automatised new_mocks.LIST

Inflate_errorbars.ipynb

@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {
     "collapsed": true
    },
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -109,7 +109,7 @@
        "1"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -134,7 +134,7 @@
        "'DES_SN002457.DAT'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 33,
    "metadata": {
     "collapsed": false
    },
@@ -181,11 +181,6 @@
     "    test_table_II = dat.data['DES_SN002457.DAT']\n",
     "    test_table_Ia = dat.data['DES_SN002542.DAT']\n",
     "    test_table_Ibc = dat.data['DES_SN005399.DAT']\n",
-    "#print test_table_II\n",
-    "\n",
-    "#test_table_Ia.replace_column('mjd', test_table_Ia['mjd']+56178)\n",
-    "#test_table_II.replace_column('mjd', test_table_II['mjd']+56178)\n",
-    "#test_table_Ibc.replace_column('mjd', test_table_Ibc['mjd']+56178)\n",
     "\n",
     "    test_table_II.rename_column('flux', 'FLUXCAL')\n",
     "    test_table_II.rename_column('flux_error', 'FLUXCALERR')\n",
@@ -208,6 +203,7 @@
     "\n",
     "    realspertype=realsperlc*(len(prototypes_Ia)+len(prototypes_II)+len(prototypes_Ibc))\n",
     "                                 \n",
+    "    logfile=open('new_mocks/new_mocks.LIST', 'w')\n",
     "    for i in range(realsperlc):\n",
     "        test_table_Ia.replace_column('FLUXCAL', type_Ia[:,i])\n",
     "        test_table_Ibc.replace_column('FLUXCAL', type_Ibc[:,i])\n",
@@ -220,29 +216,30 @@
     "        make_new_data(filename_Ibc, test_table_Ibc, 'Ibc_%s'%i, 3)\n",
     "        make_new_data(filename_II, test_table_II, 'II_%s'%i, 2)\n",
     "        make_new_data(filename_Ia, test_table_Ia, 'Ia_%s'%i, 1)\n",
-    "    \n",
-    "        new_data[(str)(i)]=test_table_II.copy()  #new_light_curve_Ia\n",
-    "        new_data[(str)(i+realspertype)]=test_table_Ia.copy()#new_light_curve_II\n",
-    "        new_data[(str)(i+2*realspertype)]=test_table_Ibc.copy()#new_light_curve_Ibc\n",
-    "    \n",
-    "#sncosmo.read_lc(new_data)\n",
-    "#new_data = Counter(new_data_Ia) + Counter(new_data_II) + Counter(new_data_Ibc)\n",
-    "\n",
-    "    for i in range(realspertype*(len(prototypes_II)+len(prototypes_Ia)+len(prototypes_Ibc))):    \n",
-    "        objs.append((str)(i))\n",
-    "    \n",
-    "    dat.object_names=np.asarray(objs)\n",
-    "    dat.data=new_data"
+    "        logfile.write('Ia_'+str(i)+'.dat\\nII_'+str(i)+'.dat\\nIbc_'+str(i)+'.dat\\n')\n",
+    "    logfile.close()\n",
+    "    "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading data...\n",
+      "11 objects read into memory.\n"
+     ]
+    }
+   ],
+   "source": [
+    "produce_mock_data_set(1.1)"
+   ]
   },
   {
    "cell_type": "code",
@@ -264,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -281,7 +278,7 @@
     "    types['Type'][np.floor(types['Type']/10)==3]=3\n",
     "    %%capture --no-stdout\n",
     " \n",
-    "    mod1Feats=snfeatures.ParametricFeatures('newling',sampler='leastsq')\n",
+    "    mod1Feats=snfeatures.ParametricFeatures('karpenka',sampler='leastsq')\n",
     "    mod1_features=mod1Feats.extract_features(dat1,nprocesses=4,chain_directory=out_int)\n",
     "    mod1_features.write('%s_karpenka.dat' %run_name, format='ascii')\n",
     "    #Unfortunately, sometimes the fitting methods return NaN for some parameters for these models.\n",
@@ -390,18 +387,7 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Reading data...\n",
-      "11 objects read into memory.\n",
-      "Reading data...\n",
-      "2001 objects read into memory.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "classifiers=['nb','svm','boost_dt']\n",
     "\n",