/scripts/tensorflow-char-rnn/char_rnn.py\u001b[0m in \u001b[0;36mget_batch\u001b[0;34m(start_points)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mindexes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_points\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 54\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexError\u001b[0m: index 1075394 is out of bounds for axis 1 with size 1075394"
]
}
],
"source": [
"t = 0\n",
"for i in it:\n",
" t += 1"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"671"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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| mit |
ayush29feb/cs231n | assignment2/Dropout.ipynb | 1 | 84252 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dropout\n",
"Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to optionally use dropout.\n",
"\n",
"[1] Geoffrey E. Hinton et al, \"Improving neural networks by preventing co-adaptation of feature detectors\", arXiv 2012"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ayush/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
]
}
],
"source": [
"# As usual, a bit of setup\n",
"\n",
"import time\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from cs231n.classifiers.fc_net import *\n",
"from cs231n.data_utils import get_CIFAR10_data\n",
"from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
"from cs231n.solver import Solver\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"# for auto-reloading external modules\n",
"# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"def rel_error(x, y):\n",
" \"\"\" returns relative error \"\"\"\n",
" return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_val: (1000, 3, 32, 32)\n",
"X_train: (49000, 3, 32, 32)\n",
"X_test: (1000, 3, 32, 32)\n",
"y_val: (1000,)\n",
"y_train: (49000,)\n",
"y_test: (1000,)\n"
]
}
],
"source": [
"# Load the (preprocessed) CIFAR10 data.\n",
"\n",
"data = get_CIFAR10_data()\n",
"for k, v in data.iteritems():\n",
" print '%s: ' % k, v.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dropout forward pass\n",
"In the file `cs231n/layers.py`, implement the forward pass for dropout. Since dropout behaves differently during training and testing, make sure to implement the operation for both modes.\n",
"\n",
"Once you have done so, run the cell below to test your implementation."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running tests with p = 0.3\n",
"Mean of input: 9.99945823678\n",
"Mean of train-time output: 10.008618983\n",
"Mean of test-time output: 9.99945823678\n",
"Fraction of train-time output set to zero: 0.699732\n",
"Fraction of test-time output set to zero: 0.0\n",
"\n",
"Running tests with p = 0.6\n",
"Mean of input: 9.99945823678\n",
"Mean of train-time output: 10.0209183784\n",
"Mean of test-time output: 9.99945823678\n",
"Fraction of train-time output set to zero: 0.399012\n",
"Fraction of test-time output set to zero: 0.0\n",
"\n",
"Running tests with p = 0.75\n",
"Mean of input: 9.99945823678\n",
"Mean of train-time output: 9.99503726269\n",
"Mean of test-time output: 9.99945823678\n",
"Fraction of train-time output set to zero: 0.2503\n",
"Fraction of test-time output set to zero: 0.0\n",
"\n"
]
}
],
"source": [
"x = np.random.randn(500, 500) + 10\n",
"\n",
"for p in [0.3, 0.6, 0.75]:\n",
" out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n",
" out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n",
"\n",
" print 'Running tests with p = ', p\n",
" print 'Mean of input: ', x.mean()\n",
" print 'Mean of train-time output: ', out.mean()\n",
" print 'Mean of test-time output: ', out_test.mean()\n",
" print 'Fraction of train-time output set to zero: ', (out == 0).mean()\n",
" print 'Fraction of test-time output set to zero: ', (out_test == 0).mean()\n",
" print"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dropout backward pass\n",
"In the file `cs231n/layers.py`, implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dx relative error: 5.44561340248e-11\n"
]
}
],
"source": [
"x = np.random.randn(10, 10) + 10\n",
"dout = np.random.randn(*x.shape)\n",
"\n",
"dropout_param = {'mode': 'train', 'p': 0.8, 'seed': 123}\n",
"out, cache = dropout_forward(x, dropout_param)\n",
"dx = dropout_backward(dout, cache)\n",
"dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)\n",
"\n",
"print 'dx relative error: ', rel_error(dx, dx_num)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fully-connected nets with Dropout\n",
"In the file `cs231n/classifiers/fc_net.py`, modify your implementation to use dropout. Specificially, if the constructor the the net receives a nonzero value for the `dropout` parameter, then the net should add dropout immediately after every ReLU nonlinearity. After doing so, run the following to numerically gradient-check your implementation."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running check with dropout = 0\n",
"Initial loss: 2.30304316117\n",
"W1 relative error: 4.80e-07\n",
"W2 relative error: 1.97e-07\n",
"W3 relative error: 1.56e-07\n",
"b1 relative error: 2.03e-08\n",
"b2 relative error: 1.69e-09\n",
"b3 relative error: 1.11e-10\n",
"\n",
"Running check with dropout = 0.25\n",
"Initial loss: 2.30235424783\n",
"W1 relative error: 1.00e-07\n",
"W2 relative error: 2.26e-09\n",
"W3 relative error: 2.56e-05\n",
"b1 relative error: 9.37e-10\n",
"b2 relative error: 2.13e-01\n",
"b3 relative error: 1.25e-10\n",
"\n",
"Running check with dropout = 0.5\n",
"Initial loss: 2.30424261716\n",
"W1 relative error: 1.21e-07\n",
"W2 relative error: 2.45e-08\n",
"W3 relative error: 8.06e-07\n",
"b1 relative error: 2.28e-08\n",
"b2 relative error: 6.84e-10\n",
"b3 relative error: 1.28e-10\n",
"\n"
]
}
],
"source": [
"N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
"X = np.random.randn(N, D)\n",
"y = np.random.randint(C, size=(N,))\n",
"\n",
"for dropout in [0, 0.25, 0.5]:\n",
" print 'Running check with dropout = ', dropout\n",
" model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
" weight_scale=5e-2, dtype=np.float64,\n",
" dropout=dropout, seed=123)\n",
"\n",
" loss, grads = model.loss(X, y)\n",
" print 'Initial loss: ', loss\n",
"\n",
" for name in sorted(grads):\n",
" f = lambda _: model.loss(X, y)[0]\n",
" grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
" print '%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))\n",
" print"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regularization experiment\n",
"As an experiment, we will train a pair of two-layer networks on 500 training examples: one will use no dropout, and one will use a dropout probability of 0.75. We will then visualize the training and validation accuracies of the two networks over time."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"(Iteration 1 / 125) loss: 8.596245\n",
"(Epoch 0 / 25) train acc: 0.252000; val_acc: 0.177000\n",
"(Epoch 1 / 25) train acc: 0.284000; val_acc: 0.184000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"cs231n/layers.py:595: RuntimeWarning: divide by zero encountered in log\n",
" loss = -np.sum(np.log(probs[np.arange(N), y])) / N\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(Epoch 2 / 25) train acc: 0.348000; val_acc: 0.215000\n",
"(Epoch 3 / 25) train acc: 0.424000; val_acc: 0.216000\n",
"(Epoch 4 / 25) train acc: 0.460000; val_acc: 0.242000\n",
"(Epoch 5 / 25) train acc: 0.534000; val_acc: 0.240000\n",
"(Epoch 6 / 25) train acc: 0.614000; val_acc: 0.259000\n",
"(Epoch 7 / 25) train acc: 0.698000; val_acc: 0.270000\n",
"(Epoch 8 / 25) train acc: 0.730000; val_acc: 0.293000\n",
"(Epoch 9 / 25) train acc: 0.774000; val_acc: 0.269000\n",
"(Epoch 10 / 25) train acc: 0.846000; val_acc: 0.258000\n",
"(Epoch 11 / 25) train acc: 0.872000; val_acc: 0.289000\n",
"(Epoch 12 / 25) train acc: 0.874000; val_acc: 0.275000\n",
"(Epoch 13 / 25) train acc: 0.904000; val_acc: 0.283000\n",
"(Epoch 14 / 25) train acc: 0.918000; val_acc: 0.269000\n",
"(Epoch 15 / 25) train acc: 0.920000; val_acc: 0.280000\n",
"(Epoch 16 / 25) train acc: 0.958000; val_acc: 0.292000\n",
"(Epoch 17 / 25) train acc: 0.972000; val_acc: 0.287000\n",
"(Epoch 18 / 25) train acc: 0.972000; val_acc: 0.303000\n",
"(Epoch 19 / 25) train acc: 0.970000; val_acc: 0.300000\n",
"(Epoch 20 / 25) train acc: 0.976000; val_acc: 0.310000\n",
"(Iteration 101 / 125) loss: 0.666234\n",
"(Epoch 21 / 25) train acc: 0.972000; val_acc: 0.313000\n",
"(Epoch 22 / 25) train acc: 0.978000; val_acc: 0.320000\n",
"(Epoch 23 / 25) train acc: 0.980000; val_acc: 0.316000\n",
"(Epoch 24 / 25) train acc: 0.988000; val_acc: 0.302000\n",
"(Epoch 25 / 25) train acc: 0.986000; val_acc: 0.302000\n",
"0.75\n",
"(Iteration 1 / 125) loss: 10.053350\n",
"(Epoch 0 / 25) train acc: 0.266000; val_acc: 0.229000\n",
"(Epoch 1 / 25) train acc: 0.322000; val_acc: 0.222000\n",
"(Epoch 2 / 25) train acc: 0.376000; val_acc: 0.236000\n",
"(Epoch 3 / 25) train acc: 0.408000; val_acc: 0.208000\n",
"(Epoch 4 / 25) train acc: 0.484000; val_acc: 0.268000\n",
"(Epoch 5 / 25) train acc: 0.572000; val_acc: 0.289000\n",
"(Epoch 6 / 25) train acc: 0.636000; val_acc: 0.256000\n",
"(Epoch 7 / 25) train acc: 0.690000; val_acc: 0.274000\n",
"(Epoch 8 / 25) train acc: 0.710000; val_acc: 0.291000\n",
"(Epoch 9 / 25) train acc: 0.728000; val_acc: 0.250000\n",
"(Epoch 10 / 25) train acc: 0.830000; val_acc: 0.289000\n",
"(Epoch 11 / 25) train acc: 0.840000; val_acc: 0.286000\n",
"(Epoch 12 / 25) train acc: 0.860000; val_acc: 0.296000\n",
"(Epoch 13 / 25) train acc: 0.858000; val_acc: 0.256000\n",
"(Epoch 14 / 25) train acc: 0.898000; val_acc: 0.293000\n",
"(Epoch 15 / 25) train acc: 0.900000; val_acc: 0.302000\n",
"(Epoch 16 / 25) train acc: 0.910000; val_acc: 0.290000\n",
"(Epoch 17 / 25) train acc: 0.860000; val_acc: 0.260000\n",
"(Epoch 18 / 25) train acc: 0.910000; val_acc: 0.304000\n",
"(Epoch 19 / 25) train acc: 0.962000; val_acc: 0.302000\n",
"(Epoch 20 / 25) train acc: 0.950000; val_acc: 0.299000\n",
"(Iteration 101 / 125) loss: 4.315906\n",
"(Epoch 21 / 25) train acc: 0.982000; val_acc: 0.321000\n",
"(Epoch 22 / 25) train acc: 0.960000; val_acc: 0.313000\n",
"(Epoch 23 / 25) train acc: 0.970000; val_acc: 0.284000\n",
"(Epoch 24 / 25) train acc: 0.978000; val_acc: 0.308000\n",
"(Epoch 25 / 25) train acc: 0.976000; val_acc: 0.326000\n"
]
}
],
"source": [
"# Train two identical nets, one with dropout and one without\n",
"\n",
"num_train = 500\n",
"small_data = {\n",
" 'X_train': data['X_train'][:num_train],\n",
" 'y_train': data['y_train'][:num_train],\n",
" 'X_val': data['X_val'],\n",
" 'y_val': data['y_val'],\n",
"}\n",
"\n",
"solvers = {}\n",
"dropout_choices = [0, 0.75]\n",
"for dropout in dropout_choices:\n",
" model = FullyConnectedNet([500], dropout=dropout)\n",
" print dropout\n",
"\n",
" solver = Solver(model, small_data,\n",
" num_epochs=25, batch_size=100,\n",
" update_rule='adam',\n",
" optim_config={\n",
" 'learning_rate': 5e-4,\n",
" },\n",
" verbose=True, print_every=100)\n",
" solver.train()\n",
" solvers[dropout] = solver"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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CcWOR0bHRlta3GrTj60KSJKmRDOAkSdJJGRi4gFxu97yRo2FcLnc7g4Mva35R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\ndLVt21aLFy/WNddco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"text/plain": [
"<matplotlib.figure.Figure at 0x10f7b1390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot train and validation accuracies of the two models\n",
"\n",
"train_accs = []\n",
"val_accs = []\n",
"for dropout in dropout_choices:\n",
" solver = solvers[dropout]\n",
" train_accs.append(solver.train_acc_history[-1])\n",
" val_accs.append(solver.val_acc_history[-1])\n",
"\n",
"plt.subplot(3, 1, 1)\n",
"for dropout in dropout_choices:\n",
" plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\n",
"plt.title('Train accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Accuracy')\n",
"plt.legend(ncol=2, loc='lower right')\n",
" \n",
"plt.subplot(3, 1, 2)\n",
"for dropout in dropout_choices:\n",
" plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\n",
"plt.title('Val accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Accuracy')\n",
"plt.legend(ncol=2, loc='lower right')\n",
"\n",
"plt.gcf().set_size_inches(15, 15)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Question\n",
"Explain what you see in this experiment. What does it suggest about dropout?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Answer\n"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
deepmind/deepmind-research | perceiver/colabs/video_autoencoding.ipynb | 1 | 38129 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6hVqbkgBFVKB"
},
"outputs": [],
"source": [
"# Copyright 2021 DeepMind Technologies Limited\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "sm0mo9ciwhKu"
},
"outputs": [],
"source": [
"# Install dependencies for Google Colab.\n",
"# If you want to run this notebook on your own machine, you can skip this cell\n",
"!pip install dm-haiku\n",
"!pip install einops\n",
"\n",
"!mkdir /content/perceiver\n",
"!touch /content/perceiver/__init__.py\n",
"!wget -O /content/perceiver/io_processors.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/io_processors.py\n",
"!wget -O /content/perceiver/perceiver.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/perceiver.py\n",
"!wget -O /content/perceiver/position_encoding.py https://raw.githubusercontent.com/deepmind/deepmind-research/master/perceiver/position_encoding.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "VHzUTH5KqNEt"
},
"outputs": [],
"source": [
"#@title Imports\n",
"\n",
"import base64\n",
"import functools\n",
"import os\n",
"import pickle\n",
"import ssl\n",
"import re\n",
"import tempfile\n",
"\n",
"from urllib import request\n",
"\n",
"import cv2\n",
"import haiku as hk\n",
"import imageio\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"import scipy.io.wavfile\n",
"\n",
"from IPython.display import HTML\n",
"\n",
"from perceiver import perceiver, io_processors\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "Bn1jTwkv3gHf"
},
"outputs": [],
"source": [
"#@title Helper functions for the UCF101 dataset\n",
"\n",
"# Utilities to fetch videos from UCF101 dataset\n",
"UCF_ROOT = 'https://www.crcv.ucf.edu/THUMOS14/UCF101/UCF101/'\n",
"_VIDEO_LIST = None\n",
"_CACHE_DIR = tempfile.mkdtemp()\n",
"# As of July 2020, crcv.ucf.edu doesn't use a certificate accepted by the\n",
"# default Colab environment anymore.\n",
"unverified_context = ssl._create_unverified_context()\n",
"\n",
"def list_ucf_videos():\n",
" \"\"\"Lists videos available in UCF101 dataset.\"\"\"\n",
" global _VIDEO_LIST\n",
" if not _VIDEO_LIST:\n",
" index = request.urlopen(UCF_ROOT, context=unverified_context).read().decode('utf-8')\n",
" videos = re.findall('(v_[\\w_]+\\.avi)', index)\n",
" _VIDEO_LIST = sorted(set(videos))\n",
" return list(_VIDEO_LIST)\n",
"\n",
"def fetch_ucf_video(video):\n",
" \"\"\"Fetchs a video and cache into local filesystem.\"\"\"\n",
" cache_path = os.path.join(_CACHE_DIR, video)\n",
" if not os.path.exists(cache_path):\n",
" urlpath = request.urljoin(UCF_ROOT, video)\n",
" print('Fetching %s =\u003e %s' % (urlpath, cache_path))\n",
" data = request.urlopen(urlpath, context=unverified_context).read()\n",
" open(cache_path, \"wb\").write(data)\n",
" return cache_path\n",
"\n",
"# Utilities to open video files using CV2\n",
"def crop_center_square(frame):\n",
" y, x = frame.shape[0:2]\n",
" min_dim = min(y, x)\n",
" start_x = (x // 2) - (min_dim // 2)\n",
" start_y = (y // 2) - (min_dim // 2)\n",
" return frame[start_y:start_y+min_dim,start_x:start_x+min_dim]\n",
"\n",
"def load_video(path, max_frames=0, resize=(224, 224)):\n",
" cap = cv2.VideoCapture(path)\n",
" frames = []\n",
" try:\n",
" while True:\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
" frame = crop_center_square(frame)\n",
" frame = cv2.resize(frame, resize)\n",
" frame = frame[:, :, [2, 1, 0]]\n",
" frames.append(frame)\n",
" \n",
" if len(frames) == max_frames:\n",
" break\n",
" finally:\n",
" cap.release()\n",
" return np.array(frames) / 255.0\n",
"\n",
"def to_gif(images):\n",
" converted_images = np.clip(images * 255, 0, 255).astype(np.uint8)\n",
" imageio.mimsave('./animation.gif', converted_images, fps=25)\n",
" with open('./animation.gif', 'rb') as f:\n",
" gif_64 = base64.b64encode(f.read()).decode('utf-8')\n",
" return HTML('\u003cimg src=\"data:image/gif;base64,%s\"/\u003e' % gif_64)\n",
"\n",
"def play_audio(data, sample_rate=48000):\n",
" scipy.io.wavfile.write('tmp_audio.wav', sample_rate, data)\n",
"\n",
" with open('./tmp_audio.wav', 'rb') as f:\n",
" audio_64 = base64.b64encode(f.read()).decode('utf-8')\n",
" return HTML('\u003caudio controls src=\"data:audio/wav;base64,%s\"/\u003e' % audio_64)\n",
"\n",
"def table(elements):\n",
" row = ['\u003ctd\u003e%s\u003c/td\u003e' % el.data for el in elements]\n",
" return HTML('\u003ctable\u003e\u003ctr\u003e%s\u003c/tr\u003e\u003c/table\u003e' % ''.join(row))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "QqXUfdsF3iZ6"
},
"outputs": [],
"source": [
"#@title Load video and audio from UCF\n",
"\n",
"video_names = list_ucf_videos()\n",
"video_path = fetch_ucf_video(video_names[0])\n",
"\n",
"# Extract audio using FFMPEG and encode as pcm float wavfile (only format readable by scipy.io.wavfile).\n",
"!yes | ffmpeg -i \"$video_path\" -c copy -f wav -map 0:a pcm_f32le -ar 48000 output.wav\n",
"\n",
"sample_rate, audio = scipy.io.wavfile.read(\"output.wav\")\n",
"if audio.dtype == np.int16:\n",
" audio = audio.astype(np.float32) / 2**15\n",
"elif audio.dtype != np.float32:\n",
" raise ValueError('Unexpected datatype. Model expects sound samples to lie in [-1, 1]')\n",
"\n",
"video = load_video(video_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "6hxYUvnpqD8Y"
},
"outputs": [],
"source": [
"#@title Kinetics 700 Classes\n",
"KINETICS_CLASSES = [\"abseiling\", \"acting in play\", \"adjusting glasses\", \"air drumming\", \n",
"\"alligator wrestling\", \"answering questions\", \"applauding\", \"applying cream\", \n",
"\"archaeological excavation\", \"archery\", \"arguing\", \"arm wrestling\", \n",
"\"arranging flowers\", \"arresting\", \"assembling bicycle\", \"assembling computer\", \n",
"\"attending conference\", \"auctioning\", \"baby waking up\", \"backflip (human)\", \n",
"\"baking cookies\", \"bandaging\", \"barbequing\", \"bartending\", \n",
"\"base jumping\", \"bathing dog\", \"battle rope training\", \"beatboxing\", \n",
"\"bee keeping\", \"being excited\", \"being in zero gravity\", \"belly dancing\", \n",
"\"bench pressing\", \"bending back\", \"bending metal\", \"biking through snow\", \n",
"\"blasting sand\", \"blending fruit\", \"blowdrying hair\", \"blowing bubble gum\", \n",
"\"blowing glass\", \"blowing leaves\", \"blowing nose\", \"blowing out candles\", \n",
"\"bobsledding\", \"bodysurfing\", \"bookbinding\", \"bottling\", \n",
"\"bouncing ball (not juggling)\", \"bouncing on bouncy castle\", \"bouncing on trampoline\", \"bowling\", \n",
"\"braiding hair\", \"breading or breadcrumbing\", \"breakdancing\", \"breaking boards\", \n",
"\"breaking glass\", \"breathing fire\", \"brush painting\", \"brushing floor\", \n",
"\"brushing hair\", \"brushing teeth\", \"building cabinet\", \"building lego\", \n",
"\"building sandcastle\", \"building shed\", \"bulldozing\", \"bungee jumping\", \n",
"\"burping\", \"busking\", \"calculating\", \"calligraphy\", \n",
"\"canoeing or kayaking\", \"capoeira\", \"capsizing\", \"card stacking\", \n",
"\"card throwing\", \"carrying baby\", \"carrying weight\", \"cartwheeling\", \n",
"\"carving ice\", \"carving marble\", \"carving pumpkin\", \"carving wood with a knife\", \n",
"\"casting fishing line\", \"catching fish\", \"catching or throwing baseball\", \"catching or throwing frisbee\", \n",
"\"catching or throwing softball\", \"celebrating\", \"changing gear in car\", \"changing oil\", \n",
"\"changing wheel (not on bike)\", \"chasing\", \"checking tires\", \"checking watch\", \n",
"\"cheerleading\", \"chewing gum\", \"chiseling stone\", \"chiseling wood\", \n",
"\"chopping meat\", \"chopping wood\", \"clam digging\", \"clapping\", \n",
"\"clay pottery making\", \"clean and jerk\", \"cleaning gutters\", \"cleaning pool\", \n",
"\"cleaning shoes\", \"cleaning toilet\", \"cleaning windows\", \"climbing a rope\", \n",
"\"climbing ladder\", \"climbing tree\", \"closing door\", \"coloring in\", \n",
"\"combing hair\", \"contact juggling\", \"contorting\", \"cooking chicken\", \n",
"\"cooking egg\", \"cooking on campfire\", \"cooking sausages (not on barbeque)\", \"cooking scallops\", \n",
"\"cosplaying\", \"coughing\", \"counting money\", \"country line dancing\", \n",
"\"cracking back\", \"cracking knuckles\", \"cracking neck\", \"crawling baby\", \n",
"\"crocheting\", \"crossing eyes\", \"crossing river\", \"crying\", \n",
"\"cumbia\", \"curling (sport)\", \"curling eyelashes\", \"curling hair\", \n",
"\"cutting apple\", \"cutting cake\", \"cutting nails\", \"cutting orange\", \n",
"\"cutting pineapple\", \"cutting watermelon\", \"dancing ballet\", \"dancing charleston\", \n",
"\"dancing gangnam style\", \"dancing macarena\", \"deadlifting\", \"dealing cards\", \n",
"\"decorating the christmas tree\", \"decoupage\", \"delivering mail\", \"digging\", \n",
"\"dining\", \"directing traffic\", \"disc golfing\", \"diving cliff\", \n",
"\"docking boat\", \"dodgeball\", \"doing aerobics\", \"doing jigsaw puzzle\", \n",
"\"doing laundry\", \"doing nails\", \"doing sudoku\", \"drawing\", \n",
"\"dribbling basketball\", \"drinking shots\", \"driving car\", \"driving tractor\", \n",
"\"drooling\", \"drop kicking\", \"drumming fingers\", \"dumpster diving\", \n",
"\"dunking basketball\", \"dyeing eyebrows\", \"dyeing hair\", \"eating burger\", \n",
"\"eating cake\", \"eating carrots\", \"eating chips\", \"eating doughnuts\", \n",
"\"eating hotdog\", \"eating ice cream\", \"eating nachos\", \"eating spaghetti\", \n",
"\"eating watermelon\", \"egg hunting\", \"embroidering\", \"entering church\", \n",
"\"exercising arm\", \"exercising with an exercise ball\", \"extinguishing fire\", \"faceplanting\", \n",
"\"falling off bike\", \"falling off chair\", \"feeding birds\", \"feeding fish\", \n",
"\"feeding goats\", \"fencing (sport)\", \"fidgeting\", \"filling cake\", \n",
"\"filling eyebrows\", \"finger snapping\", \"fixing bicycle\", \"fixing hair\", \n",
"\"flint knapping\", \"flipping bottle\", \"flipping pancake\", \"fly tying\", \n",
"\"flying kite\", \"folding clothes\", \"folding napkins\", \"folding paper\", \n",
"\"front raises\", \"frying vegetables\", \"gargling\", \"geocaching\", \n",
"\"getting a haircut\", \"getting a piercing\", \"getting a tattoo\", \"giving or receiving award\", \n",
"\"gold panning\", \"golf chipping\", \"golf driving\", \"golf putting\", \n",
"\"gospel singing in church\", \"grinding meat\", \"grooming cat\", \"grooming dog\", \n",
"\"grooming horse\", \"gymnastics tumbling\", \"hammer throw\", \"hand washing clothes\", \n",
"\"head stand\", \"headbanging\", \"headbutting\", \"helmet diving\", \n",
"\"herding cattle\", \"high fiving\", \"high jump\", \"high kick\", \n",
"\"historical reenactment\", \"hitting baseball\", \"hockey stop\", \"holding snake\", \n",
"\"home roasting coffee\", \"hopscotch\", \"hoverboarding\", \"huddling\", \n",
"\"hugging (not baby)\", \"hugging baby\", \"hula hooping\", \"hurdling\", \n",
"\"hurling (sport)\", \"ice climbing\", \"ice fishing\", \"ice skating\", \n",
"\"ice swimming\", \"inflating balloons\", \"installing carpet\", \"ironing\", \n",
"\"ironing hair\", \"javelin throw\", \"jaywalking\", \"jetskiing\", \n",
"\"jogging\", \"juggling balls\", \"juggling fire\", \"juggling soccer ball\", \n",
"\"jumping bicycle\", \"jumping into pool\", \"jumping jacks\", \"jumping sofa\", \n",
"\"jumpstyle dancing\", \"karaoke\", \"kicking field goal\", \"kicking soccer ball\", \n",
"\"kissing\", \"kitesurfing\", \"knitting\", \"krumping\", \n",
"\"land sailing\", \"laughing\", \"lawn mower racing\", \"laying bricks\", \n",
"\"laying concrete\", \"laying decking\", \"laying stone\", \"laying tiles\", \n",
"\"leatherworking\", \"letting go of balloon\", \"licking\", \"lifting hat\", \n",
"\"lighting candle\", \"lighting fire\", \"listening with headphones\", \"lock picking\", \n",
"\"long jump\", \"longboarding\", \"looking at phone\", \"looking in mirror\", \n",
"\"luge\", \"lunge\", \"making a cake\", \"making a sandwich\", \n",
"\"making balloon shapes\", \"making bubbles\", \"making cheese\", \"making horseshoes\", \n",
"\"making jewelry\", \"making latte art\", \"making paper aeroplanes\", \"making pizza\", \n",
"\"making slime\", \"making snowman\", \"making sushi\", \"making tea\", \n",
"\"making the bed\", \"marching\", \"marriage proposal\", \"massaging back\", \n",
"\"massaging feet\", \"massaging legs\", \"massaging neck\", \"massaging person's head\", \n",
"\"metal detecting\", \"milking cow\", \"milking goat\", \"mixing colours\", \n",
"\"moon walking\", \"mopping floor\", \"mosh pit dancing\", \"motorcycling\", \n",
"\"mountain climber (exercise)\", \"moving baby\", \"moving child\", \"moving furniture\", \n",
"\"mowing lawn\", \"mushroom foraging\", \"needle felting\", \"news anchoring\", \n",
"\"opening bottle (not wine)\", \"opening coconuts\", \"opening door\", \"opening present\", \n",
"\"opening refrigerator\", \"opening wine bottle\", \"packing\", \"paragliding\", \n",
"\"parasailing\", \"parkour\", \"passing American football (in game)\", \"passing American football (not in game)\", \n",
"\"passing soccer ball\", \"peeling apples\", \"peeling banana\", \"peeling potatoes\", \n",
"\"person collecting garbage\", \"petting animal (not cat)\", \"petting cat\", \"petting horse\", \n",
"\"photobombing\", \"photocopying\", \"picking apples\", \"picking blueberries\", \n",
"\"pillow fight\", \"pinching\", \"pirouetting\", \"planing wood\", \n",
"\"planting trees\", \"plastering\", \"playing accordion\", \"playing american football\", \n",
"\"playing badminton\", \"playing bagpipes\", \"playing basketball\", \"playing bass guitar\", \n",
"\"playing beer pong\", \"playing billiards\", \"playing blackjack\", \"playing cards\", \n",
"\"playing cello\", \"playing checkers\", \"playing chess\", \"playing clarinet\", \n",
"\"playing controller\", \"playing cricket\", \"playing cymbals\", \"playing darts\", \n",
"\"playing didgeridoo\", \"playing dominoes\", \"playing drums\", \"playing field hockey\", \n",
"\"playing flute\", \"playing gong\", \"playing guitar\", \"playing hand clapping games\", \n",
"\"playing harmonica\", \"playing harp\", \"playing ice hockey\", \"playing keyboard\", \n",
"\"playing kickball\", \"playing laser tag\", \"playing lute\", \"playing mahjong\", \n",
"\"playing maracas\", \"playing marbles\", \"playing monopoly\", \"playing netball\", \n",
"\"playing nose flute\", \"playing oboe\", \"playing ocarina\", \"playing organ\", \n",
"\"playing paintball\", \"playing pan pipes\", \"playing piano\", \"playing piccolo\", \n",
"\"playing pinball\", \"playing ping pong\", \"playing poker\", \"playing polo\", \n",
"\"playing recorder\", \"playing road hockey\", \"playing rounders\", \"playing rubiks cube\", \n",
"\"playing saxophone\", \"playing scrabble\", \"playing shuffleboard\", \"playing slot machine\", \n",
"\"playing squash or racquetball\", \"playing tennis\", \"playing trombone\", \"playing trumpet\", \n",
"\"playing ukulele\", \"playing violin\", \"playing volleyball\", \"playing with trains\", \n",
"\"playing xylophone\", \"poaching eggs\", \"poking bellybutton\", \"pole vault\", \n",
"\"polishing furniture\", \"polishing metal\", \"popping balloons\", \"pouring beer\", \n",
"\"pouring milk\", \"pouring wine\", \"preparing salad\", \"presenting weather forecast\", \n",
"\"pretending to be a statue\", \"pull ups\", \"pulling espresso shot\", \"pulling rope (game)\", \n",
"\"pumping fist\", \"pumping gas\", \"punching bag\", \"punching person (boxing)\", \n",
"\"push up\", \"pushing car\", \"pushing cart\", \"pushing wheelbarrow\", \n",
"\"pushing wheelchair\", \"putting in contact lenses\", \"putting on eyeliner\", \"putting on foundation\", \n",
"\"putting on lipstick\", \"putting on mascara\", \"putting on sari\", \"putting on shoes\", \n",
"\"putting wallpaper on wall\", \"raising eyebrows\", \"reading book\", \"reading newspaper\", \n",
"\"recording music\", \"repairing puncture\", \"riding a bike\", \"riding camel\", \n",
"\"riding elephant\", \"riding mechanical bull\", \"riding mule\", \"riding or walking with horse\", \n",
"\"riding scooter\", \"riding snow blower\", \"riding unicycle\", \"ripping paper\", \n",
"\"roasting marshmallows\", \"roasting pig\", \"robot dancing\", \"rock climbing\", \n",
"\"rock scissors paper\", \"roller skating\", \"rolling eyes\", \"rolling pastry\", \n",
"\"rope pushdown\", \"running on treadmill\", \"sailing\", \"salsa dancing\", \n",
"\"saluting\", \"sanding floor\", \"sanding wood\", \"sausage making\", \n",
"\"sawing wood\", \"scrambling eggs\", \"scrapbooking\", \"scrubbing face\", \n",
"\"scuba diving\", \"seasoning food\", \"separating eggs\", \"setting table\", \n",
"\"sewing\", \"shaking hands\", \"shaking head\", \"shaping bread dough\", \n",
"\"sharpening knives\", \"sharpening pencil\", \"shaving head\", \"shaving legs\", \n",
"\"shearing sheep\", \"shining flashlight\", \"shining shoes\", \"shoot dance\", \n",
"\"shooting basketball\", \"shooting goal (soccer)\", \"shooting off fireworks\", \"shopping\", \n",
"\"shot put\", \"shouting\", \"shoveling snow\", \"shredding paper\", \n",
"\"shucking oysters\", \"shuffling cards\", \"shuffling feet\", \"side kick\", \n",
"\"sieving\", \"sign language interpreting\", \"silent disco\", \"singing\", \n",
"\"sipping cup\", \"situp\", \"skateboarding\", \"ski ballet\", \n",
"\"ski jumping\", \"skiing crosscountry\", \"skiing mono\", \"skiing slalom\", \n",
"\"skipping rope\", \"skipping stone\", \"skydiving\", \"slacklining\", \n",
"\"slapping\", \"sled dog racing\", \"sleeping\", \"slicing onion\", \n",
"\"smashing\", \"smelling feet\", \"smoking\", \"smoking hookah\", \n",
"\"smoking pipe\", \"snatch weight lifting\", \"sneezing\", \"snorkeling\", \n",
"\"snowboarding\", \"snowkiting\", \"snowmobiling\", \"somersaulting\", \n",
"\"spelunking\", \"spinning plates\", \"spinning poi\", \"splashing water\", \n",
"\"spray painting\", \"spraying\", \"springboard diving\", \"square dancing\", \n",
"\"squat\", \"squeezing orange\", \"stacking cups\", \"stacking dice\", \n",
"\"standing on hands\", \"staring\", \"steer roping\", \"steering car\", \n",
"\"sticking tongue out\", \"stomping grapes\", \"stretching arm\", \"stretching leg\", \n",
"\"sucking lolly\", \"surfing crowd\", \"surfing water\", \"surveying\", \n",
"\"sweeping floor\", \"swimming backstroke\", \"swimming breast stroke\", \"swimming butterfly stroke\", \n",
"\"swimming front crawl\", \"swimming with dolphins\", \"swimming with sharks\", \"swing dancing\", \n",
"\"swinging baseball bat\", \"swinging on something\", \"sword fighting\", \"sword swallowing\", \n",
"\"tackling\", \"tagging graffiti\", \"tai chi\", \"taking photo\", \n",
"\"talking on cell phone\", \"tango dancing\", \"tap dancing\", \"tapping guitar\", \n",
"\"tapping pen\", \"tasting beer\", \"tasting food\", \"tasting wine\", \n",
"\"testifying\", \"texting\", \"threading needle\", \"throwing axe\", \n",
"\"throwing ball (not baseball or American football)\", \"throwing discus\", \"throwing knife\", \"throwing snowballs\", \n",
"\"throwing tantrum\", \"throwing water balloon\", \"tickling\", \"tie dying\", \n",
"\"tightrope walking\", \"tiptoeing\", \"tobogganing\", \"tossing coin\", \n",
"\"tossing salad\", \"training dog\", \"trapezing\", \"treating wood\", \n",
"\"trimming or shaving beard\", \"trimming shrubs\", \"trimming trees\", \"triple jump\", \n",
"\"twiddling fingers\", \"tying bow tie\", \"tying knot (not on a tie)\", \"tying necktie\", \n",
"\"tying shoe laces\", \"unboxing\", \"uncorking champagne\", \"unloading truck\", \n",
"\"using a microscope\", \"using a paint roller\", \"using a power drill\", \"using a sledge hammer\", \n",
"\"using a wrench\", \"using atm\", \"using bagging machine\", \"using circular saw\", \n",
"\"using inhaler\", \"using megaphone\", \"using puppets\", \"using remote controller (not gaming)\", \n",
"\"using segway\", \"vacuuming car\", \"vacuuming floor\", \"visiting the zoo\", \n",
"\"wading through mud\", \"wading through water\", \"waiting in line\", \"waking up\", \n",
"\"walking on stilts\", \"walking the dog\", \"walking through snow\", \"walking with crutches\", \n",
"\"washing dishes\", \"washing feet\", \"washing hair\", \"washing hands\", \n",
"\"watching tv\", \"water skiing\", \"water sliding\", \"watering plants\", \n",
"\"waving hand\", \"waxing armpits\", \"waxing back\", \"waxing chest\", \n",
"\"waxing eyebrows\", \"waxing legs\", \"weaving basket\", \"weaving fabric\", \n",
"\"welding\", \"whistling\", \"windsurfing\", \"winking\", \n",
"\"wood burning (art)\", \"wrapping present\", \"wrestling\", \"writing\", \n",
"\"yarn spinning\", \"yawning\", \"yoga\", \"zumba\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "bQpSe7DMhuln"
},
"outputs": [],
"source": [
"# Visualize inputs\n",
"table([to_gif(video), play_audio(audio)])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "uxeP5yit7hJg"
},
"outputs": [],
"source": [
"#@title Model construction\n",
"NUM_FRAMES = 16\n",
"AUDIO_SAMPLES_PER_FRAME = 48000 // 25\n",
"SAMPLES_PER_PATCH = 16\n",
"NUM_CLASSES = 700\n",
"IMG_SZ = 56\n",
"\n",
"def video_autoencoder(images, audio, subsampling):\n",
" n_audio_samples = NUM_FRAMES * AUDIO_SAMPLES_PER_FRAME\n",
" input_preprocessor = io_processors.MultimodalPreprocessor(\n",
" min_padding_size=4,\n",
" modalities={\n",
" 'audio': io_processors.AudioPreprocessor(\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" num_bands=192,\n",
" max_resolution=(n_audio_samples,),\n",
" sine_only=False,\n",
" concat_pos=True,\n",
" ),\n",
" n_extra_pos_mlp=0,\n",
" prep_type='patches',\n",
" samples_per_patch=16),\n",
" 'image': io_processors.ImagePreprocessor(\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" num_bands=32,\n",
" max_resolution=(NUM_FRAMES, IMG_SZ, IMG_SZ),\n",
" sine_only=False,\n",
" concat_pos=True,\n",
" ),\n",
" n_extra_pos_mlp=0,\n",
" prep_type='patches',\n",
" spatial_downsample=4,\n",
" temporal_downsample=1),\n",
" 'label': io_processors.OneHotPreprocessor(),\n",
" },\n",
" mask_probs={'image': 0.0, 'audio': 0.0, 'label': 1.0},\n",
" )\n",
"\n",
" output_postprocessor = io_processors.MultimodalPostprocessor(\n",
" modalities={\n",
" 'audio': io_processors.AudioPostprocessor(\n",
" samples_per_patch=SAMPLES_PER_PATCH),\n",
" 'image': io_processors.ProjectionPostprocessor(\n",
" num_outputs=3),\n",
" 'label': io_processors.ClassificationPostprocessor(\n",
" num_classes=NUM_CLASSES),\n",
" })\n",
"\n",
" encoder = encoder = perceiver.PerceiverEncoder(\n",
" num_self_attends_per_block=8,\n",
" # Weights won't be shared if num_blocks is set to 1.\n",
" num_blocks=1,\n",
" z_index_dim=28*28*1,\n",
" num_z_channels=512,\n",
" num_cross_attend_heads=1,\n",
" num_self_attend_heads=8,\n",
" cross_attend_widening_factor=1,\n",
" self_attend_widening_factor=1,\n",
" dropout_prob=0.0,\n",
" z_pos_enc_init_scale=0.02,\n",
" cross_attention_shape_for_attn='kv',\n",
" name='encoder')\n",
"\n",
" subsampled_index_dims = {\n",
" 'audio': subsampling['audio'].shape[0],\n",
" 'image': subsampling['image'].shape[0],\n",
" 'label': 1,\n",
" }\n",
" image_decoder = perceiver.BasicVideoAutoencodingDecoder(\n",
" # Autoencoding, don't pass inputs to the queries.\n",
" concat_preprocessed_input=False,\n",
" subsampled_index_dims=subsampling['image'],\n",
" output_shape=images.shape[:4],\n",
" num_z_channels=1024,\n",
" output_num_channels=512,\n",
" use_query_residual=False,\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" num_bands=32,\n",
" max_resolution=(NUM_FRAMES, IMG_SZ, IMG_SZ),\n",
" sine_only=False,\n",
" concat_pos=True,\n",
" ),\n",
" )\n",
"\n",
" decoder = perceiver.MultimodalDecoder(\n",
" # Autoencoding, don't pass inputs to the queries.\n",
" concat_preprocessed_input=False,\n",
" subsampled_index_dims=subsampled_index_dims,\n",
" # Modality specific decoders are used ONLY to generate queries.\n",
" # All modalties are decoded together using a unified decoder.\n",
" modalities={\n",
" 'audio': perceiver.BasicDecoder(\n",
" # Autoencoding, don't pass inputs to the queries.\n",
" concat_preprocessed_input=False,\n",
" subsampled_index_dims=subsampling['audio'],\n",
" output_index_dims=(n_audio_samples // SAMPLES_PER_PATCH,),\n",
" num_z_channels=1024,\n",
" output_num_channels=512,\n",
" use_query_residual=False,\n",
" position_encoding_type='fourier',\n",
" fourier_position_encoding_kwargs=dict(\n",
" num_bands=192,\n",
" max_resolution=(n_audio_samples,),\n",
" sine_only=False,\n",
" concat_pos=True,\n",
" ),\n",
" ),\n",
" 'image': image_decoder,\n",
" 'label': perceiver.ClassificationDecoder(\n",
" # Autoencoding, don't pass inputs to the queries.\n",
" concat_preprocessed_input=False,\n",
" num_classes=NUM_CLASSES,\n",
" num_z_channels=1024,\n",
" use_query_residual=False,\n",
" position_encoding_type='trainable',\n",
" trainable_position_encoding_kwargs=dict(\n",
" num_channels=1024,\n",
" init_scale=0.02,\n",
" ),\n",
" ),\n",
" },\n",
" num_outputs=None,\n",
" output_num_channels=512,\n",
" use_query_residual=False,)\n",
" \n",
" model = perceiver.Perceiver(\n",
" input_preprocessor=input_preprocessor,\n",
" encoder=encoder,\n",
" decoder=decoder,\n",
" output_postprocessor=output_postprocessor)\n",
" \n",
" return model({'image': images,\n",
" 'audio': audio,\n",
" 'label': np.zeros((images.shape[0], 700))},\n",
" is_training=False, subsampled_output_points=subsampling)\n",
"\n",
"\n",
"video_autoencoder = hk.transform_with_state(video_autoencoder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "N88EZZ7QHvbu"
},
"outputs": [],
"source": [
"#@title Model application\n",
"\n",
"\n",
"def autoencode_video(params, state, rng, images, audio):\n",
" nchunks = 128\n",
" reconstruction = {}\n",
" for chunk_idx in range(nchunks):\n",
" image_chunk_size = np.prod(images.shape[1:-1]) // nchunks\n",
" audio_chunk_size = audio.shape[1] // SAMPLES_PER_PATCH // nchunks\n",
" subsampling = {\n",
" 'image': jnp.arange(\n",
" image_chunk_size * chunk_idx, image_chunk_size * (chunk_idx + 1)),\n",
" 'audio': jnp.arange(\n",
" audio_chunk_size * chunk_idx, audio_chunk_size * (chunk_idx + 1)),\n",
" 'label': None,\n",
" }\n",
" output, state = video_autoencoder.apply(\n",
" params, state, rng, images, audio, subsampling)\n",
" reconstruction['label'] = output['label']\n",
" if 'image' not in reconstruction:\n",
" reconstruction['image'] = output['image']\n",
" reconstruction['audio'] = output['audio']\n",
" else:\n",
" reconstruction['image'] = jnp.concatenate(\n",
" [reconstruction['image'], output['image']], axis=1)\n",
" reconstruction['audio'] = jnp.concatenate(\n",
" [reconstruction['audio'], output['audio']], axis=1)\n",
" \n",
" reconstruction['image'] = jnp.reshape(reconstruction['image'], images.shape)\n",
" reconstruction['audio'] = jnp.reshape(reconstruction['audio'], audio.shape)\n",
" return reconstruction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "EVRWatw4LXFx"
},
"outputs": [],
"source": [
"#@title Load parameters from checkpoint\n",
"\n",
"!wget -O video_autoencoding_checkpoint.pystate https://storage.googleapis.com/perceiver_io/video_autoencoding_checkpoint.pystate\n",
"\n",
"rng = jax.random.PRNGKey(42)\n",
"with open(\"video_autoencoding_checkpoint.pystate\", \"rb\") as f:\n",
" params = pickle.loads(f.read())\n",
"\n",
"state = {}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FfWpBNZJLib4"
},
"outputs": [],
"source": [
"# Auto-encode the first 16 frames of the video and one of the audio channels\n",
"reconstruction = autoencode_video(params, state, rng, video[None, :16], audio[None, :16*AUDIO_SAMPLES_PER_FRAME, 0:1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cZpggBTO4eO5"
},
"outputs": [],
"source": [
"# Visualize reconstruction of first 16 frames\n",
"table([to_gif(reconstruction[\"image\"][0]), play_audio(np.array(reconstruction[\"audio\"][0]))])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oTJzBsl6xkOP"
},
"outputs": [],
"source": [
"# Kinetics 700 Labels\n",
"scores, indices = jax.lax.top_k(jax.nn.softmax(reconstruction[\"label\"]), 5)\n",
"\n",
"for score, index in zip(scores[0], indices[0]):\n",
" print(\"%s: %s\" % (KINETICS_CLASSES[index], score))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7JZRtQdwC4eE"
},
"outputs": [],
"source": [
"# Auto-encode the entire video, one chunk at a time\n",
"\n",
"# Partial video and audio into 16-frame chunks\n",
"nframes = video.shape[0]\n",
"# Truncate to be divisible by 16\n",
"nframes = nframes - (nframes % 16)\n",
"video_chunks = jnp.reshape(video[:nframes], [nframes // 16, 16, 224, 224, 3])\n",
"audio_chunks = jnp.reshape(audio[:nframes * AUDIO_SAMPLES_PER_FRAME],\n",
" [nframes // 16, 16 * AUDIO_SAMPLES_PER_FRAME, 2])\n",
"\n",
"encode = jax.jit(functools.partial(autoencode_video, params, state, rng))\n",
"\n",
"# Logically, what we do is the following code. We write out the loop to allocate\n",
"# GPU memory for only one chunk\n",
"#\n",
"# reconstruction = jax.vmap(encode, in_axes=1, out_axes=1)(\n",
"# video_chunks[None, :], audio_chunks[None, :, :, 0:1])\n",
"\n",
"chunks = []\n",
"for i in range(nframes // 16):\n",
" reconstruction = encode(video_chunks[None, i], audio_chunks[None, i, :, 0:1])\n",
" chunks.append(jax.tree_map(lambda x: np.array(x), reconstruction))\n",
"\n",
"reconstruction = jax.tree_multimap(lambda *args: np.stack(args, axis=1),\n",
" *chunks)\n",
"\n",
"reconstruction = jax.tree_map(lambda x: np.reshape(x, [-1] + list(x.shape[2:])), reconstruction)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lzwbz1mgES4d"
},
"outputs": [],
"source": [
"# Visualize reconstruction of entire video\n",
"table([to_gif(reconstruction['image'][0]), play_audio(np.array(reconstruction['audio'][0]))])"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "",
"kind": "local"
},
"name": "Perceiver IO: Video Autoencoding.ipynb",
"private_outputs": true,
"provenance": [
{
"file_id": "1qD1lvE-5c4LVw9l7H9XjA3DNtiYIcTgj",
"timestamp": 1626089023488
}
],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
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},
"nbformat": 4,
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}
| apache-2.0 |
mercybenzaquen/foundations-homework | foundations_hw/12/311 time series homework.ipynb | 1 | 616861 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied (use --upgrade to upgrade): matplotlib in /Users/mercybenzaquen/.virtualenvs/Homework12/lib/python3.5/site-packages\r\n",
"Requirement already satisfied (use --upgrade to upgrade): numpy>=1.6 in /Users/mercybenzaquen/.virtualenvs/Homework12/lib/python3.5/site-packages (from matplotlib)\r\n",
"Requirement already satisfied (use --upgrade to upgrade): python-dateutil in /Users/mercybenzaquen/.virtualenvs/Homework12/lib/python3.5/site-packages (from matplotlib)\r\n",
"Requirement already satisfied (use --upgrade to upgrade): pyparsing!=2.0.0,!=2.0.4,>=1.5.6 in /Users/mercybenzaquen/.virtualenvs/Homework12/lib/python3.5/site-packages (from matplotlib)\r\n",
"Requirement already satisfied (use --upgrade to upgrade): pytz in /Users/mercybenzaquen/.virtualenvs/Homework12/lib/python3.5/site-packages (from matplotlib)\r\n",
"Requirement already satisfied (use --upgrade to upgrade): cycler in /Users/mercybenzaquen/.virtualenvs/Homework12/lib/python3.5/site-packages (from matplotlib)\r\n",
"Requirement already satisfied (use --upgrade to upgrade): six>=1.5 in /Users/mercybenzaquen/.virtualenvs/Homework12/lib/python3.5/site-packages (from python-dateutil->matplotlib)\r\n"
]
}
],
"source": [
"!pip install matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mercybenzaquen/.virtualenvs/Homework12/lib/python3.5/site-packages/matplotlib/__init__.py:1035: UserWarning: Duplicate key in file \"/Users/mercybenzaquen/.matplotlib/matplotlibrc\", line #2\n",
" (fname, cnt))\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')\n",
"import dateutil.parser"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> First, I made a mistake naming the data set! **It's 2015 data, not 2014 data.** But yes, still use `311-2014.csv`. You can rename it.\n",
"\n",
"# Importing and preparing your data\n",
"\n",
"Import your data, but **only the first 200,000 rows**. You'll also want to change the index to be a datetime based on the **Created Date** column - you'll want to check if it's already a datetime, and parse it if not."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (8,17,48) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
},
{
"data": {
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" <thead>\n",
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" <th>Unique Key</th>\n",
" <th>Created Date</th>\n",
" <th>Closed Date</th>\n",
" <th>Agency</th>\n",
" <th>Agency Name</th>\n",
" <th>Complaint Type</th>\n",
" <th>Descriptor</th>\n",
" <th>Location Type</th>\n",
" <th>Incident Zip</th>\n",
" <th>Incident Address</th>\n",
" <th>...</th>\n",
" <th>Bridge Highway Name</th>\n",
" <th>Bridge Highway Direction</th>\n",
" <th>Road Ramp</th>\n",
" <th>Bridge Highway Segment</th>\n",
" <th>Garage Lot Name</th>\n",
" <th>Ferry Direction</th>\n",
" <th>Ferry Terminal Name</th>\n",
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" <th>Longitude</th>\n",
" <th>Location</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <td>07/06/2015 10:58:27 AM</td>\n",
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" <td>DCA</td>\n",
" <td>Department of Consumer Affairs</td>\n",
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" <td>Demand for Cash</td>\n",
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" <td>30997660</td>\n",
" <td>07/03/2015 01:26:29 PM</td>\n",
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" <td>(40.76702142171206, -73.97944780718524)</td>\n",
" </tr>\n",
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"</table>\n",
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"</div>"
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" Unique Key Created Date Closed Date Agency \\\n",
"0 31015465 07/06/2015 10:58:27 AM 07/22/2015 01:07:20 AM DCA \n",
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"\n",
" Agency Name Complaint Type Descriptor \\\n",
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"1 New York City Police Department Vending In Prohibited Area \n",
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" Location Type Incident Zip Incident Address \\\n",
"0 NaN 11360 27-16 203 STREET \n",
"1 Residential Building/House 10019 200 CENTRAL PARK SOUTH \n",
"\n",
" ... Bridge Highway Name \\\n",
"0 ... NaN \n",
"1 ... NaN \n",
"\n",
" Bridge Highway Direction Road Ramp Bridge Highway Segment Garage Lot Name \\\n",
"0 NaN NaN NaN NaN \n",
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"\n",
"[2 rows x 53 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#df = pd.read_csv(\"small-311-2015.csv\")\n",
"df = pd.read_csv(\"311-2014.csv\", nrows=200000)\n",
"\n",
"df.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
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"Created Date 200000 non-null object\n",
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"School Not Found 151897 non-null object\n",
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"Vehicle Type 34 non-null object\n",
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"Bridge Highway Name 1960 non-null object\n",
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"Ferry Terminal Name 215 non-null object\n",
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"Longitude 175825 non-null float64\n",
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"dtypes: float64(5), int64(1), object(47)\n",
"memory usage: 80.9+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
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]
},
"execution_count": 5,
"metadata": {},
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}
],
"source": [
"def parse_date (str_date):\n",
" return dateutil.parser.parse(str_date)\n",
"\n",
"df['created_dt']= df['Created Date'].apply(parse_date)\n",
"\n",
"df.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 200000 entries, 0 to 199999\n",
"Data columns (total 54 columns):\n",
"Unique Key 200000 non-null int64\n",
"Created Date 200000 non-null object\n",
"Closed Date 188913 non-null object\n",
"Agency 200000 non-null object\n",
"Agency Name 200000 non-null object\n",
"Complaint Type 200000 non-null object\n",
"Descriptor 198197 non-null object\n",
"Location Type 179328 non-null object\n",
"Incident Zip 181049 non-null object\n",
"Incident Address 152173 non-null object\n",
"Street Name 152152 non-null object\n",
"Cross Street 1 108035 non-null object\n",
"Cross Street 2 107583 non-null object\n",
"Intersection Street 1 24790 non-null object\n",
"Intersection Street 2 24530 non-null object\n",
"Address Type 177091 non-null object\n",
"City 181095 non-null object\n",
"Landmark 127 non-null object\n",
"Facility Type 80031 non-null object\n",
"Status 199998 non-null object\n",
"Due Date 152018 non-null object\n",
"Resolution Description 198936 non-null object\n",
"Resolution Action Updated Date 188529 non-null object\n",
"Community Board 200000 non-null object\n",
"Borough 200000 non-null object\n",
"X Coordinate (State Plane) 175825 non-null float64\n",
"Y Coordinate (State Plane) 175825 non-null float64\n",
"Park Facility Name 200000 non-null object\n",
"Park Borough 200000 non-null object\n",
"School Name 200000 non-null object\n",
"School Number 199907 non-null object\n",
"School Region 197128 non-null object\n",
"School Code 197128 non-null object\n",
"School Phone Number 200000 non-null object\n",
"School Address 200000 non-null object\n",
"School City 200000 non-null object\n",
"School State 200000 non-null object\n",
"School Zip 199999 non-null object\n",
"School Not Found 151897 non-null object\n",
"School or Citywide Complaint 0 non-null float64\n",
"Vehicle Type 34 non-null object\n",
"Taxi Company Borough 434 non-null object\n",
"Taxi Pick Up Location 3680 non-null object\n",
"Bridge Highway Name 1960 non-null object\n",
"Bridge Highway Direction 1959 non-null object\n",
"Road Ramp 1946 non-null object\n",
"Bridge Highway Segment 2134 non-null object\n",
"Garage Lot Name 143 non-null object\n",
"Ferry Direction 86 non-null object\n",
"Ferry Terminal Name 215 non-null object\n",
"Latitude 175825 non-null float64\n",
"Longitude 175825 non-null float64\n",
"Location 175825 non-null object\n",
"created_dt 200000 non-null datetime64[ns]\n",
"dtypes: datetime64[ns](1), float64(5), int64(1), object(47)\n",
"memory usage: 82.4+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What was the **most popular type of complaint**, and how many times was it filed?"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Blocked Driveway 21779\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"Complaint Type\"].value_counts().head(1)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make a horizontal bar graph of the **top 5 most frequent complaint types**."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10f099400>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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D5XJ6ugxTUC+KqBdAQG1PV1CpKKSlGNedzT1dginY9fMSN/WiiHohN5pOd4uIiJiUQlpE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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f19a898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[\"Complaint Type\"].value_counts().head(5).sort_values().plot(kind='barh')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which borough has the **most complaints per capita?** Since it's only 5 boroughs, you can do the math manually."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"BROOKLYN 57129\n",
"QUEENS 46824\n",
"MANHATTAN 42050\n",
"BRONX 29610\n",
"Unspecified 17000\n",
"STATEN ISLAND 7387\n",
"Name: Borough, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"Borough\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"people_bronx= 1438159\n",
"people_queens= 2321580\n",
"people_manhattan=1636268\n",
"people_brooklyn= 2621793\n",
"people_staten_island= 473279\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.020588822237318682"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complaints_per_capita_bronx= 29610/people_bronx\n",
"complaints_per_capita_bronx"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.020169022820665235"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complaints_per_capita_queens=46824/people_queens\n",
"complaints_per_capita_queens"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.025698724169879263"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complaints_per_capita_manhattan=42050/people_manhattan\n",
"complaints_per_capita_manhattan"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complaints_per_capita_staten_island=473279/people_staten_island\n",
"complaints_per_capita_staten_island"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"complaints_per_capita_brooklyn=2621793/people_brooklyn\n",
"complaints_per_capita_brooklyn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"According to your selection of data, **how many cases were filed in March?** How about May?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unique Key</th>\n",
" <th>Created Date</th>\n",
" <th>Closed Date</th>\n",
" <th>Agency</th>\n",
" <th>Agency Name</th>\n",
" <th>Complaint Type</th>\n",
" <th>Descriptor</th>\n",
" <th>Location Type</th>\n",
" <th>Incident Zip</th>\n",
" <th>Incident Address</th>\n",
" <th>...</th>\n",
" <th>Bridge Highway Direction</th>\n",
" <th>Road Ramp</th>\n",
" <th>Bridge Highway Segment</th>\n",
" <th>Garage Lot Name</th>\n",
" <th>Ferry Direction</th>\n",
" <th>Ferry Terminal Name</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Location</th>\n",
" <th>created_dt</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-07-06 10:58:27</th>\n",
" <td>31015465</td>\n",
" <td>07/06/2015 10:58:27 AM</td>\n",
" <td>07/22/2015 01:07:20 AM</td>\n",
" <td>DCA</td>\n",
" <td>Department of Consumer Affairs</td>\n",
" <td>Consumer Complaint</td>\n",
" <td>Demand for Cash</td>\n",
" <td>NaN</td>\n",
" <td>11360</td>\n",
" <td>27-16 203 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.773540</td>\n",
" <td>-73.788237</td>\n",
" <td>(40.773539552542, -73.78823697228408)</td>\n",
" <td>2015-07-06 10:58:27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-03 13:26:29</th>\n",
" <td>30997660</td>\n",
" <td>07/03/2015 01:26:29 PM</td>\n",
" <td>07/03/2015 02:08:20 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Vending</td>\n",
" <td>In Prohibited Area</td>\n",
" <td>Residential Building/House</td>\n",
" <td>10019</td>\n",
" <td>200 CENTRAL PARK SOUTH</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.767021</td>\n",
" <td>-73.979448</td>\n",
" <td>(40.76702142171206, -73.97944780718524)</td>\n",
" <td>2015-07-03 13:26:29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-09 03:55:09</th>\n",
" <td>31950223</td>\n",
" <td>11/09/2015 03:55:09 AM</td>\n",
" <td>11/09/2015 08:08:57 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Blocked Driveway</td>\n",
" <td>No Access</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>10453</td>\n",
" <td>1993 GRAND AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.852671</td>\n",
" <td>-73.910608</td>\n",
" <td>(40.85267061877697, -73.91060771362552)</td>\n",
" <td>2015-11-09 03:55:09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-03 02:18:32</th>\n",
" <td>31000038</td>\n",
" <td>07/03/2015 02:18:32 AM</td>\n",
" <td>07/03/2015 07:54:48 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Commercial</td>\n",
" <td>Loud Music/Party</td>\n",
" <td>Club/Bar/Restaurant</td>\n",
" <td>11372</td>\n",
" <td>84-16 NORTHERN BOULEVARD</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.755774</td>\n",
" <td>-73.883262</td>\n",
" <td>(40.755773786469966, -73.88326243225418)</td>\n",
" <td>2015-07-03 02:18:32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-04 00:03:27</th>\n",
" <td>30995614</td>\n",
" <td>07/04/2015 12:03:27 AM</td>\n",
" <td>07/04/2015 03:33:09 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Street/Sidewalk</td>\n",
" <td>Loud Talking</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11216</td>\n",
" <td>1057 BERGEN STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>40.676175</td>\n",
" <td>-73.951269</td>\n",
" <td>(40.67617516102934, -73.9512690004692)</td>\n",
" <td>2015-07-04 00:03:27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 54 columns</p>\n",
"</div>"
],
"text/plain": [
" Unique Key Created Date \\\n",
"created_dt \n",
"2015-07-06 10:58:27 31015465 07/06/2015 10:58:27 AM \n",
"2015-07-03 13:26:29 30997660 07/03/2015 01:26:29 PM \n",
"2015-11-09 03:55:09 31950223 11/09/2015 03:55:09 AM \n",
"2015-07-03 02:18:32 31000038 07/03/2015 02:18:32 AM \n",
"2015-07-04 00:03:27 30995614 07/04/2015 12:03:27 AM \n",
"\n",
" Closed Date Agency \\\n",
"created_dt \n",
"2015-07-06 10:58:27 07/22/2015 01:07:20 AM DCA \n",
"2015-07-03 13:26:29 07/03/2015 02:08:20 PM NYPD \n",
"2015-11-09 03:55:09 11/09/2015 08:08:57 AM NYPD \n",
"2015-07-03 02:18:32 07/03/2015 07:54:48 AM NYPD \n",
"2015-07-04 00:03:27 07/04/2015 03:33:09 AM NYPD \n",
"\n",
" Agency Name Complaint Type \\\n",
"created_dt \n",
"2015-07-06 10:58:27 Department of Consumer Affairs Consumer Complaint \n",
"2015-07-03 13:26:29 New York City Police Department Vending \n",
"2015-11-09 03:55:09 New York City Police Department Blocked Driveway \n",
"2015-07-03 02:18:32 New York City Police Department Noise - Commercial \n",
"2015-07-04 00:03:27 New York City Police Department Noise - Street/Sidewalk \n",
"\n",
" Descriptor Location Type \\\n",
"created_dt \n",
"2015-07-06 10:58:27 Demand for Cash NaN \n",
"2015-07-03 13:26:29 In Prohibited Area Residential Building/House \n",
"2015-11-09 03:55:09 No Access Street/Sidewalk \n",
"2015-07-03 02:18:32 Loud Music/Party Club/Bar/Restaurant \n",
"2015-07-04 00:03:27 Loud Talking Street/Sidewalk \n",
"\n",
" Incident Zip Incident Address \\\n",
"created_dt \n",
"2015-07-06 10:58:27 11360 27-16 203 STREET \n",
"2015-07-03 13:26:29 10019 200 CENTRAL PARK SOUTH \n",
"2015-11-09 03:55:09 10453 1993 GRAND AVENUE \n",
"2015-07-03 02:18:32 11372 84-16 NORTHERN BOULEVARD \n",
"2015-07-04 00:03:27 11216 1057 BERGEN STREET \n",
"\n",
" ... Bridge Highway Direction Road Ramp \\\n",
"created_dt ... \n",
"2015-07-06 10:58:27 ... NaN NaN \n",
"2015-07-03 13:26:29 ... NaN NaN \n",
"2015-11-09 03:55:09 ... NaN NaN \n",
"2015-07-03 02:18:32 ... NaN NaN \n",
"2015-07-04 00:03:27 ... NaN NaN \n",
"\n",
" Bridge Highway Segment Garage Lot Name Ferry Direction \\\n",
"created_dt \n",
"2015-07-06 10:58:27 NaN NaN NaN \n",
"2015-07-03 13:26:29 NaN NaN NaN \n",
"2015-11-09 03:55:09 NaN NaN NaN \n",
"2015-07-03 02:18:32 NaN NaN NaN \n",
"2015-07-04 00:03:27 NaN NaN NaN \n",
"\n",
" Ferry Terminal Name Latitude Longitude \\\n",
"created_dt \n",
"2015-07-06 10:58:27 NaN 40.773540 -73.788237 \n",
"2015-07-03 13:26:29 NaN 40.767021 -73.979448 \n",
"2015-11-09 03:55:09 NaN 40.852671 -73.910608 \n",
"2015-07-03 02:18:32 NaN 40.755774 -73.883262 \n",
"2015-07-04 00:03:27 NaN 40.676175 -73.951269 \n",
"\n",
" Location \\\n",
"created_dt \n",
"2015-07-06 10:58:27 (40.773539552542, -73.78823697228408) \n",
"2015-07-03 13:26:29 (40.76702142171206, -73.97944780718524) \n",
"2015-11-09 03:55:09 (40.85267061877697, -73.91060771362552) \n",
"2015-07-03 02:18:32 (40.755773786469966, -73.88326243225418) \n",
"2015-07-04 00:03:27 (40.67617516102934, -73.9512690004692) \n",
"\n",
" created_dt \n",
"created_dt \n",
"2015-07-06 10:58:27 2015-07-06 10:58:27 \n",
"2015-07-03 13:26:29 2015-07-03 13:26:29 \n",
"2015-11-09 03:55:09 2015-11-09 03:55:09 \n",
"2015-07-03 02:18:32 2015-07-03 02:18:32 \n",
"2015-07-04 00:03:27 2015-07-04 00:03:27 \n",
"\n",
"[5 rows x 54 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.index = df['created_dt']\n",
"#del df['Created Date']\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There were 15025 cases filed in March\n"
]
}
],
"source": [
"print(\"There were\", len(df['2015-03']), \"cases filed in March\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There were 49715 cases filed in May\n"
]
}
],
"source": [
"print(\"There were\", len(df['2015-05']), \"cases filed in May\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'd like to see all of the 311 complaints **called in on April 1st.**\n",
"\n",
"> **Surprise!** We couldn't do this in class, but it was just a limitation of our data set"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th>2015-04-01 21:37:42</th>\n",
" <td>30311691</td>\n",
" <td>04/01/2015 09:37:42 PM</td>\n",
" <td>04/01/2015 10:49:33 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Illegal Parking</td>\n",
" <td>Blocked Sidewalk</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11234</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>2015-04-01 21:37:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 23:12:04</th>\n",
" <td>30307701</td>\n",
" <td>04/01/2015 11:12:04 PM</td>\n",
" <td>04/01/2015 11:32:40 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Commercial</td>\n",
" <td>Loud Music/Party</td>\n",
" <td>Store/Commercial</td>\n",
" <td>11205</td>\n",
" <td>700 MYRTLE AVENUE</td>\n",
" <td>...</td>\n",
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" <td>2015-04-01 23:12:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 13:10:35</th>\n",
" <td>30313389</td>\n",
" <td>04/01/2015 01:10:35 PM</td>\n",
" <td>04/07/2015 04:01:08 PM</td>\n",
" <td>DPR</td>\n",
" <td>Department of Parks and Recreation</td>\n",
" <td>Root/Sewer/Sidewalk Condition</td>\n",
" <td>Trees and Sidewalks Program</td>\n",
" <td>Street</td>\n",
" <td>11422</td>\n",
" <td>245-16 149 AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.653016</td>\n",
" <td>-73.738626</td>\n",
" <td>(40.653016256598534, -73.73862588133056)</td>\n",
" <td>2015-04-01 13:10:35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 17:37:38</th>\n",
" <td>30314393</td>\n",
" <td>04/01/2015 05:37:38 PM</td>\n",
" <td>04/03/2015 11:40:54 AM</td>\n",
" <td>DPR</td>\n",
" <td>Department of Parks and Recreation</td>\n",
" <td>Maintenance or Facility</td>\n",
" <td>Hours of Operation</td>\n",
" <td>Park</td>\n",
" <td>11211</td>\n",
" <td>NaN</td>\n",
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" <td>2015-04-01 17:37:38</td>\n",
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" <tr>\n",
" <th>2015-04-01 12:32:40</th>\n",
" <td>30309207</td>\n",
" <td>04/01/2015 12:32:40 PM</td>\n",
" <td>04/17/2015 01:06:49 AM</td>\n",
" <td>DCA</td>\n",
" <td>Department of Consumer Affairs</td>\n",
" <td>Consumer Complaint</td>\n",
" <td>Installation/Work Quality</td>\n",
" <td>NaN</td>\n",
" <td>11423</td>\n",
" <td>90-71 198 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>2015-04-01 12:32:40</td>\n",
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" <th>2015-04-01 18:44:50</th>\n",
" <td>30311759</td>\n",
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" <td>06/24/2015 11:27:00 AM</td>\n",
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" <td>Department of Parks and Recreation</td>\n",
" <td>Damaged Tree</td>\n",
" <td>Entire Tree Has Fallen Down</td>\n",
" <td>Street</td>\n",
" <td>10467</td>\n",
" <td>862 EAST 213 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>40.878028</td>\n",
" <td>-73.860237</td>\n",
" <td>(40.87802828144708, -73.86023734606933)</td>\n",
" <td>2015-04-01 18:44:50</td>\n",
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" <tr>\n",
" <th>2015-04-01 16:30:15</th>\n",
" <td>30309690</td>\n",
" <td>04/01/2015 04:30:15 PM</td>\n",
" <td>04/01/2015 11:27:22 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Animal Abuse</td>\n",
" <td>Neglected</td>\n",
" <td>Residential Building/House</td>\n",
" <td>11368</td>\n",
" <td>107-15 NORTHERN BOULEVARD</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.757811</td>\n",
" <td>-73.861677</td>\n",
" <td>(40.757811195752154, -73.86167714731972)</td>\n",
" <td>2015-04-01 16:30:15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 09:04:07</th>\n",
" <td>30307990</td>\n",
" <td>04/01/2015 09:04:07 AM</td>\n",
" <td>04/06/2015 09:17:10 AM</td>\n",
" <td>DOF</td>\n",
" <td>Senior Citizen Rent Increase Exemption Unit</td>\n",
" <td>SCRIE</td>\n",
" <td>Miscellaneous</td>\n",
" <td>Senior Address</td>\n",
" <td>10027</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 09:04:07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 07:46:58</th>\n",
" <td>30308253</td>\n",
" <td>04/01/2015 07:46:58 AM</td>\n",
" <td>04/01/2015 09:32:31 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Blocked Driveway</td>\n",
" <td>No Access</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11370</td>\n",
" <td>32-51 80 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.756412</td>\n",
" <td>-73.887405</td>\n",
" <td>(40.75641194675221, -73.88740503059863)</td>\n",
" <td>2015-04-01 07:46:58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 17:12:17</th>\n",
" <td>30314214</td>\n",
" <td>04/01/2015 05:12:17 PM</td>\n",
" <td>04/09/2015 02:20:11 PM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Highway Condition</td>\n",
" <td>Pothole - Highway</td>\n",
" <td>Highway</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>West/Manhattan Bound</td>\n",
" <td>Roadway</td>\n",
" <td>Clearview Expwy (I-295) (Exit 27 S-N) - Utopia...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 17:12:17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 21:30:48</th>\n",
" <td>30307111</td>\n",
" <td>04/01/2015 09:30:48 PM</td>\n",
" <td>NaN</td>\n",
" <td>DOHMH</td>\n",
" <td>Department of Health and Mental Hygiene</td>\n",
" <td>Food Establishment</td>\n",
" <td>Food Temperature</td>\n",
" <td>Restaurant/Bar/Deli/Bakery</td>\n",
" <td>11215</td>\n",
" <td>709 5 AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.660699</td>\n",
" <td>-73.994082</td>\n",
" <td>(40.660699296661825, -73.99408169463258)</td>\n",
" <td>2015-04-01 21:30:48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 15:51:04</th>\n",
" <td>30311571</td>\n",
" <td>04/01/2015 03:51:04 PM</td>\n",
" <td>04/14/2015 09:23:30 AM</td>\n",
" <td>DPR</td>\n",
" <td>Department of Parks and Recreation</td>\n",
" <td>Maintenance or Facility</td>\n",
" <td>Hours of Operation</td>\n",
" <td>Park</td>\n",
" <td>11210</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.621474</td>\n",
" <td>-73.950711</td>\n",
" <td>(40.62147413119333, -73.95071097029123)</td>\n",
" <td>2015-04-01 15:51:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 10:43:28</th>\n",
" <td>30313817</td>\n",
" <td>04/01/2015 10:43:28 AM</td>\n",
" <td>NaN</td>\n",
" <td>DPR</td>\n",
" <td>Department of Parks and Recreation</td>\n",
" <td>Damaged Tree</td>\n",
" <td>Branch Cracked and Will Fall</td>\n",
" <td>NaN</td>\n",
" <td>10009</td>\n",
" <td>620 EAST 12TH STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.727725</td>\n",
" <td>-73.978204</td>\n",
" <td>(40.72772462544187, -73.97820435916094)</td>\n",
" <td>2015-04-01 10:43:28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 15:12:46</th>\n",
" <td>30308922</td>\n",
" <td>04/01/2015 03:12:46 PM</td>\n",
" <td>06/01/2015 06:25:48 AM</td>\n",
" <td>DOHMH</td>\n",
" <td>Department of Health and Mental Hygiene</td>\n",
" <td>Food Establishment</td>\n",
" <td>Letter Grading</td>\n",
" <td>Restaurant/Bar/Deli/Bakery</td>\n",
" <td>11238</td>\n",
" <td>663 FRANKLIN AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.675746</td>\n",
" <td>-73.956122</td>\n",
" <td>(40.67574618440852, -73.9561218336512)</td>\n",
" <td>2015-04-01 15:12:46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 06:15:42</th>\n",
" <td>30311132</td>\n",
" <td>04/01/2015 06:15:42 AM</td>\n",
" <td>04/01/2015 10:28:30 AM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Highway Condition</td>\n",
" <td>Pothole - Highway</td>\n",
" <td>Highway</td>\n",
" <td>10304</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>East/Brooklyn Bound</td>\n",
" <td>Roadway</td>\n",
" <td>Clove Rd/Richmond Rd (Exit 13) - Lily Pond Ave...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.606875</td>\n",
" <td>-74.085408</td>\n",
" <td>(40.60687536641399, -74.0854077221027)</td>\n",
" <td>2015-04-01 06:15:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 11:28:02</th>\n",
" <td>30308180</td>\n",
" <td>04/01/2015 11:28:02 AM</td>\n",
" <td>04/01/2015 11:42:53 AM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Highway Condition</td>\n",
" <td>Pothole - Highway</td>\n",
" <td>Highway</td>\n",
" <td>11432</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>West/Toward Triborough Br</td>\n",
" <td>Ramp</td>\n",
" <td>168th St (Exit 17)</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.719228</td>\n",
" <td>-73.791963</td>\n",
" <td>(40.71922760413319, -73.791962929951)</td>\n",
" <td>2015-04-01 11:28:02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 17:35:18</th>\n",
" <td>30313207</td>\n",
" <td>04/01/2015 05:35:18 PM</td>\n",
" <td>06/01/2015 06:25:54 AM</td>\n",
" <td>DOHMH</td>\n",
" <td>Department of Health and Mental Hygiene</td>\n",
" <td>Food Establishment</td>\n",
" <td>Rodents/Insects/Garbage</td>\n",
" <td>Restaurant/Bar/Deli/Bakery</td>\n",
" <td>10011</td>\n",
" <td>140 WEST 13 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.737182</td>\n",
" <td>-73.998585</td>\n",
" <td>(40.737182358685516, -73.99858548189518)</td>\n",
" <td>2015-04-01 17:35:18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 13:54:54</th>\n",
" <td>30310017</td>\n",
" <td>04/01/2015 01:54:54 PM</td>\n",
" <td>04/06/2015 10:11:11 AM</td>\n",
" <td>DOF</td>\n",
" <td>Senior Citizen Rent Increase Exemption Unit</td>\n",
" <td>SCRIE</td>\n",
" <td>Miscellaneous</td>\n",
" <td>Senior Address</td>\n",
" <td>11435</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 13:54:54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 23:49:33</th>\n",
" <td>30306774</td>\n",
" <td>04/01/2015 11:49:33 PM</td>\n",
" <td>04/02/2015 12:20:59 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Commercial</td>\n",
" <td>Loud Music/Party</td>\n",
" <td>Store/Commercial</td>\n",
" <td>10003</td>\n",
" <td>36 SAINT MARKS PLACE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.728733</td>\n",
" <td>-73.988011</td>\n",
" <td>(40.72873338955463, -73.98801059255561)</td>\n",
" <td>2015-04-01 23:49:33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 07:50:49</th>\n",
" <td>30313339</td>\n",
" <td>04/01/2015 07:50:49 AM</td>\n",
" <td>07/08/2015 02:19:25 PM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Street Condition</td>\n",
" <td>Rough, Pitted or Cracked Roads</td>\n",
" <td>Street</td>\n",
" <td>11385</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.703414</td>\n",
" <td>-73.862854</td>\n",
" <td>(40.70341423569781, -73.86285397616253)</td>\n",
" <td>2015-04-01 07:50:49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 13:50:29</th>\n",
" <td>30312146</td>\n",
" <td>04/01/2015 01:50:29 PM</td>\n",
" <td>06/01/2015 06:25:49 AM</td>\n",
" <td>DOHMH</td>\n",
" <td>Department of Health and Mental Hygiene</td>\n",
" <td>Food Establishment</td>\n",
" <td>Rodents/Insects/Garbage</td>\n",
" <td>Restaurant/Bar/Deli/Bakery</td>\n",
" <td>10028</td>\n",
" <td>1291 LEXINGTON AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.780069</td>\n",
" <td>-73.955158</td>\n",
" <td>(40.78006850471446, -73.95515761412761)</td>\n",
" <td>2015-04-01 13:50:29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 16:14:19</th>\n",
" <td>30313259</td>\n",
" <td>04/01/2015 04:14:19 PM</td>\n",
" <td>04/01/2015 04:21:53 PM</td>\n",
" <td>HRA</td>\n",
" <td>HRA Benefit Card Replacement</td>\n",
" <td>Benefit Card Replacement</td>\n",
" <td>Medicaid</td>\n",
" <td>NYC Street Address</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 16:14:19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 19:27:34</th>\n",
" <td>30308920</td>\n",
" <td>04/01/2015 07:27:34 PM</td>\n",
" <td>04/01/2015 08:45:17 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Street/Sidewalk</td>\n",
" <td>Loud Music/Party</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>10017</td>\n",
" <td>210 EAST 46 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.753104</td>\n",
" <td>-73.972096</td>\n",
" <td>(40.75310402468627, -73.97209629231209)</td>\n",
" <td>2015-04-01 19:27:34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 05:30:02</th>\n",
" <td>30314164</td>\n",
" <td>04/01/2015 05:30:02 AM</td>\n",
" <td>04/01/2015 02:57:31 PM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Highway Condition</td>\n",
" <td>Pothole - Highway</td>\n",
" <td>Highway</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>East/Queens Bound</td>\n",
" <td>Roadway</td>\n",
" <td>Williamsburg Br / Metropolitan Ave (Exit 32) -...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 05:30:02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 10:33:26</th>\n",
" <td>30311790</td>\n",
" <td>04/01/2015 10:33:26 AM</td>\n",
" <td>04/01/2015 11:19:12 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Illegal Parking</td>\n",
" <td>Blocked Sidewalk</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>10033</td>\n",
" <td>2284 AMSTERDAM AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.843149</td>\n",
" <td>-73.934539</td>\n",
" <td>(40.84314882753921, -73.93453937669832)</td>\n",
" <td>2015-04-01 10:33:26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 11:47:38</th>\n",
" <td>30310940</td>\n",
" <td>04/01/2015 11:47:38 AM</td>\n",
" <td>04/06/2015 09:23:32 AM</td>\n",
" <td>DOF</td>\n",
" <td>Senior Citizen Rent Increase Exemption Unit</td>\n",
" <td>SCRIE</td>\n",
" <td>Miscellaneous</td>\n",
" <td>Senior Address</td>\n",
" <td>11355</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 11:47:38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 11:01:27</th>\n",
" <td>30310409</td>\n",
" <td>04/01/2015 11:01:27 AM</td>\n",
" <td>04/17/2015 01:06:42 AM</td>\n",
" <td>DCA</td>\n",
" <td>Department of Consumer Affairs</td>\n",
" <td>Consumer Complaint</td>\n",
" <td>Exchange/Refund/Return</td>\n",
" <td>NaN</td>\n",
" <td>10455</td>\n",
" <td>2997 3 AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.819111</td>\n",
" <td>-73.913908</td>\n",
" <td>(40.819110789789214, -73.91390802507868)</td>\n",
" <td>2015-04-01 11:01:27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 08:51:52</th>\n",
" <td>30310350</td>\n",
" <td>04/01/2015 08:51:52 AM</td>\n",
" <td>04/03/2015 04:33:46 PM</td>\n",
" <td>DCA</td>\n",
" <td>Department of Consumer Affairs</td>\n",
" <td>Consumer Complaint</td>\n",
" <td>Cars Parked on Sidewalk/Street</td>\n",
" <td>NaN</td>\n",
" <td>11223</td>\n",
" <td>1701 WEST 8 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.605657</td>\n",
" <td>-73.981194</td>\n",
" <td>(40.60565667868274, -73.98119372058547)</td>\n",
" <td>2015-04-01 08:51:52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 14:58:55</th>\n",
" <td>30313106</td>\n",
" <td>04/01/2015 02:58:55 PM</td>\n",
" <td>04/06/2015 10:06:35 AM</td>\n",
" <td>DOF</td>\n",
" <td>Senior Citizen Rent Increase Exemption Unit</td>\n",
" <td>SCRIE</td>\n",
" <td>Rent Discrepancy</td>\n",
" <td>Senior Address</td>\n",
" <td>11201</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 14:58:55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 16:59:19</th>\n",
" <td>30309324</td>\n",
" <td>04/01/2015 04:59:19 PM</td>\n",
" <td>04/01/2015 07:48:33 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Blocked Driveway</td>\n",
" <td>Partial Access</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11210</td>\n",
" <td>650 EAST 24 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.634497</td>\n",
" <td>-73.954167</td>\n",
" <td>(40.63449684441219, -73.95416735372353)</td>\n",
" <td>2015-04-01 16:59:19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 17:12:09</th>\n",
" <td>30313532</td>\n",
" <td>04/01/2015 05:12:09 PM</td>\n",
" <td>04/30/2015 06:02:47 PM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Street Condition</td>\n",
" <td>Line/Marking - Faded</td>\n",
" <td>Street</td>\n",
" <td>11207</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.679561</td>\n",
" <td>-73.898899</td>\n",
" <td>(40.67956105192572, -73.89889884573184)</td>\n",
" <td>2015-04-01 17:12:09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 17:09:29</th>\n",
" <td>30311473</td>\n",
" <td>04/01/2015 05:09:29 PM</td>\n",
" <td>04/30/2015 05:59:38 PM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Street Condition</td>\n",
" <td>Line/Marking - Faded</td>\n",
" <td>Street</td>\n",
" <td>11203</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.658529</td>\n",
" <td>-73.939568</td>\n",
" <td>(40.6585289219231, -73.93956820621213)</td>\n",
" <td>2015-04-01 17:09:29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 18:30:22</th>\n",
" <td>30307427</td>\n",
" <td>04/01/2015 06:30:22 PM</td>\n",
" <td>05/06/2015 10:59:47 AM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Street Condition</td>\n",
" <td>Failed Street Repair</td>\n",
" <td>Street</td>\n",
" <td>11234</td>\n",
" <td>J AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.628542</td>\n",
" <td>-73.921838</td>\n",
" <td>(40.62854243316789, -73.92183818389044)</td>\n",
" <td>2015-04-01 18:30:22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 21:07:21</th>\n",
" <td>30314301</td>\n",
" <td>04/01/2015 09:07:21 PM</td>\n",
" <td>05/08/2015 11:30:22 AM</td>\n",
" <td>TLC</td>\n",
" <td>Taxi and Limousine Commission</td>\n",
" <td>Taxi Complaint</td>\n",
" <td>Driver Complaint</td>\n",
" <td>NaN</td>\n",
" <td>10001</td>\n",
" <td>511 WEST 25 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.749380</td>\n",
" <td>-74.004169</td>\n",
" <td>(40.74937996228322, -74.00416853967121)</td>\n",
" <td>2015-04-01 21:07:21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 10:50:12</th>\n",
" <td>30312508</td>\n",
" <td>04/01/2015 10:50:12 AM</td>\n",
" <td>05/08/2015 10:21:38 AM</td>\n",
" <td>TLC</td>\n",
" <td>Taxi and Limousine Commission</td>\n",
" <td>Taxi Complaint</td>\n",
" <td>Driver Complaint</td>\n",
" <td>NaN</td>\n",
" <td>10032</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.842154</td>\n",
" <td>-73.942278</td>\n",
" <td>(40.84215388602991, -73.94227827092928)</td>\n",
" <td>2015-04-01 10:50:12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 09:07:38</th>\n",
" <td>30310225</td>\n",
" <td>04/01/2015 09:07:38 AM</td>\n",
" <td>05/04/2015 10:43:15 AM</td>\n",
" <td>DPR</td>\n",
" <td>Department of Parks and Recreation</td>\n",
" <td>Root/Sewer/Sidewalk Condition</td>\n",
" <td>Trees and Sidewalks Program</td>\n",
" <td>Street</td>\n",
" <td>10307</td>\n",
" <td>647 CRAIG AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.506708</td>\n",
" <td>-74.252182</td>\n",
" <td>(40.50670803830861, -74.25218246259357)</td>\n",
" <td>2015-04-01 09:07:38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 16:18:25</th>\n",
" <td>30313554</td>\n",
" <td>04/01/2015 04:18:25 PM</td>\n",
" <td>05/08/2015 11:29:12 AM</td>\n",
" <td>TLC</td>\n",
" <td>Taxi and Limousine Commission</td>\n",
" <td>Taxi Complaint</td>\n",
" <td>Driver Complaint</td>\n",
" <td>NaN</td>\n",
" <td>11369</td>\n",
" <td>22-19 93 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.769574</td>\n",
" <td>-73.877480</td>\n",
" <td>(40.769573850244676, -73.8774799367093)</td>\n",
" <td>2015-04-01 16:18:25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 10:23:09</th>\n",
" <td>30313061</td>\n",
" <td>04/01/2015 10:23:09 AM</td>\n",
" <td>05/07/2015 02:19:57 PM</td>\n",
" <td>TLC</td>\n",
" <td>Taxi and Limousine Commission</td>\n",
" <td>Taxi Complaint</td>\n",
" <td>Driver Complaint</td>\n",
" <td>Street</td>\n",
" <td>10021</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.765546</td>\n",
" <td>-73.954702</td>\n",
" <td>(40.765545913197165, -73.95470170187454)</td>\n",
" <td>2015-04-01 10:23:09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 14:31:57</th>\n",
" <td>30312110</td>\n",
" <td>04/01/2015 02:31:57 PM</td>\n",
" <td>05/08/2015 06:05:44 PM</td>\n",
" <td>DPR</td>\n",
" <td>Department of Parks and Recreation</td>\n",
" <td>Dead Tree</td>\n",
" <td>Dead/Dying Tree</td>\n",
" <td>Street</td>\n",
" <td>11229</td>\n",
" <td>2056 EAST 29 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.601403</td>\n",
" <td>-73.943106</td>\n",
" <td>(40.60140342407911, -73.94310580244269)</td>\n",
" <td>2015-04-01 14:31:57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 18:50:19</th>\n",
" <td>30307758</td>\n",
" <td>04/01/2015 06:50:19 PM</td>\n",
" <td>05/07/2015 07:46:48 AM</td>\n",
" <td>DPR</td>\n",
" <td>Department of Parks and Recreation</td>\n",
" <td>Damaged Tree</td>\n",
" <td>Branch Cracked and Will Fall</td>\n",
" <td>Street</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.720103</td>\n",
" <td>-73.790376</td>\n",
" <td>(40.72010305201917, -73.79037648278602)</td>\n",
" <td>2015-04-01 18:50:19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 14:03:43</th>\n",
" <td>30313462</td>\n",
" <td>04/01/2015 02:03:43 PM</td>\n",
" <td>05/06/2015 12:48:52 PM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Street Condition</td>\n",
" <td>Blocked - Construction</td>\n",
" <td>Street</td>\n",
" <td>11209</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.633428</td>\n",
" <td>-74.032876</td>\n",
" <td>(40.63342806685948, -74.03287604669814)</td>\n",
" <td>2015-04-01 14:03:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 11:59:20</th>\n",
" <td>30310246</td>\n",
" <td>04/01/2015 11:59:20 AM</td>\n",
" <td>11/09/2015 03:58:34 PM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Street Condition</td>\n",
" <td>Rough, Pitted or Cracked Roads</td>\n",
" <td>Street</td>\n",
" <td>11217</td>\n",
" <td>90 PROSPECT PLACE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.679040</td>\n",
" <td>-73.974579</td>\n",
" <td>(40.67903998236064, -73.97457889877462)</td>\n",
" <td>2015-04-01 11:59:20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 09:17:40</th>\n",
" <td>30310085</td>\n",
" <td>04/01/2015 09:17:40 AM</td>\n",
" <td>05/07/2015 06:53:11 PM</td>\n",
" <td>DOT</td>\n",
" <td>Department of Transportation</td>\n",
" <td>Highway Condition</td>\n",
" <td>Graffiti - Highway</td>\n",
" <td>Highway</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>West/Staten Island Bound</td>\n",
" <td>Roadway</td>\n",
" <td>Crospey Ave Stillwell Ave (Exit 6N) - Crospey ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 09:17:40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 21:13:08</th>\n",
" <td>30314474</td>\n",
" <td>04/01/2015 09:13:08 PM</td>\n",
" <td>05/08/2015 11:27:01 AM</td>\n",
" <td>TLC</td>\n",
" <td>Taxi and Limousine Commission</td>\n",
" <td>Taxi Complaint</td>\n",
" <td>Driver Complaint</td>\n",
" <td>NaN</td>\n",
" <td>11429</td>\n",
" <td>214-16 110 AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.708131</td>\n",
" <td>-73.743041</td>\n",
" <td>(40.70813050331176, -73.74304104617282)</td>\n",
" <td>2015-04-01 21:13:08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 12:59:08</th>\n",
" <td>30308968</td>\n",
" <td>04/01/2015 12:59:08 PM</td>\n",
" <td>04/01/2015 12:59:23 PM</td>\n",
" <td>HRA</td>\n",
" <td>HRA Benefit Card Replacement</td>\n",
" <td>Benefit Card Replacement</td>\n",
" <td>Medicaid</td>\n",
" <td>Address Outside of NYC</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 12:59:08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 13:31:23</th>\n",
" <td>30308389</td>\n",
" <td>04/01/2015 01:31:23 PM</td>\n",
" <td>04/01/2015 01:32:08 PM</td>\n",
" <td>HRA</td>\n",
" <td>HRA Benefit Card Replacement</td>\n",
" <td>Benefit Card Replacement</td>\n",
" <td>Medicaid</td>\n",
" <td>NYC Street Address</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 13:31:23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 16:42:15</th>\n",
" <td>30309377</td>\n",
" <td>04/01/2015 04:42:15 PM</td>\n",
" <td>04/01/2015 04:43:11 PM</td>\n",
" <td>HRA</td>\n",
" <td>HRA Benefit Card Replacement</td>\n",
" <td>Benefit Card Replacement</td>\n",
" <td>Food Stamp</td>\n",
" <td>NYC Street Address</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 16:42:15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 13:37:07</th>\n",
" <td>30310992</td>\n",
" <td>04/01/2015 01:37:07 PM</td>\n",
" <td>04/01/2015 01:37:28 PM</td>\n",
" <td>HRA</td>\n",
" <td>HRA Benefit Card Replacement</td>\n",
" <td>Benefit Card Replacement</td>\n",
" <td>Food Stamp</td>\n",
" <td>NYC Street Address</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-01 13:37:07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 23:44:04</th>\n",
" <td>30310652</td>\n",
" <td>04/01/2015 11:44:04 PM</td>\n",
" <td>04/02/2015 01:25:52 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Derelict Vehicle</td>\n",
" <td>With License Plate</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11421</td>\n",
" <td>85-86 87 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.694888</td>\n",
" <td>-73.857927</td>\n",
" <td>(40.69488849346232, -73.85792744070989)</td>\n",
" <td>2015-04-01 23:44:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 16:32:12</th>\n",
" <td>30309028</td>\n",
" <td>04/01/2015 04:32:12 PM</td>\n",
" <td>05/20/2015 05:36:29 PM</td>\n",
" <td>TLC</td>\n",
" <td>Taxi and Limousine Commission</td>\n",
" <td>For Hire Vehicle Complaint</td>\n",
" <td>Car Service Company Complaint</td>\n",
" <td>Street</td>\n",
" <td>10451</td>\n",
" <td>215 EAST 161 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.826235</td>\n",
" <td>-73.920529</td>\n",
" <td>(40.8262353417949, -73.92052920426786)</td>\n",
" <td>2015-04-01 16:32:12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 08:26:06</th>\n",
" <td>30312622</td>\n",
" <td>04/01/2015 08:26:06 AM</td>\n",
" <td>06/01/2015 06:25:41 AM</td>\n",
" <td>DOHMH</td>\n",
" <td>Department of Health and Mental Hygiene</td>\n",
" <td>Food Establishment</td>\n",
" <td>Facility Construction</td>\n",
" <td>Restaurant/Bar/Deli/Bakery</td>\n",
" <td>11234</td>\n",
" <td>2301 FLATBUSH AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.613889</td>\n",
" <td>-73.927186</td>\n",
" <td>(40.61388875283825, -73.92718600732812)</td>\n",
" <td>2015-04-01 08:26:06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 15:08:20</th>\n",
" <td>30308371</td>\n",
" <td>04/01/2015 03:08:20 PM</td>\n",
" <td>06/01/2015 06:18:02 PM</td>\n",
" <td>TLC</td>\n",
" <td>Taxi and Limousine Commission</td>\n",
" <td>Taxi Complaint</td>\n",
" <td>Driver Complaint</td>\n",
" <td>Street</td>\n",
" <td>10128</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.781533</td>\n",
" <td>-73.958320</td>\n",
" <td>(40.78153263581957, -73.9583197488706)</td>\n",
" <td>2015-04-01 15:08:20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 10:19:21</th>\n",
" <td>30311001</td>\n",
" <td>04/01/2015 10:19:21 AM</td>\n",
" <td>06/01/2015 06:25:39 AM</td>\n",
" <td>DOHMH</td>\n",
" <td>Department of Health and Mental Hygiene</td>\n",
" <td>Food Establishment</td>\n",
" <td>Rodents/Insects/Garbage</td>\n",
" <td>Restaurant/Bar/Deli/Bakery</td>\n",
" <td>11377</td>\n",
" <td>59-21 ROOSEVELT AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.745586</td>\n",
" <td>-73.904573</td>\n",
" <td>(40.74558568959288, -73.90457292624892)</td>\n",
" <td>2015-04-01 10:19:21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 20:20:13</th>\n",
" <td>30311341</td>\n",
" <td>04/01/2015 08:20:13 PM</td>\n",
" <td>04/01/2015 10:49:32 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Blocked Driveway</td>\n",
" <td>Partial Access</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11691</td>\n",
" <td>348 BEACH 40 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.595019</td>\n",
" <td>-73.772153</td>\n",
" <td>(40.5950185756628, -73.77215306630436)</td>\n",
" <td>2015-04-01 20:20:13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 02:16:44</th>\n",
" <td>30308863</td>\n",
" <td>04/01/2015 02:16:44 AM</td>\n",
" <td>04/01/2015 02:54:17 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Commercial</td>\n",
" <td>Loud Music/Party</td>\n",
" <td>Club/Bar/Restaurant</td>\n",
" <td>10013</td>\n",
" <td>301 CHURCH STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.719322</td>\n",
" <td>-74.004470</td>\n",
" <td>(40.71932215308254, -74.00446968948569)</td>\n",
" <td>2015-04-01 02:16:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 13:12:58</th>\n",
" <td>30307673</td>\n",
" <td>04/01/2015 01:12:58 PM</td>\n",
" <td>04/01/2015 10:01:26 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Illegal Parking</td>\n",
" <td>Posted Parking Sign Violation</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>10306</td>\n",
" <td>200 ADELAIDE AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.561690</td>\n",
" <td>-74.124622</td>\n",
" <td>(40.5616902523158, -74.12462211525013)</td>\n",
" <td>2015-04-01 13:12:58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 13:17:23</th>\n",
" <td>30307732</td>\n",
" <td>04/01/2015 01:17:23 PM</td>\n",
" <td>04/01/2015 01:31:22 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Traffic</td>\n",
" <td>Congestion/Gridlock</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>10013</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.720557</td>\n",
" <td>-74.003510</td>\n",
" <td>(40.72055732795014, -74.00351016018516)</td>\n",
" <td>2015-04-01 13:17:23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 21:39:04</th>\n",
" <td>30311958</td>\n",
" <td>04/01/2015 09:39:04 PM</td>\n",
" <td>04/01/2015 09:50:48 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Vehicle</td>\n",
" <td>Car/Truck Music</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11207</td>\n",
" <td>184 JEROME STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.677739</td>\n",
" <td>-73.887888</td>\n",
" <td>(40.677739297670584, -73.8878875660618)</td>\n",
" <td>2015-04-01 21:39:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 12:53:45</th>\n",
" <td>30309365</td>\n",
" <td>04/01/2015 12:53:45 PM</td>\n",
" <td>04/02/2015 12:04:38 PM</td>\n",
" <td>DCA</td>\n",
" <td>Department of Consumer Affairs</td>\n",
" <td>Consumer Complaint</td>\n",
" <td>Overcharge</td>\n",
" <td>NaN</td>\n",
" <td>11418</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.700108</td>\n",
" <td>-73.832667</td>\n",
" <td>(40.70010803283339, -73.83266746664873)</td>\n",
" <td>2015-04-01 12:53:45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-01 10:46:01</th>\n",
" <td>30312487</td>\n",
" <td>04/01/2015 10:46:01 AM</td>\n",
" <td>04/02/2015 03:34:31 PM</td>\n",
" <td>DCA</td>\n",
" <td>Department of Consumer Affairs</td>\n",
" <td>Consumer Complaint</td>\n",
" <td>Damaged/Defective Goods</td>\n",
" <td>NaN</td>\n",
" <td>11232</td>\n",
" <td>807 42 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.645348</td>\n",
" <td>-73.998616</td>\n",
" <td>(40.64534787518196, -73.99861625677346)</td>\n",
" <td>2015-04-01 10:46:01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>573 rows × 54 columns</p>\n",
"</div>"
],
"text/plain": [
" Unique Key Created Date \\\n",
"created_dt \n",
"2015-04-01 21:37:42 30311691 04/01/2015 09:37:42 PM \n",
"2015-04-01 23:12:04 30307701 04/01/2015 11:12:04 PM \n",
"2015-04-01 13:10:35 30313389 04/01/2015 01:10:35 PM \n",
"2015-04-01 17:37:38 30314393 04/01/2015 05:37:38 PM \n",
"2015-04-01 12:32:40 30309207 04/01/2015 12:32:40 PM \n",
"2015-04-01 18:44:50 30311759 04/01/2015 06:44:50 PM \n",
"2015-04-01 16:30:15 30309690 04/01/2015 04:30:15 PM \n",
"2015-04-01 09:04:07 30307990 04/01/2015 09:04:07 AM \n",
"2015-04-01 07:46:58 30308253 04/01/2015 07:46:58 AM \n",
"2015-04-01 17:12:17 30314214 04/01/2015 05:12:17 PM \n",
"2015-04-01 21:30:48 30307111 04/01/2015 09:30:48 PM \n",
"2015-04-01 15:51:04 30311571 04/01/2015 03:51:04 PM \n",
"2015-04-01 10:43:28 30313817 04/01/2015 10:43:28 AM \n",
"2015-04-01 15:12:46 30308922 04/01/2015 03:12:46 PM \n",
"2015-04-01 06:15:42 30311132 04/01/2015 06:15:42 AM \n",
"2015-04-01 11:28:02 30308180 04/01/2015 11:28:02 AM \n",
"2015-04-01 17:35:18 30313207 04/01/2015 05:35:18 PM \n",
"2015-04-01 13:54:54 30310017 04/01/2015 01:54:54 PM \n",
"2015-04-01 23:49:33 30306774 04/01/2015 11:49:33 PM \n",
"2015-04-01 07:50:49 30313339 04/01/2015 07:50:49 AM \n",
"2015-04-01 13:50:29 30312146 04/01/2015 01:50:29 PM \n",
"2015-04-01 16:14:19 30313259 04/01/2015 04:14:19 PM \n",
"2015-04-01 19:27:34 30308920 04/01/2015 07:27:34 PM \n",
"2015-04-01 05:30:02 30314164 04/01/2015 05:30:02 AM \n",
"2015-04-01 10:33:26 30311790 04/01/2015 10:33:26 AM \n",
"2015-04-01 11:47:38 30310940 04/01/2015 11:47:38 AM \n",
"2015-04-01 11:01:27 30310409 04/01/2015 11:01:27 AM \n",
"2015-04-01 08:51:52 30310350 04/01/2015 08:51:52 AM \n",
"2015-04-01 14:58:55 30313106 04/01/2015 02:58:55 PM \n",
"2015-04-01 16:59:19 30309324 04/01/2015 04:59:19 PM \n",
"... ... ... \n",
"2015-04-01 17:12:09 30313532 04/01/2015 05:12:09 PM \n",
"2015-04-01 17:09:29 30311473 04/01/2015 05:09:29 PM \n",
"2015-04-01 18:30:22 30307427 04/01/2015 06:30:22 PM \n",
"2015-04-01 21:07:21 30314301 04/01/2015 09:07:21 PM \n",
"2015-04-01 10:50:12 30312508 04/01/2015 10:50:12 AM \n",
"2015-04-01 09:07:38 30310225 04/01/2015 09:07:38 AM \n",
"2015-04-01 16:18:25 30313554 04/01/2015 04:18:25 PM \n",
"2015-04-01 10:23:09 30313061 04/01/2015 10:23:09 AM \n",
"2015-04-01 14:31:57 30312110 04/01/2015 02:31:57 PM \n",
"2015-04-01 18:50:19 30307758 04/01/2015 06:50:19 PM \n",
"2015-04-01 14:03:43 30313462 04/01/2015 02:03:43 PM \n",
"2015-04-01 11:59:20 30310246 04/01/2015 11:59:20 AM \n",
"2015-04-01 09:17:40 30310085 04/01/2015 09:17:40 AM \n",
"2015-04-01 21:13:08 30314474 04/01/2015 09:13:08 PM \n",
"2015-04-01 12:59:08 30308968 04/01/2015 12:59:08 PM \n",
"2015-04-01 13:31:23 30308389 04/01/2015 01:31:23 PM \n",
"2015-04-01 16:42:15 30309377 04/01/2015 04:42:15 PM \n",
"2015-04-01 13:37:07 30310992 04/01/2015 01:37:07 PM \n",
"2015-04-01 23:44:04 30310652 04/01/2015 11:44:04 PM \n",
"2015-04-01 16:32:12 30309028 04/01/2015 04:32:12 PM \n",
"2015-04-01 08:26:06 30312622 04/01/2015 08:26:06 AM \n",
"2015-04-01 15:08:20 30308371 04/01/2015 03:08:20 PM \n",
"2015-04-01 10:19:21 30311001 04/01/2015 10:19:21 AM \n",
"2015-04-01 20:20:13 30311341 04/01/2015 08:20:13 PM \n",
"2015-04-01 02:16:44 30308863 04/01/2015 02:16:44 AM \n",
"2015-04-01 13:12:58 30307673 04/01/2015 01:12:58 PM \n",
"2015-04-01 13:17:23 30307732 04/01/2015 01:17:23 PM \n",
"2015-04-01 21:39:04 30311958 04/01/2015 09:39:04 PM \n",
"2015-04-01 12:53:45 30309365 04/01/2015 12:53:45 PM \n",
"2015-04-01 10:46:01 30312487 04/01/2015 10:46:01 AM \n",
"\n",
" Closed Date Agency \\\n",
"created_dt \n",
"2015-04-01 21:37:42 04/01/2015 10:49:33 PM NYPD \n",
"2015-04-01 23:12:04 04/01/2015 11:32:40 PM NYPD \n",
"2015-04-01 13:10:35 04/07/2015 04:01:08 PM DPR \n",
"2015-04-01 17:37:38 04/03/2015 11:40:54 AM DPR \n",
"2015-04-01 12:32:40 04/17/2015 01:06:49 AM DCA \n",
"2015-04-01 18:44:50 06/24/2015 11:27:00 AM DPR \n",
"2015-04-01 16:30:15 04/01/2015 11:27:22 PM NYPD \n",
"2015-04-01 09:04:07 04/06/2015 09:17:10 AM DOF \n",
"2015-04-01 07:46:58 04/01/2015 09:32:31 AM NYPD \n",
"2015-04-01 17:12:17 04/09/2015 02:20:11 PM DOT \n",
"2015-04-01 21:30:48 NaN DOHMH \n",
"2015-04-01 15:51:04 04/14/2015 09:23:30 AM DPR \n",
"2015-04-01 10:43:28 NaN DPR \n",
"2015-04-01 15:12:46 06/01/2015 06:25:48 AM DOHMH \n",
"2015-04-01 06:15:42 04/01/2015 10:28:30 AM DOT \n",
"2015-04-01 11:28:02 04/01/2015 11:42:53 AM DOT \n",
"2015-04-01 17:35:18 06/01/2015 06:25:54 AM DOHMH \n",
"2015-04-01 13:54:54 04/06/2015 10:11:11 AM DOF \n",
"2015-04-01 23:49:33 04/02/2015 12:20:59 AM NYPD \n",
"2015-04-01 07:50:49 07/08/2015 02:19:25 PM DOT \n",
"2015-04-01 13:50:29 06/01/2015 06:25:49 AM DOHMH \n",
"2015-04-01 16:14:19 04/01/2015 04:21:53 PM HRA \n",
"2015-04-01 19:27:34 04/01/2015 08:45:17 PM NYPD \n",
"2015-04-01 05:30:02 04/01/2015 02:57:31 PM DOT \n",
"2015-04-01 10:33:26 04/01/2015 11:19:12 AM NYPD \n",
"2015-04-01 11:47:38 04/06/2015 09:23:32 AM DOF \n",
"2015-04-01 11:01:27 04/17/2015 01:06:42 AM DCA \n",
"2015-04-01 08:51:52 04/03/2015 04:33:46 PM DCA \n",
"2015-04-01 14:58:55 04/06/2015 10:06:35 AM DOF \n",
"2015-04-01 16:59:19 04/01/2015 07:48:33 PM NYPD \n",
"... ... ... \n",
"2015-04-01 17:12:09 04/30/2015 06:02:47 PM DOT \n",
"2015-04-01 17:09:29 04/30/2015 05:59:38 PM DOT \n",
"2015-04-01 18:30:22 05/06/2015 10:59:47 AM DOT \n",
"2015-04-01 21:07:21 05/08/2015 11:30:22 AM TLC \n",
"2015-04-01 10:50:12 05/08/2015 10:21:38 AM TLC \n",
"2015-04-01 09:07:38 05/04/2015 10:43:15 AM DPR \n",
"2015-04-01 16:18:25 05/08/2015 11:29:12 AM TLC \n",
"2015-04-01 10:23:09 05/07/2015 02:19:57 PM TLC \n",
"2015-04-01 14:31:57 05/08/2015 06:05:44 PM DPR \n",
"2015-04-01 18:50:19 05/07/2015 07:46:48 AM DPR \n",
"2015-04-01 14:03:43 05/06/2015 12:48:52 PM DOT \n",
"2015-04-01 11:59:20 11/09/2015 03:58:34 PM DOT \n",
"2015-04-01 09:17:40 05/07/2015 06:53:11 PM DOT \n",
"2015-04-01 21:13:08 05/08/2015 11:27:01 AM TLC \n",
"2015-04-01 12:59:08 04/01/2015 12:59:23 PM HRA \n",
"2015-04-01 13:31:23 04/01/2015 01:32:08 PM HRA \n",
"2015-04-01 16:42:15 04/01/2015 04:43:11 PM HRA \n",
"2015-04-01 13:37:07 04/01/2015 01:37:28 PM HRA \n",
"2015-04-01 23:44:04 04/02/2015 01:25:52 AM NYPD \n",
"2015-04-01 16:32:12 05/20/2015 05:36:29 PM TLC \n",
"2015-04-01 08:26:06 06/01/2015 06:25:41 AM DOHMH \n",
"2015-04-01 15:08:20 06/01/2015 06:18:02 PM TLC \n",
"2015-04-01 10:19:21 06/01/2015 06:25:39 AM DOHMH \n",
"2015-04-01 20:20:13 04/01/2015 10:49:32 PM NYPD \n",
"2015-04-01 02:16:44 04/01/2015 02:54:17 AM NYPD \n",
"2015-04-01 13:12:58 04/01/2015 10:01:26 PM NYPD \n",
"2015-04-01 13:17:23 04/01/2015 01:31:22 PM NYPD \n",
"2015-04-01 21:39:04 04/01/2015 09:50:48 PM NYPD \n",
"2015-04-01 12:53:45 04/02/2015 12:04:38 PM DCA \n",
"2015-04-01 10:46:01 04/02/2015 03:34:31 PM DCA \n",
"\n",
" Agency Name \\\n",
"created_dt \n",
"2015-04-01 21:37:42 New York City Police Department \n",
"2015-04-01 23:12:04 New York City Police Department \n",
"2015-04-01 13:10:35 Department of Parks and Recreation \n",
"2015-04-01 17:37:38 Department of Parks and Recreation \n",
"2015-04-01 12:32:40 Department of Consumer Affairs \n",
"2015-04-01 18:44:50 Department of Parks and Recreation \n",
"2015-04-01 16:30:15 New York City Police Department \n",
"2015-04-01 09:04:07 Senior Citizen Rent Increase Exemption Unit \n",
"2015-04-01 07:46:58 New York City Police Department \n",
"2015-04-01 17:12:17 Department of Transportation \n",
"2015-04-01 21:30:48 Department of Health and Mental Hygiene \n",
"2015-04-01 15:51:04 Department of Parks and Recreation \n",
"2015-04-01 10:43:28 Department of Parks and Recreation \n",
"2015-04-01 15:12:46 Department of Health and Mental Hygiene \n",
"2015-04-01 06:15:42 Department of Transportation \n",
"2015-04-01 11:28:02 Department of Transportation \n",
"2015-04-01 17:35:18 Department of Health and Mental Hygiene \n",
"2015-04-01 13:54:54 Senior Citizen Rent Increase Exemption Unit \n",
"2015-04-01 23:49:33 New York City Police Department \n",
"2015-04-01 07:50:49 Department of Transportation \n",
"2015-04-01 13:50:29 Department of Health and Mental Hygiene \n",
"2015-04-01 16:14:19 HRA Benefit Card Replacement \n",
"2015-04-01 19:27:34 New York City Police Department \n",
"2015-04-01 05:30:02 Department of Transportation \n",
"2015-04-01 10:33:26 New York City Police Department \n",
"2015-04-01 11:47:38 Senior Citizen Rent Increase Exemption Unit \n",
"2015-04-01 11:01:27 Department of Consumer Affairs \n",
"2015-04-01 08:51:52 Department of Consumer Affairs \n",
"2015-04-01 14:58:55 Senior Citizen Rent Increase Exemption Unit \n",
"2015-04-01 16:59:19 New York City Police Department \n",
"... ... \n",
"2015-04-01 17:12:09 Department of Transportation \n",
"2015-04-01 17:09:29 Department of Transportation \n",
"2015-04-01 18:30:22 Department of Transportation \n",
"2015-04-01 21:07:21 Taxi and Limousine Commission \n",
"2015-04-01 10:50:12 Taxi and Limousine Commission \n",
"2015-04-01 09:07:38 Department of Parks and Recreation \n",
"2015-04-01 16:18:25 Taxi and Limousine Commission \n",
"2015-04-01 10:23:09 Taxi and Limousine Commission \n",
"2015-04-01 14:31:57 Department of Parks and Recreation \n",
"2015-04-01 18:50:19 Department of Parks and Recreation \n",
"2015-04-01 14:03:43 Department of Transportation \n",
"2015-04-01 11:59:20 Department of Transportation \n",
"2015-04-01 09:17:40 Department of Transportation \n",
"2015-04-01 21:13:08 Taxi and Limousine Commission \n",
"2015-04-01 12:59:08 HRA Benefit Card Replacement \n",
"2015-04-01 13:31:23 HRA Benefit Card Replacement \n",
"2015-04-01 16:42:15 HRA Benefit Card Replacement \n",
"2015-04-01 13:37:07 HRA Benefit Card Replacement \n",
"2015-04-01 23:44:04 New York City Police Department \n",
"2015-04-01 16:32:12 Taxi and Limousine Commission \n",
"2015-04-01 08:26:06 Department of Health and Mental Hygiene \n",
"2015-04-01 15:08:20 Taxi and Limousine Commission \n",
"2015-04-01 10:19:21 Department of Health and Mental Hygiene \n",
"2015-04-01 20:20:13 New York City Police Department \n",
"2015-04-01 02:16:44 New York City Police Department \n",
"2015-04-01 13:12:58 New York City Police Department \n",
"2015-04-01 13:17:23 New York City Police Department \n",
"2015-04-01 21:39:04 New York City Police Department \n",
"2015-04-01 12:53:45 Department of Consumer Affairs \n",
"2015-04-01 10:46:01 Department of Consumer Affairs \n",
"\n",
" Complaint Type \\\n",
"created_dt \n",
"2015-04-01 21:37:42 Illegal Parking \n",
"2015-04-01 23:12:04 Noise - Commercial \n",
"2015-04-01 13:10:35 Root/Sewer/Sidewalk Condition \n",
"2015-04-01 17:37:38 Maintenance or Facility \n",
"2015-04-01 12:32:40 Consumer Complaint \n",
"2015-04-01 18:44:50 Damaged Tree \n",
"2015-04-01 16:30:15 Animal Abuse \n",
"2015-04-01 09:04:07 SCRIE \n",
"2015-04-01 07:46:58 Blocked Driveway \n",
"2015-04-01 17:12:17 Highway Condition \n",
"2015-04-01 21:30:48 Food Establishment \n",
"2015-04-01 15:51:04 Maintenance or Facility \n",
"2015-04-01 10:43:28 Damaged Tree \n",
"2015-04-01 15:12:46 Food Establishment \n",
"2015-04-01 06:15:42 Highway Condition \n",
"2015-04-01 11:28:02 Highway Condition \n",
"2015-04-01 17:35:18 Food Establishment \n",
"2015-04-01 13:54:54 SCRIE \n",
"2015-04-01 23:49:33 Noise - Commercial \n",
"2015-04-01 07:50:49 Street Condition \n",
"2015-04-01 13:50:29 Food Establishment \n",
"2015-04-01 16:14:19 Benefit Card Replacement \n",
"2015-04-01 19:27:34 Noise - Street/Sidewalk \n",
"2015-04-01 05:30:02 Highway Condition \n",
"2015-04-01 10:33:26 Illegal Parking \n",
"2015-04-01 11:47:38 SCRIE \n",
"2015-04-01 11:01:27 Consumer Complaint \n",
"2015-04-01 08:51:52 Consumer Complaint \n",
"2015-04-01 14:58:55 SCRIE \n",
"2015-04-01 16:59:19 Blocked Driveway \n",
"... ... \n",
"2015-04-01 17:12:09 Street Condition \n",
"2015-04-01 17:09:29 Street Condition \n",
"2015-04-01 18:30:22 Street Condition \n",
"2015-04-01 21:07:21 Taxi Complaint \n",
"2015-04-01 10:50:12 Taxi Complaint \n",
"2015-04-01 09:07:38 Root/Sewer/Sidewalk Condition \n",
"2015-04-01 16:18:25 Taxi Complaint \n",
"2015-04-01 10:23:09 Taxi Complaint \n",
"2015-04-01 14:31:57 Dead Tree \n",
"2015-04-01 18:50:19 Damaged Tree \n",
"2015-04-01 14:03:43 Street Condition \n",
"2015-04-01 11:59:20 Street Condition \n",
"2015-04-01 09:17:40 Highway Condition \n",
"2015-04-01 21:13:08 Taxi Complaint \n",
"2015-04-01 12:59:08 Benefit Card Replacement \n",
"2015-04-01 13:31:23 Benefit Card Replacement \n",
"2015-04-01 16:42:15 Benefit Card Replacement \n",
"2015-04-01 13:37:07 Benefit Card Replacement \n",
"2015-04-01 23:44:04 Derelict Vehicle \n",
"2015-04-01 16:32:12 For Hire Vehicle Complaint \n",
"2015-04-01 08:26:06 Food Establishment \n",
"2015-04-01 15:08:20 Taxi Complaint \n",
"2015-04-01 10:19:21 Food Establishment \n",
"2015-04-01 20:20:13 Blocked Driveway \n",
"2015-04-01 02:16:44 Noise - Commercial \n",
"2015-04-01 13:12:58 Illegal Parking \n",
"2015-04-01 13:17:23 Traffic \n",
"2015-04-01 21:39:04 Noise - Vehicle \n",
"2015-04-01 12:53:45 Consumer Complaint \n",
"2015-04-01 10:46:01 Consumer Complaint \n",
"\n",
" Descriptor \\\n",
"created_dt \n",
"2015-04-01 21:37:42 Blocked Sidewalk \n",
"2015-04-01 23:12:04 Loud Music/Party \n",
"2015-04-01 13:10:35 Trees and Sidewalks Program \n",
"2015-04-01 17:37:38 Hours of Operation \n",
"2015-04-01 12:32:40 Installation/Work Quality \n",
"2015-04-01 18:44:50 Entire Tree Has Fallen Down \n",
"2015-04-01 16:30:15 Neglected \n",
"2015-04-01 09:04:07 Miscellaneous \n",
"2015-04-01 07:46:58 No Access \n",
"2015-04-01 17:12:17 Pothole - Highway \n",
"2015-04-01 21:30:48 Food Temperature \n",
"2015-04-01 15:51:04 Hours of Operation \n",
"2015-04-01 10:43:28 Branch Cracked and Will Fall \n",
"2015-04-01 15:12:46 Letter Grading \n",
"2015-04-01 06:15:42 Pothole - Highway \n",
"2015-04-01 11:28:02 Pothole - Highway \n",
"2015-04-01 17:35:18 Rodents/Insects/Garbage \n",
"2015-04-01 13:54:54 Miscellaneous \n",
"2015-04-01 23:49:33 Loud Music/Party \n",
"2015-04-01 07:50:49 Rough, Pitted or Cracked Roads \n",
"2015-04-01 13:50:29 Rodents/Insects/Garbage \n",
"2015-04-01 16:14:19 Medicaid \n",
"2015-04-01 19:27:34 Loud Music/Party \n",
"2015-04-01 05:30:02 Pothole - Highway \n",
"2015-04-01 10:33:26 Blocked Sidewalk \n",
"2015-04-01 11:47:38 Miscellaneous \n",
"2015-04-01 11:01:27 Exchange/Refund/Return \n",
"2015-04-01 08:51:52 Cars Parked on Sidewalk/Street \n",
"2015-04-01 14:58:55 Rent Discrepancy \n",
"2015-04-01 16:59:19 Partial Access \n",
"... ... \n",
"2015-04-01 17:12:09 Line/Marking - Faded \n",
"2015-04-01 17:09:29 Line/Marking - Faded \n",
"2015-04-01 18:30:22 Failed Street Repair \n",
"2015-04-01 21:07:21 Driver Complaint \n",
"2015-04-01 10:50:12 Driver Complaint \n",
"2015-04-01 09:07:38 Trees and Sidewalks Program \n",
"2015-04-01 16:18:25 Driver Complaint \n",
"2015-04-01 10:23:09 Driver Complaint \n",
"2015-04-01 14:31:57 Dead/Dying Tree \n",
"2015-04-01 18:50:19 Branch Cracked and Will Fall \n",
"2015-04-01 14:03:43 Blocked - Construction \n",
"2015-04-01 11:59:20 Rough, Pitted or Cracked Roads \n",
"2015-04-01 09:17:40 Graffiti - Highway \n",
"2015-04-01 21:13:08 Driver Complaint \n",
"2015-04-01 12:59:08 Medicaid \n",
"2015-04-01 13:31:23 Medicaid \n",
"2015-04-01 16:42:15 Food Stamp \n",
"2015-04-01 13:37:07 Food Stamp \n",
"2015-04-01 23:44:04 With License Plate \n",
"2015-04-01 16:32:12 Car Service Company Complaint \n",
"2015-04-01 08:26:06 Facility Construction \n",
"2015-04-01 15:08:20 Driver Complaint \n",
"2015-04-01 10:19:21 Rodents/Insects/Garbage \n",
"2015-04-01 20:20:13 Partial Access \n",
"2015-04-01 02:16:44 Loud Music/Party \n",
"2015-04-01 13:12:58 Posted Parking Sign Violation \n",
"2015-04-01 13:17:23 Congestion/Gridlock \n",
"2015-04-01 21:39:04 Car/Truck Music \n",
"2015-04-01 12:53:45 Overcharge \n",
"2015-04-01 10:46:01 Damaged/Defective Goods \n",
"\n",
" Location Type Incident Zip \\\n",
"created_dt \n",
"2015-04-01 21:37:42 Street/Sidewalk 11234 \n",
"2015-04-01 23:12:04 Store/Commercial 11205 \n",
"2015-04-01 13:10:35 Street 11422 \n",
"2015-04-01 17:37:38 Park 11211 \n",
"2015-04-01 12:32:40 NaN 11423 \n",
"2015-04-01 18:44:50 Street 10467 \n",
"2015-04-01 16:30:15 Residential Building/House 11368 \n",
"2015-04-01 09:04:07 Senior Address 10027 \n",
"2015-04-01 07:46:58 Street/Sidewalk 11370 \n",
"2015-04-01 17:12:17 Highway NaN \n",
"2015-04-01 21:30:48 Restaurant/Bar/Deli/Bakery 11215 \n",
"2015-04-01 15:51:04 Park 11210 \n",
"2015-04-01 10:43:28 NaN 10009 \n",
"2015-04-01 15:12:46 Restaurant/Bar/Deli/Bakery 11238 \n",
"2015-04-01 06:15:42 Highway 10304 \n",
"2015-04-01 11:28:02 Highway 11432 \n",
"2015-04-01 17:35:18 Restaurant/Bar/Deli/Bakery 10011 \n",
"2015-04-01 13:54:54 Senior Address 11435 \n",
"2015-04-01 23:49:33 Store/Commercial 10003 \n",
"2015-04-01 07:50:49 Street 11385 \n",
"2015-04-01 13:50:29 Restaurant/Bar/Deli/Bakery 10028 \n",
"2015-04-01 16:14:19 NYC Street Address NaN \n",
"2015-04-01 19:27:34 Street/Sidewalk 10017 \n",
"2015-04-01 05:30:02 Highway NaN \n",
"2015-04-01 10:33:26 Street/Sidewalk 10033 \n",
"2015-04-01 11:47:38 Senior Address 11355 \n",
"2015-04-01 11:01:27 NaN 10455 \n",
"2015-04-01 08:51:52 NaN 11223 \n",
"2015-04-01 14:58:55 Senior Address 11201 \n",
"2015-04-01 16:59:19 Street/Sidewalk 11210 \n",
"... ... ... \n",
"2015-04-01 17:12:09 Street 11207 \n",
"2015-04-01 17:09:29 Street 11203 \n",
"2015-04-01 18:30:22 Street 11234 \n",
"2015-04-01 21:07:21 NaN 10001 \n",
"2015-04-01 10:50:12 NaN 10032 \n",
"2015-04-01 09:07:38 Street 10307 \n",
"2015-04-01 16:18:25 NaN 11369 \n",
"2015-04-01 10:23:09 Street 10021 \n",
"2015-04-01 14:31:57 Street 11229 \n",
"2015-04-01 18:50:19 Street NaN \n",
"2015-04-01 14:03:43 Street 11209 \n",
"2015-04-01 11:59:20 Street 11217 \n",
"2015-04-01 09:17:40 Highway NaN \n",
"2015-04-01 21:13:08 NaN 11429 \n",
"2015-04-01 12:59:08 Address Outside of NYC NaN \n",
"2015-04-01 13:31:23 NYC Street Address NaN \n",
"2015-04-01 16:42:15 NYC Street Address NaN \n",
"2015-04-01 13:37:07 NYC Street Address NaN \n",
"2015-04-01 23:44:04 Street/Sidewalk 11421 \n",
"2015-04-01 16:32:12 Street 10451 \n",
"2015-04-01 08:26:06 Restaurant/Bar/Deli/Bakery 11234 \n",
"2015-04-01 15:08:20 Street 10128 \n",
"2015-04-01 10:19:21 Restaurant/Bar/Deli/Bakery 11377 \n",
"2015-04-01 20:20:13 Street/Sidewalk 11691 \n",
"2015-04-01 02:16:44 Club/Bar/Restaurant 10013 \n",
"2015-04-01 13:12:58 Street/Sidewalk 10306 \n",
"2015-04-01 13:17:23 Street/Sidewalk 10013 \n",
"2015-04-01 21:39:04 Street/Sidewalk 11207 \n",
"2015-04-01 12:53:45 NaN 11418 \n",
"2015-04-01 10:46:01 NaN 11232 \n",
"\n",
" Incident Address ... \\\n",
"created_dt ... \n",
"2015-04-01 21:37:42 NaN ... \n",
"2015-04-01 23:12:04 700 MYRTLE AVENUE ... \n",
"2015-04-01 13:10:35 245-16 149 AVENUE ... \n",
"2015-04-01 17:37:38 NaN ... \n",
"2015-04-01 12:32:40 90-71 198 STREET ... \n",
"2015-04-01 18:44:50 862 EAST 213 STREET ... \n",
"2015-04-01 16:30:15 107-15 NORTHERN BOULEVARD ... \n",
"2015-04-01 09:04:07 NaN ... \n",
"2015-04-01 07:46:58 32-51 80 STREET ... \n",
"2015-04-01 17:12:17 NaN ... \n",
"2015-04-01 21:30:48 709 5 AVENUE ... \n",
"2015-04-01 15:51:04 NaN ... \n",
"2015-04-01 10:43:28 620 EAST 12TH STREET ... \n",
"2015-04-01 15:12:46 663 FRANKLIN AVENUE ... \n",
"2015-04-01 06:15:42 NaN ... \n",
"2015-04-01 11:28:02 NaN ... \n",
"2015-04-01 17:35:18 140 WEST 13 STREET ... \n",
"2015-04-01 13:54:54 NaN ... \n",
"2015-04-01 23:49:33 36 SAINT MARKS PLACE ... \n",
"2015-04-01 07:50:49 NaN ... \n",
"2015-04-01 13:50:29 1291 LEXINGTON AVENUE ... \n",
"2015-04-01 16:14:19 NaN ... \n",
"2015-04-01 19:27:34 210 EAST 46 STREET ... \n",
"2015-04-01 05:30:02 NaN ... \n",
"2015-04-01 10:33:26 2284 AMSTERDAM AVENUE ... \n",
"2015-04-01 11:47:38 NaN ... \n",
"2015-04-01 11:01:27 2997 3 AVENUE ... \n",
"2015-04-01 08:51:52 1701 WEST 8 STREET ... \n",
"2015-04-01 14:58:55 NaN ... \n",
"2015-04-01 16:59:19 650 EAST 24 STREET ... \n",
"... ... ... \n",
"2015-04-01 17:12:09 NaN ... \n",
"2015-04-01 17:09:29 NaN ... \n",
"2015-04-01 18:30:22 J AVENUE ... \n",
"2015-04-01 21:07:21 511 WEST 25 STREET ... \n",
"2015-04-01 10:50:12 NaN ... \n",
"2015-04-01 09:07:38 647 CRAIG AVENUE ... \n",
"2015-04-01 16:18:25 22-19 93 STREET ... \n",
"2015-04-01 10:23:09 NaN ... \n",
"2015-04-01 14:31:57 2056 EAST 29 STREET ... \n",
"2015-04-01 18:50:19 NaN ... \n",
"2015-04-01 14:03:43 NaN ... \n",
"2015-04-01 11:59:20 90 PROSPECT PLACE ... \n",
"2015-04-01 09:17:40 NaN ... \n",
"2015-04-01 21:13:08 214-16 110 AVENUE ... \n",
"2015-04-01 12:59:08 NaN ... \n",
"2015-04-01 13:31:23 NaN ... \n",
"2015-04-01 16:42:15 NaN ... \n",
"2015-04-01 13:37:07 NaN ... \n",
"2015-04-01 23:44:04 85-86 87 STREET ... \n",
"2015-04-01 16:32:12 215 EAST 161 STREET ... \n",
"2015-04-01 08:26:06 2301 FLATBUSH AVENUE ... \n",
"2015-04-01 15:08:20 NaN ... \n",
"2015-04-01 10:19:21 59-21 ROOSEVELT AVENUE ... \n",
"2015-04-01 20:20:13 348 BEACH 40 STREET ... \n",
"2015-04-01 02:16:44 301 CHURCH STREET ... \n",
"2015-04-01 13:12:58 200 ADELAIDE AVENUE ... \n",
"2015-04-01 13:17:23 NaN ... \n",
"2015-04-01 21:39:04 184 JEROME STREET ... \n",
"2015-04-01 12:53:45 NaN ... \n",
"2015-04-01 10:46:01 807 42 STREET ... \n",
"\n",
" Bridge Highway Direction Road Ramp \\\n",
"created_dt \n",
"2015-04-01 21:37:42 NaN NaN \n",
"2015-04-01 23:12:04 NaN NaN \n",
"2015-04-01 13:10:35 NaN NaN \n",
"2015-04-01 17:37:38 NaN NaN \n",
"2015-04-01 12:32:40 NaN NaN \n",
"2015-04-01 18:44:50 NaN NaN \n",
"2015-04-01 16:30:15 NaN NaN \n",
"2015-04-01 09:04:07 NaN NaN \n",
"2015-04-01 07:46:58 NaN NaN \n",
"2015-04-01 17:12:17 West/Manhattan Bound Roadway \n",
"2015-04-01 21:30:48 NaN NaN \n",
"2015-04-01 15:51:04 NaN NaN \n",
"2015-04-01 10:43:28 NaN NaN \n",
"2015-04-01 15:12:46 NaN NaN \n",
"2015-04-01 06:15:42 East/Brooklyn Bound Roadway \n",
"2015-04-01 11:28:02 West/Toward Triborough Br Ramp \n",
"2015-04-01 17:35:18 NaN NaN \n",
"2015-04-01 13:54:54 NaN NaN \n",
"2015-04-01 23:49:33 NaN NaN \n",
"2015-04-01 07:50:49 NaN NaN \n",
"2015-04-01 13:50:29 NaN NaN \n",
"2015-04-01 16:14:19 NaN NaN \n",
"2015-04-01 19:27:34 NaN NaN \n",
"2015-04-01 05:30:02 East/Queens Bound Roadway \n",
"2015-04-01 10:33:26 NaN NaN \n",
"2015-04-01 11:47:38 NaN NaN \n",
"2015-04-01 11:01:27 NaN NaN \n",
"2015-04-01 08:51:52 NaN NaN \n",
"2015-04-01 14:58:55 NaN NaN \n",
"2015-04-01 16:59:19 NaN NaN \n",
"... ... ... \n",
"2015-04-01 17:12:09 NaN NaN \n",
"2015-04-01 17:09:29 NaN NaN \n",
"2015-04-01 18:30:22 NaN NaN \n",
"2015-04-01 21:07:21 NaN NaN \n",
"2015-04-01 10:50:12 NaN NaN \n",
"2015-04-01 09:07:38 NaN NaN \n",
"2015-04-01 16:18:25 NaN NaN \n",
"2015-04-01 10:23:09 NaN NaN \n",
"2015-04-01 14:31:57 NaN NaN \n",
"2015-04-01 18:50:19 NaN NaN \n",
"2015-04-01 14:03:43 NaN NaN \n",
"2015-04-01 11:59:20 NaN NaN \n",
"2015-04-01 09:17:40 West/Staten Island Bound Roadway \n",
"2015-04-01 21:13:08 NaN NaN \n",
"2015-04-01 12:59:08 NaN NaN \n",
"2015-04-01 13:31:23 NaN NaN \n",
"2015-04-01 16:42:15 NaN NaN \n",
"2015-04-01 13:37:07 NaN NaN \n",
"2015-04-01 23:44:04 NaN NaN \n",
"2015-04-01 16:32:12 NaN NaN \n",
"2015-04-01 08:26:06 NaN NaN \n",
"2015-04-01 15:08:20 NaN NaN \n",
"2015-04-01 10:19:21 NaN NaN \n",
"2015-04-01 20:20:13 NaN NaN \n",
"2015-04-01 02:16:44 NaN NaN \n",
"2015-04-01 13:12:58 NaN NaN \n",
"2015-04-01 13:17:23 NaN NaN \n",
"2015-04-01 21:39:04 NaN NaN \n",
"2015-04-01 12:53:45 NaN NaN \n",
"2015-04-01 10:46:01 NaN NaN \n",
"\n",
" Bridge Highway Segment \\\n",
"created_dt \n",
"2015-04-01 21:37:42 NaN \n",
"2015-04-01 23:12:04 NaN \n",
"2015-04-01 13:10:35 NaN \n",
"2015-04-01 17:37:38 NaN \n",
"2015-04-01 12:32:40 NaN \n",
"2015-04-01 18:44:50 NaN \n",
"2015-04-01 16:30:15 NaN \n",
"2015-04-01 09:04:07 NaN \n",
"2015-04-01 07:46:58 NaN \n",
"2015-04-01 17:12:17 Clearview Expwy (I-295) (Exit 27 S-N) - Utopia... \n",
"2015-04-01 21:30:48 NaN \n",
"2015-04-01 15:51:04 NaN \n",
"2015-04-01 10:43:28 NaN \n",
"2015-04-01 15:12:46 NaN \n",
"2015-04-01 06:15:42 Clove Rd/Richmond Rd (Exit 13) - Lily Pond Ave... \n",
"2015-04-01 11:28:02 168th St (Exit 17) \n",
"2015-04-01 17:35:18 NaN \n",
"2015-04-01 13:54:54 NaN \n",
"2015-04-01 23:49:33 NaN \n",
"2015-04-01 07:50:49 NaN \n",
"2015-04-01 13:50:29 NaN \n",
"2015-04-01 16:14:19 NaN \n",
"2015-04-01 19:27:34 NaN \n",
"2015-04-01 05:30:02 Williamsburg Br / Metropolitan Ave (Exit 32) -... \n",
"2015-04-01 10:33:26 NaN \n",
"2015-04-01 11:47:38 NaN \n",
"2015-04-01 11:01:27 NaN \n",
"2015-04-01 08:51:52 NaN \n",
"2015-04-01 14:58:55 NaN \n",
"2015-04-01 16:59:19 NaN \n",
"... ... \n",
"2015-04-01 17:12:09 NaN \n",
"2015-04-01 17:09:29 NaN \n",
"2015-04-01 18:30:22 NaN \n",
"2015-04-01 21:07:21 NaN \n",
"2015-04-01 10:50:12 NaN \n",
"2015-04-01 09:07:38 NaN \n",
"2015-04-01 16:18:25 NaN \n",
"2015-04-01 10:23:09 NaN \n",
"2015-04-01 14:31:57 NaN \n",
"2015-04-01 18:50:19 NaN \n",
"2015-04-01 14:03:43 NaN \n",
"2015-04-01 11:59:20 NaN \n",
"2015-04-01 09:17:40 Crospey Ave Stillwell Ave (Exit 6N) - Crospey ... \n",
"2015-04-01 21:13:08 NaN \n",
"2015-04-01 12:59:08 NaN \n",
"2015-04-01 13:31:23 NaN \n",
"2015-04-01 16:42:15 NaN \n",
"2015-04-01 13:37:07 NaN \n",
"2015-04-01 23:44:04 NaN \n",
"2015-04-01 16:32:12 NaN \n",
"2015-04-01 08:26:06 NaN \n",
"2015-04-01 15:08:20 NaN \n",
"2015-04-01 10:19:21 NaN \n",
"2015-04-01 20:20:13 NaN \n",
"2015-04-01 02:16:44 NaN \n",
"2015-04-01 13:12:58 NaN \n",
"2015-04-01 13:17:23 NaN \n",
"2015-04-01 21:39:04 NaN \n",
"2015-04-01 12:53:45 NaN \n",
"2015-04-01 10:46:01 NaN \n",
"\n",
" Garage Lot Name Ferry Direction Ferry Terminal Name \\\n",
"created_dt \n",
"2015-04-01 21:37:42 NaN NaN NaN \n",
"2015-04-01 23:12:04 NaN NaN NaN \n",
"2015-04-01 13:10:35 NaN NaN NaN \n",
"2015-04-01 17:37:38 NaN NaN NaN \n",
"2015-04-01 12:32:40 NaN NaN NaN \n",
"2015-04-01 18:44:50 NaN NaN NaN \n",
"2015-04-01 16:30:15 NaN NaN NaN \n",
"2015-04-01 09:04:07 NaN NaN NaN \n",
"2015-04-01 07:46:58 NaN NaN NaN \n",
"2015-04-01 17:12:17 NaN NaN NaN \n",
"2015-04-01 21:30:48 NaN NaN NaN \n",
"2015-04-01 15:51:04 NaN NaN NaN \n",
"2015-04-01 10:43:28 NaN NaN NaN \n",
"2015-04-01 15:12:46 NaN NaN NaN \n",
"2015-04-01 06:15:42 NaN NaN NaN \n",
"2015-04-01 11:28:02 NaN NaN NaN \n",
"2015-04-01 17:35:18 NaN NaN NaN \n",
"2015-04-01 13:54:54 NaN NaN NaN \n",
"2015-04-01 23:49:33 NaN NaN NaN \n",
"2015-04-01 07:50:49 NaN NaN NaN \n",
"2015-04-01 13:50:29 NaN NaN NaN \n",
"2015-04-01 16:14:19 NaN NaN NaN \n",
"2015-04-01 19:27:34 NaN NaN NaN \n",
"2015-04-01 05:30:02 NaN NaN NaN \n",
"2015-04-01 10:33:26 NaN NaN NaN \n",
"2015-04-01 11:47:38 NaN NaN NaN \n",
"2015-04-01 11:01:27 NaN NaN NaN \n",
"2015-04-01 08:51:52 NaN NaN NaN \n",
"2015-04-01 14:58:55 NaN NaN NaN \n",
"2015-04-01 16:59:19 NaN NaN NaN \n",
"... ... ... ... \n",
"2015-04-01 17:12:09 NaN NaN NaN \n",
"2015-04-01 17:09:29 NaN NaN NaN \n",
"2015-04-01 18:30:22 NaN NaN NaN \n",
"2015-04-01 21:07:21 NaN NaN NaN \n",
"2015-04-01 10:50:12 NaN NaN NaN \n",
"2015-04-01 09:07:38 NaN NaN NaN \n",
"2015-04-01 16:18:25 NaN NaN NaN \n",
"2015-04-01 10:23:09 NaN NaN NaN \n",
"2015-04-01 14:31:57 NaN NaN NaN \n",
"2015-04-01 18:50:19 NaN NaN NaN \n",
"2015-04-01 14:03:43 NaN NaN NaN \n",
"2015-04-01 11:59:20 NaN NaN NaN \n",
"2015-04-01 09:17:40 NaN NaN NaN \n",
"2015-04-01 21:13:08 NaN NaN NaN \n",
"2015-04-01 12:59:08 NaN NaN NaN \n",
"2015-04-01 13:31:23 NaN NaN NaN \n",
"2015-04-01 16:42:15 NaN NaN NaN \n",
"2015-04-01 13:37:07 NaN NaN NaN \n",
"2015-04-01 23:44:04 NaN NaN NaN \n",
"2015-04-01 16:32:12 NaN NaN NaN \n",
"2015-04-01 08:26:06 NaN NaN NaN \n",
"2015-04-01 15:08:20 NaN NaN NaN \n",
"2015-04-01 10:19:21 NaN NaN NaN \n",
"2015-04-01 20:20:13 NaN NaN NaN \n",
"2015-04-01 02:16:44 NaN NaN NaN \n",
"2015-04-01 13:12:58 NaN NaN NaN \n",
"2015-04-01 13:17:23 NaN NaN NaN \n",
"2015-04-01 21:39:04 NaN NaN NaN \n",
"2015-04-01 12:53:45 NaN NaN NaN \n",
"2015-04-01 10:46:01 NaN NaN NaN \n",
"\n",
" Latitude Longitude \\\n",
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"2015-04-01 21:37:42 40.609810 -73.922498 \n",
"2015-04-01 23:12:04 40.694644 -73.955504 \n",
"2015-04-01 13:10:35 40.653016 -73.738626 \n",
"2015-04-01 17:37:38 NaN NaN \n",
"2015-04-01 12:32:40 40.714299 -73.761158 \n",
"2015-04-01 18:44:50 40.878028 -73.860237 \n",
"2015-04-01 16:30:15 40.757811 -73.861677 \n",
"2015-04-01 09:04:07 NaN NaN \n",
"2015-04-01 07:46:58 40.756412 -73.887405 \n",
"2015-04-01 17:12:17 NaN NaN \n",
"2015-04-01 21:30:48 40.660699 -73.994082 \n",
"2015-04-01 15:51:04 40.621474 -73.950711 \n",
"2015-04-01 10:43:28 40.727725 -73.978204 \n",
"2015-04-01 15:12:46 40.675746 -73.956122 \n",
"2015-04-01 06:15:42 40.606875 -74.085408 \n",
"2015-04-01 11:28:02 40.719228 -73.791963 \n",
"2015-04-01 17:35:18 40.737182 -73.998585 \n",
"2015-04-01 13:54:54 NaN NaN \n",
"2015-04-01 23:49:33 40.728733 -73.988011 \n",
"2015-04-01 07:50:49 40.703414 -73.862854 \n",
"2015-04-01 13:50:29 40.780069 -73.955158 \n",
"2015-04-01 16:14:19 NaN NaN \n",
"2015-04-01 19:27:34 40.753104 -73.972096 \n",
"2015-04-01 05:30:02 NaN NaN \n",
"2015-04-01 10:33:26 40.843149 -73.934539 \n",
"2015-04-01 11:47:38 NaN NaN \n",
"2015-04-01 11:01:27 40.819111 -73.913908 \n",
"2015-04-01 08:51:52 40.605657 -73.981194 \n",
"2015-04-01 14:58:55 NaN NaN \n",
"2015-04-01 16:59:19 40.634497 -73.954167 \n",
"... ... ... \n",
"2015-04-01 17:12:09 40.679561 -73.898899 \n",
"2015-04-01 17:09:29 40.658529 -73.939568 \n",
"2015-04-01 18:30:22 40.628542 -73.921838 \n",
"2015-04-01 21:07:21 40.749380 -74.004169 \n",
"2015-04-01 10:50:12 40.842154 -73.942278 \n",
"2015-04-01 09:07:38 40.506708 -74.252182 \n",
"2015-04-01 16:18:25 40.769574 -73.877480 \n",
"2015-04-01 10:23:09 40.765546 -73.954702 \n",
"2015-04-01 14:31:57 40.601403 -73.943106 \n",
"2015-04-01 18:50:19 40.720103 -73.790376 \n",
"2015-04-01 14:03:43 40.633428 -74.032876 \n",
"2015-04-01 11:59:20 40.679040 -73.974579 \n",
"2015-04-01 09:17:40 NaN NaN \n",
"2015-04-01 21:13:08 40.708131 -73.743041 \n",
"2015-04-01 12:59:08 NaN NaN \n",
"2015-04-01 13:31:23 NaN NaN \n",
"2015-04-01 16:42:15 NaN NaN \n",
"2015-04-01 13:37:07 NaN NaN \n",
"2015-04-01 23:44:04 40.694888 -73.857927 \n",
"2015-04-01 16:32:12 40.826235 -73.920529 \n",
"2015-04-01 08:26:06 40.613889 -73.927186 \n",
"2015-04-01 15:08:20 40.781533 -73.958320 \n",
"2015-04-01 10:19:21 40.745586 -73.904573 \n",
"2015-04-01 20:20:13 40.595019 -73.772153 \n",
"2015-04-01 02:16:44 40.719322 -74.004470 \n",
"2015-04-01 13:12:58 40.561690 -74.124622 \n",
"2015-04-01 13:17:23 40.720557 -74.003510 \n",
"2015-04-01 21:39:04 40.677739 -73.887888 \n",
"2015-04-01 12:53:45 40.700108 -73.832667 \n",
"2015-04-01 10:46:01 40.645348 -73.998616 \n",
"\n",
" Location \\\n",
"created_dt \n",
"2015-04-01 21:37:42 (40.60980966645303, -73.92249759633725) \n",
"2015-04-01 23:12:04 (40.694643700748486, -73.95550356170298) \n",
"2015-04-01 13:10:35 (40.653016256598534, -73.73862588133056) \n",
"2015-04-01 17:37:38 NaN \n",
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"2015-04-01 18:44:50 (40.87802828144708, -73.86023734606933) \n",
"2015-04-01 16:30:15 (40.757811195752154, -73.86167714731972) \n",
"2015-04-01 09:04:07 NaN \n",
"2015-04-01 07:46:58 (40.75641194675221, -73.88740503059863) \n",
"2015-04-01 17:12:17 NaN \n",
"2015-04-01 21:30:48 (40.660699296661825, -73.99408169463258) \n",
"2015-04-01 15:51:04 (40.62147413119333, -73.95071097029123) \n",
"2015-04-01 10:43:28 (40.72772462544187, -73.97820435916094) \n",
"2015-04-01 15:12:46 (40.67574618440852, -73.9561218336512) \n",
"2015-04-01 06:15:42 (40.60687536641399, -74.0854077221027) \n",
"2015-04-01 11:28:02 (40.71922760413319, -73.791962929951) \n",
"2015-04-01 17:35:18 (40.737182358685516, -73.99858548189518) \n",
"2015-04-01 13:54:54 NaN \n",
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"2015-04-01 07:50:49 (40.70341423569781, -73.86285397616253) \n",
"2015-04-01 13:50:29 (40.78006850471446, -73.95515761412761) \n",
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"2015-04-01 05:30:02 NaN \n",
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"2015-04-01 11:47:38 NaN \n",
"2015-04-01 11:01:27 (40.819110789789214, -73.91390802507868) \n",
"2015-04-01 08:51:52 (40.60565667868274, -73.98119372058547) \n",
"2015-04-01 14:58:55 NaN \n",
"2015-04-01 16:59:19 (40.63449684441219, -73.95416735372353) \n",
"... ... \n",
"2015-04-01 17:12:09 (40.67956105192572, -73.89889884573184) \n",
"2015-04-01 17:09:29 (40.6585289219231, -73.93956820621213) \n",
"2015-04-01 18:30:22 (40.62854243316789, -73.92183818389044) \n",
"2015-04-01 21:07:21 (40.74937996228322, -74.00416853967121) \n",
"2015-04-01 10:50:12 (40.84215388602991, -73.94227827092928) \n",
"2015-04-01 09:07:38 (40.50670803830861, -74.25218246259357) \n",
"2015-04-01 16:18:25 (40.769573850244676, -73.8774799367093) \n",
"2015-04-01 10:23:09 (40.765545913197165, -73.95470170187454) \n",
"2015-04-01 14:31:57 (40.60140342407911, -73.94310580244269) \n",
"2015-04-01 18:50:19 (40.72010305201917, -73.79037648278602) \n",
"2015-04-01 14:03:43 (40.63342806685948, -74.03287604669814) \n",
"2015-04-01 11:59:20 (40.67903998236064, -73.97457889877462) \n",
"2015-04-01 09:17:40 NaN \n",
"2015-04-01 21:13:08 (40.70813050331176, -73.74304104617282) \n",
"2015-04-01 12:59:08 NaN \n",
"2015-04-01 13:31:23 NaN \n",
"2015-04-01 16:42:15 NaN \n",
"2015-04-01 13:37:07 NaN \n",
"2015-04-01 23:44:04 (40.69488849346232, -73.85792744070989) \n",
"2015-04-01 16:32:12 (40.8262353417949, -73.92052920426786) \n",
"2015-04-01 08:26:06 (40.61388875283825, -73.92718600732812) \n",
"2015-04-01 15:08:20 (40.78153263581957, -73.9583197488706) \n",
"2015-04-01 10:19:21 (40.74558568959288, -73.90457292624892) \n",
"2015-04-01 20:20:13 (40.5950185756628, -73.77215306630436) \n",
"2015-04-01 02:16:44 (40.71932215308254, -74.00446968948569) \n",
"2015-04-01 13:12:58 (40.5616902523158, -74.12462211525013) \n",
"2015-04-01 13:17:23 (40.72055732795014, -74.00351016018516) \n",
"2015-04-01 21:39:04 (40.677739297670584, -73.8878875660618) \n",
"2015-04-01 12:53:45 (40.70010803283339, -73.83266746664873) \n",
"2015-04-01 10:46:01 (40.64534787518196, -73.99861625677346) \n",
"\n",
" created_dt \n",
"created_dt \n",
"2015-04-01 21:37:42 2015-04-01 21:37:42 \n",
"2015-04-01 23:12:04 2015-04-01 23:12:04 \n",
"2015-04-01 13:10:35 2015-04-01 13:10:35 \n",
"2015-04-01 17:37:38 2015-04-01 17:37:38 \n",
"2015-04-01 12:32:40 2015-04-01 12:32:40 \n",
"2015-04-01 18:44:50 2015-04-01 18:44:50 \n",
"2015-04-01 16:30:15 2015-04-01 16:30:15 \n",
"2015-04-01 09:04:07 2015-04-01 09:04:07 \n",
"2015-04-01 07:46:58 2015-04-01 07:46:58 \n",
"2015-04-01 17:12:17 2015-04-01 17:12:17 \n",
"2015-04-01 21:30:48 2015-04-01 21:30:48 \n",
"2015-04-01 15:51:04 2015-04-01 15:51:04 \n",
"2015-04-01 10:43:28 2015-04-01 10:43:28 \n",
"2015-04-01 15:12:46 2015-04-01 15:12:46 \n",
"2015-04-01 06:15:42 2015-04-01 06:15:42 \n",
"2015-04-01 11:28:02 2015-04-01 11:28:02 \n",
"2015-04-01 17:35:18 2015-04-01 17:35:18 \n",
"2015-04-01 13:54:54 2015-04-01 13:54:54 \n",
"2015-04-01 23:49:33 2015-04-01 23:49:33 \n",
"2015-04-01 07:50:49 2015-04-01 07:50:49 \n",
"2015-04-01 13:50:29 2015-04-01 13:50:29 \n",
"2015-04-01 16:14:19 2015-04-01 16:14:19 \n",
"2015-04-01 19:27:34 2015-04-01 19:27:34 \n",
"2015-04-01 05:30:02 2015-04-01 05:30:02 \n",
"2015-04-01 10:33:26 2015-04-01 10:33:26 \n",
"2015-04-01 11:47:38 2015-04-01 11:47:38 \n",
"2015-04-01 11:01:27 2015-04-01 11:01:27 \n",
"2015-04-01 08:51:52 2015-04-01 08:51:52 \n",
"2015-04-01 14:58:55 2015-04-01 14:58:55 \n",
"2015-04-01 16:59:19 2015-04-01 16:59:19 \n",
"... ... \n",
"2015-04-01 17:12:09 2015-04-01 17:12:09 \n",
"2015-04-01 17:09:29 2015-04-01 17:09:29 \n",
"2015-04-01 18:30:22 2015-04-01 18:30:22 \n",
"2015-04-01 21:07:21 2015-04-01 21:07:21 \n",
"2015-04-01 10:50:12 2015-04-01 10:50:12 \n",
"2015-04-01 09:07:38 2015-04-01 09:07:38 \n",
"2015-04-01 16:18:25 2015-04-01 16:18:25 \n",
"2015-04-01 10:23:09 2015-04-01 10:23:09 \n",
"2015-04-01 14:31:57 2015-04-01 14:31:57 \n",
"2015-04-01 18:50:19 2015-04-01 18:50:19 \n",
"2015-04-01 14:03:43 2015-04-01 14:03:43 \n",
"2015-04-01 11:59:20 2015-04-01 11:59:20 \n",
"2015-04-01 09:17:40 2015-04-01 09:17:40 \n",
"2015-04-01 21:13:08 2015-04-01 21:13:08 \n",
"2015-04-01 12:59:08 2015-04-01 12:59:08 \n",
"2015-04-01 13:31:23 2015-04-01 13:31:23 \n",
"2015-04-01 16:42:15 2015-04-01 16:42:15 \n",
"2015-04-01 13:37:07 2015-04-01 13:37:07 \n",
"2015-04-01 23:44:04 2015-04-01 23:44:04 \n",
"2015-04-01 16:32:12 2015-04-01 16:32:12 \n",
"2015-04-01 08:26:06 2015-04-01 08:26:06 \n",
"2015-04-01 15:08:20 2015-04-01 15:08:20 \n",
"2015-04-01 10:19:21 2015-04-01 10:19:21 \n",
"2015-04-01 20:20:13 2015-04-01 20:20:13 \n",
"2015-04-01 02:16:44 2015-04-01 02:16:44 \n",
"2015-04-01 13:12:58 2015-04-01 13:12:58 \n",
"2015-04-01 13:17:23 2015-04-01 13:17:23 \n",
"2015-04-01 21:39:04 2015-04-01 21:39:04 \n",
"2015-04-01 12:53:45 2015-04-01 12:53:45 \n",
"2015-04-01 10:46:01 2015-04-01 10:46:01 \n",
"\n",
"[573 rows x 54 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['2015-04-01']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What was the most popular type of complaint on April 1st?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What were the **most popular three types of complaint** on April 1st"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Illegal Parking 67\n",
"Street Condition 64\n",
"Blocked Driveway 58\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['2015-04-01']['Complaint Type'].value_counts().head(3)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 200000 entries, 2015-07-06 10:58:27 to 2015-06-09 12:48:25\n",
"Data columns (total 54 columns):\n",
"Unique Key 200000 non-null int64\n",
"Created Date 200000 non-null object\n",
"Closed Date 188913 non-null object\n",
"Agency 200000 non-null object\n",
"Agency Name 200000 non-null object\n",
"Complaint Type 200000 non-null object\n",
"Descriptor 198197 non-null object\n",
"Location Type 179328 non-null object\n",
"Incident Zip 181049 non-null object\n",
"Incident Address 152173 non-null object\n",
"Street Name 152152 non-null object\n",
"Cross Street 1 108035 non-null object\n",
"Cross Street 2 107583 non-null object\n",
"Intersection Street 1 24790 non-null object\n",
"Intersection Street 2 24530 non-null object\n",
"Address Type 177091 non-null object\n",
"City 181095 non-null object\n",
"Landmark 127 non-null object\n",
"Facility Type 80031 non-null object\n",
"Status 199998 non-null object\n",
"Due Date 152018 non-null object\n",
"Resolution Description 198936 non-null object\n",
"Resolution Action Updated Date 188529 non-null object\n",
"Community Board 200000 non-null object\n",
"Borough 200000 non-null object\n",
"X Coordinate (State Plane) 175825 non-null float64\n",
"Y Coordinate (State Plane) 175825 non-null float64\n",
"Park Facility Name 200000 non-null object\n",
"Park Borough 200000 non-null object\n",
"School Name 200000 non-null object\n",
"School Number 199907 non-null object\n",
"School Region 197128 non-null object\n",
"School Code 197128 non-null object\n",
"School Phone Number 200000 non-null object\n",
"School Address 200000 non-null object\n",
"School City 200000 non-null object\n",
"School State 200000 non-null object\n",
"School Zip 199999 non-null object\n",
"School Not Found 151897 non-null object\n",
"School or Citywide Complaint 0 non-null float64\n",
"Vehicle Type 34 non-null object\n",
"Taxi Company Borough 434 non-null object\n",
"Taxi Pick Up Location 3680 non-null object\n",
"Bridge Highway Name 1960 non-null object\n",
"Bridge Highway Direction 1959 non-null object\n",
"Road Ramp 1946 non-null object\n",
"Bridge Highway Segment 2134 non-null object\n",
"Garage Lot Name 143 non-null object\n",
"Ferry Direction 86 non-null object\n",
"Ferry Terminal Name 215 non-null object\n",
"Latitude 175825 non-null float64\n",
"Longitude 175825 non-null float64\n",
"Location 175825 non-null object\n",
"created_dt 200000 non-null datetime64[ns]\n",
"dtypes: datetime64[ns](1), float64(5), int64(1), object(47)\n",
"memory usage: 83.9+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What month has the most reports filed?** How many? Graph it."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <td>15025</td>\n",
" <td>15025</td>\n",
" <td>15025</td>\n",
" <td>14931</td>\n",
" <td>13742</td>\n",
" <td>13833</td>\n",
" <td>10775</td>\n",
" <td>...</td>\n",
" <td>704</td>\n",
" <td>702</td>\n",
" <td>702</td>\n",
" <td>20</td>\n",
" <td>10</td>\n",
" <td>22</td>\n",
" <td>13444</td>\n",
" <td>13444</td>\n",
" <td>13444</td>\n",
" <td>15025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-30</th>\n",
" <td>20087</td>\n",
" <td>20087</td>\n",
" <td>19131</td>\n",
" <td>20087</td>\n",
" <td>20087</td>\n",
" <td>20087</td>\n",
" <td>19921</td>\n",
" <td>17250</td>\n",
" <td>17292</td>\n",
" <td>13809</td>\n",
" <td>...</td>\n",
" <td>311</td>\n",
" <td>307</td>\n",
" <td>346</td>\n",
" <td>15</td>\n",
" <td>9</td>\n",
" <td>18</td>\n",
" <td>16692</td>\n",
" <td>16692</td>\n",
" <td>16692</td>\n",
" <td>20087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-31</th>\n",
" <td>49715</td>\n",
" <td>49715</td>\n",
" <td>47090</td>\n",
" <td>49715</td>\n",
" <td>49715</td>\n",
" <td>49715</td>\n",
" <td>49287</td>\n",
" <td>42564</td>\n",
" <td>42611</td>\n",
" <td>36206</td>\n",
" <td>...</td>\n",
" <td>303</td>\n",
" <td>301</td>\n",
" <td>393</td>\n",
" <td>33</td>\n",
" <td>17</td>\n",
" <td>45</td>\n",
" <td>41381</td>\n",
" <td>41381</td>\n",
" <td>41381</td>\n",
" <td>49715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-30</th>\n",
" <td>14459</td>\n",
" <td>14459</td>\n",
" <td>13416</td>\n",
" <td>14459</td>\n",
" <td>14459</td>\n",
" <td>14459</td>\n",
" <td>14341</td>\n",
" <td>12274</td>\n",
" <td>12474</td>\n",
" <td>10460</td>\n",
" <td>...</td>\n",
" <td>83</td>\n",
" <td>81</td>\n",
" <td>99</td>\n",
" <td>16</td>\n",
" <td>5</td>\n",
" <td>18</td>\n",
" <td>12067</td>\n",
" <td>12067</td>\n",
" <td>12067</td>\n",
" <td>14459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-31</th>\n",
" <td>15047</td>\n",
" <td>15047</td>\n",
" <td>13908</td>\n",
" <td>15047</td>\n",
" <td>15047</td>\n",
" <td>15047</td>\n",
" <td>14789</td>\n",
" <td>14121</td>\n",
" <td>14395</td>\n",
" <td>11430</td>\n",
" <td>...</td>\n",
" <td>75</td>\n",
" <td>74</td>\n",
" <td>74</td>\n",
" <td>13</td>\n",
" <td>11</td>\n",
" <td>26</td>\n",
" <td>13864</td>\n",
" <td>13864</td>\n",
" <td>13864</td>\n",
" <td>15047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-08-31</th>\n",
" <td>12204</td>\n",
" <td>12204</td>\n",
" <td>11408</td>\n",
" <td>12204</td>\n",
" <td>12204</td>\n",
" <td>12204</td>\n",
" <td>12022</td>\n",
" <td>11266</td>\n",
" <td>11753</td>\n",
" <td>9556</td>\n",
" <td>...</td>\n",
" <td>53</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>18</td>\n",
" <td>11336</td>\n",
" <td>11336</td>\n",
" <td>11336</td>\n",
" <td>12204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-30</th>\n",
" <td>13679</td>\n",
" <td>13679</td>\n",
" <td>12911</td>\n",
" <td>13679</td>\n",
" <td>13679</td>\n",
" <td>13679</td>\n",
" <td>13492</td>\n",
" <td>12790</td>\n",
" <td>13024</td>\n",
" <td>10769</td>\n",
" <td>...</td>\n",
" <td>78</td>\n",
" <td>78</td>\n",
" <td>85</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>12551</td>\n",
" <td>12551</td>\n",
" <td>12551</td>\n",
" <td>13679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-10-31</th>\n",
" <td>24700</td>\n",
" <td>24700</td>\n",
" <td>23658</td>\n",
" <td>24700</td>\n",
" <td>24700</td>\n",
" <td>24700</td>\n",
" <td>24551</td>\n",
" <td>23061</td>\n",
" <td>23361</td>\n",
" <td>21244</td>\n",
" <td>...</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>103</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>20</td>\n",
" <td>23007</td>\n",
" <td>23007</td>\n",
" <td>23007</td>\n",
" <td>24700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-30</th>\n",
" <td>16476</td>\n",
" <td>16476</td>\n",
" <td>15736</td>\n",
" <td>16476</td>\n",
" <td>16476</td>\n",
" <td>16476</td>\n",
" <td>16344</td>\n",
" <td>15242</td>\n",
" <td>15279</td>\n",
" <td>13740</td>\n",
" <td>...</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>68</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>12</td>\n",
" <td>14999</td>\n",
" <td>14999</td>\n",
" <td>14999</td>\n",
" <td>16476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-12-31</th>\n",
" <td>3373</td>\n",
" <td>3373</td>\n",
" <td>3134</td>\n",
" <td>3373</td>\n",
" <td>3373</td>\n",
" <td>3373</td>\n",
" <td>3365</td>\n",
" <td>2960</td>\n",
" <td>3091</td>\n",
" <td>2776</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3026</td>\n",
" <td>3026</td>\n",
" <td>3026</td>\n",
" <td>3373</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2016-01-31</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>13 rows × 54 columns</p>\n",
"</div>"
],
"text/plain": [
" Unique Key Created Date Closed Date Agency Agency Name \\\n",
"created_dt \n",
"2015-01-31 7091 7091 6583 7091 7091 \n",
"2015-02-28 8141 8141 7631 8141 8141 \n",
"2015-03-31 15025 15025 14305 15025 15025 \n",
"2015-04-30 20087 20087 19131 20087 20087 \n",
"2015-05-31 49715 49715 47090 49715 49715 \n",
"2015-06-30 14459 14459 13416 14459 14459 \n",
"2015-07-31 15047 15047 13908 15047 15047 \n",
"2015-08-31 12204 12204 11408 12204 12204 \n",
"2015-09-30 13679 13679 12911 13679 13679 \n",
"2015-10-31 24700 24700 23658 24700 24700 \n",
"2015-11-30 16476 16476 15736 16476 16476 \n",
"2015-12-31 3373 3373 3134 3373 3373 \n",
"2016-01-31 3 3 2 3 3 \n",
"\n",
" Complaint Type Descriptor Location Type Incident Zip \\\n",
"created_dt \n",
"2015-01-31 7091 7051 6547 6418 \n",
"2015-02-28 8141 8100 7508 7515 \n",
"2015-03-31 15025 14931 13742 13833 \n",
"2015-04-30 20087 19921 17250 17292 \n",
"2015-05-31 49715 49287 42564 42611 \n",
"2015-06-30 14459 14341 12274 12474 \n",
"2015-07-31 15047 14789 14121 14395 \n",
"2015-08-31 12204 12022 11266 11753 \n",
"2015-09-30 13679 13492 12790 13024 \n",
"2015-10-31 24700 24551 23061 23361 \n",
"2015-11-30 16476 16344 15242 15279 \n",
"2015-12-31 3373 3365 2960 3091 \n",
"2016-01-31 3 3 3 3 \n",
"\n",
" Incident Address ... Bridge Highway Direction Road Ramp \\\n",
"created_dt ... \n",
"2015-01-31 5308 ... 76 75 \n",
"2015-02-28 6097 ... 121 121 \n",
"2015-03-31 10775 ... 704 702 \n",
"2015-04-30 13809 ... 311 307 \n",
"2015-05-31 36206 ... 303 301 \n",
"2015-06-30 10460 ... 83 81 \n",
"2015-07-31 11430 ... 75 74 \n",
"2015-08-31 9556 ... 53 52 \n",
"2015-09-30 10769 ... 78 78 \n",
"2015-10-31 21244 ... 88 88 \n",
"2015-11-30 13740 ... 60 60 \n",
"2015-12-31 2776 ... 7 7 \n",
"2016-01-31 3 ... 0 0 \n",
"\n",
" Bridge Highway Segment Garage Lot Name Ferry Direction \\\n",
"created_dt \n",
"2015-01-31 75 7 2 \n",
"2015-02-28 121 18 4 \n",
"2015-03-31 702 20 10 \n",
"2015-04-30 346 15 9 \n",
"2015-05-31 393 33 17 \n",
"2015-06-30 99 16 5 \n",
"2015-07-31 74 13 11 \n",
"2015-08-31 52 12 12 \n",
"2015-09-30 85 3 4 \n",
"2015-10-31 103 2 8 \n",
"2015-11-30 68 3 4 \n",
"2015-12-31 16 1 0 \n",
"2016-01-31 0 0 0 \n",
"\n",
" Ferry Terminal Name Latitude Longitude Location created_dt \n",
"created_dt \n",
"2015-01-31 8 6181 6181 6181 7091 \n",
"2015-02-28 17 7274 7274 7274 8141 \n",
"2015-03-31 22 13444 13444 13444 15025 \n",
"2015-04-30 18 16692 16692 16692 20087 \n",
"2015-05-31 45 41381 41381 41381 49715 \n",
"2015-06-30 18 12067 12067 12067 14459 \n",
"2015-07-31 26 13864 13864 13864 15047 \n",
"2015-08-31 18 11336 11336 11336 12204 \n",
"2015-09-30 10 12551 12551 12551 13679 \n",
"2015-10-31 20 23007 23007 23007 24700 \n",
"2015-11-30 12 14999 14999 14999 16476 \n",
"2015-12-31 1 3026 3026 3026 3373 \n",
"2016-01-31 0 3 3 3 3 \n",
"\n",
"[13 rows x 54 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.resample('M').count()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: .resample() is now a deferred operation\n",
"use .resample(...).mean() instead of .resample(...)\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/plain": [
"Timestamp('2015-01-31 00:00:00', offset='M')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.resample('M').index[0]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'1.11.1'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.__version__"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10a9c3e48>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qbaRrAhNCOAUGBhITE+OSc9V4U6pRo0YRFxdHQkICLVq0IDY2FoDw8HD69u1LbGwsvr6+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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a9eb940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.resample('M').count().plot(y=\"Unique Key\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10b14b748>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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TEkKcJwVIeLcDeyG8GSogEGurtrB/r9kZCSHOkwIkvJpO24lq41jWwdpaWkBC1CRSgIRX\n02k7IeZ8AYqMhqOH0EWFJmclhAApQMKLaa1h705UG8fia8o/ABo3gSMHTM5MCAFSgIQ3+/0o+Pii\nQsOcm1RUDFqeAwlRI0gBEl5L7/3j+Y9Ty2hHrzghhOmkAAnvdcHzn2IqKgZ9QAqQEDWBFCDhtS7s\nAefUsjUc2oc2ZL0eIcwmBUh4JX3mNJw8ARGtSmxXgUHQIASOmTP3nxDiD1KAhHfauwtat0NZraXf\naxktt+GEqAGkAAmvpNN2omI6lPmeiox2zJAghDCVFCDhlfTenaiLOiAUk44IQtQMUoCE19FFhY45\n36Lbl71DZDQcSEeWwhLCXFKAhPc5kA5hTVGB9ct8WzVoBH5+kPm7hxMTQlxICpDwOmV2v75YS3kO\nJITZpAAJr6P3lh6AejF5DiSE+aQACa+itQYXWkBKumILYTopQMK7nDgGFguEhl96v6gYx7MiIYRp\npAAJr+IY/9MRpdSld7SFQWEBOvukZxITQpQiBUh4l7SdcLkOCOAoUNIKEsJUUoCEVylzCYZyOJ4D\nSU84IcwiBUh4DX02B078DhGtXTsgUjoiCGEmKUDCe+zdDa3aoHx8XNpdRcbIWCAhTCQFSHiN4g4I\nLmvSDE6fQp/JcV9SQohySQESXqMiz38AlMUKEVGyRLcQJpECJLyCLiqCfWkQU84EpOVQkTIjghBm\nkQIkvMPB36BxuGPF04qQtYGEMI0UIOEV9N4dFXv+c560gIQwjxQg4R3Sdrk0ALWU5i0h8xg6P7/6\ncxJCXJIUIFHraa0r3AGhmPLxhaYt4dBvbshMCHEpUoBE7Zf5O2gNjZtU6nAlA1KFMIUUIFHr6TTH\n+j+XnYC0PJEx0hVbCBNIARK1XyVvvxVTkdHo/dITTghPc23OkmpkGAbPPPMMNpuNp556ipycHF5/\n/XWOHz9OeHg4SUlJBAYGArB06VJWr16N1WplzJgxdOnSBYD09HTmz59PYWEh3bp1Y8yYMQAUFRUx\nd+5c0tPTCQ4OJikpicaNG3v6EoWH6bSdWPoOrfwJIlpBxkF0UaHjmZAQwiM83gL64osvaNGihfP1\nsmXL6Ny5M7NmzSIuLo6lS5cCcOjQITZu3MjMmTN55plnWLhwoWO1S2DhwoWMHz+eWbNmcfToUbZs\n2QLAqlWrCAoKYvbs2YwYMYLFixd7+vKEh+mzZ+B4hmM8TyUp/wAIbQJHDlZjZkKIy/FoAcrMzGTz\n5s0MGzbMuW3Tpk0MGjQIgMGDB5OSkuLc3q9fP6xWK+Hh4TRr1oy0tDSysrLIzc2lTZs2AAwcONB5\nTEpKivNcffr0Ydu2bZ68PGGG9N0QFVPllouKjEbLcyAhPMqjBejtt9/mrrvuKvGwODs7m5CQEABC\nQkLIzs4GwG63l7h9ZrPZsNvt2O12QkNDndtDQ0Ox2+3OY4rfs1gs1K9fn5wcmWjSm+m9FZyAtDyR\n0SDPgYTwKI89A/r5559p2LAhrVq1IjU1tdz9Kt2TqQzFt+wulpqaWiKHxMREgoODqy1uRfj5+ZkS\n21vi5vy2B/8Rt+LrwjkvFbuwQ2fylrzpls/ErM/azNhyzd4ft9iSJUucX8fFxREXF+fysR4rQLt2\n7WLTpk1s3ryZgoICcnNzmTNnDiEhIWRlZTn/btiwIeBo8Zw4ccJ5fGZmJjabDZvNRmZmZqntxccU\nvzYMg9zcXIKCSs8NVtaHdPr0aXdc9mUFBwebEtsb4upz5zDSdmE0jyLPhXNeKrYObYqxby+nsrMc\ns2RXI7M+azNjyzV7f9zi2ImJiZU+3qVbcN9//z2HDh0C4MiRI0yePJkpU6Zw+PBhlwPdcccdLFiw\ngLlz5/Loo4/SqVMnHn74YXr06EFycjIAycnJxMfHAxAfH8+GDRsoKiri999/JyMjgzZt2hASEkJg\nYCBpaWlorVm7di09e/Z0HrNmzRoANm7cSKdOnVzOT9RCh/aBrTGqftV/+1P1gyC4Afx+tOp5CSFc\n4lIB+vDDD50tiXfeeYeYmBg6duzIwoULq5xAQkIC27ZtY8KECWzfvp2EhAQAIiIi6Nu3L0lJSbz0\n0kuMGzfOeXtu7NixLFiwgAkTJtC0aVO6du0KwNChQzl16hSPPPIIX3zxBXfccUeV8xM1l06r2vif\nUmQ8kBAe5dItuFOnThESEkJBQQG7d+/m8ccfx2q1Mnbs2EoFjY2NJTY2FoCgoCCee+65Mve76aab\nuOmmm0ptj46O5tVXXy213dfXl8cee6xSOYlaaO9O6NS92k6nimdE6D2o2s4phCifSy2gBg0akJGR\nwZYtW4iJicHX15fCwkJ35ybEJVV3C0jmhBPCs1xqAd1888089dRTWCwWkpKSANi2bRtRUVFuTU6I\n8ujM41BUCGHNqu+kkTFwIB2tdbX2xhRClM2lAjR48GD69u0LgL+/PwBt27bl0UcfdV9mQlyCTttR\ntQlIy6AaNgIfH7Afh9DwajuvEKJsLt2Ce/LJJ/H393cWH4CGDRvy8ssvuy0xIS6pihOQlut8K0gI\n4X4uFaCMjIxS27TWHDt2rNoTEsIV1d4D7jzVMhp9QHrCCeEJl7wFN3fuXOCPWaYvdPz4cVq2bOm+\nzIQoh8476xivExlT7edWUdEY67+r9vMKIUq7ZAFq0qRJmV8rpWjfvr3zuZAQHpW+G1pGo3zdsHRC\ny2g48K/qP68QopRLFqBbb70VcHQ4KB7sKYTZ3HX7DXAs612Qjz51EtWgkXtiCCEAF3vBde3alSNH\njrBv3z7y8vJKvDd0aBUWAhOiEnTaTizDrnfLuZVS51tB6dCph1tiCCEcXCpAn3zyCR9//DFRUVEl\nesKBFCDhWdo4B7/tgZgObouhomLQ+/eipAAJ4VYuFaAvvviCF198UQaeCvMd2g8hoaigBu6L0TIa\nveUH951fCAG42A3bz8+vxDLaQphFu2v8zwVUlIwFEsITXCpAo0aN4s033+TkyZMYhlHijxAelbYT\n3FyAaNIcTmWhz8pqukK4k0u34ObPnw/Ad9+VHh/x4YcfVm9GQlyCTtuJ5frb3BpDWawQ0QoO/gbt\nO7s1lhB1mUsF6OJBqEKYQdtPQEE+NHH/7WDHjAjpKClAQriNSwUoLCzM3XkIcVl6706I6eCZmaoj\no2HPdvfHEaIOK7cA/fOf/+Svf/0rAHPmzCn3H/1DDz3knsyEuJg7B6BeREXGYKz8zCOxhKiryi1A\n4eF/TEfftGlTjyQjxKXotJ1Ybr/XM8GaR8LxDHR+PuqisW9CiOpRbgG6cCns4il5hDCLzsuFjEMQ\n1cYj8ZSvLzRtAYf3QXR7j8QUoq5x6RkQOGbEPnLkCKdOnSqxvVOnTtWelBCl/LYHWrZG+fp5LKSK\njEEf2IuSAiSEW7hUgHbt2sVrr71GYWEhubm51KtXj7y8PEJDQ6WHnPAITwxALSUyWgakCuFGLg1E\nffvtt7nhhhtYtGgR9erVY9GiRdx8881cffXV7s5PCMDNM2CXw9ECkgIkhLu4VICOHDnCddddV2Jb\nQkICK1ascEtSQlxIG+cgfQ/EeLgFFNEKjh5AFxV5Nq4QdYRLBSgwMJDc3FwAQkJCOHToEDk5OaWW\nZhDCLY4cgAYhqOCGHg2rAuqBLRyOHvRoXCHqCpeeAfXu3ZvNmzdzxRVXMGTIEKZMmYLVaqVPnz7u\nzk+I87ff3Lf8wqWoyPMzIrRsbUp8IbyZSwVozJgxzq9vuOEG2rZtS15eHl26dHFXXkL8IW2neXOy\nRcbAgb3Qf5g58YXwYi53w75Qx44evhcv6jSdthPLiERTYqvIaIwt/zUlthDertwCNGnSJJfm3Joy\nZUq1JiTEhXRWJuTlemQC0jJFRsPB39CGgbK49MhUCOGicguQLLUtaoS08xOQmvSfv6ofDEHB8PtR\nx8wIQohqU24BGjx4sAfTEKJsZoz/KSUy2jEjghQgIaqVy8+AVq1axfr16zl58iSNGjWif//+DBky\nxDNT44s6S6ftxJI41tQcVPGMCL0GmpqHEN7GpQK0ePFiUlJSGDFiBI0bN+bEiRMsX76cI0eOMHr0\naHfnKOoonZ/nGIPTyjMTkJZHlmYQwj1cKkDJyclMnz6d0NBQ57bu3bvz1FNPSQES7rPvV4hohfIz\neTmEyBg4mI7WWlr8QlQjl57s1qtXj3r16pXaFhgY6JakhIAa8vwHUCE2sFjBfsLsVITwKi61gK67\n7jpeeeUVEhISsNlsZGZm8tlnnzFixAiOHTvm3K9JkyZuS1TUPTptJ5YBNWTC28gYOLgXQmV5eiGq\ni0sF6K233gIgNTW1xPbt27ezaNEi5+sPP/yw3HMUFhYyefJkioqKKCoqIj4+njvuuIOcnBxef/11\njh8/Tnh4OElJSc6W1dKlS1m9ejVWq5UxY8Y4Z15IT09n/vz5FBYW0q1bN+dMDUVFRcydO5f09HSC\ng4NJSkqicePGLn8YoubQhgHpu+CeR8xOBTg/Jc/+dFRXmX5KiOriUgG6VGFxla+vL5MnT8bf3x/D\nMHjuuefYtWsXmzZtonPnztx4440sW7aMpUuXcuedd3Lo0CE2btzIzJkzyczM5IUXXmD27NkopVi4\ncCHjx4+nTZs2vPTSS2zZsoWuXbuyatUqgoKCmD17Nhs2bGDx4sU8+uijVc5dmODoQagfjGrQyOxM\ngPMdETauMjsNIbyKR0f3+fs7HiYXFhZiGAZBQUFs2rSJQYMGAY6xRykpKQBs2rSJfv36YbVaCQ8P\np1mzZqSlpZGVlUVubi5t2jh6Rg0cONB5TEpKivNcffr0Ydu2bZ68PFGNasrzH6fIaNi/1+wshPAq\nLrWATpw4wUcffcS+fftKLcEwa9Ysl4MZhsHTTz/NsWPHuOqqq4iIiCA7O5uQkBDAsdRDdnY2AHa7\nnXbt2jmPtdls2O12rFZrid54oaGh2O125zHF71ksFurXr09OTg5BQUEu5yhqiLSd0LYGFaDGTSA/\nD30qC9UgxOxshPAKLhWg1157jebNm5OYmIifn1+lg1ksFmbMmMHZs2eZNm1aqWdKQLV2c9VaV9u5\nhGfpvTuxXHOz2Wk4KaX+WKK7U3ez0xHCK7hUgA4fPszUqVOxVNN8XIGBgXTr1o29e/cSEhJCVlaW\n8++GDR2LjtlsNk6c+KPba2ZmJjabzdkL7+LtxccUvzYMg9zc3DJbP6mpqSWKX2JiIsHBwdVybRXl\n5+dnSuyaHNfIsnP67BmC23Ws1jngqnrNuW06oH4/TEDwII/GrYqa/H32tth1LW6xJUuWOL+Oi4sj\nLi7O5WNdKkA9evRgx44ddOrUqeLZnXfq1Cl8fHwIDAykoKCAbdu2ccstt3Dq1CmSk5NJSEggOTmZ\n+Ph4AOLj45k9ezYjR47EbreTkZFBmzZtUEoRGBhIWloaMTExrF27lmuvvdZ5zJo1a2jbti0bN24s\nN9+yPqTTp09X+tqqIjg42JTYNTmu3pKCjm5PzpkzHo99KUbTCNjyI4UVPIdZn7WZseWavT9ucezE\nxMovleJSAbrnnnt49tlnadq0qbOFUuyBBx5wKVBWVhbz5s1Da43WmgEDBtC5c2dat27NzJkzWb16\nNWFhYSQlJQEQERFB3759SUpKwsfHh3Hjxjlvz40dO5Z58+Y5u2F37doVcMzgPWfOHB555BGCg4OZ\nMGGCyx+EqDl02k5UjDkroF6KiozBWP6B2WkI4TWUduFByYwZM8jIyKBr166lngHddtttbkvOk44c\nOWJK3Lr2W5Mrcc+9+DcsN49Bta98i7uysS9FG+cwHr4Nyz/eQgXW91jcqqjJ32dvi13X4gI0b968\nSse71ALavn07//znP0tNxyNEddMF+XB4P7Rqa3YqpSiLFSJawaHfoF31Fkch6iKXnvBGRUWZVmFF\nHbPvV2gRhfI3eQLScqjzawMJIarOpRZQXFwc06ZNY/DgwaWeAcnKqaI6OZ7/1KDxPxeLjIFfSw8f\nEEJUnEsFaPfu3dhsNn755ZdS70kBEtVJp+3E0n+Y2WmUS0VGY3y33Ow0hPAKLhWgyZMnuzsPIRwT\nkO7dBXc/ZHYq5WseBcePogvyzV+nSIhazuUluXNycvjpp5+w2+3YbDZ69OghU9yI6pVxCALrO9bf\nqaGUry8XrjxPAAAgAElEQVSEt3B0lGjd7vIHCCHK5VInhD179vDwww/z7bffsn//flauXMnDDz/M\nnj173J2fqENq3ASk5VBR0egD6WanIUSt5/J6QOPGjaN///7ObRs2bGDRokW89NJLbktO1DFpO6Em\nd0Ao1jIGpCecEFXmUgvo6NGj9O3bt8S2Pn36kJGR4ZakRN2k90oLSIi6xKUC1LRpUzZs2FBi28aN\nG2UJblFt9KmTcPoUNI80O5XLi2gNR/aji4rMzkSIWs2lW3Bjxozh5Zdf5ssvv6Rx48YcP36co0eP\n8vTTT7s7P1FX7N0NMe2rdfZrd1EB9cAW5ug0EdHK7HSEqLVcKkDt27dnzpw5/Pzzz5w8eZIePXrQ\nvXt36QUnqk2NH4B6EdXSMSOCkgIkRKVdsgAVFBSQkZFBZGQkQUFBDBw40PnegQMH8PPzq9ICdUIU\n03t3YkkYbXYarouKcSxO16/mDpoVoqa75P2OTz/9lNWrV5f5XnJyMp999plbkhJ1iy4sgEP7atW4\nmuIWkBCi8i5ZgDZs2MD1119f5nsjR45k/fr1bklK1DH70qBpBMo/wOxMXBcZDQd/c8zeIISolEsW\noOJZD8pis9mw2+1uSUrULbVlAOqFVFADCAyC4zIUQYjKumQBCggI4MSJE2W+d+LECfxr6JT5onbR\ne2vJANSLRcbIeCAhquCSBahbt268//77Zb73wQcf0L17d7ckJeoOrTXUkgGoF1OR0TIjghBVcMle\ncLfddhsTJ07kiSeeoFevXjRq1IiTJ0/y448/kpuby9SpUz2Vp/BWGYfBvx6qUajZmVSYiozBWCVL\nMwhRWZcsQCEhIUyfPp3PP/+cLVu2kJOTQ1BQED169GDkyJEyDkhUmU7bUavG/5QQGQ0H0tFao5Qy\nOxthIuOTdzhbWIBOHCs/CxVw2YGoQUFB3Hbbbdx2222eyEfUNXt3Qi28/QZAiA2UgpMnHDMjiDpJ\n79+L/v5bihqFor9dhrr6JrNTqjVq/rwnwqvptF218vkP4PhN93wrSNRN2jiH8e481M1/JujJF9Hf\nfIretsnstGoNKUDCNPr0KTh1ElrUgglIy6EiY2RAah2m13wF/v6ofsOwNG6CZfxTGItmoY8cMDu1\nWkEKkDDP3p3Quj3KYjU7k0pTkbI0Q12ls+zoz97Hcuf9zuc+qk1H1C33YMydis45ZXKGNV+5BWji\nxInOrz/66COPJCPqlto4ALWUyBi5BVdH6SX/Rg0cjrpoCRFLv6Go7v0w3pguS3ZcRrkF6MiRIxQU\nFADw+eefeywhUXfUlgXoLqlxE8g7iz6dbXYmwoP09p/Rv+1BXZdY5vvqf+4C/wD0+/9yjHUTZSq3\nF1zPnj2ZMGEC4eHhFBQUMHny5DL3mzJlituSE95LFxY6Wg6t25qdSpUoiwVanu+IENfN7HSEB+iC\nfIz33sByx3hUObPBKIsVy7jHMV5+ElavQA0d6eEsa4dyC9ADDzzArl27+P3330lLS2PIkCGezEt4\nu/1p0LQFKiDQ7EyqrPg5kJICVCfoLz5CRcagOve45H6qXiCWh57FePlJdNMWqFj5+bjYJccBdejQ\ngQ4dOlBUVMTgwYM9lJKoC/Te2rUA3SVFxsAvKWZnITxAHz2EXvMllkmzXdpfhTXF8tcnMd6YjuXJ\nl1FNW7g5w9rFpRVRhw4dSmpqKmvWrOHkyZM0atSIgQMH0qlTJ3fnJ7yUTtuJ6jnA7DSqhYqMxvj8\nQ7PTEG6mtcZYPB818rYKTR2l2nVC3XQXxtypWJ75B6q+zCBTzKVu2N999x0zZ84kJCTEOSfcrFmz\nWLlypbvzE17IMQFp7R2AWkrTCMjKROeeNTsT4UZ642rIz0MNua7Cx1oGXI3q1B3jXzPQ5865Ibva\nyaUC9Nlnn/Hss89yxx13cNVVV3H77bfz7LPPyoqoonKOHQFfX5SXTF+jrFZoEQUHfzM7FeEm+sxp\n9MdvYRl9f6XHralb/wJKoT96s5qzq71cKkCnT58mIiKixLbmzZuTk5PjlqSEd/Oq5z/nOToiyIwI\n3kp//DYq/gpUq8r32lRWK5b7nkCn/oyx9qvqS64Wc6kAdejQgXfeeYf8/HwA8vLyePfdd2nXrp1b\nkxNeau+u2jsBaXlkTjivpdN2oLdtQt14Z5XPpQKDsDz0HHrZf9C7t1VDdrWbSwXo3nvvZf/+/YwZ\nM4Z7772Xe+65h/3793Pfffe5Oz/hhbxiBoSLyJxw3kkXFWEsXoBKHIcKrF8t51RNmjvGCP3rH+g6\nvqS7S73gGjVqxJQpU8jMzHT2ggsNrX0LiAnzGTmnICsTWrQyO5Xq1SIKfj+KLixA+fqZnY2oJnrl\np9AoFBXfv1rPq2K7okaOwpjzgqNnXL3aPx6uMlwqQMVCQ0MrXXgyMzOZO3cu2dnZKKUYNmwY1113\nHTk5Obz++uscP36c8PBwkpKSCAx0fDOWLl3K6tWrsVqtjBkzhi5dugCQnp7O/PnzKSwspFu3bowZ\nMwaAoqIi5s6dS3p6OsHBwSQlJdG4ceNK5Svc49zuVGjV1vHg3osoXz9o0hwO7a/1szsIB33iGPrr\nT7A884pbFpmzDBmBceQAxv++guWhibV6Ut7K8ths2FarlT//+c+89tprTJs2ja+//prDhw+zbNky\nOnfuzKxZs4iLi2Pp0qUAHDp0iI0bNzJz5kyeeeYZFi5c6JxTaeHChYwfP55Zs2Zx9OhRtmzZAsCq\nVasICgpi9uzZjBgxgsWLF3vq8oSLivZs97oOCMVUy2j0QbkN5w201hjv/wt15Y2o8GZui6NG3QsF\n+ehP3nFbjJrMYwUoJCSEVq1aARAQEECLFi3IzMxk06ZNDBo0CIDBgweTkuIYUb5p0yb69euH1Wol\nPDycZs2akZaWRlZWFrm5ubRp0waAgQMHOo9JSUlxnqtPnz5s2yYP+Wqaot3bve75j1NUDOyXjghe\nYfMPcDwDNdy9q5sqHx8s459C/7wRY/13bo1VE122ABmGwfbt2ymqxmnFf//9d/bv30+7du3Izs4m\nJCQEcBSp7GzHrMJ2u73E7TObzYbdbsdut5e4DRgaGordbnceU/yexWKhfv360lW8BtH5+Zz7bQ9E\ntzc7FbdwtICkANV2Ou8sxgf/61jnx8fX7fFUUAMsDz+H/vgtdNoOt8erSS5bgCwWCzNmzMDHp0KP\ni8qVl5fHa6+9xpgxYwgICCj1fnXea5Vp0GsOnXEY4+Un8es72HsfuEa2hsP7ZaR7Lac/fR/VsQuq\nveemGlPNWmK551GMN2agM3/3WFyzuVRVOnbsyJ49e6o87ufcuXO8+uqrDBw4kJ49ewKOVk9WVpbz\n74YNGwKOFs+JEyecx2ZmZmKz2bDZbGRmZpbaXnxM8WvDMMjNzSUoqPS8S6mpqaSmpjpfJyYmEhwc\nXKVrqyw/Pz9TYnsybsH335L79jzqJf6FoOtuprCw0CNxL+b2aw4O5pQtjPqnT2Jt2dpzcS+hLvx8\nVWfsot9+5UzKWoL/8SaWCp6jytfcbzB5J49TsOAlgqbMQQXU80zcKlqyZInz67i4OOLi4lw+1qUC\nFBYWxksvvUR8fDyhoaElWimjRo1yOdiCBQuIiIjguuv+mEupR48eJCcnk5CQQHJyMvHx8QDEx8cz\ne/ZsRo4cid1uJyMjgzZt2qCUIjAwkLS0NGJiYli7di3XXnut85g1a9bQtm1bNm7cWO5kqWV9SKdP\nn3b5OqpTcHCwKbE9EVfn56M/+Bf61x1Ykv5OQcvWFBYWevVnrVu2JmfHL1hC/rh9bNb32MzYtfGa\ntXEO41+voG68kzPKChU8R3Vcsx4wHJ2+h+zX/47l/qcd6015IG5lBQcHk5hY9qJ8rnCpABUUFDhb\nLMXPWypq165drFu3jsjISJ588kmUUtx+++0kJCQwc+ZMVq9eTVhYGElJSQBERETQt29fkpKS8PHx\nYdy4cc7CN3bsWObNm+fsht21a1fAMWv3nDlzeOSRRwgODmbChAmVylVUnT58AOOf01FRMViefc3l\n3+ZqvZbRcDAdGGp2JqKC9NqvweqD6n+laTkopeDO+9GvPYf+9D3UTaNNy8UTlJYHJYBjCXIzeNtv\nqFpr9PqVjrmzbhmD6jesRIvZ7N/W3N4C2rEZY8USrE+85NG45fG2ny93xdbZJzGefxjL36ahWkR5\nLG65+ZzOxpj2OOqmu7D0HuSxuBXVvHnzKh3vcs+Cw4cPs3HjRrKzsxk7dixHjhyhsLCQqKjKfbOE\n99F5Z9HvLkAf+g3LEy+imkeanZLntYyBg7+hDcOl2yeiZtBL/o264qpKF5/qpoIbOlZTffVZdHgz\nVGvvnHfTpX8hGzduZNKkSdjtdtauXQtAbm4u77xTNwdPidL0gXSMFx4Df38s/+/Vull8ABXcAOoF\nwom6PcdXbaJ3bEbv3YUaeZvZqZSgIlph+fPDGPNfQp/MvPwBtZBLBWjJkiU899xz3HfffVjO/1YX\nFRXFvn373JmbqAW01hirv8CYOQl1w+1Y7n4I5e9vdlrmaikzY9cWurAA4z9vYLnjrzXy51Z17Y0a\nOgJj3jT0+dUIvIlLBSg7O7vUrTallFvmRxK1hz6bg/HGdPT332B5esZl71XXFTIzdu2hv/g/iGiN\n+lNPs1Mpl7rmZlSzCPRbs7xubKNLBSg6Otp5663Y+vXrndPhiLpH/7YH44UkVMNGWJ6egWpStYeR\n3sSxOJ20gGo6nXEInbwCy6hxZqdySUop1N0PoTN/R6/40Ox0qpVLnRDuuecepk6dyqpVq8jPz2fa\ntGkcOXKEZ5991t35iRpGa43+9lP0Vx87lifu3s/slGqeyBg4kI7WWu4S1FBaa4z/vIEakYiy1fwZ\n85WvH5YHJ2K8+Di6WSSqh3f8u3OpALVo0YLXX3+dn376iR49ehAaGkqPHj3KnEpHeC+dcwpj0Sw4\nne1YwySsqdkp1UyNQkFryLI7vhY1jv5vMpzNQQ0ZaXYqLlMNG2F5YCLG65OxhDVBRcaYnVKVudxP\n1N/fnw4dOhAbG0vHjh2l+NQx+tcdGC88imoageXJl6T4XIJS6nwrSJ4D1UT6TA76/97CMvqBWrcu\nlYqKwTL6fkenhOyTZqdTZS61gE6cOMHs2bP59ddfqV+/PmfOnKFt27Y8/PDDhIWFuTtHYSJtGOiv\nPkZ/txzLnx+u0Q9raxIV5XgOpLr0MjsVcRH9yduobn1r7dga1aM/6shBjPkvYvnbNLPTqRKXWkDz\n5s0jOjqaRYsWsXDhQhYtWkR0dDTz5s1zd37CRPrUSYxZz6O3/4Rl4mtSfCqipfSEq4l02k701hTU\nTXeZnUqVqJGjULYw9DvzanXPOJcKUHp6OqNHj3bedgsICGD06NGkp0tPH2+ld2519HJr3Q7L49Nq\nxYPamkRFyVigmkYXFWEsno9K/AsqsL7Z6VSJUgo1ZgL66EHyP/vA7HQqzaUC1LZtW9LS0kps27t3\nb5WXZxA1jzbOYXz6Hsa/Z2K5ZwKWhNG17j55jdC4KeSeQeecMjsTcZ7+bjk0tKF6DjA7lWqh/P2x\nPDiR/M8/RGccNjudSin3GdCHH/7R37xJkya89NJLdO/endDQUDIzM9m8eTNXXHGFR5IUnqFPZmIs\nfBWsVizPzUQ1bGR2SrWWsligZWtHK6hZC7PTqfN05nH0V//n6L3pRV3jVaNQ/K67hfzP3kPd94TZ\n6VRYuS2gzMxM55/CwkJ69+6Nr68vp06dwtfXl169elFQUODJXIUb6W0/YUx7DBXbFcujz0vxqQaq\nZbQ8B6ohjPf/iRp2PSrc+wZM+197M3r3NvTB38xOpcLKbQE98MADnsxDmEQXFaGXLUb/uBbLfU+g\n2nluGWKvFxkD238yO4s6T2/5AY4dRv31KbNTcQsVUA917c0Yn/4H60O1a3IAl5djyM/PJyMjg7y8\nvBLb27dvX+1JCc/Qmb9j/OsfEBiE5bnXHTM5i2qjIqMxvvjI7DTqNJ2Xi/H+v7Dc8yjK19fsdNxG\nDboW/c2njlm9YzqYnY7LXCpAa9as4c0338THxwc/P78S7y1YsMAtiQn3Kkj53rH88PD/QV11o6xd\n4w7NWsLJE+jcs2ZnUmfp5e+j2nVGdfiT2am4lfL1Q40c5WgFPfaC2em4zKUCtHjxYh5//HH+9Cfv\n/ibWBVpr9LL/kJeyFsuDE2vVb0u1jbJaoXkk5/bvhRatqu282jgHZ3LgVDaczkKfznZ+zelsdPHX\nZ3I4+6d4dP8r6+T6TPrgb+iNq7E8P8fsVDxC9RvmGDS+65daU3BdKkA+Pj7Exsa6OxfhZlpr9NJ3\n0Nt+JnjaG5xR0upxNxUZw7l9v16yAGmtIT8PTmfDqfNF5IKvS70+cxoCAiG4ITRoiAoOgQYNITgE\nWkZjKf7aPwC1/SeMV5+FZi2xDLkOuvRG+bh8573W0obhGPOTMBrVIMTsdDxC+figbrgDY9liLE9N\nrxW9/Vz6SRw1ahTvvPMOt9xyCw0ayHOC2qi45aN/2YTl8WmO/6RMWke+TomMpnDzfzGsvueLSRac\nOl9QnEUly7FvcAg0CIHghqjzxYXQcGjV9o+iEtwQghq4XETqxXWh8OoE9M8bMVYuhw8WogYORw24\nGhVic+OFm0uv+waUQl1xldmpeJTqNQD95f/BL5ugS82fucSln+JmzZrx4Ycf8vXXX5d678LxQqLm\n0p+9j976XyyPT5XOBh6kOnaBbSmwbZOjeAQ3hJhmWIq/Li44AfXcl4OPL6rXQOg10HFbKvlLjMkP\nomK7oYZcB23jasVvy64ysuzoT/+D5bEX6tyzTWWxYrnxTkcrqHOPGn/9LhWgefPmMWjQIPr161eq\nE4Ko+YzP3kf/tB7L36Y5frMWHqPCmxH0zAxO15DWpmrZGnXXA+ib/4zeuArj3Xlg9UENvhbVZ4hb\nC6Gn5C5egOo3FBXRyuxUzNGtD3zxEfqnDaieNXuyAJcK0OnTpxk1apRX/ZZUVxiff4De9D2Wv02t\nM/fCxeWpwPqoYdejh46EXb9grF6BXroY1XsQash1qGYtzU6xUvTOrRTt2oaqIx0PyqKUwnLTXRgf\n/AvdvW+NnkrLpQI0ePBg1q5dy6BBg9ydj6hGxool6P+udbR8GsjMBqI0pRR07IK1Yxe0/Th67dcY\nr0yE5pFYBl8HXXvX6P/AtNZw8gSk70b/tgf941rq3/s4ef51fL2y2K7QIAT9QzKq/zCzsymXSwUo\nLS2Nr776ik8++YSQkJK/RU+ZMsUtiYmqMb78P/QPqx0zWcu0OsIFyhaGShiNHjkK/dMGjJWfwQf/\nixo0HDVgeI34OdJ5ubA/DZ2+B52+G37bA8Y5iG7vmLn93ifw7dGHvBpyy9MsSiksCXdh/Ps1dK+B\nNXYQrksFaNiwYQwbVnOrqCjJ+PoT9PcrsTwxzat7Ogn3UD6+qN6DoPeg850WvsCY9AAqrjtq8HXQ\nNtYjt+O1YcDRQ+jfdjtbOPx+FCJaoaLbo3pegUr8CzRuIo8HyqDaxkKzCPT336CGjDA7nTK5fAtO\n1A7GN0vRa7/G8rcXUSGhZqcjajlHp4UHz3daWI3x7lxHp4UhIxzPi6qx04I+lQW/OVo2+rc9sO9X\nR5fz6PbQuj2WAcOhZSuUT838bb4msiSMxpg7Fd3vSpS/v9nplOJSAVq1alW57w0dOrTakhFVY3z7\nKTr5S0fxaSTFR1QfFRj0R6eFnVsxVn+BXvquowgNvg7VLKJC59OFBXAg/Xzr5vzttNwz0KodKrod\nlqtudHwtQwaqREW1gegO6OQVqOH/Y3Y6pbhUgNatW1fidVZWFhkZGXTo0EEKUA1hfLccvXqFrF4q\n3EopBbFdscZ2dXRaWPM1xiv/D1pEYRl8HXTpVarTgtYajmc4n9no9N1w5AA0beFo3XTqjuX626FJ\n8xo/bqU2stx4B8YrE9EDr0HVCzQ7nRJcKkCTJ08utW3VqlUcPlw7V+HzNsaqz9HfforliRdRoWFm\npyPqCGULQ900Gn39+U4L3y5zdFoYOJzCjn/C2LkVnb7H0VHA1w+i26Fat8fSoz9EtamRt4S8kWoe\niYrrjv52GeqGO8xOp4RKTwo1ePBgxo4dy1133VWd+YgKMlZ/gf5mmaOrdWi42emIOqhUp4XVK8hf\n/gFEtMZyxVVw94PyPNJk6obbMV58HD10JCqo5tzWdKkAGYZR4nVBQQFr166lfv36bklKuMZI/hL9\n1ceO4tO4idnpCOHotHD3QwQFB9eY2R8EqLCmqB790V99jLrlHrPTcXKpAN1+++2lttlsNv76179W\ne0LCNcbar9BffuTocBDW1Ox0hBA1nBoxCmPKI+grb6wxwzNcKkBz584t8drf319mxTaRse4b9Iol\njolFpfgIIVygGoWi+g1Ff7EEdcd4s9MBwKUuJ2FhYSX+SPExj7F+JXr5B1gem4oKb252OkKIWkRd\newv6x3XoE8fMTgW4TAvoctPsKKWYNGlStSYkymds+A697D9YHn8B1USKjxCiYlRwQ9SQ69DLP0Dd\nM8HsdC5dgAYMGFDmdrvdzpdffkl+fr7LgRYsWMDPP/9Mw4YNeeWVVwDIycnh9ddf5/jx44SHh5OU\nlERgoKOf+tKlS1m9ejVWq5UxY8bQpUsXANLT05k/fz6FhYV069aNMWPGAFBUVMTcuXNJT08nODiY\npKQkGjf2nvEwxsbV6KXvOlo+TSs26E8IIYqpqxIwnh2PPnqowgOIq9slb8ENHTq0xJ+ePXty6NAh\nli9fTq9evZg1a5bLgYYMGcLEiRNLbFu2bBmdO3dm1qxZxMXFsXTpUgAOHTrExo0bmTlzJs888wwL\nFy50DGYDFi5cyPjx45k1axZHjx5ly5YtgGNcUlBQELNnz2bEiBEsXry4Qh9ETWb8kIz++G3HAlsm\n/8AIIWo3FVgfddWN6M/eMzsV154BnT17lg8++IBHHnmE7Oxspk+fzl//+ldCQ13v29+hQ4dS3bY3\nbdrkXOJh8ODBpKSkOLf369cPq9VKeHg4zZo1Iy0tjaysLHJzc2nTpg0AAwcOdB6TkpLiPFefPn3Y\ntm2by7nVZMZ/16D/7y0sj/291q7RIoSoWdTQkehfU9EH9pqaxyVvwRUUFLBixQo+//xzYmNj+fvf\n/07LltX3n2B2drZzeYeQkBCys7MBxy2+du3aOfez2WzY7XasVmuJohcaGordbnceU/yexWKhfv36\n5OTkEBQUVG35epqRsg790ZtYkv6Oah5pdjpCCC+h/ANQ196Csew/WB8x7zn+JQvQgw8+iGEY3HDD\nDcTExJCdne0sEsU6depUbclU55TqxbfsypKamkpqaqrzdWJiIsHBwdUWuyL8/PzKjF3wQzK5S/5N\n8MRXsEZGeyyuu5kV18zYcs11I3Zti6tH3MKplZ9R7+gBfNrFVTr+kiVLnF/HxcURF+f6uS5ZgPz8\n/AD45ptvynxfKVVqjFBFhISEkJWV5fy7YcOGgKPFc+LECed+mZmZ2Gw2bDYbmZmZpbYXH1P82jAM\ncnNzy239lPUhmTVqO7iMEeP6pw0Y772B5dEpnG0UBm7Iray4nmBWXDNjyzXXjdi1Mu51t5Lzn39i\n/du0SsdOTEysXGwuU4DmzZtX6ROXRWtdomXSo0cPkpOTSUhIIDk5mfj4eADi4+OZPXs2I0eOxG63\nk5GRQZs2bVBKERgYSFpaGjExMaxdu5Zrr73WecyaNWto27YtGzdurNaWmSfpnzc6is+E51EtW5ud\njhDCi6l+w9BffYLeuRXVsYvH41d6MtKKmjVrFjt27OD06dPcf//9JCYmkpCQwMyZM1m9ejVhYWEk\nJSUBEBERQd++fUlKSsLHx4dx48Y5b8+NHTuWefPmObthd+3aFXD02JszZw6PPPIIwcHBTJhgfh/3\nitJbfsBYPN9RfNxw200IIS6krFbHRKVL38XS4U8eX1lW6Us9LKlDjhw5Ykrc4uaz3vojxttzsEyY\n7FhEykNxPU1uzdSN2HLNtSeuNgyMv0/AkjAa1bV3hY5t3rxqA+Jl9acaQP+S4ig+D0/ySPERQohi\nymJxLN396X/QF6184G5SgExWuPm/GG/NxvLQs6jWbc1ORwhRF3XpBb5+6E3fezSsFCAT6e0/c3bB\ny1genOhYmlgIIUyglMKSMBr96Xvoc+c8FtdjnRDqKm2cg8zjcOwwOuMQZBxGZxyGjMNQVEjQky+S\n2zzK7DSFEHVdxy7QKBS94TvUgKs9ElIKUDXRuWcdxeXYITh6/u+Mw3D8KNRvAE1boJq2gGaRWLr1\nhaYR0CgUn4YN3TLORwghKqK4FWT87yvoPkNQvr5ujykFqAKcrZmMC1ozx863ZnLPQJPmjpmqm7ZA\ndevr+LpJc1RAPbNTF0KIy1JtOkKLKPTar1HDRro9nhSgMuizZ87fMjsMGYccfx87DL8fheAG0DQC\n1aQFtIjE0uN8ayYkFGWRR2pCiNrNkjAaY/bf0VdcifIPcGssKUDnGe/O+6PQ5OX+0Zpp0gLVo98f\nrRk3f0OEEMJMKjIa1aYjetUK1LU3uzWWFKBiEa2w9OjvfDbj6RHBQghRU6gb78SY8TR60HBUoPtW\nFJB7RudZhoxAxXZF2RpL8RFC1GmqWQTqTz3R337q1jhSgIQQQpSirr8NvfoL9Onsy+9cSVKAhBBC\nlKIaN0H1HID+6mO3xZACJIQQokxqxK3o71eiT2ZefudKkAIkhBCiTCokFHXFVegVH7rl/FKAhBBC\nlEtdczP6p/Xo4xnVfm4pQEIIIcqlghughoxAL3+/2s8tBUgIIcQlqasS0Nt/Rh85UK3nlQIkhBDi\nklS9QNTVCRifvlet55UCJIQQ4rLUkJGwdxd6/95qO6cUICGEEJel/P1RI27FWLa42s4pBUgIIYRL\n1ICr4ehBdNqOajmfFCAhhBAuUT6+qOtvw1j6LlrrKp9PCpAQQgiXqT5D4FQW7NhS5XNJARJCCOEy\nZSvy4KAAAA1zSURBVLWibrgTY+m7VT6XFCAhhBAVonr0g3PnqnweKUBCCCEqRFksWO77W5XPIwVI\nCCFEhalmLat8DilAQgghTCEFSAghhCmkAAkhhDCFFCAhhBCmkAIkhBDCFFKAhBBCmEIKkBBCCFNI\nARJCCGEKH7MTqG5btmzhrbfeQmvNkCFDSEhIMDslIYQQZfCqFpBhGPz73/9m4sSJvPrqq6xfv57D\nhw+bnZYQQogyeFUBSktLo1mzZoSFheHj40P//v1JSUkxOy0hhBBl8KoCZLfbCQ0Ndb622WzY7XYT\nMxJCCFEerypAQgghag+v6oRgs9k4ceKE87Xdbsdms5XaLzU1ldTUVOfrxMREmjdv7pEcyxIcHCxx\nvTy2XHPdiF3X4gIsWbLE+XVcXBxxcXEuH+tVLaA2bdqQkZHB8ePHKSoqYv369cTHx5faLy4ujsTE\nROefCz9ATzMrdl2La2Zsuea6EbuuxS2OfeH/pRUpPuBlLSCLxcLYsWOZOnUqWmuGDh1KRESE2WkJ\nIYQog1cVIICuXbsya9Yss9MQQghxGdbnn3/+ebOTqAnCw8PrXOy6FtfM2HLNdSN2XYtb1dhKa62r\nMRchhBDCJV7VCUEIIUTtIQVICCGEKerUM6C7776bm266yWPxRo0axaZNm/j2229ZuXIl3bp1IzAw\nsMx9d+zYwb///W+uuOKKaot97NgxevXqBTjmyRs3bhy7du2qthiX8uOPP/LYY4/Rv39/j4xRMPt6\nwfM/XxWNP2XKFCIjI2nUqFGVY3n6+3uhTz75hIULF7Jy5Uq+++47WrduXeZ4P3ew2+3MmTOHJUuW\n8OWXX3Ls2DE6d+6MxVL27/JffPEFUVFRWK3WSsccNWoUeXl5dOnSBYDly5ezbds2YmNjK31OV+Nu\n2rSJr776iu+++478/Hzatm2LUqraYnhdL7hLqc4PzhUBAQFMnz7d5f2rMz9/f38OHjxIYWEhvr6+\n/PLLLzRu3LhC5zAMo9x/WJezYcMGunfvzvr167n11lvdHrM6rreqPP3zZWb8yn5/q2rPnj1s3ryZ\nGTNmYLVaycnJoaioyGPxX3nlFYYPH86gQYPQWvPPf/6T999/n9GjR5e5/4oVKxg4cCB+fn6Vjunj\n48OPP/7ITTfdRFBQUKXPU1EX/v916tQpZs2axdmzZ0lMTKy2GHWqAAHk5+czY8YMzpw5w7lz5xg1\nahTx8fEcP36cF198kQ4dOrBnzx5sNhtPPvkkvr6+lY5VVv8OwzB477332LFjB4WFhQwfPpwrr7wS\ngLNnz/Lyyy+TkZFBp06dGDduXKVjA3Tr1o2ff/6Z3r178/3339O/f3927twJOCZufeuttygsLMTP\nz48HHniAZs2akZyczI8//kheXh5aayZPnlzhuHl5efz6669MmTKFadOmceutt7Jjxw4+/PBD6tWr\nV+r67r77bq688kq2b9/O2LFjad++vceud/LkyfzlL38hKioKgEmTJjFu3DgiIyMrHF9rzY4dO/js\ns894+umnAXjzzTeJiYlh0KBBPPjggwwaNIiffvoJwzBISkqq1hk4Lhe/upT3/S0v7s8//8y7775L\nQEAA7dq149ixY879KiorK4vg4GBni6L4P+T09HTeeecd8vPzCQ4O5oEHHuD/t3NusTF1bRz/ze5B\nO6WVphqhitEIRdVZlSrzStUxLjS4UEFCkJDoGIeEltADIhLlorRCUBdNxCmaahs9jBJNSmo0pCl1\naJmKaGuGTrvnvZDZn/k6433tGe9Iun9X3Xvtrv961ulZ61l79sCBA8nIyGD48OEYjUZEUWTz5s1E\nRUXJ0q6vr8ff31+qS5VKRWpqKtu2bSMlJYXCwkIeP36MIAhotVpsNhufPn0iIyODAQMGsH//flm6\nPj4+aLVabt68yapVqxzSTCYTZ86coaOjg+DgYLZs2UJgYCA6nY7c3Fzg+5y3Y8cOcnNzZS8og4OD\n2bRpE3v27CElJeWn89i1a9eoqqpCEARiY2NZs2aNy3z73BmQn58fOp2OrKws9u/fz4ULF6S01tZW\nkpOTOX78OGq1mgcPHril1dXVhV6vZ9euXRw7dgyAsrIy1Go1R44cITMzk9LSUkwmEwCNjY1s2LCB\nEydO0Nra6pa+SqVi1qxZVFdXY7VaaW5udhh4ERERHDx4kOzsbFJSUrh8+bKU1tTURFpamiznA/Do\n0SMmTpxIWFgYwcHBNDU1/dS+b9++MXr0aHJycmQ7H7n2arVaysvLAWhpacFqtcpyPv9fFleEhISQ\nnZ3NggULuH79uls6cvQ9gav2daZrtVrJy8tj3759ZGZm0t7e7lb5YmJiaGtrY8eOHZw9exaj0UhP\nTw8FBQXs3LmTzMxMEhMTuXLlivQ/XV1d5OTksGHDBs6cOSNb+/Xr12g0God7gYGBhIWFcffuXdra\n2jh27BhHjx5lzpw5JCcnExoayoEDB2Q7H/herwsXLqSyshKLxeKQlp+fT2JiIkePHmX27Nnk5+ej\nVqsZMWIERqMRgNraWmJjY2U7Hzvh4eGIokh7e7vLeayuro7a2loyMzPJyclh+fLlP82zz+2AAC5d\nukRDQwMqlYpPnz7x+fNn4HsF2ycfjUbDhw8f3NLp169frxDckydPaG5upqamBgCLxUJLSwu+vr5E\nRUUxaNAgAOLj42loaGDGjBmy9SMjIzGZTFRXVzN58mSHtC9fvnDq1ClaWlpQqVT09PRIaTExMS7P\nqv4NVVVVLFmyBIC4uDiqqqqYMmWKS/sEQXDLTjty7J05cyZFRUWsXbuW8vJyEhMT3S7Hz7CfUWk0\nGh4+fPhbtX4XrtrXGW/fvmXw4MFSODQ+Pp7S0lLZ2vaw0LNnz6ivr+fkyZOsWLGC5uZm6QsoNpvN\n4ZwrPj4egLFjx/L161fMZrNb/dsZRqORpKQkybkGBQUBzqMgcggICGDu3Lncvn3bIZz3/PlzdDod\nAAkJCVy6dAn43i4Gg4Ho6GgMBgNJSUkeKYcdV/PYkydPmDdvnhQ5steDK/qUA7LZbFRUVNDR0UF2\ndjaCILB161asViuAQ7hNEATpvqfLsH79emJiYhzuG43GXitDT6xkp0yZwsWLF0lPT6ejo0O6f/Xq\nVcaPH09aWhomk4mMjAwprV+/frL1Ojs7efr0Ka9fv0alUiGKIiqVqpdDgP/Z5+/v77FV+6/a6+/v\nz4QJE3j48CH379//pTM7Z/j4+CCKonTd1dXlkG7vY4IgODh9T/FP+u7iqn2nTZvmUtfTPzVUqVRE\nR0cTHR1NZGQkxcXFREZGcujQIZfP/1gWuX0tIiJCmnDtWCwW2trapIXV72TRokXo9XrmzZsn3XNl\ny9SpUyksLKSzs5OmpibGjx/vtv779+8RBIHg4GCX81hdXd0v5dnnQnAWi4WQkBAEQaC+vt7h69me\nHijO8ps4cSLFxcXS5NPS0iIN1hcvXmAymRBFEYPBwJgxY9zWnj9/PitXrmTYsGEO6WazWXpzyB6C\n8gQ1NTUkJCSQm5vLqVOnOH36NOHh4Tx79ozGxkan9nmi3t2xd/78+RQUFBAVFeXWylilUjFo0CDe\nvHlDd3c3X758ob6+XnZ+f6K+q/YVRZG3b9/20h0yZAgfPnyQxpnBYHBL/927d7S2tkrXL1++JCIi\ngvb2dp4/fw5AT08Pb968kZ6xazY0NBAUFERgYKAs7QkTJtDV1UVFRQXw/Tz3woULJCYmEhsbS0lJ\nieSEOzs7AVCr1ZjNZll6dux9u3///sTFxVFWVialjR49mqqqKgAqKyulMRUQEIBGo+H8+fNMnjxZ\nltP9cVy2t7dz9uxZkpOTAefz2Ldv34iJiaG8vFya0+z14Io+swMSRRE/Pz/mzJlDVlYWOp0OjUbD\n0KFDpWc8HTt3lp9Wq8VkMqHX67HZbISEhEhb6KioKM6dO8f79+8ZN26cFK5xRzs0NJSFCxf2Sl+2\nbBm5ubkUFRU53Z3IxWAw9Ir7Tp8+nZKSEkaNGuXUPk/Uuzv2ajQa1Gq1w8ryVxFFEV9fX0JDQ4mL\ni2Pnzp2Eh4czcuTIXmX8HfwbfU/grH1nzJiBwWBwquvv78/GjRs5fPgwAQEBjBo1yq16+Pr1KwUF\nBZjNZgRBYPDgwWzatIm//vqL/Px8zGYzoiiyePFi6UPEfn5+6PV6enp62LJli3zjAZ1OR15eHkVF\nRdhsNiZNmsTq1asRBIF3796RlpaGr68vWq2WpKQktFotR44cITQ0VPY50I/1tXTpUoqLi6Xr9evX\nc/r0aW7cuCG9hGBn1qxZnDhxwiG68StYrVb0ej3d3d34+PiQkJAghV5dzWOxsbG8evWK3bt34+fn\nx6RJk3q9OOGArY/Q1NRk27t3r7eL0Wd5+vSpLSsry9vFcMrHjx9t27dvdysPb/cvb+v/DIvFIv2d\nl5dnu3Xr1n+mnZ6ebmtsbPzP9BR+jT6xAyopKeHOnTusW7fO20VR+MOoqKigsLCQ1NRU2Xl4u395\nW/+fKC0t5d69e3R3dzNy5EjpdV0FBeVjpAoKCgoKXqHPvYSgoKCgoPBnoDggBQUFBQWvoDggBQUF\nBQWvoDggBQUFBQWvoDggBQUFBQWvoDggBQUFBQWv8DcdA5EBtBSCmwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ad52fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax= df.groupby(df.index.month).count().plot(y='Unique Key', legend=False)\n",
"ax.set_xticks([1,2,3,4,5,6,7,8,9,10,11, 12])\n",
"ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
"ax.set_ylabel(\"Number of Complaints\")\n",
"ax.set_title(\"311 complains in 2015\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What week of the year has the most reports filed?** How many? Graph the weekly complaints."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10f08aa58>"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5y88VuXhkVid/3iJXJBHBbXw1iYCYte6vdEW6yyeREUpaqh3rE7mYTCWjrVk0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\nevO/7Ds7qq5RET01mrM7zpK8Mtml8R2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JBKCwsJDk5GSGDRtGWFgYp06duuzztLa2Mn78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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f2a8f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#df.resample('W').count().head(5)\n",
"df.resample('W').count().plot(y=\"Unique Key\", color= \"purple\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Noise complaints are a big deal. Use `.str.contains` to select noise complaints, and make an chart of when they show up annually. **Then** make a chart about when they show up every day (cyclic)."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unique Key</th>\n",
" <th>Created Date</th>\n",
" <th>Closed Date</th>\n",
" <th>Agency</th>\n",
" <th>Agency Name</th>\n",
" <th>Complaint Type</th>\n",
" <th>Descriptor</th>\n",
" <th>Location Type</th>\n",
" <th>Incident Zip</th>\n",
" <th>Incident Address</th>\n",
" <th>...</th>\n",
" <th>Bridge Highway Direction</th>\n",
" <th>Road Ramp</th>\n",
" <th>Bridge Highway Segment</th>\n",
" <th>Garage Lot Name</th>\n",
" <th>Ferry Direction</th>\n",
" <th>Ferry Terminal Name</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Location</th>\n",
" <th>created_dt</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-07-03 02:18:32</th>\n",
" <td>31000038</td>\n",
" <td>07/03/2015 02:18:32 AM</td>\n",
" <td>07/03/2015 07:54:48 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Commercial</td>\n",
" <td>Loud Music/Party</td>\n",
" <td>Club/Bar/Restaurant</td>\n",
" <td>11372</td>\n",
" <td>84-16 NORTHERN BOULEVARD</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.755774</td>\n",
" <td>-73.883262</td>\n",
" <td>(40.755773786469966, -73.88326243225418)</td>\n",
" <td>2015-07-03 02:18:32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-04 00:03:27</th>\n",
" <td>30995614</td>\n",
" <td>07/04/2015 12:03:27 AM</td>\n",
" <td>07/04/2015 03:33:09 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Street/Sidewalk</td>\n",
" <td>Loud Talking</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11216</td>\n",
" <td>1057 BERGEN STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.676175</td>\n",
" <td>-73.951269</td>\n",
" <td>(40.67617516102934, -73.9512690004692)</td>\n",
" <td>2015-07-04 00:03:27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-09-09 21:59:03</th>\n",
" <td>31492526</td>\n",
" <td>09/09/2015 09:59:03 PM</td>\n",
" <td>09/09/2015 11:17:39 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Street/Sidewalk</td>\n",
" <td>Loud Talking</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11238</td>\n",
" <td>238 SAINT JAMES PLACE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.683308</td>\n",
" <td>-73.963775</td>\n",
" <td>(40.68330795503152, -73.96377504548408)</td>\n",
" <td>2015-09-09 21:59:03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-04-28 18:26:58</th>\n",
" <td>30502370</td>\n",
" <td>04/28/2015 06:26:58 PM</td>\n",
" <td>04/28/2015 07:29:34 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Commercial</td>\n",
" <td>Car/Truck Music</td>\n",
" <td>Store/Commercial</td>\n",
" <td>10035</td>\n",
" <td>1911 MADISON AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.804617</td>\n",
" <td>-73.941505</td>\n",
" <td>(40.80461674564084, -73.9415053197214)</td>\n",
" <td>2015-04-28 18:26:58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-05-21 19:01:52</th>\n",
" <td>30668699</td>\n",
" <td>05/21/2015 07:01:52 PM</td>\n",
" <td>05/21/2015 09:56:29 PM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Street/Sidewalk</td>\n",
" <td>Loud Talking</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>10026</td>\n",
" <td>8 WEST 111 STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.797731</td>\n",
" <td>-73.949399</td>\n",
" <td>(40.79773121644539, -73.94939942634502)</td>\n",
" <td>2015-05-21 19:01:52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 54 columns</p>\n",
"</div>"
],
"text/plain": [
" Unique Key Created Date \\\n",
"created_dt \n",
"2015-07-03 02:18:32 31000038 07/03/2015 02:18:32 AM \n",
"2015-07-04 00:03:27 30995614 07/04/2015 12:03:27 AM \n",
"2015-09-09 21:59:03 31492526 09/09/2015 09:59:03 PM \n",
"2015-04-28 18:26:58 30502370 04/28/2015 06:26:58 PM \n",
"2015-05-21 19:01:52 30668699 05/21/2015 07:01:52 PM \n",
"\n",
" Closed Date Agency \\\n",
"created_dt \n",
"2015-07-03 02:18:32 07/03/2015 07:54:48 AM NYPD \n",
"2015-07-04 00:03:27 07/04/2015 03:33:09 AM NYPD \n",
"2015-09-09 21:59:03 09/09/2015 11:17:39 PM NYPD \n",
"2015-04-28 18:26:58 04/28/2015 07:29:34 PM NYPD \n",
"2015-05-21 19:01:52 05/21/2015 09:56:29 PM NYPD \n",
"\n",
" Agency Name Complaint Type \\\n",
"created_dt \n",
"2015-07-03 02:18:32 New York City Police Department Noise - Commercial \n",
"2015-07-04 00:03:27 New York City Police Department Noise - Street/Sidewalk \n",
"2015-09-09 21:59:03 New York City Police Department Noise - Street/Sidewalk \n",
"2015-04-28 18:26:58 New York City Police Department Noise - Commercial \n",
"2015-05-21 19:01:52 New York City Police Department Noise - Street/Sidewalk \n",
"\n",
" Descriptor Location Type Incident Zip \\\n",
"created_dt \n",
"2015-07-03 02:18:32 Loud Music/Party Club/Bar/Restaurant 11372 \n",
"2015-07-04 00:03:27 Loud Talking Street/Sidewalk 11216 \n",
"2015-09-09 21:59:03 Loud Talking Street/Sidewalk 11238 \n",
"2015-04-28 18:26:58 Car/Truck Music Store/Commercial 10035 \n",
"2015-05-21 19:01:52 Loud Talking Street/Sidewalk 10026 \n",
"\n",
" Incident Address ... \\\n",
"created_dt ... \n",
"2015-07-03 02:18:32 84-16 NORTHERN BOULEVARD ... \n",
"2015-07-04 00:03:27 1057 BERGEN STREET ... \n",
"2015-09-09 21:59:03 238 SAINT JAMES PLACE ... \n",
"2015-04-28 18:26:58 1911 MADISON AVENUE ... \n",
"2015-05-21 19:01:52 8 WEST 111 STREET ... \n",
"\n",
" Bridge Highway Direction Road Ramp Bridge Highway Segment \\\n",
"created_dt \n",
"2015-07-03 02:18:32 NaN NaN NaN \n",
"2015-07-04 00:03:27 NaN NaN NaN \n",
"2015-09-09 21:59:03 NaN NaN NaN \n",
"2015-04-28 18:26:58 NaN NaN NaN \n",
"2015-05-21 19:01:52 NaN NaN NaN \n",
"\n",
" Garage Lot Name Ferry Direction Ferry Terminal Name \\\n",
"created_dt \n",
"2015-07-03 02:18:32 NaN NaN NaN \n",
"2015-07-04 00:03:27 NaN NaN NaN \n",
"2015-09-09 21:59:03 NaN NaN NaN \n",
"2015-04-28 18:26:58 NaN NaN NaN \n",
"2015-05-21 19:01:52 NaN NaN NaN \n",
"\n",
" Latitude Longitude \\\n",
"created_dt \n",
"2015-07-03 02:18:32 40.755774 -73.883262 \n",
"2015-07-04 00:03:27 40.676175 -73.951269 \n",
"2015-09-09 21:59:03 40.683308 -73.963775 \n",
"2015-04-28 18:26:58 40.804617 -73.941505 \n",
"2015-05-21 19:01:52 40.797731 -73.949399 \n",
"\n",
" Location \\\n",
"created_dt \n",
"2015-07-03 02:18:32 (40.755773786469966, -73.88326243225418) \n",
"2015-07-04 00:03:27 (40.67617516102934, -73.9512690004692) \n",
"2015-09-09 21:59:03 (40.68330795503152, -73.96377504548408) \n",
"2015-04-28 18:26:58 (40.80461674564084, -73.9415053197214) \n",
"2015-05-21 19:01:52 (40.79773121644539, -73.94939942634502) \n",
"\n",
" created_dt \n",
"created_dt \n",
"2015-07-03 02:18:32 2015-07-03 02:18:32 \n",
"2015-07-04 00:03:27 2015-07-04 00:03:27 \n",
"2015-09-09 21:59:03 2015-09-09 21:59:03 \n",
"2015-04-28 18:26:58 2015-04-28 18:26:58 \n",
"2015-05-21 19:01:52 2015-05-21 19:01:52 \n",
"\n",
"[5 rows x 54 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Complaint Type'].str.contains(\"Noise\")].head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"noise_df= df[df['Complaint Type'].str.contains(\"Noise\")]\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10bab60b8>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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I6YlWbd8uCG289hmboFA4sLdxzymEqJVdhSYxMdHReQhRjd6b6ZCFylRQCJaU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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ae2eb00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"noise_graph= noise_df.groupby(noise_df.index.month).count().plot(y='Unique Key', legend=False)\n",
"noise_graph.set_xticks([1,2,3,4,5,6,7,8,9,10,11, 12])\n",
"noise_graph.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
"noise_graph.set_ylabel(\"Number of Noise Complaints\")\n",
"noise_graph.set_title(\"311 noise complains in 2015\")"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10f3925f8>"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dxPx/6+B0E8ajz6Bc6VaHFBXkjL8LjFu/jP7dL9FtctYvRKTQ71YFe+1kZmHc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\nQiMFYjbBDWkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e86e978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"noise_df.groupby(by=noise_df.index.hour)['Unique Key'].count().plot()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10c04b6d8>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Gc/z4cd5++22OHz9OcXFxlXnvvfeeU4IJIdyPSvlpcLOfevEQAhwsNMuWLeM3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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bf69278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"noise_graph= noise_df.groupby(noise_df.index.dayofweek).count().plot(y='Unique Key', legend=False)\n",
"noise_graph.set_xticks([1,2,3,4,5,6,7])\n",
"noise_graph.set_xticklabels(['Mon', 'Tues', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun'])\n",
"noise_graph.set_ylabel(\"Number of Noise Complaints\")\n",
"noise_graph.set_title(\"311 noise complains in 2015\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Which were the top five days of the year for filing complaints?** How many on each of those days? Graph it."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"created_dt\n",
"2015-10-28 2697\n",
"2015-11-09 2529\n",
"2015-05-04 2465\n",
"2015-05-11 2293\n",
"2015-10-29 2258\n",
"Name: Unique Key, dtype: int64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"daily_count= df['Unique Key'].resample('D').count().sort_values(ascending=False)\n",
"top_5_days= daily_count.head(5)\n",
"top_5_days"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10c0991d0>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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bzbjvvvuwZs0aLFu2DNu2bcMPP/yA4uJiJCYmIj8/HzabDUVFRQCAo0ePYvfu3cjLy8MT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\nRESK/D8zndtx1y5r9gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bf76cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = top_5_days.plot(kind='bar') # I dont know how to put names to the labels\n",
"ax.set_title(\"Top 5 days\")\n",
"ax.set_xlabel(\"Day\")\n",
"ax.set_ylabel(\"Complaints\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What hour of the day are the most complaints?** Graph a day of complaints."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10b57f1d0>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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tiYmJdO/e3SWJ1RoyZPq6pVNOoP/7b/TBJNTwu1ETpqI8Pd2dVoUoDw/UzQPR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\n7EVYX8vJAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c0b1cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hour_graph= df.groupby(df.index.hour).count().plot(y='Unique Key', legend=False)\n",
"hour_graph.set_xticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23])\n",
"hour_graph.set_title(\"A day of complaints\")\n",
"hour_graph.set_xlabel(\"Hours\")\n",
"hour_graph.set_ylabel(\"Complaints\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the hours has an odd number of complaints. What are the most common complaints at that hour, and what are the most common complaints the hour before and after?"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"twelve_am_complaints= df[df.index.hour <1]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unique Key</th>\n",
" <th>Created Date</th>\n",
" <th>Closed Date</th>\n",
" <th>Agency</th>\n",
" <th>Agency Name</th>\n",
" <th>Complaint Type</th>\n",
" <th>Descriptor</th>\n",
" <th>Location Type</th>\n",
" <th>Incident Zip</th>\n",
" <th>Incident Address</th>\n",
" <th>...</th>\n",
" <th>Bridge Highway Direction</th>\n",
" <th>Road Ramp</th>\n",
" <th>Bridge Highway Segment</th>\n",
" <th>Garage Lot Name</th>\n",
" <th>Ferry Direction</th>\n",
" <th>Ferry Terminal Name</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Location</th>\n",
" <th>created_dt</th>\n",
" </tr>\n",
" <tr>\n",
" <th>created_dt</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-07-04 00:03:27</th>\n",
" <td>30995614</td>\n",
" <td>07/04/2015 12:03:27 AM</td>\n",
" <td>07/04/2015 03:33:09 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Street/Sidewalk</td>\n",
" <td>Loud Talking</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>11216</td>\n",
" <td>1057 BERGEN STREET</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.676175</td>\n",
" <td>-73.951269</td>\n",
" <td>(40.67617516102934, -73.9512690004692)</td>\n",
" <td>2015-07-04 00:03:27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-09 00:00:00</th>\n",
" <td>31042454</td>\n",
" <td>07/09/2015 12:00:00 AM</td>\n",
" <td>07/20/2015 12:00:00 AM</td>\n",
" <td>DOHMH</td>\n",
" <td>Department of Health and Mental Hygiene</td>\n",
" <td>Standing Water</td>\n",
" <td>Other - Explain Below</td>\n",
" <td>Other</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-07-09 00:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-09 00:00:00</th>\n",
" <td>31037751</td>\n",
" <td>07/09/2015 12:00:00 AM</td>\n",
" <td>NaN</td>\n",
" <td>DOHMH</td>\n",
" <td>Department of Health and Mental Hygiene</td>\n",
" <td>Standing Water</td>\n",
" <td>Puddle in Ground</td>\n",
" <td>3+ Family Apartment Building</td>\n",
" <td>10016</td>\n",
" <td>379 THIRD AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.741537</td>\n",
" <td>-73.981163</td>\n",
" <td>(40.741536747969185, -73.98116258383294)</td>\n",
" <td>2015-07-09 00:00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-29 00:26:39</th>\n",
" <td>30956584</td>\n",
" <td>06/29/2015 12:26:39 AM</td>\n",
" <td>06/29/2015 04:27:24 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Park</td>\n",
" <td>Loud Talking</td>\n",
" <td>Park/Playground</td>\n",
" <td>11377</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.741280</td>\n",
" <td>-73.902565</td>\n",
" <td>(40.741280237793646, -73.90256544457489)</td>\n",
" <td>2015-06-29 00:26:39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-07-02 00:09:59</th>\n",
" <td>30986795</td>\n",
" <td>07/02/2015 12:09:59 AM</td>\n",
" <td>07/02/2015 12:37:47 AM</td>\n",
" <td>NYPD</td>\n",
" <td>New York City Police Department</td>\n",
" <td>Noise - Street/Sidewalk</td>\n",
" <td>Loud Music/Party</td>\n",
" <td>Street/Sidewalk</td>\n",
" <td>10035</td>\n",
" <td>20 PALADINO AVENUE</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>40.800365</td>\n",
" <td>-73.931212</td>\n",
" <td>(40.80036497064086, -73.9312115560449)</td>\n",
" <td>2015-07-02 00:09:59</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 54 columns</p>\n",
"</div>"
],
"text/plain": [
" Unique Key Created Date \\\n",
"created_dt \n",
"2015-07-04 00:03:27 30995614 07/04/2015 12:03:27 AM \n",
"2015-07-09 00:00:00 31042454 07/09/2015 12:00:00 AM \n",
"2015-07-09 00:00:00 31037751 07/09/2015 12:00:00 AM \n",
"2015-06-29 00:26:39 30956584 06/29/2015 12:26:39 AM \n",
"2015-07-02 00:09:59 30986795 07/02/2015 12:09:59 AM \n",
"\n",
" Closed Date Agency \\\n",
"created_dt \n",
"2015-07-04 00:03:27 07/04/2015 03:33:09 AM NYPD \n",
"2015-07-09 00:00:00 07/20/2015 12:00:00 AM DOHMH \n",
"2015-07-09 00:00:00 NaN DOHMH \n",
"2015-06-29 00:26:39 06/29/2015 04:27:24 AM NYPD \n",
"2015-07-02 00:09:59 07/02/2015 12:37:47 AM NYPD \n",
"\n",
" Agency Name \\\n",
"created_dt \n",
"2015-07-04 00:03:27 New York City Police Department \n",
"2015-07-09 00:00:00 Department of Health and Mental Hygiene \n",
"2015-07-09 00:00:00 Department of Health and Mental Hygiene \n",
"2015-06-29 00:26:39 New York City Police Department \n",
"2015-07-02 00:09:59 New York City Police Department \n",
"\n",
" Complaint Type Descriptor \\\n",
"created_dt \n",
"2015-07-04 00:03:27 Noise - Street/Sidewalk Loud Talking \n",
"2015-07-09 00:00:00 Standing Water Other - Explain Below \n",
"2015-07-09 00:00:00 Standing Water Puddle in Ground \n",
"2015-06-29 00:26:39 Noise - Park Loud Talking \n",
"2015-07-02 00:09:59 Noise - Street/Sidewalk Loud Music/Party \n",
"\n",
" Location Type Incident Zip \\\n",
"created_dt \n",
"2015-07-04 00:03:27 Street/Sidewalk 11216 \n",
"2015-07-09 00:00:00 Other NaN \n",
"2015-07-09 00:00:00 3+ Family Apartment Building 10016 \n",
"2015-06-29 00:26:39 Park/Playground 11377 \n",
"2015-07-02 00:09:59 Street/Sidewalk 10035 \n",
"\n",
" Incident Address ... \\\n",
"created_dt ... \n",
"2015-07-04 00:03:27 1057 BERGEN STREET ... \n",
"2015-07-09 00:00:00 NaN ... \n",
"2015-07-09 00:00:00 379 THIRD AVENUE ... \n",
"2015-06-29 00:26:39 NaN ... \n",
"2015-07-02 00:09:59 20 PALADINO AVENUE ... \n",
"\n",
" Bridge Highway Direction Road Ramp Bridge Highway Segment \\\n",
"created_dt \n",
"2015-07-04 00:03:27 NaN NaN NaN \n",
"2015-07-09 00:00:00 NaN NaN NaN \n",
"2015-07-09 00:00:00 NaN NaN NaN \n",
"2015-06-29 00:26:39 NaN NaN NaN \n",
"2015-07-02 00:09:59 NaN NaN NaN \n",
"\n",
" Garage Lot Name Ferry Direction Ferry Terminal Name \\\n",
"created_dt \n",
"2015-07-04 00:03:27 NaN NaN NaN \n",
"2015-07-09 00:00:00 NaN NaN NaN \n",
"2015-07-09 00:00:00 NaN NaN NaN \n",
"2015-06-29 00:26:39 NaN NaN NaN \n",
"2015-07-02 00:09:59 NaN NaN NaN \n",
"\n",
" Latitude Longitude \\\n",
"created_dt \n",
"2015-07-04 00:03:27 40.676175 -73.951269 \n",
"2015-07-09 00:00:00 NaN NaN \n",
"2015-07-09 00:00:00 40.741537 -73.981163 \n",
"2015-06-29 00:26:39 40.741280 -73.902565 \n",
"2015-07-02 00:09:59 40.800365 -73.931212 \n",
"\n",
" Location \\\n",
"created_dt \n",
"2015-07-04 00:03:27 (40.67617516102934, -73.9512690004692) \n",
"2015-07-09 00:00:00 NaN \n",
"2015-07-09 00:00:00 (40.741536747969185, -73.98116258383294) \n",
"2015-06-29 00:26:39 (40.741280237793646, -73.90256544457489) \n",
"2015-07-02 00:09:59 (40.80036497064086, -73.9312115560449) \n",
"\n",
" created_dt \n",
"created_dt \n",
"2015-07-04 00:03:27 2015-07-04 00:03:27 \n",
"2015-07-09 00:00:00 2015-07-09 00:00:00 \n",
"2015-07-09 00:00:00 2015-07-09 00:00:00 \n",
"2015-06-29 00:26:39 2015-06-29 00:26:39 \n",
"2015-07-02 00:09:59 2015-07-02 00:09:59 \n",
"\n",
"[5 rows x 54 columns]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"twelve_am_complaints.head()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"HEAT/HOT WATER 4534\n",
"Rodent 2112\n",
"PAINT/PLASTER 1946\n",
"UNSANITARY CONDITION 1820\n",
"PLUMBING 1502\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"twelve_am_complaints['Complaint Type'].value_counts().head(5)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"one_am_complaints= df[df.index.hour == 1]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Noise - Commercial 1025\n",
"Noise - Street/Sidewalk 897\n",
"Blocked Driveway 479\n",
"Illegal Parking 400\n",
"Noise - Vehicle 249\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"one_am_complaints['Complaint Type'].value_counts().head(5)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Noise - Street/Sidewalk 1599\n",
"Noise - Commercial 1503\n",
"Blocked Driveway 973\n",
"Illegal Parking 882\n",
"Noise - Vehicle 478\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eleven_pm_complaints= df[df.index.hour == 23]\n",
"eleven_pm_complaints['Complaint Type'].value_counts().head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So odd. What's the **per-minute breakdown** of complaints between 12am and 1am? You don't need to include 1am."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unique Key</th>\n",
" <th>Created Date</th>\n",
" <th>Closed Date</th>\n",
" <th>Agency</th>\n",
" <th>Agency Name</th>\n",
" <th>Complaint Type</th>\n",
" <th>Descriptor</th>\n",
" <th>Location Type</th>\n",
" <th>Incident Zip</th>\n",
" <th>Incident Address</th>\n",
" <th>...</th>\n",
" <th>Bridge Highway Direction</th>\n",
" <th>Road Ramp</th>\n",
" <th>Bridge Highway Segment</th>\n",
" <th>Garage Lot Name</th>\n",
" <th>Ferry Direction</th>\n",
" <th>Ferry Terminal Name</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Location</th>\n",
" <th>created_dt</th>\n",
" </tr>\n",
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" <td>95</td>\n",
" <td>95</td>\n",
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" <td>82</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>77</td>\n",
" <td>77</td>\n",
" <td>77</td>\n",
" <td>82</td>\n",
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" <tr>\n",
" <th>10</th>\n",
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" <td>89</td>\n",
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" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>78</td>\n",
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" <td>0</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>89</td>\n",
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" <tr>\n",
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" <td>99</td>\n",
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" <td>101</td>\n",
" <td>101</td>\n",
" <td>100</td>\n",
" <td>92</td>\n",
" <td>99</td>\n",
" <td>84</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>99</td>\n",
" <td>99</td>\n",
" <td>99</td>\n",
" <td>101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>99</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>97</td>\n",
" <td>96</td>\n",
" <td>96</td>\n",
" <td>81</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>95</td>\n",
" <td>95</td>\n",
" <td>95</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>98</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>99</td>\n",
" <td>93</td>\n",
" <td>94</td>\n",
" <td>83</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>86</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>85</td>\n",
" <td>84</td>\n",
" <td>74</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>99</td>\n",
" <td>96</td>\n",
" <td>97</td>\n",
" <td>83</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>96</td>\n",
" <td>96</td>\n",
" <td>96</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>82</td>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>78</td>\n",
" <td>77</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>91</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>92</td>\n",
" <td>91</td>\n",
" <td>89</td>\n",
" <td>81</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>90</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>85</td>\n",
" <td>89</td>\n",
" <td>77</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>91</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>92</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>76</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>98</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>99</td>\n",
" <td>93</td>\n",
" <td>99</td>\n",
" <td>85</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>99</td>\n",
" <td>99</td>\n",
" <td>99</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>109</td>\n",
" <td>109</td>\n",
" <td>108</td>\n",
" <td>109</td>\n",
" <td>109</td>\n",
" <td>109</td>\n",
" <td>109</td>\n",
" <td>101</td>\n",
" <td>106</td>\n",
" <td>96</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>103</td>\n",
" <td>103</td>\n",
" <td>103</td>\n",
" <td>109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>104</td>\n",
" <td>104</td>\n",
" <td>99</td>\n",
" <td>104</td>\n",
" <td>104</td>\n",
" <td>104</td>\n",
" <td>104</td>\n",
" <td>98</td>\n",
" <td>103</td>\n",
" <td>91</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>101</td>\n",
" <td>101</td>\n",
" <td>101</td>\n",
" <td>104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>92</td>\n",
" <td>92</td>\n",
" <td>87</td>\n",
" <td>92</td>\n",
" <td>92</td>\n",
" <td>92</td>\n",
" <td>92</td>\n",
" <td>91</td>\n",
" <td>86</td>\n",
" <td>80</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>86</td>\n",
" <td>86</td>\n",
" <td>86</td>\n",
" <td>92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>79</td>\n",
" <td>79</td>\n",
" <td>76</td>\n",
" <td>79</td>\n",
" <td>79</td>\n",
" <td>79</td>\n",
" <td>78</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>96</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>96</td>\n",
" <td>96</td>\n",
" <td>91</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>96</td>\n",
" <td>96</td>\n",
" <td>96</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>79</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>81</td>\n",
" <td>72</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>91</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>90</td>\n",
" <td>88</td>\n",
" <td>81</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>90</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>87</td>\n",
" <td>93</td>\n",
" <td>83</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>86</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>86</td>\n",
" <td>83</td>\n",
" <td>82</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>87</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>88</td>\n",
" <td>86</td>\n",
" <td>87</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>86</td>\n",
" <td>86</td>\n",
" <td>86</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>87</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>85</td>\n",
" <td>86</td>\n",
" <td>79</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>86</td>\n",
" <td>86</td>\n",
" <td>86</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>84</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>86</td>\n",
" <td>84</td>\n",
" <td>74</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>98</td>\n",
" <td>98</td>\n",
" <td>94</td>\n",
" <td>98</td>\n",
" <td>98</td>\n",
" <td>98</td>\n",
" <td>94</td>\n",
" <td>95</td>\n",
" <td>91</td>\n",
" <td>79</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>77</td>\n",
" <td>77</td>\n",
" <td>75</td>\n",
" <td>77</td>\n",
" <td>77</td>\n",
" <td>77</td>\n",
" <td>77</td>\n",
" <td>74</td>\n",
" <td>76</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" <td>73</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>85</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>88</td>\n",
" <td>85</td>\n",
" <td>84</td>\n",
" <td>75</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>78</td>\n",
" <td>78</td>\n",
" <td>73</td>\n",
" <td>78</td>\n",
" <td>78</td>\n",
" <td>78</td>\n",
" <td>78</td>\n",
" <td>74</td>\n",
" <td>72</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>82</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>84</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>81</td>\n",
" <td>69</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>91</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>82</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>106</td>\n",
" <td>106</td>\n",
" <td>100</td>\n",
" <td>106</td>\n",
" <td>106</td>\n",
" <td>106</td>\n",
" <td>106</td>\n",
" <td>102</td>\n",
" <td>99</td>\n",
" <td>88</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>98</td>\n",
" <td>98</td>\n",
" <td>98</td>\n",
" <td>106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>108</td>\n",
" <td>108</td>\n",
" <td>105</td>\n",
" <td>108</td>\n",
" <td>108</td>\n",
" <td>108</td>\n",
" <td>108</td>\n",
" <td>101</td>\n",
" <td>102</td>\n",
" <td>96</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>101</td>\n",
" <td>101</td>\n",
" <td>101</td>\n",
" <td>108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>95</td>\n",
" <td>95</td>\n",
" <td>92</td>\n",
" <td>95</td>\n",
" <td>95</td>\n",
" <td>95</td>\n",
" <td>95</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>68</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>72</td>\n",
" <td>70</td>\n",
" <td>65</td>\n",
" <td>63</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>89</td>\n",
" <td>81</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>88</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>97</td>\n",
" <td>97</td>\n",
" <td>93</td>\n",
" <td>97</td>\n",
" <td>97</td>\n",
" <td>97</td>\n",
" <td>97</td>\n",
" <td>91</td>\n",
" <td>94</td>\n",
" <td>78</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>78</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>80</td>\n",
" <td>77</td>\n",
" <td>78</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>78</td>\n",
" <td>78</td>\n",
" <td>78</td>\n",
" <td>81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>88</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>84</td>\n",
" <td>85</td>\n",
" <td>75</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>85</td>\n",
" <td>85</td>\n",
" <td>85</td>\n",
" <td>93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>74</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>74</td>\n",
" <td>74</td>\n",
" <td>64</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>74</td>\n",
" <td>74</td>\n",
" <td>74</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>79</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>74</td>\n",
" <td>74</td>\n",
" <td>74</td>\n",
" <td>82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>68</td>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>69</td>\n",
" <td>68</td>\n",
" <td>70</td>\n",
" <td>60</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>112</td>\n",
" <td>112</td>\n",
" <td>108</td>\n",
" <td>112</td>\n",
" <td>112</td>\n",
" <td>112</td>\n",
" <td>111</td>\n",
" <td>108</td>\n",
" <td>106</td>\n",
" <td>96</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>106</td>\n",
" <td>106</td>\n",
" <td>106</td>\n",
" <td>112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>71</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>73</td>\n",
" <td>71</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>71</td>\n",
" <td>71</td>\n",
" <td>71</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>85</td>\n",
" <td>85</td>\n",
" <td>83</td>\n",
" <td>85</td>\n",
" <td>85</td>\n",
" <td>85</td>\n",
" <td>85</td>\n",
" <td>81</td>\n",
" <td>83</td>\n",
" <td>74</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>82</td>\n",
" <td>85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>79</td>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>83</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>70</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>81</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>66</td>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>70</td>\n",
" <td>69</td>\n",
" <td>66</td>\n",
" <td>61</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>66</td>\n",
" <td>66</td>\n",
" <td>66</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>59</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>57</td>\n",
" <td>56</td>\n",
" <td>47</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>87</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>89</td>\n",
" <td>83</td>\n",
" <td>85</td>\n",
" <td>75</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>85</td>\n",
" <td>85</td>\n",
" <td>85</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>75</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>74</td>\n",
" <td>76</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>61</td>\n",
" <td>61</td>\n",
" <td>56</td>\n",
" <td>61</td>\n",
" <td>61</td>\n",
" <td>61</td>\n",
" <td>61</td>\n",
" <td>54</td>\n",
" <td>55</td>\n",
" <td>50</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>55</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>77</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>77</td>\n",
" <td>76</td>\n",
" <td>66</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>76</td>\n",
" <td>80</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>60 rows × 54 columns</p>\n",
"</div>"
],
"text/plain": [
" Unique Key Created Date Closed Date Agency Agency Name \\\n",
"0 17116 17116 16721 17116 17116 \n",
"1 109 109 108 109 109 \n",
"2 91 91 88 91 91 \n",
"3 99 99 97 99 99 \n",
"4 106 106 103 106 106 \n",
"5 94 94 91 94 94 \n",
"6 106 106 103 106 106 \n",
"7 106 106 103 106 106 \n",
"8 95 95 94 95 95 \n",
"9 82 82 80 82 82 \n",
"10 89 89 87 89 89 \n",
"11 101 101 99 101 101 \n",
"12 100 100 99 100 100 \n",
"13 100 100 98 100 100 \n",
"14 88 88 86 88 88 \n",
"15 100 100 100 100 100 \n",
"16 83 83 82 83 83 \n",
"17 93 93 91 93 93 \n",
"18 91 91 90 91 91 \n",
"19 93 93 91 93 93 \n",
"20 100 100 98 100 100 \n",
"21 109 109 108 109 109 \n",
"22 104 104 99 104 104 \n",
"23 92 92 87 92 92 \n",
"24 79 79 76 79 79 \n",
"25 100 100 96 100 100 \n",
"26 84 84 79 84 84 \n",
"27 94 94 91 94 94 \n",
"28 94 94 90 94 94 \n",
"29 87 87 86 87 87 \n",
"30 89 89 87 89 89 \n",
"31 89 89 87 89 89 \n",
"32 87 87 84 87 87 \n",
"33 98 98 94 98 98 \n",
"34 77 77 75 77 77 \n",
"35 89 89 85 89 89 \n",
"36 78 78 73 78 78 \n",
"37 84 84 82 84 84 \n",
"38 91 91 91 91 91 \n",
"39 106 106 100 106 106 \n",
"40 108 108 105 108 108 \n",
"41 95 95 92 95 95 \n",
"42 72 72 68 72 72 \n",
"43 89 89 89 89 89 \n",
"44 97 97 93 97 97 \n",
"45 81 81 78 81 81 \n",
"46 93 93 88 93 93 \n",
"47 76 76 74 76 76 \n",
"48 82 82 79 82 82 \n",
"49 70 70 68 70 70 \n",
"50 112 112 108 112 112 \n",
"51 75 75 71 75 75 \n",
"52 85 85 83 85 85 \n",
"53 83 83 79 83 83 \n",
"54 70 70 66 70 70 \n",
"55 60 60 59 60 60 \n",
"56 89 89 87 89 89 \n",
"57 76 76 75 76 76 \n",
"58 61 61 56 61 61 \n",
"59 80 80 77 80 80 \n",
"\n",
" Complaint Type Descriptor Location Type Incident Zip Incident Address \\\n",
"0 17116 17116 17098 17098 16983 \n",
"1 109 109 105 103 90 \n",
"2 91 90 81 88 72 \n",
"3 99 99 94 96 83 \n",
"4 106 105 101 103 93 \n",
"5 94 93 89 90 80 \n",
"6 106 105 101 101 90 \n",
"7 106 106 101 102 90 \n",
"8 95 95 92 92 80 \n",
"9 82 81 80 78 71 \n",
"10 89 89 87 87 78 \n",
"11 101 100 92 99 84 \n",
"12 100 97 96 96 81 \n",
"13 100 99 93 94 83 \n",
"14 88 88 85 84 74 \n",
"15 100 99 96 97 83 \n",
"16 83 83 78 77 63 \n",
"17 93 92 91 89 81 \n",
"18 91 91 85 89 77 \n",
"19 93 92 88 88 76 \n",
"20 100 99 93 99 85 \n",
"21 109 109 101 106 96 \n",
"22 104 104 98 103 91 \n",
"23 92 92 91 86 80 \n",
"24 79 78 75 75 67 \n",
"25 100 100 96 96 91 \n",
"26 84 83 83 81 72 \n",
"27 94 94 90 88 81 \n",
"28 94 94 87 93 83 \n",
"29 87 86 83 82 65 \n",
"30 89 88 86 87 73 \n",
"31 89 89 85 86 79 \n",
"32 87 87 86 84 74 \n",
"33 98 94 95 91 79 \n",
"34 77 77 74 76 69 \n",
"35 89 88 85 84 75 \n",
"36 78 78 74 72 66 \n",
"37 84 82 82 81 69 \n",
"38 91 91 88 88 82 \n",
"39 106 106 102 99 88 \n",
"40 108 108 101 102 96 \n",
"41 95 95 88 88 78 \n",
"42 72 72 70 65 63 \n",
"43 89 87 87 89 81 \n",
"44 97 97 91 94 78 \n",
"45 81 80 77 78 67 \n",
"46 93 93 84 85 75 \n",
"47 76 76 74 74 64 \n",
"48 82 82 76 76 66 \n",
"49 70 69 68 70 60 \n",
"50 112 111 108 106 96 \n",
"51 75 75 73 71 61 \n",
"52 85 85 81 83 74 \n",
"53 83 83 81 81 70 \n",
"54 70 70 69 66 61 \n",
"55 60 60 57 56 47 \n",
"56 89 89 83 85 75 \n",
"57 76 76 74 76 65 \n",
"58 61 61 54 55 50 \n",
"59 80 80 77 76 66 \n",
"\n",
" ... Bridge Highway Direction Road Ramp Bridge Highway Segment \\\n",
"0 ... 0 0 0 \n",
"1 ... 0 0 0 \n",
"2 ... 0 0 1 \n",
"3 ... 1 1 1 \n",
"4 ... 1 1 1 \n",
"5 ... 0 0 0 \n",
"6 ... 2 2 2 \n",
"7 ... 1 1 2 \n",
"8 ... 1 1 1 \n",
"9 ... 1 1 1 \n",
"10 ... 0 0 0 \n",
"11 ... 0 0 1 \n",
"12 ... 0 0 0 \n",
"13 ... 1 1 1 \n",
"14 ... 0 0 0 \n",
"15 ... 0 0 0 \n",
"16 ... 0 0 0 \n",
"17 ... 0 0 0 \n",
"18 ... 0 0 0 \n",
"19 ... 0 0 0 \n",
"20 ... 0 0 0 \n",
"21 ... 0 0 0 \n",
"22 ... 0 0 1 \n",
"23 ... 0 0 0 \n",
"24 ... 0 0 0 \n",
"25 ... 0 0 0 \n",
"26 ... 0 0 0 \n",
"27 ... 1 1 2 \n",
"28 ... 1 1 1 \n",
"29 ... 0 0 0 \n",
"30 ... 0 0 0 \n",
"31 ... 0 0 0 \n",
"32 ... 1 1 1 \n",
"33 ... 0 0 0 \n",
"34 ... 0 0 0 \n",
"35 ... 1 1 1 \n",
"36 ... 1 1 1 \n",
"37 ... 0 0 0 \n",
"38 ... 1 1 1 \n",
"39 ... 0 0 0 \n",
"40 ... 0 0 0 \n",
"41 ... 0 0 0 \n",
"42 ... 0 0 0 \n",
"43 ... 0 0 0 \n",
"44 ... 0 0 0 \n",
"45 ... 0 0 0 \n",
"46 ... 0 0 0 \n",
"47 ... 1 1 1 \n",
"48 ... 1 1 2 \n",
"49 ... 1 1 1 \n",
"50 ... 2 2 2 \n",
"51 ... 1 1 1 \n",
"52 ... 1 1 1 \n",
"53 ... 1 1 1 \n",
"54 ... 0 0 0 \n",
"55 ... 0 0 0 \n",
"56 ... 0 0 0 \n",
"57 ... 0 0 0 \n",
"58 ... 1 1 1 \n",
"59 ... 0 0 0 \n",
"\n",
" Garage Lot Name Ferry Direction Ferry Terminal Name Latitude \\\n",
"0 0 0 0 17093 \n",
"1 0 0 0 102 \n",
"2 0 0 0 87 \n",
"3 0 0 0 95 \n",
"4 0 0 0 103 \n",
"5 1 0 0 88 \n",
"6 0 0 0 101 \n",
"7 0 0 0 102 \n",
"8 0 0 0 90 \n",
"9 0 0 0 77 \n",
"10 0 0 0 84 \n",
"11 0 0 0 99 \n",
"12 0 0 0 95 \n",
"13 0 0 0 93 \n",
"14 0 0 0 84 \n",
"15 0 1 1 96 \n",
"16 0 0 0 76 \n",
"17 0 0 0 88 \n",
"18 0 0 0 89 \n",
"19 0 0 0 88 \n",
"20 0 0 0 99 \n",
"21 0 0 0 103 \n",
"22 0 0 0 101 \n",
"23 0 0 0 86 \n",
"24 0 0 0 75 \n",
"25 0 0 0 96 \n",
"26 0 0 0 81 \n",
"27 0 0 0 87 \n",
"28 0 0 0 93 \n",
"29 0 1 1 82 \n",
"30 0 0 0 86 \n",
"31 0 0 0 86 \n",
"32 0 0 0 82 \n",
"33 0 0 0 91 \n",
"34 0 0 0 73 \n",
"35 0 0 0 84 \n",
"36 0 0 0 72 \n",
"37 0 0 0 81 \n",
"38 0 0 1 88 \n",
"39 0 0 0 98 \n",
"40 0 0 0 101 \n",
"41 0 0 0 88 \n",
"42 0 0 0 65 \n",
"43 0 0 0 88 \n",
"44 0 0 0 93 \n",
"45 0 0 0 78 \n",
"46 0 0 0 85 \n",
"47 0 0 0 74 \n",
"48 0 0 0 74 \n",
"49 0 0 0 70 \n",
"50 0 0 0 106 \n",
"51 0 0 0 71 \n",
"52 0 0 1 82 \n",
"53 0 0 0 81 \n",
"54 0 0 0 66 \n",
"55 0 0 0 56 \n",
"56 0 0 0 85 \n",
"57 0 0 0 75 \n",
"58 0 0 0 55 \n",
"59 0 0 0 76 \n",
"\n",
" Longitude Location created_dt \n",
"0 17093 17093 17116 \n",
"1 102 102 109 \n",
"2 87 87 91 \n",
"3 95 95 99 \n",
"4 103 103 106 \n",
"5 88 88 94 \n",
"6 101 101 106 \n",
"7 102 102 106 \n",
"8 90 90 95 \n",
"9 77 77 82 \n",
"10 84 84 89 \n",
"11 99 99 101 \n",
"12 95 95 100 \n",
"13 93 93 100 \n",
"14 84 84 88 \n",
"15 96 96 100 \n",
"16 76 76 83 \n",
"17 88 88 93 \n",
"18 89 89 91 \n",
"19 88 88 93 \n",
"20 99 99 100 \n",
"21 103 103 109 \n",
"22 101 101 104 \n",
"23 86 86 92 \n",
"24 75 75 79 \n",
"25 96 96 100 \n",
"26 81 81 84 \n",
"27 87 87 94 \n",
"28 93 93 94 \n",
"29 82 82 87 \n",
"30 86 86 89 \n",
"31 86 86 89 \n",
"32 82 82 87 \n",
"33 91 91 98 \n",
"34 73 73 77 \n",
"35 84 84 89 \n",
"36 72 72 78 \n",
"37 81 81 84 \n",
"38 88 88 91 \n",
"39 98 98 106 \n",
"40 101 101 108 \n",
"41 88 88 95 \n",
"42 65 65 72 \n",
"43 88 88 89 \n",
"44 93 93 97 \n",
"45 78 78 81 \n",
"46 85 85 93 \n",
"47 74 74 76 \n",
"48 74 74 82 \n",
"49 70 70 70 \n",
"50 106 106 112 \n",
"51 71 71 75 \n",
"52 82 82 85 \n",
"53 81 81 83 \n",
"54 66 66 70 \n",
"55 56 56 60 \n",
"56 85 85 89 \n",
"57 75 75 76 \n",
"58 55 55 61 \n",
"59 76 76 80 \n",
"\n",
"[60 rows x 54 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"twelve_am_complaints.groupby(twelve_am_complaints.index.minute).count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like midnight is a little bit of an outlier. Why might that be? Take the 5 most common agencies and graph the times they file reports at (all day, not just midnight)."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"NYPD 80000\n",
"HPD 39388\n",
"DOT 22308\n",
"DPR 15505\n",
"DOHMH 8250\n",
"Name: Agency, dtype: int64"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Agency'].value_counts().head(5)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_NYPD = df[df['Agency'] == 'NYPD']"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_HPD = df[df['Agency'] == 'HPD']"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_DOT = df[df['Agency'] == 'DOT']"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_DPR= df[df['Agency'] == 'DPR']"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_DOHMH= df[df['Agency'] == 'DOHMH']"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10b612198>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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VFUUyMjLI3bt3ibOzM1FWViYPHjyQ2E6a5+H+/fuEw+GIOBIYGBgQNptNbt++zVyXjo4O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"text/plain": [
"<matplotlib.figure.Figure at 0x10b612ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"all_graph = df_NYPD.groupby(by= df_NYPD.index.hour).count().plot(y='Unique Key', label='NYPD complaints')\n",
"all_graph.set_xticks([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23])\n",
"all_graph.set_title(\"A day of complaints by the top 5 agencies\")\n",
"all_graph.set_xlabel(\"Hours\")\n",
"all_graph.set_ylabel(\"Complaints\")\n",
"\n",
"\n",
"df_HPD.groupby(by= df_HPD.index.hour).count().plot(y='Unique Key', ax=all_graph , label='HPD complaints')\n",
"\n",
"df_DOT.groupby(by= df_DOT.index.hour).count().plot(y='Unique Key', ax=all_graph , label='DOT complaints')\n",
"\n",
"df_DPR.groupby(by= df_DPR.index.hour).count().plot(y='Unique Key', ax=all_graph , label='DPR complaints')\n",
"\n",
"df_DOHMH.groupby(by= df_DOHMH.index.hour).count().plot(y='Unique Key', ax=all_graph , label='DOHMH complaints')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Graph those same agencies on an annual basis - make it **weekly**. When do people like to complain? When does the NYPD have an odd number of complaints?"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"May and June are the months with more complaints, followed by October, November and December. \n",
"In May the NYPD and HPD have an odd number of complaints\n"
]
},
{
"data": {
"image/png": 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JUiWShYFqSM4EKNVEyZm5OCm56HbvwFGj1i9aJEnSvScLA9XQ3r17a3wTgVT7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\nGRBCCCES3P8DSxzc/bbs18IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c1b3a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"all_graph = df_NYPD.groupby(by= df_NYPD.index.weekofyear).count().plot(y='Unique Key', label='NYPD complaints')\n",
"#all_graph.set_xticks([1,50])\n",
"all_graph.set_title(\"A year of complaints by the top 5 agencies\")\n",
"all_graph.set_xlabel(\"Weeks\")\n",
"\n",
"ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
"\n",
"df_HPD.groupby(by= df_HPD.index.week).count().plot(y='Unique Key', ax=all_graph , label='HPD complaints')\n",
"\n",
"df_DOT.groupby(by= df_DOT.index.hour).count().plot(y='Unique Key', ax=all_graph , label='DOT complaints')\n",
"\n",
"df_DPR.groupby(by= df_DPR.index.hour).count().plot(y='Unique Key', ax=all_graph , label='DPR complaints')\n",
"\n",
"df_DOHMH.groupby(by= df_DOHMH.index.hour).count().plot(y='Unique Key', ax=all_graph , label='DOHMH complaints')\n",
"\n",
"plt.legend(bbox_to_anchor=(0, 1), loc='best', ncol=1)\n",
"\n",
"print(\"\"\"May and June are the months with more complaints, followed by October, November and December. \n",
"In May the NYPD and HPD have an odd number of complaints\"\"\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Maybe the NYPD deals with different issues at different times? Check the most popular complaints in July and August vs the month of May. Also check the most common complaints for the Housing Preservation Bureau (HPD) in winter vs. summer."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Illegal Parking 3444\n",
"Blocked Driveway 3258\n",
"Noise - Street/Sidewalk 3165\n",
"Street Condition 1480\n",
"Noise - Commercial 1201\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"August_July = df[\"2015-07\":\"2015-08\"]\n",
"August_July_complaints = August_July['Complaint Type'].value_counts().head(5)\n",
"August_July_complaints"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Blocked Driveway 4114\n",
"Illegal Parking 3975\n",
"HEAT/HOT WATER 3583\n",
"Noise - Street/Sidewalk 3385\n",
"Noise - Commercial 2263\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"May = df['2015-05']\n",
"May_complaints= May['Complaint Type'].value_counts().head(5)\n",
"May_complaints"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# August_July_vs_May= August_July_complaints.plot(y='Unique Key', label='August - July complaints')\n",
"# August_July_vs_May.set_ylabel(\"Number of Complaints\")\n",
"# August_July_vs_May.set_title(\"August-July vs May Complaints\")\n",
"# May['Complaint Type'].value_counts().head(5).plot(y='Unique Key', ax=August_July_vs_May, label='May complaints')\n",
"\n",
"# August_July_vs_May.set_xticks([1,2,3,4,5])\n",
"# August_July_vs_May.set_xticklabels(['Illegal Parking', 'Blocked Driveway', 'Noise - Street/Sidewalk', 'Street Condition', 'Noise - Commercial'])\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"HEAT/HOT WATER 12408\n",
"UNSANITARY CONDITION 4774\n",
"PAINT/PLASTER 4306\n",
"PLUMBING 3388\n",
"HPD Literature Request 3305\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Most popular complaints of the HPD\n",
"df_HPD['Complaint Type'].value_counts().head(5)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"HEAT/HOT WATER 617\n",
"UNSANITARY CONDITION 510\n",
"HPD Literature Request 462\n",
"PAINT/PLASTER 444\n",
"PLUMBING 309\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summer_complaints= df_HPD[\"2015-06\":\"2015-08\"]['Complaint Type'].value_counts().head(5)\n",
"summer_complaints"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"UNSANITARY CONDITION 8\n",
"GENERAL 3\n",
"PAINT/PLASTER 3\n",
"APPLIANCE 2\n",
"WATER LEAK 2\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"winter_complaints= df_HPD[\"2015-01\":\"2015-02\"]['Complaint Type'].value_counts().head(5)\n",
"winter_complaints"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"HEAT/HOT WATER 353\n",
"UNSANITARY CONDITION 182\n",
"PLUMBING 138\n",
"PAINT/PLASTER 136\n",
"DOOR/WINDOW 103\n",
"Name: Complaint Type, dtype: int64"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"winter_complaints_dec= df_HPD[\"2015-12\"]['Complaint Type'].value_counts().head(5)\n",
"winter_complaints_dec"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"winter_results= df_HPD[\"2015-12\"]['Complaint Type'].value_counts() + df_HPD[\"2015-01\":\"2015-02\"]['Complaint Type'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"APPLIANCE 32.0\n",
"DOOR/WINDOW NaN\n",
"ELECTRIC NaN\n",
"FLOORING/STAIRS 57.0\n",
"GENERAL 66.0\n",
"HEAT/HOT WATER NaN\n",
"HPD Literature Request NaN\n",
"OUTSIDE BUILDING NaN\n",
"PAINT/PLASTER 139.0\n",
"PLUMBING 139.0\n",
"SAFETY NaN\n",
"UNSANITARY CONDITION 190.0\n",
"WATER LEAK 88.0\n",
"Name: Complaint Type, dtype: float64"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"winter_results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
remenska/iSDM | notebooks/species_synonyms.ipynb | 1 | 105808 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import logging\n",
"import numpy as np\n",
"import pandas as pd\n",
"root = logging.getLogger()\n",
"root.addHandler(logging.StreamHandler())\n",
"import datetime\n",
"%matplotlib inline\n",
"from shapely.prepared import prep\n",
"from shapely import speedups\n",
"speedups.enable()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pygbif import species # http://pygbif.readthedocs.org/en/latest/\n",
"from pygbif import occurrences"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# all these synonyms should pop-up below (for those that have occurrences):\n",
"# http://www.gbif.org/species/4286327/synonyms"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from iSDM.species import GBIFSpecies"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Enabled Shapely speedups for performance.\n"
]
}
],
"source": [
"gasterosteus_aculeatus = GBIFSpecies(name_species=\"Gasterosteus aculeatus\")"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading data from: ../data/fish/selection/gbif/Gasterosteus aculeatus4286327.pkl\n",
"Succesfully loaded previously saved data.\n"
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" <td>Driedoornige stekelbaars</td>\n",
" <td>NaN</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>http://www.inbo.be/en/norms-for-data-use</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>HUMAN_OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-28</td>\n",
" <td>NaN</td>\n",
" <td>Arendonk 4 Fuik 2</td>\n",
" <td>Belgium Datum 1972</td>\n",
" <td>NaN</td>\n",
" <td>Driedoornige stekelbaars</td>\n",
" <td>NaN</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>http://www.inbo.be/en/norms-for-data-use</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>HUMAN_OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-28</td>\n",
" <td>NaN</td>\n",
" <td>Arendonk 4 Fuik 1</td>\n",
" <td>Belgium Datum 1972</td>\n",
" <td>NaN</td>\n",
" <td>Driedoornige stekelbaars</td>\n",
" <td>NaN</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>HUMAN_OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>17291438</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>HUMAN_OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>17321055</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>http://www.inbo.be/en/norms-for-data-use</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>HUMAN_OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2015-04-28</td>\n",
" <td>NaN</td>\n",
" <td>Arendonk 4 Fuik 3</td>\n",
" <td>Belgium Datum 1972</td>\n",
" <td>NaN</td>\n",
" <td>Driedoornige stekelbaars</td>\n",
" <td>NaN</td>\n",
" <td>2015.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87544</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14691302</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87545</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14691501</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87546</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14691512</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87547</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14691380</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87548</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14691458</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87549</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14691397</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87550</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14691223</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87551</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>OBSERVATION</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>14691199</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87552</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Seguimiento</td>\n",
" <td>NaN</td>\n",
" <td>UNKNOWN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>583991</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87553</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1839.2.4.12</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Northeast Atlantic Ocean</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87554</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1894.2.22.5-6</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87555</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1931.6.26.1-3</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Northeast Atlantic Ocean; North Sea</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87556</th>\n",
" <td>http://www.vertnet.org/resources/norms.html</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9740</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>summer 1956</td>\n",
" <td>NaN</td>\n",
" <td>around Yakobi Is., and in Lynn Canal</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87557</th>\n",
" <td>http://www.vertnet.org/resources/norms.html</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6704</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1976</td>\n",
" <td>NaN</td>\n",
" <td>North Fork of Chickamin River, Indian Creek, ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87558</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>26538</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[no verbatim date data]</td>\n",
" <td>NaN</td>\n",
" <td>Saachuru-Kale; Rec'd 24 Dec. 1885.</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87559</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6761</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[no verbatim date data]</td>\n",
" <td>NaN</td>\n",
" <td>Nahant [Massachusetts Bay]</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87560</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6779</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1856-1862</td>\n",
" <td>NaN</td>\n",
" <td>San Francisco, mid-1800's.</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87561</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1853.10.1.1</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87562</th>\n",
" <td>Open Access, http://creativecommons.org/public...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>Gasterosteus aculeatus (YPM ICH 003575)</td>\n",
" <td>YPM ICH 003575</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>sticklebacks; pipefishes; ray-finned fishes; v...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87563</th>\n",
" <td>Open Access, http://creativecommons.org/public...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>Gasterosteus aculeatus (YPM ICH 023480)</td>\n",
" <td>YPM ICH 023480</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>sticklebacks; pipefishes; ray-finned fishes; v...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87564</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>37987</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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"<p>87574 rows × 167 columns</p>\n",
"</div>"
],
"text/plain": [
" accessrights \\\n",
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"... ... ... ... \n",
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"\n",
" basisofrecord behavior bibliographiccitation \\\n",
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"... ... ... ... \n",
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"87572 PRESERVED_SPECIMEN NaN NaN \n",
"87573 PRESERVED_SPECIMEN NaN NaN \n",
"\n",
" catalognumber class classkey ... verbatimdepth \\\n",
"0 2714337 Actinopterygii 204 ... NaN \n",
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"... ... ... ... ... ... \n",
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"87552 583991 Actinopterygii 204 ... NaN \n",
"87553 1839.2.4.12 Actinopterygii 204 ... NaN \n",
"87554 1894.2.22.5-6 Actinopterygii 204 ... NaN \n",
"87555 1931.6.26.1-3 Actinopterygii 204 ... NaN \n",
"87556 9740 Actinopterygii 204 ... NaN \n",
"87557 6704 Actinopterygii 204 ... NaN \n",
"87558 26538 Actinopterygii 204 ... NaN \n",
"87559 6761 Actinopterygii 204 ... NaN \n",
"87560 6779 Actinopterygii 204 ... NaN \n",
"87561 1853.10.1.1 Actinopterygii 204 ... NaN \n",
"87562 YPM ICH 003575 Actinopterygii 204 ... NaN \n",
"87563 YPM ICH 023480 Actinopterygii 204 ... NaN \n",
"87564 37987 Actinopterygii 204 ... NaN \n",
"87565 5209 Actinopterygii 204 ... NaN \n",
"87566 3064 Actinopterygii 204 ... NaN \n",
"87567 4714 Actinopterygii 204 ... NaN \n",
"87568 0000-7075 Actinopterygii 204 ... NaN \n",
"87569 0000-7114 Actinopterygii 204 ... NaN \n",
"87570 0000-7083 Actinopterygii 204 ... NaN \n",
"87571 0000-7076 Actinopterygii 204 ... NaN \n",
"87572 0000-7096 Actinopterygii 204 ... NaN \n",
"87573 1898-0809 Actinopterygii 204 ... NaN \n",
"\n",
" verbatimelevation verbatimeventdate \\\n",
"0 NaN 2016-02-24 11:25:06 \n",
"1 NaN 2016-02-22 \n",
"2 NaN 2016-02-23 \n",
"3 NaN NaN \n",
"4 NaN 2016-03-18 \n",
"5 NaN 2016-03-27 \n",
"6 NaN Sat Apr 30 2016 11:08:00 GMT-0700 (PDT) \n",
"7 NaN 2016-04-30 11:08:32 \n",
"8 NaN 2016-04-21 11:51:18 AM PDT \n",
"9 NaN 2016-05-21 11:01:10 \n",
"10 NaN NaN \n",
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"12 NaN NaN \n",
"13 NaN 2016-05-16 14:53:51 \n",
"14 NaN 2016-06-18 10:53:01 AM PDT \n",
"15 NaN NaN \n",
"16 NaN NaN \n",
"17 NaN NaN \n",
"18 NaN 2015-01-19 \n",
"19 NaN NaN \n",
"20 NaN 2015-03-07 14:31:50 \n",
"21 NaN 2015-04-29 \n",
"22 NaN 2015-04-29 \n",
"23 NaN 2015-04-28 \n",
"24 NaN 2015-04-29 \n",
"25 NaN 2015-04-28 \n",
"26 NaN 2015-04-28 \n",
"27 NaN NaN \n",
"28 NaN NaN \n",
"29 NaN 2015-04-28 \n",
"... ... ... \n",
"87544 NaN NaN \n",
"87545 NaN NaN \n",
"87546 NaN NaN \n",
"87547 NaN NaN \n",
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"87549 NaN NaN \n",
"87550 NaN NaN \n",
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"87552 NaN NaN \n",
"87553 NaN NaN \n",
"87554 NaN NaN \n",
"87555 NaN NaN \n",
"87556 NaN summer 1956 \n",
"87557 NaN 1976 \n",
"87558 NaN [no verbatim date data] \n",
"87559 NaN [no verbatim date data] \n",
"87560 NaN 1856-1862 \n",
"87561 NaN NaN \n",
"87562 NaN NaN \n",
"87563 NaN NaN \n",
"87564 NaN NaN \n",
"87565 NaN NaN \n",
"87566 NaN NaN \n",
"87567 NaN NaN \n",
"87568 NaN NaN \n",
"87569 NaN NaN \n",
"87570 NaN NaN \n",
"87571 NaN NaN \n",
"87572 NaN NaN \n",
"87573 NaN NaN \n",
"\n",
" verbatimlabel verbatimlocality \\\n",
"0 NaN Astoria Waterfront \n",
"1 NaN Maacama Creek, CA \n",
"2 NaN pinnacles np \n",
"3 NaN NaN \n",
"4 NaN Mountain Lake \n",
"5 NaN Font estramar \n",
"6 NaN 100 Shaffer Rd, Santa Cruz, CA, US \n",
"7 NaN Younger Lagoon Reserve, Santa Cruz, UC Natural... \n",
"8 NaN Mill Valley, CA 94941 \n",
"9 NaN Pinnacles National Park \n",
"10 NaN NaN \n",
"11 NaN Mouth of Colewort Creek, Lewis and Clark Natio... \n",
"12 NaN NaN \n",
"13 NaN Soldotna, Headquarters Lake \n",
"14 NaN Blue Dot Farm, Marin County, US-CA, US \n",
"15 NaN NaN \n",
"16 NaN NaN \n",
"17 NaN NaN \n",
"18 NaN Owen Beach \n",
"19 NaN NaN \n",
"20 NaN Golden Gate Park, San Francisco, California, U... \n",
"21 NaN De Mosten - Bospoel Fuik 3 \n",
"22 NaN De Mosten - Bospoel Fuik 2 \n",
"23 NaN De Mosten - Bospoel Fuik3 \n",
"24 NaN De Mosten - Bospoel Fuik 1 \n",
"25 NaN Arendonk 4 Fuik 2 \n",
"26 NaN Arendonk 4 Fuik 1 \n",
"27 NaN NaN \n",
"28 NaN NaN \n",
"29 NaN Arendonk 4 Fuik 3 \n",
"... ... ... \n",
"87544 NaN NaN \n",
"87545 NaN NaN \n",
"87546 NaN NaN \n",
"87547 NaN NaN \n",
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"87550 NaN NaN \n",
"87551 NaN NaN \n",
"87552 NaN NaN \n",
"87553 NaN NaN \n",
"87554 NaN NaN \n",
"87555 NaN NaN \n",
"87556 NaN around Yakobi Is., and in Lynn Canal \n",
"87557 NaN North Fork of Chickamin River, Indian Creek, ... \n",
"87558 NaN Saachuru-Kale; Rec'd 24 Dec. 1885. \n",
"87559 NaN Nahant [Massachusetts Bay] \n",
"87560 NaN San Francisco, mid-1800's. \n",
"87561 NaN NaN \n",
"87562 NaN NaN \n",
"87563 NaN NaN \n",
"87564 NaN NaN \n",
"87565 NaN NaN \n",
"87566 NaN NaN \n",
"87567 NaN NaN \n",
"87568 NaN NaN \n",
"87569 NaN NaN \n",
"87570 NaN NaN \n",
"87571 NaN NaN \n",
"87572 NaN NaN \n",
"87573 NaN NaN \n",
"\n",
" verbatimsrs verbatimtaxonrank \\\n",
"0 NaN NaN \n",
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"3 NaN NaN \n",
"4 NaN NaN \n",
"5 NaN NaN \n",
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"14 NaN NaN \n",
"15 NaN NaN \n",
"16 NaN NaN \n",
"17 NaN NaN \n",
"18 NaN NaN \n",
"19 NaN NaN \n",
"20 NaN NaN \n",
"21 Belgium Datum 1972 NaN \n",
"22 Belgium Datum 1972 NaN \n",
"23 Belgium Datum 1972 NaN \n",
"24 Belgium Datum 1972 NaN \n",
"25 Belgium Datum 1972 NaN \n",
"26 Belgium Datum 1972 NaN \n",
"27 NaN NaN \n",
"28 NaN NaN \n",
"29 Belgium Datum 1972 NaN \n",
"... ... ... \n",
"87544 NaN NaN \n",
"87545 NaN NaN \n",
"87546 NaN NaN \n",
"87547 NaN NaN \n",
"87548 NaN NaN \n",
"87549 NaN NaN \n",
"87550 NaN NaN \n",
"87551 NaN NaN \n",
"87552 NaN NaN \n",
"87553 NaN NaN \n",
"87554 NaN NaN \n",
"87555 NaN NaN \n",
"87556 NaN NaN \n",
"87557 NaN NaN \n",
"87558 NaN NaN \n",
"87559 NaN NaN \n",
"87560 NaN NaN \n",
"87561 NaN NaN \n",
"87562 NaN NaN \n",
"87563 NaN NaN \n",
"87564 NaN NaN \n",
"87565 NaN NaN \n",
"87566 NaN NaN \n",
"87567 NaN NaN \n",
"87568 NaN NaN \n",
"87569 NaN NaN \n",
"87570 NaN NaN \n",
"87571 NaN NaN \n",
"87572 NaN NaN \n",
"87573 NaN NaN \n",
"\n",
" vernacularname \\\n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 NaN \n",
"11 NaN \n",
"12 NaN \n",
"13 NaN \n",
"14 NaN \n",
"15 NaN \n",
"16 NaN \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 Driedoornige stekelbaars \n",
"22 Driedoornige stekelbaars \n",
"23 Driedoornige stekelbaars \n",
"24 Driedoornige stekelbaars \n",
"25 Driedoornige stekelbaars \n",
"26 Driedoornige stekelbaars \n",
"27 NaN \n",
"28 NaN \n",
"29 Driedoornige stekelbaars \n",
"... ... \n",
"87544 NaN \n",
"87545 NaN \n",
"87546 NaN \n",
"87547 NaN \n",
"87548 NaN \n",
"87549 NaN \n",
"87550 NaN \n",
"87551 NaN \n",
"87552 NaN \n",
"87553 NaN \n",
"87554 NaN \n",
"87555 NaN \n",
"87556 NaN \n",
"87557 NaN \n",
"87558 NaN \n",
"87559 NaN \n",
"87560 NaN \n",
"87561 NaN \n",
"87562 sticklebacks; pipefishes; ray-finned fishes; v... \n",
"87563 sticklebacks; pipefishes; ray-finned fishes; v... \n",
"87564 NaN \n",
"87565 NaN \n",
"87566 NaN \n",
"87567 NaN \n",
"87568 NaN \n",
"87569 NaN \n",
"87570 NaN \n",
"87571 NaN \n",
"87572 NaN \n",
"87573 NaN \n",
"\n",
" waterbody year \n",
"0 NaN 2016.0 \n",
"1 NaN 2016.0 \n",
"2 NaN 2016.0 \n",
"3 NaN 2016.0 \n",
"4 NaN 2016.0 \n",
"5 NaN 2016.0 \n",
"6 NaN 2016.0 \n",
"7 NaN 2016.0 \n",
"8 NaN 2016.0 \n",
"9 NaN 2016.0 \n",
"10 NaN 2016.0 \n",
"11 NaN 2016.0 \n",
"12 NaN 2016.0 \n",
"13 NaN 2016.0 \n",
"14 NaN 2016.0 \n",
"15 NaN 2016.0 \n",
"16 NaN 2016.0 \n",
"17 NaN 2016.0 \n",
"18 NaN 2015.0 \n",
"19 NaN 2015.0 \n",
"20 NaN 2015.0 \n",
"21 NaN 2015.0 \n",
"22 NaN 2015.0 \n",
"23 NaN 2015.0 \n",
"24 NaN 2015.0 \n",
"25 NaN 2015.0 \n",
"26 NaN 2015.0 \n",
"27 NaN 2015.0 \n",
"28 NaN 2015.0 \n",
"29 NaN 2015.0 \n",
"... ... ... \n",
"87544 NaN NaN \n",
"87545 NaN NaN \n",
"87546 NaN NaN \n",
"87547 NaN NaN \n",
"87548 NaN NaN \n",
"87549 NaN NaN \n",
"87550 NaN NaN \n",
"87551 NaN NaN \n",
"87552 NaN NaN \n",
"87553 Northeast Atlantic Ocean NaN \n",
"87554 NaN NaN \n",
"87555 Northeast Atlantic Ocean; North Sea NaN \n",
"87556 NaN NaN \n",
"87557 NaN NaN \n",
"87558 NaN NaN \n",
"87559 NaN NaN \n",
"87560 NaN NaN \n",
"87561 NaN NaN \n",
"87562 NaN NaN \n",
"87563 NaN NaN \n",
"87564 NaN NaN \n",
"87565 Puget sound NaN \n",
"87566 NaN NaN \n",
"87567 Green lake NaN \n",
"87568 NaN NaN \n",
"87569 seine NaN \n",
"87570 rhin NaN \n",
"87571 NaN NaN \n",
"87572 NaN NaN \n",
"87573 seine NaN \n",
"\n",
"[87574 rows x 167 columns]"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gasterosteus_aculeatus.load_data(\"../data/fish/selection/gbif/Gasterosteus aculeatus4286327.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"species_df = gasterosteus_aculeatus.get_data()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Gasterosteus aculeatus Linnaeus, 1758',\n",
" 'Gasterosteus aculeatus subsp. aculeatus',\n",
" 'Gasterosteus aculeatus subsp. williamsoni Girard, 1854',\n",
" 'Gasterosteus cataphractus (Pallas, 1814)',\n",
" 'Gasterosteus bispinosus Walbaum, 1792',\n",
" 'Gasterosteus williamsoni Girard, 1854',\n",
" 'Gasterosteus atkinsi Bean, 1879',\n",
" 'Gasterosteus atkinsii Bean, 1879',\n",
" 'Gasterosteus aculeatus subsp. gymnurus Cuvier, 1829',\n",
" 'Gasterosteus brachycentrus Cuvier, 1829',\n",
" 'Gasterosteus cuvieri Girard, 1850',\n",
" 'Gasterosteus trachurus Cuvier, 1829',\n",
" 'Gasterosteus plebeius Girard, 1854',\n",
" 'Gasterosteus inopinatus Girard, 1854',\n",
" 'Gasterosteus leiurus Cuvier, 1829',\n",
" 'Gasterosteus intermedius Girard, 1856',\n",
" 'Gasterosteus loricatus Reinhardt, 1837',\n",
" 'Gasterosteus argentatissimus Blanchard, 1866',\n",
" 'Gasterosteus serratus Ayres, 1855',\n",
" 'Gasterosteus biaculeatus Mitchill, 1815',\n",
" 'Gasterosteus pugetti Girard, 1856',\n",
" 'Gastrosteus aculeatus (Linnaeus, 1758)',\n",
" 'Gasterosteus argyropomus Cuvier, 1829',\n",
" 'Gasterosteus spinulosus Yarrell, 1835',\n",
" 'Gasterosteus santaeannae Regan, 1909',\n",
" 'Gasterosteus noveboracensis Cuvier, 1829',\n",
" 'Gasterosteus algeriensis Sauvage, 1874',\n",
" 'Gasterosteus bailloni Blanchard, 1866'], dtype=object)"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# all synonyms (including the original) for occurrences of \"Gasterosteus aculeatus\"\n",
"species_df.scientificname.unique()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([4286327, 7193464, 6169883, 4286335, 4286334, 4352589, 4352581,\n",
" 4352569, 6169875, 4286342, 4286343, 4286332, 4352562, 4352575,\n",
" 4286336, 4352566, 4352584, 4352574, 4352567, 4286348, 4352572,\n",
" 4352560, 4286341, 4286331, 4352591, 4286329, 4352582, 4352576])"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"species_df.taxonkey.unique()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>accessrights</th>\n",
" <th>associatedoccurrences</th>\n",
" <th>associatedreferences</th>\n",
" <th>associatedsequences</th>\n",
" <th>basisofrecord</th>\n",
" <th>behavior</th>\n",
" <th>bibliographiccitation</th>\n",
" <th>catalognumber</th>\n",
" <th>class</th>\n",
" <th>classkey</th>\n",
" <th>...</th>\n",
" <th>verbatimdepth</th>\n",
" <th>verbatimelevation</th>\n",
" <th>verbatimeventdate</th>\n",
" <th>verbatimlabel</th>\n",
" <th>verbatimlocality</th>\n",
" <th>verbatimsrs</th>\n",
" <th>verbatimtaxonrank</th>\n",
" <th>vernacularname</th>\n",
" <th>waterbody</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>78041</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11475</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1885.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78076</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11145</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1882.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78226</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6249</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1844.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80161</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>PRESERVED_SPECIMEN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1896.10.3.1-5</td>\n",
" <td>Actinopterygii</td>\n",
" <td>204</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 167 columns</p>\n",
"</div>"
],
"text/plain": [
" accessrights associatedoccurrences associatedreferences \\\n",
"78041 NaN NaN NaN \n",
"78076 NaN NaN NaN \n",
"78226 NaN NaN NaN \n",
"80161 NaN NaN NaN \n",
"\n",
" associatedsequences basisofrecord behavior bibliographiccitation \\\n",
"78041 NaN PRESERVED_SPECIMEN NaN NaN \n",
"78076 NaN PRESERVED_SPECIMEN NaN NaN \n",
"78226 NaN PRESERVED_SPECIMEN NaN NaN \n",
"80161 NaN PRESERVED_SPECIMEN NaN NaN \n",
"\n",
" catalognumber class classkey ... verbatimdepth \\\n",
"78041 11475 Actinopterygii 204 ... NaN \n",
"78076 11145 Actinopterygii 204 ... NaN \n",
"78226 6249 Actinopterygii 204 ... NaN \n",
"80161 1896.10.3.1-5 Actinopterygii 204 ... NaN \n",
"\n",
" verbatimelevation verbatimeventdate verbatimlabel verbatimlocality \\\n",
"78041 NaN NaN NaN NaN \n",
"78076 NaN NaN NaN NaN \n",
"78226 NaN NaN NaN NaN \n",
"80161 NaN NaN NaN NaN \n",
"\n",
" verbatimsrs verbatimtaxonrank vernacularname waterbody year \n",
"78041 NaN NaN NaN NaN 1885.0 \n",
"78076 NaN NaN NaN NaN 1882.0 \n",
"78226 NaN NaN NaN NaN 1844.0 \n",
"80161 NaN NaN NaN NaN NaN \n",
"\n",
"[4 rows x 167 columns]"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# take one taxonkey as an example\n",
"# 4 records for synonym \"Gasterosteus brachycentrus Cuvier\": http://www.gbif.org/species/4286342\n",
"species_df[species_df.taxonkey==4286342]"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"78041 Gasterosteus brachycentrus Cuvier, 1829\n",
"78076 Gasterosteus brachycentrus Cuvier, 1829\n",
"78226 Gasterosteus brachycentrus Cuvier, 1829\n",
"80161 Gasterosteus brachycentrus Cuvier, 1829\n",
"Name: scientificname, dtype: object"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"species_df[species_df.taxonkey==4286342].scientificname"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
TakesxiSximada/TIL | jupyter/use-extension/FISH_FISH_FISH.ipynb | 1 | 25237 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" # FISH FISH FISH "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# h1\n",
"## h2\n",
"### h3\n",
"#### h4"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1485584149.313308"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import time\n",
"time.time()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from PIL import Image\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"im = Image.open(\"./img/nbextensions-tab.png\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"im_list = np.asarray(im)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x1154e3550>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt.imshow(im_list)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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uUfJ3ikGmvZr6gAi6UkNVKAaCGxZ1I4S4F/haStmlirHUzhZ9itMU\nQjwphMgXQuTX1NT0pWm3KCevGBKUk1d4Mb25dZMA3CeEqAR+DyQKIXYBXwkhIgD0z6/1+ueBmS7t\nZ+hlbkgp35RSGqSUhsmTJ1/HFBQKhULRHT06einlC1LKGVLK2WgPWQ9IKR8F9gGP69UeB7L15X3A\nI0KIECFEJDAP+Gt/BudJAi/97cNse1tF1/SFG2HH65Zd9AM8SeDZq0+RYu5dvpis3HMDNTSfwqOU\nIHRrx217NMnA9Mw86r1budMrCbyOtr8CsoQQ64AvgdUAUsovhBBZQDHaI74fSSn7lVDjv5/7zjVl\n20oa2ff4NOppYCxjKGyEyBN5XBw7CYrPczBwCmvv9VMFo37SWzuu3X6E9Cfn8bNdp7CdvUIUrZQC\nq0dBVmMA6ab9wBjyzUsGfQ7ewC+zv7hGAm/J1jK23D0bqOFAcRPfDvyK3xRA4sIprP+0gQ3fbOe1\ng+dYOwrSG6HhaBHbbJC+fAwVRxuIvX8GVZ+eY9qds4icHz0k8xpsPNmxvtjKY/8wHeiQ+0uOaCO7\nqA4CQ4EgnnkIHl2zBMqtWHNtHDwDD98SxCZrKxmbY7CfrqakupXYZcPz+OyOPr0wJaX8s5TyXn3Z\nJqVcIaWcJ6X8rpTy7y71zFLKuVLKKCnlDY90mjY/BkdcTQhwIKeOyJsWAFeVk+8Dne2Y99ztFJ4J\nIjx8KmnPLifjqSkkr1zIxhc0m2Y8tYThIqbcayIiMCYswFZcRmK0llTrxTXxxC+YBReuEP+dGFbf\nHcczT84iatE8Hr03DNC0jbMaITI6hh3lDBsn3xW7M2wYExZgSD3ilPt7cU08ac8uhzb3Yy4r08Zr\nJbDxhRgiV63grseXs+llTd4ydlnsUAzf61G5bhSKQcZgOkK++fahHobCD1C5bhQKL0U5ecVgoxy9\nQqFQ+Dle7eiVlOCNQdnxxqEk8G4Myo6Di1c7es9SgoeAan25K8mxMtanuuTDSNUlA1OPOCUF15ss\nHCivo7RRkxe0A5v0HCObzf6VEKk3duyQVeyQ99uUasH91NCA/WyRbndbp+RcDc71tXvK2PReJdlb\nLWx+r0SXAcxhc9ohkjJPoSWk0kINHVJ5m94rY5vZokkWmg9pEneNZ0hKO4Rl+343VbGhwpMEnmZH\ncJ2TwbSflD1a7iDHsQc4Jf/s1dqDQ+vXmjRexXsHnRKJ1hotD0zK9mMDPJuhw5MdS/do369Vz1O1\n9u1ip7ygK6WNYH3vEFW5VtLeK6G+/DjbLGXYzxZjMO13kZp0lxWt2LsfGL45sLza0Xti309vp+Lo\nORp0x1TRoL9V29TOo8Glzvwtr79sxG4v07/sVuyNDRjvnAHAEtNBXjcbqdqpxZEXAlnFdbxk0tpm\nN/q/HFlnO1a2QcXeHKCdIl16ccvLS/XkcY4fSBBLtteRbzay1qRJADpOEBV2yDdHU/VeDi1ARfNV\nkp81km2t577yCeSbV1BS28SHa+bp9fXvQpfKW3ryFOPuX0IJk9h9Swu7F13FVm7DVttEna2dfPNK\nZzZDb2LfT2+H9gbsdMypK7Y8rkUvHWibx+ZcG/ETNGm86fcuJ0M/9p7+r0IMJgsZTy0a6KF7FeGL\n50N7A09narrN6Y9Fa/KCZiOFl8vcHP7Ce5dRWN5A1qeVLM+084xxLku2n0FTIdOkJu01Wk6cLLNF\n9wkjMJgODsXUvAKvdvRPvvVXErfkuKU1PZ97hIf32RgzX0t1ENlUQ+zMUOqB4Pnx+gGhZxAMnqt9\nycFBBI8ag+XgKQAyNi4jyWTBvrIjFPO1jCKW6Ff2YcDBjf6TSqE3dpw/NpDIVVpsc8yqlbodx+jO\nNUS7UjIfYd8DQdoPMTUGg8lCsON7CAaYxX3nOqXcGh3AG2OrMJgsuKaaiwyGTSYL++6bQOycKRif\nM/Jo9DhCAkcAIwgZE0R5yWWSIzQNX4Oe0Gso+Y//bSBxSw6JWzpe7DmfewTDi4cJpmNOMILSY6c0\nG7bZnP9FTp+o/ddjnBlC/FErzuMUSHH8F1lrI99s1LKy+ime7Bg2JwLDi4c5+Nwiff6H0JyzhdgJ\nc50XcMH6mz8flLQQBexe2sL6zCL23TdB76kVg8lC/RlN23m1aYnzJDGUWsNDjQqvVCgUCh9FhVcq\nFAqFAlCOXqFQKPwer3b0XUnguT2JbzzVbR9Z5d1Ljg0HBsKOjnvI3hANM1h4ksDrCs2+R1zWPcsE\ndlXuz3iUEqw+TvaeQ24ylvXg9gDeEQnmoHSPhV2p7lE5jmO6sNG1rONZgGdZQno8/n0dr3b0niTw\n8n+q5wRxSN8FBjilxRwRIJazl7A1NrCtwIa9LQhL0Rkqaoavw++tHR3LmyyOMEELtsYG7cF1WxCG\n1INOO+42deQUMZgs5L5tcX4PWX4WnurAkwSeIfUg9eWVrN1bBvZKDKYc7LVngAAI1B62WjKtQAsp\nJguGNCtJJguGVwv08iYA1u495Xzg7e94smNwxEI2H2vC3qSlplyvP7x3fYCvRYIZyTJbqK9tIDgw\ngOBgSNp6hKriSq1SYJjzwb+mJZ0DXGXt3lNU5R52kyWEjmPVHhjgDH/1R7za0XuSwDO8Uswbd45x\nk74bC9TXajHh1rM2YqdP5A/Wr5jzjTGUnCxjWthoHn7tb4M8eu+ht3YEzY5LF07DetYGhPIH61fY\ngJKTmrasw452h3QemnZkbklHEGbkVKgq9r848K6kBA/sLaHQegrL2yeBVi6e7RDS2XaojE1FNggc\npRXU2jStXluNXj4GQ5qVQusZIIiwKP/XZvBkxwrLQQ5uWMivD1aQVXCGXJdtdj0tcdJ2K7l7D7J0\naShuMfGhIWzLKNFXrjqja55Kt8LMcKCdQusZCj9vcJMlrCovZultoVTVVBMMZJiNZPmwFm93+EXU\njdViJd4YDzWnYPK8Gziy4UYDMGaoB+GTWGtaiJ+stIoVg8uwirqJN+ox78rJXyfKyfcX5eQV3oxf\nOHqFQqFQdI1XO3qPEniZeaRndkjgOR7S7+pCps2Seci5nJTq+WGL3e4hB4aHsqp2yC06x1ofe5Xa\nkx1d55ymy7R5orttGh26bpsyi/DntGmeJPA6H4+d6UqCseLjI85jtspOv+yW/rZvpunwZMdtOw6T\nnplH7lnP+Wjs1cWkZ+ZReaaYbTusnn+zii7x2Xv0Wbll3JUwl+Xmw4TNCSc8KIhnokew4ySkr5qN\nwXyYsDlh2Eq+Jv/lZRjSrOTraQ0MphxNnqytDggglnZagHWLgth4rJX0leM4fLSO7z8YxuGPbCQ/\nZSQr1cKqDQs5f7SESOMyvPwc2SNJJgs2YGMEpFVr0nax3/0W6amHMSSOc8qxGUwW0pePoaWmkfBb\nJ1OY/RWx98/A+t/nWG0ykvvxEcbOmUdL+SnG3RLNtIhxXCw6xvSYeXyQe5HkhNlDOs8bSeKWnGsk\n8AB2Fddx4NNTFJbXoD3W1pzQhjB4zRZAFO2sWxRE4kMryDZbsDZC7CjIjppHxkNzKW2EyFFQUl4J\nU2cTcqGS423hrJ4P6/dWsmWVpgS2q7iObRnHcETqAMQCrz8wjuxDdSTMCeLKCJgUFcE0L1as6s6O\nr2XkEYX2W9xxrNVpN/tJK9aaUOInN7Fk5yXCaCecjt/s7p+uJHKib/8m+4Of36NvYOmiSKoaYfcL\nS7EVnQcgIXo2q/WghX2mpdiKzgId0Q4A6y1nOuTJAsOIoh3jKChlDIkPrQACiF0Wy13zAwgLjyD2\nVi2Hhh3gUhWRxuVAgFcm2OoL4QBhYaxKHIdD2q7CDtva6KS5qW17o6idyJhFTvk7xyVowncmEzsn\njPjvTCYqYhwh9koiYxbR0o5fOfmeSF8Xx5YI2PLsMnbHB0FEBI8+qz0z6ji2IKsRHutC7TJ2zmwK\nK+uImjOb6W2aM399VQxVekx4MJBhWkYUEOXY77NTOJBTx6PPLQWu8s1vzSZ88sQBm+fAE+C0l6vd\nABISOgwXjvtvdjg6+b7gs1f0Q4Ed7ce2K9XCoy/7d4KkbZl5jATWrlFCy33B9SW0WCB9GCfSUgw8\nvb2i75WjF0JUor2kdhVok1IahBCTgD8As4FKYLWU8pJe/wVgnV7/x1LKD7vr31ccvUKhUHgTA3Hr\nZrmU8lsunT4P5Egp5wE5+jpCiGjgEeAWwAhsE0KM8NShQqFQKAae67mxlQzs1Jd3Aqtcyn8vpWyR\nUlYAp4Fv92cHSgJPoVAorp/eOnoJfCyEKBBCPKmXTZFSVuvLF4Ap+vJ04KxL23N6WZ/xJIGnUCgU\nir7RW0e/VEr5LeAu4EdCiO+4bpTajf4+PdUVQjwphMgXQuTX1NT03MBBrZZlzqAniCpthJQ9Zewq\nbiBlTxlZevga7WcwvK3VXa9nasw+OXwTmykUiuFLYG8qSSnP659fCyH+G+1WzFdCiAgpZbUQIgL4\nWq9+Hpjp0nyGXta5zzeBN0F7GOtpv0++9VdOX6hn8rgQ/vDjpQAY0sog0EYYsGdjPFX6XZ3gQAgf\nE8S48WD5tYWng0N5I6oNg+kM+akxGF4uInm+esVfoVAMP3qMuhFCjAYCpJT1+vJHwC+AFYBNSvkr\nIcTzwCQp5UYhxC3AO2gng2loD2rnSSmvdrUPFXWjUCgUfae3UTe9uaKfAvy30HKZBwLvSCktQggr\nkCWEWAd8CawGkFJ+IYTIAorRkuD+qDsnr1AoFIqBpUdHL6UsBxZ6KLehXdV7amMGzNc9OoVCoVBc\nN+q9YYVCofBzlKNXKBQKP6dXUTeDza9//euhHoJCoVB4LT//+c/7VN8rHX1fJ6FQKBSKrlG3bhQK\nhcLPUY5eoVAo/Bzl6BUKhcLPUY5eoVAo/Bzl6BUKhcLP8QtHr6dncNJc8g5i9TvXlHfHE0Jworn/\n++xVm9Xv9LlNb/d54u0net2+P2NXKBS+i9c6eiEEQggud1pv1pcfXCTcHJZj+4NC0NzWBKea3Mo7\n01y0A7HoCYQQ3PNvBwCIDtXqvlvSfE1b10/H8rFmeH/zPVrZc+9w4v/dw+IntT7//eAF3nlusfv+\nO42pohnuEYKfrtbqXWjr2Lb4uXe7tYsnG7mWdenMm48567mO3b3tT7vvQ6FQ+BReGUfPhffJqZYk\nTu1wsMWtkpsDLzvXt1glm35zDw/+vxMANElJqBA8+HkTIwPfhQjgbyClhHPvI4TALVNnYKhzuxCC\ndcBP3i3nlfsjnfuQUsLpdxBiE3/8Z20s5a2SyEBtedFIWPzS+84++N06jl1ORMpXEGIiAJ9VX4Jz\nFdo+I0JZ7DIOx34X/yKPS8/9XybepzncplbJib996tE0jn0d2LyYn7xbzSv3T0UIQfHv1rnVufCn\nTZxog5s7f8MjF7n14xz7qz/wXK5QKHwer7yib75YTfRU97LIQIAJbusjp0Zc0/YHMSOv7XDGEs87\nuivRbXXxzdf2x00/4FLTFpr1y+aKi24jdS5VV2tiWw/84yJAG8Nnxz5j0dQJLI5f7LwtdMzDMBbN\nHsnICdq+P7N+xshAWBzfxZh1Lnx6jJvnTXXbtytT70ikua3bLq4Zu0Kh8E+80tGPjFlHhH7roLhJ\nIi99Rqi+nnGi6Zr6dwOhXVx9arcgJpJ3qfu8+5eAlJtDtavYu95Cns3puH0xElJ2a1e7KyI69nOs\neaRzHxER154kFi9a7Lwqvlk//0h5ydnvT94t5lLnNvGLe3Ul/YP3JU/cqvWzbrero16k9T95BYs8\nnPMcuN7+8TR2hULhP/QoPDIYDJbwyD133+Nc/p/3/2fA93e9LF50Dw4fvORfdmD6x6ndNwC3W1QX\nPvp31v3mgHObL8xZoVD0nt4KjwwrR69QKBT+RG8dvVfeulEoFArFjcMrruiFEPVA6VCPYxAIBy72\nWMv3GQ7zHA5zhOExT1+e4zellJN7quQt4ZWlvfn3w9cRQuSrefoHw2GOMDzmORzmqG7dKBQKhZ+j\nHL1CoVD4Od7i6N8c6gEMEmqe/sNwmCMMj3n6/Ry94mGsQqFQKAYOb7miVygUCsUAMeSOXghhFEKU\nCiFOCyGeH+rx9BchxEwhxEEhRLEQ4gshxHq9fJIQ4iMhxCn9c6JLmxf0eZcKIZKGbvR9QwgxQghx\nTAjxnr7uj3OcIITYI4QoEUKcEELc4afz/Bf9eC0SQmQKIUb6wzyFEOlCiK+FEEUuZX2elxAiTgjx\nub7tt8JXM/1JKYfsDxgBlAFzgGDgOBA9lGO6jrlEAIv15bHASSAaSAOe18ufB36tL0fr8w0BInU7\njBjqefRyrs8B7wDv6ev+OMedwBP6cjBaRj2/micwHagAQvX1LOB/+8M8ge8Ai4Eil7I+zwv4K3A7\nIIAPgLuGem79+RvqK/pvA6ellOVSSjvweyB5iMfUL6SU1VLKz/TleuAE2g8pGc1poH+u0peTgd9L\nKVuklBXAaTR7eDVCiBnAPcBbLsX+NsfxaI5iB4CU0i6lvIyfzVMnEAgVQgQCo4Aq/GCeUsq/AH/v\nVNyneQkhIoBxUsojUvP6v3Np41MMtaOfDpx1WT+nl/k0QojZwCLgKDBFSulIL3kBmKIv++rcXwM2\nAu0uZf42x0igBvgv/RbVW0KI0fjZPKWU54FXgDNANVArpdyPn83Thb7Oa7q+3Lnc5xhqR+93CCHG\nAH8ENkgp61y36VcFPhvmJIS4F/haSlnQVR1fn6NOINq//W9IKRcBV9D+1XfiD/PU71Eno53YpgGj\nhRCPutbxh3l6wl/n1RVD7ejPAzNd1mfoZT6JECIIzclnSCkdWoBf6f8Con9+rZf74twTgPuEEJVo\nt9kShRC78K85gnbldk5KeVRf34Pm+P1tnt8FKqSUNVLKVuBdYAn+N08HfZ3XeX25c7nPMdSO3grM\nE0JECiGCgUeAfUM8pn6hP43fAZyQUr7qsmkf8Li+/DiQ7VL+iBAiRAgRCcxDe/DjtUgpX5BSzpBS\nzkb7rg5IKR/Fj+YIIKW8AJwVQkTpRSuAYvxsnmi3bG4XQozSj98VaM+W/G2eDvo0L/02T50Q4nbd\nPv/LpY1vMdRPg9EEok6iPek2DfV4rmMeS9H+FSwE/qb/3Q2EATnAKeBjYJJLG5M+71J87Gk+8A90\nRN343RyBbwH5+ve5F5jop/N8GSgBioC30SJPfH6eQCbac4dWtP/Q1vVnXoBBt00ZsBX9JVNf+1Nv\nxioUCoWfM9S3bhQKhUIxwChHr1AoFH6OcvQKhULh5yhHr1AoFH6OcvQKhULh5yhHr1AoFH6OcvQK\nhULh5yhHr1AoFH7O/w9SoIGzSDWHgAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1129f7d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"gist": {
"data": {
"description": "Untitled.ipynb",
"public": false
},
"id": "b08dc23127f496ee0f01b9567fedede0"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
quiltdata/quilt-compiler | docs/Walkthrough/Installing a Package.ipynb | 1 | 7363 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Searching for packages\n",
"\n",
"As explained in [\"Uploading a Package\"](Uploading%20a%20Package.md), packages are managed using *registries*. There is a one local registry on your machine, and potentially many remote registries elsewhere \"in the world\". Use `list_packages` to see the packages available on a registry:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['aneesh/cli-push',\n",
" 'examples/hurdat',\n",
" 'aleksey/hurdat']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import quilt3\n",
"\n",
"# list local packages\n",
"list(quilt3.list_packages())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['aleksey/hurdat',\n",
" 'examples/hurdat',\n",
" 'quilt/altair',\n",
" 'quilt/hurdat',\n",
" 'quilt/open_fruit',\n",
" 'quilt/open_images']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# list remote packages\n",
"list(quilt3.list_packages(\"s3://quilt-example\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installing a package\n",
"\n",
"To make a remote package and all of its data available locally, `install` it.\n",
"\n",
"The examples in this section use the `examples/hurdat` [demo package](https://open.quiltdata.com/b/quilt-example/tree/examples/hurdat/):"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading manifest: 100%|██████████| 5/5 [00:00<00:00, 7049.25entries/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully installed package 'examples/hurdat', tophash=f8d1478 from s3://quilt-example\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"quilt3.Package.install(\n",
" \"examples/hurdat\",\n",
" \"s3://quilt-example\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that unless this registry is public, you will need to be logged into a user who has read access to this registry in order to install from it:\n",
"\n",
"```python\n",
"# only need to run this once\n",
"# ie quilt3.config('https://your-catalog-homepage/')\n",
"quilt3.config('https://open.quiltdata.com/')\n",
"\n",
"# follow the instructions to finish login\n",
"quilt3.login()\n",
"```\n",
"\n",
"Data files that you download are written to a folder in your local registry by default. You can specify an alternative destination using dest:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading manifest: 100%|██████████| 5/5 [00:00<00:00, 9027.77entries/s]\n",
"Copying objects: 100%|██████████| 3.62M/3.62M [00:00<00:00, 303MB/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully installed package 'examples/hurdat', tophash=f8d1478 from s3://quilt-example\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"quilt3.Package.install(\n",
" \"examples/hurdat\", \n",
" \"s3://quilt-example\", \n",
" dest=\"./\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, you can install a specific version of a package by specifying the corresponding top hash:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading manifest: 100%|██████████| 5/5 [00:00<00:00, 11491.24entries/s]\n",
"Copying objects: 100%|██████████| 35.4k/35.4k [00:02<00:00, 14.3kB/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Successfully installed package 'examples/hurdat', tophash=058e62c from s3://quilt-example\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"quilt3.Package.install(\n",
" \"examples/hurdat\", \n",
" \"s3://quilt-example\", \n",
" top_hash=\"058e62c\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Browsing a package manifest\n",
"\n",
"An alternative to `install` is `browse`. `browse` downloads a package manifest without also downloading the data in the package."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading manifest: 100%|██████████| 5/5 [00:00<00:00, 7541.00entries/s]\n",
"Loading manifest: 100%|██████████| 5/5 [00:00<00:00, 10710.68entries/s]\n"
]
}
],
"source": [
"# load a package manifest from a remote registry\n",
"p = quilt3.Package.browse(\"examples/hurdat\", \"s3://quilt-example\")\n",
"\n",
"# load a package manifest from the default remote registry\n",
"quilt3.config(default_remote_registry=\"s3://quilt-example\")\n",
"p = quilt3.Package.browse(\"examples/hurdat\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`browse` is advantageous when you don't want to download everything in a package at once. For example if you just want to look at a package's metadata.\n",
"\n",
"## Importing a package\n",
"\n",
"You can import a local package from within Python:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading manifest: 100%|██████████| 5/5 [00:00<00:00, 9637.65entries/s]\n"
]
}
],
"source": [
"from quilt3.data.examples import hurdat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This allows you to manage your data and code dependencies all in one place in your Python scripts or Jupyter notebooks."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
moranconnorj/code_guild | wk0/notebooks/wk0.1.ipynb | 1 | 11247 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# wk0.1 A curious case of conditionals\n",
"\n",
"## Intro to git and github\n",
"\n",
"Read these:\n",
"* https://help.github.com/articles/set-up-git/\n",
"* https://www.atlassian.com/git/tutorials/syncing/git-push\n",
"\n",
"## A few challenges\n",
"\n",
"Complete *reverse_string* and *primes* challenges in challenge folder."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reviewing some ideas from yesterday"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# Example from yesterday\n",
"\n",
"def t(num):\n",
" if 10 <= num < 15:\n",
" print(\"hot\")\n",
" elif num > 15:\n",
" print(\"hotter\")\n",
" else:\n",
" print(\"cold\")\n",
"\n",
"# What does this function do?\n",
"for i in range(4):\n",
" for j in range(10):\n",
" if j % 3 == 0:\n",
" continue\n",
" if j > 7 and j % 2 == 0:\n",
" break\n",
" else:\n",
" print('i equals', i)\n",
" print('j equals', j)\n",
" print('------------')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cold\n"
]
}
],
"source": [
"def t(num):\n",
" if 10<= num < 15:\n",
" print(\"hot\")\n",
" elif num > 15:\n",
" print(\"hotter\")\n",
" else:\n",
" print(\"cold\")\n",
"t(5)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-12-3b39866f40bc>, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-12-3b39866f40bc>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m i in range(4):\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"i in range(4):\n",
" for j in range(10):\n",
" if j % 3 == 0:\n",
" continue\n",
" if j > 7 and j % 2 == 0:\n",
" break\n",
" else:\n",
" print('i equals', i)\n",
" print('j equals', j)\n",
" print('-----------')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = 1\n",
"assert a == 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## While Conditional.: return discussion\n",
"\n",
"* [Follow up on yesterday's discussion on conditionals](http://www.pythonlearn.com/html-008/cfbook004.html)\n",
"\n",
"[more on conditionals](https://docs.python.org/3.5/tutorial/datastructures.html#more-on-conditions):\n",
"* is and is not\n",
" * [difference between is and ==](https://stackoverflow.com/questions/132988/is-there-a-difference-between-and-is-in-python)\n",
" \n",
"Try evaluating each of the lines below in your python REPL. What's going on? \n",
" \n",
"```\n",
"a = [1, 2, 3]\n",
"b = a\n",
"b is a\n",
"b == a\n",
"b = a[:]\n",
"b is a\n",
"b == a\n",
"```\n",
"If you have a hypothesis about the difference between is and ==, devise some tests you could try out to disprove your hypothesis. Implement them. Were you correct?\n",
"\n",
"* in and not in\n",
"* and, or\n",
"* comparison priority\n",
"> not has the highest priority and or the lowest, so that A and not B or C is equivalent to (A and (not B)) or C.\n",
"\n",
"* Short circuit operators\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# More string and list practice\n",
"\n",
"* Add elements from a list L to the end of another list at least three different ways."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l = [1, 2, 3]\n",
"b = [4, 5, 6]\n",
"\n",
"l+b\n",
"l[:]+b[:2]\n",
"l[::-1]+b[1:]\n",
"\n",
"l.extend(b)\n",
"\n",
"l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Insert elements into a list at least two different ways."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[3, 2, 2]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1, 2, 3]\n",
"b = [4, 5, 6]\n",
"\n",
"b[2] = 3\n",
"b[::-1] = l\n",
"b[2]=l[1]\n",
"b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Insert an element into a string (this might take more than one step...)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hellllllo World!'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1, 2, 3]\n",
"str2 = 'World!'\n",
"\n",
"str2+str(l[2])\n",
"\n",
"str2 = list(str2)\n",
"str2.insert(3, 'HI')\n",
"str2 = ''.join(str2)\n",
"str2\n",
"\n",
"str1 = 'Hello World!'\n",
"str1 = str1[:3] + 'lllll' + str1[4:]\n",
"str1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Remove an item from a list by value."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 3]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1, 2, 3]\n",
"l.remove(2)\n",
"l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Remove an item from a list by index."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[2, 3]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1, 2, 3]\n",
"l.pop(0)\n",
"l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Remove all items from a list two different ways."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1, 2, 3]\n",
"del l[:]\n",
"l.clear()\n",
"l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Return the index in the list of the first item whose value is x. "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = [1, 2, 3]\n",
"l.index(2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Return the number of times x appears in the list."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l.count(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Capitalize the first element in a string"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hello'"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"str1 = 'hello'\n",
"str1.capitalize()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Capitalize all elements in a string"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'HELLO WORLD !'"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"str1.upper()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Strip leading, trailing, and all whitespace (ex. leading: ' asdf ' --> 'asdf ', trailing:' asdf ' --> ' asdf', all:' asdf ' --> 'asdf')."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hello World !'"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"str1 = 'Hello World ! '\n",
"str1.strip(' ')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Split a sentence into a list of words (no whitespace), how would you split on comma or semi-colon?"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Split', 'a', 'sentence', 'into', 'a', 'list']"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = 'Split a sentence into a list'\n",
"a.split()\n",
"\n",
"a = a.replace(' ', ':')\n",
"\n",
"a.split(':')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Read up on how to format strings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
subutai/htmresearch | projects/neural_correlations/EXP1-Random/.ipynb_checkpoints/NeuCorr_Exp1-checkpoint.ipynb | 10 | 18512 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EXP 1-Random"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import random\n",
"import matplotlib\n",
"matplotlib.use('Agg')\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from nupic.bindings.algorithms import TemporalMemory as TM\n",
"from htmresearch.support.neural_correlations_utils import *\n",
" \n",
"uintType = \"uint32\"\n",
"random.seed(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"symbolsPerSequence = 10\n",
"numSequences = 1000\n",
"epochs = 1\n",
"totalTS = epochs * numSequences * symbolsPerSequence"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tm = TM(columnDimensions = (2048,),\n",
" cellsPerColumn=8,\n",
" initialPermanence=0.21,\n",
" connectedPermanence=0.3,\n",
" minThreshold=15,\n",
" maxNewSynapseCount=40,\n",
" permanenceIncrement=0.1,\n",
" permanenceDecrement=0.1,\n",
" activationThreshold=15,\n",
" predictedSegmentDecrement=0.01,\n",
" )\n",
"\n",
"sparsity = 0.02\n",
"sparseCols = int(tm.numberOfColumns() * sparsity)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feed sequences to the TM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create sequences\n",
"allSequences = []\n",
"for s in range(numSequences):\n",
" sequence = generateRandomSequence(symbolsPerSequence, tm.numberOfColumns(), sparsity)\n",
" allSequences.append(sequence)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"spikeTrains = np.zeros((tm.numberOfCells(), totalTS), dtype = \"uint32\")\n",
"columnUsage = np.zeros(tm.numberOfColumns(), dtype=\"uint32\")\n",
"spikeCount = np.zeros(totalTS, dtype=\"uint32\")\n",
"ts = 0\n",
"\n",
"entropyX = []\n",
"entropyY = []\n",
"\n",
"negPCCX_cells = []\n",
"negPCCY_cells = []\n",
"\n",
"numSpikesX = []\n",
"numSpikesY = []\n",
"\n",
"numSpikes = 0\n",
"\n",
"negPCCX_cols = []\n",
"negPCCY_cols = []\n",
"\n",
"# Randomly generate the indices of the columns to keep track during simulation time\n",
"colIndicesLarge = np.random.permutation(tm.numberOfColumns())[0:125] # keep track of 125 columns = 1000 cells\n",
"\n",
"for epoch in range(epochs):\n",
" # shuffle sequences\n",
" print \"\"\n",
" print \"Epoch: \" + str(epoch)\n",
" seqIndices = np.random.permutation(np.arange(numSequences))\n",
" \n",
" for s in range(numSequences):\n",
" if s > 0 and s % 100 == 0:\n",
" print str(s) + \" sequences processed\"\n",
" for symbol in range(symbolsPerSequence):\n",
" tm.compute(allSequences[seqIndices[s]][symbol], learn=True)\n",
" \n",
" for cell in tm.getActiveCells():\n",
" spikeTrains[cell, ts] = 1\n",
" numSpikes += 1\n",
" spikeCount[ts] += 1\n",
" \n",
" # Obtain active columns:\n",
" activeColumnsIndices = [tm.columnForCell(i) for i in tm.getActiveCells()]\n",
" currentColumns = [1 if i in activeColumnsIndices else 0 for i in range(tm.numberOfColumns())]\n",
" for col in np.nonzero(currentColumns)[0]:\n",
" columnUsage[col] += 1\n",
" \n",
" if ts > 0 and ts % int(totalTS * 0.1) == 0:\n",
" numSpikesX.append(ts)\n",
" numSpikesY.append(numSpikes) \n",
" numSpikes = 0\n",
" \n",
" #print \"++ Analyzing correlations (cells at random) ++\" \n",
" subSpikeTrains = subSample(spikeTrains, 1000, tm.numberOfCells(), ts)\n",
" (corrMatrix, numNegPCC) = computePWCorrelations(subSpikeTrains, removeAutoCorr=True)\n",
" negPCCX_cells.append(ts)\n",
" negPCCY_cells.append(numNegPCC) \n",
" bins = 300\n",
" plt.hist(corrMatrix.ravel(), bins, alpha=0.5) \n",
" # Set range for plot appropriately!\n",
" plt.xlim(-0.05,0.1)\n",
" plt.xlabel(\"PCC\")\n",
" plt.ylabel(\"Frequency\")\n",
" plt.savefig(\"cellsHist_\" + str(ts))\n",
" plt.close()\n",
" entropyX.append(ts)\n",
" entropyY.append(computeEntropy(subSpikeTrains)) \n",
"\n",
" #print \"++ Analyzing correlations (whole columns) ++\"\n",
" ### First the LARGE subsample of columns:\n",
" subSpikeTrains = subSampleWholeColumn(spikeTrains, colIndicesLarge, tm.getCellsPerColumn(), ts)\n",
" (corrMatrix, numNegPCC) = computePWCorrelations(subSpikeTrains, removeAutoCorr=True)\n",
" negPCCX_cols.append(s)\n",
" negPCCY_cols.append(numNegPCC) \n",
" #print \"++ Generating histogram ++\"\n",
" bins = 300\n",
" plt.hist(corrMatrix.ravel(), bins, alpha=0.5)\n",
" plt.xlim(-0.05,0.1)\n",
" plt.xlabel(\"PCC\")\n",
" plt.ylabel(\"Frequency\")\n",
" plt.savefig(\"colsHist_\" + str(ts))\n",
" plt.close() \n",
" \n",
" ts += 1\n",
" \n",
"print \"*** DONE ***\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sparsityTraceX = []\n",
"sparsityTraceY = []\n",
"for i in range(totalTS - 1000):\n",
" sparsityTraceX.append(i)\n",
" sparsityTraceY.append(np.mean(spikeCount[i:1000 + i]) / tm.numberOfCells())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(sparsityTraceX, sparsityTraceY)\n",
"plt.xlabel(\"Time\")\n",
"plt.ylabel(\"Sparsity\")\n",
"plt.savefig(\"sparsityTrace\")\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# plot trace of negative PCCs\n",
"plt.plot(negPCCX_cells, negPCCY_cells)\n",
"plt.xlabel(\"Time\")\n",
"plt.ylabel(\"Negative PCC Count\")\n",
"plt.savefig(\"negPCCTrace_cells\")\n",
"plt.close()\n",
"\n",
"plt.plot(negPCCX_cols, negPCCY_cols)\n",
"plt.xlabel(\"Time\")\n",
"plt.ylabel(\"Negative PCC Count\")\n",
"plt.savefig(\"negPCCTrace_cols\")\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(numSpikesX, numSpikesY)\n",
"plt.xlabel(\"Time\")\n",
"plt.ylabel(\"Num Spikes\")\n",
"plt.savefig(\"numSpikesTrace\")\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# plot entropy\n",
"plt.plot(entropyX, entropyY)\n",
"plt.xlabel(\"Time\")\n",
"plt.ylabel(\"Entropy\")\n",
"plt.savefig(\"entropyTM\")\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.hist(columnUsage)\n",
"plt.xlabel(\"Number of times active\")\n",
"plt.ylabel(\"Number of columns\")\n",
"plt.savefig(\"columnUsage\")\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ISI analysis (with Poisson model too)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"subSpikeTrains = subSample(spikeTrains, 1000, tm.numberOfCells(), totalTS)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"isi = computeISI(subSpikeTrains)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Print ISI distribution of TM\n",
"\n",
"#bins = np.linspace(np.min(isi), np.max(isi), 50)\n",
"bins = 100\n",
"plt.hist(isi, bins)\n",
"plt.xlim(0,600)\n",
"# plt.xlim(89500,92000)\n",
"plt.xlabel(\"ISI\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.savefig(\"isiTM\")\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print np.mean(isi)\n",
"print np.std(isi)\n",
"print np.std(isi)/np.mean(isi)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Generate spike distribution\n",
"spikeCount = []\n",
"for cell in range(np.shape(subSpikeTrains)[0]):\n",
" spikeCount.append(np.count_nonzero(subSpikeTrains[cell,:]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins = 25\n",
"plt.hist(spikeCount, bins)\n",
"plt.xlabel(\"Spike Count\")\n",
"plt.ylabel(\"Number of cells\")\n",
"plt.savefig(\"spikesHist_TM\")\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"firingRate = 18\n",
"pSpikeTrain = poissonSpikeGenerator(firingRate,np.shape(subSpikeTrains)[1],np.shape(subSpikeTrains)[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"isi = computeISI(pSpikeTrain)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Print ISI distribution of Poisson model\n",
"\n",
"#bins = np.linspace(np.min(isi), np.max(isi), 50)\n",
"bins = 100\n",
"plt.hist(isi, bins)\n",
"plt.xlim(0,600)\n",
"# plt.xlim(89500,92000)\n",
"plt.xlabel(\"ISI\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.savefig(\"isiPOI\")\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print np.mean(isi)\n",
"print np.std(isi)\n",
"print np.std(isi)/np.mean(isi)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Generate spike distribution\n",
"spikeCount = []\n",
"for cell in range(np.shape(pSpikeTrain)[0]):\n",
" spikeCount.append(np.count_nonzero(pSpikeTrain[cell,:]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins = 25\n",
"plt.hist(spikeCount, bins)\n",
"plt.xlabel(\"Spike Count\")\n",
"plt.ylabel(\"Number of cells\")\n",
"plt.savefig(\"spikesHist_POI\")\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Raster Plots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"subSpikeTrains = subSample(spikeTrains, 100, tm.numberOfCells(), -1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rasterPlot(subSpikeTrains, \"TM\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pSpikeTrain = poissonSpikeGenerator(firingRate,np.shape(subSpikeTrains)[1],np.shape(subSpikeTrains)[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rasterPlot(pSpikeTrain, \"Poisson\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quick Accuracy Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"simpleAccuracyTest(\"random\", tm, allSequences)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Elad Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Sample from both TM_SpikeTrains and Poisson_SpikeTrains. 10 cells for 1000 (?) timesteps\n",
"wordLength = 10\n",
"firingRate = 18 \n",
"\n",
"# generate all 2^N strings:\n",
"binaryStrings = list(itertools.product([0, 1], repeat=wordLength))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"trials = 10\n",
"\n",
"x = [] #observed\n",
"y = [] #predicted by random model\n",
"\n",
"for t in range(trials):\n",
" print \"Trial: \" + str(t)\n",
" # sample from spike trains\n",
" subSpikeTrains = subSample(spikeTrains, wordLength, tm.numberOfCells(), 0)\n",
" pSpikeTrain = poissonSpikeGenerator(firingRate,np.shape(subSpikeTrains)[1],np.shape(subSpikeTrains)[0]) \n",
" for i in range(2**wordLength):\n",
" if i == 0:\n",
" continue\n",
"# if i % 100 == 0:\n",
"# print str(i) + \" words processed\"\n",
" binaryWord = np.array(binaryStrings[i], dtype=\"uint32\")\n",
" x.append(countInSample(binaryWord, subSpikeTrains))\n",
" y.append(countInSample(binaryWord, pSpikeTrain))\n",
"# print \"**All words processed**\" \n",
"# print \"\"\n",
"print \"*** DONE ***\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.loglog(x, y, 'bo',basex=10)\n",
"plt.xlabel(\"Observed\")\n",
"plt.ylabel(\"Predicted\")\n",
"plt.plot(x,x,'k-')\n",
"plt.xlim(0,np.max(x))\n",
"plt.savefig(\"EladPlot\")\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Save TM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"saveTM(tm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# to load the TM back from the file do:\n",
"with open('tm.nta', 'rb') as f:\n",
" proto2 = TemporalMemoryProto_capnp.TemporalMemoryProto.read(f, traversal_limit_in_words=2**61)\n",
"tm = TM.read(proto2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis of input"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"overlapMatrix = inputAnalysis(allSequences, \"random\", tm.numberOfColumns())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# show heatmap of overlap matrix\n",
"plt.imshow(overlapMatrix, cmap='spectral', interpolation='nearest')\n",
"cb = plt.colorbar()\n",
"cb.set_label('Overlap Score')\n",
"plt.savefig(\"overlapScore_heatmap\")\n",
"plt.close()\n",
"# plt.show()\n",
"\n",
"# generate histogram\n",
"bins = 60\n",
"(n, bins, patches) = plt.hist(overlapMatrix.ravel(), bins, alpha=0.5)\n",
"\n",
"plt.xlabel(\"Overlap Score\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.savefig(\"overlapScore_hist\")\n",
"\n",
"plt.xlim(0,0.15)\n",
"plt.xlabel(\"Overlap Score\")\n",
"plt.ylabel(\"Frequency\")\n",
"plt.savefig(\"overlapScore_hist_ZOOM\")\n",
"plt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = []\n",
"trials = 1000\n",
"for t in range(trials):\n",
" pSpikeTrain = poissonSpikeGenerator(18,1000,1)\n",
" x.append(np.count_nonzero(pSpikeTrain))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins = 25\n",
"plt.hist(x, bins)\n",
"plt.savefig(\"test_spikePOI\")\n",
"plt.close()"
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| agpl-3.0 |
spatchcock/monetary_economics_python | notebooks/wip/two_region.ipynb | 1 | 211110 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"%matplotlib inline \n",
"\n",
"# number of time steps\n",
"N = 300\n",
"\n",
"# relative size of rUK to Scotland - this is basically used to define the relative government spends\n",
"scaling = 9.0\n",
"\n",
"# fixed spending rate of rUK\n",
"G_UK = 50\n",
"\n",
"# fiscal transfer to Scotland\n",
"# taken as a fraction of the rUK spend (based on relative size factor) \n",
"# with another fraction for the proportion devolved (with the remainder provided by Sottish tax and spend) \n",
"FT_S = 0.5*(G_UK/(scaling))\n",
"\n",
"# tax rates\n",
"theta_UK = 0.25\n",
"theta_S = 0.25\n",
"\n",
"# savings rates (actually spending rates!)\n",
"alpha_UK = 0.95\n",
"alpha_S = 0.95\n",
"\n",
"# Scottish (rUK) export rate (relative to GDP) - approximate value from table 6.11 of Active Citizen\n",
"EX_S = 0.31\n",
"# Scottish (rUK) import rate (relative to GDP) - approximate value from table 6.11 of Active Citizen\n",
"IM_S = 0.41\n",
"# UK import rate (relative to GDP) - simply Scottish export rate scaled by relative sizes of economies\n",
"IM_UK = EX_S/scaling\n",
"\n",
"# containers - rUK\n",
"C_UK = np.zeros(N) # consumption\n",
"Y_UK = np.zeros(N) # income\n",
"Y_d_UK = np.zeros(N) # disposable income\n",
"T_UK = np.zeros(N) # tax revenue\n",
"H_h_UK = np.zeros(N) # private savings\n",
"H_g_UK = np.zeros(N) # government debt\n",
"CA_UK = np.zeros(N) # current account\n",
"\n",
"# containers - Scotland\n",
"C_S = np.zeros(N) # consumption\n",
"Y_S = np.zeros(N) # income\n",
"Y_d_S = np.zeros(N) # disposable income\n",
"T_S = np.zeros(N) # tax revenue\n",
"H_h_S = np.zeros(N) # private savings\n",
"H_g_S = np.zeros(N) # government debt\n",
"CA_S = np.zeros(N) # current account\n",
"# Scottish G is not a constant\n",
"G_S = np.zeros(N) # Scottish government spending\n",
"\n",
"\n",
"for t in range(1, N):\n",
" \n",
" ### UK ###\n",
" \n",
" # calculate consumer spending\n",
" C_UK[t] = alpha_UK*Y_d_UK[t-1] \n",
" \n",
" CA_UK[t] = IM_S*Y_S[t-1] - IM_UK*Y_UK[t-1]\n",
" \n",
" # calculate total income (consumer spending plus constant government spending plus CA)\n",
" Y_UK[t] = G_UK + C_UK[t] + CA_UK[t]\n",
" \n",
" # calculate the tax take\n",
" T_UK[t] = theta_UK * Y_UK[t]\n",
" \n",
" # calculate disposable income\n",
" Y_d_UK[t] = Y_UK[t] - T_UK[t]\n",
" \n",
" # calculate the change in private savings\n",
" H_h_UK[t] = H_h_UK[t-1] + (1-alpha_UK)*Y_d_UK[t-1] \n",
" \n",
" # calculate the change in government debt from rUK operations\n",
" H_g_UK[t] = H_g_UK[t-1] + T_UK[t]- G_UK\n",
" \n",
" ### SCOTLAND ###\n",
" \n",
" # calculate consumer spending\n",
" C_S[t] = alpha_S*Y_d_S[t-1] \n",
" \n",
" # calculate government spending (tax take plus fiscal transfer)\n",
" G_S[t] = T_S[t-1] + FT_S\n",
" \n",
" # calculate CA\n",
" CA_S[t] = - IM_S*Y_S[t-1] + IM_UK*Y_UK[t-1]\n",
" \n",
" # calculate total income (consumer spending plus constant government spending + CA)\n",
" Y_S[t] = G_S[t] + C_S[t] + CA_S[t]\n",
" \n",
" # calculate the tax take\n",
" T_S[t] = theta_S * Y_S[t]\n",
" \n",
" # calculate disposable income\n",
" Y_d_S[t] = Y_S[t] - T_S[t]\n",
" \n",
" # calculate the change in private savings\n",
" H_h_S[t] = H_h_S[t-1] + (1-alpha_S)*Y_d_S[t-1] \n",
" \n",
" # calculate the change in government debt from Scottish operations\n",
" # this has already been modified for rUK operations - just add on Scottish value\n",
" H_g_UK[t] = H_g_UK[t] + T_S[t]- G_S[t]\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure(figsize=(14, 10))\n",
"\n",
"consumption_plot = fig.add_subplot(341, xlim=(0, N), ylim=(0, np.max([np.max(Y_UK),np.max(Y_S)])*1.1))\n",
"consumption_plot.plot(range(N), C_UK, lw=3)\n",
"consumption_plot.plot(range(N), C_S, lw=3)\n",
"consumption_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('consumption')\n",
"\n",
"gov_plot = fig.add_subplot(342, xlim=(0, N), ylim=(0, np.max([np.max(Y_UK), np.max(Y_S)])*1.1))\n",
"gov_plot.plot(range(N), np.repeat(G_UK,N), lw=3)\n",
"gov_plot.plot(range(N), G_S, lw=3)\n",
"gov_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('government spending')\n",
"\n",
"ca_plot = fig.add_subplot(343, xlim=(0, N), ylim=(np.min([np.min(CA_UK),np.min(CA_S)])*1.1, np.max([np.max(CA_UK),np.max(CA_S)])*1.1))\n",
"ca_plot.plot(range(N), CA_UK, lw=3)\n",
"ca_plot.plot(range(N), CA_S, lw=3)\n",
"ca_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('trade balance')\n",
"\n",
"income_plot = fig.add_subplot(344, xlim=(0, N), ylim=(0, np.max([np.max(Y_UK),np.max(Y_S)])*1.1))\n",
"income_plot.plot(range(N), Y_UK, lw=3)\n",
"income_plot.plot(range(N), Y_S, lw=3)\n",
"income_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('income')\n",
"\n",
"tax_plot = fig.add_subplot(345, xlim=(0, N), ylim=(0, np.max([np.max(T_UK),np.max(T_S)])*1.1))\n",
"tax_plot.plot(range(N), T_UK, lw=3)\n",
"tax_plot.plot(range(N), T_S, lw=3)\n",
"tax_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('tax revenue')\n",
"\n",
"tax_gdp_plot = fig.add_subplot(346, xlim=(0, N), ylim=(0, 1))\n",
"tax_gdp_plot.plot(range(N), T_UK/Y_UK, lw=3)\n",
"tax_gdp_plot.plot(range(N), T_S/Y_S, lw=3)\n",
"tax_gdp_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('tax revenue (% GDP)')\n",
"\n",
"deficit_plot = fig.add_subplot(347, xlim=(0, N), ylim=(np.min([np.min(T_UK-G_UK),np.min(T_S-G_S)])*1.1, np.max([np.max(T_UK-G_UK),np.max(T_S-G_S)])*1.1))\n",
"deficit_plot.plot(range(N), T_UK-np.repeat(G_UK,N), lw=3)\n",
"deficit_plot.plot(range(N), T_S-G_S, lw=3)\n",
"deficit_plot.plot(range(N), ((T_UK-np.repeat(G_UK,N))+(T_S-G_S)), lw=3)\n",
"deficit_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('government budget')\n",
"\n",
"debt_plot = fig.add_subplot(348, xlim=(0, N), ylim=(np.min(H_g_UK)*1.1,0))\n",
"debt_plot.plot(range(N), H_g_UK, lw=3)\n",
"debt_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('government debt')\n",
"\n",
"gov_balance_gdp_plot = fig.add_subplot(3,4,9, xlim=(0, N), ylim=(-0.2, 0.2))\n",
"gov_balance_gdp_plot.plot(range(N-1), (T_UK[1:]-np.repeat(G_UK,N-1))/Y_UK[1:], lw=3)\n",
"gov_balance_gdp_plot.plot(range(N-1), (T_S[1:]-G_S[1:])/Y_S[1:], lw=3)\n",
"gov_balance_gdp_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('government balance (% GDP)')\n",
"\n",
"private_balance_gdp_plot = fig.add_subplot(3,4,10, xlim=(0, N), ylim=(-0.2,0.2))\n",
"private_balance_gdp_plot.plot(range(N-1), np.diff(H_h_UK)/Y_UK[1:], lw=3)\n",
"private_balance_gdp_plot.plot(range(N-1), np.diff(H_h_S)/Y_S[1:], lw=3)\n",
"private_balance_gdp_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('private balance (% GDP)')\n",
"\n",
"ca_gdp_plot = fig.add_subplot(3,4,11, xlim=(0, N), ylim=(-0.2,0.2))\n",
"ca_gdp_plot.plot(range(N-1), np.divide(CA_UK[1:],Y_UK[1:]), lw=3)\n",
"ca_gdp_plot.plot(range(N-1), np.divide(CA_S[1:], Y_S[1:]), lw=3)\n",
"ca_gdp_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('trade balance (% GDP)')\n",
"\n",
"# space subplots neatly\n",
"plt.tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:44: RuntimeWarning: invalid value encountered in divide\n",
"-c:45: RuntimeWarning: invalid value encountered in divide\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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krUnX6v2T168Hnn/etL1/f+C77xRLg0gSWq5fUxOPC+kJ27NtZWrSAfQ7fRbf\nL39ElXyI7KXFvv7JJ5+gVq1aiIyMRHWraezatWsrEl8QBMTFiRgyxLQ+aBCwZo0ioYlkoamadK3e\nP/nSJctyvXrq5UFERKQH165dw6lTp3DXqoi0e/fuisV3db/z8F8iogpzc3PDO++8g2nTppkn2wRB\nwOnTpxXLgTPpRBaSX3Xl6NGj2Lx5M7777jvzQ23Wg/T69eWNZbR6C8Y1nvnz51dom54Zrf0xLllb\nsmQJunfvjp49e+LDDz9Er169MGXKFEVzcHHPlT2G0dofa3ONbfbs2fjrr79w5swZpKWlIS0tTdEB\nOqDOIJ3tnnG1StJB+siRI/HSSy9hw4YN2LJli/nxMC+99BJ8fX0RFBRk3padnY2IiAi0aNECPXv2\nxPXr180/mzFjBpo3b45WrVph586dD319JQfpRHr39ddfl9m2bNmyhz5P7n5ORMqYP38+EhISEBgY\niPj4eCQlJcHLy0vRHJzdOJNOJKXmzZvD3d29wr8vx3s6Z9KJLCStSW/Tpg3++OOPB15YxpZ9+/bB\nw8MDw4cPx7FjxwAA7777LurWrYt3330Xs2bNwrVr1zBz5kykpKRg6NChSExMREZGBp599lmcPHnS\nfGqOeces6gCef95Ulw4AcXHA4MH27yuRkrRQvxYXF4dvvvkG+/btQ7du3czbb926BWdnZ+zevfuB\nz5e7nxM5Okdpz6GhoTh48CCCg4Nx4MABuLm5oU2bNkhJSanya+7YsQNvvvkmioqKMGrUKLz33nvm\nn9mqSR+OXVj+YY8qxyNSkxb7enR0NP744w88/fTT5pr0B92CTer3dEEQMHeuiLfeMq2/+SYwd668\n+0wkJ03VpHfu3BkpKSlo27ZtpZ7XrVs3pKenl9q2efNm7N27FwAQExOD8PBwzJw5E5s2bcKQIUPg\n6uqKwMBANGvWDAkJCXjsscfKfX3OpBPZ7/HHH4e/vz8uX76Mt99+2/yHx9PTEx06dHjo8+Xu50Sk\njEceeQTXrl1DdHQ0IiIi4OPjg8DAwCq/XlFREcaMGYNdu3ahYcOG6Ny5M6KiotC6detyn+NUjTPp\nRFKKjo5GdHS0eaJNFMUHTrrJ8Z7OmXQiC0kH6SNHjkTXrl3h5+dX6lu4o0ePVvq1Ll68CF9fXwCA\nr6+v+X7rFy5cKNWpAwICkJGRYfM1RowYgcDAQBw/DgDeAIJRt244AEt9Qni4tOsl2+R6/fLW582b\nh+DgYMVEOeHpAAAgAElEQVTicX/l37/k5GS7PvhKrVGjRmjUqBEOHDgg2WtK2c8BwNvbW5F2UbKN\n7Z77a+/+aa2fV8TGjRsBAFOmTEF4eDhu3ryJ3r17V/n1EhIS0KxZM/NxGDx4MDZt2vTAQbpQTZma\n9JL/KyUZLa6asdXcZ60ZMWIE8vLycPLkSQBAq1at4OrqWqnXsPc9PS5uBIBAAEBSkjf27NHv+/me\nPXuQnJyMN998U7F49+8r91f6/Ssp8bj/C6wqESXUpEkTcdOmTeJff/0lpqWlmR8VkZaWJrZr1868\n7u3tXernPj4+oiiK4pgxY8RVq1aZt8fGxoobNmwo83rWu9awoSgCpsfZs5XZo8qLj4+XNwDjGjKu\nxF3VLuvXrxebNWsmenp6ih4eHqKHh4fo6elZoefK2c+VZLT2x7jK0FI/f5iDBw+K8+bNE+fPny8e\nOnTIrtdat26dOGrUKPP6ypUrxTFjxpjXAYgIhojwe4/eEPuMmWD+eXx8fKn/M6nWS7bJ9frlrc+d\nO1fReGrvb3x8vDh37lzF99fWvsu5fx9++KH44YcfijExMZrs6/Hx8eKjjz4qduvWTezWrZvYqFEj\ncc+ePQ98jpTv6QDESZMsn9c/+sjePaoY6/8rpakVm3GVYW8/l/SvxGOPPVbl597f0Vu2bClmZmaK\noiiKFy5cEFu2bCmKoijOmDFDnDFjhvn3evXqJR44cKDM61kfmJo1LZ3+5s0qp0ikGi29oTdp0kRM\nSUmp0nPl7OdEjs5R2vPUqVPFdu3aiZMnTxY/+OADsX379uJHdnyiXr9+/cMH6VNKP0Z+9pld+0Ck\nJi329ZCQEPHEiRPm9dTUVDEkJOSBz5HyPR2A+O67ls/rM2favUtEqrK3nzvZPxdvERISgqFDhyIu\nLg4bNmzAhg0bqnwLtqioKCxfvhwAsHz5ckRHR5u3r1mzBvn5+UhLS8OpU6fQpUuXcl+noADIyTEt\nOzkBHh5VSoeI7vHz83vgaaiVIVU/JyLlrFq1ComJiZg6dSo++ugjHDhwACtXrqzy6zVs2BDnzp0z\nr587dw4BAQGlf+mLg8DBVy3rLqxJJ5JSYWEhWrZsaV5v0aIFCgsLK/Ua9r6nsyadyELSQXpubi6q\nVauGnTt3YuvWrdi6dWuFbsE2ZMgQPP7440hNTcUjjzyCZcuWYcKECfjxxx/RokUL/PTTT5gwYQIA\n0xXkX3jhBbRp0wZ9+vTBZ5999sALW9y4YVn29gYqeeH5SrOue1AS4+o7rpaEhoZi0KBBlf4yTs5+\nrjSjtT/GJWsNGzbEnTuWQfLdu3fLDqorITQ0FKdOnUJ6ejry8/Oxdu1aREVFlf6lzE5Abh3zapET\n75Oul7hqxmZft+jUqRNGjRqFPXv2ID4+HqNGjUJoaGi5vy/Hezrvk864eoprL0kvHGfr/skVERcX\nZ3P7rl27bG5///338f7771fota1u0Qhv70qnRkT3uXHjBtzd3cvc53TAgAEPfJ6c/ZyI5Dd27FgA\ngJeXF9q2bYuePXsCAH788Ue7znRxcXHBokWL0KtXLxQVFSE2Ntb22TqFlns4i86cSSeS0uLFi/Hp\np5+ab7nWrVs3jB49utzfl+M9nTPpRBaS3id95MiRpV/83rdkS5culSpEhZXcm+7gQaBzZ9O2jh2B\nQ4cUT4XIblq8p6oW8LiQnmi9PX/99delbs8EWHIWBAExMTGyxDXFFIGuc4Be4wEAQxu/hdXD58gS\nj0huWuzrOTk5cHNzg7OzMwDTrRHz8vJQo0YNReILgoDhw0WsWGFa//prQKY/KUSK0NR90vv27Wt+\nA79z5w42btyIBg0aSBmi0jiTTiSt1NRUjB49GllZWfjjjz9w9OhRbN68GZMmTVI7NSKS0YgRI9RN\noMAyk14oyH+6O5GRPPPMM9i9ezc87l28KTc3F7169cKvv/6qWA6cSSeykLQm/bnnnsPAgQMxcOBA\nDBs2DOvWrcPBgwelDFFpSg/SjVZvwbjG8/LLL2P69Omodu8dNCgoqNzT3vTKaO2PcUkTCiwzeoWQ\n/3R3o7U/1uYaW15ennmADgCenp7IzVX2yzDWpDOunuLaS9JB+v1OnjyJy5cvyxnioawH6V5e6uVB\npBe5ubkICwszrwuCAFdXVxUzIiJDsKpJLwBn0omkVLNmTRyyqgk9ePAg3N3dH/AM6eXlWZY5k05G\nJ2lNuoeHh/l0d0EQ4Ovri5kzZ2LgwIFShaiwkjqAuXOBceNM2954A5g3T/FUiOympfq1Pn36YOHC\nhXj++eeRlJSE9evX46uvvsL27dsVz0VLx4XIXo7WnnNzcxWpVzXXpLfYCgyNBAD0atwXO4ZvlT02\nkRy02NcTExMxePBg+Pv7AwAyMzOxdu3aB17hXUqCICAiQsSPP5rWd+wAevVSJDSRLDRVk3779m0p\nX04S1inxHulE9lu0aBFeeeUVnDhxAg0aNEDjxo2xevVqtdMiIoX8+uuvGDVqFG7duoVz584hOTkZ\nX375JT777DN5A1vVpOcVcyadSEqdO3fG8ePHkZqaCkEQ0LJlS8XPkrM+3b16dUVDE2mOpKe7//LL\nL+aB+sqVKzFu3DicOXNGyhCVpvQg3Wj1FoxrPE2bNsXu3btx5coVpKam4pdffkFgYKDaaSnKaO2P\nccnam2++iR07dqBu3boAgODgYOzdu1f+wFY16XeLWJOul7hqxmZfL+3gwYM4evQoDh06hLi4OKwo\nudS6QliTzrh6imsvSWfSX3vtNRw5cgRHjhzBnDlzEBsbi+HDhyvz5l2OnBzLcs2aqqVBpBvXrl3D\nihUrkJ6ejsLCQgCmU3pK7q1KRPr36KOPllp3cZH044RtVjXpuQWcSSeS0rBhw3D69GkEBwebb8MG\nAMOHD1csB17dnchC0pr0kJAQJCUlYerUqWjYsCFGjRqFjh074vDhw1KFqLCSOoARI4Dly03bli4F\n7ruVO5FD0FL9WteuXdG1a1cEBQXByclJ9nskP4iWjguRvRylPT/33HN46623MGbMGPz2229YsGAB\nDh48iDVr1sgSz1yTXicVGNsKANC8dnOcHHtSlnhEctNiX2/dujVSUlLM15ZSmiAICAoSceyYaf3I\nEaB9e1VSIZKEpmrSPT09MX36dKxatQr79u1DUVERCgoKpAxRaaxJJ5JWXl4e5syZo3YaRKSSxYsX\n44033kBGRgYaNmyInj174tNPP5U/sNXp7pxJJ5JWu3btkJmZiQYNGqiWA2fSiSwkrUlfu3Ytqlev\njqVLl8LPzw8ZGRl4++23pQxRaaxJZ1w9xNWSoUOH4ssvv0RmZiays7PNDyMxWvtjXLJWr149fPPN\nN7h06RIuX76M1atXo06dOvIHtjrd/U4ha9L1ElfN2OzrFpcvX0abNm3Qs2dPREZGIjIyElFRUYrm\nYD1IV+qadWz3jKtVks6k+/v7Y/z48eb1Rx99VJVTYK1xJp1IWm5ubnjnnXcwbdo0ODmZvucTBAGn\nT59WOTMiktPYsWPNyyWn8VmfGiv7dSk4k04kmylTpqidAu5d5gaAcoN0Iq2StCZ9w4YNmDBhAi5e\nvGg+B18QBNy8eVOqEBVW8gEiJARITjZtO3wYCAlRPBUiu2mpfq1x48ZITEw0X9lZTVo6LkT20np7\n/vrrrwGYbsGWkpKCQYMGQRRFrFu3Dm3btsXnn38uS1xzTbpQDHxouaBV8eRi1epnieyh9b6uBkEQ\n4OcnIivLtJ6RAah45j2R3TRVk/7uu+9i69ataN26tZQvaxfrmXRe3Z3Ifs2bN4e7u/vDf5GIdGXE\niBEATDXp+/fvN99D+R//+AeefPJJ+RMQnYACN8D1LgDgbuFduLvybxGRPZ544gn88ssv8PDwKPOl\nl9ITbdYz6UrcMIJIyyStSffz89PUAB1gTTrj6iOultSoUQPBwcF45ZVXMHbsWIwdOxavv/662mkp\nymjtj3HJ2vXr10t9cL916xauX7+uTHAFb8NmtPbH2lxj+uWXXwAAt2/fxq1bt0o9lD4TVo3T3dnu\nGVerJP2eKjQ0FIMGDUJ0dDSq3bssoyAIGDBggJRhKoU16UTSio6ORnR0dKltPOWUyDgmTJiAjh07\nIjw8HACwd+9e5epZC2oA7tcAKHPxOCJSjvUNoTiTTkYnaU16yalw939gX7ZsmVQhKkwQBBQXi3B2\nBkr2sLAQcHZ+8POItIj1a7bxuJCeOFJ7zszMxG+//QZBEBAWFgY/Pz/ZYplr0gFgbHOgzp8AgNQx\nqWhRp4VscYnk4kh9XSmCIKB6dRF5eab1O3cANzd1cyKyh6Zq0ksuKqMVeXmWAXq1ahygE0lh//79\nmDp1KtLT01F479w0Xt2dyFjc3Nzg7++Pu3fv4uTJkzh58iS6d+8uf2CrK7zfKeBMOpGecCadyELS\nmvRz586hf//+qFevHurVq4eBAwfi/PnzUoaolDtW799KXefKaPUWjGs8sbGxGDduHPbv34/ExEQk\nJiYiISFB7bQUZbT2x7hkbcmSJejevTt69+6NKVOmoFevXsqd7s6adN3FVTM2+7q2FBdblpWaWGO7\nZ1ytknSQPnLkSERFReHChQu4cOECIiMjMXLkSClDVMrdu5ZlXoyaSBre3t7o06cPfH19UbduXfOD\niIxh/vz5SEhIQKNGjRAfH4+kpCR4eXkpE9x6Jp016US65OIC8FI3ZHSS1qR36NABR44ceeg2JQiC\ngL/+EtG0qWk9MBBIS1M8DSJJaKl+bcKECSgqKsKAAQNQvXp18/aOHTsqnouWjguRvRylPYeGhuLg\nwYMIDg7GgQMH4ObmhjZt2iAlJUWWeKVq0of2BVpsAwBsGbIFf2vxN1liEsnJUfq6kqz7uZtb6bNh\niRyRpmrS69Spg5UrV2Lo0KEQRRFr1qxRdYaNM+lE0jtw4AAEQcDBgwdLbY+Pj1cpIyJSUkBAAK5d\nu4bo6GhERETAx8cHgYGBygS3Ot2dNelE+qTU7deItEzS092XLVuGb7/9Fn5+fvD398e6detUubJ7\nCetv4ZS6QqTR6i0Y11iKiooQFRWF+Pj4Mg8jMVr7Y1yy9v3338PHxwdTpkzBv/71L4waNQrff/+9\nrDF79DD9W9fLcro7a9L1EVfN2Ozr2qTkRePY7hlXqyQdpE+ePBkrVqzA5cuXcfnyZSxbtky5i8nY\nwJl0Imk5OzsjLi5O7TSISCWFhYVo1aqVeT08PBxRUVGoVq2arHE3bwa2bgX+1ttqJp016US6xCu7\nE0lckx4cHIzk5OSHblOCIAj48UcRERGm9WeeAXbvVjwNIkloqX7trbfeQkFBAQYNGoSaNWtCFEUI\ngsCadCI7OUp77tevHxYsWIBGjRopEs/6uLz137cw78A8AMDsnrMxrus4RXIgkpKj9HUlWdekN2gA\nZGSomw+RvTRVky6KIrKzs1G7dm0AQHZ2NoqKiqQMUSlq3IKNSO+SkpIgCAImT55carvRTnknMqrs\n7Gy0bdsWXbp0Qc2aNQGYPoxs3rxZ9tjuLqxJJ9I7zqQTSXy6+/jx49G1a1d88MEHmDRpErp27Yp3\n3nlHyhCVYn26O2vSGdeR42rJnj17WJNusPbHuGTt3//+N7Zu3YrJkydj/Pjx5ocSarha1aQXsiZd\nD3HVjM2+rk2sSWdcPcS1l6SD9OHDh+O7775D/fr14efnh40bN2L48OFShqgUzqQTSS8rKwuxsbHo\n3bs3ACAlJQVfffWVylkRkVJ++OEHhIeHl3ps27ZNkdicSSfSP17dnUjimnQtEQQBX3wh4tVXTeuj\nRgFLlqibE1FVaal+rXfv3hg5ciSmTZuGo0ePoqCgACEhIfj9998Vz0VLx4XIXo7SnkNCQpCUlFRq\nW1BQEI4dOyZLPOvjsjhxMUZvGw0AeC30NSzuu1iWmERycpS+riTrmvS2bQEVPlIQScrefi7pTLrW\ncCadSHpXrlzBoEGD4OzsDABwdXWFCwvIiHRv8eLFCAoKQmpqKoKCgsyPwMBAtG/fXpEc3F0tb+Zy\n34KNiNTBmXQinQ/SWZPOuHqJqyUeHh64evWqef3AgQPw8vJSMSPlGa39MS4BwNChQ7FlyxZERUVh\n69at2LJlC7Zs2YJDhw5h9erViuRgXZMu9+nuRmt/rM0lrWBNOuPqIa69dD39xZl0IunNnj0bkZGR\nOH36NB5//HFcvnwZ69evVzstIpKZl5cXvLy8sGbNGtVysK5J50w6kT7x5Dwindekv/uuiI8/Nq1P\nnw5MnKhuTkRVpbX6tcLCQpw4cQKiKKJly5aoVq2aKnlo7bgQ2YPt2Tbr47Lr9C5ErIwAADzT+Bns\nHr5bzdSIqoR9vSzrmvRu3YCff1Y3HyJ7sSb9AaxPd+dMOpE02rdvj48//hju7u4ICgpSbYBORMZT\n6hZsnEkn0iXOpBPpfJBufbo7a9IZ15HjasnmzZvh7OyMF154AaGhofjkk09w9uxZtdNSlNHaH+OS\nVih5CzajtT/W5pJWsCadcfUQ1166HqTn51uWlRqkE+ldYGAg3nvvPRw6dAhxcXE4evQoGjdurHZa\nRGQAnEkn0j9e3Z1I5zXpQ4aIiIszra9eDQwdqm5ORFWltfq19PR0rF27Ft9++y2cnZ0xaNAgjB8/\nXvE8tHZciOxhxPa8bt06TJkyBSdOnEBiYiI6duxY5nesj8vZG2fRaF4jAEBArQCce+ucovkSScGI\nff1hrGvSo6KATZvUzYfIXvb2c11XfeTlWZZZNkskjbCwMOTn5+OFF17AunXr0KRJE7VTIiIHFRQU\nhI0bN+LVV1+t0O9zJp1I/ziTTmSg092VGqQbrd6CcY1n+fLlSEpKwsSJEw07QDda+2NckkurVq3Q\nokWLCv8+a9L1F1fN2Ozr2sSadMbVQ1x7cZBORJXi5+eHt956C506dUKnTp0wfvx43LhxQ+20iMgA\n3F2tBumFd3jKMJEO8eruRDqvSQ8PF1Hy5cnu3cAzz6iaElGVaal+bcCAAQgKCkJMTAxEUcTKlStx\n9OhRfPfdd4rnoqXjQmQvvbbniIgIZGVlldk+ffp0REZGAgCefvppzJ49u9ya9JiYGAQGBgIApiVO\nQ2H9QiAQuD3xNhJ/TQQAhIeHA7DMmnCd61pZT05OxvXr1wGYrumyfPlyXfZ1e1jXpI8YASxbpmo6\nRHaz9z1d84P0wMBA1KpVC87OznB1dUVCQgKys7MxaNAgnDlzBoGBgfj222/h7e1d6nmCIODxx0X8\n+qtpfd8+4MknVdgBIglo6cN7hw4dcOTIkYduq6yq9HUtHRciexm5PT9skG59XHw/8cWlnEsAgMzx\nmfDz8FMsTyIp6L2vV/X9vGSQPmoUsGSJSskTScTefq75090FQcCePXuQlJSEhIQEAMDMmTMRERGB\nkydPokePHpg5c6bN51qf7l69uhLZGq/egnGNx93dHfv27TOv79+/HzVq1HjAMyrGnr6uNKO1P8Yl\nJVT0w0yt6rXMyzfzbsqVjuHaH2tzSSr2vp+zJp1x9RDXXg5R9XH/G/fmzZuxd+9eAEBMTAzCw8Nt\ndnbWpBNJ7/PPP8fw4cPNdeg+Pj5Yvny5JK9dlb4uCJKEJiIVbNy4Ea+//jquXLmCvn37IiQkBNu3\nb3/gc5QapBNR1VX1szvAq7sTAQ5wunuTJk3g5eUFZ2dnvPrqq3j55Zfh4+ODa9euATD9Eahdu7Z5\nvYQgCKhVKwY3bwYCACZM8EavXsGaqlHiOtfLW583bx6Sk5PNNZhTp07V3KlxN2+aPhzXqlXrIb9Z\nMVXp66bT42IABN7b4g0gGED4vfU99/7lOte1uD4PQDIs7Vd7/VwL7j9l8OnlT2NP+h4AwO7hu/FM\n42dUyoyoavR+uru97+ePPQYMGuSN4GB+bue646xLfe0JzQ/SMzMz4e/vj8uXLyMiIgILFy5EVFRU\nqY5du3ZtZGdnl3qeIAho3FhEWppp/c8/gaZNlcycSDpaekOfPXv2vTdTCy8vL3Tq1AnBwcFVft2q\n9HXrGjYix6edfq4l9//967emHzanbgYAbBy0EdGtotVKjahKtPSeLgd738/feQf4+GOlsyaSlu5r\n0v39/QEA9erVQ//+/ZGQkABfX1/zlWIzMzNRv359m8/lfdIZVy9xteTQoUP4/PPPkZGRgfPnz+OL\nL77A9u3b8fLLL2PWrFlVft2q9nVRVP4RH7+HcRlX8gdVDGvS9RVXzdh8T5eHPZ/dAcDZWZE0AbDd\nM652aXqQnpubi1u3bgEAcnJysHPnTgQFBSEqKspcA7t8+XJER9v+Fp016UTSO3fuHA4fPozZs2dj\nzpw5OHToEC5duoS9e/fi66+/rtJr2tvXicg4WJNOpF1SvJ8rOUgn0ipNn+6elpaG/v37AwAKCwvx\n4osvYuLEicjOzsYLL7yAs2fPPvAWbF5eIu5d2wrXrgH3/QqRw9DSqXGtWrXC0aNHUe3eN195eXlo\n3749UlNTERISgqSkpEq/ZlX7upaOC5G92J5tu/+4TNw9ETP3my44Ne2ZaXi/2/tqpUZUJXru6/a8\nn+Pe6e6TJwNTp6qRPZF07O3nmr66e+PGjZGcnFxme+3atbFr166HPp8z6UTSe/HFFxEWFobo6GiI\noogtW7Zg6NChyMnJQZs2bar0mvb2dSIyjlrVOJNOpFVSvJ8reQs2Iq3S9Onu9mJNOuPqJa6WfPDB\nB/jyyy/h5eUFHx8ffPHFF/jwww9Rs2ZNrF69Wu30FGG09se4pCWsSddXXDVjs69rE2vSGVcPce2l\n6++qiopM/woC61uIpNS5c2d07txZ7TSIyIC83LzMy9fvXlcxEyKSAz+zE2m8Jt0e1rUt1asDd++q\nmw+RPfRcv2YPHhfSE7Zn2+4/LttObUPfb/oCAHo3643tL25XKzWiKmFfL8v6c/t//gO8/ba6+RDZ\nS/e3YJMC69GJiIj0obZ7bfNy9p3sB/wmETkizqQTcZAuOaPVWzAuGZHR2h/jkpYoNUg3WvtjbS5p\nBWvSGVcPce1liEF69epqZ0BERERSsB6kX829qmImRCQHzqQTGaQmPTAQSEtTNR0iu7B+zTYeF9IT\ntmfb7j8uhcWFcP2Xq+lnEFDwQQGcnfipnhwH+3pZ1p/bFy8GXntN3XyI7MWa9ApgTToREZE+uDi5\nwKu66QrvIkTcyLuhckZEJCXOpBNxkC45o9VbMC4ZkdHaH+OS1ihRl2609sfaXNIK1qQzrh7i2ssQ\ng3RXV7UzICIiIqmwLp1IvziTTmSQmvSwMODAAXXzIbIH69ds43EhPWF7ts3Wcem9qjf++9d/AQCb\nB29GZMtINVIjqhL29bKsP7evXAkMG6ZuPkT2Yk16Bbi4qJ0BERERScXPw8+8fDHnooqZEJHUOJNO\nZJBBupKnuxut3oJxyYiM1v4Yl7TGepCedTtLlhhGa3+szSWtYE064+ohrr0MMUjnTDoREZF++Nb0\nNS9zJp1IXziTTmSQmvTevYHt29XNh8gerF+zjceF9ITt2TZbxyXuWByGfjcUAPBcm+ew7vl1aqRG\nVCXs62VZf27//nugXz918yGyF2vSK4BXdyciItIPXw+rmfTbnEkn0hPOpBMZZJCu5OnuRqu3YFwy\nIqO1P8YlrbGuSc+4lSFLDKO1P9bmklawJp1x9RDXXhykExERkUN51OtR8/L5m+dRLBarmA0RSYkz\n6UQGqUkfMgT45ht18yGyB+vXbONxIT1he7atvONS5+M6yL6TDQDIGJeBBp4NlE6NqErY18uy/ty+\naxfQo4e6+RDZizXpFcCadCIiIn1p5NXIvHzm+hkVMyEiKXEmncggg3TWpDOuo8clbTFa+2Nc0qJG\n3pZB+tkbZyV/faO1P9bmklawJp1x9RDXXhykExERkcMJ9A40L/917S/1EiEiSXEmncggNen/8z/A\nokXq5kNkD9av2cbjQnrC9mxbecfl84Of4x8//AMAMKz9MKzsv1Lp1IiqhH29LOvP7QcOAGFh6uZD\nZC97+7kh5pg5k05ERKQvreu2Ni8fv3xcxUyISEoVnUkvFotxt/Au7hTcQZFYBFEUId4b6JcslwyS\nrJeJHIEhhq9K16SHh4crF5BxDRGXtMVo7Y9xSYta17MM0k9cOYFisRhOgnRVfEZrf2q2eyPuM5Xv\n/kG6KIrYf3Y/Np/cjMSMRKRfT8fFnIu4W3jX/mDpAALtfxmHis24DsEQg3Re3Z2IiEhf6tWoh3o1\n6uFy7mXkFOTgz+w/0aJOC7XTIiI7WQ/SUy6nIOb7GBy8cFC9hIhUYIia9EmTgH/9S918iOzB+jXb\neFxIT9iebXvQcen7TV9sO7UNALCy/0oMaz9MydSIqoR9vSzrz+2//w60bQvsOr0L0WuikVOQU+7z\nqjtXh7urO1ydXM2vI0Aod9kUh0h+58edZ036w7AmnYiISH+6NOhiHqQfOH+Ag3QiHXB2BtKvp2Pg\ntwPNA/TqztUxvMNw9G3eF63qtkIDzwaoWa2mpCUuRFISxtn3hZAhWjbvk864jh6XtMVo7Y9xSase\nf+Rx8/Ku07skfW2jtT/eL5q0wtkZGLt9LG7m3QQABNQKQMLLCfgy8kv0a9UPLeu2hGd1T0kG6Gz3\njKtVhphjflBNemFxIbJuZ+Fm3k3cyruFnIIcFIvFEEXR9C/EUusPc+zcMdxKvSVh9hXDuPqOS0RE\nZXVr1A3uLu64U3gHqVdTcfraaTTxaaJ2WkRkh5TrB7H15FYAgAAB655fh/a+7VXOikhZhqhJnz0b\nGDfO8rPEjEQsP7IcP6X9hD+z/0RBcYE6SRJV1BSwfs0G1vWRnrA92/aw4xIZF2n+QP9R+Ef44KkP\nlEqNqErY18uy/tw+aHUs1p5aCgAY3G4w4gbGqZgZUdXY288Ndbr7rbxbGLJhCLr8bxd8mvgpjl85\nzgE6ERGRSt555x20bt0aHTp0wIABA3Djxo1Kv8awIEsd+heHvkBeYZ6UKRKRklzuYmv6t+bVN8Le\nUP8MpxUAACAASURBVDEZIvUY5nT3G3dvoM/qPvi/8/9X5ue+NX3h7eaNWtVroYZrDTg7OcNJcIIA\nwfSvIJjXH3ZVyKspV1GnTR25doVxDRp3K7YqHpPKZ7R7+jIuyaVnz56YNWsWnJycMGHCBMyYMQMz\nZ86s1Gv0a9UPvjV9cTHnIjJuZWD2/83G+93etzs3o7U/3iedNCEwHjkFtwEAzWs3R1jDMFnDsd0z\nrlYZYpDu4gK8vOXlUgP0F9q+gFc7vYrODTrDs7qnZLGM1gAZVxnCUN4yhIj0JyIiwrwcFhaGDRs2\nVPo13Fzc8H639/HGDtOM2+T4yfDz8MPI4JG83RKRo2nxg3kxsmUk+zAZliFq0sd9uhNzLvcy/2xh\nn4UY02WMSpkRVR7r12zjcSE9MXp7joyMxJAhQzB06NBS2wVBQExMDAIDAwEA3t7eCA4ONn9humfP\nHhQVF2FS2iQcOH8ASDc9r+uTXdGvZT84n3VGHfc66BPRB+4u7vi/ff8HZydnPPP0M3ASnLB3714A\nKPV6XOe6lOvJycm4fv06ACA9PR3Lly83dF+3xfy5/dUQwD8ZAPDfYf9Fz6Y91U2MqIrsfU83xCC9\n+cwncOrurwCAEcEjsKzfMhUzI6o8o394Lw+PC+mJXttzREQEsrKyymyfPn06IiMjAQDTpk3D4cOH\nbc6kV/S4ZN3OQo8VPZByOaVS+Qmo+ExdZWb15HpdcnyFkwt12dftIQgC4JoDTKwFOBVBgIBr712D\nl5uX2qkRVYm97+n6P93d94h5gO7q5Ip/P/1vWcMZ7TRsxiUtunbnGn459wtOXT2FszfP4mbeTeTk\n5yC3IBdFYhFEUSx1e0URYoVvswgA105cg08rH5n3gnGNFlevfvzxxwf+/Ouvv8a2bduwe/duu+L4\nefghYVQC3v7xbXx1+KsKXxhWRDkfotIBBN73u3INrKxf1kZcRagVV83YasUl2/wPAU5FAIDW9Vor\nMkBnTTrjapX+B+nt1pgXn2vzHBrWaqhiMkQkp6TMJPzr539hU+qmCg+4qyQLgJt8L8+4Bo1rQDt2\n7MB//vMf7N27F25u9h/0mtVqYnHfxZjy1BRsTt2MxAuJOH7lOK7kXsGV3CvIK8xDYXEhCosLUSQW\nyft3gogqJ+CAeVHuC8YRaZ3+T3cf3Raobzr1bdPgTYhqGaVuYkRVoNfTYO1lfVwW/rYQ43eO520V\nyXFNkXGmVqOaN2+O/Px81K5dGwDQtWtXfPbZZ6V+R86/f5UZpFcmh3Jn5+18XdKHai7V+P9+H0EQ\ngBcGAm1MJS9f/O0LvNLpFZWzIqo6nu7+ID5/mQfobi5ueLbJsyonRERyWJSwCK/veL3UttAGoejc\noDMaezeGj7sParrWRA3XGnBxcoEgCOZbKlrfarEit1kkkkuPKT3UTkFxp06dUjW+k+BU8V/mnwYi\neTVMMC9yJp2MTt+D9JZbzIsRTSJQw7WG7CGNVm/BuKS27ae2m2+9BJgG58v6LUO7+u1ki2m09se4\nZERGa3+szSXVeZ0DYJpYa1u/rSIh2e4ZV6sq8RWy9uzYsQOtWrVC8+bNMWvWrLK/0HybeTGyRaQi\nOSUnJysSh3GNFdfIHtbPh3431HzKaljDMOwdsVfWATpgvPbHuGRERmt/arZ7I+6zET30c/s9HXw7\nwMVJmXlEtnvG1SqHHaQXFRVhzJgx2LFjB1JSUhAXF4fjx4+X/qVGP5sX+zTvo0heJffBVBrj6juu\nUVWkn1+/a/o/CagVgE2DNylyxozR2h/jkhEZrf2p2e6NuM9GU6HP7fd0atBJsbzY7hlXqxx2kJ6Q\nkIBmzZohMDAQrq6uGDx4MDZt2lT6l1zyAACNa7ZBQK0AFbIkIntUqJ/fszx6OXw9fBXOkIiIiB6m\nMu/nnfyVG6QTaZXDDtIzMjLwyCOPmNcDAgKQkZFh83cfq9dLqbSQnp6uWCzGNU5co6poP3d3cUe3\nR7splpfR2h/jkhEZrf2p2e6NuM9GU5nP7UoO0tnuGVerHPYWbBs2bMCOHTuwZMkSAMCqVavw22+/\nYeHChQDAKzST7jhoV7UL+zkZjRH7+cOwn5MeGa2v8/2cjMiQt2Br2LAhzp07Z14/d+4cAgIsp7Qb\n7Y8fkR6xnxMR+zmR4+P7OVHlOOzp7qGhoTh16hTS09ORn5+PtWvXIioqSu20iEhC7OdERESOj+/n\nRJXjsDPpLi4uWLRoEXr16oWioiLExsaidevWaqdFRBJiPyciInJ8fD8nqhyHnUkHgD59+iA1NRV/\n/vknJk6caN5e0fswSiEwMBDt27dHSEgIunTpAgDIzs5GREQEWrRogZ49e0py6f+XXnoJvr6+CAoK\nMm97UJwZM2agefPmaNWqFXbu3Clp3ClTpiAgIAAhISEICQnB9u3bJY977tw5PP3002jbti3atWuH\nBQsWAJB/n8uLK/c+3717F2FhYQgODkabNm3M7VmJ/2OtYz9nP2c/138/Lw/7Ofs5+7l+8P2c/Zz9\nvBJxRZ0pLCwUmzZtKqalpYn5+flihw4dxJSUFNniBQYGilevXi217Z133hFnzZoliqIozpw5U3zv\nvffsjvPzzz+Lhw8fFtu1a/fQOH/88YfYoUMHMT8/X0xLSxObNm0qFhUVSRZ3ypQp4uzZs8v8rpRx\nMzMzxaSkJFEURfHWrVtiixYtxJSUFNn3uby4SuxzTk6OKIqiWFBQIIaFhYn79u1T5P/YEbGfs5+z\nn+sf+zn7Ofu5/rGfs5+zn9vm0DPptlTmPoxSEe+72MXmzZsRExMDAIiJicH3339vd4xu3brBx8en\nQnE2bdqEIUOGwNXVFYGBgWjWrBkSEhIkiwvYvsCHlHH9/PwQHBwMAPDw8EDr1q2RkZEh+z6XF1eJ\nfa5RowYAID8/H0VFRfDx8VHk/9gRsZ+zn7Of6x/7Ofs5+7n+sZ+zn7Of26a7QXpl7sMoBUEQ8Oyz\nzyI0NNR8W4mLFy/C19cXAODr64uLFy/KEru8OBcuXCh1xUw5jsHChQvRoUMHxMbGmk/lkCtueno6\nkpKSEBYWpug+l8R97LHHAMi/z8XFxQgODoavr6/51B01/4+1jP2c/Vyq2Ozn2sV+zn4uVWz2c+1i\nP2c/lyq23vq57gbpSt9n8ZdffkFSUhK2b9+OTz/9FPv27SuTjxI5PSyOlDn84x//QFpaGpKTk+Hv\n74/x48fLFvf27dsYOHAg5s+fD09PzzKvLdc+3759G8899xzmz58PDw8PRfbZyckJycnJOH/+PH7+\n+WfEx8eXeV2l/o+1jv28/J9Lhf2c/Vxt7Ofl/1wq7Ofs52pjPy//51JhP3fMfq67QfrD7sMoNX9/\nfwBAvXr10L9/fyQkJMDX1xdZWVkAgMzMTNSvX1+W2OXFuf8YnD9/Hg0bNpQsbv369c0Nb9SoUebT\nNaSOW1BQgIEDB+Lvf/87oqOjASizzyVxhw0bZo6r1D4DgJeXF/r27YtDhw6p9n+sdezn7Of2xmY/\n1z72c/Zze2Ozn2sf+zn7ub2x9drPdTdIV/I+jLm5ubh16xYAICcnBzt37kRQUBCioqKwfPlyAMDy\n5cvNDUZq5cWJiorCmjVrkJ+fj7S0NJw6dcp8BUspZGZmmpc3btxovoKklHFFUURsbCzatGmDN998\n07xd7n0uL67c+3zlyhXzqTh37tzBjz/+iJCQENX+j7WO/Zz9nP1c/9jP2c/Zz/WP/Zz9nP28/J3T\nnW3btoktWrQQmzZtKk6fPl22OKdPnxY7dOggdujQQWzbtq051tWrV8UePXqIzZs3FyMiIsRr167Z\nHWvw4MGiv7+/6OrqKgYEBIhLly59YJxp06aJTZs2FVu2bCnu2LFDsrhfffWV+Pe//10MCgoS27dv\nL/br10/MysqSPO6+fftEQRDEDh06iMHBwWJwcLC4fft22ffZVtxt27bJvs9Hjx4VQ0JCxA4dOohB\nQUHixx9/LIrig9uSVMfaUbGfs5+zn+sf+zn7Ofu5/rGfs5+zn5cliKKNS98RERERERERkeJ0d7o7\nERERERERkaPiIJ2IiIiIiIhIIzhIJyIiIiIiItIIDtKJiIiIiIiINIKDdLLpxo0bWLx4MQDTbQye\nf/55lTMiIqmxnxPpH/s5kf6xn+sPr+5ONqWnpyMyMhLHjh1TOxUikgn7OZH+sZ8T6R/7uf64qJ0A\nadOECRPw119/ISQkBM2bN8fx48dx7Ngx/H/27jw8prN94Ph3sohdCEITPwliCRERFVollqBUbLW2\nJUqrlGotrVeXl7aWLjRKtdpXS7X2XZfUUhRFiLViCU3aiC2hKkREkvP748hMIoktM3Nm5tyf68pl\nzsnM3PcczzOTZ55t/vz5rFmzhrS0NOLi4hgzZgzp6eksWrQINzc3fvrpJ8qXL8/p06cZMWIEycnJ\nlCxZkq+++oo6depo/bKEELlIPRfC8Uk9F8LxST13QA+1g7tweAkJCUqDBg3y3f7mm2+UWrVqKdeu\nXVOSk5OVsmXLKnPnzlUURVFee+01JTIyUlEURWnTpo0SFxenKIqi7N69W2nTpo0Gr0IIcTdSz4Vw\nfFLPhXB8Us8dj/SkiwIpuWZBKHfMiGjdujWlSpWiVKlSuLu706VLFwACAgI4fPgw169f5/fff88z\nHyYjI8M6iQsh7pvUcyEcn9RzIRyf1HPHI4108cDc3NyMt52cnIzHTk5OZGZmkp2dTfny5Tlw4IBW\nKQohikjquRCOT+q5EI5P6rl9ktXdRYHKlClDamrqAz0m55u7MmXK4Ovry4oVK4znDx8+bPYchRBF\nI/VcCMcn9VwIxyf13PFII10UyMPDg8cff5yAgABef/11DAYDAAaDwXg75zj37Zzj77//nnnz5tGo\nUSMaNGjAunXrrPsChBD3JPVcCMcn9VwIxyf13PHIFmxCCCGEEEIIIYSNkJ50IYQQQgghhBDCRkgj\nXQghhBBCCCGEsBHSSBdCCCGEEEIIIWyENNKFEEIIIYQQQggbIY10IYQQQgghhBDCRkgjXQghhBBC\nCCGEsBHSSBdCCCGEEEIIIWyENNKFEEIIIYQQQggbIY10IYQmnn/+eTw9PQkICCj0Pq+88gp+fn4E\nBgZy4MABK2YnhLCmqKgo6tati5+fHx988IHW6QghLEDquRD3TxrpQghNDBo0iKioqEJ//9NPP3Hq\n1Cni4uL48ssvGTZsmBWzE0JYS1ZWFiNGjCAqKorY2FgWL17MsWPHtE5LCGFGUs+FeDDSSBdCaOKJ\nJ56gfPnyhf5+3bp1DBw4EICQkBCuXLnChQsXrJWeEMJKoqOjqVWrFj4+Pri6utK3b1/Wrl2rdVpC\nCDOSei7Eg3HROgFLMRgMWqcghFkpiqJ1ClaVlJREtWrVjMfe3t6cOXMGT09P4zmp58LR6K2eQ8F1\nfc+ePcZjqefCEemtrks9F3pUlHru0D3piqJY/ee///2vxJW4Zv/Rqztfe0Ef4noqBxLXsePq1X39\ncT4R/D/z10U50FtcPb5mPbq/RriCm5tCRobjlwE9lnu9xS0qh26kCyHsl5eXF4mJicbjM2fO4OXl\npWFGQghLuLOuJyYm4u3tne9+ydeTrZmWEMKM7ree37wJsbHWzEwI2ySNdDNLSEiQuBJXmEF4eDjf\nfvstALt378bd3T3PUHct6a38SVxhSU2aNCEuLo6EhAQyMjJYunQp4eHh+e536cYlspVsq+Wlt/Kn\nZbnX42vWm/ut5wAxMdbLS8q9xLVVDjsnXSuNGjWSuBJX3Id+/fqxbds2UlJSqFatGpMmTeLWrVsA\nDB06lE6dOvHTTz9Rq1YtSpUqxTfffKNxxiZ6K38SV1iSi4sLs2fPpkOHDmRlZTF48GDq1auX737Z\nSjZX0q9QoUQFq+Slt/KnZbnX42vWm/ut5wC7dsHzz1snLyn3EtdWGRRzDJq3QQaDwSzzAYSwBVKe\nCybXRTgSKc8FMxgMMFG9ffzl49SpWEfTfIQoKqnr+alz1tVrUrMmnDqlbT5CFFVR67kMdxdCCCGE\nXUhJS9E6BSGEhZQoof57+rQ00oWQRrqZbd26VeJKXOHg9Fb+JK6wFS2+acHxlONWiaW38qdludfj\naxb5tWtnur1kiXViSrmXuLZKGulCCCGEsBtBc4OYsWsGWdlZWqcihDCj/v1Nt7//HmRGgNAzmZMu\nhB2Q8lwwuS7CkUh5LljuOem51faoTc96PQmuGky1ctXwKOFB6WKlcXFyyfPjZLj//oj728v59n15\ngPs+wPMKx+fs5Cx1/Q4Gg4Fr1xQ8PeH6dfXc3r3QpIm2eQnxsIr6mS6NdCHsgJTngsl1EY5EynPB\nDAYDa46t4c9//uTbw99y8PxBrVMSomgmInX9DjnvfwMGwMKF6rnu3WHVKm3zEuJhycJxNkZv8y0k\nrtAjvZU/iSu01rVuV15r/hrRQ6KZ2GoiZYqVsXzQBMuHkLgax9YqrijUmDGm26tXw4EDlo0nc9Il\nrq2SfdKFEEIIYRdcnV35b+h/GfvYWH4+9TPRSdEcuXiE5OvJpKSlcCPzBpnZmcafW1m3ULi/now7\nezwUlEKHqd/vcxb0vPe8P8oDDaU3F63iahlby7iiYIGB0KOHqQd92DDYuROcnbXNSwhrk+HuQtgB\nKc8Fk+siHImU54LJdRGORsp0frmvybFjamP91i31dx98AK+/rmFyQjwEGe4uhBBCCCGEcAj16sFb\nb5mOJ0yAzZu1y0cILUgj3cz0Nt9C4go90lv5k7hCj/RW/mRurrAl48dDs2bq7aws6N0bTp82fxwp\n9xLXVkkjXQghhBBCCGEzihWDlSuhShX1+PJlaN8ezp7VNi8hrEXmpAthB6Q8F0yui3AkUp4L9qDX\n5eRJiI6GP/6A5GRISYEbNyAzU+2Ry8w03RZCCzExUtfvVFg937ULWreGmzfVY39/+O038PCwcoJC\nPCDZJ70QD3Jh0tPVuS47dqhDaZKS4Pp1SEtTP9gzMkBR1B+4+20hLOHff+UDvSDSqBGORMpzwe73\nuvz0E7zzDsTEWCEpIYpE6vqd7lbPf/hB3TM9M1M9btwYNmyQhrqwbUX+TFcc1P28tKwsRZk+XVEq\nVcppapvjZ4sZn0viStycH6xQa+yPVtdly5YtElfimp3U84Ld67pkZyvK6NGO9H6vt7h6fM1YqfbY\nj3tdk0WLFMVgMF3DwEBFuXix6HG1er/XMrbEtY6i1nPd7pOeng59+8LatVpnIoQQQoiHNWkSzJhh\nOnZzg7ZtITgYvL3V3rZSpcDVFVxc1P2Wc/4tZBt0QO2RDw62fP56j6tlbK3iPvqo9WPau3791NGt\nQ4aozfRDh6BNG9i0CTw9tc5OCPPT7XD3iAhYsMB0XK0a9OoFQUHq7bJloWRJKFFCXbzCYDD9qM9f\n+G0hzK18eRkaVxAZHiwciZTngt3tuhw8qDaysrPV4/Bw+N//oFIlKyYoxAOSup7f/V6Tb7+FQYNM\ndb5uXfj1V6ha1cIJCvGAZE56Ie52YX75BTp2NB2/+ip8+KH6LbsQtkg+0Asm10U4EinPBbvbdenS\nRZ2vCtCqldqr5qLbMYLCXkhdz+9BrsmiRfDcc6aGeu3aakPdy8uCCQrxgIpaz3W3BZuiwH//azru\n318dJmeuBrre9gCUuEKP9Fb+JK6wRfHxpga6wQCffWbeBrreyp/sFy3sRf/+sGSJOmUF1B0dWrWC\nv/568OeSci9xbZXuGukxMbBnj3q7WDH4+GMZoi6EEELYm4ULTbeffBLq19cuFyGEdfXqBcuXmzrZ\nTp+Gli3h1Clt8xLCXHQ33H30aPjkE/X2c8+pc1uEsHUyNK5gcl2EI5HyXLDCrsujj8K+fertxYvV\nxWCFsAdS1/N72Guyfj08/bS6XTKoc9M3bVL3UxdCSzLc/QGtW2e6/cwz2uUhhBBCiIeTnGxqoDs7\n511nRgihHznrUpQooR6fO6cOfT94UNu8hCgqXS2v8tdf6nAYUFdub93a/DG2bt1KaGio+Z9Y4uo6\nrrAteit/ElfYmt9/N90OCQF391y/PH9endd26pR6+9o19Sc9XV1pKmer5dy3jdtXm2y9dIlQDw/r\nvCAdx9UytpavWZhPWBhERUHnzmpVT0lR/8aPilLfH+5Gy/d7vX3G6S1uUemqkZ573YAnnlDnpAsh\nhBDCvuSsLQPQvPntGxs2wLvvws6dmuQkhNBOy5bqMPeOHeHKFfWnXTv48Uf1d0LYG13NSX/lFZg1\nS7397rvw9tsaJCbEQ5D5awWT6yIciZTnghV0Xdq1g82b1dvLlmTTa98b6kqwQtgBA0hdv4O53v8O\nHlR71lNS1OMSJWDNGmjfvshPLcQDKWqZ1lVP+v79ptvBwdrlIYQQQoiHd/So6Xa7DePg6xmmEy4u\navd6QABUqwZly0Lp0lC8ODg5qVu65Pyb85NzLIQ1PPWU1hk4rEaNYNs29Yu8c+fgxg113vqKFeq/\nQtgNxUHd+dKyshSlVCnTxLNz5ywTd8uWLZZ5Yomr67gOXFWLRKvrorfyJ3GtQ+p5we68Lv/8Y/os\nD3PdkndWeZcuinLmjFni6q38aRVXy9hS122Hua9JXJyi/N//md4aXFwUZenS/PeTci9xLaWoZVo3\nq7snJcH16+ptDw+oUkXbfIQQQgjx4I4dy7ml8LHreNMvnnoKVq8GLy8t0hJC2JBateC336BmTfU4\nMxP69YMFC7TNS4j7pZs56b/+Cm3bqrebN8+7MqwQtk7mqhZMrotwJFKeC3bndZk/HwYNgkeJJprb\nSze7uUFcnDq8XQgbJ3U9P0tdk7Nn1aHvpi/34PPP4aWXzB5KiDxkn/T7dPKk6bafn3Z5CCGEEHqz\nfPly6tevj7OzM/tzLxADTJ06FT8/P+rWrcuGDRvu+VwJCeq/Ecw3nezXTxroQoh8HnlEnaMeGGg6\nN2wYzJhR+GOEsAW6aaTHxZluW7KRvjX3Pm9WJHEdO66wLXorfxJXFFVAQACrV6+m5R17IcXGxrJ0\n6VJiY2OJiopi+PDhZGdn3/W5/v4bQCGcdaaTAweaPWe9lT8ty70eX7OwnkqVYMsWaNrUdG7MGHj/\nfSn3Etd22UUjPSsri6CgILrcXpbx8uXLhIWFUbt2bdq3b8+VK1fu+Rzqh7qqRg1LZSqEEEKIO9Wt\nW5fatWvnO7927Vr69euHq6srPj4+1KpVi+jo6Ls+119/QQP+wJsk9UT58tCihSXSFkLcYeLEiXh7\nexMUFERQUBA///yz8XeFjYqJiYkhICAAPz8/Ro0apUXalC8PGzfCE0+Yzr39Nnz1lbq0nBC2xi62\nYJs5cyb+/v6kpqYCMG3aNMLCwnj99df54IMPmDZtGtOmTbvrc5w5Y7ptyRFxoaGhlntyiavbuI4o\nKiqKV199laysLIYMGcIbb7yR5/cpKSk8++yznD9/nszMTMaOHUtERIQ2yd5Bb+VP4gpLOXv2LM2a\nNTMee3t7k5SUlO9+ERER+Pj4AHDokDuPYfrmfWuDBrBjh/H/LafXxF6Pc87ZSj7WOs792q0VPzQ0\n1CrxDh48aOxQSsiZr2GnDAYDo0ePZvTo0XnO5x4Vk5SURLt27YiLi8NgMDBs2DDmzZtH06ZN6dSp\nE1FRUXTs2NHquZctC1FR0K2b2mAHWLQolIoVITLS+rsw6u0zTm9xi8rmF447c+YMERERvPnmm8yY\nMYP169dTt25dtm3bhqenJ+fPnyc0NJTjx4/nedydk/WrVTM11P/8E3x9rfkqhCgaR1tkJisrizp1\n6rBp0ya8vLx49NFHWbx4MfXq1TPeZ+LEidy8eZOpU6eSkpJCnTp1uHDhAi4upu8WHe26CH2z9/Ic\nFhbG+fPn852fMmWKcSRc69atmT59Oo0bNwZg5MiRNGvWjGeeeQaAIUOG0KlTJ3r06GF8fO7roijq\ndudfZQxgAAvVO0yfDnc0GISwZfZc1ydNmkTp0qUZM2ZMnvNTp07FycnJ+IV7x44dmThxItWrV6dN\nmzYcu71y25IlS9i6dStffPFFnsdb85qkp0Pv3rB+vencCy+oC8o5O1slBaEDDr9w3GuvvcZHH32E\nk5Mp1QsXLuDp6QmAp6cnFy5cuOtzZGbCuXOm40cesUiqgP7mW0hc8TCio6OpVasWPj4+uLq60rdv\nX9auXZvnPlWrVuXq1asAXL16FQ8PjzwNdC3prfxJXHE/Nm7cyJEjR/L95DTQC+Ll5UViYqLx+MyZ\nM3jdZQu1K1cgIwOCiTGdzNUTb056K38yN1fcr1mzZhEYGMjgwYONIwTOnj2Lt7e38T45o2LuPO/l\n5VXgaBlQR8xMnDiRiRMnEhkZmef/Z+vWrWY7Ll4cXnllK61abQXUc199tZWOHbeSmWn+eIUdR0ZG\nWvT5CzvOuW2teHp5vZGRkcbya46Rn7bxF28hfvjhBypXrkxQUFCeC5GbwWDAUMj4lJzhcampkJXl\nDjSiUqVQ3Nwca7gWwMGDB60aT16vZeNFRkZy8OBB4/BOR5OUlES1XPNOvL292bNnT577vPDCC7Rp\n04ZHHnmE1NRUli1bVuBz5R4G6+7uTqNGjRymHOit3Ovt9Tp6PS9M7p6F8PBw+vfvz+jRo0lKSiIu\nLo6muVd3usOFC+BKBrXJtWVLQIAl0xVCdwobFTN58mSGDRvGO++8A8Dbb7/NmDFjmDdvnlnizp8/\nv9Df5bxvmuu4XbtQQkOhc+etqNPnQ9m0Cfr2hUWLzB9Pjh3/+M5zCxYsoChserj7hAkTWLhwIS4u\nLqSnp3P16lV69OjB3r172bp1K1WqVOHcuXO0bt36rsPd9+41rejYqBEcOGDtVyJE0djz0LiCrFy5\nkqioKL766isAvvvuO/bs2cOsWbOM93n//fdJSUkhMjKS06dPExYWxqFDhyhTpozxPo52XYS+OXJ5\nXr16Na+88gopKSmUK1cuz4JTU6ZM4euvv8bFxYWZM2fSoUOHPI/NfV22bYOXQ//gD243zH18DUWi\nBwAAIABJREFUID7emi9FiCJzlLqekJBAly5dOHLkiHFtqPHjxwPqcPdJkyZRvXp1WrdubRzuvnjx\nYrZt26bpcPfcsrPh5ZchdzqdO8OKFWqPuxAPy6GHu0+ZMoXExETi4+NZsmQJbdq0YeHChYSHhxu/\nnViwYAHdunW76/NcvGi6fXuUvBBCQ3cOcU1MTMwzHA7g999/p1evXgDUrFkTX19fTpw4YdU8hRDm\n0b17dxITE7lx4wbnz5/PsyL0hAkTOHXqFMePH8/XQL/ThQtQn6OmE/XrWyplIUQBzuWaP7p69WoC\nbo9kCQ8PZ8mSJWRkZBAfH28cFVOlShXKli3Lnj17UBSFhQsX3vPvdmtycoI5c+C110znfvwRnnoK\nrl/XLi8hbLqRfqecYe3jx49n48aN1K5dm19//dX4rV1hkpNNtytVsmSG+YdLWovEdey4jqZJkybE\nxcWRkJBARkYGS5cuJTw8PM996taty6ZNmwB1HYoTJ05Qw0b2T9Rb+ZO4wlZcuAA1OW06UcC2buai\nt/KnZbnX42u2V2+88QYNGzYkMDCQbdu28cknnwDg7+9P79698ff358knn2TOnDnGv9vnzJnDkCFD\n8PPzo1atWpqs7F6YrVu3YjCo60++9Zbp/ObN0KED/PuvZWNrQeLaB5uek55bq1ataNWqFQAVKlQw\n/vF+P1JSTLcrVjR3ZkKIB+Xi4sLs2bPp0KEDWVlZDB48mHr16jF37lwAhg4dyoQJExg0aBCBgYFk\nZ2fz4YcfUqFCBY0zF0Jo6eJF8CHBdEK2ahHCqr799ttCfzdhwgQmTJiQ73xwcDBHjhyxZFpFZjDA\ne+9ByZKQ8xJ27oR27eCXX0D+/BDWZtNz0osi9zyAN96ADz9Uz0+ebKp8QtgLR5m/Zm5yXYQjkfJc\nsNzX5aWXoOfcMMK4/UX9+vXquFQh7IjU9fxs6ZrMnAmvvmo6DgiATZugcmXtchL2x6HnpJuLNYe7\nCyGEEMIy/vnnjp50na2ML4SwvFGjYO5ctXcd4MgRaNkSCtk5TgiLkEa6meltvoXEFXqkt/IncYWt\nuHxJ4f/423TCgo10vZU/mZMu9KiwMvDii/Dtt+rCcgAnTqgN9YQEy8e2NIlrH3TRSJc56UIIIYT9\ny065jBsZAGSWKgulS2uckRDCUT37LCxdCi63V/D680944gk4eVLbvIQ+6GJOep06pgp19Cj4+2uY\nmBAPwZbmatkSuS7CkUh5Llju69Kx2lGizjQAIMO3NsX+lG0Zhf2Rup6fLV+TH3+Enj3h5k312NNT\nnaPeoIG2eQnbJnPS70Pu7RPc3bXLQwhHsn//fsaNG0dISAienp5UqVKFkJAQxo0bx4EDB7ROTwjh\ngIpfOW86qFJFu0SEELrRubPaUC9ZUj2+cAFCQ2H/fk3TEg5OGulmprf5FhJXnzp16sT06dNp0qQJ\nixcv5q+//iI+Pp7FixcTHBzMxx9/TOfOnbVO02L0Vv4krrAF2dlQ+pqpke7ibdlGut7Kn8xJF3p0\nv2WgbVt1K7YyZdTjS5egTRvYtcvysc1N4toHu9kn/WHdvAnp6eptFxcoUULbfIRwBN988w2enp75\nzteoUYMaNWrQt29fLl68qEFmQoiiSk9Pp3jx4vc8Z21Xr4Inpka6U1XpSRdCWE+LFrB5M3TooO40\n8e+/EBYGP/yg9qwLYU4OPyf94kV17giAh0feReSEsBe2NldLURTWrFnDqVOnaNiwIR06dNAkD1u7\nLkIUha2U58aNG7P/jnGcBZ2zlpzrEh8PK2qMYxwfq7+YOhXGj9ckJyGKwlbqui2xp2ty+DC0a2fa\nPap4cVi9Gjp21DYvYVuKWqYdvif9yhXTbZmPLoR5DB8+nNjYWB577DHefvtt9uzZwzvvvKN1WkKI\nIjh37hxnz54lLS2N/fv3oygKBoOBq1evkpaWpnV6XLkCHlwynZDtWoQQGmjYEH77TR0Cf/asOmI3\nPByWLYNu3bTOTjgKh5+Tnns+erlylo+nt/kWEleffvvtN3799VemTp3K1q1bWbNmjdYpWZXeyp/E\n1YcNGzYwduxYkpKSGDNmDGPHjmXMmDHMmDGDKVOmaJ0eqalQgcumEx4eFo2nt/Inc9KFHj1sGahb\nV22oV6+uHt+6BU8/DUuWWD52UUlc++DwPemysrsQ5lesWDGcnZ0BKFmypN0MURNCFG7gwIEMHDiQ\nFStW8PTTT2udTj7Xrt3RSK9QQbtkhBC6V7OmqUf91CnIyoL+/eHGDRg0SOvshL1z+DnpK1ZAr17q\nue7dYdUqbfMS4mHY2lytEiVKUKtWLePx6dOnqVmzJqDmevjwYavkYWvXRYiisJXyfP78ed58802S\nkpKIiooiNjaWXbt2MXjwYE3yybkuS5dC/b4NaMBR9ReHDqnjToWwM7ZS122JPV+Tc+fUOeqxsaZz\ns2fDyy9rl5PQnsxJvwdrD3cXQg+OHTumdQpCCAuJiIhg0KBBTJ48GQA/Pz969+6tWSM9h/SkC1Gw\nHTt20KJFizzndu7cyeOPP65RRvpStSps2wbt28OBA+q5ESMgLQ3GjdM2N2G/HH5Oemqq6XbZspaP\np7f5FhJXn3x8fPDx8cHd3Z3k5GSSk5MpX7688byj01v5k7j6kpKSQp8+fYxTWlxdXXFx0f47/dSr\nilUb6XorfzIn3X6NHDky37kRI0ZokIn9MVcZqFgRfv0VmjUznXv9dZg0CQrrTNVbuddb3KLS/lPX\nwq5fN90uVUq7PIRwJDdv3mTo0KGsWbMGX19fFEUhISGB7t27M3fuXIoVK6Z1ikKIh1S6dGkuXTKt\nor57927K2cBQtBuXb1CcmwDccnbDtUQJjTMSQlu7du3i999/Jzk5mRkzZhiH1qamppKdna1xdvrj\n7g4bNkCXLmrPOsDEiWqP+rRpYDBomp6wMw4/J/0//1ErBsDkyTBhgrZ5CfEwbG2u1ttvv82ff/7J\nF198QZkyZQD1j4Lhw4fj4+PDe++9Z5U8bO26CFEUtlKeY2JiGDlyJEePHqV+/fokJyezYsUKAgMD\nNckn57pMHn6GNz+vBkBqmaqUuXpWk3yEKCpz1fVt27axZcsW5s6dy0svvWQ8X6ZMGbp06YKfn1+R\nY1iLrbz/mUNamroO1oYNpnMjRsDMmeDk8GOYRY6ilmmHb6S/8grMmqWei4yEUaO0zUuIh2FrH171\n69cnOjqaUncMT7l27RohISEcPXrUKnnY2nURoihsqTzfunWLEydOAFCnTh1cXV01yyXnurzf5whv\nLVMXirtUxR+Pc9Z5nxHC3Mxd1xMSEvDx8eHff//FYDBQ1hrzO83Mlt7/zOHmTejTB9auNZ17/nn4\n8ku4PZNIOLiilmmH/z7n2jXTbWsMd9fbfAuJq0/Ozs75GuigDpN10sHXxHorfxJXX1auXMn69es5\nefIkJ0+eZP369WzevJmLFy9qmlf2lavG21mlLN8I0Vv5kznp9is5OZmAgAAaNmxIQEAAgYGB7Nu3\nT+u07IKlyoCbGyxfDn37ms59/TU895y6p7olY9+LxLUPupqTXrq0dnkI4WguX76c75yiKBhk0pUQ\ndu3rr79m165dtG7dGlD/wGncuDHx8fG88847DBgwQJvEcq0Eq5Quo00OQtig559/njlz5vDEE08A\n6mrvzz//vNW2QxUFc3WF776D4sVh/nz13OLF6j7qS5ZompqwAw4/3P2pp+DHH9Vz69apizkIYW9s\nbRiYj4/PXRvj8fHxVsnD1q6LEEVhK+W5ffv2LFy4EE9PTwAuXLjAc889x+LFi2nZsqXVprPkyLku\n7wYs550/eqs5Pd4Dzx0rrZqHEOZi7roeFBTEgZy9v25r3Lgx+/fvN1sMS7OV9z9LyM6GkSNhzhzT\nuY4dYeVKKFlSu7yEZck+6feQe7i79KQLYR4JCQlapyCEsJDExERjAx2gcuXKJCYm4uHhoenODU7X\nTT3phrLSky5ETEwMAK1atWLo0KH069cPgKVLl9KqVSstUxO5ODnB7Nlqg/zjj9VzUVHQuTOsXy/t\nE1Ewh588au0t2PQ230Li6lNmZibXcn0Dtnv3bn777Td+++03UnMNSXVUeit/EldfWrduTefOnVmw\nYAHz588nPDyc0NBQrl+/jru7u2Z5FUs3zUk3lJM56Y4SV8vY9l7Xx4wZw9ixYzl06BAnTpxg0qRJ\nTJo0iWPHjnHw4EGt07ML1ioDBgN8+CH89795Y7dvD1euWCWFPHG1oLe4RSU96UKIB/bGG29QuXJl\n3njjDQD69etHgwYNSE9Pp3HjxnzwwQcaZyiEeFizZ89m1apV7NixA4PBwMCBA+nZsycGg4EtW7Zo\nlpfrTdMXgE7u0pMuhL02PvTKYFD3TS9ZEm7/+cSuXdC2LfzyC1SsqGl6wsY4/Jz0//s/SExUzyUk\nQPXqmqYlxEOxtblajRo1Yu/evcZtmXLmwymKQosWLdi5c6dV8rC16yJEUUh5LljOdZlT5nWGX/sI\ngCvjp+I+dbzGmQnxcMxd18+fP8+bb75JUlISUVFRxMbGsmvXLgYPHmy2GJamt/e/2bPVeeo56teH\nTZugShXtchLmJVuw3YP0pAthftnZ2Xn2Tc7pOTcYDHmGwQsh7Efp0qUpU6ZMgT9F3Xd53Lhx1KtX\nj8DAQHr06MG///5r/N3UqVPx8/Ojbt26bNiwodDnKH7L1JPuIj3pQhhFRETQvn17zp49C4Cfnx+f\nfPKJxlmJuxkxAv73P7V3HeDoUWjZ0tSxKITDN9JlTrrEdYS4tubWrVtcvWqaH9q+fXsA/v33X27e\nvKlVWlajt/IncfXh2rVrpKamMmrUKD744AOSkpJISkriww8/ZNSoUUV67vbt23P06FEOHTpE7dq1\nmTp1KgCxsbEsXbqU2NhYoqKiGD58ONnZ2QU+R4lMUyPdtYLlG+l6K38yJ91+paSk0KdPH5ydnQFw\ndXXFxcXhZ7SahZZloGbNrXz3Hdz+byMuDp54Av7807Jx9Vbf7LWeW72RnpCQwKZNmwBIS0vL84e+\nuWVmQkaGetvJCdzcLBZKCF154YUX6Nu3L3/99ZfxXEJCAn379mXIkCEaZiaEKKp169YxfPhwypYt\nS9myZRk2bBhr164t0nOGhYXh5KT+yRESEsKZM2cAWLt2Lf369cPV1RUfHx9q1apFdHR0vscrCpTM\nsm4jXQh7Ubp0aS5dumQ83r17N+XKldMwI3G/+veH5cvVPdUB/vpLbagfP65tXkJ7Vp2T/uWXX/LV\nV19x+fJlTp8+zcmTJxk2bBibN282eyyDwUBqqkKZ25/jpUrlHfouhD2xxblaX3zxBVOmTDEOby9d\nujT/+c9/GDZsmNVysMXrIsTDspXy3Lx5c15++WXjdk5Llizhs88+4/fffzfL83fp0oV+/frRv39/\nRo4cSbNmzXjmmWcAGDJkCE8++SQ9e/Y03t9gMPDsswNx+m4bviTgDjSaPp3Q0aMBUy9JaGioHMux\nTR4fPHiQK7eX8E5ISGDBggVmresxMTGMHDmSo0ePUr9+fZKTk1mxYgWBgYFmi2FptvL+p5Wff4Ye\nPSA9XT2uVEmdo96wobZ5iYdX1DJt1UZ6YGAg0dHRNGvWjAMHDgAQEBDAkSNHzB7LYDCQnKxQqZJ6\n7OEBKSlmDyOEVdjyh1fOaJiizll9GLZ8XYR4ULZSnuPj4xk1apSxUf74448zc+ZMfHx87vq4sLAw\nzp8/n+/8lClT6NKlCwCTJ09m//79rFy5EqDARnqnTp3o0aOH8fEGg4FLlxROeTSlKXvVk7t2QbNm\nRX2pQmjCEnX91q1bnDhxAoA6derkWTfmYSxfvpyJEydy/Phx9u7dS+PGjY2/mzp1Kl9//TXOzs58\n+umnxilvMTExREREkJ6eTqdOnZg5cyYAN2/eZMCAAezfvx8PDw+WLl1K9TtWcraV9z8tbdkCXbqY\npuqWLw8bNkCTJtrmJR6OXS0c5+bmhluuMeeZmZkYclZMsIAbN0y3ixe3WJg89DbfQuKKnCGxeqK3\n8idx9cXX15d169aRkpJCSkoKa9euvWcDHWDjxo0cOXIk309OA33+/Pn89NNPfP/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YAAAg\nAElEQVQydd5fv37dovFy96RbeP0qIXQrISGBU6dO0a5dO9LS0sjMzKSsTH4Swu7Y9HZOuT/Qi0tP\nuhC5FTT6RYhhw9RF455/HrKz4fhxaNlSbahXr651duJerDrcvVevXgwdOpQrV67w5Zdf0rZtW4YM\nGWKxeFo00vU230Li6tuXX35prNcAZ86coXv37hpnZXl6K38SVx+qV69OaGgou3fvplWrVoSGhhIa\nGkpwcDAuLlb9Tj8fwy3TcHcnKzXS9Vb+HGlurq3HNbcTJ07Qtm1b6tevD8Dhw4d5//33Nc7KPjh6\nuR84EBYvVrdqAzh9Gh59dCunTlk8dD5Szx+MVRvp48aNo2fPnvTs2ZOTJ0/y3nvv8corr1gs3s1c\nU9ikJ10I8/vss8/YsWOHsee8du3aXJRJT0LYtZUrV+Ln50fZsmUpU6YMZcqU0Xx0jHOuRrqhhPSk\nC5HbCy+8wJQpU4zbngYEBLB48WKNsxK2ondvWLXK1BZKTlZ71GNjtc1L3J1V56RPnz6dvn374uXl\nZfFYBoOB5s0Vdu1Sj3fsgMcft3hYISzCVudqNW3alOjoaIKCgjhw4ACZmZk0btzYIutMFMRWr4sQ\nD8NWynPNmjVtajsng8HA8qojefrcLABOj/iEmrNe1TgrIR6euet6kyZN2Ldvn/GzGKBRo0YcPHjQ\nbDEszVbe/xzZxo3Qtatpi+qKFWHDBggK0jYvR1XUMm3VnvTU1FTat29PixYtmD17NhcuXLBoPJmT\nLoRltWrVismTJ5OWlsbGjRvp1asXXbp00TotIUQR2OJ2Tk6ZuYa7S0+6EHlUqlSJU7nGL69YsYKq\nVatqmJGwRWFhEBUFpUurxykp6qrve/Zom5comNW3YDt69CifffYZ586do2XLlrRt29Zi8WROusR1\nlLi2atq0aVSqVImAgADmzp1Lp06ddDEPTm/lT+Lqiy1u5+SUafpAdyopc9IdKa6WsR2lrs+ePZuh\nQ4dy/PhxHnnkET755BM+//xzrdOyC3or9y1bwocfbsXdXT2+cgXatYNt2ywfW+r5g9FkJZjKlStT\npUoVPDw8SE5Otlgc6UkXwrKcnZ158cUXefHFF7VORQhhJv/++y8lSpRgw4YNec736NFDo4zAJcvU\nk+5spUa6EPaiZs2abN68mevXr5OdnU2ZMmW0TknYsHr1YMsWtWc9JQWuXYMnn4TVq6FDB62zEzms\nOid9zpw5LFu2jIsXL9KrVy/69OmDv7+/RWIZDAZ8fRXi49Xj06ehRg2LhBLC4mx1rpavr2++cwaD\ngT///NMq8W31ugjxMKQ8F8xgMPBzyR50TFN788/PWk6VEU9rnJUQD8/cdf2ff/7h22+/JSEhgczM\nTGOMTz/91GwxLE3e/6wvNlbtRT93Tj0uVgyWL4fwcG3zchR2tU/633//TWRkJI0aNbJKPOlJF8Ky\n9u7da7ydnp7OihUruHTpkoYZCSGK6sSJEwwfPpzz589z9OhRDh8+zLp163jrrbc0yyl3T7pLKelJ\nFyK3Tp060bx5cxo2bIiTkxOKomAwGLROS9g4f3/47Tdo2xb+/lttN/XsCd99B336aJ2dsOqc9GnT\nppGamso333wDQHJyMvE5Xd0WIHPSJa6jxLVVFStWNP54e3vz6quv8uOPP2qdlsXprfxJXH2xxe2c\nXLJzNdJLWucDXW/lT29zc7WMa243b95kxowZDBo0iIEDBxIREcHAgQO1Tssu6L3c16qlNtRr1lSP\nMzOhf39YsMCyca3JXuu5VXvSJ06cSExMDCdOnGDQoEFkZGTw7LPPsnPnTovEk550ISwrJibG+G19\ndnY2+/btIysrS+OshBBFkZaWRkhIiPHYYDDg6uqqYUbgmruRXlp60oXIrX///nz55Zd06dIFNzdT\n/ahQoYKGWQl7Ub262lBv1w6OHYPsbIiIgLQ0GDZM6+z0y6pz0gMDAzlw4ADBwcHGfRwbNmxokT2V\nDQYDxYsrpKerx9evQ8mSZg8jhFXY6lyt0NBQYyPdxcUFHx8fxo4dS506dawS31avixAPw1bK85NP\nPsmsWbPo1asXBw4cYMWKFcybN4+ff/5Zk3wMBgO7DSGEKOo+QTe3/I5baHNNchHCHMxd12fPns2b\nb76Ju7s7Tk5OxhjWWh/GHGzl/U/PkpPVxeQOHTKd+/hjGDNGu5zsmV3NSXdzczO+eQBcv37dovGk\nJ10Iy7LXIURCiMLNnj2bF1980bidk6+vL99//32RnvPtt99m3bp1GAwGPDw8mD9/PtWqVQNg6tSp\nfP311zg7O/Ppp5/Svn37fI8vpsicdCEKM336dE6fPk3FihW1TkXYsUqV1FXfO3aE6Gj13Nixao/6\nW2+BLHNgXVadk96rVy+GDh3KlStX+PLLL2nbti1DhgyxWLzsbPVfgwGcnS0WJg+9zbeQuPqWnp7O\n999/z+TJk3n33XeZNGkS7777rtZpWZzeyp/E1Zec7ZxSUlI4ceIEO3fuxMfHp0jP+frrr3Po0CEO\nHjxIt27dmDRpEgCxsbEsXbqU2NhYoqKiGD58ONk5H965uGH9Ldj0Vv70PjfXnvn5+VGiRAmt07BL\nUu7zKl8eNm6EJ54wnXvnHZgwAYo60MEWX68ts1pPuqIo9OnTh+PHj1OmTBlOnjzJe++9R1hYmMVj\nFysm3/4IYQldu3bF3d2d4OBgihcvrnU6QggzsMR2Trn3bb527Zqxx2/t2rX069cPV1dXfHx8qFWr\nFtHR0TRr1izP43M30nGTnnQhcitZsiSNGjWidevWxjnp9rYFm7AdZctCVBR066Y22AGmTVN71D/5\nBJys2sWrX1abk64oCgEBAfzxxx/WCHd7nqz60sqUgatXrRJWCIuw1blaDRo0sFqdLoitXhchHoat\nlOfmzZvTvHlzAgIC8mznVNTVot98800WLlxIiRIliI6Oply5cowcOZJmzZrxzDPPADBkyBCefPJJ\nevbsaXycwWCgFyXxJw0A93feoVHr1oSGhgKmXhI5lmNbPT548CBXrlwBICEhgQULFpi1rs+fPz/f\nOXPUWWuylfc/YZKeDr17w/r1pnNDhsAXX1hvhLI9K2qZturCcQMHDuTll1+madOmFo+Vu5FesaK6\nGIIQ9spWP7xefPFFRowYQcOGDTWJb6vXRYiHYSvluXHjxuzfv/+BHxcWFsb58+fznZ8yZQpdunQx\nHk+bNo0TJ07wzTffFNhI79SpEz169DDe32AwcJGKVCJFPXH+PHh6PnB+QtgKW6nrtkSuiW26dQue\nfRaWLTOdy9mizcWqK5vZn6KWaasOWNi9ezfNmzenRo0aBAQEEBAQYJU/7otZcdE4vc23kLj6tn37\ndoKDg6ldu7ZV67TW9Fb+JK6+5GzndO7cOS5fvmz8uZeNGzdy5MiRfD+5G+g5z793714AvLy8SExM\nNP7uzJkzeHl55XvuYuRaCdZKw931Vv5kbq792rFjB2FhYfj5+eHr64uvry81atTQOi27IOX+7lxd\nYdEiyD0oY9Ei6NMn7wLd5o5rTvZaz636Hcgvv/xizXBG1mykC6EnWm3JJISwnOLFizNu3DgmT55s\ntu2c4uLi8PPzA9R56EFBQQCEh4fTv39/Ro8eTVJSEnFxcQWOtpM56UIUbvDgwURGRtK4cWOczTQO\nefny5UycOJHjx4+zd+9eGjduDKjD9evVq0fdunUBdXrMnDlzAIiJiSEiIoL09HQ6derEzJkzAbh5\n8yYDBgxg//79eHh4sHTpUqpXr26WPIXlOTvD11+rW1l//rl6btUqdc76ypUgaxZahlWHu1tT7uHu\ntWvDiRPa5iNEUdjyMLDt27dz6tQpBg0aRHJyMteuXcPX19cqsW35ugjxoGylPPv6+rJ3716zbuf0\n9NNPc+LECZydnalZsyaff/45lStXBtTh8F9//TUuLi7MnDmTDh065HmswWAgCwNOtz/TycyUCZHC\nrpm7roeEhLBnzx6zPR/A8ePHcXJyYujQoUyfPj1PI71Lly4cOXIk32OaNm3K7Nmzadq0KZ06deKV\nV16hY8eOzJkzhz/++IM5c+awdOlSVq9ezZIlS/I81lbe/0ThFEXdkm3GDNO51q1h3TooXVq7vGyV\nXe2TrhXpSRfCMiZOnEhMTAwnTpxg0KBBZGRk8Oyzz7Jz506tUxNCPCRLbOe0YsWKQn83YcIEJkyY\ncNfHGxvoIEsLC3GH1q1bM27cOHr06GFc3R0wNqwfRk5P+f06d+4cqampxpEwAwYMYM2aNXTs2JF1\n69YZt13s2bMnI0aMeOi8hHYMBvj4YyhVCt57Tz23ZQt06AA//QTlymmbn6PRRSPd1dV6sbZu3Wpc\n0dOaJK5jx7VVq1ev5sCBAwQHBwPq/NLU1FSNs7I8vZU/iasvtrydUwauFLPSnqp6K39alns9vmZz\n2r17NwaDgX379uU5v2XLFovEi4+PJygoiHLlyvH+++/TokULkpKS8Pb2Nt7Hy8uLpKQkAJKSkqhW\nrRoALi4ulCtXjsuXL1OhQoU8zxsREYGPjw8A7u7uNGrUyOKr8Oec02rV/1dffdVq8e58rQ/7+Hff\nhfPnt/LVVwCh/P47NG26lY8+gvBwx3u993t85y4ORWXVRnpsbCz+/v55zlnjDVJ60oWwDDc3N+Oc\nVYDr169rmI0Qwhy6detGt27d8pwzWKlhfC+ZBlfkI10Ik6ysLMLDwxk9evQDP/Z+d2TI7ZFHHiEx\nMZHy5cuzf/9+unXrxtGjRx84dkEK2koux51tBXMd39nYsnQ8Rzn+8stQGjSAUaPU45MnQ3nzTQgJ\nUTff0Do/LY7vPLdgwQKKRLGi+vXrK9OmTVOys7OV69evKyNGjFBCQkIKvf/ff/+thIaGKv7+/kr9\n+vWVmTNnKoqiKJcuXVLatWun+Pn5KWFhYco///yT77GAos6eUJTHH7fYSxLCKqxcVe/bhx9+qLz4\n4ouKj4+PMnfuXCUkJMRYT+/l559/VurUqaPUqlVLmTZtWqH3i46OVpydnZWVK1fm+52tXhchHoaU\n54KR82EOylXnclqnI0SRmbuuN2nSxKzPl1toaKgSExNzz9+fPXtWqVu3rvH8okWLlJdeeklRFEXp\n0KGDsmvXLkVRFOXWrVtKxYoV8z2PvP/Zp6++UhSDwfgWrdSurSiJiVpnZRuKWqatOrFrz549JCYm\n0rx5c5o2bUrVqlX5/fffC72/q6srn3zyCUePHmX37t189tlnHDt2jGnTphEWFsbJkydp27Yt06ZN\nu2tcaw53F0JPxo0bR8+ePenZsycnT57kvffe45VXXrnn47KyshgxYgRRUVHExsayePFijh07VuD9\n3njjDTp27CgLyghhJba8nVOmQT7QhbhTixYtGDFiBNu3b2f//v3ExMSwf/9+sz1/7s/flJQUsrKy\nAPjzzz+Ji4ujRo0aVK1albJly7Jnzx4URWHhwoV07doVUHdxyOlVXLFiBW3btjVbbkJbQ4bAwoWm\ntTxPnoSWLSE+Xtu8HIFVG+kuLi6UKFGCGzdukJ6eTo0aNfIMlb1TlSpVaNSoEQClS5emXr16JCUl\nsW7dOgbe3rBv4MCBrFmz5h5xzfca7kVvewBKXH2bPn069evX5+OPP+bjjz8mLCzsvh4XHR1NrVq1\n8PHxwdXVlb59+7J27dp895s1axZPP/00lSpVMnfqRaK38idx9WXw4MGMHj2aHTt2sHfvXvbu3Ut0\ndLTWaQGQZbDeB7reyp+W5V6Pr9mcDhw4wNGjR3nnnXcYM2YMY8eOZcyYMUV6ztWrV1OtWjV2795N\n586defLJJwHYtm0bgYGBBAUF0atXL+bOnYu7uzsAc+bMYciQIfj5+VGrVi06duwIqO8ply5dws/P\nj8jIyHt2rlmTlPuie+YZWLrU1CkaHw9PPJF/Zy1Heb3WYtU56U2bNiU8PJx9+/aRkpLC0KFDWbly\nJcuXL7/nYxMSEjhw4AAhISFcuHABT09PADw9Pblw4UIhj4oAfEhIgMhI6y1AYcnnL+z44MGDVo0n\nr9ey8SIjIzl48KBxARVblZqaSvv27Slfvjx9+/alV69exrp5N7kXkQHw9vbOt31MUlISa9eu5ddf\nf2Xv3r2FzonVaqEZSz6/Xsu93l6vrdZzd3d34x/ktibLSXrShbjTne9d5tC9e3e6d++e73zO6LmC\nBAcHF7g1m5ubG8uWLTN7jsJ29OwJa9ZAjx5w8yYkJak96ps2QUCA1tnZJ6vuk753714effTRPOe+\n/fZbBgwYcNfHXbt2jVatWvH222/TrVs3ypcvzz///GP8fYUKFbh8+XKex+TeJ71LF3UPPyHsla3v\nH3ro0CGWLVvGihUr8Pb2ZvPmzXe9/8qVK4mKiuIrdWlQvvvuO/bs2cOsWbOM9+nVqxdjx44lJCSE\niIgIunTpku8PA1u/LkI8CFspz+PHjycrK8us2zkVhcFgMG7AdrZEDR5JO61JHkKYi7nr+vnz53nz\nzTdJSkoyTiPbtWsXgwcPNlsMS7OV9z9RNJs3Q3g4pKWpxxUqwIYNcHsTIF2xq33ScxroFy9eJD09\nHYBWrVrd9TG3bt2iZ8+ePPfcc8bVZj09PTl//jxVqlTh3LlzVK5c+a7PYc3h7kLoUeXKlalSpQoe\nHh4kJyff8/5eXl4kJiYajxMTE/Ns3QIQExND3759AXUO3M8//4yrqyvh4eHmTV4IkYe1t3N6ENKT\nLkR+ERERDBo0iMmTJwPg5+dH79697aqRLhxD27Zqo7xTJ7h6FS5fhjZt4Oef4bHHtM7Ovlh1Tvq6\ndeuMC9G0atUKHx8fOnXqVOj9FUVh8ODB+Pv7G/fVg7wLUCxYsCDfVjF3svY+6VqQuI4d11bNmTOH\n0NBQ2rZtS0pKCv/73/84fPjwPR/XpEkT4uLiSEhIICMjg6VLl+ZrfP/555/Ex8cTHx/P008/zeef\nf24zDXS9lT+Jqx852zlt2bIl348tyHaSOemOFlfL2I5S11NSUujTpw/Ot1fvcnV1xUV6qO6LlHvz\ne/xxtUe9QgX1+OpVaN8eZsywbNzC2Gs9t2oj/a233mLXrl3Url2b+Ph4Nm/eTEhISKH337lzJ999\n9x1btmwhKCiIoKAgoqKiGD9+PBs3bqR27dr8+uuvjB8//q5x5X1KCMv4+++/iYyMJDY2lkmTJuHv\n739fj3NxcWH27Nl06NABf39/+vTpQ7169Zg7dy5z5861cNZCiMI4OzuzePFirdMoVLb0pAuRT+nS\npbl06ZLxePfu3ZQrV07DjITeNWkCW7dCzmDn69fhP/9Re9TF/bHqnPTg4GBiYmIIDAxk//79ODs7\n07Bhw/vqeXtQueekDxgARd1PXggt2fJcre3bt3Pq1CkGDRpEcnIy165dw9fX1yqxbfm6CPGgbKU8\nv/baa9y6dYs+/8/enYdVUbZ/AP8eFvcF4nVB8BUVRFA2QdD6WZjiVmBpidurFvqapaZZqW2illta\nbmGU+5JSWoKlqJmm+SYkQmi4gIoiiZpKIi4IPL8/Th5BQNBzzsycme/nurhi5pyZ+57puTk+Z+aZ\nJzwctWvXhhACOp1OEWPSMx5rD9fLynjSPNGjMnWtJyUlYcyYMfjjjz/Qpk0bXLp0CRs3boSPj4/J\nYpibUv7+kWkdP66/BT47W79sawts2KB/wJzaWdSYdHt7e+Tl5aFTp04YNGgQGjZsiDp16pg9LudJ\nJzKPyMhIJCUl4fjx43jppZdQUFCAwYMHY//+/XKnRkSPKDk5GTqdDh988EGp9Uq45b3Ymh/oRPfz\n9/fH3r17cezYMQgh4O7ujmrVqsmdFhHc3YG9e/Ud9cxM4M4doF8/YPVqYOBAubNTNklvd4+NjUWt\nWrXw6aefokePHnB1dcWWLVvMHpfzpDOupcdVqu+++w6xsbGoXbs2AP0D4fLy8mTOyvy01v4YV1v2\n7Nmj2DHpwppj0tUWV87Yaql1b29vzJkzBzVr1oSXlxc76A+B7d78WrTQd9SdnfVxi4qAwYOBpUul\niW+pdS5pJ33atGmwtraGra0thg0bhrFjx2LOnDlmj8sr6UTmUb16dVhZ3fszkp+fL2M2RGQKOTk5\niIiIQI8ePQAAaWlpWLZsmcxZ6fFKOlFZcXFxsLa2Rr9+/RAQEIC5c+fi7NmzcqdFZNC0KbBgAdC2\nrX5ZCGDECKDEzLt0H0nHpPv5+SE5ObnUOi8vLxw+fNjksUqOSR83Dvj0U5OHIJKMUsdqffzxx8jI\nyMCOHTswefJkLF++HAMHDsTYsWMlia/U80L0KJTSnnv06GGYzik1NRV37tyBn58fjhw5Iks+Jcek\np/27BzzP8MlDZNnMWevp6emYPn061q1bh6KiIrPEMAel/P0j87p8Wf+k90OH7q2bNQuYOFG+nMzF\nIsakL1myBFFRUTh58iS8vLwM6/Py8vDEE0+YPT6f7k5kekIIhIeH49ixY6hbty5OnDiB6dOnIyQk\nRO7UiMgId6dzmjVrFgBlTedUbMMr6UTlyczMRExMDL7++mtYW1tLcqcq0cNycNBPz9arF/Drr/p1\nkyYBN24AkZGATidreooiye3uAwcOxJYtWxAWFobvv/8eW7ZswZYtW5CUlIR169aZPT7nSWdcS4+r\nVL169UK3bt0wd+5czJ07VzMddK21P8bVFiVP5yQkvN1da+2PY3MtV1BQEJ5//nkUFxfjm2++QWJi\nIiZMmCB3WhaB7V76uHZ2wI4dQOfO916bNg146y39bfDmimtpJPlqvH79+qhfvz42bNggRbgyFHIB\ngEhVdDod/P39kZiYiMDAQLnTISITmTdvHkJDQ3Hq1Ck8/vjjhumclEDwA52ojFWrVqF169Zyp0FU\nZXXqAD/8APTte2/u9Hnz9FfUFy8GrCR9apoySTomXUolx6RPmwa8/768+RAZQ6ljtdzd3ZGRkYFm\nzZoZnvCu0+mQmpoqSXylnheiR6Gk9lxYWKiY6ZxKjklP9RoE79S1suVCZAqmrvXc3FxMnToVe/fu\nBQAEBwfjgw8+UMwdMFWhpL9/JJ3bt4EBA4Dvvru3btgw/ZPfra1lS8skLGJMutz4xTuReWzfvl3u\nFIjIxLy9vdG/f3+Eh4ejZcuWcqdTiuB0LURlvPzyy/Dy8sI333wDIQTWrFmDl156Cd9++63cqRE9\nUPXqQEwMMHQosH69ft3KlcDNm8CaNdqeoUsTNxNwnnTGtfS4SuXi4lLuj9pprf0xrrYoeTonIeGD\n47TW/jg213KdPHkSU6dORYsWLdCyZUtERkbi5MmTcqdlEdju5Y9ra6vvkEdE3FsXEwO88IL+Sru5\n4iqdJjrpWv4WhoiI6GG4uLhg4sSJSEpKwvr165GamormzZvLnRYAQMdb44jKqFmzJvbt22dY/uWX\nX1CrVi0ZMyJ6ONbWwBdfAGPG3FsXFweEhenHqWuRJsakL1oEjB4tbz5ExuBYrfLxvJCaKKk93z+d\nU3h4uGxPiy45Jj3lqbHw3bNAljyITMXUtZ6SkoIhQ4bg77//BgDY29tj1apV8PHxMVkMc1PS3z+S\njxDA5MnA7Nn31j35JPD990DduvLl9Sg4Jr0K+MU7kXmkpaXB09Oz1Lo9e/YgODhYnoSIyGhBQUEo\nKChAv3798M0336BFixZyp3QP50knKsPX1xepqam4du0aAKBevXoyZ0T0aHQ6YOZMoHZt4IMP9Ov2\n7gVCQvRPgbe3lzc/KfF2dxNT2jgPxlVHXKXq168fZs+eDSEEbty4gTFjxmDSpElyp2V2Wmt/jKst\nq1atQnJyMiZPnqysDjqkfXCc1tofx+Zarnnz5uGTTz7B0qVLsXTpUnzyySdYtmwZUlJS5E5N8dju\nlRdXp9PPyjV37r11CQnA008Dly6ZL67SaKKTzivpROaRkJCArKwsdOzYEYGBgXB0dMT//vc/udMi\nIiM0btwY48ePh7+/P/z9/TFhwgTDbbSy40NmiMpISkrC559/juzsbJw7dw7R0dHYtm0bRowYgdkl\n7xsmsiATJgCffXZvOSUFCA4Gzp+XLSVJaWJM+rp1wMCB8uZDZAyljtW6ffs23nvvPezYsQP5+fn4\n8MMP0b9/f8niK/W8ED0KpbTnPn36wMvLC0OHDjVM55SamirbdE4lx6QnPz8Vft9+IEseRKZi6lrv\n1KkTtm3bhjp16gAArl+/jl69eiE+Ph7+/v44evSoyWKZi1L+/pHyrFypf/J7cbF+2dUV2LUL+Pe/\nZU2rUsa2aV5JJ6JHFhgYiBo1auDgwYPYt28fvvrqK7z44otyp0VERlDydE46XkknKuPSpUuoVq2a\nYdnW1hYXLlxArVq1UKNGDRkzIzLesGH6C67W1vrljAygUydAIR9LZsNOuokpfZwH41pmXKVaunQp\npk+fDltbWzg6OiIuLg6hoaFyp2V2Wmt/jKst5pzOad68ebCyssKVK1cM62bOnAk3Nze0bt0aO3bs\neOD2HJOuvrhyxlZLrQ8aNAhBQUGYOnUqIiMj8fjjj2PgwIHIz88v83BXKo3t3jLi9u8PbNoE3P0u\n6uxZfUe9KjeJWGqda+IaM794JzKP9u3bAwAuXryIW7duAQCeeuopOVMiIiN9/vnn5U7nZKysrCzs\n3LkTzZo1M6xLS0tDTEwM0tLSkJ2dja5du+LEiROwsqrgGgI/0InKeP/999GjRw/s378fOp0O0dHR\nCAgIAACsW7dO5uyITKN3b/3c6c89B9y6pR+b/tRTwM6dgAXNNlhlmhiTvnUr0LOnvPkQGUOpY7Xi\n4uIwYcIE/Pnnn2jYsCHOnDkDDw8P/PHHH5LEV+p5IXoUSmvPpp7O6cUXX8T777+P3r17IykpCY89\n9hhmzpwJKysrTJw4EQDQo0cPREZGokOHDobtSs2TPmIxfL94zST5EMlFabWuBDwnVFU//ww8+yxw\n/bp+2c4O2L4dCAyUN6/7cZ70KuCYdCLzeO+99/Drr78iJCQEycnJ2L17N9asWWcgABYAACAASURB\nVCN3WkRkhHnz5v3zRfc99evXh7+/P3x9fR9pn7GxsXB2doa3t3ep9X/++WepDrmzszOys7PLbD8M\ngAuACynxcJ9/B76+vggODgZw71ZGLnNZqcspKSnIzc0FAGRmZoKIHt3dq+c9ewK5ufqfrl2BH37Q\n3wKvGkKlAAhACECI3buli7tbymCMq5m4Si3Vdu3aCSGE8Pb2FoWFhUIIIby8vCSLL9d50Vr7Y1xp\nKKXOBwwYINzc3MQbb7whxo8fL1q1aiX69u0rAgICxKxZsyrcrmvXrqJt27ZlfmJjY0VQUJD4+++/\nhRBCuLi4iL/++ksIIcTo0aPF2rVrDfuIiIgQmzZtKrVf3P0wB8Tvry8zwxGXT2vtT664csbWeq0r\nidY+z+WMrZa4hw4J8a9/GT4eRM2aQuzYYf64VWVsm9bENWZeSScyD3t7e+Tl5aFTp04YNGgQGjZs\naJgChogsU1ZWFg4dOmSo5WnTpqFXr174+eef4e/vb7g1/X47d+4sd/2RI0dw+vRp+PwzaPDcuXPw\n9/dHQkICnJyckJWVZXjvuXPn4OTkVGFuOlt+oBMREeDnB+zZo7+KnpMD3Lypvw1+40ZADc8w1sSY\n9AMHgKAgefMhMoZSx2rl5+ejRo0aKC4uxrp163Dt2jUMGjQIDg4OksTX6XRApCShiMwvEoqo89at\nWyM1NdUwpdPt27fh7e2N48ePw8/PD8nJyUbtv3nz5oYx6WlpaRg4cCASExMND47LyMgodbt9yTHp\nhyevg9eMgUbFJ5KbUj/T5cRzQo8qPR3o0gW4+32vjQ3w1VeA3DMCc570KuCVdCLzmDZtGqytrWFr\na4thw4Zh7NixmDNnjtxpEZERzD2dU8kOuKenJ/r16wdPT0/07NkTUVFRZcbDl2TFK+lEknjrrbfg\n4eEBHx8f9OnTxzDbA1DxtIlJSUnw8vKCm5sbXn/9dcP627dvIzw8HG5ubujQoQPOnDkj6bGQurm5\nAfv2AS1a6JcLC/VTtq1eLW9extJEJ93aWrpYljj3IOMqP65SlTen8datW2XIRGKZjMu46vX+++/j\niy++QP369WFvb4/o6GhMmTIFtWvXNsl0TqdOncJjjz1mWH7nnXeQkZGBY8eOoXv37g/cVsrb3bX2\nOcP5oqmkbt264Y8//sDvv/+OVq1aYebMmQBKT5sYHx+PV1991XC1cNSoUVi2bBnS09ORnp6O+Ph4\nAMCyZcvg4OCA9PR0jB8/vsIhM3Jgu1dH3GbNgL17gdat9cvFxcDQoUB0tOXWuSa+kuaVdCLTWrJk\nCaKionDy5El4eXkZ1ufl5eGJJ56QNBcxRfrb4/bs2WN4ci/jMq6p6CIrvoIstfbt26N9+/Zyp1GG\nVTV+oBNJISQkxPB7UFAQNm3aBEA/U8OAAQNga2sLFxcXuLq6IiEhAc2aNUNeXh4C/5kHa8iQIdi8\neTN69OiBuLg4TJ06FQDQt29fjB49WvoDItVzctJPz9atG/D77/p1r7wCvPoqIMNHutE08Wkn5ZV0\nOf5hx7jqj6s0AwcORM+ePTFp0iTMnj3b8C163bp1JRuPLiettT/GJaWQ8kq61tqfnO1ei8dsSZYv\nX44BAwYAqHjaRFtbWzg7OxvWOzk5GaZTzM7ORtOmTQEANjY2qF+/Pq5cuVLqjhoAGDZsGFxcXAAA\ndnZ2mphq8S4p4wcHB6v6eH/6CXj88T04fhwAghEVFYzbt/dg8GDLmmpREw+OO34caNVK3nyIjMEH\nqpSP54XUhO25fCUfHHdi8Xa0eq2brPkQGUsptR4SEoKcnJwy62fMmIHQfx6P/dFHH+HQoUOGK+lj\nxoxBhw4dMGjQIADA8OHD0bNnT7i4uGDSpEmGWR727duHOXPmYMuWLfDy8sL27dvRpEkTAICrqysS\nExNLddKVck5IHa5d0z/pfd++e+smTwY++gh4wGNPTIoPjqsCjklnXEuPS8qitfbHuKQU1hLe7q61\n9sexudqzc+dOHD58uMzP3Q76ypUrsXXr1lLPoihv2kRnZ2c4OTnh3LlzZdbf3ebs2bMAgMLCQvz9\n999lrqLLhe1enXHr1QO2bdNPzwbo486cCYwbp59V3RKwk05EREQWgfOkE0kjPj4eH3/8MWJjY1Gj\nRg3D+rCwMGzYsAEFBQU4ffo00tPTERgYiMaNG6NevXpISEiAEAJr1qxB7969DdusWrUKALBx40Z0\n6dJFlmMibaldG9iyBejY8d66hQuBkSP1D5ZTOsXf7v7yyy/jhx9+QMOGDXH48GEAwJUrVxAeHo4z\nZ87AxcUFX3/9Nezs7EptV/J297NngX+GwhBZJDXeBhYfH49x48ahqKgIw4cPL/O013Xr1mHOnDkQ\nQqBu3bpYsmQJvL29S71HjeeFtIvtuXwlb3fPXLcfLgMflzUfImNZQq27ubmhoKDAcMW7Y8eOiIqK\nAqC/HX758uWwsbHBggULDLMyJCUlYdiwYbh58yZ69eqFhQsXAtBPwfaf//wHycnJcHBwwIYNGwxj\nz++yhHNClqmgABg8GPjmm3vrBg8GVqww78PFjW3Tiu+k79u3D3Xq1MGQIUMMnfS3334b//rXv/D2\n229j9uzZuHr1KmbNmlVqu5Kd9Oxs4J9hMEQWSW0fXkVFRXB3d8ePP/4IJycntG/fHuvXr4eHh4fh\nPb/++is8PT1Rv359xMfHIzIyEgcOHCi1H7WdF9I2tufyleykn92YgH/3DZQ1HyJjsdbL4jkhcyos\nBF5+GViz5t66vn2Br74CqlUzT0zVj0nv1KkT7O3tS62Li4vD0KFDAQBDhw7F5s2bH7gPjklnXEuP\nqzaJiYlwdXWFi4sLbG1t0b9/f8TGxpZ6T8eOHVG/fn0A+ulfSo51k5vW2h/jklJYVbOVLJbW2h/H\n5pIWsd1rI66NDbBypf5W97s2bQL69AFu3ZIlrUpZ5OCuCxcuoFGjRgCARo0a4cKFCxW8cxgAF8yb\nBzRpIs1UDneZc2qB8pZTUlIkjcfjNW+8+fPnIyUlpcztYGpRcjoWQD+FS0JCQoXvX7ZsGXr16lXu\na3JM2XIX2z2P15hltde5OVhXt8h/thARkcysrIAlS4BatYBPP9Wv++EH/VPgY2P1Y9iVRPG3uwP6\nueZCQ0MNt7vb29vj6tWrhtcfe+wxXLlypdQ2JW93v3IFuO9iPJFFUdttYJs2bUJ8fDy+/PJLAMDa\ntWuRkJCARYsWlXnv7t278dprr2H//v1l7qpR23khbWN7Ll/J290v7E5Do2CPB76fSOlY62XxnJBU\nhAA++AD48MN76554Qt9h/+cGTpNQ/e3u5WnUqJFhXsfz58+jYcOGD3w/n+5OpCz3T+GSlZVlmKql\npNTUVIwYMQJxcXFlOuhEpD1WEk7BRkRE6qPTAdOn6+dMv2v/fv10bfdd85WVRXbSS07lsGrVKjz3\n3HMPfD/HpDOupcdVm4CAAKSnpyMzMxMFBQWIiYlBWFhYqfecPXsWffr0wdq1a+Hq6ipTpuXTWvtj\nXFIKKW9311r7k7Pda/GYSRnY7rUb95137t32DgAHDwLBwcDFi5KkVSnFd9IHDBiAxx9/HMePH0fT\npk2xYsUKTJo0CTt37kSrVq3w008/YdKkSQ/cB6+kEymLjY0NFi9ejO7du8PT0xPh4eHw8PBAdHQ0\noqOjAQDTpk3D1atXMWrUKPj5+SEwkE90JtI6K45JJyIiExk3DoiO1l9dB4DDh4Enn9TPDCY3ixiT\n/ihKjkkvKABspXsgLJHJcaxW+XheSE3YnstXckz69RN/oo6bo6z5EBmLtV4WzwnJae1aYOhQoLhY\nv9yiBbBrF2DMc101OSb9YfFKOhERkeXjmHQiIjK1wYOBmBjA5p+PmFOngE6dgBMn5MtJE510KwmP\nUmnjLRhXHXFJWbTW/hiXlIJj0tUXV87YrHViu2fcu154Adi8GaheXb987pz+1vcjR8yX24OovpNu\nwy/diYiIVMGmBj/UiYjIPJ55Rj8VW61a+uULF/QPkzt0SPpcVD8mvXp14NYtubMhMg7HapWP54XU\nhO25fCXHpIv8G9DVqilrPkTGYq2XxXNCSvLLL0CvXkBenn65fn1g2zagY8eq74Nj0ivB8ehERETq\noLPllXQiIjKv//s//YPj7O31y3//DYSEAFLesc9OuokpfbwF41pmXFIWrbU/xiXFkPBDXWvtj2Nz\nSYvY7hm3Iu3b6zvlDRrol/PzgZ49gfh4k6X2QOykExERkeIVwUraJ8ESEZGmeXsDe/cCTZrol2/d\nAsLC9A+YMzfVj0l3cAD++kvubIiMw7Fa5eN5ITVhey7f3THpt1EN1cVtudMhMhprvSyeE1KykyeB\nLl2AM2f0y9bWwJo1wIABFW/DMemV4JV0IiIiy3cHtnKnQEREGtSyJbBvH+Dmpl8uKgIGDQKWLzdf\nTHbSTczSxlswrmXEJWXRWvtjXFKCIp20D43TWvvj2FzSIrZ7xq2qpk31t763aaNfFgKIiAAWLzbJ\n7stgJ52IiIgUrxB8sjsREcmncWP9w+T8/O6tGzMGmDPH9LFUPya9WTMgM1PubIiMw7Fa5eN5ITVh\ney7f3THpF60aoWFRjtzpEBmNtV4WzwlZktxc/ZPeDxy4t27KFP2PTqdf5pj0SvBKOhERkeWT+nZ3\nIiKi8tjZATt2AMHB99ZNnQpMnKi/Dd4UVP+J96BO+vWC6zhx+QTO/n0WubdycePODdy8cxM3C29C\nCAEBYfgGRODef+9fV9KZlDNo5tvM9AdSCcZVd1xSlj179iC45F9mxmVceqDIyEgsXboUDf6ZcHbG\njBno2bMnAGDmzJlYvnw5rK2tsXDhQnTr1q3cfcgxJl1L7U/Odq/FYyZlYLtn3EdVty7www9Anz7A\n9u36dR9/DNy4ASxcaPz+NddJLywuxPrD6xGdFI0D5w6gSBSZNmAmgGum3SXjMi4RkSXT6XR44403\n8MYbb5Ran5aWhpiYGKSlpSE7Oxtdu3bFiRMnYFXOfOi8kk5EREpSqxYQGwv0739v7vTPPtN31I2l\n+jHpbdoAR47o1/2Z9yfCN4bjl7O/yJob0UOLBMdqlYNj2EhN1Nyep06dijp16mDChAml1s+cORNW\nVlaYOHEiAKBHjx6IjIxEhw4dDO+5Oyb9VDV3tLh9TMq0icxCzbX+qHhOyJLduQMMGQJs2FByrXFt\nWvVfS9+9kn4p/xI6reiEU1dPGV7TQYdWDq3Q8rGW+Fetf6GWbS3UtKmJGjY1YG1lbXiP7p8nAOig\nq3AdkTl9gA/kToGIyCiLFi3C6tWrERAQgHnz5sHOzg5//vlnqQ65s7MzsrOzy2w7DEC9oit4LDIS\ndnZ28PX1Ndy+eHd6HS5zWanLKSkpyM3NBQBk8mnGRKpjawusXau/sm6yudOFSgEQgBB+fvrl5zc8\nLxAJgUgIq6lW4p1d74gL1y+YPO7u3btNvk/GZVwVl6pR5DovWmt/jCsNS6/zrl27irZt25b5iY2N\nFRcuXBDFxcWiuLhYvPvuu+Lll18WQggxevRosXbtWsM+IiIixKZNm0rtF/rn8IhjNXwkPR6ttT+5\n4soZm7WuHFr7PJczNuOaT1GREK+9JsQ/j48zal+qv5JuYwPsPr0b3x37zrBu44sb8bzH8zJmRURE\npC47d+6s0vuGDx+O0NBQAICTkxOysrIMr507dw5OTk5lttkd+DZ0jo3hbppUiYiITM7KCli0SH9F\n/eOPjduX6sekd+gA1HutO3ac3AEAGOY7DCt6r5A3OaKHxLFa5eN5ITVRc3s+f/48HB0dAQCffvop\nfvvtN3z11VdIS0vDwIEDkZiYaHhwXEZGhmFIGaDu80LaxDZdFs8JqYkQgJUVx6Q/0J3aZ7DzpP7b\nfR10+OBJju0lIiKS0sSJE5GSkgKdTofmzZsjOjoaAODp6Yl+/frB09MTNjY2iIqKKtVBJyIisjSm\n+BgrO8eJylxp/I1hPvOQliFobt/crPHuPjBEaoyr7rikLFprf4xLxlq9ejVSU1Px+++/Y/PmzWjU\nqJHhtXfeeQcZGRk4duwYunfvLmOWpWmt/cnZ7rV4zKQMbPeMq1Sq76RfbfC94ffwNuEyZkJERERE\npHxvvfUWPDw84OPjgz59+uDvv/8GoH86fc2aNeHn5wc/Pz+8+uqrhm2SkpLg5eUFNzc3vP7664b1\nt2/fRnh4ONzc3NChQwecOXNG8uMhsjTqHpNuex26d+pD6IoAAOcnnEfjOo1lzozo4XGsVvl4XkhN\n2J7Lx/NCamMJbXrnzp3o0qULrKysMGnSJADArFmzkJmZidDQUBw+fLjMNoGBgVi8eDECAwPRq1cv\njB07Fj169EBUVBSOHDmCqKgoxMTE4LvvvsOG0hNKW8Q5IXoYxrZpdV9Jb5Jk6KB7NvBkB52IiIiI\nqBIhISGwstJ3E4KCgnDu3LkHvv/8+fPIy8tDYGAgAGDIkCHYvHkzACAuLg5Dhw4FAPTt2xe7du0y\nY+ZE6qDuTrpTguHXDs4dJAmptfEWjEtapLX2x7ikRVprfxybSxVZvnw5evXqZVg+ffo0/Pz8EBwc\njF9++QUAkJ2dDWdnZ8N7nJyckJ2dbXitadOmAAAbGxvUr18fV65ckfAIKsZ2z7hKpe6nuzvf66QH\nOQXJmAgRERERkXKEhIQgJyenzPoZM2YgNDQUAPDRRx+hWrVqGDhwIACgSZMmyMrKgr29PQ4dOoTn\nnnsOf/zxh0nyGTZsGFxcXAAAdnZ28PX1RXBwMIB7HS1TL99lrv0/aDklJUXSeHIvq/14U1JSkJub\nC0D/7AZjqXtM+htOQD39t3gpI1Pg09hH5qyIHg3HapWP54XUhO25fDwvpDaW0qZXrlyJL7/8Ert2\n7UKNGjXKfU/nzp0xb948ODo64umnn8bRo0cBAOvXr8fevXuxZMkS9OjRA5GRkejQoQMKCwvh6OiI\nS5culdqPpZwToqrimPQH+aeDXtu2Nto0bCNzMkREREREyhcfH4+PP/4YsbGxpTrof/31F4qK9M97\nOnXqFNLT09GiRQs4OjqiXr16SEhIgBACa9asQe/evQEAYWFhWLVqFQBg48aN6NKli/QHRGRh1N1J\n/4d/E3/YWElzZ7/WxlswLmmR1tof45IWaa39cWwulTRmzBhcv34dISEhpaZa+/nnn+Hj4wM/Pz+8\n+OKLiI6Ohp2dHQAgKioKw4cPh5ubG1xdXdGjRw8AQEREBC5fvgw3NzfMnz8fs2bNku247sd2z7hK\npYlOekCTAMlipaSkSBaLcbUTl5RFa+2PcUmLtNb+5Gz3WjxmpUtPT8eZM2eQnJyM5ORkREVFAdA/\nnf3IkSNITk5GUlISnnnmGcM2/v7+OHz4MDIyMrBw4ULD+urVq+Prr79Geno6Dhw4YBh3rgRs94yr\nVBbdSY+Pj0fr1q3h5uaG2bNnV/i+do3bSZbT3QcGSI1x1R1XjapSv2PHjoWbmxt8fHyQnJwscYYV\n01r7Y1zSIq21PznbvRaPmZSB7Z5xlcpiO+lFRUUYPXo04uPjkZaWhvXr1xseVnE//yb+EmdHRA9S\nlfrdunUrMjIykJ6eji+++AKjRo2SKVsiIiIiIulYbCc9MTERrq6ucHFxga2tLfr374/Y2Ngy77Mp\nroNWDq0ky8sUj9xnXMZVu6rUb1xcHIYOHQoACAoKQm5uLi5cuCBHumVorf0xLmmR1tqfnO1ei8dM\nysB2z7hKZbFTsG3cuBHbt2/Hl19+CQBYu3YtEhISsGjRIgD/TMFGpCIWWqrlqqx+ASA0NBSTJ0/G\n448/DgDo2rUrZs+eDX//e3fGsM5JbdRU56bCOic1Yq2XxjonNTKmzqV55LkZVFbM/ONHpFxV/TC+\nv47v3451TqR+rHMi9WOdE5Vmsbe7Ozk5ISsry7CclZUFZ2dnGTMioqqqSv3e/55z587ByclJshyJ\niIiIiORgsZ30gIAApKenIzMzEwUFBYiJiUFYWJjcaRFRFVSlfsPCwrB69WoAwIEDB2BnZ4dGjRrJ\nkS4RERERkWQs9nZ3GxsbLF68GN27d0dRUREiIiLg4eEhd1pEVAUV1W90dDQAYOTIkejVqxe2bt0K\nV1dX1K5dGytWrJA5ayIiIiIiCQgV2rZtm3B3dxeurq5i1qxZZo3VrFkz4eXlJXx9fUX79u2FEEJc\nvnxZdO3aVbi5uYmQkBBx9epVo+O89NJLomHDhqJt27aGdQ+KM2PGDOHq6irc3d3F9u3bTRp3ypQp\nwsnJSfj6+gpfX1+xdetWk8c9e/asCA4OFp6enqJNmzZiwYIFQgjzH3NFcc19zDdv3hSBgYHCx8dH\neHh4iEmTJklyvJaMdc46Z52rH+ucdc46Vz/WOeucdV6W6jrphYWFomXLluL06dOioKBA+Pj4iLS0\nNLPFc3FxEZcvXy617q233hKzZ88WQggxa9YsMXHiRKPj7N27Vxw6dKhU0VUU548//hA+Pj6ioKBA\nnD59WrRs2VIUFRWZLG5kZKSYN29emfeaMu758+dFcnKyEEKIvLw80apVK5GWlmb2Y64orhTHnJ+f\nL4QQ4s6dOyIoKEjs27dPkv/Hloh1zjpnnasf65x1zjpXP9Y565x1Xj6LHZNekarOn25K4r4nUpac\n33no0KHYvHmz0TE6deoEe3v7KsWJjY3FgAEDYGtrCxcXF7i6uiIxMdFkcYHyn8JpyriNGzeGr68v\nAKBOnTrw8PBAdna22Y+5orhSHHOtWrUAAAUFBSgqKoK9vb0k/48tEeucdc46Vz/WOeucda5+rHPW\nOeu8fKrrpGdnZ6Np06aGZWdnZ8P/LHPQ6XTo2rUrAgICDHM+X7hwwfCAq0aNGuHChQtmiV1RnD//\n/LPUk7LNcQ4WLVoEHx8fREREIDc316xxMzMzkZycjKCgIEmP+W7cDh06ADD/MRcXF8PX1xeNGjVC\n586d0aZNG1n/HysZ65x1bqrYrHPlYp2zzk0Vm3WuXKxz1rmpYqutzlXXSa/q/Mumsn//fiQnJ2Pb\ntm347LPPsG/fvjL5SJFTZXFMmcOoUaNw+vRppKSkwNHRERMmTDBb3OvXr6Nv375YsGAB6tatW2bf\n5jrm69ev44UXXsCCBQtQp04dSY7ZysoKKSkpOHfuHPbu3Yvdu3eX2a9U/4+VjnVe8eumwjpnncuN\ndV7x66bCOmedy411XvHrpsI6t8w6V10nXer50x0dHQEADRo0wPPPP4/ExEQ0atQIOTk5AIDz58+j\nYcOGZoldURxzzy/dsGFDQ8MbPny44XYNU8e9c+cO+vbti//85z947rnnAEhzzHfjDh482BBXqmMG\ngPr16+OZZ55BUlKSbP+PlY51zjo3NjbrXPlY56xzY2OzzpWPdc46Nza2WutcdZ10KedPv3HjBvLy\n8gAA+fn52LFjB7y8vBAWFoZVq1YBAFatWmVoMKZWUZywsDBs2LABBQUFOH36NNLT0xEYGGiyuOfP\nnzf8/t1338HLy8vkcYUQiIiIgKenJ8aNG2dYb+5jriiuuY/5r7/+MtyKc/PmTezcuRN+fn6y/T9W\nOtY565x1rn6sc9Y561z9WOesc9Z5xQenOlu3bhWtWrUSLVu2FDNmzDBbnFOnTgkfHx/h4+Mj2rRp\nY4h1+fJl0aVLF5NO5dC/f3/h6OgobG1thbOzs1i+fPkD43z00UeiZcuWwt3dXcTHx5ss7rJly8R/\n/vMf4eXlJby9vUXv3r1FTk6OyePu27dP6HQ64ePjY5g+Ydu2bWY/5vLibt261ezHnJqaKvz8/ISP\nj4/w8vISc+bMEUI8uC2Z6lxbKtY565x1rn6sc9Y561z9WOesc9Z5WTohynn0HRERERERERFJTnW3\nuxMRERERERFZKnbSiYiIiIiIiBSCnXQiIiIiIiIihWAnnYiIiIiIiEgh2Emncv39999YsmQJAP00\nBi+++KLMGRGRqbHOidSPdU6kfqxz9eHT3alcmZmZCA0NxeHDh+VOhYjMhHVOpH6scyL1Y52rj43c\nCZAyTZo0CSdPnoSfnx/c3Nxw9OhRHD58GCtXrsTmzZtx48YNpKenY8KECbh16xa++uorVK9eHVu3\nboW9vT1OnjyJ0aNH49KlS6hVqxa+/PJLuLu7y31YRFQC65xI/VjnROrHOlehR5rBnVQvMzNTtG3b\ntszvK1asEK6uruL69evi0qVLol69eiI6OloIIcT48ePF/PnzhRBCPP300yI9PV0IIcSBAwfE008/\nLcNRENGDsM6J1I91TqR+rHP14ZV0KpcoMQpC3DcionPnzqhduzZq164NOzs7hIaGAgC8vLyQmpqK\n/Px8/O9//ys1HqagoECaxImoyljnROrHOidSP9a5+rCTTg+tevXqht+trKwMy1ZWVigsLERxcTHs\n7e2RnJwsV4pEZCTWOZH6sc6J1I91bpn4dHcqV926dZGXl/dQ29z95q5u3bpo3rw5Nm7caFifmppq\n8hyJyDiscyL1Y50TqR/rXH3YSadyOTg44IknnoCXlxfefvtt6HQ6AIBOpzP8fne55O93l9etW4dl\ny5bB19cXbdu2RVxcnLQHQESVYp0TqR/rnEj9WOfqwynYiIiIiIiIiBSCV9KJiIiIiIiIFIKddCIi\nIiIiIiKFYCediIiIiIiISCHYSSciIiIiIiJSCHbSiYiIiIiIiBSCnXQiIiIiIiIihWAnnYiIiIiI\niEgh2EknIiIiIiIiUghZO+nx8fFo3bo13NzcMHv27DKvr1u3Dj4+PvD29sYTTzyB1NTUKm9LRMrA\nOidSP9Y5ERGRCQmZFBYWipYtW4rTp0+LgoIC4ePjI9LS0kq953//+5/Izc0VQgixbds2ERQUVOVt\niUh+rHMi9WOdExERmZZsV9ITExPh6uoKFxcX2Nraon///oiNjS31no4dO6J+/foAgKCgIJw7d67K\n2xKR/FjnROrHOiciIjItG7kCZ2dno2nTpoZlZ2dnJCQkVPj+ZcuWoVev2TIIMQAAIABJREFUXlXe\nVqfTmThjInkJIeRO4aGxzokeDuucdU7aYIm1TkTSke1K+sN86O7evRvLly83jFWr6rZCCMl/pkyZ\nwriMa/IfS8U6Z1zGrfqPpWKdM66lxmatE5FSyXYl3cnJCVlZWYblrKwsODs7l3lfamoqRowYgfj4\neNjb2z/UtkQkL9Y5kfqxzomIiExLtivpAQEBSE9PR2ZmJgoKChATE4OwsLBS7zl79iz69OmDtWvX\nwtXV9aG2lUtmZibjMi79g3XOuIyrfqxzxrXU2Kx1IlIq2a6k29jYYPHixejevTuKiooQEREBDw8P\nREdHAwBGjhyJadOm4erVqxg1ahQAwNbWFomJiRVuqwS+vr6My7j0D9Y54zKu+rHOGddSY7PWiUip\ndEKlg2N0Oh3H/ZBqsD2Xj+eF1ITtuXw8L6Q2bNNEVBnZbncnIiIiIiIiotLYSTexPXv2MC7jkspp\nrf0xLmmR1tqfnO1ei8dMRPQg7KQTERERERERKQTHpBNZALbn8vG8kJqwPZeP54XUhm2aiCrDK+lE\nRERERERECsFOuolpbVwV45IWaa39MS5pkdbaH8ekExEpBzvpRERERERERArBMelEFoDtuXw8L6Qm\nbM/l43khtWGbJqLK8Eo6ERERERERkUKwk25iWhtXxbikRVprf4xLWqS19scx6UREysFOOhERERER\nEZFCcEw6kQVgey4fzwupCdtz+XheSG3YpomoMrySTkRERERERKQQ7KSbmNbGVTEuaZHW2h/jkhZp\nrf1xTDoRkXKwk05ERERERESkEByTTmQB2J7Lx/NCasL2XD6eF1IbtmkiqoysV9Lj4+PRunVruLm5\nYfbs2WVeP3bsGDp27IgaNWpg3rx5pV5zcXGBt7c3/Pz8EBgYKFXKRPSQWOdE6sc6JyIiMh3ZOulF\nRUUYPXo04uPjkZaWhvXr1+Po0aOl3uPg4IBFixbhzTffLLO9TqfDnj17kJycjMTERKnSrpTWxlUx\nLj0I65xxGVf9WOeMa6mxWetEpFSyddITExPh6uoKFxcX2Nraon///oiNjS31ngYNGiAgIAC2trbl\n7oO3ChEpG+ucSP1Y50RERKZlI1fg7OxsNG3a1LDs7OyMhISEKm+v0+nQtWtXWFtbY+TIkRgxYkSZ\n9wwbNgwuLi4AADs7O/j6+iI4OBjAvW9P1bJ8d51S8uHxGrc8f/58pKSkGNqvpWKdm3b57jql5MPj\nNW6Zda7HOi+9fHedUvKRarnksUsVPzg4WJJ4KSkpyM3NBQBkZmaCiKgysj04btOmTYiPj8eXX34J\nAFi7di0SEhKwaNGiMu+dOnUq6tSpgwkTJhjWnT9/Ho6Ojrh06RJCQkKwaNEidOrUyfA6H8pBamKp\n7Zl1TlR1ltqeWedED4dtmogqY1WVNx09ehTbtm3D9u3bcezYMZMEdnJyQlZWlmE5KysLzs7OVd7e\n0dERgP4Wuueff14x49ju/0aYcRlXy1jnjMu46sc6Z1xLjc1aJyKlqrCTfvr0aYwdOxaurq545ZVX\nsHr1aqxYsQIjR45Ey5Yt8frrrxt1y05AQADS09ORmZmJgoICxMTEICwsrNz33v9t440bN5CXlwcA\nyM/Px44dO+Dl5fXIuRCRebDOidSPdU5ERGRaFd7u3q9fP4wYMQLBwcFlHvRy584d7N69G0uXLsXX\nX3/9yMG3bduGcePGoaioCBEREZg8eTKio6MBACNHjkROTg7at2+Pa9euwcrKCnXr1kVaWhouXryI\nPn36AAAKCwsxaNAgTJ48ufSB8VYiUhFLbs+sc6KqseT2zDonqjq2aSKqjGxj0s2NfwBJTdiey8fz\nQmrC9lw+nhdSG7ZpIqpMhbe7nzhxAr1790abNm0wYMAAZGdnS5mXxdLauCrGJS3SWvtjXNIirbU/\njkknIlKOCjvpL7/8Mp599lls2rQJ7dq1w5gxY6TMi4iIiIiIiEhzKrzd3dfXFykpKYZlPz8/JCcn\nS5aYsXgrEakJ23P5eF5ITdiey8fzQmrDNk1ElbGp6IVbt27h0KFDAPRPY7158yYOHToEIQR0Oh3a\ntWsnWZJEREREREREWlDh7e6NGzfGhAkTMGHCBLz55puG5TfffBMTJkyQMkeLorVxVYxLWqS19se4\npEVaa38ck05EpBwVXknnHy4iIiIiIiIiaT1wCrbLly9j3bp1OHbsGHQ6HTw8PDBgwAA4ODhImeMj\n4XgfUhO25/LxvJCasD2Xj+eF1IZtmogqU+Ht7kePHkXbtm2RlJQEd3d3uLq6IjExEW3btsWxY8ek\nzJGIiIiIiIhIEyrspL/33ntYsGABVq1ahddffx3jx4/H6tWrsXjxYrz77rtS5mhRtDauinFJi7TW\n/hiXtEhr7Y9j0omIlKPCTvrhw4fRr1+/Muv79u2Lw4cPmzUpIiIiIiIiIi2qcEz6g+ZFt4Q50zne\nh9TEnO354sWL+Oabb7B3715kZmZCp9OhWbNmePLJJ/Hiiy+iYcOGZolrCqxzUhO25/LxvJDasE0T\nUWUqfLr7pUuX8Mknn5T7R+TSpUtmTYqIpBEREYGTJ0+iZ8+eeOWVV+Do6AghBM6fP4/ExET069cP\nrq6uWLp0qdypEhERERFpQoW3uw8fPhx5eXm4fv16qZ+8vDyMGDFCyhwtitbGVTGuZRs7diz27NmD\niRMnonPnzmjdujU8PDzw9NNPY9KkSdizZw/Gjh0rd5qKo7X2x7ikRVprfxyTTkSkHBVeSY+MjJQw\nDSKSg4+PD1JSUpCRkYE2bdrAw8OjzHu8vb1lyIyIzCE/Px9ZWVnQ6XRwdnZG7dq15U6JiIiI7lPh\nmPQjR47g5MmT6N27NwBg3Lhx+Pvvv6HT6TB69Gi0a9dO0kQfFsf7kJqYqz1PmzYNa9euhb+/Pw4c\nOIDJkyfjv//9r8njmAvrnNTEXO05Ly8PX375JTZs2IC//voLjRo1ghACFy5cgIODAwYNGoQRI0ag\nTp06Jo9tCqxzUhu2aSKqTIWd9GeffRaTJ0/GE088AQDw9PTE9OnTkZ+fj2+//RabN2+WNNGHxT+A\npCbmas+enp44ePAgatWqhcuXL6N79+44ePCgyeOYC+uc1MRc7blLly7o378/QkND0bhx41Kv5eTk\nIC4uDjExMdi1a5fJY5sC65zUhm2aiCpT4Zj08+fPGzroAFC3bl307dsXQ4YMMdmD4+Lj49G6dWu4\nublh9uzZZV4/duwYOnbsiBo1amDevHkPta1ctDauinEtW/Xq1VGrVi0AgIODA4qLi00eg3XOuIwr\nr127dmHEiBFlOugA0LhxY/z3v/81uoPOOmdcS4yttlonIvWocEx6Xl5eqeWEhATD7xcvXjQ6cFFR\nEUaPHo0ff/wRTk5OaN++PcLCwkqNiXVwcMCiRYvKXLWvyrZEVLlTp04hNDS03GWdToe4uDij9s86\nJ5LfhQsXMGPGDGRkZMDb2xuTJ09GvXr1TLZ/1jkREZFpVdhJb9KkCQ4cOIAOHTqUWv/rr7/CycnJ\n6MCJiYlwdXWFi4sLAKB///6IjY0t9cHcoEEDNGjQAD/88MNDbyuX4OBgxmVcixEbG1tqecKECYbf\ndTqd0ftnnTMu48pvyJAhCAgIwJgxY/D9999j7NixWLlypcn2zzpnXEuNrbZaJyL1qLCTPmfOHISH\nh2PYsGFo164dhBA4dOgQVq5ciZiYGKMDZ2dno2nTpoZlZ2fnUlfrTbHtsGHDDB/8dnZ28PX1NfxB\nvnuLE5e5rMTl+fPnIyUlxdB+zaXkP1DuDmNp0KCByfbPOucyl+Wv85ycHHz00UcAgB49esDPz8+k\n+2edc5nLD15OSUlBbm4uACAzMxNERJUSD5CTkyPee+890adPH9GnTx/x/vvvi5ycnAdtUmUbN24U\nw4cPNyyvWbNGjB49utz3RkZGirlz5z7UtpUcmtns3r2bcRnX5MzVnouLi8WUKVOEg4ODsLOzE3Z2\ndsLBwUFERkaaZP+sc8Zl3KozV3v28vISly9fFpcvXxZ//fVXqeXLly8bvX8p6rzn2p5iwMYB4kbB\nDaPzrSqttT+54soZW221TkTqUeGVdABo1KgRpk+fbpYvB5ycnJCVlWVYzsrKgrOzs9m3JaJ7Pv30\nU+zfvx+//fYbmjdvDkA/Lv2VV17BJ598gjfeeMOo/bPOieR37do1+Pv7G5aFEIZlnU6HU6dOGbV/\nKep8W8Y2AICbgxumBk81Kl8iIiKlq3AKNnMrLCyEu7s7du3ahSZNmiAwMBDr168vdxxaZGQk6tat\naxgvW5VtOb0FqYm52rOvry927txZ5hb3S5cuISQkBCkpKUbtn3VOVHWW2p6lqHNE6n93e8wNJ8ac\nkOKwiMzGUmudiKTzwCvpZg1sY4PFixeje/fuKCoqQkREBDw8PBAdHQ0AGDlyJHJyctC+fXtcu3YN\nVlZWWLBgAdLS0lCnTp1ytyWih1NYWFjuGPQGDRqgsLDQ6P2zzomUobCwEFu3bsXx48cBAB4eHujR\nowdsbIz/Z4CUdV5YbPzfJSIiIqV7qCvpN2/exJ07d0w6dYu5yPUt5Z49ewwPC2FcxjUVc7VnPz8/\nJCcnP/RrSsE6Z1w1xTVXe87OzsbTTz+Nxo0bl3oQ7IULF7B79240adLE5DFNqeSV9Kb1muLs+LOS\nxNVa+5Mrrpyx1VbrRKQeVf4KfenSpdi4cSOKiooQEBCAmTNnmjMvIpJAamoq6tatW+5rN2/elDgb\nIjKHd955B6NGjcK4ceNKrV+4cCEmT56MVatWyZTZw+OVdCIi0oIKr6THxsaid+/ehuXw8HDD1Gve\n3t5ITU2VJsNHxG8pSU3YnsvH80JqYq727O7ubrjNvSQhBNzd3XHihLLHeJe8kt6gVgNcfOuirPkQ\nGYufXURUGauKXjh8+DDCwsIMD47y9vZGREQEhg8fjrZt20qWIBFJJzs7G2fPnsXZs2dNMiadiORX\ns2bNctfrdDrUqlVL4myMUySK5E6BiIjI7CrspL/33nuIjo7GZ599huHDhyMiIgKTJk3C2LFj8dVX\nX0mZo0XZs2cP4zKuxZgxYwamTr03nVHHjh3xzDPPICQkBB9//LGMmSmb1tof41q2a9eu4dtvv8Wm\nTZsMP3eXr127Jnd6D0XK29211v7kbPdaPGYiogd54Jj0OnXqYP78+UhPT8d///tfBAQE4O2335Yq\nNyIys2+++Qb79u0zLDs4OCA5ORlFRUV48sknMXnyZBmzIyJTePLJJ7Fly5ZyX3vqqackzsY4HJNO\nRERaUOGY9HfffRe//fYb7ty5g7CwMIwfPx6xsbFYsGABhg0bhiFDhkid60PheB9SE6me7r5y5UoM\nGzYMANCuXTscOnTI5DFNiXVOasL2XL6SY9KrWVfD7fduy5oPkbFY60RUmQqvpH///ff4/fffUVxc\nDH9/f4wfPx69e/dGr169EBUVJWWORGQm+fn5KCgoQLVq1QDA0EG/ffs28vLyZMyMiEwlKysLmZmZ\n6NSpEwBg3rx5uH79OnQ6HQYOHAhXV1eZM6w6XkknIiItqHBMetu2bTFixAgMGTKk1ByStra2eP31\n16XIzSJpbVwV41q2F154Aa+88gry8/MN665fv46RI0fihRdekDEzZdNa+2Ncy/bWW28hNzfXsPzF\nF1+gTp06AIApU6bIldYjKRbFksXSWvvjmHQiIuWo8Er6unXrkJqaimrVqqF169ZS5kREEpk2bRre\ne+89NGvWDP/+978BAGfPnkVERASmT58uc3ZEZArHjx9HaGioYblmzZqYMGECAOD//u//5EqLiIiI\nKlDhmPSff/650gfK7N69G507dzZLYsbieB9SE3O35xs3biAjIwMA4OrqajHTMrHOSU3M1Z49PDxw\n9OhRw/Lly5fh4OAAAGjdujWOHTtm8pimVHJMOgCIKax5smz87CKiylR4JX3Lli1466230LVrVwQE\nBMDR0RHFxcXIycnBwYMH8eOPP6Jz586K7aQTUdXVqlUL3t7ecqdBRGZQr149HD9+HO7u7gBg6KAf\nO3YM9erVkzM1IiIiKkeFY9Lnzp2LXbt2wdPTEzt37sT06dPx0Ucf4ccff0Tbtm2xe/duzJkzR8pc\nLYLWxlUxLmmR1tof41q2qVOnIjQ0FKtWrcLhw4dx+PBhrFy5EqGhoYiMjJQ7PcXSWvvjmHQiIuV4\n4DzpdevWxeDBgzF48GCp8iEiIiIT6tGjB7799lvMnj0bCxcuBAC0adMG3333Hdq2bStzdkRERHS/\nCsekWzqO9yE1kao9X7x4EQsXLsSNGzcwatQouLm5mT2mMVjnpCZsz+XjmHRSG9Y6EVWmwtvdiUh7\nJkyYgG7duuH555/HwIED5U6HiIiIiEhz2Ek3Ma2Nq2Jcy9a9e3fs3bvXsFxQUIDmzZujefPmuH37\ntoyZKZvW2h/jkhZprf1xTDoRkXJU2knPz8/H9OnTMWLECABAeno6vv/+e5MEj4+PR+vWreHm5obZ\ns2eX+56xY8fCzc0NPj4+SE5ONqx3cXGBt7c3/Pz8EBgYaJJ8iLQmJiYGcXFx6N+/P06ePIkPP/wQ\nkydPxtixYxEVFWWSGKxzIvVjnRMREZmQqMSLL74oZs2aJTw9PYUQQly/fl14e3tXtlmlCgsLRcuW\nLcXp06dFQUGB8PHxEWlpaaXe88MPP4iePXsKIYQ4cOCACAoKMrzm4uIiLl++XOH+q3BoRBbD3O05\nIyND9O/fX7zxxhviypUrJtsv65yo6qRqz3FxceKpp54SgYGBYvHixUbvT4o6R+S9HyJLx88uIqpM\npVfST548iYkTJ6JatWoAgNq1a5vky4HExES4urrCxcUFtra26N+/P2JjY0u9Jy4uDkOHDgUABAUF\nITc3FxcuXCj5BYNJciHSqoyMDLz55ptYtmwZ5s6di969e6N///5YuHAhioqKjN4/65xIfiWvWgPA\n6tWr8dNPP+HXX3/FkiVLjN6/1HVeWFxodM5ERERK9sAp2ACgevXquHnzpmH55MmTqF69utGBs7Oz\n0bRpU8Oys7MzEhISKn1PdnY2GjVqBJ1Oh65du8La2hojR4403I5f0rBhw+Di4gIAsLOzg6+vL4KD\ngwHcG4dk6uW768y1/4qW58+fL8nx8XilOd758+cjJSXF0H7NZcCAAZg/fz7y8/MxZMgQ7Nq1C506\ndcLq1asREhKCn376yaj9s85Nu6z2dq+145WqzpcsWQIhBKZPn47GjRujadOm+PDDD2FlZQUnJyej\n9y9FnWMzADv9r5/U/gSBAYGqaQdaa/flLaekpGDcuHGSHm/JY5Xi+HJzcwEAmZmZICKqVGWX2rdv\n3y6efPJJ8a9//UsMGDBA/Pvf/xY//fST0ZfwN27cKIYPH25YXrNmjRg9enSp9zz77LPil19+MSx3\n6dJFJCUlCSGEyM7OFkIIcfHiReHj4yP27t1batsqHJpZ7N69m3EZ1+TM1Z69vb1Fdna2OHHihOjQ\noUOp1/Lz843eP+uccRm36szZnlNSUkRYWJiYOnWqyMvLEzt37hSxsbHi1q1bRu9bijovebv7lRum\nG5LzIFprf3LFlTO2GmudiNTBqrJOfLdu3bBp0yasWLECAwcORFJSEjp37mz0lwNOTk7IysoyLGdl\nZcHZ2fmB7zl37pzhW/8mTZoAABo0aIDnn38eiYmJRudkCne/OWVcxrUEUVFRGDNmDN5//318/vnn\npV6rVauW0ftnnTMu4yqDj48PYmNj4evri969e+PPP/9EWFiYSe6Mk7rObxdJM/OE1tqfnO1ei8dM\nRPQglXbSv/32W9jY2ODZZ5/Fs88+CxsbG2zevNnowAEBAUhPT0dmZiYKCgoQExODsLCwUu8JCwvD\n6tWrAQAHDhyAnZ0dGjVqhBs3biAvLw+A/unzO3bsgJeXl9E5EWnNE088gU2bNmHDhg3w8fEx+f5Z\n50TyW7JkCR5//HF07NgRN27cQHx8PK5evYpu3bqVmoLxUUld57cLOT0kERGpW6Wd9KlTp8LOzs6w\nbGdnh8jISKMD29jYYPHixejevTs8PT0RHh4ODw8PREdHIzo6GgDQq1cvtGjRAq6urhg5cqRhSqic\nnBx06tQJvr6+CAoKwrPPPotu3boZnZMplBzfxLiMq3TPPPMMvvnmG9y4caPMa/n5+YiJiUGvXr0e\nef+sc8ZlXPlFRUVh//792LNnD+bMmQNbW1u8/vrriImJMcmX7lLXuVRX0rXW/uRs91o8ZiKiB6n0\nwXGinCeumuKpzwDQs2dP9OzZs9S6kSNHllpevHhxme1atGiBlJQUk+RApGUrVqzA4sWLMWXKFFhb\nW8PR0RFCCOTk5KCwsBDh4eFYtWqVUTHkqPOiIiD7zyIcPnMOF/Iu49rNG7h2Mx+3CwsgICCEqPC/\ndz3oWdNZx/9A3MlLj5SbMRhX3XHNxcnJCTNnzkR+fj48PDwM6+3t7fHJJ5+YJIaUdc4r6UREpHY6\nUV4vvISXXnoJ9vb2eO211yCEwGeffYarV69i5cqVEqX4aHQ6HaduItWQoj3n5OTgzJkzAIBmzZqh\ncePGZo1nCiXPy82bwMIvcvHZb4twrk4sRMPfAWtO1UQWJNI8Uw7evn0b27dvR7Vq1RASEgJra2uT\nxzAnnU4HRN5b/m3EbwhoEiBbPkTG4r9RiagylV5JX7RoEaZPn47w8HAAQEhICD777DOzJ0ZE0mrc\nuLFFdMzLc/w48NTwrbjw+H8Atytyp0OkKNnZ2WXGiN/v5MmTaNmypUQZGYdX0omISO0q7aTXqVMH\ns2fPliIXVdizZ48sTwtlXHXHpYrl5wNdhiTiQvfnAOs7pV6zudUI1Qoaw1bUhi1qwwq20MEKOugA\n6Az/hdDpr9bd/W8lbmddQvWmDcxzQIyr2bjZ2GiW/U6ePBn5+fkICwtDQECAYVjL+fPncfDgQcTF\nxaFu3brYsGGDWeKbmpRj0rX0OSPn55sWj5mI6EEq7aQfP34cc+fORWZmJgoL9beO6nQ6/PTTT2ZP\njoioMgsXFSPbf7ihg26nc8aHT3+EQQFhsKthV8nWj0Zr/6BkXGnoPq38C6JHERMTg4yMDGzYsAHv\nvvtuqWEt//d//4dFixahRYsWZoltDrySTkREalfpmHRvb2+MGjUK7dq1M4xj0+l08Pf3lyTBR8Xx\nPqQmUrTnGzduICsrC+7u7maNY0o6nQ5OwT8gO/gZAEB1XW0cHXMYze2by5wZ0cPj51b57h+T/l34\nd3iu9XOy5UNkLNY6EVWm0ivptra2GDVqlBS5EJFM4uLi8NZbb+H27dvIzMxEcnIypkyZgri4OLlT\nq1R2/Xu3CI9o91920IlUjlfSiYhI7SqdJz00NBSfffYZzp8/jytXrhh+qHxam+uTcdUhMjISCQkJ\nsLe3BwD4+fnh1KlTMmdVRa7xhl8H+4ZLElJr7Y9xSUk4T7q64soZm7VOREpV6ZX0lStXQqfTYe7c\nuaXWnz592mxJEZG0bG1tYWdXevy2lVWl3+EpQ93zAIDqxfaclolIAwqKCuROgYiIyKwqHZNuqTje\nh9TE3O355ZdfRpcuXTBr1ix8++23WLhwIe7cuYPPP//cbDFNoeRYVbdqT+LE5J9lzYfIGOau8+Li\nYqxbtw6nT5/GBx98gLNnzyInJweBgYFmi2kK949JX9xzMV4LfE22fIiMxX+jElFlqnSp7MiRI/j6\n66+xevVqww8RqceiRYvwxx9/oHr16hgwYADq1auH+fPny53WQ3Gzay13CkSK9uqrr+LXX3/FV199\nBUA/xeqrr74qc1YPT6rb3YmIiORSaSc9MjISY8aMwejRo7F79268/fbbFvEwKblobVwV46pD7dq1\nMWPGDBw8eBAHDx7ERx99hBo1asid1kNxtZfuqfRaa3+Mqw4JCQmIiopCzZo1AQCPPfYY7ty5I3NW\nD0+qB8dprf1xTDoRkXJU2knfuHEjfvzxRzg6OmLFihX4/fffkZubK0VuRCSRrl27lqrrK1euoHv3\n7jJm9PDcHFrKnQKRolWrVg1FRUWG5UuXLlnOsydK4JV0IiJSu0rHpLdv3x6//fYb/P398dNPP6Fe\nvXpo3bo1jh8/LlWOj4TjfUhNzN2efX19kZKSUuk6pSk5VvW7Z/fjOf/HZc2HyBjmrvO1a9fi66+/\nRlJSEoYOHYqNGzfiww8/RL9+/cwW0xTuH5M+6f8m4f/bu/ewKsp9D+DfAVEUQfCGBiolCiZ3UFNT\nUcRNpmi1Nd1ZotXZ2tF25U6ttiesY+HuuBOstNxl9JTuTPOWys6d4KVEDEEMU0xBEYFUQBREbu/5\ng1hAXAZYa2bW5ft5nvU4M8zM7zeL9+fiXTPvzNshbzdar6oKKCoCSkqAysqa+fov/klAxsLPj3+j\nElHLZO/uPmzYMBQWFuLZZ59FUFAQ7OzsMGoU/xAmMifW1ta4dOkSBgwYAADIysoyuTNsrj26a50C\nkVGbM2cOAgMD8d133wEAdu3ahSFDhmicVdvVXu6ekgJ88QVw4gTw008Anw5LRETmQvav8A8++ABO\nTk5YsGABvv32W8TGxmLTpk1q5GaSLG1cFeOah1WrVmHMmDGYM2cO5syZg7Fjx+Ktt97SOq02UbOT\nbmntj3HNQ2JiIlxcXLBo0SIsWrQILi4uOH78uNZptdnNkruYPRsICADWrAEOH1aqg56gxE4Z16hi\naxWXiKhlzZ5JT05OrrnErAknT55EQECAYkkRkbrCwsKQnJyMxMRESJKEtWvXomfPnlqn1SY9ujhp\nnQKRUVuwYAFSUlJ083Z2do2WmYLtO+/i5r8aL5ckoFs3wM4OsLEBrK1rXh06AFZWNa+2uH0b6NrV\nMDkzrnHG1ipuWpr6MYnItDQ7Jj04OLjZTjoAxMfH6x08Li4OL7zwAqqqqvDMM89g2bJljdZ5/vnn\nsX//fnTp0gWffvop/P39W7Utx6STOVGjPefk5CArKwuVlZW62h+D3h6NAAAgAElEQVQ7dqze+1W6\nzhEJSOX2qF5VrHeuRFrS4t4TPj4+SDNAj0GNOtdJewL4+nMAwIwZwBNPAP7+gItLTaecyNjxb1Qi\nkiU0UllZKQYOHCgyMzNFeXm58PX1FWfOnGmwzt69e8VDDz0khBAiMTFRjBgxotXbanhoRAandHte\nunSpGDBggHjooYfElClTdC99qVHniISweXmA3rkSaU3pOp8+fbqIjo4W5eXl4u7du2Lt2rVi2rRp\neu9XrTrXvWY+KgAhNm/WO3UiTfBvVCKS06qLv06fPo2tW7fis88+0730lZSUBHd3d7i5ucHGxgaz\nZs3Crl27Gqyze/duzJ07FwAwYsQIFBUVIS8vr1XbasXSxlAyrnnYsWMHzp07h3379mHPnj26l77U\nqnObSnVvGmdp7Y9xzcOGDRvw/fffw8XFBa6urkhMTMRHH32k935V/zy3KcVzzwGzZ+udeossrf3x\nOelERMZD9u7ukZGROHToENLT0/Hwww9j//79ePDBB/HUU0/pFTgnJwf9+vXTzbu6uja6gU1T6+Tk\n5ODq1auy2wJAREQE3NzcAACOjo7w8/NDcHAwgLr/mA09X0up/Tc3X3sJo1rxeLzKxlu7di1SU1N1\n7VdpAwcORHl5OTp16mTQ/apR59gJCNsiREZGss55vCZ1vGrXubOzM7788kuD71etOofjb9OlGQhZ\nlgAgGIDptwNLa/dNzaempqp+vGofX1FREYCap6cQEcmRfU66l5cXTp06hYCAAJw6dQr5+fl44okn\n8J///EevwNu3b0dcXBw2btwIoOb5rcePH8e6det060ydOhXLly/H6NGjAQATJ07E6tWrkZWVJbst\nx/uQOVG6PT/66KM4deoUQkJCdB11SZIQExOj137VqHNEAi5FM3Dl3a165UqkNaXr/Ndff8XGjRt1\n956ojfnJJ5/otV+16ryWY2kQClef0CtnIi3xb1QikiN7Jr1z586wtrZGhw4dcPPmTfTu3RvZ2dl6\nB3ZxcWmwn+zsbLi6ura4zpUrV+Dq6oqKigrZbYmo9cLDwxEeHt5gWUs3jmwtNep8ht06eA500ztX\nInM3bdo0jB07FqGhobCyqhntZip1juOLgBHvAQDsu5fqnTMREZFRkxu0vnDhQlFQUCDWr18v3N3d\nha+vr4iIiNB7MHxFRYW47777RGZmprh7967sjWaOHTumu9FMa7ZtxaEpIj4+nnEZ1+C0as/6Yp0z\nLuO2ntLt2dfXV5H9qlHncLqgu3Gc27tuihzH71la+9MqrpaxzbXWicj0yZ5J/+CDDwDUPF81LCwM\nxcXF8PHx0fvLgQ4dOuC9997DH/7wB1RVVeHpp5/GkCFD8OGHHwIA/vznP2Py5MnYt28f3N3dYWdn\nh02bNrW4LRG1T0ZGBl599VWcOXMGd+7cAVBzhu3ixYt67Zd1TmQ8pkyZgr179+Lhhx826H5VqfOK\nLrrJ0kqeSSciIvMmOyZdCIGvv/4aR48ehSRJGDNmDB555BG18ms3jvchc6J0ex49ejRWrlyJl156\nCXv27MGmTZtQVVWFN998U7GYhsA6J3OidHvu2rUrSktL0bFjR9jY2OhiFhcXKxbTECRJwpTHbuIb\n724AAPuO9ih+xbhzJmoJP7uISI5sJ33hwoW4cOECZs+eDSEEtm7divvuu093ht1Y8T9AMidKt+eA\ngACcPHkS3t7eOH36dINlxox1TuaE7blpkiShvLIcHf+3IwDAWrJGxYoKg4ynJ9ICa52I5Mg+Jz0+\nPh5xcXGYN28e5s+fj3379uHgwYNq5GaSfv8oE8ZlXFNga2uLqqoquLu747333sPXX3+NkpISrdMy\nWpbW/hjXfBQWFiIpKQmHDx/WvUyBjbUNbKxqzv5XiSpUVFcoHtPS2p+W7d4Sj5mIqCWyY9Ld3d1x\n+fJl3XNcL1++DHd3d6XzIiIVrV27FqWlpYiJicGKFStQXFyM2NhYrdMiIgPauHEjYmJikJ2dDX9/\nfyQmJmLkyJEm88V7Z5vOqLhb0zkvrShFR+uOGmdERESkjGYvd586dSoAoLi4GElJSRg+fDgkSUJS\nUhKGDRuGQ4cOqZpoW/FSIjInbM9N4/tC5kTp9uzl5YUTJ05g5MiRSE1NxdmzZ/HKK69gx44disU0\nhNr3pe+avsi7nQcAyHkpB/fY36NxZkTtw88uIpLT7Jn0JUuWAGj6PxKOAyMyD7VfxjVFkiTs3r1b\nxWyISEm2trbo3LkzAKCsrAyenp44d+6cxlm1Xhebend4r+Ad3omIyHw120kPDg5WMQ3zkZCQoMl7\nx7jmHVcptV/GUdtYWvtjXPPQr18/FBYWYvr06QgNDYWTk5NuKJspULuTbmntT8t2b4nHTETUEtkx\n6ZaioADIzARyc4GSkppXWRlQXQ0IUffv71+/d+EC8OOP6ufPuOYdVyn844TIctRe1h4ZGYng4GAU\nFxcjLCxM46xar34nvaScN7YkIiLzJfsINlPVmvE+V68C69YB27cD58+rlBhRuyg7fi0jIwOvvvoq\n0tPTUVZWVhNRknDx4kXFYhoCx/WROVGqPRcUFLT48+7duxs8piHVvi8TP5uI7zK/AwB8O+dbhA4M\n1TgzovbhZxcRyZE9kx4dHY2//OUvsstMzbZtQEREzRlzIks3b948rFy5Ei+99BLi4uKwadMmVFVV\naZ0WERlAQEBAi/eSyczMVDGb9nPo5KCbvnn3poaZEBERKUv2TLq/vz9SUlIaLPPz80Nqaqqiiemr\npW8pP/0UmDev4bJOnQB3d8DVFXBwAOzsAFtbwMoKkKS6f5t61ZednYB+/YIVOaaWMK55x12zRtlv\n3QMCAnDy5El4e3vj9OnTDZYZM63ORlja+EnGVQfPrjWt9n2J2BmB2FM1j4b8OPxjzPefr2hcS2t/\nHJOuHtY6Eclp9kz6li1bsHnzZmRmZja4A/StW7fQo0cPVZJTwi+/AAsW1M27uwPvvAM89FBNR11f\nCQmAFp9xjGvecdesUXb/tra2qKqqgru7O9577z3cc889KOFlJkRmp7CwEOfPn9cNawGAsWPHaphR\n63Wz7aabLr5brGEmREREymr2TPqlS5eQmZmJ5cuXY/Xq1bpv/Ozt7eHr64sOHYz7nnPNfUs5Y0bN\npe4A4OUFHD4MODmpnBxRGyn9rXtSUhKGDBmCoqIirFixAsXFxVi6dCkeeOABxWIaAs9GkDlRuj1v\n3LgRMTExyM7Ohr+/PxITEzFy5EgcPHhQsZiGUPu+rIhfgf89/L8AgJXBK/E/4/5H48yI2oefXUQk\np9me9oABAzBgwAAkJiaqmY+iLl0Cvv66bv6TT9hBJwKA4cOHAwCEEIiJiYGDg4PMFkRkaqKjo3Hi\nxAmMHDkS8fHxOHv2LF555RWt02o1h451/y/xTDoREZkzK7kVtm/fjkGDBsHBwQH29vawt7c32T/g\nN2+ueZQaAEycCAwbZvgYCQkJht8p41p8XKWdOHEC3t7eupevry9+NKdnzRmYpbU/xjUPtra26Ny5\nMwCgrKwMnp6eOHfunMZZtZ7aN46ztPanZbu3xGMmImqJ7DXrS5cuxTfffIMhQ4aokY+idu6sm547\nV7s8iIzN/Pnz8cEHH2DMmDEAgKNHj2L+/PlIS0vTODMiMpR+/fqhsLAQ06dPR2hoKJycnODm5qZ1\nWq1Wv5POM+lERGTOZO/uPnr0aHz//fdq5WMwvx/vU1gI1D4K1toauHaNl7qT6VB6/FpTT3Hg3d2J\n1KVme05ISEBxcTHCwsLQsWNHVWK2V+37sjdjL6ZsmQIACHMPw/4n9mucGVH78LOLiOTIXu4eFBSE\nxx9/HFu2bMH27duxfft2fF1/YHc7FBQUIDQ0FIMHD8akSZNQVFTU5HpxcXHw9PTEoEGDsHr1at3y\nyMhIuLq6wt/fH/7+/oiLi5ONWX9ovb8/O+hEAJCcnIzk5GSMGzcOf/7zn5GQkICEhAQsXLgQ48aN\n02vfWtQ5EbUsOTkZ0dHRSEtLg6urq94ddDXrnGfSiYjIUsh20m/evInOnTvj22+/xTfffINvvvkG\ne/bs0StoVFQUQkNDkZGRgZCQEERFRTVap6qqCosWLUJcXBzOnDmDLVu24OeffwZQ8w3kSy+9hJSU\nFKSkpCAsLEw25g8/1E2PHq1X+i2ytHFVjGvalixZgr/+9a84deoUMjIysHLlSqxcuRI///wzUlNT\n9dq3FnWuFktrf4xrHt544w1ERESgoKAA169fx7x58/Dmm2/qtU8167x+J72orOkvAwzJ0tofx6QT\nERkP2THpn376qcGD7t69G4cOHQIAzJ07F8HBwY0+2JOSkuDu7q4bLzdr1izs2rVLNza+rZcJnTpV\nN/3bjayJLJ6Sf6BoUedE1LzPP/8caWlpsLW1BQC88sor8PX1xYoVK9q9TzXrvEeXHrrpG6U32p0z\nERGRsZPtpJ87dw7PPfcc8vLykJ6ejrS0NOzevRt/+9vf2h00Pz8fzs7OAABnZ2fk5+c3WicnJwf9\n+vXTzbu6uuL48eO6+XXr1uGzzz5DUFAQ1qxZA0dHx0b7iIiI0P1RcOyYIwA/AMEYOrSucxIcHAzA\n9OdrlxlLPjxe/ebXrl2L1NRUk7qp0+9pUeeOjo7w8/Mzmt8j2z2Pt6V5tevcxcUFd+7c0XXSy8rK\n4Orqqtc+1axz1/6uwBEAtsC1PtcghIAkSZr/HtnuDTNf/9jVih8cHKxKvNTUVN1QkKysLBARyZG9\ncdzYsWPxzjvvYMGCBUhJSYEQAl5eXkhPT29xx6GhocjLy2u0fNWqVZg7dy4KCwt1y7p3746CgoIG\n623fvh1xcXHYuHEjgJozAMePH8e6devw66+/olevXgCAFStWIDc3Fx9//HHDA6t3U46yMsDOrubx\na1ZWwO3bwG9PoSEyCcZ6kxljqnMiU6dUe168eDEAIDs7G0lJSZg0aRIA4MCBAxg+fDh27NjR4vbG\nVOf2b9vjdvltAEDhskI42jbu0BMZO352EZEc2TPppaWlGDFihG5ekiTY2NjI7vjAgQPN/szZ2Rl5\neXno06cPcnNz0bt370bruLi4IDs7WzefnZ2t+8a//vrPPPMMpk6d2mIu58/XPR/93nuV7aDX//Zb\nTYxr3nGNlTHVuZosrf0xrmkLDAyEJEkICgrC9OnTAdR8lgcHB0OSJNntjanOe3bpqeukXy+9rmgn\n3dLan5bt3hKPmYioJbKd9F69euGXX37RzW/btg19+/bVK2h4eDhiY2OxbNkyxMbG6v5oqC8oKAjn\nz59HVlYW7rnnHnz55ZfYsmULACA3N1eXw44dO+Dt7d1ivEuX6qbd3fVKncgslZSU4B//+AcuX76M\njRs34vz58zh37hymTJnS7n2qXedE1LSIiAjF9q12nffs0hNZRVkAajrp7t35oU5ERGZIyPjll1/E\nhAkThK2trejbt68YNWqUyMzMlNusRTdu3BAhISFi0KBBIjQ0VBQWFgohhMjJyRGTJ0/Wrbdv3z4x\nePBgMXDgQPHWW2/plj/55JPC29tb+Pj4iGnTpom8vLxGMeof2vvvCwHUvJ55Rq/UiTTRilLVy4wZ\nM0RUVJS4//77hRBC3L59W/j4+Oi1T7XrnMjUmWJ7VrvOwz4PE4iEQCTEnnN7FDwyIuWYYq0Tkbpk\nx6TXKikpQXV1Nezt7ZX7xsCA6o/3eeUVoPZmsytXAv/zPxomRtQOSo9fCwwMRHJyMvz9/ZGSkgIA\n8PX1xan6j0UwQhzXR+aE7blp9d+XJ3c8ic/TPgcAbJq2CRF+ERpmRtQ+rHUikmMlt0JhYSGio6Px\nt7/9Da+++ioWL16M559/Xo3cDOby5brp/v2VjfX7u5SqhXHNO67SOnXqhDt37ujmL1y4gE6dOmmY\nkXGztPbHuOaltLRU6xTarbdd3Rj23Fu5isaytPanZbu3xGMmImqJbCd98uTJuHTpEnx8fBAUFITA\nwEAEBgaqkZvB1LtfDeo9BYaIfhMZGYmwsDBcuXIFf/rTnzBhwgSsXr1a67SIyIB++OEH3H///fDw\n8AAApKam4rnnntM4q7Zxta97ZFzOrRwNMyEiIlKO7OXuAQEBOHnypFr5GEz9S4k8PICMjJrl6enA\n/fdrmBhRO6hxadz169eRmJgIABgxYoTusUjGjJcMkjlRuj0PHz4c27Ztw7Rp03TDWoYOHSr7SFWt\n1X9fvkr/CjO3zQQAhHuEY9esXVqmRtQu/OwiIjmyZ9L/9Kc/4aOPPkJubi4KCgp0L1Ny7VrdtAn0\nO4hUFxISgp49e2LKlCmYMmUKevXqhZCQEK3TIiID6/+7MV8dOsg+5MWo9OtWdzncleIrGmZCRESk\nHNlOuq2tLV5++WU88MADukvdg4KC1MjNICorgcLCmmlJArp3VzaepY2rYlzTdufOHdy4cQPXrl1r\n8CVcVlYWcnJ4KWlzLK39Ma556N+/P77//nsAQHl5Of7v//4PQ4YM0TirtnF1qLvcXelOuqW1P45J\nJyIyHrJfoa9ZswYXLlxAz5491cjH4Oqf9O/eHbC21i4XImPz4YcfIjo6GlevXm1wrwl7e3ssWrRI\nw8yIyNDWr1+Pv/zlL8jJyYGLiwsmTZqE999/X+u02qRP1z6wlqxRJarwa8mvuFNxB51tOmudFhER\nkUHJjkmfNGkSduzYATs7O7VyMoja8T5nzgBDh9Ys8/AAzp7VNi+i9lB6/FpMTIzJPbUB4Lg+Mi9s\nz037/fviHuOOC4UXAACnFpyCj7OPVqkRtQtrnYjkyJ5J79KlC/z8/DB+/HjdI5kkSUJMTIziyRnC\n9et10yZ6MQCR4p5//nn89NNPOHPmDMrKynTLn3rqKQ2zIiJDWLx4sW66tnMgSZJumal8ntfy7Omp\n66SfvX6WnXQiIjI7smPSp0+fjtdeew2jRo3SjUk3pUew1b9pnBqddEsbV8W45iEyMhKLFy/GokWL\nEB8fj6VLl2L37t1ap2W0LK39Ma5pq/3cvnv3Lk6ePInBgwfD3d0dKSkpKC8v1zq9NvPs6ambPntd\nucvjLK39cUw6EZHxkD2THhERoUIayrlxo266Rw/t8iAyZtu2bcOpU6cQEBCATZs2IT8/H0888YTW\naRGRAdR+jq9fvx5Hjx6FjY0NAGDhwoV48MEHNcysfep30k//elrDTIiIiJQhOyb96NGjWLlyJbKy\nslBZWVmzkSTh4sWLqiTYXrWX9L3zDrB0ac2yF18E/vEPbfMiag+lx68NGzYMJ06cQGBgIA4ePAgH\nBwd4enri3LlzisU0BI7rI3OidHv28PDADz/8gB6/fWNdUFCAkSNHmlydn8w9icCPaq7o69+tPy69\ncEmr1IjahZ9dRCRH9kz6008/jbVr1yIgIADWJnhr9Fu36qYdHLTLg8iYBQUFobCwEM8++yyCgoJg\nZ2eHUaNGaZ0WERnQ8uXLERAQgODgYADAoUOHEBkZqWlO7eHd2xu2HWxRVlmGyzcvI+92Hvp07aN1\nWkRERAYjOybd0dERDz30EJydndGzZ0/dy1TU76Tb2ysfz9LGVTGueVi/fj2cnJywYMECfPvtt4iN\njcWmTZu0TstoWVr7Y1zzMG/ePCQmJuKRRx7Bo48+isTERJMc0mZjbYOge4J08wczDyoSx9LaH8ek\nExEZD9lO+vjx4/Hyyy/j2LFjOHnypO5lKoqL66Z5Jp2oaVOnTsXmzZtRUlKCe++9F76+vlqnREQK\nsLW1Rd++feHo6IiMjAwcPnxY65Ta5Q8D/6Cb3pOxR8NMiIiIDE92THpwcHCDR7XUio+PVywpQ6gd\n7zNzJvDVVzXLtmwBZs3SNi+i9lB6/FpCQgK+/PJL7Nu3D0FBQZg9ezamTJkCW1tbxWIaAsf1kTlR\nuj1v3LgRMTExuHLlCvz8/JCYmIiRI0fi4EFlzkQbSlPvS2peKvw/9AcAdLHpgqsvXUU3225apEfU\nZvzsIiI5LZ5Jr6qqQnh4OOLj4xu9TIXal7sTmaLg4GCsX78eFy5cwIIFC7B161b07t1b67SIyICi\no6ORlJSEAQMGID4+HikpKejWzTQ7tr7OvvDu7Q0AKK0oxfof12ucERERkeG02Em3trbGli1bDB60\noKAAoaGhGDx4MCZNmoSioqIm15s/fz6cnZ3h7e3dru0B9S93t7RxVYxrPu7cuYPt27djw4YNOHHi\nBObOnavX/tSsc7VZWvtjXPNga2uLzp07AwDKysoM8gQHrepckiQsHr5YN7/qyCqcuXam/QfSBEtr\nfxyTTkRkPGTHpD/44INYtGgRjhw5gpMnTyI5OVnvMelRUVEIDQ1FRkYGQkJCEBUV1eR68+bNQ1xc\nXLu3B3gmnag1Zs6cCU9PTxw8eBCLFi3CL7/8gnXr1um1TzXrnIjkubq6orCwENOnT0doaCjCw8Ph\n5uam1z61rPMIvwgM6TkEAHC7/DbGbBqDf/30L1RWV7bvYIiIiIyEJmPSPT09cejQITg7OyMvLw/B\nwcE4e/Zsk+tmZWVh6tSpOH36dJu2rx3vc++9QFZWzbILF4D77mt32kSaUXr82r///W9MnDjRoI9Z\nVLPOicyBmu05ISEBxcXFCAsLQ8eOHdu9H63rPCU3BQ9uehClFaW6Zd06dYN/X3+4ObrBydYJdh3t\n0KVDF1hbWUOCBCvJCpIkQYKk+7d2GZEaFo9YzM8uImqR7HPSlbgUKD8/H87OzgAAZ2dn5OfnK7J9\nREQE8vPdfptzRFqaH+67LxhA3XHVPi+W85w3pvm1a9ciNTVV77Nccr777juEhITg9u3b2LVrl265\nEAKSJOHRRx9t977VrPPa98nR0RF+fn5G83vkPOdbmlerzgGgsrISXl5eug5wbQ760rrO/fv6Y83g\nNXj1u1dR2KcQAHDz3E0knEsA3H7bOOu3fznPea3m8wCU/TZvPCO3iMiIyZ5Jz8vLw2uvvYacnBzE\nxcXhzJkzOHbsGJ5++ukWdxwaGoq8vLxGy1etWoW5c+eisLBQt6x79+4oKChocj9NffPu5OQku33t\nN+8dOwIVFTXLysqATp1aTFtvCQkJBvvjh3EZt5ZSZ9hef/11rFy5EhEREU2eRZJ7Vrqx1LnaLK39\nMa46lG7P06ZNQ0xMDAYMGNCm7Uyhzm+U3sDa42uxKWUTcm7ltOq4ZGWhrqOlJkuLq2VsreJGgmfS\niahFsmfSIyIiMG/ePKxatQoAMGjQIMycOVO2k37gwIFmf1Z7WVufPn2Qm5vb5rtIt3b7ioq6Drq1\ntfIddCJTs3LlSlRXV+Ohhx7C448/3ubtjaHOiah1CgoKMHToUAwfPhx2dnYAajrAu3fvbnE7U6jz\nHl164M3xb+KN4DdwsfAizhecx+Wbl3Hr7i2UVJSgpKIE1aIaQggIiAbTQtTM/15Opxy4+Li0Kx99\nWFpcLWNrFfd9vK96TCIyLbJn0oOCgvDjjz/C398fKSkpAAA/Pz+kpqa2O+jSpUvRo0cPLFu2DFFR\nUSgqKmr2ZjFNffPemu0lSUJxsdDd0b1r14Y3kSMyJUqfYQsMDERycrJB96lWnfNsBJkLpdvzoUOH\nGu1fkiSMGzeu3ftknRO1Hds0EckSMsaNGyeuX78u/Pz8hBBCHDt2TIwdO1ZusxbduHFDhISEiEGD\nBonQ0FBRWFgohBAiJydHTJ48WbferFmzRN++fUXHjh2Fq6ur+OSTT1rcvj4AIj9fCKDm1bOnXikT\naaoVpaqXZcuWiXfeeUdcvnxZ3LhxQ/fSh1p1TmQulG7PL7/8cqNlS5cu1WufrHOitmObJiI5smfS\nk5OTsXjxYqSnp2Po0KG4du0atm3bBl9fX+W/QdCDJEm4dEmgduhdv37A5cvKx7W0MZSMqw6lv3V3\nc3NrNCZdkiRcvHhRsZiGwDHpjGtOcZVuz/WviKvl7e3d4My2MWKdm3dcLWOba60TkemTHZMeGBiI\nw4cP4+zZsxBCwMPDQ6/HtaiprKxu2tZWuzyIjF1W7XMKicjsrF+/Hh988AEuXLgAb29v3fJbt25h\n9OjRGmZGRERETZE9k+7j44NZs2bh8ccfx8CBA9XKS2+SJCE1VcDPr2be2xtIS9M2J6L2Uvpb9zt3\n7uCDDz7A0aNHIUkSxowZg4ULF8LWyL/d4tkIMidKteebN2+isLAQy5cvx+rVq3Ux7O3t0aNHD4PH\nMzTWOZkbtmkikiPbSc/KysKXX36JrVu3QpIkzJo1CzNnzkT//v3VyrFdJElCYqLAAw/UzA8fDhw/\nrm1ORO2l9Af6jBkz4ODggDlz5kAIgc2bN+PmzZv46quvFItpCPxDh8wJ23PT+L6QuWGbJiI5VnIr\nuLm5YdmyZUhOTsaWLVuQlpaGe++9V43c9HbnTt20WicEExIS1AnEuBYVV2np6en4+OOPMX78eEyY\nMAH//Oc/kZ6ernVaRsvS2h/jkiWytPanZbu3xGMmImqJ7Jh0oOHZdGtra/z9739XOi+D4Jh0otYJ\nCAjAsWPHMHLkSABAYmIiAgMDNc6KiIiIiMjyyF7uPmLECJSXl2PmzJl4/PHHcd9996mVm14kScKO\nHQKPPFIzP20asHOntjkRtZfSl8Z5enoiIyMD/fr1gyRJuHz5Mjw8PNChQwdIkoQ0I72hAy8ZJHPC\n9tw0vi9kbtimiUiO7Jn02NhYeHp6qpGLwWlxuTuRKYqLi9M6BSIiIiIiQivGpPfp0wcvvvgiAgMD\nERgYiCVLluDmzZtq5KY3LS53t7RxVYxrHtzc3Fp8UUOW1v4YlyyRpbU/jkknIjIesp30+fPnw8HB\nAV999RW2bt0Ke3t7zJs3T43c9Fa/k965s3Z5EBEREREREbWG7Jh0X19fnDp1SnaZsZEkCWvWCCxZ\nUjP/wgvAu+9qmxNRe3H8WtP4vpA5YXtuGt8XMjds00QkR/ZMeufOnXHkyBHd/NGjR9GlSxdFkzIU\n3t2diIiIiIiITIlsJ33Dhg347//+bwwYMAADBgzAokWLsCv+cCEAABDWSURBVGHDBjVy05sWl7tb\n2rgqxiVLZGntj3HJElla++OYdCIi4yF7d3c/Pz+kpaWhuLgYAODg4KB4UobS0t3dq6qrkJafhp9+\n/QlZRVkoLi/GzbKbKKkoQbWohhAC1aK6Zhp103Kun7mOnnk9DXwk8hjXvOMSEREREZFlkB2TvmbN\nGkiS1GBZt27dEBgYCD8/P0WT04ckSVi0SOC992rmo6OB558H7lbexervV2PDjxuQeztX2ySJWisS\nHL/WBI7rI3PC9tw0vi9kbtimiUiO7Jn05ORk/Pjjj5g6dSqEENi7dy+8vb2xYcMG/PGPf8SyZcvU\nyLNdfj8m/UrxFYR9Hob0a+naJUVERERERETUDNlOenZ2Nk6ePImuXbsCAN544w1MnjwZhw4dQmBg\noFF30svL6810uIvJX0xu0EHvbdcbo/qNgkcPD3Tv3B0OnRxgZ2MHaytrWElWsJKsIEGqm5YkSJAa\nB6rnp6Sf4DXcS6EjYlxLjTs9crrqMal5CQkJCA4OZlzGJTNmae1Py3ZvicdMRNQS2U76tWvX0LFj\nR928jY0N8vPz0aVLF9i285bpBQUFePzxx3Hp0iW4ublh69atcHR0bLTe/PnzsXfvXvTu3RunT5/W\nLY+MjMQ///lP9OrVCwDw9ttvIywsrNH2lZV10/8pWYPTBTX7sLGywT/+8A8sCFqADlayb0GbdMvr\nhmDPYIPuk3EZ1xSpVedEpB3WORERkeHJjkl/88038fXXX2P69OkQQmDPnj0IDw/HX//6V/zXf/0X\nvvjiizYHXbp0KXr27ImlS5di9erVKCwsRFRUVKP1jhw5gq5du+Kpp55q8KG+cuVK2Nvb46WXXmr+\nwCQJM2YIfPUVgA53YP96f9yqug4AiAmLweIRi9ucN5FWTHH8mlp1bmrvC1FzTLE9s86J2o5tmojk\nyD6CbcWKFfjoo4/QrVs3ODk54cMPP8Trr78OOzu7dnXQAWD37t2YO3cuAGDu3LnYuXNnk+uNGTMG\nTk5OTf6sNf+5VVT8NuH+b10HvX+3/lgQtKDtSRNRm6hV50SkHdY5ERGR4bXqWu9hw4Zh2LBhBgua\nn58PZ2dnAICzszPy8/PbvI9169bhs88+Q1BQENasWdPk5XU//hgBwA3ATiARQB9gzpNzYGNto3s2\nZu1YJEPN1y5Tav/Nza9duxZ+fn6qxePxKn98qampcHNzg6lSq84jIiJ075Ojo6Mq7aJ2Gds9j1ff\n42Ods84trd03NZ+amooXXnhB1eOtf6xqHF9RUREAICsrC0REsoRCJk6cKLy8vBq9du3aJRwdHRus\n6+Tk1Ox+MjMzhZeXV4Nl+fn5orq6WlRXV4vXXntNzJ8/v9F2AMSkSUIAQuAvbgKREIiEOJZ9zDAH\n2Iz4+HhF98+4lhlXwVLVizHUuRYsrf0xrjpY56xzS4yrZWzWOhEZK9kx6Urw9PREQkIC+vTpg9zc\nXIwfPx5nz55tct2srCxMnTq1wRi21vxckiRMmCBw8NgNYFlPAEBH64649cotdLTu2NSuiIyWKY5f\nU6vOTe19IWqOKbZn1jlR27FNE5Ec2THpSggPD0dsbCwAIDY2FtOnt+3xUrm5ubrpHTt2wNvbu8n1\nKioA9D2pm/dx9mEHnUglatU5EWmHdU5ERGR4mnTSly9fjgMHDmDw4ME4ePAgli9fDgC4evUqHn74\nYd16s2fPxqhRo5CRkYF+/fph06ZNAIBly5bBx8cHvr6+OHToEN59990m41RUAOh1Rjfv38dfuYP6\nTf3xTWpiXPOOa4rUqnMtWFr7Y1xqDuuccU05NmudiIyVYR8S3krdu3fHf/7zn0bL77nnHuzdu1c3\nv2XLlia3/+yzz1oVp6ICQM9Luvn7nO5rW6JE1G5q1TkRaYd1TkREZHiajElXgyRJ8PUVOOXxGHD/\n1wCAzY9uxmzv2RpnRtR2HL/WNL4vZE7YnpvG94XMDds0EcnR5HJ3tVRUAOh2WTffv1t/7ZIhIiIi\nIiIikmH+nXTHusvdBzgOUDympY2rYlyyRJbW/hiXLJGltT+OSSciMh5m3Ukvr6wA7K4BAKwkK/Tt\n2lfjjIiIiIiIiIiaZ9Zj0vsMzEfek84AAMdO3VG4/IbGWRG1D8evNY3vC5kTtuem8X0hc8M2TURy\nzPpMeoV1kW7asZOThpkQERERERERyTPzTnqhbtrRVp1OuqWNq2JcskSW1v4YlyyRpbU/jkknIjIe\n5t1J71DXSXdSqZNORERERERE1F5mPSbdxn8zKqb9CQDwqMcMbJ+1VeOsiNqH49eaxveFzAnbc9P4\nvpC5YZsmIjlmfSa9yqZuTHr3LjyTTkRERERERMbNrDvp1Z3qLnfvoVIn3dLGVTEuWSJLa3+MS5bI\n0tofx6QTERkPs+6kw5Zj0omIiIiIiMh0mPWYdEx9FgjcCABY//B6LAhaoHFWRO3D8WtN4/tC5oTt\nuWl8X8jcsE0TkRzzPpPe4Y5u0s7GTsNEiIiIiIiIiOSZeSe9TDdp28FWlZCWNq6KcckSWVr7Y1yy\nRJbW/jgmnYjIeLCTbmCpqamqxGFcy4pLxsXS2h/jkiWytPanZbu3xGMmImqJJp30goIChIaGYvDg\nwZg0aRKKiooarZOdnY3x48dj6NCh8PLyQkxMTJu2B6BJJ73ZXBiXcS2ManWuAUtrf4xLzWGdM64p\nxzam9kZEVJ8mnfSoqCiEhoYiIyMDISEhiIqKarSOjY0N3n33XaSnpyMxMRHvv/8+zp492+rtAWjS\nSSeiGqrVORFphnVORERkeJp00nfv3o25c+cCAObOnYudO3c2WqdPnz7w8/MDAHTt2hVDhgxBTk5O\nq7cH0KCT3qlDJ0MeQrOysrJUicO4lhXXFKlW5xqwtPbHuNQc1jnjmnJs1joRGStNHsHm5OSEwsKa\nZ5gLIdC9e3fdfFOysrIwbtw4pKeno2vXrq3aXpIk5Q6ASAOm9rgW1jlR27HOWedkGUyt1olIXR2U\n2nFoaCjy8vIaLV+1alWDeUmSWvwAvn37Nv74xz8iOjoaXbt2bfTz5rbnf35EymOdE5k/1jkREZG6\nFOukHzhwoNmfOTs7Iy8vD3369EFubi569+7d5HoVFRV47LHHMGfOHEyfPr3N2xORsljnROaPdU5E\nRKQuTcakh4eHIzY2FgAQGxvb4AO7lhACTz/9NO6//3688MILbd6eiLTFOicyf6xzIiIiw9NkTHpB\nQQFmzpyJy5cvw83NDVu3boWjoyOuXr2KZ599Fnv37sXRo0cxduxY+Pj46C5/e/vttxEWFtbs9kRk\nPFjnROaPdU5ERKQAYYb2798vPDw8hLu7u4iKilI01oABA4S3t7fw8/MTw4YNE0IIcePGDTFx4kQx\naNAgERoaKgoLC/WOM2/ePNG7d2/h5eWlW9ZSnLfeeku4u7sLDw8P8e9//9ugcV9//XXh4uIi/Pz8\nhJ+fn9i3b5/B416+fFkEBweL+++/XwwdOlRER0cLIZQ/5ubiKn3Md+7cEcOHDxe+vr5iyJAhYvny\n5aocryljnbPOWefmj3XOOmedE5ElMrtOemVlpRg4cKDIzMwU5eXlwtfXV5w5c0axeG5ubuLGjRsN\nlr388sti9erVQgghoqKixLJly/SOc/jwYXHy5MkGH67NxUlPTxe+vr6ivLxcZGZmioEDB4qqqiqD\nxY2MjBRr1qxptK4h4+bm5oqUlBQhhBC3bt0SgwcPFmfOnFH8mJuLq8Yxl5SUCCGEqKioECNGjBBH\njhxR5XdsiljnrHPWufljnbPOWedEZKk0GZOupKSkJLi7u8PNzQ02NjaYNWsWdu3apWhM8bsRA0o8\n93XMmDFwcnJqVZxdu3Zh9uzZsLGxgZubG9zd3ZGUlGSwuEDTd9s1ZNzmnqur9DG39DxfpY+5S5cu\nAIDy8nJUVVXByclJld+xKWKds85Z5+aPdc46Z50TkaUyu056Tk4O+vXrp5t3dXXV/aesBEmSMHHi\nRAQFBWHjxo0AgPz8fDg7OwOouXNtfn6+IrGbi3P16lW4urrq1lPiPVi3bh18fX3x9NNPo6ioSNG4\nWVlZSElJwYgRI1Q95tq4DzzwAADlj7m6uhp+fn5wdnbG+PHjMXToUE1/x8aMdc46N1Rs1rnxYp2z\nzg0Vm3VORKbG7DrpLT2jVQnff/89UlJSsH//frz//vs4cuRIo3zUyEkujiFzWLhwITIzM5Gamoq+\nfftiyZIlisW9ffs2HnvsMURHR8Pe3r7RvpU65t8/z1eNY7ayskJqaiquXLmCw4cPIz4+vtF+1fod\nGzvWefM/NxTWOetca6zz5n9uKKxz1jkRGSez66S7uLggOztbN5+dnd3gG0pD69u3LwCgV69eeOSR\nR5CUlKR77isARZ/72lyc378HV65cgYuLi8Hi9u7dW/cB88wzz+guyzJ03Nrn6j755JO6x/KoccxN\nPc9XrWMGgG7duuHhhx9GcnKyZr9jY8c6Z53rG5t1bvxY56xzfWOzzonIVJldJz0oKAjnz59HVlYW\nysvL8eWXXyI8PFyRWKWlpbh16xYAoKSkBN9++y28vb1Ve+5rc3HCw8Pxr3/9C+Xl5cjMzMT58+cx\nfPhwg8XNzc3VTe/YsQPe3t4Gjyuaea6u0sfcXFylj/n69eu6S+7u3LmDAwcOwN/fX7PfsbFjnbPO\nWefmj3XOOmedE5HF0uBmdYrbt2+fGDx4sBg4cKB46623FItz8eJF4evrK3x9fcXQoUN1sW7cuCFC\nQkIM+siWWbNmib59+wobGxvh6uoqPvnkkxbjrFq1SgwcOFB4eHiIuLg4g8X9+OOPxZNPPim8vb2F\nj4+PmDZtmsjLyzN43CNHjghJkoSvr6/uMSn79+9X/Jibirtv3z7FjzktLU34+/sLX19f4e3tLf7+\n978LIVpuS4Z6r00V65x1zjo3f6xz1jnrnIgskSREE7e4JCIiIiIiIiLVmd3l7kRERERERESmip10\nIiIiIiIiIiPBTjoRERERERGRkWAnnYiIiIiIiMhIsJNOTbp58ybWr18PoOZxJTNmzNA4IyIyNNY5\nkfljnRMRmR7e3Z2alJWVhalTp+L06dNap0JECmGdE5k/1jkRkenpoHUCZJyWL1+OCxcuwN/fH4MG\nDcLPP/+M06dP49NPP8XOnTtRWlqK8+fPY8mSJSgrK8PmzZvRqVMn7Nu3D05OTrhw4QIWLVqEa9eu\noUuXLti4cSM8PDy0Piwiqod1TmT+WOdERCZI28e0k7HKysoSXl5ejaY3bdok3N3dxe3bt8W1a9eE\ng4OD+PDDD4UQQrz44oti7dq1QgghJkyYIM6fPy+EECIxMVFMmDBBg6MgopawzonMH+uciMj08Ew6\nNUnUGwUhfjciYvz48bCzs4OdnR0cHR0xdepUAIC3tzfS0tJQUlKCH374ocG4t/LycnUSJ6JWY50T\nmT/WORGR6WEnndqsU6dOumkrKyvdvJWVFSorK1FdXQ0nJyekpKRolSIR6Yl1TmT+WOdERMaJd3en\nJtnb2+PWrVtt2qb2G3p7e3vce++92LZtm255WlqawXMkIv2wzonMH+uciMj0sJNOTerRowdGjx4N\nb29vLF26FJIkAQAkSdJN187Xn66d/+KLL/Dxxx/Dz88PXl5e2L17t7oHQESyWOdE5o91TkRkevgI\nNiIiIiIiIiIjwTPpREREREREREaCnXQiIiIiIiIiI8FOOhEREREREZGRYCediIiIiIiIyEiwk05E\nRERERERkJNhJJyIiIiIiIjIS/w95B+XF5GmEYAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f38f5cafa50>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some thoughts...\n",
"\n",
"- rUK = blue, Scotland = green, aggregate = red\n",
"\n",
"- top row of plots shows income identity, first three plots sum to last one\n",
"- middle row is government\n",
"- bottom row is sectoral balances as percent of GDP\n",
"- government has deficit in both regions if private sector are saving (i.e. alpha < 1)\n",
"- in absolute terms rUK has bigger deficit, but relative to GDP it is much smaller\n",
"- government balance equals private balance + trade balance for each regions (bottom row of plots)\n",
"- if alpha = 1, private balance obviously goes to zero (no savings) and trade deficit perfectly balances government deficit (in this case we see the government have a balanced budget on aggregate (red) - the surplus from the rUK exactly matches the fiscal transfer to Scotland)\n",
"- any absolute trade deficit needs to be less than G at the outset otherwise it explodes (i.e. more money being spent than exists, G is only source of funds)\n",
"- basically economies grow until amount flowing out (IM and savings; both function of Y) equals amount flowing in (EX, G and FT)\n",
"- absolute trade balances are obsiously equal and opposite but relative to GDP Scotlands deficit is much larger than rUKs surplus (by factor of 9, again, obviously)\n",
"\n",
"Would be good to\n",
"- add investment\n",
"- use realistic figures for rUK government spend and Scottish block grant (very crude implementation here), spending propensities\n",
"- could add spending out of wealth (Modigliani consumption function) but probably guessing parameters\n",
"- could handle Scottish tax differently (i.e. Scottish rate)\n",
"- could add bonds (and interest rate and liquidity preference) but possibly overkill\n",
"- if modelling as distinct countries need to central banks with reserves (e.g. gold)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### NO SAVING ###\n",
"\n",
"# savings rates (actually spending rates!)\n",
"alpha_UK = 1.0\n",
"alpha_S = 1.0\n",
"\n",
"\n",
"for t in range(1, N):\n",
" \n",
" ### UK ###\n",
" \n",
" # calculate consumer spending\n",
" C_UK[t] = alpha_UK*Y_d_UK[t-1] \n",
" \n",
" CA_UK[t] = IM_S*Y_S[t-1] - IM_UK*Y_UK[t-1]\n",
" \n",
" # calculate total income (consumer spending plus constant government spending plus CA)\n",
" Y_UK[t] = G_UK + C_UK[t] + CA_UK[t]\n",
" \n",
" # calculate the tax take\n",
" T_UK[t] = theta_UK * Y_UK[t]\n",
" \n",
" # calculate disposable income\n",
" Y_d_UK[t] = Y_UK[t] - T_UK[t]\n",
" \n",
" # calculate the change in private savings\n",
" H_h_UK[t] = H_h_UK[t-1] + (1-alpha_UK)*Y_d_UK[t-1] \n",
" \n",
" # calculate the change in government debt from rUK operations\n",
" H_g_UK[t] = H_g_UK[t-1] + T_UK[t]- G_UK\n",
" \n",
" ### SCOTLAND ###\n",
" \n",
" # calculate consumer spending\n",
" C_S[t] = alpha_S*Y_d_S[t-1] \n",
" \n",
" # calculate government spending (tax take plus fiscal transfer)\n",
" G_S[t] = T_S[t-1] + FT_S\n",
" \n",
" # calculate CA\n",
" CA_S[t] = - IM_S*Y_S[t-1] + IM_UK*Y_UK[t-1]\n",
" \n",
" # calculate total income (consumer spending plus constant government spending + CA)\n",
" Y_S[t] = G_S[t] + C_S[t] + CA_S[t]\n",
" \n",
" # calculate the tax take\n",
" T_S[t] = theta_S * Y_S[t]\n",
" \n",
" # calculate disposable income\n",
" Y_d_S[t] = Y_S[t] - T_S[t]\n",
" \n",
" # calculate the change in private savings\n",
" H_h_S[t] = H_h_S[t-1] + (1-alpha_S)*Y_d_S[t-1] \n",
" \n",
" # calculate the change in government debt from Scottish operations\n",
" # this has already been modified for rUK operations - just add on Scottish value\n",
" H_g_UK[t] = H_g_UK[t] + T_S[t]- G_S[t]\n",
" \n",
"\n",
"fig = plt.figure(figsize=(14, 10))\n",
"\n",
"consumption_plot = fig.add_subplot(341, xlim=(0, N), ylim=(0, np.max([np.max(Y_UK),np.max(Y_S)])*1.1))\n",
"consumption_plot.plot(range(N), C_UK, lw=3)\n",
"consumption_plot.plot(range(N), C_S, lw=3)\n",
"consumption_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('consumption')\n",
"\n",
"gov_plot = fig.add_subplot(342, xlim=(0, N), ylim=(0, np.max([np.max(Y_UK), np.max(Y_S)])*1.1))\n",
"gov_plot.plot(range(N), np.repeat(G_UK,N), lw=3)\n",
"gov_plot.plot(range(N), G_S, lw=3)\n",
"gov_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('government spending')\n",
"\n",
"ca_plot = fig.add_subplot(343, xlim=(0, N), ylim=(np.min([np.min(CA_UK),np.min(CA_S)])*1.1, np.max([np.max(CA_UK),np.max(CA_S)])*1.1))\n",
"ca_plot.plot(range(N), CA_UK, lw=3)\n",
"ca_plot.plot(range(N), CA_S, lw=3)\n",
"ca_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('trade balance')\n",
"\n",
"income_plot = fig.add_subplot(344, xlim=(0, N), ylim=(0, np.max([np.max(Y_UK),np.max(Y_S)])*1.1))\n",
"income_plot.plot(range(N), Y_UK, lw=3)\n",
"income_plot.plot(range(N), Y_S, lw=3)\n",
"income_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('income')\n",
"\n",
"tax_plot = fig.add_subplot(345, xlim=(0, N), ylim=(0, np.max([np.max(T_UK),np.max(T_S)])*1.1))\n",
"tax_plot.plot(range(N), T_UK, lw=3)\n",
"tax_plot.plot(range(N), T_S, lw=3)\n",
"tax_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('tax revenue')\n",
"\n",
"tax_gdp_plot = fig.add_subplot(346, xlim=(0, N), ylim=(0, 1))\n",
"tax_gdp_plot.plot(range(N), T_UK/Y_UK, lw=3)\n",
"tax_gdp_plot.plot(range(N), T_S/Y_S, lw=3)\n",
"tax_gdp_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('tax revenue (% GDP)')\n",
"\n",
"deficit_plot = fig.add_subplot(347, xlim=(0, N), ylim=(np.min([np.min(T_UK-G_UK),np.min(T_S-G_S)])*1.1, np.max([np.max(T_UK-G_UK),np.max(T_S-G_S)])*1.1))\n",
"deficit_plot.plot(range(N), T_UK-np.repeat(G_UK,N), lw=3)\n",
"deficit_plot.plot(range(N), T_S-G_S, lw=3)\n",
"deficit_plot.plot(range(N), ((T_UK-np.repeat(G_UK,N))+(T_S-G_S)), lw=3)\n",
"deficit_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('government budget')\n",
"\n",
"debt_plot = fig.add_subplot(348, xlim=(0, N), ylim=(np.min(H_g_UK)*1.1,0))\n",
"debt_plot.plot(range(N), H_g_UK, lw=3)\n",
"debt_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('government debt')\n",
"\n",
"gov_balance_gdp_plot = fig.add_subplot(3,4,9, xlim=(0, N), ylim=(-0.2, 0.2))\n",
"gov_balance_gdp_plot.plot(range(N-1), (T_UK[1:]-np.repeat(G_UK,N-1))/Y_UK[1:], lw=3)\n",
"gov_balance_gdp_plot.plot(range(N-1), (T_S[1:]-G_S[1:])/Y_S[1:], lw=3)\n",
"gov_balance_gdp_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('government balance (% GDP)')\n",
"\n",
"private_balance_gdp_plot = fig.add_subplot(3,4,10, xlim=(0, N), ylim=(-0.2,0.2))\n",
"private_balance_gdp_plot.plot(range(N-1), np.diff(H_h_UK)/Y_UK[1:], lw=3)\n",
"private_balance_gdp_plot.plot(range(N-1), np.diff(H_h_S)/Y_S[1:], lw=3)\n",
"private_balance_gdp_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('private balance (% GDP)')\n",
"\n",
"ca_gdp_plot = fig.add_subplot(3,4,11, xlim=(0, N), ylim=(-0.2,0.2))\n",
"ca_gdp_plot.plot(range(N-1), np.divide(CA_UK[1:],Y_UK[1:]), lw=3)\n",
"ca_gdp_plot.plot(range(N-1), np.divide(CA_S[1:], Y_S[1:]), lw=3)\n",
"ca_gdp_plot.grid()\n",
"# label axes\n",
"plt.xlabel('time')\n",
"plt.ylabel('trade balance (% GDP)')\n",
"\n",
"# space subplots neatly\n",
"plt.tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:103: RuntimeWarning: invalid value encountered in divide\n",
"-c:104: RuntimeWarning: invalid value encountered in divide\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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mly+bls8AgKcn4EAT/EROIz09Ha+++qp5/5VXXsGqVasEloiI9DJ37ly89dZb\n8PDwMD/VQVEUZGZm6lMAlyIAnGUjMpqcHMu2l5e4chA5Ek1z0q9evQpPT88Kj+mhdB7AgQNA27am\n7WbNSi59J3ImInPZJk6ciI4dO5pnz9esWYPExETMmTNH97Iwp4+Mim3bQlEUINbqQGwR5sxRMHGi\nqBIR2c5R+viRI0cwbtw4pKen49ChQzhw4AA2btyIV155RbcyKIqCU6dUNGxo2g8MBE6f1i08kd04\n1HPSu3XrVqljIqSmWrYbNRJWDCKntmDBAowYMQIeHh7w8PDA8OHDsWDBAvj4+KBmzZqii0dEdnbx\n4kUkJiZi+/bt5h9deVzhTDqRRp544gnz6hgACAkJwcqVK3UvR26uZVvAvB6RQ9JkkH7mzBns3bsX\nOTk52LdvH/bu3Yt9+/YhPj4eOdZrWAT66y/LdnCwfWPJlnfBHBd5ZGdno6ioCAUFBSgoKEBRURGy\nsrKQlZWl35JXByBju2OdaeHChejRowf69u2L119/Hf369UNsbKy+hfDIYk464xoitiPIyclB586d\nzfuKophTWfR09aplu3p1fWLK2O5YZ+eiSU765s2bsWTJEqSlpWHSpEnm4z4+Pnjrrbe0CGGzkyct\n27ffLq4cRM5s586daNu2Lby9vbFs2TIkJSXh2WefRSMuTyEyvHfffRe7d+9G165dsXXrVhw+fBhT\np07VtxDVslCjRgN9YxIZlJ+fH/744w/z/tq1a9Gggf79izPpRGVpmpO+du1aPPzww1qdzial8wAe\newxYbroxLBYvBmJixJSLyFYic9lCQkKwf/9+HDx4EDExMRg9ejTWrFmDbdu26V4WR8npI9Kao7bt\nDh06YM+ePQgNDUVCQgI8PT3RsmVLpKSk2C1mmZz0j/fi+yXt0Lev3UIS2Z2j9PHjx4/jySefxC+/\n/ILatWvjjjvuwIoVKxBs7yWnVhRFwbZtKnr2NO137w7onUVDZA8O9Zz08PBwTJgwwSGfoXzmjGWb\nd3Ynqho3Nze4uLhgw4YNePrppzFmzBgsWrRIdLGISAcNGzbExYsXMXDgQERERKB27dq6fpkHoNty\ndyIZNG7cGD/++COuXLmCoqIi+Pj4CCmH9Uy6XsvdiRydpjeOc+RnKKenW7btvZJHtrwL5rjIoziF\nZfny5ejfvz8KCwuRn58vuli6k7Hdsc60fv161K5dG7GxsXjjjTcwZswYbNiwQd9CVGNOOuMaI7Yj\nuHjxIt5+Wy3pAAAgAElEQVR991288soreOmllzBhwgQ888wzupdDxHJ3Gdsd6+xcNJ1Jd+RnKHMm\nnch2q1atwsqVK7Fo0SIEBATg5MmTeOGFF0QXi4jsKCMjo8yxNm3aADDdTLJOnTr6FcYjmzPpRBp5\n4IEH0LVrV7Rp0wYuLi5QVdWUYqIzETeOI3J0muakO+ozlK9ds/xmztXVtO/qqnuRiDThKLlsovE6\nkFE5WtsODg6+6Rf3EydO2C12mZz0jQtweuMTCAy0W0giu3OUPt6uXTvs27dPaBkURcGiRSpGjTLt\nR0cDS5YILRKRJhwqJ33BggWYO3cuHnvsMQBAUVERatSogQULFkBRFGGPaDp71rJdvz4H6ERERJWV\nmpoquggWOi13J5JBVFQUFixYgMjISFSrVs18XNfVMeBMOlF5NM1Jd9RnKJ8/b9muX9/+8WTLu2CO\nC8lGxnbHOhNgymFNTEzE9u3bzT+60mm5u4ztTra4omM7Ak9PT7zwwgvo0qUL2rdvj/bt26NDhw66\nl4M56caOKzK2M/dxTWfSAeDAgQNITU1FQUGB+djgwYO1DnNL/vnHsu0AN5onclrvvvsunn322QqP\nEZHxLFy4EPPmzcOpU6cQFhaGhIQEdO3aFT/99JNuZXDxzIK7u27hiAxtzpw5OH78OOrVqye0HJxJ\nJypL05z0kSNH4uDBg2jVqhVcXCyT9IsXL9YqRKVZ5wGsXAlERZmO/+tfwOrVuheHSDMic9nCwsKQ\nlJRU4lhoaCiSk5N1L4uj5PQRac1R23br1q2xe/dudO3aFcnJyTh8+DCmTp2K9evXV/mccXFxeO65\n51BYWIgxY8bgxRdfLPH3pXPSPQ48hWvrPqpyPCJH4Ch9vG/fvli/fj1qCMwhURQFr7yi4s03TfvT\npwNW96AmcloOlZO+a9cuHDp0SMidIW+GM+lEtlm5ciU+//xznDhxApGRkebjWVlZqMtORSQFT09P\nVL8+zXX16lU0b94cR44cqfL5CgsLMX78ePzwww8IDAxEx44dMWDAALRo0eKG73Gtnl3leERUkpeX\nF0JDQ9GrVy9zTrqiKJg3b56u5RCx3J3I0Wmak96xY0ekpKRoeUpN6D1Ily3vgjkuxtetWzdMmjQJ\nzZs3x/PPP49JkyZh0qRJmDNnDr7//nvRxdOdjO2OdaaGDRvi4sWLGDhwICIiIjBgwAAEBwdX+XyJ\niYm46667EBwcDHd3dzzyyCP46quvbvoeV88rVY53K2Rsd7LFFR3bEQwcOBAvv/wy7r77bnTo0MGc\nl643EcvdZWx3rLNz0XQmfeTIkejatSsCAgJK/EbuwIEDWoa5ZZxJJ7JNo0aN0KhRIyQkJIguChEJ\nUrysPTY2FuHh4cjMzMR9991X5fOlpaWhYcOG5v2goCDs2rWr7As3APA1beblpyA+Ph7h4eEALF/A\ntN4vZq/z32w/OTlZ13ii90XWtzhVS4/2FB8f71hPSgAQExODa9eu4ejRowCA5s2bw13ATR84k05U\nlqY56Y0bN8Y777yD1q1bl8hJt+U37VVlnQcwYgTw+eem40uXAo8/rntxiDQjMpdt3bp1mDJlCs6e\nPWsug6jHKzpKTh+R1hy5be/duxc7duyAoii455570K5duyqfa926dYiLi8PChQsBAMuXL8euXbsw\nf/5882tK56T7XOqGzHd2VjkmkSNwlD4eHx+P6OhoNGrUCABw8uRJLF26FD179tStDIqiICpKNX9P\nX77c9L2dyNk5VE56/fr1MWDAAC1PqQnOpBNpY/Lkyfjmm29umjNKRMY0ffp0rFmzBoMHD4aqqhg5\nciQefvhhvFrFuzwFBgbi1KlT5v1Tp04hKCjo5m9yy6lSLCIqa+LEidi8eTOaNWsGADh69CgeeeQR\n7Nu3T9dycCadqCxNc9LDwsIQFRWFlStXYt26dVi3bh2+/PJLLUNUycWLlu06dewfT7a8C+a4yCMg\nIIADdMjZ7lhnWr58OXbv3o1p06Zh+vTpSEhIwLJly6p8vg4dOuDYsWNITU1FXl4eVq1aVf4v+ucf\nNm+q7sxJZ1xjxHYEBQUF5gE6ADRt2rTEI5T1cu2aZft6tqzdydjuWGfnoulMek5ODjw8PLB58+YS\nx0U/J/3yZct2rVriykHk7Dp06IBhw4Zh4MCB8PDwAGBaziO6jxOR/QUGBiI3Nxee16e6rl69WvHM\n9024ubnhvffeQ79+/VBYWIjRo0eX/0vAfMvjoQpd9RmkE8mgffv2GDNmDB599FGoqooVK1agQ4cO\nupcjL8+yrdcgncjRaZqT7kis8wAaNADS003HT58GAgMFFozIRiJz2WJiYsxlsLZ48WLdy+IoOX1E\nWnO0tj1hwgQApuXoiYmJ6Nu3LwBgy5Yt6NSpk03PSa+IoiiAZwYwxbQMzqOoFq5Nu2S3eER6cJQ+\nfvXqVbz//vvYudN0n4fu3btj3Lhx5ps/60FRFHTvruLnn03727YBPXroFp7Ibmzt55oO0keOHFny\n5Ne/yC9atEirEJVmfWFq1AByrqexZWUB3t66F4dIM47y4S4arwMZlaO17SVLlpg/z61vGKmqKhRF\nQXR0tN1iK4oCuF4FXjXN3ruobiiMzbdbPCI9OEofv3LlCjw9PeHq6goAKCwsxLVr1+Dl5aVbGRRF\nQefOKoof7PDrr0CXLrqFJ7IbW/u5pjnpDz74IPr374/+/fujd+/euHz5MmrUqFHxG+0oP98yQHdx\nMQ3Y7U22vAvmuMjjyJEj6N27N1q1agUAOHDgAN58803BpdKfjO2OdZZXTEwMoqOjER0djZiYGPN+\n8Z92V+gBFJkGEUVKAfIK8yp4g+1kbHeyxRUd2xHce++9yLW6a1tOTg769OmjezmYk27suCJjO3Mf\n1zQn/eGHHy6xHxUVhbvvvlvLELfM+slQNWsCpVbpEtEteOKJJ/C///0PY8eOBQCEhIRg+PDheOWV\nVwSXjIiMSwHyagCepg/0K3lX4FHdQ3CZiJzftWvX4G21vNTHxwc5Ofo/QcE6J92DXZsIgJ1z0g8f\nPoz+/fvjjz/+sFeIGypeYvDnn0DjxqZjjRoBqam6F4VIUyKXyXXo0AF79uxBWFgYkpKSAAChoaFI\nTk7WvSyOslyQSGts2xamZfYqMOk2wOcMAOD0/51GYE3eXIacl6P08bvvvhvz5s1D+/btAQB79uzB\nhAkT8Ouvv+pWBkVR0LixiuPHTfvHjgF33aVbeCK7cajnpHt7e5vz1hRFgb+/P2bNmqVliFvGO7sT\nacfPz6/EL93Wrl2LBg0aCCwREektJydH15xVAKaZ9Ouu5PMO70RamDt3LoYOHWr+HD9z5gxWrVql\nezk4k05UlqY56dnZ2cjKykJWVhYyMzNx7NgxDBkypML3jRo1Cv7+/ggJCTEfy8jIQEREBJo2bYq+\nffvi0iXL3VxnzJiBJk2aoHnz5mUe91aaiEG6bHkXzHGRx3vvvYennnoKhw8fxm233YZ33nkHH374\nYaXea89+rjcZ2x3rTL/88gtatmxpfq5ycnIyxo0bp09wq8ewXcmz/yBdxnYnW1zRsR1Bx44d8fvv\nv+PDDz/ERx99hMOHD9/0EWz2+hxnTrqx44qM7cx9XNNB+s6dO5GdnQ0AWLZsGSZOnIi//vqrwveN\nHDkScXFxJY7NnDkTEREROHr0KHr37o2ZM2cCAFJSUrBq1SqkpKQgLi4O48aNQ1FR0Q3PzZl0Iu00\nbtwYP/74Iy5cuIAjR45g586dCA4OrtR77dnPicj+nnvuOcTFxaFevXoATKku27Zt0yc4Z9KJ7GLP\nnj04cOAA9u7di5UrV+Kzzz674Wvt9TnOmXSisjTNSQ8JCcH+/ftx8OBBxMTEYPTo0VizZk2lPsRT\nU1MRGRmJgwcPAgCaN2+Obdu2wd/fH+np6QgPD8fhw4cxY8YMuLi44MUXXwQA3HfffYiNjUWXUs9r\nKM4DWLYMePxx07GoKGDFCq1qSySGyFy2ixcv4rPPPkNqaioKCgrM5Zk3b16l3q9lP3eUnD4irTlq\n2+7UqRMSExNL3JOibdu22L9/v91imnPSH+sLNN4CAIgbEYd+d/WzW0wie3OUPv7oo4/izz//RGho\nqPkxbAAwf/78G77HHt/Xq1dXUXyT+StXAL2zaYjswaFy0t3c3ODi4oINGzbg6aefxpgxY6r8jPSz\nZ8/C398fAODv74+zZ88CAP7+++8SHTwoKAhpaWnlniMmJgYXLgRf3/NFVlYogHAAluUP4eHc575j\n78fHx2PJkiUAUOlZa3t54IEH0LVrV7Rp0wYuLi7m5yRXla39PCYmxnxNfH19ERoa6hD/ZtznvrP2\n8Zu5/fbbsXPnTgBAXl4e5s2bhxYtWugTPN/yrZ0z6UTa2Lt3L1JSUoR+jgNAbm4MgGAAwEcf+aJd\nO36Wc9/59jX/LFc11L17d/U///mPetddd6lnzpxRCwoK1NatW1fqvSdOnCjxWl9f3xJ/X7t2bVVV\nVXX8+PHq8uXLzcdHjx6trlu3rsz5iqv2v/+pKmD6mTjxlqtUJVu3btUnkORxRcYWWWeNu+0tCQsL\ns+n9WvZzkddBxnbHOutHZNu+mXPnzqnDhw9X/fz81Hr16qlRUVHqhQsX7BoTgOkzfHCUilioiIX6\nWfJndo2pqnK2O9niioztKH384YcfVtPS0m7pPfb4vl78Pd3F5ZaKYhMZ2x3rrC9b+7mmM+mrVq3C\n559/jkWLFiEgIAAnT57E888/X6VzFS+bCQgIwJkzZ1C/fn0AQGBgIE6dOmV+3enTpxEYeONHsVxP\nkQcA1Khxw5cRUSVERUVhwYIFiIyMRDWru7vUqVOnSufTqp8Tkf35+fnh888/FxM8nznpRFo7f/48\nWrZsiU6dOpk/0xVFwcaNGyt9Di0/x/W6aRyRM7Drc9JvRekcl8mTJ6Nu3bp48cUXMXPmTFy6dAkz\nZ85ESkoKoqKikJiYiLS0NPTp0wd//PFHmaU6xXkAL7wAzJ5tOjZrFjB5st41I9KWyFy29957Dy+/\n/DJ8fX3h4uJiLs+ff/5Zqfdr2c8dJaePSGuO1rYnTJhg3i4um3VfrOw9KarCnJPe7/+ArnMBAHP6\nzsHErhPtFpPI3hyljxcv1S2teClveezxfR0wXYtatQCrm8MTOTWHyklft24dpkyZgrNnz5oLpSgK\nMjMzb/q+4cOHY9u2bbhw4QIaNmyI6dOnY8qUKRg6dCg+/fRTBAcHY/Xq1QCAli1bYujQoWjZsiXc\n3NzwwQcf3DSXhjPpRNqZM2cOjh8/br67862wZz8nIvtp3749ANMj2FJSUjBs2DCoqoo1a9agVatW\n+hRC50ewEcngZoPx8tj7c5wz6URWbFosX8qdd96ppqSkaHnKKiuu2mOPWXLSFy/WJ7ZseRfMcdGX\nxt32lkRERKjZ2dnC4lsTeR1kbHess35Etu2b6dSpk5qXl2fez8vLUzt16mTXmCjOV73nLXNO+otb\nXrRrTFWVs93JFldkbNF9vFu3bqqqqmqNGjVUb2/vEj8+Pj66lgVWOelBQfrFlbHdsc76srWfazqT\nHhAQoN+dXivpitUv3L29xZWDyAi8vLwQGhqKXr16lchfs+dyVyJyDJcuXUJmZibq1q0LAMjKysIl\nvdamMiedSDPFT2nItl5u6gA4k05koWlO+rPPPov09HQMHDgQHh4epgCKgsGDB2sVotKK8wD69QM2\nbzYd++474P77dS8KkaZE5rIVP1rCmqIoiI6O1r0sjpLTR6Q1R23bixcvRmxsrHmJ7LZt2xAbG4uY\nmBi7xVQUBe3aqbja4lOkNBkDABgZOhKLHqra412JHIGj9nERrHPSW7QAUlLElodIKw6Vk3758mVU\nr14dm4tHxdeJGKQX40w6kXbs+WWciBzbyJEjcd9992HXrl1QFAWzZs1CQECA3ePu3QusPOiFqC9N\n+5xJJzImzqQTWbhoebIlS5ZgyZIlWLx4cYkfkUTcOO5Gd8tkXOPEFllnkXbs2IGIiAg0adIEd9xx\nB+644w7ceeedooulOxnbHetMAODp6YkGDRrA19cXR48exfbt23WJW8ND3xvHydjuZIsrOjaVdX0R\nri5kbHess3PRdCb91KlTeOaZZ7Bjxw4AQI8ePfDuu+8iKChIyzC3xHomnXd3J7LN6NGjMXfuXLRr\n1w6urq6ii0NEOlq4cCHmzZuH06dPIzQ0FAkJCejatSt++uknu8eu4c6cdCKj40w6kYWmOel9+vTB\niBEj8OijjwIAVqxYgRUrVmDLli1ahai04jyABg2A9HTTsdOngcBA3YtCpCmRuWydO3fGrl27hMQu\njTl9ZFSO2rZbt26N3bt3o2vXrkhOTsbhw4cxdepUrF+/3m4xi69FwukEdP20KwCgU2An7BrjGP8P\nEVWFo/ZxEaxz0vv0AQQMGYjswtZ+ruly9/Pnz2PkyJFwd3eHu7s7YmJicO7cOS1D3DLOpBNpp1ev\nXnjhhRfw66+/Yt++feYfIjI+T09PVK9eHQBw9epVNG/eHEeOHNEltvVMenaeY92Rmoi04e4uugRE\njkPTQXrdunWxbNkyFBYWoqCgAMuXL0e9evW0DHFLVLXkIN3LS5+4suVdMMdFHgkJCdizZw9eeukl\nTJo0yfwjGxnbHetMQUFBuHjxIgYOHIiIiAgMGDAAwcHBusT29rDc+VWPQbqM7U62uKJjU1nMSTdm\nXJGxnbmPa5qTvnjxYowfPx4TJ04EAHTr1k3ojeMKCoCiItO2q6u+nZ/IaAoLCzFgwABz/yYiuWzY\nsAEAzI9hy8zMxH333adL7JrVapq3s65l6RKTiPTFmXQiC01z0qOjozF37lzUrl0bAJCRkYHnn38e\nixbp/zxTRVFw+bKKWrVM+zVqlLzTO5GzEpnL1rFjR+zevVtI7NKY00dG5Yhtu6CgAK1bt8bhw4d1\njVt8LfIK81DtTdNdpVwVV+S/mn89l5XI+ThiHxfFOid9+HDg88/FlodIKw6Vk75//37zAB0A6tSp\nIzRfNTfXsn09jY6IbHDPPfdg/Pjx+Pnnn7Fv3z7s3buXOelEEnBzc0OzZs3w119/aXK+NWvWoFWr\nVnB1da3U/yEerh6o5moapBeqhcgtyK3gHUTkbDiTTmSh6SBdVVVkZGSY9zMyMlBYWKhliFsiapAu\nW94Fc1zkkZSUhEOHDuG1117DpEmT8PzzzzMnXYK4ImPLWGdHlZGRgVatWuHee+9FZGQkIiMjMWDA\ngCqdKyQkBOvXr0ePHj0q/R49l7zL2O5kiys6NpWl5yBdxnbHOjsXTXPSJ02ahK5du2Lo0KFQVRVr\n1qzByy+/rGWIW3L1qmWbM+lEtnPm/+yIyDZvvvlmmaV7VV1y3rx581t+T81qNXE+5zwAIPNaJvy9\n/asUm4gcE2fSiSw0zUkHgEOHDuGnn36Coii499570bJlSy1PX2mKomDfPhXt2pn227QB9u8XUhQi\nTYnMZUtPT8fLL7+MtLQ0xMXFISUlBb/++itGjx6te1mY00dG5ahte/Lkyfjvf/9b4tiLL76IWbNm\nVfmcvXr1wpw5c9Cu+MO6FEVREB0djeDgYHy892OkF6QDAcDet/aiXYN25l8choeHAwD3ue+Q+8Xb\nqampAIClS5c6ZB8XwTonffx4YP58seUh0oqtn+WaD9IdhaIo2LlTxd13m/Y7dwYSEsSWiUgLIr/A\n33fffRg5ciT+85//4MCBA8jPz0dYWBh+++033cviqAMZIls5atsOCwtDUlJSiWMhISE4ePBgua+P\niIhAenp6meNvvfUWIiMjAVRukF58LXou6Yntf20HAGyN3orw4PCqVoVIKEft4yJYD9L/7/+At98W\nWx4irTjUjeMcjajl7rLlXTDHRR4XLlzAsGHD4OrqCgBwd3eHm5umWTNOQcZ2xzrL68MPP0RISAiO\nHDmCkJAQ809wcDDatGlzw/dt2bIFBw8eLPNTPEC/VT4ePubtzGuZVTpHZcnY7mSLKzo2lcWcdGPG\nFRnbmfu4ob9dW984ztNTXDmIjMLb2xv//POPeT8hIQG1ip9zSESGFBUVhfvvvx9TpkzBrFmzzDMD\nPj4+qFu3rs3nr+xMA5+VTmRszEknsjD0cvc1a1T861+m/UGDgC+/FFsmIi2IXCa3d+9eTJgwAYcO\nHUKrVq1w/vx5rF27Fm3bttW9LFwuSEYlQ9tev349nnnmGVy4cAG1atVCWFgYNm3aVOZ11tdi7Ddj\n8fHejwEAHzzwAf7d8d+6lplIKzL08cqyXu4eGwu8/rrQ4hBpxtZ+Ls1MOu/uTmS79u3bY/v27Th8\n+DBUVUWzZs3g4eEhulhE5GQGDRqEQYMG3dJ7fKpZlrtn5XEmnchoOJNOZMGcdDuQLe+COS7yaNOm\nDf773/+ievXqCAkJkXaALmO7Y51JtJoeluXuzElnXGePTWUxJ92YcUXGduY+buhBOnPSibS1ceNG\nuLq6YujQoejQoQNmz56NkydPii4WEUnAOifd3oN0ItIfZ9KJLAydkz5zpoopU0z7zz8P/O9/YstE\npAVHyWU7duwY3njjDaxYsQKFhYW6x3eU60CkNbZtC+trsShpEUZvHA0AiAmNweKHFossGlGVsY9b\nWOekv/ce8PTTYstDpBXmpN+EqOXuREaWmpqKVatWYfXq1XB1dcV///tf0UUiIglwJp3I2DiTTmQh\nzXJ35qQbL67I2M6c42KLzp07Y9CgQSgqKsKaNWuQmJiISZMmiS6W7mRsd6wzicbnpDOukWJTWcxJ\nN2ZckbGduY8beiadOelE2lq6dCmaN28uuhhEJCE+J53I2DiTTmRh6Jz0J59UsWCBaf+DD4B/85Gq\nZAAic9kuXbqEadOmYfv27QCA8PBwvPbaa6hVq5buZWFOHxkV27aF9bU4dO4QWn/YGgDQol4LpDyd\nIrJoRFXGPm5hnZP+xRfAsGFiy0OkFVv7uaGXu+flWbarVRNXDiKjGDVqFGrWrIk1a9Zg9erV8PHx\nwciRI0UXi4gkwOekExkbZ9KJLAw9SL92zbKt5yBdtrwL5rjI4/jx45g2bRruvPNONG7cGLGxsTh+\n/LjoYulOxnbHOpNoet44TsZ2J1tc0bGpLDcdk3BlbHess3Mx9CDdeibdw0NcOYiMonr16vj555/N\n+zt27ICXl5fAEhGRLKxvHJd1LYvLhYkMhjPpRBaGzknv31/FN9+Y9r/6ChgwQGyZiLQgMpctOTkZ\njz/+OC5fvgwAqF27NpYuXYq2bdvqXhbm9JFRsW1blL4WNd6qgZz8HABA5pTMEkvgiZwF+7iFdU76\nli1Anz5iy0OkFT4n/SY4k06krdDQUBw4cACZmaalpjVr1qzgHURE2qntWds8SL909RIH6UQGwpl0\nIgtDL3dnTrqx44qM7cw5LraYM2cO3n77bXzyySf45JNP8Pbbb+PTTz9FcnKy6KLpSsZ2xzqTI6hT\nvY55+5/cf+wWR8Z2J1tc0bGpLD4n3ZhxRcZ25j4uzSCdM+lEttu7dy8++ugjpKWl4fTp0/j444+x\nadMmPPHEE5g1a5bo4hGRwVkP0jNyMwSWhIi0xpl0IgtD56S3a6di3z7T/u7dQIcOYstEpAWRuWzd\nu3fHpk2b4O3tDQDIzs7GAw88gLi4OLRv3x6///67bmVhTh8ZFdu2RelrMXjVYKw/vB4AsOZfa/Bw\ny4dFFY2oytjHLaxz0pOSgNBQseUh0orhc9KDg4NRs2ZNuLq6wt3dHYmJicjIyMCwYcPw119/ITg4\nGKtXr4avr2+Z94pa7k5kVOfPn4eH1bIUd3d3nD17Fl5eXvD09KzyeW3p50QkD86kEzkmLT7HOZNO\nZOHwy90VRUF8fDySkpKQmJgIAJg5cyYiIiJw9OhR9O7dGzNnziz3vaJuHCdb3gVzXOQxYsQIdO7c\nGdOmTUNsbCy6deuGqKgoXLlyBS1btqzyeW3p5yLI2O5YZ3IEJXLSc5iTzrjOG9totPgcZ066MeOK\njO3MfdzhZ9IBlFkqsHHjRmzbtg0AEB0djfDw8HI7PmfSibT16quv4r777sPOnTuhKAo+/vhjdLie\nR7JixQqbzl2Vfq4oNoUkIidTYib9KmfSiRxJVb+vF+NMOpGFww/SFUVBnz594OrqiqeeegpPPPEE\nzp49C39/fwCAv78/zp49W+57z5+PARAMAFi61Bc9e4YiPDwcgOU3K/bYDw8Pt+v5b7ZfTM/4Iusr\nar/4mB7x4uPjsWTJEgCm5WSidezYER07dtT0nFXv5zEo7uOAL4BQAOHX9+Ov/2mP/XA7n/9m+6jg\n7+21X3xMr3iOso8K/l6L/XgAS67vB4NurG71uuZtey53t/6/Xm+iYssWV3Rso7Hl+3rxZ/n8+cDt\nt/siNFSf7+si94vx+7p994uPOeP3dYe/cdyZM2fQoEEDnD9/HhEREZg/fz4GDBiAixcvml9Tp04d\nZGSU/LBWFAW1a6softmFC0DduiByeka84UxV+rn1zWaIjMV4fbyqSv9/ty5lHR5eY7pZ3IBmA/DV\nI1+JKhpRlfFz3ML6s/zMGSAgQM9SE9mPrf3cRcOy2EWDBg0AAH5+fhg0aBASExPh7++P9PR0AKb/\nFOrXr1/ue0U9gq30b8kY13ixRdbZiKraz1VVzM/WrfFSxWWd9f2hG6tfw/L/wLkr5+wWh59rxo8r\nOrbR2PJ9vZibjut7ZWx3rLNzcehBek5ODrKysgAAV65cwebNmxESEoIBAwZg6dKlAIClS5di4MCB\n5b7f+sZxzEkncky29nMikoe/t795256DdCKqPK0+x/UcpBM5Oode7n7ixAkMGjQIAFBQUIARI0Zg\n6tSpyMjIwNChQ3Hy5MkbPtKh9FLYoiLeZIqMwWjL5Kraz412HYiKsW1blL4Wl65eQu1ZtQEA3h7e\nyJqaJapoRFVmtD6u1ff1zEzAx0fv0hPZh6393KEH6baw7vQeHiWXvhM5M6N9uFcVrwMZFdu2Relr\noezqk0cAACAASURBVKoqPP/jibxC01K5Ky9dgZe7l6jiEVUJ+7iF9ff1nBygenWx5SHSiuFz0rWg\n91J32fIumONCspGx3bHOpLUXXngBLVq0QNu2bTF48GBcvny5wvcoiqJLXrqM7U62uKJjU1nMSTdm\nXJGxnbmPSzFI1/OmcURERFSxvn374tChQ9i/fz+aNm2KGTNmVOp9/jUseelns2/0SCcicjaurqJL\nQOQ4pFjuftttQFqa2PIQaYXL5Ex4HcioZGzb69evx7p167B8+fISx8u7Fg9+/iC+O/ad6X3D1mNg\nc95UkpyLjH38Roq/ryuK6f5RREZhaz+X4j6KvLM7ERGR41q0aBGGDx9e7t/FxMQgODgYAODr6wsl\nx3IX2Pit8fBN90V4eLhp//rSRu5z35H2i7dTU1NB5eOd3YlKUQ0KgPmps82a6Rt769at+gaUNK7I\n2CLrbOBue0tEXgcZ2x3rrB8j9fE+ffqorVu3LvOzceNG82vefPNNdfDgweW+v7xrMT1+uopYqIiF\nOuWHKXYpt4ztTra4ImMbqY/bqvj7uqenvnFlbHess75s7edS/N6KOelERET627Jly03/fsmSJfju\nu+/w448/VvqcQTWDzNunM09XuWxE5Dg4k05UkhQ56e3bA3v2iC0PkVaYy2bC60BGJUvbjouLw6RJ\nk7Bt2zbUq1ev3NeUdy22HN+Cvsv7AgDCg8OxNXqr3ctKpCVZ+nhlFH9f9/UFLl4UXRoi7fARbJXg\n7i66BERERGRtwoQJyM7ORkREBMLCwjBu3LhKvc96Jv3U5VP2Kh4R6Yh3dicqiYN0O5DtWYB87iLJ\nRsZ2xzqT1o4dO4a//voLSUlJSEpKwgcffFCp991e63bz9snLJ1FYVKh52WRsd7LFFR2bStJ7ubuM\n7Y51di4cpBMREZHTqOFRw/ys9PyifJzK5Gw6kbPjTDpRSVLkpPftC3z/vdjyEGmFuWwmvA5kVGzb\nFje6Fncvuhu/nPoFAPDDYz+g95299S4aUZWxj1sUf1+//Xbgr79El4ZIO8xJrwTOpBMRERlH49qN\nzdvHLx4XWBIi0gLv7k5UEgfpdiBb3gVzXEg2MrY71pkcSZM6Tczbhy8c1vz8MrY72eKKjk0l6b3c\nXcZ2xzo7Fw7SiYiIyKm0qt/KvP3bud8EloSItMCZdKKSpMhJj4oCVqwQWx4irTCXzYTXgYyKbdvi\nRtfi6D9H0ey9ZgCABt4N8Pekv/UuGlGVsY9bFH9fDwkBDhwQXRoi7djaz6X4vRVn0omIiIyjce3G\n8HTzxNWCqziTfQbnr5yHXw0/0cUioiqqzHL37LxsHLlwBOdzzuNCzgVk52UjvzAf+UX5yCvMQ35h\nPorUojLvU1F2oFTuMf7ihBwIB+l2EB8fj/DwcH2DShhXZGyRdSbxZGx3rDM5ElcXV7T1b4tdabsA\nALv/3o0Hmjyg2fllbHeyxRUdm0q60XL33879hgV7F2DTH5vwR8Yf2gVMBRCs3emcIraouCJji4qr\nASly0pnnQkREZCxdgrqYtxNOJwgsCRHZqvRM+pW8K4jZEIOQD0MwP3G+tgN0IicgRU76hAnAvHli\ny0OkFeaymfA6kFGxbVvc7Fp88dsXGL5uOAAgPDgcW6O36lk0oipjH7co/r5+993Ajh2mYxdyLqDP\nZ32w/+z+Eq91c3FD07pNEegTiHpe9eBTzQcerh5wd3GHu6s73F3c4epS/rp5BUrljilljxFVxevh\nrzMnvSLMSSciIjKWno16mrd3ntyJ7LxseHt4CywREVVV8arXwqJCjPhyRIkBemTTSDzT+Rl0v707\nqrlVE1RColvzOl636f0cpFvJzc/FhZwLyC3IxbWCa8grzMO1wmvmm1AU/zbE+mYTpY+pqorkhGSE\ndgnVsAaVI1tckbFF1pnEY+6m8eOKjk0Va+DTAG382+DA2QPIL8rHluNbMKjFIE3OLWO7ky2u6NhU\nUvFy93d3vYvNxzebj3/c/2M82f5JzePJ2O5YZ+ci7SD9WsE1fH30a/x44kckpiXieMZxXL52WZuA\nqQCOanMqxnXQ2KLiEhGRWf+m/XHgrOm5TSt/W6nZIJ2I9OXmZspDn7FjhvnYS91fsssAncgZSJGT\nPm0a8NprpuOqqmLp/qV48YcXce7KOXEFJKqqWD4mBGBOHxkX27ZFRdfit3O/IeTDEACAh6sH/nru\nLwR4B+hVPKIqYR+3KP6+/sADQO9X3sakzZMAAI1qNcKxCcfg7sqcVXJOfE56JRTPpBcWFWLM12Ow\nJHlJua9zc3GDn5cfvNy9UM2tGqq5VkM1t2pwVSw3oSi+oYT1zSZKH+NNJ8ie4hEvughERA6hdf3W\n6BzYGbvSdiGvMA+zf5mN2X1niy4WEd0iF1cV8xPnm/en3DOFA3SSmhSD9OKbUYz7blyJAfptPrdh\nZOhIhAeHo61/W9T1qgsXxfan0smWd8EcF30pMfwlkGgytjvWmRzV5LsnY8jqIQCAebvmYXCLwejW\nsJtN55Sx3ckWV3RsKumKTxJSL6UCAGpVq4WRoSPtGk/Gdsc6OxcpBunu7sB3x77Dgr0LzMei20bj\n/QfeRw2PGgJLRkRERLYY1HwQ7m54N3ae2on8onwMXjUYm0ZsQliDMNFFI6JKSq+93rwd2SySd3En\n6UmRk/7O/Kt4O78pTmWeAgD8q+W/8MXDX2gya06kN+aymfA6kFGxbVtU9lqkXkpFhwUd8E/uPwCA\naq7V8GT7J/Fom0cRFhDGZbPkUNjHLYq/r9ec2gqZ1VIAAF8O/ZI3gSSnZ2s/l2KQHv3OYiy9PAoA\nUM+rHn5/+nfU86onsHREVccPdxNeBzIqtm2LW7kW21K3IXJlJLLyskocd3dxx52174RfDT/U9qyN\nWp614O7iDlcXV7gqrnBzcTNv2+OeMtb3sNH0vLz/jdOa3Xc2+/h1iqIANU8CE28HAFR3q44Lky/A\ny91LcMmIbMMbx1VIxdYcy40oJnebbPcBumx5F8xxIWdQWFSIvMI85BflI68wD3mFeSgoKijzuvL+\nQ1VR8ljCjgR0vruz3cp6Iwk7EtDlni66xxUZW8Y6U9X0DO6J3U/sxqPrH8Wev/eYj+cX5ePIP0dw\n5J8jlT9ZKoBgrUvo4LFliys6Nlk0/MW82a1hN10G6PzuKkdsZ/6+bvxBelACTuYnATD9dm50u9GC\nC0RE9nTp6iV8e/Rb/Hr6Vxw4ewBpWWk4f+V8mdk1m6QC2Kvd6W4p7j4BcUXGFhVXdGyqkmb1miFx\nTCI2H9+Mlb+txE8nfjKnuhGRg7IapHdt2FVgQYgch/GXu/d9Hug2BwAwOmw0PhnwidiCEdmIS2FN\nSl+Hf3L+wfTt0/Hxno9xrfCawJIR2Si2/BUdMtLi/7vMa5lIvZSKi7kXcfHqRWRey0RBUQEKiwpN\nf6qF5m2tlV6Fo9l52T6c2uR7JvPf8DpFUYAnOgKBuwEA30V9h/ub3C+4VES243L3ijT92rw5pMUQ\ngQUhInvZnbYbQ1YPuemMmQIFHq4e5h93V3e4ubiVmy9aXq5nZV9HZKtUpIougqHUrFYTbfzbiC4G\nkdlkTBZdBMcSkGTe7BLEFCMiwOiD9LpHgXpHAQBe7l7odUcvXcLKlnfBHBcSaXfabtz72b3Izss2\nH2vj3wZDWgxB+wbt0aRuE/h5+cHX01ezQbWM7Y511o/ynPF/+fPqq69i48aNUBQFdevWxZIlS9Cw\nYUPRxTKTsd3JFld0bLLialrF0qJeC9SuXluXkDK2O9bZuRj7GWRNvzFvRtwZAU83T13CJicn6xJH\n9rgiY4usM1n8fv533L/ifvMA3dfTF2v+tQbJTyXjtZ6v4cGmD6Jp3aaoXb22prPeMrY71pm0NHny\nZOzfvx/JyckYOHAgpk2bJrpIJcjY7mSLKzo2laVnPrqM7Y51di5OPUiPi4tD8+bN0aRJE8yaNavs\nC6yWukc2jdStXJcuXdItlsxxRcYWWWeZVNTHI5ZFmJ+LXLd6XewctRMPt3zY7svQZWx3rDNpycfH\nx7ydnZ2NevUc67GoMrY72eKKji2LCr+rW+kW1E2nUsnZ7lhn5+K0y90LCwsxfvx4/PDDDwgMDETH\njh0xYMAAtGjRwvKiRj+bNx9o8oCAUhJRVVWmj6dlpQEAvD28sWnEJrT0aymquER0i15++WUsW7YM\nXl5eSEhIuOHrYmJiEBwcDADw9fVFaGioeflifHw8AGi+X8xe57/ZfmpqqtD4MtU3NTW1xHJYe7an\n0nWVQaW+q1vhnd2JrKhO6pdfflH79etn3p8xY4Y6Y8YM8z4AFbGmnxZvd9S1bNHR0brGkzWuyNgi\n6+zE3faW3EofX5K0RNeyydjuWGf9GKWP9/l/9u48roqq/wP4Z1jcNVxRwaeLgCKCgJhLi2KKpiW5\ni5aJj5bpo21umb8KK0UreyzJMiu1jTRwLSXNR1IzQRHSJFlMElAUXFFU5DK/P653k1W5M3Pvnc/7\n9eLlzHDvfM8ZznHuuWfOOf37i35+fuV+tmzZYva6qKgoMSIiosJzKHUt1Fju1BZXydj2UserU919\nXBSN9/L6bzYXtWVa2dKmxnLHPMurtvXcZpdgi42Nxc8//4xVq1YBAL755hskJiZi+fLlADjrMtk3\nG622d4V1nNRMDXVc79SpUxg8eDD+/PPPcr9jPSd7pYY6Xt19HGAdJ/tWm3pus4+7V1ep1fCfH5E9\nYx0nsl+ZmZnw9vYGAGzevBlBQUEVvo71nMh21aQBzjpOVDGbbaS7ubkhJ8e4JnJOTg7c3d0VTBER\nWRLrOJH9mjdvHtLT0+Ho6AhPT0988sknSieJiCyM93Gie2ezj7uXlpaiY8eO2LVrF9q2bYvu3bsj\nJiam0skoiMi2sI4TERHZLt7Hie6dzfakOzk5ITo6GgMHDoRWq8WkSZNY6YnsCOs4ERGR7eJ9nOje\n2fQ66YMGDUJ6ejqysrIwb948w/G7WZOxtjQaDbp06YKgoCB0794dAHDhwgWEhoaiQ4cOGDBggMXW\n6Pv3v/8NV1dX+Pv7G45VFSsqKgre3t7w8fHBjh07LBo3MjIS7u7uCAoKQlBQELZv327xuIDu0ai+\nffuic+fO8PPzw0cffQRA+nxXFlfqfN+4cQM9evRAYGAgfH19DeVajr+zNbKGOg7IV8+VquOVxZaj\nnqutjgOs5zVlj/dy1nH56nhVsXkvl09l93HAPus4oL7P66zjEtXxWs0Nb4VKS0tFT09P8eTJk2JJ\nSYkYEBAgpqWlSRZPo9GI58+fNzs2e/ZsccmSJaIoiuLixYvFuXPnWiTWnj17xMOHD4t+fn7Vxjp2\n7JgYEBAglpSUiCdPnhQ9PT1FrfbelraoKG5kZKS4dOnScq+1ZFxRFMUzZ86IKSkpoiiKYlFRkdih\nQwcxLS1N8nxXFleOfF+7dk0URVG8deuW2KNHD3Hv3r2y/J1thdx1XBTlq+dK1fHKYstR3tVYx0WR\n9bw69novZx2Xr45XFZv3cuXZax0XRfV9Xmcdl6aO23RPekWSkpLg5eUFjUYDZ2dnhIeHY/PmzZLG\nFO8Y1r9lyxZMmDABADBhwgRs2rTJInEeeeQRNG3atEaxNm/ejLFjx8LZ2RkajQZeXl5ISkqyWFyg\n4hk5LRkXAFq3bo3AwEAAQKNGjdCpUyfk5eVJnu/K4gLS57tBgwYAgJKSEmi1WjRt2lSWv7OtUKKO\nA/LUc6XqeGWxAenLuxrrOMB6Xh17vZezjstXx6uKDfBerjR7reOA+j6vs45LU8ftrpGel5eHdu3a\nGfbd3d0NfywpCIKA/v37o1u3boZ1IM+ePQtXV1cAgKurK86ePStZ/MpinT592mwGTSmuw/LlyxEQ\nEIBJkyYZHueQMm52djZSUlLQo0cPWfOtj9uzZ08A0ue7rKwMgYGBcHV1NTzCo+Tf2drIXccBZeu5\n0n97Oeu5Wuo4wHpeHTXdy5X+u6uhjpvG5r3cOqipjlcVy94+r7OOWy7PdtdIr8majJb022+/ISUl\nBdu3b8fHH3+MvXv3lkuPXGmqLpYl0zF16lScPHkSqampaNOmDWbOnClp3KtXr2LEiBH48MMP0bhx\n43LnlyrfV69exciRI/Hhhx+iUaNGsuTbwcEBqampyM3NxZ49e7B79+5y55Xr72yNlMiftdRzuf/2\nctZzNdVxgPW8Omq9l7OOSxeb93LrotY6XpNYtvp5nXXcsnXc7hrpcq/J2KZNGwBAy5YtMWzYMCQl\nJcHV1RX5+fkAgDNnzqBVq1aSxa8s1p3XITc3F25ubhaL26pVK0Phmzx5suGRDSni3rp1CyNGjMD4\n8eMxdOhQAPLkWx/36aefNsSVM9/33XcfHn/8cSQnJyv2d7ZGSqy7qmQ9V/JvL1d5V2sdB1jPK6Om\neznruLT5Vrqes45XTE11HLD/z+us45av43bXSO/WrRsyMzORnZ2NkpISrFu3DmFhYZLEKi4uRlFR\nEQDg2rVr2LFjB/z9/REWFoa1a9cCANauXWsoMFKoLFZYWBi+//57lJSU4OTJk8jMzDTMZmkJZ86c\nMWxv3LjRMJOkpeOKoohJkybB19cXL730kuG41PmuLK7U+S4sLDQ8knP9+nXs3LkTQUFBiv2drZGc\ndRxQvp4r+beXo56rrY4DrOc1oaZ7Oeu4dPnmvdx6qamOA/b9eZ11XKI6fk/T2Vm5bdu2iR06dBA9\nPT3FRYsWSRbn77//FgMCAsSAgACxc+fOhljnz58X+/XrJ3p7e4uhoaHixYsXLRIvPDxcbNOmjejs\n7Cy6u7uLX375ZZWxFi5cKHp6eoodO3YU4+PjLRb3iy++EMePHy/6+/uLXbp0EZ988kkxPz/f4nFF\nURT37t0rCoIgBgQEiIGBgWJgYKC4fft2yfNdUdxt27ZJnu8jR46IQUFBYkBAgOjv7y++++67oihW\nXaYseb1thVx1XBTlredK1fGKYstVz9VWx0WR9bym7PFezjouXx2vLDbv5dbDHuu4KKrv8zrruDR1\nXBDFCqa+IyIiIiIiIiLZ2d3j7kRERERERES2io10IiIiIiIiIivBRjoRERERERGRlWAjnYiIiIiI\niMhKsJFOlbp8+TI++eQTALqlDEaNGqVwiojIkljHiewf6zmRfWMdt0+c3Z0qlZ2djSFDhuDo0aNK\nJ4WIJMA6TmT/WM+J7BvruH1yUjoBZL1effVVnDhxAkFBQfD29sZff/2Fo0ePYs2aNdi0aROKi4uR\nmZmJmTNn4saNG/juu+9Qt25dbNu2DU2bNsWJEycwffp0FBQUoEGDBli1ahU6duyodLaI6DbWcSL7\nx3pOZN9Yx+3UPa3gTqqQnZ0t+vn5ldtevXq16OXlJV69elUsKCgQmzRpIq5cuVIURVF8+eWXxWXL\nlomiKIqPPvqomJmZKYqiKB44cEB89NFHFcgFEVWGdZzI/rGeE9k31nH7xJ50qpRoMhJCvGNURN++\nfdGwYUM0bNgQLi4uGDJkCADA398fR44cwbVr17B//36zcTElJSXyJJyIaoR1nMj+sZ4T2TfWcfvE\nRjrdk7p16xq2HRwcDPsODg4oLS1FWVkZmjZtipSUFKWSSES1wDpOZP9Yz4nsG+u47eLs7lSpxo0b\no6io6K7eo/8Gr3HjxvDw8EBsbKzh+JEjRyyeRiK6d6zjRPaP9ZzIvrGO2yc20qlSzZs3x0MPPQR/\nf3/MmTMHgiAAAARBMGzr90239fvffvstvvjiCwQGBsLPzw9btmyRNwNEVCXWcSL7x3pOZN9Yx+0T\nl2AjIiIiIiIishLsSSciIiIiIiKyEmykExEREREREVkJNtKJiIiIiIiIrAQb6URERERERERWgo10\nIiIiIiIiIivBRjoRERERERGRlWAjnYiIiIiIiMhKsJFOREREREREZCXYSCci2f373/+Gq6sr/P39\nK33NCy+8AG9vbwQEBCAlJUXG1BEREZElxMfHw8fHB97e3liyZInSySGyGWykE5HsJk6ciPj4+Ep/\nv23bNmRlZSEzMxOfffYZpk6dKmPqiIiIqLa0Wi2mT5+O+Ph4pKWlISYmBn/99ZfSySKyCWykE5Hs\nHnnkETRt2rTS32/ZsgUTJkwAAPTo0QOXLl3C2bNn5UoeERER1VJSUhK8vLyg0Wjg7OyM8PBwbN68\nWelkEdkEJ6UTIBVBEJROApFkRFFUOgmSysvLQ7t27Qz77u7uyM3Nhaurq+EY6zjZM3uv4zXFek72\nSg11vKJ7eWJiotlrWMfJntWmntt1T7ooior8vPnmm4xr57GVzLNa3JnXim7kLHf2H1uNeSZzNb1u\nZWUi1q8X0bu3CEEQAdTm581avt8WY6strpKx1aHmDXARb76pjv/flYzNPMv7U1t225OupOzsbMa1\n89hK5lkN3NzckJOTY9jPzc2Fm5ubgikyp8ZyxzyTNTt3Dhg5Eti711JnzLbUiWwottriKh3b/t15\nL8/JyYG7u3uFry0tlStVOmq8tzDPtoWNdCKyOmFhYYiOjkZ4eDgOHDgAFxcXs0fdiYj0Tp8GevcG\nTpwwHnNwAAICdD/u7kCrVkDjxkDduoCzM1Cnju5fR0fzc+k7/t57D5g927h/5+8r27eEJUuAuXMt\nf17GtZ7Y/frJH1MJ3bp1Q2ZmJrKzs9G2bVusW7cOMTExFb5W7kY6kbVjI10CERERjGvnsZXMsz0Y\nO3Ysfv31VxQWFqJdu3ZYsGABbt26BQCYMmUKBg8ejG3btsHLywsNGzbE6tWrFU6xOTWWO+aZrFFZ\nGfDMM8YGuoMD8MorwMyZQOvW935eZ+cIhIRYJIl3zcFBmdhqi6t0bDVwcnJCdHQ0Bg4cCK1Wi0mT\nJqFTp04VvlbuRroa7y3Ms20RREs8NG+FBEGwyHgAImvDsq3D60D2imXbqLpr8fHHwPTp+tcCcXHA\nsGEyJY7oHrGOG+nGrYt48UVg2TKlU0NkObWt53Y9cZxSEhISGNfOYyuZZ1KeGssd80zWprgYeOst\n4/7cuZZroKux3KktrtKxyZxWK288NZY75tm2sJFOREREioiPj4ePjw+8vb2xZMmSu3rvunW6CeMA\n3bjzyEjLp4+I5MEx6UTm+Lg7kY1h2dbhdSB7pZayrdVq0bFjR/zyyy9wc3PDAw88gJiYGLMxq1Vd\ni0ceAfbt022/+65uojciW6CWOl4T+sfdJ08GVq1SOjVEllPbes6J44iIiEh2SUlJ8PLygkajAQCE\nh4dj8+bN5SaW+injJ/Rv3x91neoajuXnA7/9ptt2cNBNHlepsjLg8mXg2jWgpAS4dcv4b1mZ+Wsr\n+0BV0XE2sogshj3pRObYSJdAQkICQhSYLlRtcZWMrWSeSXlqLHfMM1laXl4e2rVrZ9h3d3dHYmJi\nudc9MeYJvDjwRbjUc4GLiwsCAwNx4kTI7TZyAvz9AVfXEABAwq5dQEoKQvLygNRUJBw5Aly6hJDb\njfGE2+cMuf1vZfv6YzV9vSX3UwG8JGM8/b5+W654+n2l8gsAywAEyhBPv50NqozcjXQ13luYZ9vC\nRjoRERHJTqjpIuNDgbGTxqKHew/DIeNjsSF4+unbmxs2IGTuXCArC8bfmqvpfkIt389929gPvOOY\nlPFMt9eC7sSedCJzHJNOZGNYtnV4HcheqaVsHzhwAJGRkYiPjwcAREVFwcHBAXPnzjW8RhAEIBLY\nOnYrnujwBADdU+b/+heQm6t7zaGkMgTHvQZUNfFckyZAo0ZAnTq6H2dn3Y+jY8Wvr+gLhJoeI6qG\nkJSkijpeE/ox6aNGAevXK50aIsvhmHQiIiKyOd26dUNmZiays7PRtm1brFu3DjExMRW+9ty1c4bt\n3FxjA71RIyDw58XmDXQXF90g9X79AH9/oG1boG5dEFkNfrlTDnvSicxxCTYJqG0tQK67SGqjxnLH\nPJOlOTk5ITo6GgMHDoSvry/GjBlTbtI4PdNG+uHDxuMTvPfDMfIN44EnngD+/hv48EMgLAzw8Lin\nBroay53a4iodm8wpMSZdKaxr9h/XEtiTTkRERIoYNGgQBg0aVO3rTBvpqan6LRFz82YAWq1u9+GH\ngY0bASd+tCGyNexJJzLHMelENoZlW4fXgewVy7aRfkw6ABybdgy+LX0xbBiwaRPwGLZjOwbrflmv\nHpCerhusTmTlWMeN9GPSQ0OBHTuUTg2R5XBMugSKi4E//wRycoCCAuDiReDmTePSqiUlFX/jd+ff\ngcuqEhERWYbfCj881eUpHM2NBOCJ5/Gp8ZdTprCBTmTD2JNOZI496bdduQJ8+imwbp3uUbrbS6re\nowSUX4hDDmqLq2RspeICAL+BB5TtiVDjep/Ms3zYy2Zk2pNuoHXCfYfCcW7796iD25/s09OBDh0s\nFleN5U5tcZWMzTpupO9J790b+PVX+eKqsdwxz/JiT7oFbNig+xK+sFDplBAREZGpjs074srNKzhz\n9YzugGMpnqj/Derc/v0hNwGjtg9Eo12NUN+pPuo51UMdxzoQBAEChBr9e6fCtEK0yG8hXyatILba\n4iodm8yxJ53InOp70qOjgRkzzI85OAAdOwLe3kDLlkDz5rrhbs7OxiVWnZxqtmQql1UlS5s6ld/A\nA+yJIPvFsm1kei325+zH8z+8hqNFv2LtRuCZP3Svmf8osKi3gokkuluRYB2/Td+T3r07kJiodGqI\nLIc96bWwZw/wwgvG/XbtgDffBEaOBO67T7l0EVVl6lSlU0BEJL8H2z2I5+vtxn8+SkDI2ScBFAEA\nfmmvbLqIqPbYk05kTrWN9KtXgfHjjRO59egB/PSTrte8ttQ27oJjXEht1FjumGeyBrm5Av71T3v8\n63YDHY0aYdeHOci/UYjiW8W4fus6rpdexy3tLYgQIYpitf9W5M+kP+HX3U/GnCkfW21xlYw9NHKo\n7DGtnRLrpKvt3sI82xbVNtKXLwdOndJtN2sGxMVZpoFORERE0jh1CghGsvHAAw+gUQMXeDVwhLwN\n0gAAIABJREFUsWic+/LvQ4hPiEXPae2x1RZX6dhkTqtVOgVE1sXqx6RrNBo0adIEjo6OcHZ2RlJS\nEi5cuIAxY8bgn3/+gUajwfr16+HiYn6DrmocwK1bukfbz57V7a9aBUyeLHVOiCyD41V1eB3IXrFs\nG915LXr3BgbunY/5WKQ7MGsW8N57CqWO6N6wjhvpx6R37AgcP650aogsp7b13MGCaZGEIAhISEhA\nSkoKkpKSAACLFy9GaGgoMjIy0K9fPyxevPiuzrltm7GB3qYNMGGCpVNNRERElnbqFBCEFOOB4GDl\nEkNEFsMx6UTmrL6RDpSfAXPLli2YcLtlPWHCBGzatOmuzhcba9yeMEE3a7slJSQkWPaEjGt1sZXM\nMylPjeWOeSaliSJw+jTgA5PuNn9/SWKpsdypLa7SscmcEmPSlcK6Zv9xLcHqx6QLgoD+/fvD0dER\nU6ZMwbPPPouzZ8/C1dUVAODq6oqz+m7xO0RERECj0QAAXFxcEBgYiIcfDsFPPwFAAgBg1KgQAMY/\non5yAVvcT01Ntar0yLGvJ3f81NRU2eIlJCRgzZo1AGAoz0REanPxIuB46zo0yNYdcHAAvLwUTRMR\nWQZ70onMWf2Y9DNnzqBNmzYoKChAaGgoli9fjrCwMFy8eNHwmmbNmuHChQtm76tsHMDBg0D37rpt\nNzcgJ4frlpNt4Vg2HV4Hslcs20am1yItDQjvfARHEKD7pZcXkJmpYOqI7g3ruJF+TLqrK5Cfr3Rq\niCzH7sekt2nTBgDQsmVLDBs2DElJSXB1dUX+7Zp85swZtGrVqsbn27PHuN23LxvoREREtuDsWaAD\nMowHOnZULjFEZFGc3Z3InFU30ouLi1FUpFsL9dq1a9ixYwf8/f0RFhaGtWvXAgDWrl2LoUNrvt7k\nvn3G7UcesWhyDdQ27oJjXEht1FjumGdSWn4+jI+6A4Cnp2Sx1Fju1BZX6dhkjmPS7TeukrFtuY5b\n9Zj0s2fPYtiwYQCA0tJSPPXUUxgwYAC6deuG0aNH44svvjAswVZTKSaTwvbsaekUExERkRTOnr2j\nkX7//YqlhYgsi2PSicxZ/Zj0e1XROIALF4DmzXXbdesCRUWWn9mdSGocy6bD60D2imXbyPRavPoq\n8NCSIRiCH3W/jI0FRoxQMHVE94Z13Eg/Jr1ePeD6daVTQ2Q5dj8m3ZL++MO47efHBjoREZGtOH+e\nPelE9oo96UTmVNVI/+sv47ZES6sCUN+4C45xIbVRY7ljnklpFy4A/8Ip4wEJG+lqLHdqi6t0bDLH\nMen2G1fJ2LZcx1XVSM/gpLBEREQ26VpBMe7DFQBAmZMz0KKFwikiIksqK1M6BUTWQ1Vj0gcPBrZv\n123HxQHDhyuQMKJa4lg2HV4Hslcs20am12KQz9/Ynq6b0b2kdTvUOXOqqrcSWS3WcSP9mHQAuHkT\nqFNH2fQQWQrHpN8F0570Dh2USweR2sXHx8PHxwfe3t5YsmRJud8XFhbiscceQ2BgIPz8/LBmzRr5\nE0lEVqXO+XzDtujaWsGUEJEUOC6dyEg1jXStFvjnH+N++/bSxVLbuAuOcaG7odVqMX36dMTHxyMt\nLQ0xMTH4y3TCCADR0dEICgpCamoqEhISMHPmTJRa0d1bjeWOeSalNbhyxrDt6NZG0lhqLHdqi6t0\nbCpPztu8Gssd82xbVNNIz883Vv6WLYEGDZRND5FaJSUlwcvLCxqNBs7OzggPD8fmzZvNXtOmTRtc\nuaIbe3rlyhU0b94cTk5OSiSXiKzA9etAsxJjT7qjO3vSieyNFX0XT6Q41XzqPWUydO1f/5I2VkhI\niLQBGFfx2Erm2dbl5eWhXbt2hn13d3ckJiaavebZZ5/Fo48+irZt26KoqAjr16+v8FwRERHQaDQA\nABcXFwQGBhr+NvpvT6XYDwkJkfT8Ve3ryR1ff0zu/Cq9b5p3qeIlJCQYhnToyzOZu3ABaA1jI11o\nI21POu9r9h9X6dhUnlYrXyw1ljvm2baoZuK4deuA8HDd9rBhwIYNCiWMqJZsfcKZuLg4xMfHY9Wq\nVQCAb775BomJiVi+fLnhNe+88w4KCwuxbNkynDhxAqGhofjjjz/QuHFjw2ts/ToQVYZl20h/Lf78\nE9jn/zyex0rdL6Kjgf/8R9nEEd0j1nEj04nj8vKAtm2VTQ+RpXDiuBqSsyddbeMuOMaF7oabmxty\ncnIM+zk5OXB3dzd7zf79+zFq1CgAgKenJzw8PJCeni5rOquixnLHPNO9+OGHH9C5c2c4Ojri8OHD\nZr+LioqCt7c3fHx8sGPHjirPc+UK0BznjQckXn5NjeVObXGVjk3l3bolXyw1ljvm2baoppGel2fc\nvqM9QEQy6tatGzIzM5GdnY2SkhKsW7cOYWFhZq/x8fHBL7/8AgA4e/Ys0tPT0V7K2R6JSBL+/v7Y\nuHEjevfubXY8LS0N69atQ1paGuLj4zFt2jSUVbFI8uXLQAsUGg80by5VkolIIXI20omsnWoedw8P\n1z3yDgDffAM89ZRCCSOqJXt4TG779u146aWXoNVqMWnSJMybNw8rV+oeY50yZQoKCwsxceJEnDp1\nCmVlZZg3bx7GjRtndg57uA5EFbHHst23b18sXboUXbt2BaDrRXdwcMDcuXMBAI899hgiIyPRs2dP\ns/fpr8W6dUCn8C7ogqO6Xxw+DAQFyZoHIkuxxzp+r0wfd09LAzp1UjY9RJZS23qumonjzp41bru6\nKpcOIgIGDRqEQYMGmR2bMmWKYbtFixbYunWr3MkiIpmcPn3arEHu7u6OPNNH3kxERETgwgUNvJGN\ndgACAYTc7km3lgkGuc/9qvb129nZ2aDKsSedyIRop+7Mmo+PKAK6n6NHpY29e/duaQMwruKxlcyz\nHVfbu6LkdVBjuWOe5WNrdbx///6in59fuZ8tW7YYXhMSEiImJycb9qdPny5+8803hv1JkyaJcXFx\n5c6tvxbvvVsmXkdd44386lUJc6TOcqe2uErGtrU6LiUAhmp96JB8cdVY7phnedW2nrMnnYiIiO7Z\nzp077/o9d04gmZubCzc3t0pff/18MerhJgDgllM9ODdocPcJJSKrxp50IiNVjEm/eROoV0933NER\nKCkBHFQzZR7ZG45l0+F1IHtlj2W7b9++eP/99xEcHAxAN3HcuHHjkJSUhLy8PPTv3x9ZWVm3x6ca\n6a9F5L9PIXL1/QCAovvc0PhSrux5ILIUe6rjkZGR+Pzzz9GyZUsAwKJFiwzD2aKiovDll1/C0dER\nH330EQYMGFDu/aZj0vfsAR55RLakE0mKY9Jr4Nw543arVmygE9XW4cOHERMTgz179iA7OxuCIOD+\n++9H7969MW7cOARxQiciArBx40a88MILKCwsxOOPP46goCBs374dvr6+GD16NHx9feHk5IQVK1aU\na6CbEi9cNGzfatRUjqQTUQ0IgoBXXnkFr7zyitlx0xUc9F/EZWRkwKGKD+HsSScyUkVztdBk1Zbb\nX/RJSm1rAXLdRXUZPHgwli5dim7duiEmJgb//PMPTp48iZiYGAQHB+P999/H448/rnQyJaXGcsc8\n070YNmwYcnJycP36deTn52P79u2G37322mvIysrC8ePHMXDgwCrPo7142bBd1ug+ydKrp8Zyp7a4\nSse2JxX1Fm7evBljx46Fs7MzNBoNvLy8kJSUVOV5uE66fcZVMrYt13FV9KSfP2/c5tKqRLWzevVq\nuFYwsUP79u3Rvn17hIeH45zp4ytEZBNu3LiBevqxYVUcU4Jw2dhIF++TvpFORDW3fPlyfPXVV+jW\nrRuWLl0KFxeXu1rBAYgAoMHatcBff7kgMDBQlhn3pTx/VfupqamKrzigxAoHSsRPTU2VLV5CQgLW\nrFkDANBoNKgtVYxJX78eGDNGd3zECCA2VsGEEdWS0mPZRFHEpk2bkJWVhS5dulTbAyYVpa8DkVSU\nKttdu3bF4cOHqz0mJ/21eNPrGyw4MR4AUBA6Fi13fKdYmohqy9buX6GhocjPzy93fOHChejZs6dh\nPPrrr7+OM2fO4IsvvsCMGTPQs2dPPPXUUwCAyZMnY/DgwRg+fLjZOUzHpG/YAAwbJm1eiOTCMek1\nYNqT3qyZcukgsgfTpk1DWloaHnzwQbz++utITEzEG2+8oXSyiOgenTlzBqdPn0ZxcTEOHz4MURQh\nCAKuXLmC4uJipZMHAHAuvmLYdnBhTzqRnGq6gsPkyZMxZMgQAHe/ggPAMelEplQxJv3CBeO2HI+7\nq23cBce4qMuePXvwv//9D1FRUUhISMCmTZuUTpLs1FjumGf7tWPHDsyaNQt5eXmYOXMmZs2ahZkz\nZ+KDDz7AokWLlE4eAKDOdePj7g5Nm0geT43lTm1xlY5tL86cOWPY3rhxI/z9/QEAYWFh+P7771FS\nUoKTJ08iMzMT3bt3r/JcHJNun3GVjG3LdVwVPemmjXT2pBPVTp06deDo6AgAaNCggU09skdE5U2Y\nMAETJkxAbGwsRo4cqXRyKlTvprGR7tSMPelE1mLu3LlITU2FIAjw8PDAypUrAeCuV3AA2JNOZEoV\nY9IjIoC1a3XHP/8cmDRJuXQR1ZbSY9nq168PLy8vw/6JEyfg6ekJQJe2I0eOyJIOpa8DkVSUKtv5\n+fmYP38+8vLyEB8fj7S0NPz++++YpOBNU38tVjlPxbOlnwIAbrwfjXoz/6NYmohqi/cvI9Mx6StX\nAs89p2x6iCyFY9JrgD3pRJbz119/KZ0EIpJAREQEJk6ciIULFwIAvL29MXr0aEUb6QBQVgY0LDX2\npNdpyZ50oqrs27cPDz/8sNmx3377DQ899JBCKaoZ9qQTGaluTLocjXS1jbvgGBd10Wg00Gg0cHFx\nQUFBAQoKCtC0aVPDcTVQY7ljnu1fYWEhxowZYxjO4uzsDCcn5b/Lv34daALTieM4Jp1xbT+2lGbM\nmFHu2PTp0xVIyd3hmHT7jKtkbFuu48rffWtAq9WiW7ducHd3x9atW3HhwgWMGTMG//zzDzQaDdav\nXw8XF5dK33/FeG8Hl1clqp2bN29iypQp2LRpEzw8PCCKIrKzszFs2DCsXLkSderUUTqJRHQPGjVq\nhPMmy6EcOHAA91nBTfPaNeA+GHvSeSMnqtjvv/+O/fv3o6CgAB988IHhUduioiKUlZUpnLrqsSed\nyMgmxqR/8MEHSE5ORlFREbZs2YI5c+agRYsWmDNnDpYsWYKLFy9i8eLFZu8xHQdw//3AqVO643//\nDXh4yJ0DIstReizb66+/jr///huffvopGjduDED3AWDatGnQaDR4++23ZUmH0teBSCpKle3k5GTM\nmDEDx44dQ+fOnVFQUIDY2FgEBATInhY9QRDw998iLrUPQhBSdQcPHQKCgxVLE1FtSVXHf/31V+ze\nvRsrV67E888/bzjeuHFjDBkyBN7e3haPWVumY9IXLgRee03Z9BBZSm3rudU30nNzcxEREYH58+fj\ngw8+wNatW+Hj44Nff/0Vrq6uyM/PR0hICI4fP272PtML07QpcOmS7nhhoTzLsBFJRenGaefOnZGU\nlISGDRuaHb969Sp69OiBY8eOyZIOpa8DkVSULNu3bt1Ceno6AKBjx45wdnZWJB16giDg6FERdfw7\noAMydQePHwc6dlQ0XUS1IXUdz87OhkajweXLlyEIApo0kX6IyL0ybaRHRgJvvqlocogsprb13OrH\npL/88st477334OBgTOrZs2fh6uoKAHB1dcXZs2crfG9ERAQiIyNx+XIkgGUAEqD/fyohIcFsnIIl\n9/XbUp2/sv1ly5bJGk/p/FaUBrniL1u2TNbrGxERYSjPSnN0dCzXQAd0j8qa1lN7Zvq3UkNcJWOr\nMc9KiYuLw9atW5GRkYGMjAxs3boVu3btwrlz5xRN17VrQCNcNR6o4P8fS1NjuVNbXKVjS6mgoAD+\n/v7o0qUL/P39ERAQgEOHDimdrGpxTLp9xlUytk3XcdGKbd26VZw2bZooiqK4e/du8YknnhBFURRd\nXFzMXte0adNy79Vn7epVUQR0P/XrS5zg23bv3i1PIJXHVTK2knlWutr6+/uL58+fL/dTWFgo+vv7\ny5YOJa+DGssd8ywfpcr24MGDxaZNm4rDhw8Xhw8fLjZr1kzs37+/6OnpKa5du1aRNAEQd+0SxUto\nYryZX7woeVw1lju1xVUyttR13M/PT9yzZ49hf+/evbLen+8GAEPVnjNHvrhqLHfMs7xqW8+t+nH3\n1157DV9//TWcnJxw48YNXLlyBcOHD8fBgweRkJCA1q1b48yZM+jbt2+lj7ufPg24uemOuboC+fkK\nZITIgpR+zFuj0dx+PK1iJ0+elCUdSl8HIqkoVbYHDBiAr7/+2vCk2tmzZzF+/HjExMSgd+/esg1l\nMSUIAjZvKsPgoc5wglZ3sKQEUPgxfKLakLqOBwUFISUlxexY165dcfjwYcli3ivTx91ffhn44ANl\n00NkKXa9TvqiRYuwaNEiALrJMN5//318/fXXmDNnDtauXYu5c+di7dq1GDp0aKXnMJ3Z3YqH5BDZ\njOzsbKWTQEQSyMnJMTTQAaBVq1bIyclB8+bNFV214fqlm4YG+i2HOoqPkyeyVsnJyQCAPn36YMqU\nKRg7diwAYN26dejTp4+SSasRzu5OZGRTA0j1vXevvvoqdu7ciQ4dOuB///sfXn311Urfo8Tya2ob\nd8ExLupSWlqKq1eN40MPHDiAPXv2YM+ePSgqKlIwZfJRY7ljnu1f37598fjjj2Pt2rVYs2YNwsLC\nEBISgmvXrlW5zKnUbl26Zti+6ST9eHRAneVObXGVji2FmTNnYtasWfjjjz+Qnp6OBQsWYMGCBfjr\nr7+QmpqqdPKqxTHp9hlXydi2XMetuifdVJ8+fQzfAjZr1gy//PJLjd532WRpVfakE9Xe3Llz0apV\nK8ydOxcAMHbsWPj5+eHGjRvo2rUrlixZonAKieheREdHY8OGDdi3bx8EQcCECRMwYsQICIKA3bt3\nK5Yu7RXjl4K36sjTSCeyRbbcIAHYk05kyqrHpNeGfhxAXBwwcqTu2NChwMaNyqaLqLaUHosdGBiI\ngwcPGh451Y99E0URDz/8MH777TdZ0qH0dSCSCsu2kSAI+HLWMUx8vzMA4GwzH7ie/0vhVBHVjtR1\nPD8/H/Pnz0deXh7i4+ORlpaG33//HZMmTZIs5r0yHZM+fjzw1VfKpofIUux+CbbaMn36lj3pRLVX\nVlZmNiZU33MuCILZY/BEZBsaNWqExo0bV/hjDesrlxUZH3cvZU86UbUiIiIwYMAAnD59GgDg7e2N\n//73vwqnqnrsSScysvtG+lV5l1YFoL5xFxzjoi63bt3CFZPJHgYMGAAAuHz5Mm7evKlUsmSlxnLH\nPNuvq1evoqioCC+++CKWLFmCvLw85OXl4d1338WLL76odPIgmtzIS+s3kiWmGsud2uIqHVtKhYWF\nGDNmDBwdHQEAzs7OcHKy/hGuHJNun3GVjG3LdVz2Rnp2drZhPHlxcbHZh30pXDN+AS9bI53Inj37\n7LMIDw/HP//8YziWnZ2N8PBwTJ48WcGUEVFtbNmyBdOmTUOTJk3QpEkTTJ06FZs3b1Y6WcBV441c\nW483cqLqNGrUCOfPnzfsHzhwAPfJNXtyLbAnnchI1jHpn332GVatWoULFy7gxIkTyMjIwNSpU7Fr\n1y6Lx9KPA3jjDeDtt3XH3nwTiIy0eCgiWVnDeNVPP/0UixYtMjze3qhRI8ybNw9Tp06VLQ3WcB2I\npKBU2e7Vqxf+85//GJZt+v777/Hxxx9j//79sqdFTxAEfN4/BpN+0aUps+toeCevUyw9RJYgdR1P\nTk7GjBkzcOzYMXTu3BkFBQWIjY1FQECAZDHvlemY9MceA7ZvVzY9RJZiU+ukf/zxx0hKSkLPnj0B\nAB06dMC5c+ckjcmedCLLe/755/H8888bnoSxhnGrRFQ73333HV588UW89NJLAICHHnoI3333ncKp\nAnC92LAp1m+gYEKIbENwcDB+/fVXpKenAwA6duxoNpeMtSopUToFRNZD1sfd69ati7p16xr2S0tL\nDWufS0WJRrraxl1wjIt66R+LVRs1ljvm2f55eHhgy5YtKCwsRGFhITZv3gyNRqN0siDcuG7crl9f\nlphqLHdqi6t0bCnExcVhw4YNiIuLw9atW5GRkYGMjAxs3boVGzZsUDp51ZKzka7Gcsc82xZZe9L7\n9OmDhQsXori4GDt37sSKFSswZMgQSWOyJ52IiKh6EydONNvXf4n+5ZdfKpEcYzpMGuloIE8jncgW\nbd26FYIg4Ny5c9i/fz8effRRAMDu3bvx4IMPYvjw4QqnsGoqmXuWqEZkHZOu1WrxxRdfYMeOHQCA\ngQMHYvLkyZL0puvHAQwfblwbPTYWGDHC4qGIZGUPY7Hj4+Px0ksvQavVYvLkyZg7d2651yQkJODl\nl1/GrVu30KJFi3LfhtrDdSCqiFJlOzY21nA/vn79OjZu3Ii2bdti+fLl93zO2bNn48cff0SdOnXg\n6emJ1atXGyawioqKwpdffglHR0d89NFHhpUiTAmCgDVeb2FC1hsAgBPh8+EZ8849p4fIGkhdx0ND\nQ/HVV1+hTZs2AIAzZ85gwoQJhs/f1sR0THqXLsAffyibHiJLsakx6Y6Ojnjuuefw3HPPyRZTiSXY\niNQkMzMTCxYsQHFxMWbPno1evXpV+XqtVovp06fjl19+gZubGx544AGEhYWhU6dOhtdcunQJ//nP\nf/Dzzz/D3d0dhYWFUmeDSPVGjhxptj9u3Dg89NBDtTrngAEDsGTJEjg4OODVV19FVFQUFi9ejLS0\nNKxbtw5paWnIy8tD//79kZGRAQeH8qPwHG7eMG43ZE86UXVycnLQunVrw76rqytOnTqlYIpqhmPS\niYxkHZPu4eFR7qd9+/aSxuSYdPuNq2RsWx7jUls3btww23/99dexaNEifPjhhzWa3T0pKQleXl7Q\naDRwdnZGeHh4uWWevvvuO4wYMQLu7u4AgBYtWlguAxagxnLHPKtPRkYGCgoKanWO0NBQQ8O7R48e\nyM3NBQBs3rwZY8eOhbOzMzQaDby8vJCUlFThORxLjI+7OzSsV6v01JQay53a4iodW0r9+/fHwIED\nsWbNGqxevRqDBw9GaGio0smqlpyPu6ux3DHPtkXWnvSDBw8atm/cuIHY2FizdRylwDHpRJY1ZMgQ\njB8/Hs888wwAwNnZGf/88w8EQYCjo2O178/Ly0O7du0M++7u7khMTDR7TWZmJm7duoW+ffuiqKgI\nL774IsaPH1/uXBEREYaJrVxcXBAYGIiQkBAAxv+Y7Wk/NTVVsfipqamK5F/Pnq93QkIC1qxZAwCK\nTtTWqFEjw+PugiDA1dUVS5Yssdj5v/zyS8PybqdPnzas9ALo/h/Iy8ur8H2fXdyGjNvbQtrv6JOQ\nwHJnR/tq+H9Nv52dnQ05REdHY8OGDdi7dy8AYMqUKRg2bJgssWuDPelERrKOSa9I165dcfjwYYuf\nVz8OwNsbyMrSHTt+HOjY0eKhiGSl9Fjs0tJSfPLJJ/jxxx8xf/58+Pj44MMPP8T169fx3HPPwcfH\np8r3x8XFIT4+HqtWrQIAfPPNN0hMTDQb9zp9+nQcPnwYu3btQnFxMXr16oWffvoJ3t7ehtcofR2I\npGJrZTs0NBT5+fnlji9atMgwOezChQtx+PBhxMXFAQBmzJiBnj174qmnngIATJ48GYMHDy43sZUg\nCIhtHIERRWsAAPmLvkTreeYT3BHZGlur41IyHZPeogVQy4d3iKyGTY1JT05ONnxLX1ZWhkOHDkGr\n1Uoakz3pRJbl5OSEGTNm4JlnnsFbb72FFStWYOHChfD09KzR+93c3JCTk2PYz8nJMTzWrteuXTu0\naNEC9evXR/369dG7d2/88ccfZo10IrIsURSxYcMG7Nu3Dw4ODnj44Ydr1Pu2c+fOKn+/Zs0abNu2\nDbt27TIcu/P/gdzcXLi5uVX4fqdS4+Puzo3ledydiOTHnnQiI1nHpM+cOdPwM2/ePCQnJ2P9+vWS\nxuSYdPuNq2RsJfOstAMHDmDkyJGYOnUqIiIi8M4772D+/PmYOXMmLl26VO37u3XrhszMTGRnZ6Ok\npATr1q1DWFiY2WuefPJJ7Nu3D1qtFsXFxUhMTISvr69UWbpraix3zLP9mzZtGlauXIkuXbqgc+fO\n+PTTTzFt2rRanTM+Ph7vvfceNm/ejHr1jA3ssLAwfP/99ygpKcHJkyeRmZmJ7t27V3iOOiaNdKfG\nXCedce0jNpXHMen2GVfJ2LZcx2XtSVfiQhUXG7cbNJA9PJHdmTJlCrZt24Zr165h4sSJ+O233/D9\n99/j119/xejRo6td4sXJyQnR0dEYOHAgtFotJk2ahE6dOmHlypWG8/v4+OCxxx5Dly5d4ODggGef\nfdaqGulE9mj37t1IS0szTPQWERFR63o3Y8YMlJSUGCat6tWrF1asWAFfX1+MHj0avr6+cHJywooV\nKypdjrVOmbGRXuc+zu5OVJ0PP/wQL774YrXHrE1JCSCKgAQrMxPZHFnHpN+4cQNxcXHIzs6GVquF\nKIoQBAFvvPGGxWMJgoBbt0Q4O+v3Aa2WFZ9sn9Jj2YKDg7F8+XJcu3YNixYtwu7duw2/09dpOSh9\nHYikolTZfuKJJxAdHW2YvC47OxvTp0/Hjz/+KHta9ARBwK94BL2hmwCr9H+/wqlvb8XSQ2QJUtfx\noKAgpKSkmB0LDAw0TJRnTQRBgJOTiNJS3X5JCQyf3YlsmU2NSX/yySfh4uKC4OBgs8fepHLd+OU7\n6tdnA53IEr777jusXLkSderUwVdffWX2O7ka6ERkOfrJ3YqKitCpUyd0794dgiAgKSkJDzzwgMKp\nA+rD5HF3mZZgI7JFMTEx+O6773Dy5ElDvQZ0dbt58+YKpqxqdeqAjXSiO8jaSM/Ly8NRqw8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0BAAAIDA9GvXz+zWaCjoqLg7e0NHx8f7Nixo/qTcUw649pRbCnt27cPoaGh8Pb2hoeHBzw8PNC+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\nRERERBbi/wEHL0AVcwAscQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f38f5c99690>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
stuart-knock/bokeh | examples/compat/seaborn/seaborn.ipynb | 11 | 2938 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Some examples of seaborn-based plots rendered by Bokeh"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from bokeh import mpl\n",
"from bokeh.plotting import show"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy import optimize\n",
"\n",
"# Set the palette colors.\n",
"sns.set(palette=\"Set2\")\n",
"\n",
"\n",
"# Build the sin wave\n",
"def sine_wave(n_x, obs_err_sd=1.5, tp_err_sd=.3):\n",
" x = np.linspace(0, (n_x - 1) / 2, n_x)\n",
" y = np.sin(x) + np.random.normal(0, obs_err_sd) + np.random.normal(0, tp_err_sd, n_x)\n",
" return y\n",
"\n",
"sines = np.array([sine_wave(31) for _ in range(20)])\n",
"\n",
"# Generate the Seaborn plot with \"ci\" bars.\n",
"ax = sns.tsplot(sines, err_style=\"ci_bars\", interpolate=False)\n",
"xmin, xmax = ax.get_xlim()\n",
"x = np.linspace(xmin, xmax, sines.shape[1])\n",
"out, _ = optimize.leastsq(lambda p: sines.mean(0) - (np.sin(x / p[1]) + p[0]), (0, 2))\n",
"a, b = out\n",
"xx = np.linspace(xmin, xmax, 100)\n",
"plt.plot(xx, np.sin(xx / b) + a, c=\"#444444\")\n",
"\n",
"plt.title(\"Seaborn tsplot with CI in bokeh.\")\n",
"\n",
"show(mpl.to_bokeh(notebook=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib as mplc\n",
"\n",
"# Generate the pandas dataframe\n",
"data = np.random.multivariate_normal([0, 0], [[1, 2], [2, 20]], size=100)\n",
"data = pd.DataFrame(data, columns=[\"X\", \"Y\"])\n",
"mplc.rc(\"figure\", figsize=(6, 6))\n",
"\n",
"# Just plot seaborn kde\n",
"sns.kdeplot(data, cmap=\"BuGn_d\")\n",
"\n",
"plt.title(\"Seaborn kdeplot in bokeh.\")\n",
"\n",
"show(mpl.to_bokeh(notebook=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We generated random data\n",
"data = 1 + np.random.randn(20, 6)\n",
"\n",
"# And then just call the violinplot from Seaborn\n",
"sns.violinplot(data, color=\"Set3\")\n",
"\n",
"plt.title(\"Seaborn violin plot in bokeh.\")\n",
"\n",
"show(mpl.to_bokeh(notebook=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |