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fonnesbeck/ELSO Survival (PyMC 2).ipynb

Created June 11, 2014 01:29
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ELSO Survival (PyMC 2).ipynb
{
"metadata": {
"name": "",
"signature": "sha256:5b0ac573b765f9be9d5e89eb503db46bd11d8c79be98c1a53f28484889809f65"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import pymc as pm\n",
"\n",
"pm.__version__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"'2.3.2'"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu = pd.read_csv('data/flu.csv', index_col=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"(1406, 60)"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Turn into table of unique patients, with appropriate organism columns"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_only = flu.groupby('PatientID')['OrganismName'].apply(lambda s: \n",
" len([x for x in s if str(x).startswith('Influenza')])==len(s)).astype(int)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create indictors for coinfection type"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for org in flu.Type.unique():\n",
" flu[org] = (flu.Type==org).astype(int)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_unique = flu.drop_duplicates(subset=['PatientID']).set_index('PatientID')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_unique['flu_only'] = flu_only"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add time from admission to time on ECMO"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"admit_through_emco = flu_unique.AdmitToTimeOnHours.add(flu_unique.HoursECMO, fill_value=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See how many have no time information from admission, and drop these"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"missing = admit_through_emco.isnull()\n",
"missing.sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"1"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete = flu_unique[missing ^ True]\n",
"admit_through_emco.dropna(inplace=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Confirm no null here\n",
"admit_through_emco.isnull().sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"0"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete['admit_through_emco'] = admit_through_emco"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Time off ECMO through to event (death or discharge)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"off_ecmo_to_event = flu_complete.TimeOffToDCDateHours.add(flu_complete.TimeOffToDeathDateHours, \n",
" fill_value=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"off_ecmo_to_event.isnull().sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"51"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Not sure why add() does not work here\n",
"flu_complete['time_to_event'] = (admit_through_emco.values + off_ecmo_to_event.fillna(0).values)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete.time_to_event.isnull().sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"0"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clean covariates"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete.Race.value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"W 554\n",
"A 102\n",
"B 83\n",
"H 82\n",
"O 56\n",
"dtype: int64"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete['non_white'] = (flu_complete.Race!='W').astype(int)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete['male'] = (flu_complete.Sex=='M').astype(int)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete['PO2'][flu_complete.PO2 > 200] = None"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python2.7/site-packages/pandas/core/series.py:632: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame\n",
" self.where(~key, value, inplace=True)\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# ECMO Type\n",
"flu_complete['VA'] = flu_complete.Mode.isin(['VA', 'VV-VA'])\n",
"# Set \"Other\" type to NA (there are only a couple)\n",
"flu_complete.VA[flu_complete.Mode=='Other'] = np.nan"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create oxygen index"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete['OI'] = flu_complete.FiO2 * flu_complete.MAP / flu_complete.PO2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete.OI.hist(bins=20)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1153ef190>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x1153e6b90>"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete[flu_complete.OI>250][['pH', 'PCO2', 'PO2', 'HCO3', \n",
" 'SaO2', 'FiO2']]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"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>pH</th>\n",
" <th>PCO2</th>\n",
" <th>PO2</th>\n",
" <th>HCO3</th>\n",
" <th>SaO2</th>\n",
" <th>FiO2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>PatientID</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>C2CFFB42-3BED-4ED3-888D-213F16088627</th>\n",
" <td> 7.12</td>\n",
" <td> 70.0</td>\n",
" <td> 8.0</td>\n",
" <td> 23.0</td>\n",
" <td> 4.0</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B94B0EE6-9085-4963-99F1-92A626E2DAB9</th>\n",
" <td> 7.27</td>\n",
" <td> 9.0</td>\n",
" <td> 4.0</td>\n",
" <td> 34.0</td>\n",
" <td> 53.0</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EB457A6D-41CB-48C0-A305-CEB4B034E3B2</th>\n",
" <td> 7.32</td>\n",
" <td> 6.3</td>\n",
" <td> 7.5</td>\n",
" <td> 22.5</td>\n",
" <td> 83.0</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EB0E87F5-39EA-4330-A104-E90254929813</th>\n",
" <td> 7.33</td>\n",
" <td> 6.4</td>\n",
" <td> 5.6</td>\n",
" <td> 23.2</td>\n",
" <td> 71.9</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
" pH PCO2 PO2 HCO3 SaO2 FiO2\n",
"PatientID \n",
"C2CFFB42-3BED-4ED3-888D-213F16088627 7.12 70.0 8.0 23.0 4.0 100\n",
"B94B0EE6-9085-4963-99F1-92A626E2DAB9 7.27 9.0 4.0 34.0 53.0 100\n",
"EB457A6D-41CB-48C0-A305-CEB4B034E3B2 7.32 6.3 7.5 22.5 83.0 100\n",
"EB0E87F5-39EA-4330-A104-E90254929813 7.33 6.4 5.6 23.2 71.9 100"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"covariates = ['male', 'AgeYears', 'HoursECMO', 'pH', 'PCO2', 'HCO3', 'OI', \n",
" 'SaO2', 'viral', 'fungal', 'bacterial', 'admit_through_emco', 'VA']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get counts of missing values"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete[covariates].isnull().sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"male 0\n",
"AgeYears 0\n",
"HoursECMO 1\n",
"pH 95\n",
"PCO2 85\n",
"HCO3 167\n",
"OI 396\n",
"SaO2 147\n",
"viral 0\n",
"fungal 0\n",
"bacterial 0\n",
"admit_through_emco 0\n",
"VA 14\n",
"dtype: int64"
]
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to drop the missing values for some of the covariates, and not use some covariates with many missing values."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"drop_subset = ['HoursECMO', 'VA']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete = flu_complete.dropna(subset=drop_subset)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flu_complete[['pH', 'PCO2', 'PO2', 'HCO3', \n",
" 'SaO2', 'FiO2']].hist(bins=25);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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PsgbtbOBbwDbhYfB1M/tSL2Pt6/5J69SLurhxxmkO04ccb2F9h5n43Dgt0t2NUySlzo0j\naRZh+tYxSZsTFmz4IGECqFwLmNTd8J16UcfGvlvqbvPe2A8Gpc6NY2arzGws/n6KMHf3DtRgrH3z\nKJE6yHI5/ZXlbCBLufYWtNRZfp3ph93V2ba7im6VNIewkMl1+Fh7xxlQygtc6het/ml5tG1rupni\neHPCPDn/n5ld2mq4maQvA780s/Nj+lnAFWb2vcS5tf6kdeqFu3EKzQfluGCKkJHdjdPBJlLSh4NS\n16CNCqYC3wXOM7PGMMtWw81+D8xOXL5jTBtHHYOqfL+e+4HRxN+VOJMLf2MvgE7DdQj/br8FfLEp\nPXW4GWEStDHCXDpzCav5qOnaPKOOJuBDLwdPTh5Z1GToJeElZoSwDOFthJFmEIYiX03KUOTEtbnK\nqhuylGu7sqzz0MsihmlO9qGXWXz2ewOHEWa8XB63hdRkARPH6SMvAJ82s9cBewF/K2k3ajIRmuO0\no6vpEgpTWhP/pTMY1NVnL+lS4Ctx28/MHopDlUfN7LVN59bC5gfVZ+/DNAOlDr2UdLakhyTdmkhr\nOX+3pCWSfiNphaQD82TKcepOxpFpjlMbsnTQngN8meC3b9D4bG0EVC0GGgFVjcVLdgCukTQuoKpI\nRms4X4vL6a+sKogj074LHGNma5Odh2atZ4Gtw9w4G2jsz29Km990PHmsurlxur0+b/lM2rlxGhsw\nB7g1sb+C8DYDMAtYEX8vIXbaxv0rgb1S5BXSWeEdtIMnJ48satJBG7LCVOAq4NhE2gpClDnAdo36\n0HRd9wXVJd5B6x207bZMPvv4yXq5mb0+7q8fY6/wWvOYmW3VYoz9D83su03yLItex4H6+OyjrZ8L\nPGpmn06kL41pp8dpvmeY2eKma2th8+6zH2xKH2ffDrN8i5c4zgDSGJl2i6TlMW0JYSTaRZI+SpwI\nrZrsjcfHpjtJ8jb2PQVUQTH+y0Zav/ydWfab8+b5mbh/xhlndPW8A6OJvyupAjP7L1oPati/n3lJ\nI70vJO2NOLcGxvvM60mrf3IjIyOl9xXVuj8qi6+HiT773AFV5j77SS0njyxq5LPPuxVl8+1oLtf0\ncht+n33r9OxbUc+gaHqx+SxTHF9IWKhha8Kwss8DP6DF/N2STiTMZb+OMFrhqhSZ1kmv4zSoi8++\nF8q0+fbumn750IuQUZ+81bV9KnU++zLwxt7pBm/sO8pmEBvUeudtInVos0oNqsqDpIUxqOo3cRx+\nKUwcO1y9LJfTX1l1oV82357RAZdfNqNdnNvs2cmooca2XXhjL2kKIXx8IcGHf2icP6RwxsbGaifL\n5fRXVh3op823p+xyHfTnVn7+62zbPQ+9TOGtwN0WVqlC0rcJq1fdWbSiNWvWdD6pz7Jcznha+ZMb\nI3uGhFw2//TTT/Pggw+mHttuu+2YPn36uLTOQymLqw/VyC+b8vPfqCetnlWVrqAyGvsdgPsS+/cD\nezaf9PTTT/Pss8+mCthyyy2ZOnVqCVmrP0kjOeWUU8Yd66ehpBlrIz/d5yPNV5pN54CQyeab+dGP\nfsTBBx/CZpttPy79ueceYN269LrRjY/ZqZpsdp9GKXUh7zCeVhtwMPCNxP5hwJebzrHjjju+5bCn\n66+/PnXIUautV9rJ7kZncTLMYFGmoWPd5qO785P6F63PR/f33s0wuO6G01nB9ptny2rzzfzgBz+w\nKVM2sWnTthu3TZmySZtya1c+aTbTrYx2aYsKkFHFuY30LOXTTl+3W68y0vOW104LH40jaS/gZDNb\nGPeXAC+Z2emJc4pV6kxarAajcdzmnX6S1+bLaOw3Bn4NvBN4ALgeONTMCvfZO04dcJt3BoHCffZm\ntk7SJwkzA04BvulG7wwzbvPOIFBJUJXjOI7TX0oJqkoiabakEUm3S7pN0tExveVqVy3kbCbpOklj\nku6Q9I955CTkTYnr6V6eV46klZJuiXKu70HODEmXSLoz3tueOeW8RhvWCV4u6QlJR+eUtSQ+s1sl\nXSBp05xyjokybpN0TNYy0hCtkFZ0wFW3ZZNDfiF1toOOQutzGz091/M2sgup/23kF9IurKcPIxVm\nAfPi780Jvs3dCJOpfTamn0CcTK2DrGnx78bAL4F98siJ5x4HnA9cFvfz5OdeYGZTWlLOY4RFqtcC\nqwirfk2Px94F/AR4EniWsADG++O9bQmcCfwKeAT4A8EX/N6EnlcAFxJmFV0D/Bfw1sTxjYAHCbOQ\ndnVvhInv7gE2jfvfARblkPPHwK3AZgT3xtXAzlnkAPsSlv1rnoBvwnVsmIBvasz73cBGZdt2Rjub\nEvMzJ+ZvDNitR5mZyyan/CLr7ErgmRZ14GBC/8ZTsZ5cFe2+oeMcwpDWx6MtfwPYvMt76bmet5Hd\nqf73Kv9c4Kj4u9Eu5JZfhfFfSpgONnW1q4wypgE3AK/LI4cw9fI1wALCoizklHMv8EdNaUk5vwN+\nF39vT2j4/hH4H8AThAnjdiA0rH8KfD2eOzMa//nANoQJ534frzk4njOXsEbctoQBvB8DHk5UpAOB\nn+a5t6j/18BW0cguBw7IIed/AGcl9v838Nmscih4hbQqNuBtwJWJ/cXA4gLkZiqbgu4hd52NdeQd\nberAIcAMYDlhqcfngV3i+X8T69Bm8ZwrgH/pIt+F1PMO99au/ueWT2jY70lJz99uFm3cGQz0v4Et\ngMcT6Urut7l+I8Kb0VpgaUzLI+diwpvRfgkjyCPnnmikNwIfS5FzL7A2sb8U+I9YBsfHtHmERavP\nIbzJf4OwGMa65vwQGsqVbfLzBPCm+Pts4BM93NvHYzmvBs7LIwd4LeGfxkzCP+ifA1/KKoeJDVrq\ndYQ1kv8icews4j/FqjdCo9Z2DH5OuZnKpiA9vdTZ9Y193G/UgZXAZ5L1Ocp8ETglTQfwIeCWLvJe\nSD1vI79T/c8tv0W7ML0X+aX77BuoaZHm5DELObdOMszsJTObR/iP/aeSFnQrR9L7gNVmtpwWIW1Z\n8wPsbWZvAt4N/K2kfdPERb2zgfcQPmlnA5fE4xsDewBnmtkewNOEBuGFlPxcDOwkaZeU+5pHWEfg\nbkmbEFxCF+e5N0k7E74a5hDexjaXdFi3csxsBXA6sAz4IaFiv9itnBayO13XtcyS6Hs+8pZpM0XU\n2YaoKC9ZB3YCLkrWZ8LKKM8TviLTdOwH3JYx70XW81a0rf89yk9rF8Ytddmt/L409pKmEozmPDO7\nNCY/JGlWPJ5c7aojZvYE8J/An+SQ83bgIEn3Enze75B0Xp78mNmD8e/DwPcJc6Ssl0N4YNMlPQ78\nlDDt3hnxWGNSlPuB+83shrh/CcF9siYlP41rtk7mQ9LLgfMIgT1rCcZ3U8wXOe7tzcDPzexRM1sH\nfI/gjliVo4zONrM3m9l+hK+Tu3Lkp0Gr6zKvkFYBzXmbTXjmRZO7PqVRYJ0VcGlTHfhSPNaoP8n6\n/ATBNTlOh6QDgL8krKeRhcLqeSs61f8e5ae1C3uQow426MdoHAHfBO4wszMShy4jdPoR/17afG2T\nnK0bPc+SXkb477+8WzlmdqKZzTazuQR/4bVmdniO/EyTtEX8PZ3gI7+1Sc504DtmtpWZzTGzTxI6\nbQG2i/lZBdwnadeYvj+hw/WelPxsF/cfSeTjZQSf+s9tQ8TmoQQDb9DVvRH8gntJell8fvsDd0Q9\n3chB0jbx707AnwEX5MhPp/u4DDhE0iaS5gK7EDr+6sCNwC6S5sQvro8Q8ls0ect0AkXV2YgBH2iq\nAw37fU3Ul6zPvyV0aq/XoRChfD7BNXd3lnsoqp63ImP9zy2/RbtwOznqYFJoqRthxMxLhE/45XFb\nSPDjXkN401sGzOgg5/UE39UYcAvwmZjelZwmmfuxoZe+2/zMjXkZI3xaLkmR8wzw/qbrRMJnH9Pe\nSOhwvpnwFr00Pthx+SH0vq9MXLcpYQTDeYm06YTKtEUiresyIvQP3E4w4HMJI0nyyPlJlDMGLMia\nH8I/qwcIn/X3AUe2uw44kTDqZQXwrrLtuss68G5C38XdDTvpUV5XZVNVnY2yxvnsE3XgQcLb6/r6\nTHj5vJPwotPQsS9hhbz39HA/uet5G5lZ6n+vz6G5XdiyF/mVV4Rh3tIMPaYfTHh7PwJ4eTTyfYB/\nTRjMfxM6WbcljEY4lPCJ++fxnKmE//LfB6ZUfa+++Za2takDH472fGi071nR3lcCW8Vz/jg29H9e\n9X0Mw9a3DlpnA2b2XcLn/FEEn+4q4B+In2Rm9hih8d+M4D55hNBhepiZNTpd3w68l/D5u0bS2rjt\n3c97cZw8mNlFwOHApwn2fTvhS3VvM3s8nnYc8EfA2Qn7vjVVoNORLAuOn01oVFab2eubjh0P/BOw\ndWygGjP+HUUYdXG0mS0rI+OOUxZu884wkuXN/hyCv24ccRjVAQR3QyNtd8Ib6+7xmjMl+deDM2i4\nzTtDR0ejNLOfEobMNfPPhE68JB8ALjSzFyws0XY3YTiS4wwMbvPOMJLrDUTSBwhjQG9pOrQ948cQ\n30+YDsBxBhq3eWfQ6Xo+e0nTCMPcDkgmt7lkQqeAfNUepyCsDytVuc07dSKvzed5s9+ZEEZ/c4xO\n2xG4SdK2dBHJ2O9hR4sWLXKdQ6a3jwykzU8sK2vaFqVls7K8VmW7g5SnXui6sTezW81sWzObayE6\n7X5gDzN7iHpHMjpOLobf5pv/CTjDSMfGXtKFhNkKd5V0n6Qjm05Zbx1mdgdwEWFs+A8Jsy7Wwnrm\nzJnjOodQbxkMi813Zk7VGRhHHW2ojnnKS0efvZkd2uH4q5r2TwVO7TFfhTN//nzXOYR6y2BYbL4z\n84FTqs7EeupoQ3XMU16yvNmnLYH2TwpLZd0s6XuStkwcq+3ycI6TBbd5ZxjJG1S1DHidmb2RMCHP\nEvAAE2docJt3ho6O0yUASJpDWOnl9SnHPkSYevSwGDb+ksWpdiVdSZhj/ZdN1wyOW9OpLZKwkoZe\nDpvNh1mL0/SnpavnkR9OOfRi80W8gRxFWBsSahpgIil1c5yc1N7mHaeZroOqkkj6e+B5M7ugzWmp\nrwhHHHHE+p7uGTNmMG/evPWdIaOjowCF7Qe+SJg4EsJiOQsmNPiNt5mi9DfSir6fdvvNusvW19gf\nGxvj2GOPLVVf4/fKlSupikGx+fQ6MErolG38Hms6xvrj/bTZxn4/bGgQ6nCz/sJsPuNA/jkkFjeO\naUcAPwM2S6QtBhYn9q8E9kyRZ/0EMBgxsMTGhP2iGRkZKVxmHXVWpTc+s7KCVwba5puZaO8W60Ra\nejV5rcp221G3PPVi87l89pIWAl8A9jOz5BJ5uxOWnXsr4VP2GuDV1qSk3/7LdH9lc5r7KQeNfvrs\nB83mm3Gf/XDQi813dOPEAJP9gK0l3QecRBiJsAlwdXSF/MLMPmFmd0hqBJisY6ACTBwn4DbvDCOZ\n3uwLV1rJm/0IG/yV0I83+9HR0b4HZVShsyq9Zb7ZF0093+xHgQUp6a2LtMx7qMp221G3PJU6GqdF\ngMlMSVdLukvSMkkzEsc8wMQZaNzmYeJ8Of6xMuhkWZZwX+Ap4FsJ/+VS4BEzWyrpBMICwYsT/su3\nsMF/uauZvdQk0332Ts+U9WY/DDbfTLc++1bneh2pllLf7C191Z6DgHPj73OBD8bffV21x8fPO2VQ\nZ5t3nLzkDara1sL0rgAPAdvG3xUEmEz81Exv/EfLzUYKybGyw6yzSr19pEY2XxSjVWdgHHW0oTrm\nKS89BVVBGPSp9qvwlBZgsoHG/vz4d6RpX4QAkvlN5zdfT1f6s+avLgEiZQfEDFSASQ9UafP9Cqpq\nvU9p+e2HDQ1aHW78LsLm846zXwHMN7NVkrYDRszstZIWA5jZafG8K4GTzOy6JnmF+C+z+eKzprk/\nctDo8zj7Wth8XtxnPxxUMTfOZWxY02wRcGkifcBX7XGcVNzmnYEmy9DLxqo9r9GGVXtOAw6QdBfw\njrhf81V7Rvuv0X32A8nw2HwnRqvOwDjqaEN1zFNesozGOdTMtjezTcxstpmdY2aPmdn+hHm/dwB+\nKukCSZsCXwPujbKPT45HdpxBwG3eGUZyR9BGn+a1wG5m9gdJ3yFM+/o6UsYjN13rPnunZ/odQVsH\nm8+Qxzb46G61AAAfCUlEQVRH3Wc/6FThswd4EngBmCZpY2Aa8ACtxyM7zqAzIDbv0a/ORHI39mb2\nGGEWwN8RDH6NmV1N6/HIFTPa8YyiA7TcZz9cDJ7Nd2K06gyMo442VMc85SV3Yy9pZ8JqIHMIgSWb\nSzoseU78bh2g1wp/G3JaM5w270wWegmqejPwczN7FEDS94C3AaskzUqMR16ddnG5QVXN+820uj59\nvy4BHln251e8qk7Z8osKMMlJ5Tafv0400uY3HW91fqt9Cs1v3YKY6rbf+N23oKrUC6U3AucTJoB6\nDvg3wvjiVwKPmtnpMeBkxqB00PoiDoNFBR20ldt8hjySvSO2Vbp30NaVSjpozexm4FvAjcAtMfnr\ntBiPXD2j/dfoPvuhYvBsvhOjVWdgHHW0oTrmKS89zY1jZkuBpU3JjwH79yLXceqK27wzqPS0UlUM\nHjmLMM7YgCOB3wDfIXzargQ+bGZrmq5zN47TM/1240Sdldp8hvzhbpzhpapx9gD/D7jCzHYD3gCs\nABYDV5vZrsCP4r7jDAtu885A0svQyy2Bfc3sbAAzW2dmT1C7AJMGo/3X6D77oaJONl/Mwj2jFelt\nkZsa2lAd85SXXt7s5wIPSzpH0q8kfUPSdAY2wMRxOlIzm68iUtajcweVXoZevhn4BfB2M7tB0hnA\nWuCTZrZV4rzHzGxm07UD47NPw/2W9aCCoZeV23xCHr375luldy/D60R/6MXmexmNcz9wv5ndEPcv\nAZZQ26Cq0ZTjzden7VuTPNUm4GKy7Td+VxhUVbnNN/YDo2QPkur2/Fb76efXxUaGbb/xu9KgKgBJ\nPwH+yszuknQyYWIoqGVQ1QjjDT/rm33+ETqjo6NNlbN8qtBZld6KRuNUavMJefT+Bj8KLEhJr+bN\nvirbbUfd8lTVmz3Ap4DzJW0C/JYwDG0KcJGkjxKHofWow3HqhNu8M5D09GafW+lA+ex97H1dqeLN\nPi/1fLNvle4++7pS5Th7JE2RtFzS5XF/pqSrJd0laZmv2uMMG27zziDSc2MPHENYf7Pxr72mASaj\n/dfo4+yHlQGx+U6MVp2BcdTRhuqYp7z01NhL2hF4DyF8vPFpUdOgKsfpHbd5Z1DpdTTOxcCpwMuB\nvzOz90t6vDHmWMGx+FhyDHJMd5+90zMVjcap1OYT8nCf/eSjktE4kt4HrDaz5ZLmp51jZiYp1QoG\nZ5z9RHl1GYM72fYbv6saZ18Hm/dx9pNrv/G7EJs3s1wb4e3mPuBe4EHgaeA8wsRQs+I52wErUq61\nIgAMrGlrlTbS4bzssrIyMjJSyH12QxU6q9Ibn0VuG+52q4PNJ+99om12mz7Shd23Ty+Cqmy3HXXL\nUy82n9tnb2YnmtlsM5sLHAJca2aHA5cBi+Jpi4BL8+qoK2VNBOXUm8ls887gU8g4e0n7Aceb2UGS\nZgIXATsxBPPZux+/vlQ5zr4qm0/Iw332k49ebH5ggqpavz17Yz9Z8aAqb+wnG5UEVUmaLWlE0u2S\nbpN0dEwvMcDEmrZuGC0uG1k1+jj7oaIamy/TbThakJxi8lhHG6pjnvLSyzj7F4BPm9nrgL2Av5W0\nGwMbYNIbnYw97bj7+geOCm2+lxedflD3/DmFuXEkXQp8JW77mdlDkmYBo2b22qZzc7pxinLZpKUV\nLz95j63y75+/+anajVO2zSeupQhXS5luHLft/lDlrJeNDMwB3gRch69U5UwC3OY70+rL1f8JVEPP\njb2kzYHvAseY2drkAzYrNsBkA63257fYBzgDOLbD9b3IH51wvLWbZrz8IgMykmXVzwCQsbExjj32\n2FL1NX5XuHgJ0F+b3xBENRr/NvYbac3Hadpvd/5Ym/Nb7Wc9H8avIdE4viDsVWRDeWwuWa8mZVBV\n/O88FbgKODaRVkqACV0EPaWnjXQ4r1f5nXT2FqCVFQ+qKj2wqm82n7zPbDbXbfpIibLbnZu+1S2A\nyWy4gqp6WYNWhEmfHjWzTyfSl1LCqj2D6LPPIj9v+Tv999n32+YT15LN5opK9+GbdaWScfaS9gF+\nAtzChqe3BLieLgJMfv3rX/PCCy+Mk73RRhux++67N+tjGBv7NNzos1FBY9+zzT/zzDPcc889qfK3\n2WYbttlmmzS9TIbGPg2vC+MZ6KCq7bd/FWvXbsxGG20CgNmLvPDCfTz77FPN19BbwztCUWvQ5tPZ\nnwCt0VFfg7aONGz+uuuuY5995jNt2s7jjv/hDw/x939/NJ/73OfSrqWcBnmUotagLUbGxDpadWNf\nVX1qRSVBVe2QtFDSCkm/kXRCu3PXrYOnnrqSJ5+8jSefvI21a2/gpZfKyNVY51OGQOfYWBX3WZ3e\nutCNzU+f/ob19t7Y/vCHv+Hzn/98n2Mx6vbM6paf4bLrwht7SVMI444XArsDh8bAk4pZ0/mUmujs\npcKvWVPFfVantw4Ua/NpfZdlUbdnVrf8DJddl/Fm/1bgbjNbaWYvAN8GPtCNgOeff3qSR5pOrOwe\nfVtrerZ5JzutotFb1QmPXg8UElTVxA6EOb8b3A/s2f6SHwG3x9/Pxr/ZOjOzs7LH66vWObE8Whns\nKaec0l5SCX7Qqse+V0xXNv/CC48Dlzel3lVCtjqxsgKd7Vg5IaV1o5zu989+fvYGv1Gfeq037fQ1\nyy7jn1HhHbSSDgYWmtnH4v5hwJ5m9qnEOd7F7hRCHTpo3eadfpLX5st4s/89MDuxP5vwprOeOlRQ\nxykQt3mn9pThs78R2EXSHEmbAB8hrOTjOMOK27xTewp/szezdZI+SQgpnwJ808zuLFqP49QFt3ln\nEKgkqMpxHMfpL6UEVbWjm+CTHvWslHSLpOWSro9pha4oJOlsSQ9JujWR1qxjoaSfS1oj6WlJz8a8\nHZhB/hGSbo3XPSjpTEnnNXRKWiTpRknPSVoX05dLendCxpJY1iuy6GyRj65XaOpVbxudJ0u6P95n\n4fdaBv2y+Q55KL0+ZMhDu/pyX7TjZht6Jtp+4c+zRX4qs6/S61neGdTybIRP3LuBOYTZA8eA3UrS\ndS8wsyltKfDZ+PsE4LQedexLmNP81hY6Pg88R/Dh7g7cTAi8eVcsh43ayD4eWAUcGMvtlcB/AncA\nbwFuBf4G2Bs4BTiZ4Ds+ISFj91jGU2OZt9XZJi+zgHnx9+bAr4HdWpVnEXrb6DwJOC7l/ELudZBt\nvur6kCEPLesLYZ6ENSk2NAp8pozn2SI/ldlX2fWs32/2/Q4+aR4BcRBh1kLi3w/2ItzMfgo83kbH\ndcBUM/sO4T4vMLMrzewqwgiO6yU9IulhSf8uaUsASS8nNN6fNLNlZvaimf038GFga2C/qP9rZvYz\n4CVgLXA+ofFv8AHgQjN7wcxWEozhrTnuc5WZjcXfTwF3EsaWtyrPnvW20QnpQReF3GsJ1CngqtT6\n0IlYXy4HXhHfXh8DPg5cGE95miYbIgyQf5QSnmeL+gsV2VfZ9azfjX1a8MkOLc7tFQOuiW6Oj8W0\nfqwolNTxc0CS/o2wZuljifNWAf9BmP98N8JwvZPjsbcDmwHfSwo2s6eBKwhvJM18Kl6/U+Izb3vG\nDwHsubyVbYWmQvUmdP4yJn1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9xyyoqwi04ow6u87iqEJnlXprQc8dJG95y1tS01evXs0r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\n",
"text": [
"<matplotlib.figure.Figure at 0x10f256810>"
]
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Survival Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Surivival time, in days"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"obs_t = flu_complete.time_to_event.values/24."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.hist(obs_t, bins=25);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEACAYAAABS29YJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmkZjTdM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"text": [
"<matplotlib.figure.Figure at 0x114b7f9d0>"
]
}
],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fate of each patient"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"died = (flu_complete.fate=='Died').astype(int).values"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unique failure times"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"times = np.unique((flu_complete.time_to_event[flu_complete.fate=='Died'].values/24).astype(int))\n",
"times"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 33,
"text": [
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
" 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
" 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
" 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 53, 54,\n",
" 55, 59, 61, 62, 63, 67, 68, 73, 77, 108, 121, 125, 126,\n",
" 133, 142, 150, 201, 227, 235, 301])"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"N_obs = len(obs_t)\n",
"T = len(times) - 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate risk set"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Y = np.array([[int(obs >= t) for t in times] for obs in obs_t])\n",
"Y"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 35,
"text": [
"array([[1, 1, 1, ..., 0, 0, 0],\n",
" [1, 1, 1, ..., 0, 0, 0],\n",
" [1, 1, 1, ..., 0, 0, 0],\n",
" ..., \n",
" [1, 1, 1, ..., 0, 0, 0],\n",
" [1, 1, 1, ..., 0, 0, 0],\n",
" [1, 1, 1, ..., 0, 0, 0]])"
]
}
],
"prompt_number": 35
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Counting process. Jump = 1 if $\\text{obs}_t \\in [ t_j, t_{j+1} )$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dN = np.array([[Y[i,j]*(times[j+1] >= obs_t[i])*died[i] for i in range(N_obs)] for j in range(T)])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Process for one patient\n",
"dN[:,1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0])"
]
}
],
"prompt_number": 37
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Covariates"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"standardize = lambda x: (x - x[np.isnan(x) ^ True].mean()) / x[np.isnan(x) ^ True].std()\n",
"center = lambda x: (x - x[np.isnan(x) ^ True].mean())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"male, AgeYears, HoursECMO, pH, PCO2, HCO3, OI, SaO2, viral, fungal,bacterial, admit_through_emco, va = \\\n",
" flu_complete[covariates].values.T\n",
"SaO2 /= 100.\n",
"SaO2 -= 1e-6\n",
"\n",
"HoursECMO_std = standardize(HoursECMO)\n",
"AgeYears_std = standardize(AgeYears)\n",
"pH_std = standardize(pH)\n",
"PCO2_std = standardize(PCO2)\n",
"HCO3_std = standardize(HCO3)\n",
"SaO2_std = standardize(SaO2)\n",
"OI_std = standardize(OI)\n",
"admit_through_emco_std = standardize(admit_through_emco)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pymc import MCMC, Normal, Poisson, Uniform, Lambda, Laplace, Exponential, Beta\n",
"from pymc import Bernoulli, Lognormal, observed, Matplot, deterministic"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"SaO2[np.isnan(SaO2)] = 0.911\n",
"OI_std[np.isnan(OI_std)] = 0\n",
"pH_std[np.isnan(pH_std)] = 0\n",
"PCO2_std[np.isnan(PCO2_std)] = 0"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def survival_model():\n",
" \n",
" mu_pH = Normal('mu_pH', 0, 0.0001, value=0)\n",
" sigma_pH = Uniform('sigma_pH', 0, 500, value=10)\n",
" tau_pH = sigma_pH**-2\n",
" pH_masked = np.ma.masked_values(pH_std, value=0)\n",
" x_pH = Normal('x_pH', mu_pH, tau_pH, value=pH_masked, observed=True)\n",
" \n",
" mu_PCO2 = Normal('mu_PCO2', 0, 0.0001, value=0)\n",
" sigma_PCO2 = Uniform('sigma_PCO2', 0, 500, value=10)\n",
" tau_PCO2 = sigma_PCO2**-2\n",
" PCO2_masked = np.ma.masked_values(PCO2_std, value=0)\n",
" x_PCO2 = Normal('x_PCO2', mu_PCO2, tau_PCO2, value=PCO2_masked, observed=True)\n",
" \n",
" mu_OI = Normal('mu_OI', 0, 0.0001, value=0)\n",
" sigma_OI = Uniform('sigma_OI', 0, 500, value=10)\n",
" tau_OI = sigma_OI**-2\n",
" OI_masked = np.ma.masked_values(OI_std, value=0)\n",
" x_OI = Normal('x_OI', mu_OI, tau_OI, value=OI_masked, observed=True)\n",
" \n",
" a_SaO2 = Exponential('a_SaO2', 1, value=1)\n",
" b_SaO2 = Exponential('b_SaO2', 1, value=1)\n",
" SaO2_masked = np.ma.masked_values(SaO2, value=0.911)\n",
" x_SaO2 = Beta('x_SaO2', a_SaO2, b_SaO2, value=SaO2_masked, observed=True)\n",
" SaO2_std = Lambda('SaO2_std', lambda s=x_SaO2: (s - s.mean())/s.std())\n",
" \n",
" imputed_vars = [x_pH, x_PCO2, x_OI, SaO2_std]\n",
" \n",
" X = np.c_[[male, AgeYears_std, admit_through_emco_std, bacterial, viral, fungal, va]].T\n",
" \n",
" # Intercept for survival rate\n",
" beta0 = Normal('beta0', 0.0, 0.001, value=0)\n",
" # Treatment effect\n",
" beta = Normal('beta', 0, 0.001, value=[0]*(X.shape[1]+len(imputed_vars)))\n",
"\n",
" # Survival rates\n",
" lam = Lambda('lam', lambda b0=beta0, b=beta, x=X, z=imputed_vars: np.exp(b0 + np.dot(np.c_[x,np.transpose(z)], b)))\n",
"\n",
" @observed\n",
" def survival(value=obs_t, lam=lam, f=died):\n",
" \"\"\"Exponential survival likelihood, accounting for censoring\"\"\"\n",
" return (f*np.log(lam) - lam*value).sum()\n",
"\n",
" return locals()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M = MCMC(survival_model())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"M.sample(50000, 40000)"
],
"language": "python",
"metadata": {},
"outputs": [
{
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{
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},
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},
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],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.summary_plot([M.beta],\n",
" custom_labels=['male', 'AgeYears', 'Admit to ECMO', 'bacterial', 'viral', 'fungal',\n",
" 'pH', 'PCO2', 'OI', 'SaO2', 'VA'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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tOvemax1nrFn7uTddsbk3nZmZFZeD0TRJukTS4pr389OQ5mZmton81O7pGx8v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\nORiZmVnu/j/Fn18QQ3xg1AAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10f254cd0>"
]
}
],
"prompt_number": 48
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Same model above, but with a Weibull hazard function"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def weibull_survival_model():\n",
" \n",
" mu_pH = Normal('mu_pH', 0, 0.0001, value=0)\n",
" sigma_pH = Uniform('sigma_pH', 0, 500, value=10)\n",
" tau_pH = sigma_pH**-2\n",
" pH_masked = np.ma.masked_values(pH_std, value=0)\n",
" x_pH = Normal('x_pH', mu_pH, tau_pH, value=pH_masked, observed=True)\n",
" \n",
" mu_PCO2 = Normal('mu_PCO2', 0, 0.0001, value=0)\n",
" sigma_PCO2 = Uniform('sigma_PCO2', 0, 500, value=10)\n",
" tau_PCO2 = sigma_PCO2**-2\n",
" PCO2_masked = np.ma.masked_values(PCO2_std, value=0)\n",
" x_PCO2 = Normal('x_PCO2', mu_PCO2, tau_PCO2, value=PCO2_masked, observed=True)\n",
" \n",
" mu_OI = Normal('mu_OI', 0, 0.0001, value=0)\n",
" sigma_OI = Uniform('sigma_OI', 0, 500, value=10)\n",
" tau_OI = sigma_OI**-2\n",
" OI_masked = np.ma.masked_values(OI_std, value=0)\n",
" x_OI = Normal('x_OI', mu_OI, tau_OI, value=OI_masked, observed=True)\n",
" \n",
" a_SaO2 = Exponential('a_SaO2', 1, value=1)\n",
" b_SaO2 = Exponential('b_SaO2', 1, value=1)\n",
" SaO2_masked = np.ma.masked_values(SaO2, value=0.911)\n",
" x_SaO2 = Beta('x_SaO2', a_SaO2, b_SaO2, value=SaO2_masked, observed=True)\n",
" SaO2_std = Lambda('SaO2_std', lambda s=x_SaO2: (s - s.mean())/s.std())\n",
" \n",
" imputed_vars = [x_pH, x_PCO2, x_OI, SaO2_std]\n",
" \n",
" X = np.c_[[male, AgeYears_std, admit_through_emco_std, bacterial, viral, fungal]].T\n",
" \n",
" # Intercept for survival rate\n",
" beta0 = Normal('beta0', 0.0, 0.001, value=0)\n",
" # Treatment effect\n",
" beta = Normal('beta', 0, 0.001, value=[0]*(X.shape[1]+len(imputed_vars)))\n",
"\n",
" # Survival rates\n",
" lam = Lambda('lam', lambda b0=beta0, b=beta, x=X, z=imputed_vars: np.exp(b0 + np.dot(np.c_[x,np.transpose(z)], b)))\n",
"\n",
" alpha = Exponential('alpha', 1, value=1)\n",
"\n",
" @observed\n",
" def survival(value=obs_t, lam=lam, f=died, alpha=alpha):\n",
" # Weibull survival log-likelihood\n",
" return (f*(tt.log(alpha) + (alpha-1)*np.log(value*lam) + np.log(lam)) - (lam*value)**alpha).sum()\n",
"\n",
" return locals()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"W = MCMC(survival_model())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"W.sample(50000, 40000)"
],
"language": "python",
"metadata": {},
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}
],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.summary_plot([W.beta],\n",
" custom_labels=['male', 'AgeYears', 'Admit to ECMO', 'bacterial', 'viral', 'fungal',\n",
" 'pH', 'PCO2', 'OI', 'SaO2'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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7zMSa0US8VdJnyMrlFuDIXHNjZtYiXDNqHhes2dRzzajY6v4+M+r/jMzMbHpy\nMDIzs9w5GNmMV+/ZdGZWHG4zah4XbEFI8iNYWofbjIrNbUZmZlZcDkZmZpY7ByMzM8udg5GZmeXO\nwchmvNWrV+edBTMbhXvTNY8L1mzquTddsbk3nZmZFZeDkZmZ5c7ByMzMcudgNEaSLpG0sOrzfEmV\nkb5jZmYjczAau6E3ydoERATlcplyudz0R/X42XRmxedgVEeq8Vwv6euSNknaIGnbodm5Zm6aiwiW\nLz+ORYs2smjRRpYv725qQDrhhBOatm6zeqbygmsmcNfuOiTNB24COiPiCkmnAZuAtwG7AI+mRbcC\nNkdER80qWr5gNcGQPVm7ph+U2lIK0bU7u+Dqpre3HYAlS/pZv34tmuhBMf25a/c43R4RV6TxrwOv\nTeOrImJBRCwA3kKL1JSksQ3TLT2zyVKpVOjtbWdgYBUDA6vo7d2HSsVNyyOZnXcGCq76akdVn1Uz\nvSUMVS4mfuIPoBtoT5/7gbVMtChr8+XKkNn04ZrRyHaXtH8aXwVclmdmiiJiYsPgILzrXcGcObcx\nZ85tLF0aDA5OfL21g1leSqUSXV19tLX10NbWQ1dXP6VSKe9sFZprRiO7AfgbSaeTXb7/B3AIz2wP\n8qlvDCSxYcPnnrxtUSqVmnov3c+ms6kmibPPXle1j690e9Eo3IGhjtSB4fyIGO/ljAvWbOoVogOD\n1eUODOPkHczMbAq4ZtQ8LlizqeeaUbG5ZmRmZsXlYGRmZrlzMLIZz8+mMys+txk1jwu2IPw4oJbi\nNqNic5uRmZkVl4ORmZnlzsHIzMxy52BkZma5czCyGc/PpjMrPvemax4XrNnUc2+6YnNvOjMzKy4H\nIzMzy52DkZmZ5c7ByMzMctcSwUjSZknXSqpIOlvStmn68ySdJelGSVdJ+p6kF6d5+0i6WNL1kn4l\n6fiq9R0uaaOksqSfSurIa9vsmSKCcrlMuVwmIvxsOrNpoCV600l6OCLa0vjXgasj4guSrgDOiIiv\npHkdwDzgaqACfDgiLkrB61zguxHxZUmvATZFxIOSuoA1EbF/TbIzv2ALKCJYvryb3t52AJYs6WfD\nhs/52XStw73piq3u79OKwegooAM4hyyIvGGY5T8AvC4ijqya9kLgkojYvWbZZwOViNitZjUzv2AL\nQA2dekS9n6MFdv9WU7hgFBFUKhUASqUSamynnanctRtA0mxgCVAG2slqQMPZu3ZeRNwEzJU0t2bZ\nDwDfn+Smq0JGAAAMT0lEQVSsthxpfMNUp2s2FkM19c7OPjo7+1ix4njX0uuYnXcGpsi2kq5N4z8B\nTgc+PMp3Rj31SDoAeD/QObHs2USPz6cCRQDdZNcaAP1TlgezWpVKhd7edgYGVgHQ29tDpVKho8PN\nzLVaJRg9GhELqidI6geW1ll+E/D6muVfCAxExED63AGcCnRFxAOTn+XWNbEaiIB1ZE1+ACuBzzUt\nXQcws8nRUrfpqkXExcDWkj44NE1Sh6TXAt8AXivpwDR9W+Bk4J/S592BbwF/GRE3TnnmZ7iIiQ4i\nooPBwRJLl3az1VbvpK2th2XLuhkcjElY/1OD2UhKpRJdXX20tfXQ1tZDV1c/pVIp72wVUqt0YHgo\nIuYNM30X4CRgIfAn4GbgYxHxG0ntwCnALsAs4MyIODF971TgncBtaVWPR8R+Nauf+QU7DbjxuOW4\nA0OxtXZvupy4YM2mXuGCkT2Ne9OZmVlxORiZmVnuHIzMzCx3DkY24/nZdGbF5w4MzeOCLQhJ/q/3\n1uEODMXmDgxmZlZcDkZmZpY7ByMzM8udg5GZmeXOwchmvNWrV+edBTMbhXvTNY8L1mzquTddsbk3\nnZmZFZeDkZmZ5c7ByMzMcudgZGZmuXMwapCk3SR9R9KvJN0o6SRJW0paLOn8vPNn9fnZdGbF5950\nDVD2asYrgS9FxFclbQF8Bbgf+B7wiYg4pOZrLV2wRXq7pZ9N11Lcm67Y3Jtugt4IPBoRXwWIiEHg\n48D7gTl5ZqyIIoLly7vp7Oyjs7OPFSuOH1cwiAjK5TLlctnBxGwamMgx65pRAyQdA8yPiL+rmX4N\ncAZwcCvXjCZS6am3+w0FtN7edgCWLOln/fq146phuWbUUlwzykmDx2zd38fBqAGSPgrs2UrBKMe7\namPSyO7rYNRSHIxyUi6X6ezsY2BgFQBtbT1cdlk7HR0d1Yv5Nt0EbQIWVk+QNA/YHbgxlxw1WcT4\nh8HBYOnS44CeNHTTrGNVGn0YWs7MisvBqAER8SNgjqT3AEiaBfwrWa3oj3nmrYgkcfbZ69i4sZ2N\nG9sZHFxLhMYV0Nraemhr62HZsm4GB2NcwXH16tUN1aDMbPxKpRJdXX1PHrNdXf2USqWGv+/bdA2S\ntBvwZeClZEH8e8AngEXAsRFxaM1XXLATVKQeeTZt+DZdjho4Zt1mlAMXrNnUczAqNrcZmZlZcTkY\nmZlZ7hyMzMwsdw5GNuP52XRmxecODM3jgi0I/9NrS3EHhmJzBwYzMysuByMzM8udg5GZmeXOwcjM\nzHLnYGQz3urVq/POgpmNwr3pmscFazb13Juu2NybzszMisvByMzMcudgZGZmuXMwMjOz3DkY2Yzn\nZ9OZFV/L9KaT1A2sBDYDg8BREfHzEZY/HjiCrJfMncDREbFJ0rbAOcAL07rOj4jPDLOK1ijYSdDs\nN7r62XQtxb3piq21e9NJeg3wVmBBRLwcOBC4fYTljwb2BzoiYi/g88B5krZOi/xzRLwMWAB0Supq\n6gbMYBHB8uXddHb20dnZx4oVxztwmLWglqgZSXoH8L6IOLRm+v8DDgG2BS6PiKPS9NuA10fELVXL\nnglcEhGn16zjJKASEafVJDvzC3YCxlL52bixDIy/1uSaUUtxzWiMmn1nokbdlbdKMNoOuAyYA1wE\nrI+In0h6dkQ8kJY5Ezgb+Alwc0TsWLOOY4A9IuLYqmnbA1cDB1YHrmTmF+wImrs/P9NIu7GDUUtx\nMBqDoTsTvb3tACxZ0s/69WubGZBa+zZdRDwCLAQ+BNwLrJf0XuCNkn4mqQy8Edib+jvV0wpR0mzg\nm8C/DxOIWl5EY8PgYLB06XG0tfXQ1tYDdDOW43poPWY2dpVKhd7edgYGVjEwsIre3n2erCVNtdm5\npJqDiBgELgUulVQBPgyUgIURcaek1cA2EfGwpEck7RkRN1etYiHw46rPXwFuiIiTp2obZoJnXnAJ\nWAcMHQArGcvF7WgXcBF+Np3ZdNASNSNJL5H04qpJC4DryS7B75M0F1hWNf9fgJMlbZO+fxDQCfSk\nz2uBecDHpyD7M8rwNSQR0ZEG1a01LVvWzeBgNFzrGqoxuWu32fBKpRJdXX1PHmNdXf2USqVc8tIq\nbUb7AqcA2wNPAL8GjgI+RnYpfhdwA3BrRHw2fecfgPeQdd/+HVnX7n5JuwG3Ab8EHktJnFLbsYFp\neg+5aKa4cdWmP7cZjZE7MMx8LlizqedgVGyt3YHBzMyKzcHIzMxy52BkM547MJgVn9uMmscFWxD+\np9eW4jajYnOb0XRxySWXOL1pbqaXaR6/4XTdb/LM93RL28GoYGb6iWW6nlTGYqaXqYNR46ZbQMgz\nbQcjMzPLnYORmZnlzh0YmkSSC9YsBxEx5Z0YfLw3rt7v42BkZma58206MzPLnYORmZnlzsEoZ5J2\nkHShpF9J+mF6e+xwy20v6RxJv5S0SdL+zUwvLTtL0rWSzh9PWo2mJ+kFkn4sqV9SX3qr7ljT6ZJ0\nvaRfS/r7OsucnOZvlLRgPNszljQlHZ7SKkv6qaSOZqZXtdyrJD0h6Z3NTk/S4rSP9Em6pJnpSdpJ\nUq+k61J6R04kvWaSdLqku9O703JLJ/0+D6bf6FpJx09i2hM+bp8mIjzkOAD/DHwqjf898I91lvsq\n8P40Pht4VjPTS/P/DvgGcF4ztw94HvCKND6X7HUeLxtDGrOAG4H5wJbAdbXfB94CfD+Nvxr42QR/\nt0bSfM3Q7wR0TSTNRtKrWu5i4LvAu5q8fdsD/cBu6fNOTU5vDfD5obSA+4DZE/kdmzUAryN7b1ol\nz3SAxRM5fkdJe9TjFril0fW5ZpS/Q8kCDenv22sXkPQs4HWR3pkUEU9ExIPNSi+luRvZCfy/mdgj\nVkZNLyLuiojr0vgA2buidh1DGvsBN0bELRHxOHAWcFi9fETElcD2knYey4aMNc2IuKLqd7oS2K2Z\n6SUfBc4B7p1AWo2mtwo4NyLuAIiI3zc5vd+RvdSS9Pe+iHhiAmk2TUT8H/BAQdJpSu/CBo/bhnvI\nORjlb+eIuDuN3w0Md4LcE7hX0hmSrpF0qqQ5TUwP4AvAJ4HBcaYz1vQAkDSf7ErvyjGk8Xzg9qrP\nd6Rpoy0zkeDQSJrVPgB8v5npSXo+2Qn8P9KkiXSVbWT7XgzskG7VXCXpPU1O71RgH0m/BTYCfzuB\n9FpFAIvS7eLvS9q7GYmM87h9mtmTlRmrT9KFZFXaWt3VHyIi6vy/wmxgX7K3zf5C0knAp4F/aEZ6\nkt4G3BMR10paPFwak5le1Xrmkl3V/2260mpUoyfd2ivEiZysG/6upAOA95O9ur6Z6Z0EfDqVs5jY\nFXEj6W1Jtl8eCMwBrpD0s4j4dZPSOw64LiIWS/oL4EJJL4+Ih8eRXqu4BnhBRPxR0hLg28BLJjOB\n2uNWUjewNM3eVdK1afyyiPhovfU4GE2BiHhTvXmp8fF5EXGXpF2Ae4ZZ7A7gjoj4Rfp8DlkwalZ6\ni4BDJb0F2AaYJ+nMiDiiSekhaUvgXODrEfHteuur407gBVWfX0BWZiMts1uaNl6NpEnqtHAq0BUR\nE7lt00h6C4GzsjjETsASSY9HxHlNSu924PcR8SjwqKSfAC8HxhOMGklvEbAOICJ+I+lmYC/gqnGk\n1xKqA3VE/EDSlyXtEBH3T8b6hztuI2Id6XeSdHNENNRZyLfp8nce8N40/l6yK5eniYi7gNslDV3R\nHETWcNys9I6LiBdExJ7Au4GL6wWiyUgvXcWfBmyKiJPGkcZVwIslzZe0FbAipVubjyNSevsDf6i6\nfTgeo6YpaXfgW8BfRsSNE0irofQi4oURsWf63c4BPjLOQNRQesB3gNcq63U5h6xjyKYmpnc92b5P\nau/bC7hpnOm1BEk7p+MLSfuRPehgsgLRRI/bp2tGLwsPY+qRsgNwEfAr4IfA9mn6rsD3qpZ7OfAL\nsnvl32L8vekaSq9q+Tcwsd50o6YHvJasbeo64No0dI0xnSVkvXluBD6Tph0FHFW1zBfT/I3AvpPw\n242YJlnnj/uqtunnzUyvZtkzgHc2Oz3gE2QXRhXgmCaX507A+en3qwCrJvobNmsAvgn8FvgzWQ3y\nfU1O57GUzvtryuxvgL50bF0O7D+JaY963AI3Nbo+Pw7IzMxy59t0ZmaWOwcjMzPLnYORmZnlzsHI\nzMxy52BkZma5czAyM7PcORiZmVnuHIzMzCx3/x/fUkPHT/8XJQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x113032d50>"
]
}
],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.interpolate import Rbf"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def rbf_survival_model():\n",
" \n",
" node_loc = standardize(flu_complete.AgeYears).quantile([0.1, 0.25, 0.5, 0.75, .9]).values\n",
" rbf_nodes = Normal('rbf_nodes', 0, 0.0001, value=[0]*len(node_loc))\n",
" @deterministic\n",
" def age_rbf(nodes=rbf_nodes):\n",
" r = Rbf(node_loc, nodes)\n",
" return r(AgeYears_std)\n",
" \n",
" # evaluate RBF at several nodes\n",
" age_mean = AgeYears[np.isnan(AgeYears) ^ True].mean()\n",
" age_std = AgeYears[np.isnan(AgeYears) ^ True].std()\n",
" @deterministic\n",
" def age_rbf_x(nodes=rbf_nodes):\n",
" r = Rbf(node_loc, nodes)\n",
" vals = [10, 20, 30, 40, 50, 60, 70]\n",
" return r((vals - age_mean)/age_std)\n",
" \n",
" mu_pH = Normal('mu_pH', 0, 0.0001, value=0)\n",
" sigma_pH = Uniform('sigma_pH', 0, 500, value=10)\n",
" tau_pH = sigma_pH**-2\n",
" pH_masked = np.ma.masked_values(pH_std, value=0)\n",
" x_pH = Normal('x_pH', mu_pH, tau_pH, value=pH_masked, observed=True)\n",
" \n",
" mu_PCO2 = Normal('mu_PCO2', 0, 0.0001, value=0)\n",
" sigma_PCO2 = Uniform('sigma_PCO2', 0, 500, value=10)\n",
" tau_PCO2 = sigma_PCO2**-2\n",
" PCO2_masked = np.ma.masked_values(PCO2_std, value=0)\n",
" x_PCO2 = Normal('x_PCO2', mu_PCO2, tau_PCO2, value=PCO2_masked, observed=True)\n",
" \n",
" mu_OI = Normal('mu_OI', 0, 0.0001, value=0)\n",
" sigma_OI = Uniform('sigma_OI', 0, 500, value=10)\n",
" tau_OI = sigma_OI**-2\n",
" OI_masked = np.ma.masked_values(OI_std, value=0)\n",
" x_OI = Normal('x_OI', mu_OI, tau_OI, value=OI_masked, observed=True)\n",
" \n",
" a_SaO2 = Exponential('a_SaO2', 1, value=1)\n",
" b_SaO2 = Exponential('b_SaO2', 1, value=1)\n",
" SaO2_masked = np.ma.masked_values(SaO2, value=0.911)\n",
" x_SaO2 = Beta('x_SaO2', a_SaO2, b_SaO2, value=SaO2_masked, observed=True)\n",
" SaO2_std = Lambda('SaO2_std', lambda s=x_SaO2: (s - s.mean())/s.std())\n",
" \n",
" imputed_vars = [x_pH, x_PCO2, x_OI, SaO2_std]\n",
" \n",
" X = np.c_[[male, admit_through_emco_std, bacterial, viral, fungal, va]].T\n",
" \n",
" # Intercept for survival rate\n",
" #beta0 = Normal('beta0', 0.0, 0.001, value=0)\n",
" # Treatment effect\n",
" beta = Normal('beta', 0, 0.001, value=[0]*(X.shape[1]+len(imputed_vars)))\n",
"\n",
" # Survival rates\n",
" lam = Lambda('lam', lambda b0=age_rbf, b=beta, x=X, z=imputed_vars: \n",
" np.exp(b0 + np.dot(np.c_[x,np.transpose(z)], b)))\n",
"\n",
" @observed\n",
" def survival(value=obs_t, lam=lam, f=died):\n",
" \"\"\"Exponential survival likelihood, accounting for censoring\"\"\"\n",
" return (f*np.log(lam) - lam*value).sum()\n",
"\n",
" return locals()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 126
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"R = MCMC(rbf_survival_model())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 127
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"R.sample(100000, 90000)"
],
"language": "python",
"metadata": {},
"outputs": [
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{
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"input": [
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{
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"text": [
"\r",
" [-----------------99%----------------- ] 99164 of 100000 complete in 214.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 99362 of 100000 complete in 215.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 99551 of 100000 complete in 215.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 99742 of 100000 complete in 216.0 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------99%----------------- ] 99944 of 100000 complete in 216.5 sec"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\r",
" [-----------------100%-----------------] 100000 of 100000 complete in 216.6 sec"
]
}
],
"prompt_number": 76
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.summary_plot([R.beta],\n",
" custom_labels=['male', 'Admit to ECMO', 'bacterial', 'viral', 'fungal',\n",
" 'pH', 'PCO2', 'OI', 'SaO2', 'VA'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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JbyibNQW4naz28oSkCcCssuVfBk6XtGHa/p1AD7AoTZ8MTASOaUH41gIR+Qz9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\nzCx3TkZmZpa7/wNAx4MD3nHm6QAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x114ed5850>"
]
}
],
"prompt_number": 129
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below is an example of how the age effect varies over different values of age (decades). It is used as the intercept (mean) of the model."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Matplot.summary_plot([R.age_rbf_x], custom_labels=np.array([10, 20, 30, 40, 50, 60, 70], 'str'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x15d647f90>"
]
}
],
"prompt_number": 132
}
],
"metadata": {}
}
]
}
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