ipython notebook to generate mock data from 3 simulations

Generate_Mock_Lightcurves.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural networks not available in this version of scikit-learn. Neural networks are available from development version 0.18.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload #Use this to reload modules if they are changed on disk while the notebook is running\n",
+    "from __future__ import division\n",
+    "from snmachine import sndata, snfeatures, snclassifier, tsne_plot\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import time, os, pywt,subprocess\n",
+    "from sklearn.decomposition import PCA\n",
+    "from astropy.table import Table,join,vstack\n",
+    "from astropy.io import fits\n",
+    "import sklearn.metrics \n",
+    "import sncosmo\n",
+    "%matplotlib nbagg\n",
+    "from astropy.table import Column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SN numbers with classification code\n",
+    "SN001695   - 33\n",
+    "\n",
+    "2457 - 33\n",
+    "\n",
+    "2542 - 1\n",
+    "\n",
+    "5399 - 2\n",
+    "\n",
+    "13481 - 2\n",
+    "\n",
+    "13866 - 1 \n",
+    "\n",
+    "16742 - 21\n",
+    "\n",
+    "17270 - 32\n",
+    "\n",
+    "27266 -32\n",
+    "\n",
+    "64968 - 23\n",
+    "\n",
+    "149147 -21"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "dataset='spcc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# WARNING...\n",
+    "#Multinest uses a hardcoded character limit for the output file names. I believe it's a limit of 100 characters\n",
+    "#so avoid making this file path to lengthy if using nested sampling or multinest output file names will be truncated\n",
+    "\n",
+    "#Change outdir to somewhere on your computer if you like\n",
+    "outdir=os.path.join('output_%s_no_z' %dataset,'')\n",
+    "out_features=os.path.join(outdir,'features') #Where we save the extracted features to\n",
+    "out_class=os.path.join(outdir,'classifications') #Where we save the classification probabilities and ROC curves\n",
+    "out_int=os.path.join(outdir,'int') #Any intermediate files (such as multinest chains or GP fits)\n",
+    "\n",
+    "subprocess.call(['mkdir',outdir])\n",
+    "subprocess.call(['mkdir',out_features])\n",
+    "subprocess.call(['mkdir',out_class])\n",
+    "subprocess.call(['mkdir',out_int])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#Data root\n",
+    "rt=os.path.join('SPCC_SUBSET','')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading data...\n",
+      "11 objects read into memory.\n"
+     ]
+    }
+   ],
+   "source": [
+    "dat=sndata.Dataset(rt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "read_from_file=True #We can use this flag to quickly rerun from saved features\n",
+    "run_name=os.path.join(out_features,'%s_all' %dataset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'DES_SN001695.DAT'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dat.object_names[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "test_table = dat.data['DES_SN001695.DAT'] #get_lightcurve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "type_II = []\n",
+    "for i in range(len(dat.data['DES_SN001695.DAT']['flux'])):\n",
+    "    type_II.append(np.random.normal(dat.data['DES_SN001695.DAT']['flux'][i], dat.data['DES_SN001695.DAT']['flux_error'][i], 1000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "type_Ia = []\n",
+    "for i in range(len(dat.data['DES_SN002542.DAT']['flux'])):\n",
+    "    type_Ia.append(np.random.normal(dat.data['DES_SN002542.DAT']['flux'][i], dat.data['DES_SN002542.DAT']['flux_error'][i], 1000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "type_Ibc = []\n",
+    "for i in range(len(dat.data['DES_SN005399.DAT']['flux'])):\n",
+    "    type_Ia.append(np.random.normal(dat.data['DES_SN005399.DAT']['flux'][i], dat.data['DES_SN005399.DAT']['flux_error'][i], 1000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "type_Ia = np.array(type_Ia)\n",
+    "type_Ibc = np.array(type_Ibc)\n",
+    "type_II = np.array(type_II)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "test_table.replace_column('flux', type_II[:,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#test_table.write('testing.dat',format = 'ascii')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<Table length=128>\n",
+       "<table id=\"table4357163664\">\n",
+       "<thead><tr><th>mjd</th><th>filter</th><th>flux</th><th>flux_error</th><th>zp</th><th>zpsys</th></tr></thead>\n",
+       "<thead><tr><th>float64</th><th>str4</th><th>float64</th><th>float64</th><th>float64</th><th>str2</th></tr></thead>\n",
+       "<tr><td>0.0</td><td>desg</td><td>3.874</td><td>4.787</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>0.0160000000033</td><td>desr</td><td>-5.456</td><td>5.335</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>0.0310000000027</td><td>desi</td><td>-10.3</td><td>5.439</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>0.0619999999981</td><td>desz</td><td>1.439</td><td>4.691</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>2.0</td><td>desg</td><td>-0.7297</td><td>2.238</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>2.14</td><td>desr</td><td>-2.837</td><td>3.03</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>2.156</td><td>desi</td><td>-4.236</td><td>4.2</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>2.187</td><td>desz</td><td>-5.086</td><td>3.518</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>9.984</td><td>desg</td><td>0.03404</td><td>1.702</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>10.0</td><td>desr</td><td>0.8236</td><td>1.251</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
+       "<tr><td>135.918</td><td>desi</td><td>4.008</td><td>3.081</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>139.894</td><td>desz</td><td>8.095</td><td>4.252</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>143.863</td><td>desg</td><td>4.442</td><td>2.914</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>143.871</td><td>desr</td><td>0.7718</td><td>1.365</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>143.894</td><td>desi</td><td>3.976</td><td>3.218</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>148.867</td><td>desz</td><td>5.982</td><td>2.609</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>155.871</td><td>desg</td><td>-0.109</td><td>1.816</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>155.879</td><td>desr</td><td>0.3982</td><td>1.383</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>156.871</td><td>desi</td><td>1.149</td><td>2.177</td><td>27.5</td><td>ab</td></tr>\n",
+       "<tr><td>157.871</td><td>desz</td><td>16.46</td><td>3.394</td><td>27.5</td><td>ab</td></tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<Table length=128>\n",
+       "      mjd       filter   flux  flux_error    zp   zpsys\n",
+       "    float64      str4  float64  float64   float64  str2\n",
+       "--------------- ------ ------- ---------- ------- -----\n",
+       "            0.0   desg   3.874      4.787    27.5    ab\n",
+       "0.0160000000033   desr  -5.456      5.335    27.5    ab\n",
+       "0.0310000000027   desi   -10.3      5.439    27.5    ab\n",
+       "0.0619999999981   desz   1.439      4.691    27.5    ab\n",
+       "            2.0   desg -0.7297      2.238    27.5    ab\n",
+       "           2.14   desr  -2.837       3.03    27.5    ab\n",
+       "          2.156   desi  -4.236        4.2    27.5    ab\n",
+       "          2.187   desz  -5.086      3.518    27.5    ab\n",
+       "          9.984   desg 0.03404      1.702    27.5    ab\n",
+       "           10.0   desr  0.8236      1.251    27.5    ab\n",
+       "            ...    ...     ...        ...     ...   ...\n",
+       "        135.918   desi   4.008      3.081    27.5    ab\n",
+       "        139.894   desz   8.095      4.252    27.5    ab\n",
+       "        143.863   desg   4.442      2.914    27.5    ab\n",
+       "        143.871   desr  0.7718      1.365    27.5    ab\n",
+       "        143.894   desi   3.976      3.218    27.5    ab\n",
+       "        148.867   desz   5.982      2.609    27.5    ab\n",
+       "        155.871   desg  -0.109      1.816    27.5    ab\n",
+       "        155.879   desr  0.3982      1.383    27.5    ab\n",
+       "        156.871   desi   1.149      2.177    27.5    ab\n",
+       "        157.871   desz   16.46      3.394    27.5    ab"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#dat.data['DES_SN001695.DAT']"
+   ]
+  },
+  {
+   "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
+}