SNCatalog_example_lightCurves.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example notebook showing the use of SNCatalog. To run this notebook, we will need:\n",
    "- The LSST simulations stack setup (sims_catUtils) \n",
    "- A version of SNCosmo. You may use the one in the stack by setup sncosmo\n",
    "- Have the OpSim database enigma_1189_sqlite.db on disk. This is used to obtain samples of points (in time) with given fivesigmadepth. The second code block of the **Setup** section is used to do this. Please change the path to point to your OpSim database.\n",
    "\n",
    "For this example to work without connecting to a server, we will create a database of fake galaxies on disk. These galaxies should be thought of as positions only, and do not have specific physical properties set to reasonable values. Neither will the frequency of supernovae in galaxies be set to reasonable values to enable us to sample some light curves quickly. \n",
    "\n",
    "We only do this at the location of DDF with FieldID 290. Our steps are : \n",
    "\n",
    "1. **Setup** Some setup functionality\n",
    "2. **Fake Galaxy Catalog** : Create a Fake galaxy database for galaxies at locations around the field of view we chose. This will be done using ObsMetaData\n",
    "3. **Instance Catalogs of SNIa** : We create Instance Catalogs of SNIa hosted on the galaxies in the database created in the previous step. These instance Catalogs include expected values of measured flux/magnitude and their uncertainties. The uncertainties are based on the observing conditions which are read off from Observation MetaData. The ObservationMetaData are based on OpSim Observations. Here we choose to use a Deep Drilling Field (FieldID 290) on certain dates where we know there are observations. \n",
    "4. **Rebuilding Light Curves**: For each Instance Catalog, we write the catalog to disk. We then read in all of the catalogs, and rebuild the light curve by grouping observations corresponding to a SNIa identified by a unique ID."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sqlite3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "opsimPath = os.path.join('/Users/rbiswas/data/LSST/OpSimData')\n",
    "opsimDB = os.path.join(opsimPath,'enigma_1189_sqlite.db')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/manual/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
      "  \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n",
      "Duplicate object type id 25 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n",
      "Duplicate object type id 40 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n",
      "Duplicate object type id 40 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n",
      "Duplicate object type id 40 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n",
      "Duplicate object type id 40 specified: \n",
      "Output object ids may not be unique.\n",
      "This may not be a problem if you do not want globally unique id values\n"
     ]
    }
   ],
   "source": [
    "from lsst.sims.catUtils.mixins import CosmologyMixin\n",
    "from lsst.sims.utils import ObservationMetaData\n",
    "from lsst.sims.catUtils.utils import ObservationMetaDataGenerator\n",
    "from lsst.sims.catalogs.generation.db import CatalogDBObject\n",
    "from lsst.sims.catalogs.measures.instance import InstanceCatalog\n",
    "import eups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from lsst.sims.catUtils.mixins import SNIaCatalog\n",
    "from lsst.sims.catUtils.supernovae import SNObject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cleanDB(dbname, verbose=True):\n",
    "    '''\n",
    "    Deletes the database dbname from the disk.\n",
    "    Parameters\n",
    "    ----------\n",
    "    dbname: string, mandatory\n",
    "        name (abs path) of the database to be deleted\n",
    "    verbose: Bool, optional, defaults to True\n",
    "\n",
    "    '''\n",
    "\n",
    "    if os.path.exists(dbname):\n",
    "        if verbose:\n",
    "            print \"deleting database \", dbname\n",
    "        os.unlink(dbname)\n",
    "    else:\n",
    "        if verbose:\n",
    "            print 'database ', dbname, ' does not exist'\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lsst.sims.utils import spatiallySample_obsmetadata as sample_obsmetadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _createFakeGalaxyDB(dbname, ObsMetaData, size=10000, seed=1):\n",
    "    '''\n",
    "    Create a local sqlite galaxy database having filename dbname with variables\n",
    "    id, raJ2000, decJ2000 and redshift, having number of rows =size, and having\n",
    "    overlap with ObsMetaData.\n",
    "    '''\n",
    "    cleanDB(dbname)\n",
    "    conn = sqlite3.connect(dbname)\n",
    "    curs = conn.cursor()\n",
    "    curs.execute('CREATE TABLE if not exists gals (id INT, raJ2000 FLOAT,\\\n",
    "                  decJ2000 FLOAT, redshift FLOAT)')\n",
    "\n",
    "    np.random.seed(seed)\n",
    "    samps = sample_obsmetadata(ObsMetaData, size=size)\n",
    "\n",
    "    for count in range(size):\n",
    "        id = 1000000 + count\n",
    "\n",
    "        # Main Database should have values in degrees\n",
    "        ra = np.degrees(samps[0][count])\n",
    "        dec = np.degrees(samps[1][count])\n",
    "        redshift = np.random.uniform()\n",
    "        row = tuple([id, ra, dec, redshift])\n",
    "        exec_str = insertfromdata(tablename='gals', records=row,\n",
    "                                     multiple=False)\n",
    "        curs.execute(exec_str, row)\n",
    "\n",
    "    conn.commit()\n",
    "    conn.close()\n",
    "    return samps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def insertfromdata(tablename, records, multiple=True):\n",
    "    \"\"\"\n",
    "    construct string to insert multiple records into sqlite3 database\n",
    "    args:\n",
    "        tablename: str, mandatory\n",
    "            Name of table in the database.\n",
    "        records: set of records\n",
    "        multiple:\n",
    "    returns:\n",
    "    \"\"\"\n",
    "    if multiple:\n",
    "        lst = records[0]\n",
    "    else:\n",
    "        lst = records\n",
    "    s = 'INSERT INTO ' + str(tablename) + ' VALUES '\n",
    "    s += \"( \" + \", \".join([\"?\"]*len(lst)) + \")\"\n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class myGalaxyCatalog(CatalogDBObject):\n",
    "    '''\n",
    "    Create a like CatalogDBObject connecting to a local sqlite database\n",
    "    '''\n",
    "\n",
    "    objid = 'mytestgals'\n",
    "    tableid = 'gals'\n",
    "    idColKey = 'id'\n",
    "    objectTypeId = 0\n",
    "    appendint = 10000\n",
    "    database = 'testdata/galcat.db'\n",
    "    # dbAddress = './testData/galcat.db'\n",
    "    raColName = 'raJ2000'\n",
    "    decColName = 'decJ2000'\n",
    "    driver = 'sqlite'\n",
    "\n",
    "    # columns required to convert the ra, dec values in degrees\n",
    "    # to radians again\n",
    "    columns = [('id', 'id', int),\n",
    "               ('raJ2000','raJ2000 * PI()/ 180. '),\n",
    "               ('decJ2000','decJ2000 * PI()/ 180.'),\n",
    "               ('redshift', 'redshift')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughputs: We will need these to evaluate fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from astropy.table import Table\n",
    "import sncosmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "throughputsdir = os.getenv('THROUGHPUTS_DIR')\n",
    "\n",
    "from astropy.units import Unit\n",
    "bandPassList = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "banddir = os.path.join(os.getenv('THROUGHPUTS_DIR'), 'baseline')\n",
    "\n",
    "for band in bandPassList:\n",
    "\n",
    "    # setup sncosmo bandpasses\n",
    "    bandfname = banddir + \"/total_\" + band + '.dat'\n",
    "\n",
    "\n",
    "    # register the LSST bands to the SNCosmo registry\n",
    "    # Not needed for LSST, but useful to compare independent codes\n",
    "    # Usually the next two lines can be merged,\n",
    "    # but there is an astropy bug currently which affects only OSX.\n",
    "    numpyband = np.loadtxt(bandfname)\n",
    "    sncosmoband = sncosmo.Bandpass(wave=numpyband[:, 0],\n",
    "                                   trans=numpyband[:, 1],\n",
    "                                   wave_unit=Unit('nm'),\n",
    "                                   name=band)\n",
    "    sncosmo.registry.register(sncosmoband, force=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lsst.sims.photUtils import Bandpass\n",
    "from lsst.sims.photUtils import BandpassDict\n",
    "\n",
    "bandpassnames = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "LSST_BandPass = BandpassDict.loadTotalBandpassesFromFiles()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fake Galaxy Catalog DataBase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This ObservationMetaData Instance in instantiated at the location of the DDF field of interest. As the SNe inherit from the galaxy catalog, these galaxies must be in the region of interest. The mjd parameter does not play a role as galaxies are static objects. Similarly the bandpassName atribute has no effect.\n",
    "\n",
    "The positions of the galaxies are randomly selected within the region specified by the ObservationMetaData. This is done through the `lsst.sims.utils.sample_obsmetadata` function which samples the spatial region of an OnsMetaData instance. We will choose our Observation region to be around RA of 349.3 degrees and Dec of -63.3 degrees. These coordinates are called centralRA, centralDec respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# These are in degrees\n",
    "centralRA = 349.3\n",
    "centralDec = -63.3\n",
    "galaxyPatchSize = np.degrees(0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "obsMetaDataforCat = ObservationMetaData(boundType='circle',\n",
    "                                        boundLength=galaxyPatchSize,\n",
    "                                        pointingRA=centralRA,\n",
    "                                        pointingDec=centralDec,\n",
    "                                        bandpassName=['r'],\n",
    "                                        mjd=49350.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us check a few properties of this instance of ObservationMetaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6.05494608]), array([-1.15447464]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_obsmetadata(obsMetaDataforCat, size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.323944878270581"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obsMetaDataforCat.boundLength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obsMetaDataforCat._boundLength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the directory where we will store the galaxy database and the SNIa catalogs. Create the values of positions for the Fake galaxies and store in the the database `testSNData/galcat.db`. This will clobber previous files of the same name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "database  testSNData/galcat.db  does not exist\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists('testSNData'):\n",
    "    os.makedirs('testSNData')\n",
    "testdbName = 'testSNData/galcat.db'\n",
    "vals = _createFakeGalaxyDB(dbname=testdbName,\n",
    "                           ObsMetaData=obsMetaDataforCat,\n",
    "                           size=1000000,\n",
    "                           seed=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We cat now visualize the location of the galaxies (in blue) and the centralRA, and centralDec in red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113245b50>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130c03d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(vals[0][:1000], vals[1][:1000], '.')\n",
    "plt.axvline(2. * np.pi, color='k', lw=2.)\n",
    "plt.axvline(0., color='k', lw=2.)\n",
    "plt.axhline(np.pi, color='k', lw=2.)\n",
    "plt.axhline(-np.pi, color='k', lw=2.)\n",
    "plt.plot([np.radians(centralRA)], [np.radians(centralDec)], 'rs', markersize=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "galDB = myGalaxyCatalog(database=testdbName)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class galCopy(InstanceCatalog):\n",
    "    column_outputs = ['id', 'raJ2000', 'decJ2000', 'redshift']\n",
    "    override_formats = {'raJ2000': '%8e', 'decJ2000': '%8e'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "galphot = galCopy(db_obj=galDB, obs_metadata=obsMetaDataforCat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "galphot.write_catalog('gals.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  960028 gals.dat\r\n"
     ]
    }
   ],
   "source": [
    "!wc -l gals.dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#id, raJ2000, decJ2000, redshift\r\n",
      "1000000, 6.054946e+00, -9.029905e-01, 0.0376\r\n",
      "1000001, 6.206597e+00, -1.139951e+00, 0.7608\r\n",
      "1000002, 5.846492e+00, -1.072297e+00, 0.9737\r\n",
      "1000003, 5.997601e+00, -9.194755e-01, 0.1566\r\n",
      "1000004, 5.919813e+00, -9.140243e-01, 0.8494\r\n",
      "1000005, 5.892604e+00, -9.430854e-01, 0.5251\r\n",
      "1000006, 5.939565e+00, -1.270477e+00, 0.1618\r\n",
      "1000007, 6.019215e+00, -8.845809e-01, 0.3991\r\n",
      "1000008, 6.044819e+00, -9.364637e-01, 0.7685\r\n"
     ]
    }
   ],
   "source": [
    "!head gals.dat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instance Catalogs of SNIa\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would like to create a number of supernova instance catalogs and then build the light curves from the catalogs. To do this correctly, we would like to use the observation_metadata associated with a number of conscutive OpSIM pointings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/rbiswas/data/LSST/OpSimData/enigma_1189_sqlite.db\n"
     ]
    }
   ],
   "source": [
    "# In order to use the OpSim which is part of sims_data, uncomment the following two lines to override the \n",
    "# Opsim database setup in the **setup** section\n",
    "\n",
    "#opsimPath = os.path.join(eups.productDir('sims_data'),'OpSimData')\n",
    "#opsimDB = os.path.join(opsimPath,'opsimblitz1_1133_sqlite.db')\n",
    "print opsimDB\n",
    "\n",
    "\n",
    "# from Tuscon AHM notebook from Scott\n",
    "# This OPSIM DB is provided in sims_data. This creates a list of opsim pointings\n",
    "# that I have checked. This is a tuned notebook\n",
    "generator = ObservationMetaDataGenerator(driver='sqlite', database=opsimDB) #database = opsimPath, driver='sqlite')\n",
    "obsMetaDataResults = generator.getObservationMetaData(limit=1000,\n",
    "                                                      fieldRA=(centralRA-2.0, centralRA +2.0), \n",
    "                                                      fieldDec=(centralDec-2.0, centralDec+2.0),\n",
    "                                                      expMJD=(49500., 49590.),\n",
    "                                                      boundLength=0.015,\n",
    "                                                      boundType='circle')\n",
    "                                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A short block to check what you should be seeing\n",
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('sqlite:///' + opsimDB)\n",
    "\n",
    "dff = pd.read_sql_query('SELECT * FROM SUMMARY WHERE FIELDID is 290 and expMJD < 49590 and expMJD > 49490', engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>expMJD</th>\n",
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       "      <th>...</th>\n",
       "      <th>moonBright</th>\n",
       "      <th>darkBright</th>\n",
       "      <th>rawSeeing</th>\n",
       "      <th>wind</th>\n",
       "      <th>humidity</th>\n",
       "      <th>slewDist</th>\n",
       "      <th>slewTime</th>\n",
       "      <th>fiveSigmaDepth</th>\n",
       "      <th>ditheredRA</th>\n",
       "      <th>ditheredDec</th>\n",
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       "      <td>6.080937</td>\n",
       "      <td>-1.098547</td>\n",
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       "      <td>114377</td>\n",
       "      <td>1189</td>\n",
       "      <td>363</td>\n",
       "      <td>290</td>\n",
       "      <td>6.097944</td>\n",
       "      <td>-1.10516</td>\n",
       "      <td>y</td>\n",
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       "      <td>49491.434829</td>\n",
       "      <td>138</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>94.872840</td>\n",
       "      <td>0.482168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4.646860</td>\n",
       "      <td>21.551827</td>\n",
       "      <td>6.080937</td>\n",
       "      <td>-1.098547</td>\n",
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       "      <th>2</th>\n",
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       "      <td>290</td>\n",
       "      <td>6.097944</td>\n",
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       "      <td>z</td>\n",
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       "      <td>148</td>\n",
       "      <td>...</td>\n",
       "      <td>580.725466</td>\n",
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       "      <td>0.504399</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4.863491</td>\n",
       "      <td>22.260523</td>\n",
       "      <td>6.042673</td>\n",
       "      <td>-1.095241</td>\n",
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       "      <td>1189</td>\n",
       "      <td>363</td>\n",
       "      <td>290</td>\n",
       "      <td>6.097944</td>\n",
       "      <td>-1.10516</td>\n",
       "      <td>r</td>\n",
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       "      <td>166</td>\n",
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       "      <td>44.945417</td>\n",
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       "      <td>0.566910</td>\n",
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       "      <th>4</th>\n",
       "      <td>131614</td>\n",
       "      <td>1189</td>\n",
       "      <td>363</td>\n",
       "      <td>290</td>\n",
       "      <td>6.097944</td>\n",
       "      <td>-1.10516</td>\n",
       "      <td>y</td>\n",
       "      <td>14535396</td>\n",
       "      <td>49521.233761</td>\n",
       "      <td>168</td>\n",
       "      <td>...</td>\n",
       "      <td>142.789597</td>\n",
       "      <td>118.370643</td>\n",
       "      <td>0.661373</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140105</td>\n",
       "      <td>6.262073</td>\n",
       "      <td>21.270300</td>\n",
       "      <td>6.097944</td>\n",
       "      <td>-1.091934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   obsHistID  sessionID  propID  fieldID   fieldRA  fieldDec filter   expDate  \\\n",
       "0     114367       1189     363      290  6.097944  -1.10516      y  11960383   \n",
       "1     114377       1189     363      290  6.097944  -1.10516      y  11960769   \n",
       "2     121892       1189     363      290  6.097944  -1.10516      z  12808885   \n",
       "3     130397       1189     363      290  6.097944  -1.10516      r  14358187   \n",
       "4     131614       1189     363      290  6.097944  -1.10516      y  14535396   \n",
       "\n",
       "         expMJD  night     ...       moonBright  darkBright  rawSeeing  wind  \\\n",
       "0  49491.430360    138     ...         0.000000   95.330805   0.414974     0   \n",
       "1  49491.434829    138     ...         0.000000   94.872840   0.482168     0   \n",
       "2  49501.250988    148     ...       580.725466  131.390206   0.504399     0   \n",
       "3  49519.182728    166     ...        44.945417  139.174707   0.566910     0   \n",
       "4  49521.233761    168     ...       142.789597  118.370643   0.661373     0   \n",
       "\n",
       "   humidity  slewDist  slewTime  fiveSigmaDepth  ditheredRA  ditheredDec  \n",
       "0         0  0.055193  4.672322       21.637128    6.080937    -1.098547  \n",
       "1         0  0.055193  4.646860       21.551827    6.080937    -1.098547  \n",
       "2         0  0.055193  4.863491       22.260523    6.042673    -1.095241  \n",
       "3         0  0.055025  4.833909       23.874776    6.080937    -1.091934  \n",
       "4         0  0.140105  6.262073       21.270300    6.097944    -1.091934  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "837\n"
     ]
    }
   ],
   "source": [
    "# How many pointings do we have? \n",
    "print (len(obsMetaDataResults))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the frequency of occurances of observations on the days selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U8zdGxOckHQ2sj4itdQxwgTG5Z2IDcc+k3vG4Z9IuVfZM+vmmxddExDtnFyLif0n6v1UM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ec2ae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(map(lambda x: x.mjd.TAI, obsMetaDataResults ), bins=np.arange(49490, 49590,1))\n",
    "plt.xlabel('MJD')\n",
    "plt.ylabel('Visits per Night')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the spatial location of all the galaxies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def coords(x):\n",
    "    return np.radians(x.pointingRA), np.radians(x.pointingDec)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "v = zip(*map(coords, obsMetaDataResults))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11fa8a690>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+Rp37iavhVUssdwPXVDP0zsjMNzPz3Qxex7A8Is5uu6a6IuKDwEvVb0dBN+/2uigzz2fw\n28WfVsuOXbAYOB/426r+XcD17ZY0vIg4FLgU+Pp8bRc6zF8ETpv1+Z4XFmlCqrXCu4F/yMx/arue\nUVW/Ij8KrGi7liFcBFxarTt/BXhfRNzZck1Dycz/rv58BfgGg6XTLniBwVuN/Fv1+d0Mwr1rfgN4\nsvr6H9BCh/la4MyIOD0iDgNWAp27ok93Z1UAXwI2Z+Zn2y5kWBFxQkQcWx0fweCtIzpz8TYzb8jM\n0zLz7Qy+9x/JzCvarquuiFhS/VZHRBwJ/BrwVLtV1ZOZLwHPR8Sy6qFLgM0tljSq36PGEgvUeG+W\ncWTmGxHxMeAhBj84bsvMpxdyzKZFxF1ADzg+Ip4DbtpzUWXaRcRFwOXAxmrdOYEbMvPBdiur7RTg\njupq/iLga5l5f8s1HUxOAr5RvRXHYuDLmflQyzUNYxXw5Wqp4ofAlS3XM5SIWMLg4ucf12rvi4Yk\nqfu8ACpJBTDMJakAhrkkFcAwl6QCGOaSVADDXJIKYJhLUgEMc0kqwP8DE7xuvAqe9nEAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f611fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(v[0], v[1], 'ko', markersize=4)\n",
    "plt.plot(vals[0][:1000], vals[1][:1000], '.')\n",
    "plt.axvline(2. * np.pi, color='k', lw=2.)\n",
    "plt.axvline(0., color='k', lw=2.)\n",
    "plt.axhline(np.pi, color='k', lw=2.)\n",
    "plt.axhline(-np.pi, color='k', lw=2.)\n",
    "plt.plot(v[0], v[1], 'ro', markersize=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if not os.path.exists('testSNData/NewLightCurves'): \n",
    "    os.makedirs('testSNdata/NewLightCurves')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "column_outputs=['flux_u', 'flux_g', 'flux_r', 'flux_i', 'flux_z', 'flux_y',\n",
    "                'mag_u', 'mag_g', 'mag_r', 'mag_i', 'mag_z', 'mag_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def ourWriteCatalog(obsMetaDataResults):\n",
    "    fnameList = []\n",
    "    for obsind, obsMetaData in enumerate(obsMetaDataResults):\n",
    "        if obsind % 50:\n",
    "            print(obsind) \n",
    "        #print obsMetaData.mjd\n",
    "        newcatalog = SNIaCatalog(db_obj=galDB, \n",
    "                                 obs_metadata=obsMetaData, \n",
    "                                 column_outputs=['t0', 'cosmologicalDistanceModulus', 'mwebv','time', 'band', 'flux', 'flux_err', 'mag', 'mag_err'])\n",
    "        #print ('Instantiated newcatalog')\n",
    "        newcatalog.midSurveyTime=49540\n",
    "        newcatalog.snFrequency =100.\n",
    "        newcatalog.suppressDimSN = True\n",
    "        fname =  'testSNData/NewLightCurves/SNCatalog_' + \"{0:d}\".format(obsind)\n",
    "        #print ('printing name' , fname)\n",
    "        newcatalog.write_catalog(fname)\n",
    "        fnameList.append(fname)\n",
    "        #print (obsMetaData.mjd.TAI)\n",
    "    return fnameList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "fnamelist = ourWriteCatalog(obsMetaDataResults)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/manual/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators; you can avoid this warning by specifying engine='python'.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "dfs = []\n",
    "_ = map(lambda x: dfs.append(pd.read_csv(x, index_col=None, sep=', ')), fnamelist)\n",
    "all_lcs = pd.concat(dfs)\n",
    "all_lcs.rename(columns={'#snid': 'snid'}, inplace=True)\n",
    "lcs = all_lcs.groupby('snid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def SNCosmoModel(lc):\n",
    "    f = np.unique(np.asarray(lc[['z', 'c', 'x1', 't0', 'x0','snra', 'sndec']]))\n",
    "    f = np.asarray(lc[['z', 'c', 'x1', 't0', 'x0', 'snra', 'sndec']])[0]\n",
    "    #SNO = SNObject(ra=f['snra'][0], dec=f['sndec'][0])\n",
    "    SNO = SNObject(ra=f[-2], dec=f[-1])\n",
    "    SNO.set(z=f[0], c=f[1], x1=f[2], t0=f[3], x0=f[4])\n",
    "    sn = SNO.equivalentSNCosmoModel()\n",
    "    return sn,  SNO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "image/png": 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H74E5qvq+e/wNmC1OZt0SqnrQT3aSqo5R1fdwDI9u2RlMVTcCM3G8vgfc5qM4hlOOMtIG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\n9FzNLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f071810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QHejvtj/mO4c7pqUPNowwoqpBbc2kFfdlZQYwW1XfCKL/b0ANVf3Tr93WAsMI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Vuy2lHSIiKSZu7+irvvDg/nETy5BDCAYG7NTnf/jCCh6VZKFbG0sczdV7DnROSBiWojVLxIn7vv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\nIoWFhfTp0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LehpEVEoonl6aEt7v5zYCPwkpk1dvf57n4H0Cq54VWOu+8CrgZmAh8CU919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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12083edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1VfVpNzR5hqrWC9JvGY4PylBVHSoiuYCBOMrF33Ecb4vhJG2qiVOgrSGO8uLf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\n/AxgUUT0R8RWkiqPXcO8t3ZK9u/zylwvo32/WZk5H+zJ+SA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1210ab810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AHwMXmNmyKMDdVOBwM3u2hu/jSkLgvF5mdn0qeSRtBJxGOFCyjZldGZO3DfBHM7tEUiNC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\n3MDbFKlJsrf0kB8tXryYv/3tbzz11FM0a9aMtm3bsnXrVj7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kAg0PBWfr1q0cfPDBjBkz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FBeYit1TlrMLJtcZlpGRER9B6/9dUT834JElgeSKoANwHnAVmAZcHlErM/oMwe4ISI+JulM4NsR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\nmbZE95I3M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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1258e4890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lEjcmgpK8F6hqhn/grPi5zH2eE2eZYaMA9TQc+vbtG5bzRFqsxKkaO7FanElz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\nZlYInrSYmZlZIXjSYmZmZoXw/5SoPS/+hQsFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x125fb5110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9nH766Xz99decdtppfpuTYwYOHMiOHTuYPHmy36YYGRAV7SERqSwi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126c3f850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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puxNxPCi4euwTkSU4y0HB/R0DugDH3P5G4iSMCnYJB1J7n7bEJU5af4B7g9qe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uAAAg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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126e65190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EKe3t70p+5UJm4wVE5B/u5bOBPjcMIzCqmiG/MG5Dxbk4/Y1896ddgG88XsBs\nnQB7gWEYYSTZvUBVM/UL53z67RQ+bwXMdK9rAUtSkNX08PLLL6frvnCRldfPyt891tZ3f78iuRcU\nAq5wr/PgZBS28pMJaj+wvcDWt/Ujt35Ke0FU1GmJFCJSFyeaf42IrMQ5kuqPE4yrqvqhOgG3rURk\nC05m0v3eaWwYRgS5HvjEjWvJBkxyf/97YvuBYcQEmdpoUae+SvYg5J7IAHUMw/AQVV0DBCr8NtLv\nve0HhhGlREtxuUxNfHy8rZ8F17b1vV8/2vD652Hr2/qxvr44x0dGMIiI2s/LMCKDiGRYIG6o2F5g\nGJEjpb3APC2GYRiGYcQEZrQYhmEYhhETmNFiGIZhZBh2rGaEghktRkicOnWKffv28ccff3itimEY\nUcjhw4d56623aNSoEVdeeSXZs2enUKFCxMfHM2LECA4ePOi1ikYMYUaLkWZUlVmzZvHXv/6VQoUK\nUblyZQoXLkzNmjUZNWoUiYmJXqtoGIbHnDt3jjfffJMyZcqwatUq+vbty5YtWzh9+jRr167lmWee\nYfHixVSoUIE33niDM2fOeK2yEQNY9lAasIwBOH78OF27dmXTpk0MGDCAli1bUqBAAc6cOcPcuXMZ\nMGAAOXLkYMKECRQtWtRrdY0MQlUJtWWFZQ9lHvbs2cMdd9xB9uzZGT16NGXLlk1WduPGjTz55JP8\n8ccffP7555QsWTIDNTWiEcseMsLCkSNHaNasGVdffTWrV6/mzjvvpECBAgDkyJGDZs2asWDBAho3\nbkyjRo3Ys2ePxxobkWLbtm3ceOON3HvvvVSuXJmdO3d6rZIRJWzYsIE6derw17/+lTlz5qRosABU\nqFCBWbNm0aFDB2rXrs2KFSsySFMjVEaOHEm1atWoXr06pUuXpkmTJhFf0zwtaSArP12pKh06dOCa\na67hgw8+IFu2lO3dwYMHM378eBYtWsQVV1yRQVoaGcW2bdsoU6YMixcv5tZbbw3LnOZpiX02b95M\nfHw8r732Gvfdd1+a7582bRo9e/Zk2rRp1K1bN/UbjKjg7NmzNGnShOeee45WrVqFPF9Ke4EZLWkg\nK29Uw4YN47PPPuPHH38kZ86cQd3z+OOPs337dqZPn56qkWPEFtu2baNx48b88ssvYZvTjJbYZufO\nndSrV4+XXnqJBx54IN3zzJ49m+7du/Pdd99RtWrVMGpoRIrHHnuMwoUL8/LLL4dlPjseMkJi69at\nDBo0iEmvJ5eIAAAgAElEQVSTJgVtsAAMGTKEI0eO8Nprr0VQO8Mr8uXL57UKaUJEionIHBH5PxFZ\nIyJPBpBpKCKHRWSF+3rRC11jjZMnT9KuXTt69uwZksEC0KxZM9577z1atWrFpk2bwqShESk+/vhj\nduzYETaDJTUydcNEIzw899xzPPXUU5QuXTpN9+XMmZPPP/+c6tWr07RpU2rVqhUhDQ0viEFPw1mg\nj6quEpH8wHIRma2qG/zk5qtqGw/0i0lUlYceeogKFSrw/PPPh2XOjh07cvToUZo2bcqCBQsoUaJE\nWOY1wsvy5ct56623WLhwYYataUaLkSILFy5kyZIlfPzxx+m6v2jRorz33nt069aNlStXcvnll4dX\nQcMzQs0WymhUdS+w170+JiLrgaKAv9ESW1/MY8aOHcvPP//MTz/9FNb/J+6//34OHTrEbbfdxo8/\n/mh7RxTy7rvvcujQIRo1agRAjRo1+PDDDyO6ZqaPaRGRMcBtwD5VrRLg84bAdOBXd+hLVX01mbmy\n3Dl2kyZNuOeee7j33ntDmqdHjx6ICGPGjAmTZkZmIyNjWkTkBiABqKSqx3zGGwJfADuBXcAzqrou\nwP1Zbi8IxKZNm6hbty5z586lUqVKYZ9fVenZsyf79u1j2rRpFhuXRcjSgbgiUg84BnyagtHSNxh3\ncFbbqH766Sc6d+7Mli1byJEjR0hz/fHHH1SrVo033niDDh06hElDIzORUUaLezSUAAxS1ekBPktU\n1RMi0hIYpqrlA8yhvmf48fHxxMfHR1TvaENViY+Pp2PHjjz55CXhQWHj9OnTNG3alLp16zJ48OCI\nrWN4R0JCAgkJCeffDxw4MHxGi4jkA/5U1XOhKJmRiEhJ4KsUjJZ+qnp7EPNkKaOlY8eONGzYMGwb\n0pIlS2jbti0rV66kSJEiYZnTyDxkhNEiIpcBXwOzVHVYEPJbgVtU9aDfeJbaCwLx2WefMXToUJYu\nXUr27NkjutZvv/1GXFwcgwYN4u67747oWob3hJQ9JCLZRKSriMwUkf045797RGSdiLwpIilXDooN\naovIKvc73uS1MtHAli1bmD9/fsiZAL7UqlWLxx57jPvuu89K/Rte8RGwLjmDRUQK+1zH4TzYWXMc\nP44ePcqzzz7Le++9F3GDBaBQoUJMnz6dp59+mrVr10Z8PSN6CeaAcC5QBngBuE5Vi6vqtUA9YAnw\nTxHpFkEdI81yoISqVgVGAP/2WJ+o4MMPP+Tee+8Ne1rr3/72N/744w9GjBgR1nkNIzVEpC5wN9BY\nRFa6Kc0tRKSniDzsinUSkbUishIYCtzpmcJRzNChQ2nSpAlxcXEZtmblypV588036dy5M8eOHUv9\nBiNTkurxkIjkUNUUO1kFI+MlKR0PBZAN6A52P8sS59inTp2iRIkSLFiwgPLlLznOD5lNmzZRp04d\nlixZkmqJbyPzkpZz7GgjKx8PHTx4kPLly7N06VLKlCmT4evff//9nD17lk8//TTmMtiM4Ag5ENcN\nZm0MXAecAw4AS1R1djgVjRRupsBXqlo5wGeFVXWfex0HTFbVG5KZJ0tsVJMmTWLkyJHMmTMnYmsM\nHTqUqVOnMm/evAxxLxvRj1XEjQ369+/P77//zsiRIz1Z/8SJE8TFxdG7d++wHl8b0UNIRouI9Ady\nACtxsnCyAwWAOEBVNTzVhCKEiEwA4oGCwD7gZSAnju4fisjjwKPAGeAk0FtVlyYzV5bYqJo2bcoD\nDzzAXXfdFbE1EhMTiY+Pp3379vTu3Tti6xixgxkt0c/Ro0cpVaoUy5cv54YbbvBMj/Xr11O/fn1+\n/PFHKlSo4JkeRmQI1Whpo6ozkvmsk6pODYOOMUFW2Kj27NnDTTfdxO7du8mTJ09E19qyZQu1atWy\njccAzGiJBYYMGcLSpUuZOHGi16rw7rvv8sknn7Bo0SIuu8zqpGYmQjVaXnIvVwLHcY6H8gFVgGtU\ntV8YdY1qssJGNXToUFatWpXuCrhpZcSIEUyYMIEFCxbYMVEWx4yW6Obs2bOULVuWKVOmhK2zdyio\nKs2bN6d+/fq89NJLqd9gxAwhpTyr6iBgEVAd6AjchXM09F/gmTDqaUQBEyZMoGvXrhm23mOPPUau\nXLkYMmRIhq1pGEbamT59OsWLF48KgwWcP2wfffQRw4cPZ/ny5V6rY2QQmb4ibjjJ7E9Xmzdvpn79\n+uzcuTND3a1bt24lLi6O+fPnU7FixQxb14guzNMS3TRt2pQePXrQpUsXr1W5iAkTJvDqq6+yfPny\niB9pGxlDSJ4WI+swbdo0OnTokOHnw6VKlWLQoEHcd999nD17NkPXNgwjdX755Rd+/vnnqGzB0aVL\nFypVqsTf/vY3r1UxMoB0Gy0iUlJEfgynMoa3zJgxg7Zt23qyds+ePSlQoAD/+te/PFnfMIzkGTVq\nFPfccw+5cuXyWpVLEBHef/99Jk2axNy5c71Wx4gwIR0PiciVqno4jPpENZnZJXzgwAHKli3L/v37\nPduYtm3bRo0aNSLWMdaIbux4KDo5c+YMxYsXZ968eVGd5ffNN9/w2GOPsXbtWvLnz++1OkYIhHw8\nJCL1ROTvIvKeiAx3r5tlJYMlszNz5kyaNm3q6ZNUyZIlef3117nvvvs4cyZqCywbRpZi9uzZlC5d\nOqoNFoBWrVoRHx/P889HdekwI0SCaZjYH2gCrAKmAjOA/wOaiMg/IquekVHMmDGDNm3aeK0GDzzw\nAIUKFeL111/3WhUjkyEixURkjoj8n4isEZGA7ctF5B0R2ew2Ua2a0XpGG+PGjaN79+5eqxEUQ4YM\nYdq0acybN89rVYwIYcXl0kBmdQn/+eefFC5cmF9++YVChQp5rQ47d+6kevXqfPfdd1SrVs1rdYwM\nItLHQyJyHU7T11Uikh+nWWpbVd3gI9MSeEJVW4tITWCYqtYKMFem3Av8OXLkCCVKlODXX3+lYMGC\nXqsTFDNmzKBPnz6sXr2avHnzeq2OkQ5CPR66WUReEpHbRKSRiDQQkZYi8hxwyS+zEXvMnTuXKlWq\nRIXBAlCsWDHeeust7rnnHk6dOuW1OkYmQVX3quoq9/oYsB4o6ifWFvjUlVkKXCEihTNU0Sjiiy++\noHHjxjFjsAC0adOGuLg4XnzxRa9VMSKAFZczouZoyJdu3bpRrlw5BgwY4LUqRibEbaJaFfDvM1YU\n2OHzfheXGjZZhokTJ0ZdXZZgeOedd/j8889ZvHix16oYYcaKy6WBzOgSVlWKFSvGnDlzoi7Qbv/+\n/dx88818+eWX1K5d22t1jAiTUdlD7tFQAjBIVaf7ffYV8LqqLnLffw88q6or/OQy3V7gz2+//UaZ\nMmXYvXs3+fLl81qdNDNlyhT+/ve/s3LlSnLnzu21OkYaSGkvsC5TWZwVK1aQP3/+qDNYAK699lre\nffdd7r33XlatWmXn00bIiMhlOAkF4/wNFpddQHGf98XcsUvw9QLGx8cTHx8fNj2jgWnTptG8efOY\nNFgAOnfuzKRJk3jllVcYPHiw1+oYKZCQkEBCQkJQsmn2tIjIFap6JKvVaIHM+XQ1YMAAjh8/zptv\nvum1KsnSrVs3ChYsyLBhw7xWxYggGeFpEZFPgd9UtU8yn7cCHncDcWsBQ7NqIG6zZs146KGH6Ny5\ns9eqpJs9e/ZQpUoVEhIS+Mtf/uK1OkaQhLuM/73uv/ekXyUjWpgxYwa3336712qkyPDhw/niiy+Y\nM2eO16oYMYyI1AXuBhqLyEoRWSEiLUSkp4g8DKCq3wBbRWQLMBJ4zEOVPeP3339n6dKltGrVymtV\nQuL6669n4MCBPPLIIyQmJnqtjhEGQuk9FCuVK8eIyD4RWZ2CTJasy7Bjxw62b99OnTp1vFYlRa66\n6ipGjRpFjx49OHr0qNfqGDGKqv6oqtlVtaqqVlPV6qr6raqOVNUPfeSeUNWyqnqzfyxLVmHmzJk0\nadIkZo+GfOnZsyenT59m7NixXqtihIGs0DBxLNA8uQ/dugxlVLUc0BP4IKMU85qvvvqKVq1aZXiD\nxPTQsmVLmjVrRp8+Ab36hmGEkenTp0ddRmF6yZ49OyNHjuSFF15g//79XqtjhEimN1pUdSFwKAWR\nLFuXIRpTnVPirbfeYs6cOcycOdNrVQwj0/Lnn3/y/fff07p1a69VCRtVq1ale/fu9OvXz2tVjBCJ\n/kfsyJNcXYZ93qgTGr5R2AkJCeczGvyzG/744w8WLVrE5MmTM17JdHL55ZczduxYunbtyurVq2Oq\n4JVhxApz5syhSpUqXHPNNV6rElYGDhzITTfdxJw5c2jcuLHX6hjpJD1GS0zEskSKaE9z9NVJRJJN\nI5s1axZ16tShQIECGadcGGjYsCF33HEHvXr1YsKECV6rY4RAWtIcjYxjxowZtG3b1ms1wk7+/PkZ\nPnw4jz76KKtXr/a0OayRftKT8nyTqq5L+jdCeoUVESkJfKWqVQJ89gEwV1Unue83AA1V9RJPS6yl\nObppYwE/69q1Kw0bNqRnz54ZrFXonDx5kmrVqjFo0KCYTsc0LiajisuFg1jbC4IlMTGRYsWKkZCQ\nQPny5b1WJyK0a9eO6tWr8/e//91rVYxkCGvKc5KhEisGi4uQvIdoBm76tluX4XAggyUzcfr0aWbN\nmhWzT1N58uThk08+oVevXuzaFbDul2EY6WD58uVcccUVmdZgARg2bBjvvPMOv/zyi9eqGOkgzUaL\niBQXkRsjoUwkEJEJOL2TyovIdhG5P6vXZZg7dy4VK1bkuuuu81qVdFOzZk0ef/xx7rnnHqu/YBhh\nItaC89NDyZIleeaZZ+jVq1eynmgjeknP8dAQ4CRO8GotYLyqzo6AblFHrLmEkzseeuSRRyhTpgzP\nPBPb/S7PnTtHo0aNuO2223j22We9VscIETse8p4qVarwwQcfRH3tplA5ffr0+SPmDh06eK2O4UdK\ne0F6jJYGqjpfRFqr6kwR6aaqn4VF0ygn1jaqQEZLYmIiRYsWZf78+ZQrV84jzcLH9u3bqVGjBt98\n8w01atTwWh0jBMxo8ZatW7dSq1Ytdu/eTfbs2b1WJ+LMmzeP7t27s27dOvLnz++1OoYP4S7j31dE\nHgWSSiVuT7dmRoazdOlSChYsmCkMFoASJUowYsQIunTpwrFjx7xWxzBilhkzZnDbbbdlCYMFnEzE\n+Ph4C8iNMdJjtPQB5gFXisgwoHd4VTIiybRp02jfvr3XaoSVO+64gwYNGvDkk096rYoRxaTW0kNE\nGorIYbcn0QoReTGjdfSSrBDP4s/bb7/N559/zpIlS7xWxQiSNB8PXTJBDKU+h0qsuYT9j4dUlfLl\nyzNp0iSqV6/uoWbh59ixY9xyyy288sor3HnnnV6rY6SDSB8PiUg94BjwaTLlDxoCfVU11b/csbYX\npMahQ4coWbIke/fuJW/evF6rk6FMnjyZAQMGsGLFCnLnzu21OgbhPx66iKxisGQG1q1bdz4ALbOR\nP39+Pv/8c3r16sXmzZu9VseIQoJo6QFZtHjmrFmziI+Pz3IGC0Dnzp2pWLEigwYN8loVIwjSk/Kc\nT0TKiEhdEekoIm9HQjEj/HzxxRe0b98ekcy5L1evXp2BAwfSqVMnTp486bU6RmxS2+32PlNEbvJa\nmYxi+vTpMVu3KVREhHfffZfRo0ezYkWWbOodU6Qne+gNoAhuXAvwm6pmiZ7fseYS9j8eqlSpEh9+\n+GGmTmdUVbp160bu3LkZM2aM1+oYaSAjsodSqY6dH0hU1RNu9/dhqhqwylqs7QUpcfr0aQoXLsyG\nDRsoXDhL9IoNyLhx43jjjTdYtmyZHRN5TEp7QZp7D6nqsyJSHqgG7FZVa7kbA6xfv57Dhw9Tq1Yt\nr1WJKCLCyJEjiYuLY+zYsdx///1eq2TECKp6zOd6loi8JyJXq+rBQPLR3ocsWBISEqhYsWKWNlgA\nunXrxldffcVzzz3HsGHDvFYnS5GWPmSpelrcp4/7gBPARFU94fNZU6Caqr6RXmVjiVh7uvL1tLzy\nyiscPHiQoUOHeqxVxrBu3ToaNmzIDz/8QJUqlzxUG1FIBnlabsDxtFQO8FnhpBYeIhIHTFbVG5KZ\nJ6b2gpR44oknKFasGM8//7zXqnjOoUOHuPnmmxk5ciQtW7b0Wp0sS6iBuP8CigNNgG9E5Hyklqr+\nB5gfFi2NiDJlypQs1VzwpptuYujQoXTq1ImjR496rY4RBaTW0gPoJCJrRWQlMBTI9GloqpolU52T\n46qrrmLcuHE88MAD7NuXqVvQxSzBeFoeV9V33evrgJZZJYbFn1h7ukrytKxfv56mTZuyfft2smUL\nOWEspnj00Uf57bffmDx5cqYNQM4sWEXcjGflypXccccdbNq0yX4/fOjfvz+rVq3i66+/znJ7ZjQQ\nqqflz6QLVd0L/BEuxYyMYcqUKXTs2DFL/vINGTKEX3/9leHDh3utimFEHdOnT6dNmzZmsPgxcOBA\njh49amnQUUgwf8VeEJERItJDRKoC5x8vROTayKlmhIspU6Zwxx13eK2GJ+TOnZupU6fy2muvsXDh\nQq/VMYyoYsaMGVk21TklcuTIwdSpUxk1ahRff/211+oYPgRzPPQi8F+gJhCHkzW0DfgRuFZV74m0\nktFCrLmERYR169Zl2aMhX7799lt69OjB0qVLKV68uNfqGAGw46GMZceOHVSrVo29e/dy2WVpTiTN\nEixevJi2bduycOFCypcPmP1uRICQjodU9VVV/VZVB6pqa1UtAtwNLMcJ0I1qRKSFiGwQkU0i8lyA\nzzN1v5GsfDTkS4sWLejTpw/t2rXjxIkTqd9gGJmcGTNm0Lp1azNYUqB27doMGjSI9u3bW0B/lBCM\npyXZOgUi0kBVozZ7SESyAZtwMp92A8uAu1R1g49Mpu03IiJUqlSJ999/n3r16nmtjueoKt27dycx\nMZHx48fbOX6UYZ6WjKV58+Y8/PDDdOzY0WtVohpV5dFHH2XHjh3MmDEjy3TB9pJQA3HXikgzn8ly\niUgRgGg2WFzigM2quk1VzwATgUAHuDGxUaaHgwcPZuoKuGlBRBg1ahSbN29m4MCBXqtjGJ5x5MgR\nFi9eTPPmzb1WJeoREYYPH86pU6fo27ev1+pkeYKt0/KwiLwqzuPFKaCoiPQXkWivVFYU2OHzfqc7\n5k+m7TdiR0MXkydPHr7++mvGjx/PiBEjvFbHMDzhu+++o169euTPn99rVWKCHDlyMGXKFL799lve\nf/99r9XJ0gRzmHlMVTuJSG/gWxHprqrLgGUiMi3C+mUEy4ESPv1G/g1kmoirrJo1lBKFCxdm9uzZ\n1K9fn4IFC9KlSxevVTKMDCUrN0hML1dddRVff/019erVo2zZsjRt2tRrlbIkwRgttYAPVXWIiCwC\nvhaR51V1Dk51yWhmF1DC530xd+w8mbXfyKJFzn8ai3gPTKlSpZg1axZNmjThiiuuoFWrVl6rlOVI\nS78RI3ycOXOGWbNm8eabb3qtSsxRtmxZJk+eTKdOnZg3bx4VK1b0WqWsh6qm+AI+AHoD5dz3VwPT\ngZeBp1O738sXkB3YApQEcgKrgIp+MoV9ruOA/6Uwn8YKNWvWVECfeuopr1WJahYtWqTXXHONTp06\n1WtVsjzu75fn+0Ywr1jaC/z54Ycf9NZbb/VajZhm7NixWrp0ad2/f7/XqmRKUtoLUvW0qOojACKS\nx31/EGjrpg/3x+nREZWo6jkReQKYjRO/M0ZV14tIT+dj/RCn38ijwBngJJmg34iq8vvvvwPw4ouZ\nKoM77NSuXZvvvvuOVq1acezYMe69916vVTKMiGK9hkLnvvvuY+PGjXTo0IHvv/+eXLlyea1SliHV\nlOcUbxaprqorwqhPVBMraY6rVq2iffv2/O9//yMW9I0GNmzYQLNmzejduzdPP/20pUN7QKRTnkVk\nDHAbsE9VA7b+FpF3gJbAceA+VV2VjFxM7AX+qCqlS5dmxowZVK58SaNrIw0kJibSuXNn8uXLxyef\nfGJ7RhgJKeVZUvgvkWSwpCRjZDwTJ07krrvu8lqNmOLGG29k/vz5fPzxx3Tv3p3jx497rZIRfsYC\nyeb4uoH4ZVS1HNAT52g8U7F27VoAKlWq5LEmsU+2bNn49NNPWbduHa+//rrX6mQZgsmFnSsivUTE\nN6AVEckpIo1F5BPAfOpRgqoyadIk7rwz5k+5MpwbbriBxYsXky1bNmrVqsWmTZu8VskII6q6EDiU\ngkhb4FNXdilwhYgUzgjdMoqkrCF7zgwP+fLlY8aMGXzwwQdMmDDBa3WyBMEYLS2Ac8DnIrJbRNaJ\nyK/AZqALMFRVP46gjkYa+Omnn8iVKxc333yz16rEJHnz5uWTTz7h8ccfp06dOrz//vskJiZ6rZaR\nMfjXddpF4LpOMYvFs4SfIkWKMHPmTJ5++ml++OEHr9XJ9AQTiPsn8B7wnojkAAoBJ1X1cKSVM9JO\n0tGQPUmlHxHhkUceoUGDBvTo0YNJkyYxevRoypYt67VqRhQRK+UPkti9ezdbtmyhfv36XquS6ahc\nuTKTJ0/mjjvuYPbs2VStWtVrlWKKtJQ/CCkQN6sR7cF3586do0SJEnz//fdUrFgxKZjJa7VimnPn\nzjFs2DAGDx7ME088wTPPPEO+fPm8VitTkhG9h0SkJPBVoEBcEfkAmKuqk9z3G4CGqrovgGxU7wWB\nGDlyJPPnz2f8+PFeq5JpmTJlCr1792bBggWUKlXKa3VillB7DxkxwoIFCyhUqJAVPAoj2bNnp0+f\nPixfvpyNGzdSoUIFPvnkEzsyil2E5HuNzQDuARCRWsDhQAZLrDJjxgyrghthOnfuTP/+/WncuDHb\ntm3zWp1MiXla0kC0P13df//9VKpU6XxTL/O0hJ/FixfTt29fDh48SL9+/ejWrRu5c+f2Wq1MQQak\nPE8A4oGCwD6cApk5uVCzCREZgRPHdxy4P7mSDtG+F/hz7NgxihQpws6dOylQoIDX6mR6hg0bxjvv\nvENCQgLFixf3Wp2YI6W9IGijRURuUtV1fmPxqpoQuoqxQTRvVMeOHaN48eKsX7+e6667DjCjJVKo\nKvPmzeONN95g1apVPPzww3Tr1s1iXkIkI46HwkU07wWB+PLLLxk5ciTfffed16pkGd5++23ef/99\nEhISKFo0U8VzR5xwHQ9NFpHnxCGPiAwHLDk9SpgyZQr169c/b7AYkUNEiI+P55tvvuG7777j0KFD\n1K1b93y2UVI1YsOIFqZPn25ZQxlMnz596NmzJ/Xq1WP9+vVeq5NpSIunJR/wT+AW4HJgPPBPVc0y\nh/vR/HTVoEEDevfuTfv27c+Pmacl4zhz5gyzZ89m3LhxzJo1izp16tC2bVvatGlDkSJFvFYvJjBP\nS2Q4e/Ys1113HStWrKBEiRKp32CElXHjxtGvXz+mTJlCgwYNvFYnJgiXpyWpN08eIDewNSsZLNHM\n6tWr2bJlC61bt/ZalSxLjhw5aN26NRMnTmTHjh3cf//9LFiwgEqVKhEXF8drr73GsmXLOHfunNeq\nGlmMRYsWUaJECTNYPKJ79+6MHz+eTp06WQG6MJAWT8vPON2dB+HUavkAOK2qnSOnXnQRrU9XDz74\nIDfccMMlzRHN0+I9Z86cYd68eXz11Vf88MMP7Nq1iwYNGtC4cWMaNWrETTfdxGWXpVouKUtgnpbI\n0K9fP/Lnz39RXRkj41m9ejUdOnSgWbNmvP322xbAnwLhCsStoar/9RvrrqrjwqBjTBCNG9WBAwco\nX748mzZt4pprrrnoMzNaoo+9e/eSkJDAnDlzmDdvHrt27eLmm2/mlltuoUaNGlStWpVy5cqRJ08e\nr1XNcMxoCT+qSvny5Zk8eTLVqlXzWp0sz5EjR3jooYfYvHkzkydPply5cl6rFJWEy2j5e6BxVX0l\nBN1iimjcqAYMGMDOnTsZPXr0JZ+Z0RL9HDlyhJUrV/Lf//6X5cuX8/PPP/Prr79SuHBhypcvT4UK\nFShVqhRFixY9/ypSpEimfEozoyX8rF+/nubNm7Nt2zarkh0lqCoffPABL730Ei+++CK9evUie/bs\nXqsVVYTLaOnr8zY3Tov39araI3QVI4eItACG4sTvjFHVfwaQicl29AcOHODGG29k2bJllC5d+pLP\nzWiJTc6ePcv27dvZuHEjGzduZNu2bezatYtdu3axc+dO9uzZQ4ECBShSpAgFCxbk6quvvuh11VVX\ncfnll5MvXz7y5s17/uX7Pnfu3OTMmTOqNkszWsLPP/7xD3bs2MG7777rtSqGH5s2beLhhx/mxIkT\njBo1yvrF+RAWoyXApLmA71Q1PgTdIoqIZAM2AU2A3cAy4C5V3eAj0xJ4QlVbi0hNYJiq1kpmvqja\nqHr37s2ZM2cYMWJEwM/NaMmcJCYmcuDAAfbs2cPBgwc5ePAghw4dOn998OBBjh07xvHjxzlx4gQn\nTpy45PrPP//k1KlTZM+enVy5cpErVy5y5syZputwy11//fVmtISZmjVrMnjwYJo0aeK1KkYAVJWP\nPvqIF154gc6dOzNgwIBLjvmzIpEyWq4Clqlq1FbUcktxv6yqLd33z+NUv/ynj4x/v5H1QHy09xvZ\nsGEDdevWZd26dRQuXDigjBktRkqoKmfPnuXUqVOcPn2aU6dOXXKd0mfhvmf//v1mtISRHTt2ULVq\nVfbu3UuOHDm8VsdIgd9//51XXnmF8ePH069fP5566qksGdeWREpGS9BpCyKyBkj6Lc0OXANEezyL\nf6v5nUBcKjJJ7eijtufI6dOn6dq1K4MHD07WYDGM1BARcuTIETV/0CzmIrz8+9//5vbbb4+a/75G\n8hQsWJBhw4bxxBNP8Pzzz3PjjTfy6quv0rVr16g6wo0G0pJreZvP9Vlgn6qeDbM+UU+XLl3OX/tu\nsv4bbnKfJSeXLVs2rr32WooUKULRokW5+eabKVu27CXyiYmJ9O7dmxIlSvDwww+H/oUMI4uQWnyb\niIzWSIoAABfQSURBVDTEKevwqzv0paq+mrFaho8vv/yS3r17e62GkQbKlSvHF198wYIFC3j++ed5\n9dVX6d+/P127djXjMwlVzbQvoBbwrc/754Hn/GQ+AO70eb8BKJzMfBro1b59e/3ss8/Ov8aNG6fj\nxo3T9u3bB5Rv166dfvLJJ/rxxx+ff40ePVqbNGkSUL5Zs2a6cOFCnTt3rrZp00br16+vv//+u778\n8ssB5V9++WV1fdfqS2ry/pi8yUdSfu7cufryyy+ff7n/v0ZqL8gGbAFKAjmAVcCNfjINgRlBzhfw\nO0cLBw4c0CuuuEJPnDjhtSpGOklMTNQffvhB4+PjtVSpUvrhhx/qqVOnvFYrQ0hpL0g1pkVE/uDC\nsdBFH7kTR23LUBHJDmzECcTdA/wEdFHV9T4yrYDH1QnErQUM1SgJxN2+fTsLFy5k4cKFLFu2jLx5\n81K7dm1eeeUVcubMmer9FtNixBKRzB4KMr6tIdBPVW8PYr4M3QvSykcffcS3337L5MmTvVbFCAML\nFy5k0KBBrF27lscee4yePXtSqFAhr9WKGCEF4orIZ6raTUSeVtWhEdEwgrgu4WFccAn/Q0R6kgXa\n0ZvRYsQSETZaOgLNVfVh9303IE5Vn/SRaQh8gRP7tgt4Rv062/vIRvVecNttt3H33XdfdJxtxD4/\n//wzw4YNY9q0aXTs2JGnnnqKypUre61W2AnVaPk/oCkwC4jH8bCcR1UPhkfN6CfaNyp/zGgxYoko\nMFryA4mqesIthTBMVcsnM1/U7gVHjx6lWLFi7Ny5kwIFotYRboTAgQMHGDlyJO+99x5ly5blgQce\noFOnTuTLl89r1cJCqEbLk8CjQGmcpw/fiVRVL61qlkmJ5o0qiYSEBBISEs5fx8fHAxAfH3/+2jCi\nkQw4Hhqgqi3c95ccDwW4ZytwS6AHMxHRl19++fz7aPr9mjhxIuPGjWPmzJleq2JEmNOnTzNz5kw+\n+ugjFi5cSKdOnejRowe1atWKqWw8379bAAMHDgy9TouIvK+qj4ZFwxglFowWw4hVImy0BBPfVljd\n+kwiEgdMVtUbkpkvaveCzp0707x5cx588EGvVTEykN27dzNu3DjGjh3L6dOn6dSpE507d6ZGjRox\nZcBAhIrLZUWieaMyjFgn0mX8U4tvE5HHcbzKZ4CTQG9VXZrMXFG5Fxw+fJiSJUuydetWrr76aq/V\nMTxAVfn555+ZMmUKU6ZM4cyZM3Tq1Inbb7+d2rVrx0TqtBktYSJaNyrDyAxY76HQGT16NN988w1f\nfvml16oYUYCqsmbNGqZOnco333zDL7/8QtOmTWnZsiUtWrTg+uuv91rFgJjREiaidaMyjMyAGS2h\n07BhQ3r37k27du28VsWIQvbu3cu3337LrFmz+M9//sO1115Lw4YNadCgAQ0bNqRYsWJeqwiY0RI2\nonWjMozMgBktofG///2PGjVqsHv37qDqOBlZm3PnzrFmzRrmzZvH/PnzmT9/PgUKFKBhw4bUrFmT\nW265hUqVKpE7d+4M182MljARjRuVYWQWzGgJjRdffJEjR44wfPhwr1UxYpDExETWr1/P/PnzWbZs\nGcuXL2fz5s1UqFCBatWqUbFiRcqXL0+5cuUoU6YMuXLlipguZrSEiWjcqAwjs2BGS/o5deoUJUuW\nZO7cuVSsWNFrdYxMwp9//snq1atZuXIlGzduZNOmTf/f3v0H21GXdxx/f5JAYyCJgRJCuSaC+ANi\nrUF7jegdGC0mUIUwWCUWFf1DR0J1kFICMlLqNJLOFCsN6ISiIIMgGlMC5UfIJHUuoRowBoImcEWT\nkGsSKJBgJHYuN0//2L3J5uaem/vj7J6zez+vmTP37J7v2ed7Docn39397rN0dHSwefNmpkyZQktL\nC8cff/y+v1OmTGHSpEkHPQY7+bcud3k2M7PmtGTJEqZPn+4Bi9XV2LFjaW1tpbW19YD1XV1dbNmy\nhc7OTrZu3UpnZydbtmxhzZo1vPzyywc8du7cydixYxk3bhxjx4496HHYYYcxevRoRo8ezahRow55\nV2sfaRmEZtu7MqsSH2kZutNOO43LL7+c8847r9FdMTtARLB792727NnDH//4xwMee/bsoauri+7u\nbvbu3Ut3dzfd3d3MmTPHR1rMzKpo1apVvPDCC3zkI4e8z6NZ4SQxfvx4xo8fX5ftjarLVszMrHAR\nwTXXXMNXv/pVxozxPqhVnwctZmYltXLlSrZv3+67OduI4UGLmVkJvfrqq1x88cVcd911PspiI4Yn\n4g5Cs02+M6sST8QdnEsvvZTt27dz5513NrQfZvXWXy6o7JEWSZMkLZf0tKSHJE2s0W6TpCck/ULS\nmjz6kr3ldiOM5Pgj+bM7/oEkzZa0UdIzkq6o0eYGSR2S1kl6Z737UI/vIyK4/vrrWbJkCYsWLSo8\n/nA4vuMPV2UHLcB8YEVEvBVYCVxZo91e4IyImBERrTXaDEsVfihljT+SP7vj7ydpFLAImAVMB+ZK\neluvNmcBb4qINwOfB75d734M9/vYsmULF154Ibfeeivt7e0cffTRhcYfLsd3/OGq8qDlXOC29Plt\nQK07iIlqfw9mBq1AR0Rsjogu4C6SHJF1LvA9gIj4GTBR0rHFdvNgu3btYunSpcydO5cZM2Ywbdo0\nVq9ezbRp0xrdNbPCVXn21uSI2AEQEdslTa7RLoCHJXUDiyPi5sJ6aGZFOR54LrO8lWQg01+bznTd\njr42uHjxYrLzWgbyfM2aNSxcuJAHH3yQWbNmceSRR/bZ/sUXX2Tz5s2sW7eOZ599lpkzZ3L++eez\naNGiQR9dMauSUk/ElfQwkN0TEskg5Grg1og4KtP2xYg46P92ScdFxDZJxwAPA5dExCM14pX3yzIr\ngbwm4ko6H5gVEZ9Lly8EWiPii5k29wJfj4hH0+UVwD9ExNo+tudcYJajSlbEjYgza70maYekYyNi\nh6QpwPM1trEt/fuCpKUke199DlrKcmWDmR2kE5iaWW5J1/Vu84ZDtAGcC8wapcpzOZYBF6XPPw3c\n07uBpHGSjkyfHwF8CHiqqA6aWWEeA06SNE3S4cAFJDkiaxnwKQBJM4GdPaeYzaw5lPpIyyEsBO6W\n9FlgM/AxSE4HATdHxIdJTi0tTQ/1jgHuiIjljeqwmeUjIrolXQIsJ9lZuyUiNkj6fPJyLI6I+yWd\nLenXwB+AzzSyz2Z2sFLPaTEzM7ORo8qnh8zMzKxCKj9okXRLOin3yX7a5FoF08waz7nArPwqP2gB\nvktSBbNPRVTBNLOm4FxgVnKVH7SkNVde7qdJU1bBNLP6ci4wK7/KD1oGoFYVTDMbWZwLzJqcBy1m\nZmZWClWu0zJQA66C6dLdZvlqcKVZ5wKzJlHJMv6DoPTRl2XAPOAHA6mC+corrww6+IIFC7jqqqsG\n/b56GcnxR/JnL1v8CRMm5NwbwLnA8R2/6eP3lwsqP2iR9H3gDOBoSVuAa4DDcRVMsxHFucCs/Co/\naImITwygzSVF9MXMGse5wKz8Kj8RV9JsSRslPSPpij5eP13STklr08fV9e5DW1tbvTfp+CWI7fiN\nj5/lXOD4jl/++JW+95CkUcAzwAeB35Hc6fWCiNiYaXM6cFlEnDOA7cVQzmOb2aFNmDAht4m4zgVm\n5dFfLqj6kZZWoCMiNkdEF3AXSQGp3hp5xYKZ5c+5wKwCqj5o6V0sait9F4t6b3qvkf+SdEoxXTOz\nAjkXmFVA5SfiDsDPgakR8Wp675H/BN7S4D6ZWfGcC8yaXNUHLZ3A1MzyQcWiImJ35vkDkm6SdFRE\nvNTXBhcsWLDveVtbW8MnNpmVVXt7O+3t7UWFcy4wa1KDyQVVn4g7GniaZPLdNmANMDciNmTaHNtT\nQEpSK3B3RLyxxvY8+c4sJzlPxHUuMCuJ/nLBgI+0SPpyf69HxPWD7VjeIqJb0iXAcpL5O7dExAZJ\nnyctKAV8VNIXgC5gD/DxxvXYzPLgXGBWDQM+0iLpmv5ej4hr69KjJua9K7P85Hmkpd6cC8zyU5cj\nLWUdlEiaDfwb+/euFvbR5gbgLJLS3RdFxLpie2lmeXMuMCu/wZweuqG/1yPii8PvTn2lBaUWkSko\nJemeXgWlzgLeFBFvlvQe4NvAzIZ02Mxy4VxgVg2DuXro57n1Ij/7CkoBSOopKLUx0+Zc4HsAEfEz\nSROzE/LMrBKcC8wqYDCnh27LsyM56augVOsh2nSm60qdqObNm0d7ezttbW3ceOONje6OWaON2Fxg\nViWDrtMiaRVw0OzdiPhAXXrU5CZMmNDoLgzKpk2buP322xvdDbPKKVsuMKuCoRSX+/vM87HA+cBr\n9elO3R2yoFS6/IZDtNmnLFcMzJs3j0ceeYT3v//9PtJipZDzIGDE5gKzsukvFwx60BIRvee2rJa0\nZrDbKchjwEmSppEUlLoAmNurzTJgHvADSTOBnVU4h+2BitkBRmwuMKuSoZweOiqzOAp4NzCxbj2q\no4EUlIqI+yWdLenXJJc5fqaRfTaz+nMuMKuGQZfxl/Rb9s9peQ3YBPxTRDxS364Nj6RJwA+AaSR9\n/FhE7Oqj3SZgF7AX6IqI3pPzsm1dUMosJzmX8a9rPnAuMMtPf7lg1BC2dwpwI/AE8BTwAPD40LuX\nm/nAioh4K7ASuLJGu73AGRExo78Bi5mVmvOBWQUMZdByG3AycAPw7ySDmGa8POVckr6S/p1To50Y\n2vdgZuXhfGBWAUO5eujtEXFKZnmVpF/Vq0N1NLlnEl1EbJc0uUa7AB6W1A0sjoibC+uhmRXF+cCs\nAoYyaFkraWZE/BQgLXfdkNNDkh4Gjs2uIkk6V/fRvNbknfdFxDZJx5Akqw3NNj/HzA7N+cCs+oYy\naHkX8KikLenyVOBpSetJZuG/o269O4SIOLPWa5J29JTgljQFeL7GNralf1+QtJSkSmbNJLVgwYJ9\nz9va2mhraxtq981GtPb2dtrb2+u2vaLzgXOBWX0MJhcM5eqhaf293nNvj0aTtBB4KSIWSroCmBQR\n83u1GQeMiojdko4guRzy2ohYXmObvmLALCc5Xz1U13zgXGCWn/5ywaAHLWWR1pO5m6TC5WaSSxx3\nSjoOuDkiPizpBGApyaHiMcAdEXFdP9t0ojLLSc6DlrrmA+cCs/yMyEFLHpyozPKT56Cl3pwLzPJT\n7zotpSDpo5KektQt6dR+2s2WtFHSM+lhYzOrGOcDs2qo7KAFWA+cB/ykVgNJo4BFwCxgOjBX0tvq\n3ZF6TjZ0/PLEdvzGx89oinzQ6O/D8R2/7PErO2iJiKcjooPkssdaWoGOiNgcEV3AXSRFqOqqCj+U\nssYfyZ/d8fdrlnzQ6O/D8R2/7PErO2gZoOOB5zLLW9N1ZjbyOB+YNbmh1GlpGv0Uk/pKRNzbmF6Z\nWSM4H5hVX+WvHpK0CrgsItb28dpM4B8jYna6PJ+kQN7CGtuq9pdl1mB5Xz1Ur3zgXGCWr1q5oNRH\nWgahViJ8DDgpLZi3DbgAmFtrI2W5HNPM+jXsfOBcYNYYlZ3TImmOpOeAmcB9kh5I1x8n6T6AiOgG\nLiGpfPlL4K6I2NCoPptZPpwPzKqh8qeHzMzMrBoqe6SlWUi6TNLetIx4z7orJXVI2iDpQznF/Zd0\n++skLZE0ocj4aZxCC3VJapG0UtIvJa2X9MV0/SRJyyU9LekhSRNz7scoSWslLSs6vqSJkn6Y/rf9\npaT3FBz/0rSI25OS7pB0eNHff7NyLnAucC4YfnwPWnIkqQU4k+ReJz3rTgY+BpwMnAXcJCmP8+PL\ngekR8U6gA7gyjX9KEfFVUOG+Xl4DvhwR04H3AvPSmPOBFRHxVmAl6XeRoy8Bv8osFxn/m8D9EXEy\n8BfAxqLiS/oz4O+AU9O7vY8hmRNS9PffdJwLnAtSzgXDjO9BS76+AVzea925JOfKX4uITSRJpLXe\ngSNiRUTsTRd/CrSkz88pIj4FFe7LiojtEbEufb4b2EDyuc8Fbkub3QbMyasP6T9OZwP/kVldSPx0\nD7otIr4LkP433lVU/NRo4AhJY4DXAZ0Fx29WzgXOBRQVv8q5wIOWnEg6B3guItb3eql3AatO8i9g\n9Vng/oLjN7RQl6Q3Au8kSdLHRsQOSJIZMDnH0D3/OGUnixUV/wTgfyV9Nz0kvVjSuKLiR8TvgH8F\ntpD8rnZFxIqi4jcr5wLngsw654Jhxh8plzznQrWLWV0NXEVyOLgR8fcV05L0FaArIu7Msy/NRNKR\nwI+AL0XEbh1cUyOX2eeS/hrYERHrJJ3RT9O8Zr+PAU4F5kXE45K+QXI4tqjP/3qSPalpwC7gh5L+\ntqj4jeRc0JycC6qXCzxoGYaI6DMRSXo78EbgifQccQuwVlIryahzaqZ5S7qubvEz/biI5PDkBzKr\nO4E31CP+IdTtcw5GeijyR8DtEXFPunqHpGMjYoekKcDzOYV/H3COpLNJDoeOl3Q7sL2g+FtJ9ugf\nT5eXkCSqoj7/XwG/iYiXACQtBU4rMH7DOBf0y7nAuaBuucCnh3IQEU9FxJSIODEiTiD5Ac2IiOeB\nZcDH05nUJwAnAWvq3QdJs0kOTZ4TEf+XeWkZcEHe8ckU6pJ0OEmhrmU5xOntO8CvIuKbmXXLgIvS\n558G7un9pnqIiKsiYmpEnEjyeVdGxCeBewuKvwN4TtJb0lUfJKk3UsjnJzkUPFPS2PQf6A+STEIs\nKn7TcS4AnAucC+qZCyLCj5wfwG+AozLLVwK/Jpkc9qGcYnaQXKmwNn3cVGT8NM5s4Om0L/ML+J7f\nB3QD64BfpJ97NnAUsCLty3Lg9QX05XRgWfq8sPgkVwk8ln4HPwYmFhz/mvR39STJRLvDGvH9N+vD\nucC5wLlgePFdXM7MzMxKwaeHzMzMrBQ8aDEzM7NS8KDFzMzMSsGDFjMzMysFD1rMzMysFDxoMTMz\ns1LwoMWGRMltz7+QWT5O0t0N6stlkvZKOipdPkzSd5TcEv0Xkk7PtF0laWO6fq2kP+21rfPTbZ2a\nWfdpSc8ouZ36p2r04XBJd0nqkPQ/kqYO5v1mZeZ8cFAfnA/ykndhHT+q+SApTb6+CfrRAjwI/Ja0\naBdwMXBL+vwY4PFM+1UkFUn72taRwE+AR0luqQ4wCXiWpDDT63ue9/HeL5AW7QI+TnL33AG/3w8/\nyvxwPjjovc4HOT18pMWG6uvAieneycK0RPd62LcnsVTSckm/kTRP0qVp20eV3EwLSSdKekDSY5J+\nkik5PRg9d1LNOgVYCRARLwA7Jb0783qt3/3XgOuAbKnzWcDyiNgVETtJqjjO7uO92Vuu/4j993gZ\n6PvNysz54EDOBznxoMWGaj7wbEScGhFXpOuy5ZWnA3OAVuCfgd0RcSrJ7eF7DokuBi6JiL8kSTTf\nGkwHJJ1DclOw9b1eeoLkZmWj03uqvIsDbwx3a5owr85sawbQEhEP9NrW8cBzmeXOdF1v+9pFRDew\nKz08PdD3m5WZ80GNds4H9eW7PFteVkXEq8CrknYC96Xr1wN/LukIkrt+/jC9oRYk96YYEEmvA64C\nsne37dnOd4CTSe67sRlYTXIfEoBPRMS2NP6PJV0I3AFcT3IDr3rRoZuYjRjOB1YXHrRYXrKHVCOz\nvJfkdzcKeDnd26pJ0oPAZJLz0J/LvPQmkvPoT6RJrgX4uaTWSO6g++XMNlYDzwBExLb07x8kfZ9k\nz28Z8Hbgv9NtTQGWpXtuncAZmbgtJOfBe9tKsvf2O0mjgQkR8ZKkgb7frMqcD5wP6sKnh2yofg+M\nH+qbI+L3wG8lfbRnnaR39NFudnrI+XO91j8VEVMi4sSIOIEkScyIiOclvU7SuHSbZwJdEbExPTx8\ndLr+MODDwFMR8UpEHJPZ1k+Bj0TEWuAh4EwlV0dMItmTe6iPj3Qv+/fM/ob0HPog3m9WZs4HB3I+\nyImPtNiQpHsNqyU9CTwA3NRf8xrrLwS+lZ5LHgPcRXIb8yF1if2HYCcDD0nqJtkz+mS6/k/S9WOA\n0SS3SL+5v21FxMuSvgY8nq6/Np1Ah6Rrgcci4j7gFuB2SR3Ai8AFh3q/WVU4HzgfFEURtX4/ZmZm\nZs3Dp4fMzMysFDxoMTMzs1LwoMXMzMxKwYMWMzMzKwUPWszMzKwUPGgxMzOzUvCgxczMzErBgxYz\nMzMrhf8HLLE7FYXMGf0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126ff5890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figs = []\n",
    "for snid in lcs.groups.keys():\n",
    "    print(snid)\n",
    "    df = lcs.get_group(snid).query(\"band!='u'\")\n",
    "    strarr = df.to_records()\n",
    "    data = Table(strarr)\n",
    "    data['zp'] = 0.\n",
    "    data['zpsys'] = 'ab'\n",
    "    data.rename_column('flux_err', 'fluxerr')\n",
    "    data['fluxerr'] = data['fluxerr'].astype('float')\n",
    "    data['mag_err'] = data['mag_err'].astype('float')\n",
    "    \n",
    "    sn = SNCosmoModel(df)\n",
    "    fig = sncosmo.plot_lc(data, zp=0., color='k', model=sn[0])#, model=sn, color='k')\n",
    "    fig.set_label(str(snid))\n",
    "    figs.append(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "figs[0].savefig('SN_lc_zpt4.pdf')\n",
    "figs[1].savefig('SN_lc_morepts.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "obsMetaData = obsMetaDataResults[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "newcatalog = SNIaCatalog(db_obj=galDB, \n",
    "                         obs_metadata=obsMetaData, \n",
    "                         column_outputs=['t0', 'cosmologicalDistanceModulus', 'mwebv','time', 'band', 'flux', 'flux_err', 'mag', 'mag_err'])\n",
    "newcatalog.midSurveyTime=49540\n",
    "newcatalog.snFrequency =100.\n",
    "newcatalog.suppressDimSN = True\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49501.250988\n"
     ]
    }
   ],
   "source": [
    "fname = 'SN_morepts.dat'\n",
    "newcatalog.write_catalog(fname)\n",
    "#fnameList.append(fname)\n",
    "print (obsMetaData.mjd.TAI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#snid, snra, sndec, z, t0, c, x1, x0, cosmologicalDistanceModulus, mwebv, time, band, flux, flux_err, mag, mag_err\r\n",
      "1339921, 6.100259e+00, -1.104457e+00, 0.6692, 49499.9427, -6.163219e-03, 1.6875, 4.726145e-06, 42.9858, 0.0249, 49501.2510, z, 4.239256e-10, 2.488731e-10, 23.4318, 5.015137e-01\r\n",
      "1668879, 6.100052e+00, -1.104502e+00, 0.0681, 49499.9122, -1.395187e-01, 1.3202, 1.009967e-03, 37.3485, 0.0249, 49501.2510, z, 2.031476e-08, 2.603080e-10, 19.2305, 1.470040e-02\r\n",
      "1772620, 6.100033e+00, -1.104829e+00, 0.8841, 49499.5839, -1.166386e-01, -0.2276, 3.006398e-06, 43.7404, 0.0249, 49501.2510, z, 3.184562e-10, 2.488111e-10, 23.7424, 6.268650e-01\r\n",
      "1772714, 6.100617e+00, -1.104794e+00, 0.8399, 49499.7557, -4.040957e-02, 0.6075, 3.043377e-06, 43.6008, 0.0249, 49501.2510, z, 3.117216e-10, 2.488071e-10, 23.7656, 6.370970e-01\r\n",
      "1944733, 6.100261e+00, -1.104532e+00, 0.7279, 49499.9891, 5.811638e-02, 0.4292, 2.897491e-06, 43.2126, 0.0249, 49501.2510, z, 2.750203e-10, 2.487855e-10, 23.9016, 6.995318e-01\r\n"
     ]
    }
   ],
   "source": [
    "!cat SN_morepts.dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['testSNData/NewLightCurves/SNCatalog_0', 'testSNData/NewLightCurves/SNCatalog_1', 'testSNData/NewLightCurves/SNCatalog_2', 'testSNData/NewLightCurves/SNCatalog_3', 'testSNData/NewLightCurves/SNCatalog_4', 'testSNData/NewLightCurves/SNCatalog_5', 'testSNData/NewLightCurves/SNCatalog_6', 'testSNData/NewLightCurves/SNCatalog_7', 'testSNData/NewLightCurves/SNCatalog_8', 'testSNData/NewLightCurves/SNCatalog_9', 'testSNData/NewLightCurves/SNCatalog_10', 'testSNData/NewLightCurves/SNCatalog_11', 'testSNData/NewLightCurves/SNCatalog_12', 'testSNData/NewLightCurves/SNCatalog_13', 'testSNData/NewLightCurves/SNCatalog_14', 'testSNData/NewLightCurves/SNCatalog_15', 'testSNData/NewLightCurves/SNCatalog_16', 'testSNData/NewLightCurves/SNCatalog_17', 'testSNData/NewLightCurves/SNCatalog_18', 'testSNData/NewLightCurves/SNCatalog_19', 'testSNData/NewLightCurves/SNCatalog_20', 'testSNData/NewLightCurves/SNCatalog_21', 'testSNData/NewLightCurves/SNCatalog_22', 'testSNData/NewLightCurves/SNCatalog_23', 'testSNData/NewLightCurves/SNCatalog_24', 'testSNData/NewLightCurves/SNCatalog_25', 'testSNData/NewLightCurves/SNCatalog_26', 'testSNData/NewLightCurves/SNCatalog_27', 'testSNData/NewLightCurves/SNCatalog_28', 'testSNData/NewLightCurves/SNCatalog_29', 'testSNData/NewLightCurves/SNCatalog_30', 'testSNData/NewLightCurves/SNCatalog_31', 'testSNData/NewLightCurves/SNCatalog_32', 'testSNData/NewLightCurves/SNCatalog_33', 'testSNData/NewLightCurves/SNCatalog_34', 'testSNData/NewLightCurves/SNCatalog_35', 'testSNData/NewLightCurves/SNCatalog_36', 'testSNData/NewLightCurves/SNCatalog_37', 'testSNData/NewLightCurves/SNCatalog_38', 'testSNData/NewLightCurves/SNCatalog_39', 'testSNData/NewLightCurves/SNCatalog_40', 'testSNData/NewLightCurves/SNCatalog_41', 'testSNData/NewLightCurves/SNCatalog_42', 'testSNData/NewLightCurves/SNCatalog_43', 'testSNData/NewLightCurves/SNCatalog_44', 'testSNData/NewLightCurves/SNCatalog_45', 'testSNData/NewLightCurves/SNCatalog_46', 'testSNData/NewLightCurves/SNCatalog_47', 'testSNData/NewLightCurves/SNCatalog_48', 'testSNData/NewLightCurves/SNCatalog_49', 'testSNData/NewLightCurves/SNCatalog_50', 'testSNData/NewLightCurves/SNCatalog_51', 'testSNData/NewLightCurves/SNCatalog_52', 'testSNData/NewLightCurves/SNCatalog_53', 'testSNData/NewLightCurves/SNCatalog_54', 'testSNData/NewLightCurves/SNCatalog_55', 'testSNData/NewLightCurves/SNCatalog_56', 'testSNData/NewLightCurves/SNCatalog_57', 'testSNData/NewLightCurves/SNCatalog_58', 'testSNData/NewLightCurves/SNCatalog_59', 'testSNData/NewLightCurves/SNCatalog_60', 'testSNData/NewLightCurves/SNCatalog_61', 'testSNData/NewLightCurves/SNCatalog_62', 'testSNData/NewLightCurves/SNCatalog_63', 'testSNData/NewLightCurves/SNCatalog_64', 'testSNData/NewLightCurves/SNCatalog_65', 'testSNData/NewLightCurves/SNCatalog_66', 'testSNData/NewLightCurves/SNCatalog_67', 'testSNData/NewLightCurves/SNCatalog_68', 'testSNData/NewLightCurves/SNCatalog_69', 'testSNData/NewLightCurves/SNCatalog_70', 'testSNData/NewLightCurves/SNCatalog_71', 'testSNData/NewLightCurves/SNCatalog_72', 'testSNData/NewLightCurves/SNCatalog_73', 'testSNData/NewLightCurves/SNCatalog_74', 'testSNData/NewLightCurves/SNCatalog_75', 'testSNData/NewLightCurves/SNCatalog_76', 'testSNData/NewLightCurves/SNCatalog_77', 'testSNData/NewLightCurves/SNCatalog_78', 'testSNData/NewLightCurves/SNCatalog_79', 'testSNData/NewLightCurves/SNCatalog_80', 'testSNData/NewLightCurves/SNCatalog_81', 'testSNData/NewLightCurves/SNCatalog_82', 'testSNData/NewLightCurves/SNCatalog_83', 'testSNData/NewLightCurves/SNCatalog_84', 'testSNData/NewLightCurves/SNCatalog_85', 'testSNData/NewLightCurves/SNCatalog_86', 'testSNData/NewLightCurves/SNCatalog_87', 'testSNData/NewLightCurves/SNCatalog_88', 'testSNData/NewLightCurves/SNCatalog_89', 'testSNData/NewLightCurves/SNCatalog_90', 'testSNData/NewLightCurves/SNCatalog_91', 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'testSNData/NewLightCurves/SNCatalog_460', 'testSNData/NewLightCurves/SNCatalog_461', 'testSNData/NewLightCurves/SNCatalog_462', 'testSNData/NewLightCurves/SNCatalog_463', 'testSNData/NewLightCurves/SNCatalog_464', 'testSNData/NewLightCurves/SNCatalog_465', 'testSNData/NewLightCurves/SNCatalog_466', 'testSNData/NewLightCurves/SNCatalog_467', 'testSNData/NewLightCurves/SNCatalog_468', 'testSNData/NewLightCurves/SNCatalog_469', 'testSNData/NewLightCurves/SNCatalog_470', 'testSNData/NewLightCurves/SNCatalog_471', 'testSNData/NewLightCurves/SNCatalog_472', 'testSNData/NewLightCurves/SNCatalog_473', 'testSNData/NewLightCurves/SNCatalog_474', 'testSNData/NewLightCurves/SNCatalog_475', 'testSNData/NewLightCurves/SNCatalog_476', 'testSNData/NewLightCurves/SNCatalog_477', 'testSNData/NewLightCurves/SNCatalog_478', 'testSNData/NewLightCurves/SNCatalog_479', 'testSNData/NewLightCurves/SNCatalog_480', 'testSNData/NewLightCurves/SNCatalog_481', 'testSNData/NewLightCurves/SNCatalog_482', 'testSNData/NewLightCurves/SNCatalog_483', 'testSNData/NewLightCurves/SNCatalog_484', 'testSNData/NewLightCurves/SNCatalog_485', 'testSNData/NewLightCurves/SNCatalog_486', 'testSNData/NewLightCurves/SNCatalog_487', 'testSNData/NewLightCurves/SNCatalog_488', 'testSNData/NewLightCurves/SNCatalog_489', 'testSNData/NewLightCurves/SNCatalog_490', 'testSNData/NewLightCurves/SNCatalog_491', 'testSNData/NewLightCurves/SNCatalog_492', 'testSNData/NewLightCurves/SNCatalog_493', 'testSNData/NewLightCurves/SNCatalog_494', 'testSNData/NewLightCurves/SNCatalog_495', 'testSNData/NewLightCurves/SNCatalog_496', 'testSNData/NewLightCurves/SNCatalog_497', 'testSNData/NewLightCurves/SNCatalog_498', 'testSNData/NewLightCurves/SNCatalog_499', 'testSNData/NewLightCurves/SNCatalog_500', 'testSNData/NewLightCurves/SNCatalog_501', 'testSNData/NewLightCurves/SNCatalog_502', 'testSNData/NewLightCurves/SNCatalog_503', 'testSNData/NewLightCurves/SNCatalog_504', 'testSNData/NewLightCurves/SNCatalog_505', 'testSNData/NewLightCurves/SNCatalog_506', 'testSNData/NewLightCurves/SNCatalog_507', 'testSNData/NewLightCurves/SNCatalog_508', 'testSNData/NewLightCurves/SNCatalog_509', 'testSNData/NewLightCurves/SNCatalog_510', 'testSNData/NewLightCurves/SNCatalog_511', 'testSNData/NewLightCurves/SNCatalog_512', 'testSNData/NewLightCurves/SNCatalog_513', 'testSNData/NewLightCurves/SNCatalog_514', 'testSNData/NewLightCurves/SNCatalog_515', 'testSNData/NewLightCurves/SNCatalog_516', 'testSNData/NewLightCurves/SNCatalog_517', 'testSNData/NewLightCurves/SNCatalog_518', 'testSNData/NewLightCurves/SNCatalog_519', 'testSNData/NewLightCurves/SNCatalog_520', 'testSNData/NewLightCurves/SNCatalog_521', 'testSNData/NewLightCurves/SNCatalog_522', 'testSNData/NewLightCurves/SNCatalog_523', 'testSNData/NewLightCurves/SNCatalog_524', 'testSNData/NewLightCurves/SNCatalog_525', 'testSNData/NewLightCurves/SNCatalog_526', 'testSNData/NewLightCurves/SNCatalog_527', 'testSNData/NewLightCurves/SNCatalog_528', 'testSNData/NewLightCurves/SNCatalog_529', 'testSNData/NewLightCurves/SNCatalog_530', 'testSNData/NewLightCurves/SNCatalog_531', 'testSNData/NewLightCurves/SNCatalog_532', 'testSNData/NewLightCurves/SNCatalog_533', 'testSNData/NewLightCurves/SNCatalog_534', 'testSNData/NewLightCurves/SNCatalog_535', 'testSNData/NewLightCurves/SNCatalog_536', 'testSNData/NewLightCurves/SNCatalog_537', 'testSNData/NewLightCurves/SNCatalog_538', 'testSNData/NewLightCurves/SNCatalog_539', 'testSNData/NewLightCurves/SNCatalog_540', 'testSNData/NewLightCurves/SNCatalog_541', 'testSNData/NewLightCurves/SNCatalog_542', 'testSNData/NewLightCurves/SNCatalog_543', 'testSNData/NewLightCurves/SNCatalog_544', 'testSNData/NewLightCurves/SNCatalog_545', 'testSNData/NewLightCurves/SNCatalog_546', 'testSNData/NewLightCurves/SNCatalog_547', 'testSNData/NewLightCurves/SNCatalog_548', 'testSNData/NewLightCurves/SNCatalog_549', 'testSNData/NewLightCurves/SNCatalog_550', 'testSNData/NewLightCurves/SNCatalog_551', 'testSNData/NewLightCurves/SNCatalog_552', 'testSNData/NewLightCurves/SNCatalog_553', 'testSNData/NewLightCurves/SNCatalog_554', 'testSNData/NewLightCurves/SNCatalog_555', 'testSNData/NewLightCurves/SNCatalog_556', 'testSNData/NewLightCurves/SNCatalog_557', 'testSNData/NewLightCurves/SNCatalog_558', 'testSNData/NewLightCurves/SNCatalog_559', 'testSNData/NewLightCurves/SNCatalog_560', 'testSNData/NewLightCurves/SNCatalog_561', 'testSNData/NewLightCurves/SNCatalog_562', 'testSNData/NewLightCurves/SNCatalog_563', 'testSNData/NewLightCurves/SNCatalog_564', 'testSNData/NewLightCurves/SNCatalog_565', 'testSNData/NewLightCurves/SNCatalog_566', 'testSNData/NewLightCurves/SNCatalog_567', 'testSNData/NewLightCurves/SNCatalog_568', 'testSNData/NewLightCurves/SNCatalog_569', 'testSNData/NewLightCurves/SNCatalog_570', 'testSNData/NewLightCurves/SNCatalog_571', 'testSNData/NewLightCurves/SNCatalog_572', 'testSNData/NewLightCurves/SNCatalog_573', 'testSNData/NewLightCurves/SNCatalog_574', 'testSNData/NewLightCurves/SNCatalog_575', 'testSNData/NewLightCurves/SNCatalog_576', 'testSNData/NewLightCurves/SNCatalog_577', 'testSNData/NewLightCurves/SNCatalog_578', 'testSNData/NewLightCurves/SNCatalog_579', 'testSNData/NewLightCurves/SNCatalog_580', 'testSNData/NewLightCurves/SNCatalog_581', 'testSNData/NewLightCurves/SNCatalog_582', 'testSNData/NewLightCurves/SNCatalog_583', 'testSNData/NewLightCurves/SNCatalog_584', 'testSNData/NewLightCurves/SNCatalog_585', 'testSNData/NewLightCurves/SNCatalog_586', 'testSNData/NewLightCurves/SNCatalog_587', 'testSNData/NewLightCurves/SNCatalog_588', 'testSNData/NewLightCurves/SNCatalog_589', 'testSNData/NewLightCurves/SNCatalog_590', 'testSNData/NewLightCurves/SNCatalog_591', 'testSNData/NewLightCurves/SNCatalog_592', 'testSNData/NewLightCurves/SNCatalog_593', 'testSNData/NewLightCurves/SNCatalog_594', 'testSNData/NewLightCurves/SNCatalog_595', 'testSNData/NewLightCurves/SNCatalog_596', 'testSNData/NewLightCurves/SNCatalog_597', 'testSNData/NewLightCurves/SNCatalog_598', 'testSNData/NewLightCurves/SNCatalog_599', 'testSNData/NewLightCurves/SNCatalog_600', 'testSNData/NewLightCurves/SNCatalog_601', 'testSNData/NewLightCurves/SNCatalog_602', 'testSNData/NewLightCurves/SNCatalog_603', 'testSNData/NewLightCurves/SNCatalog_604', 'testSNData/NewLightCurves/SNCatalog_605', 'testSNData/NewLightCurves/SNCatalog_606', 'testSNData/NewLightCurves/SNCatalog_607', 'testSNData/NewLightCurves/SNCatalog_608', 'testSNData/NewLightCurves/SNCatalog_609', 'testSNData/NewLightCurves/SNCatalog_610', 'testSNData/NewLightCurves/SNCatalog_611', 'testSNData/NewLightCurves/SNCatalog_612', 'testSNData/NewLightCurves/SNCatalog_613', 'testSNData/NewLightCurves/SNCatalog_614', 'testSNData/NewLightCurves/SNCatalog_615', 'testSNData/NewLightCurves/SNCatalog_616', 'testSNData/NewLightCurves/SNCatalog_617', 'testSNData/NewLightCurves/SNCatalog_618', 'testSNData/NewLightCurves/SNCatalog_619', 'testSNData/NewLightCurves/SNCatalog_620', 'testSNData/NewLightCurves/SNCatalog_621', 'testSNData/NewLightCurves/SNCatalog_622', 'testSNData/NewLightCurves/SNCatalog_623', 'testSNData/NewLightCurves/SNCatalog_624', 'testSNData/NewLightCurves/SNCatalog_625', 'testSNData/NewLightCurves/SNCatalog_626', 'testSNData/NewLightCurves/SNCatalog_627', 'testSNData/NewLightCurves/SNCatalog_628', 'testSNData/NewLightCurves/SNCatalog_629', 'testSNData/NewLightCurves/SNCatalog_630', 'testSNData/NewLightCurves/SNCatalog_631', 'testSNData/NewLightCurves/SNCatalog_632', 'testSNData/NewLightCurves/SNCatalog_633', 'testSNData/NewLightCurves/SNCatalog_634', 'testSNData/NewLightCurves/SNCatalog_635', 'testSNData/NewLightCurves/SNCatalog_636', 'testSNData/NewLightCurves/SNCatalog_637', 'testSNData/NewLightCurves/SNCatalog_638', 'testSNData/NewLightCurves/SNCatalog_639', 'testSNData/NewLightCurves/SNCatalog_640', 'testSNData/NewLightCurves/SNCatalog_641', 'testSNData/NewLightCurves/SNCatalog_642', 'testSNData/NewLightCurves/SNCatalog_643', 'testSNData/NewLightCurves/SNCatalog_644', 'testSNData/NewLightCurves/SNCatalog_645', 'testSNData/NewLightCurves/SNCatalog_646', 'testSNData/NewLightCurves/SNCatalog_647', 'testSNData/NewLightCurves/SNCatalog_648', 'testSNData/NewLightCurves/SNCatalog_649', 'testSNData/NewLightCurves/SNCatalog_650', 'testSNData/NewLightCurves/SNCatalog_651', 'testSNData/NewLightCurves/SNCatalog_652', 'testSNData/NewLightCurves/SNCatalog_653', 'testSNData/NewLightCurves/SNCatalog_654', 'testSNData/NewLightCurves/SNCatalog_655', 'testSNData/NewLightCurves/SNCatalog_656', 'testSNData/NewLightCurves/SNCatalog_657', 'testSNData/NewLightCurves/SNCatalog_658', 'testSNData/NewLightCurves/SNCatalog_659', 'testSNData/NewLightCurves/SNCatalog_660', 'testSNData/NewLightCurves/SNCatalog_661', 'testSNData/NewLightCurves/SNCatalog_662', 'testSNData/NewLightCurves/SNCatalog_663', 'testSNData/NewLightCurves/SNCatalog_664', 'testSNData/NewLightCurves/SNCatalog_665', 'testSNData/NewLightCurves/SNCatalog_666', 'testSNData/NewLightCurves/SNCatalog_667', 'testSNData/NewLightCurves/SNCatalog_668', 'testSNData/NewLightCurves/SNCatalog_669', 'testSNData/NewLightCurves/SNCatalog_670', 'testSNData/NewLightCurves/SNCatalog_671', 'testSNData/NewLightCurves/SNCatalog_672', 'testSNData/NewLightCurves/SNCatalog_673', 'testSNData/NewLightCurves/SNCatalog_674', 'testSNData/NewLightCurves/SNCatalog_675', 'testSNData/NewLightCurves/SNCatalog_676', 'testSNData/NewLightCurves/SNCatalog_677', 'testSNData/NewLightCurves/SNCatalog_678', 'testSNData/NewLightCurves/SNCatalog_679', 'testSNData/NewLightCurves/SNCatalog_680', 'testSNData/NewLightCurves/SNCatalog_681', 'testSNData/NewLightCurves/SNCatalog_682', 'testSNData/NewLightCurves/SNCatalog_683', 'testSNData/NewLightCurves/SNCatalog_684', 'testSNData/NewLightCurves/SNCatalog_685', 'testSNData/NewLightCurves/SNCatalog_686', 'testSNData/NewLightCurves/SNCatalog_687', 'testSNData/NewLightCurves/SNCatalog_688', 'testSNData/NewLightCurves/SNCatalog_689', 'testSNData/NewLightCurves/SNCatalog_690', 'testSNData/NewLightCurves/SNCatalog_691', 'testSNData/NewLightCurves/SNCatalog_692', 'testSNData/NewLightCurves/SNCatalog_693', 'testSNData/NewLightCurves/SNCatalog_694', 'testSNData/NewLightCurves/SNCatalog_695', 'testSNData/NewLightCurves/SNCatalog_696', 'testSNData/NewLightCurves/SNCatalog_697', 'testSNData/NewLightCurves/SNCatalog_698', 'testSNData/NewLightCurves/SNCatalog_699', 'testSNData/NewLightCurves/SNCatalog_700', 'testSNData/NewLightCurves/SNCatalog_701', 'testSNData/NewLightCurves/SNCatalog_702', 'testSNData/NewLightCurves/SNCatalog_703', 'testSNData/NewLightCurves/SNCatalog_704', 'testSNData/NewLightCurves/SNCatalog_705', 'testSNData/NewLightCurves/SNCatalog_706', 'testSNData/NewLightCurves/SNCatalog_707', 'testSNData/NewLightCurves/SNCatalog_708', 'testSNData/NewLightCurves/SNCatalog_709', 'testSNData/NewLightCurves/SNCatalog_710', 'testSNData/NewLightCurves/SNCatalog_711', 'testSNData/NewLightCurves/SNCatalog_712', 'testSNData/NewLightCurves/SNCatalog_713', 'testSNData/NewLightCurves/SNCatalog_714', 'testSNData/NewLightCurves/SNCatalog_715', 'testSNData/NewLightCurves/SNCatalog_716', 'testSNData/NewLightCurves/SNCatalog_717', 'testSNData/NewLightCurves/SNCatalog_718', 'testSNData/NewLightCurves/SNCatalog_719', 'testSNData/NewLightCurves/SNCatalog_720', 'testSNData/NewLightCurves/SNCatalog_721', 'testSNData/NewLightCurves/SNCatalog_722', 'testSNData/NewLightCurves/SNCatalog_723', 'testSNData/NewLightCurves/SNCatalog_724', 'testSNData/NewLightCurves/SNCatalog_725', 'testSNData/NewLightCurves/SNCatalog_726', 'testSNData/NewLightCurves/SNCatalog_727', 'testSNData/NewLightCurves/SNCatalog_728', 'testSNData/NewLightCurves/SNCatalog_729', 'testSNData/NewLightCurves/SNCatalog_730', 'testSNData/NewLightCurves/SNCatalog_731', 'testSNData/NewLightCurves/SNCatalog_732', 'testSNData/NewLightCurves/SNCatalog_733', 'testSNData/NewLightCurves/SNCatalog_734', 'testSNData/NewLightCurves/SNCatalog_735', 'testSNData/NewLightCurves/SNCatalog_736', 'testSNData/NewLightCurves/SNCatalog_737', 'testSNData/NewLightCurves/SNCatalog_738', 'testSNData/NewLightCurves/SNCatalog_739', 'testSNData/NewLightCurves/SNCatalog_740', 'testSNData/NewLightCurves/SNCatalog_741', 'testSNData/NewLightCurves/SNCatalog_742', 'testSNData/NewLightCurves/SNCatalog_743', 'testSNData/NewLightCurves/SNCatalog_744', 'testSNData/NewLightCurves/SNCatalog_745', 'testSNData/NewLightCurves/SNCatalog_746', 'testSNData/NewLightCurves/SNCatalog_747', 'testSNData/NewLightCurves/SNCatalog_748', 'testSNData/NewLightCurves/SNCatalog_749', 'testSNData/NewLightCurves/SNCatalog_750', 'testSNData/NewLightCurves/SNCatalog_751', 'testSNData/NewLightCurves/SNCatalog_752', 'testSNData/NewLightCurves/SNCatalog_753', 'testSNData/NewLightCurves/SNCatalog_754', 'testSNData/NewLightCurves/SNCatalog_755', 'testSNData/NewLightCurves/SNCatalog_756', 'testSNData/NewLightCurves/SNCatalog_757', 'testSNData/NewLightCurves/SNCatalog_758', 'testSNData/NewLightCurves/SNCatalog_759', 'testSNData/NewLightCurves/SNCatalog_760', 'testSNData/NewLightCurves/SNCatalog_761', 'testSNData/NewLightCurves/SNCatalog_762', 'testSNData/NewLightCurves/SNCatalog_763', 'testSNData/NewLightCurves/SNCatalog_764', 'testSNData/NewLightCurves/SNCatalog_765', 'testSNData/NewLightCurves/SNCatalog_766', 'testSNData/NewLightCurves/SNCatalog_767', 'testSNData/NewLightCurves/SNCatalog_768', 'testSNData/NewLightCurves/SNCatalog_769', 'testSNData/NewLightCurves/SNCatalog_770', 'testSNData/NewLightCurves/SNCatalog_771', 'testSNData/NewLightCurves/SNCatalog_772', 'testSNData/NewLightCurves/SNCatalog_773', 'testSNData/NewLightCurves/SNCatalog_774', 'testSNData/NewLightCurves/SNCatalog_775', 'testSNData/NewLightCurves/SNCatalog_776', 'testSNData/NewLightCurves/SNCatalog_777', 'testSNData/NewLightCurves/SNCatalog_778', 'testSNData/NewLightCurves/SNCatalog_779', 'testSNData/NewLightCurves/SNCatalog_780', 'testSNData/NewLightCurves/SNCatalog_781', 'testSNData/NewLightCurves/SNCatalog_782', 'testSNData/NewLightCurves/SNCatalog_783', 'testSNData/NewLightCurves/SNCatalog_784', 'testSNData/NewLightCurves/SNCatalog_785', 'testSNData/NewLightCurves/SNCatalog_786', 'testSNData/NewLightCurves/SNCatalog_787', 'testSNData/NewLightCurves/SNCatalog_788', 'testSNData/NewLightCurves/SNCatalog_789', 'testSNData/NewLightCurves/SNCatalog_790', 'testSNData/NewLightCurves/SNCatalog_791', 'testSNData/NewLightCurves/SNCatalog_792', 'testSNData/NewLightCurves/SNCatalog_793', 'testSNData/NewLightCurves/SNCatalog_794', 'testSNData/NewLightCurves/SNCatalog_795', 'testSNData/NewLightCurves/SNCatalog_796', 'testSNData/NewLightCurves/SNCatalog_797', 'testSNData/NewLightCurves/SNCatalog_798', 'testSNData/NewLightCurves/SNCatalog_799', 'testSNData/NewLightCurves/SNCatalog_800', 'testSNData/NewLightCurves/SNCatalog_801', 'testSNData/NewLightCurves/SNCatalog_802', 'testSNData/NewLightCurves/SNCatalog_803', 'testSNData/NewLightCurves/SNCatalog_804', 'testSNData/NewLightCurves/SNCatalog_805', 'testSNData/NewLightCurves/SNCatalog_806', 'testSNData/NewLightCurves/SNCatalog_807', 'testSNData/NewLightCurves/SNCatalog_808', 'testSNData/NewLightCurves/SNCatalog_809', 'testSNData/NewLightCurves/SNCatalog_810', 'testSNData/NewLightCurves/SNCatalog_811', 'testSNData/NewLightCurves/SNCatalog_812', 'testSNData/NewLightCurves/SNCatalog_813', 'testSNData/NewLightCurves/SNCatalog_814', 'testSNData/NewLightCurves/SNCatalog_815', 'testSNData/NewLightCurves/SNCatalog_816', 'testSNData/NewLightCurves/SNCatalog_817', 'testSNData/NewLightCurves/SNCatalog_818', 'testSNData/NewLightCurves/SNCatalog_819', 'testSNData/NewLightCurves/SNCatalog_820', 'testSNData/NewLightCurves/SNCatalog_821', 'testSNData/NewLightCurves/SNCatalog_822', 'testSNData/NewLightCurves/SNCatalog_823', 'testSNData/NewLightCurves/SNCatalog_824', 'testSNData/NewLightCurves/SNCatalog_825', 'testSNData/NewLightCurves/SNCatalog_826', 'testSNData/NewLightCurves/SNCatalog_827', 'testSNData/NewLightCurves/SNCatalog_828', 'testSNData/NewLightCurves/SNCatalog_829', 'testSNData/NewLightCurves/SNCatalog_830', 'testSNData/NewLightCurves/SNCatalog_831', 'testSNData/NewLightCurves/SNCatalog_832', 'testSNData/NewLightCurves/SNCatalog_833', 'testSNData/NewLightCurves/SNCatalog_834', 'testSNData/NewLightCurves/SNCatalog_835', 'testSNData/NewLightCurves/SNCatalog_836']\n"
     ]
    }
   ],
   "source": [
    "print (fnamelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#snid, snra, sndec, z, t0, c, x1, x0, cosmologicalDistanceModulus, mwebv, time, band, flux, flux_err, mag, mag_err\r\n",
      "1249577, 6.099453e+00, -1.104632e+00, 0.1076, 49500.4653, -2.250614e-01, -0.5877, 4.988215e-04, 38.4027, 0.0249, 49521.2633, y, 7.148599e-09, 6.968267e-10, 20.3644, 1.011126e-01\r\n",
      "1772620, 6.100033e+00, -1.104829e+00, 0.8841, 49499.5839, -1.166386e-01, -0.2276, 3.006398e-06, 43.7404, 0.0249, 49521.2633, y, 1.958945e-10, 6.940332e-10, 24.2699, 1.643339e+00\r\n",
      "1777221, 6.099567e+00, -1.104924e+00, 0.3970, 49499.5548, -3.104283e-02, -1.1600, 2.150050e-05, 41.6052, 0.0249, 49521.2633, y, 5.962636e-10, 6.941943e-10, 23.0614, 8.382788e-01\r\n"
     ]
    }
   ],
   "source": [
    "!cat testSNData/NewLightCurves/SNCatalog_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "snid_vals = lcs.groups.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>snid</th>\n",
       "      <th>snra</th>\n",
       "      <th>sndec</th>\n",
       "      <th>z</th>\n",
       "      <th>t0</th>\n",
       "      <th>c</th>\n",
       "      <th>x1</th>\n",
       "      <th>x0</th>\n",
       "      <th>cosmologicalDistanceModulus</th>\n",
       "      <th>mwebv</th>\n",
       "      <th>time</th>\n",
       "      <th>band</th>\n",
       "      <th>flux</th>\n",
       "      <th>flux_err</th>\n",
       "      <th>mag</th>\n",
       "      <th>mag_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1944733</td>\n",
       "      <td>6.100261</td>\n",
       "      <td>-1.104532</td>\n",
       "      <td>0.7279</td>\n",
       "      <td>49499.9891</td>\n",
       "      <td>0.058116</td>\n",
       "      <td>0.4292</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>43.2126</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49501.2510</td>\n",
       "      <td>z</td>\n",
       "      <td>2.750203e-10</td>\n",
       "      <td>2.48785e-10</td>\n",
       "      <td>23.9016</td>\n",
       "      <td>0.699532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1944733</td>\n",
       "      <td>6.100261</td>\n",
       "      <td>-1.104532</td>\n",
       "      <td>0.7279</td>\n",
       "      <td>49499.9891</td>\n",
       "      <td>0.058116</td>\n",
       "      <td>0.4292</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>43.2126</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49519.1827</td>\n",
       "      <td>r</td>\n",
       "      <td>7.247616e-11</td>\n",
       "      <td>5.60998e-11</td>\n",
       "      <td>25.3495</td>\n",
       "      <td>0.622432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      snid      snra     sndec       z          t0         c      x1  \\\n",
       "4  1944733  6.100261 -1.104532  0.7279  49499.9891  0.058116  0.4292   \n",
       "4  1944733  6.100261 -1.104532  0.7279  49499.9891  0.058116  0.4292   \n",
       "\n",
       "         x0  cosmologicalDistanceModulus   mwebv        time band  \\\n",
       "4  0.000003                      43.2126  0.0249  49501.2510    z   \n",
       "4  0.000003                      43.2126  0.0249  49519.1827    r   \n",
       "\n",
       "           flux     flux_err      mag   mag_err  \n",
       "4  2.750203e-10  2.48785e-10  23.9016  0.699532  \n",
       "4  7.247616e-11  5.60998e-11  25.3495  0.622432  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lcs.get_group(snid_vals[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3000.,   3001.,   3002., ...,  11998.,  11999.,  12000.])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sncosmo.get_bandpass('g').wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from lsst.sims.photUtils import Bandpass\n",
    "from lsst.sims.photUtils import BandpassDict\n",
    "\n",
    "bandpassnames = ['u', 'g', 'r', 'i', 'z', 'y']\n",
    "LSST_BandPass = BandpassDict.loadTotalBandpassesFromFiles()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['redshift'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-235-2265f723a011>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'redshift'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'x1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m't0'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'x0'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'snra'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sndec'\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[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/manual/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1961\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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   1962\u001b[0m             \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1963\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1964\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1965\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/manual/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2005\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2006\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2007\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0m\u001b[1;32m   2008\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/manual/anaconda/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[1;32m   1148\u001b[0m                 \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\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-> 1150\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%s not in index'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mobjarr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\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   1151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['redshift'] not in index\""
     ]
    }
   ],
   "source": [
    "np.unique(np.asarray(lc[['z', 'c', 'x1', 't0', 'x0','snra', 'sndec']]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def SNCosmoModel(lc):\n",
    "    f = np.unique(np.asarray(lc[['z', 'c', 'x1', 't0', 'x0','snra', 'sndec']]))\n",
    "    f = np.asarray(lc[['z', 'c', 'x1', 't0', 'x0', 'snra', 'sndec']])[0]\n",
    "    #SNO = SNObject(ra=f['snra'][0], dec=f['sndec'][0])\n",
    "    SNO = SNObject(ra=f[-2], dec=f[-1])\n",
    "    SNO.set(z=f[0], c=f[1], x1=f[2], t0=f[3], x0=f[4])\n",
    "    sn = SNO.equivalentSNCosmoModel()\n",
    "    return sn,  SNO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lc = lcs.get_group(snid_vals[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.97000000e-01,  -3.10428300e-02,  -1.16000000e+00,\n",
       "         4.94995548e+04,   2.15005000e-05,   6.09956700e+00,\n",
       "        -1.10492400e+00])"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(lc[['z', 'c', 'x1', 't0', 'x0', 'snra', 'sndec']])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "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>snid</th>\n",
       "      <th>snra</th>\n",
       "      <th>sndec</th>\n",
       "      <th>z</th>\n",
       "      <th>t0</th>\n",
       "      <th>c</th>\n",
       "      <th>x1</th>\n",
       "      <th>x0</th>\n",
       "      <th>cosmologicalDistanceModulus</th>\n",
       "      <th>mwebv</th>\n",
       "      <th>time</th>\n",
       "      <th>band</th>\n",
       "      <th>flux</th>\n",
       "      <th>flux_err</th>\n",
       "      <th>mag</th>\n",
       "      <th>mag_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49521.2338</td>\n",
       "      <td>y</td>\n",
       "      <td>5.963909e-10</td>\n",
       "      <td>6.19611e-10</td>\n",
       "      <td>23.0612</td>\n",
       "      <td>0.773524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49521.2527</td>\n",
       "      <td>y</td>\n",
       "      <td>5.963094e-10</td>\n",
       "      <td>8.51704e-10</td>\n",
       "      <td>23.0613</td>\n",
       "      <td>0.963265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49521.2629</td>\n",
       "      <td>y</td>\n",
       "      <td>5.962654e-10</td>\n",
       "      <td>7.53871e-10</td>\n",
       "      <td>23.0614</td>\n",
       "      <td>0.887359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49521.2633</td>\n",
       "      <td>y</td>\n",
       "      <td>5.962636e-10</td>\n",
       "      <td>6.94194e-10</td>\n",
       "      <td>23.0614</td>\n",
       "      <td>0.838279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49525.3453</td>\n",
       "      <td>y</td>\n",
       "      <td>5.752254e-10</td>\n",
       "      <td>6.78796e-10</td>\n",
       "      <td>23.1004</td>\n",
       "      <td>0.846182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      snid      snra     sndec      z          t0         c    x1        x0  \\\n",
       "1  1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "1  1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2  1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2  1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2  1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "\n",
       "   cosmologicalDistanceModulus   mwebv        time band          flux  \\\n",
       "1                      41.6052  0.0249  49521.2338    y  5.963909e-10   \n",
       "1                      41.6052  0.0249  49521.2527    y  5.963094e-10   \n",
       "2                      41.6052  0.0249  49521.2629    y  5.962654e-10   \n",
       "2                      41.6052  0.0249  49521.2633    y  5.962636e-10   \n",
       "2                      41.6052  0.0249  49525.3453    y  5.752254e-10   \n",
       "\n",
       "      flux_err      mag   mag_err  \n",
       "1  6.19611e-10  23.0612  0.773524  \n",
       "1  8.51704e-10  23.0613  0.963265  \n",
       "2  7.53871e-10  23.0614  0.887359  \n",
       "2  6.94194e-10  23.0614  0.838279  \n",
       "2  6.78796e-10  23.1004  0.846182  "
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "_sn, _snlc = SNCosmoModel(lc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<SNObject at 0x11f4c4890>\n",
      "source:\n",
      "  class      : SALT2Source\n",
      "  name       : 'salt2-extended'\n",
      "  version    : 1.0\n",
      "  phases     : [-20, .., 50] days\n",
      "  wavelengths: [300, .., 18000] Angstroms\n",
      "effect (name='host' frame='rest'):\n",
      "  class           : OD94Dust\n",
      "  wavelength range: [909.09, 33333.3] Angstroms\n",
      "effect (name='mw' frame='obs'):\n",
      "  class           : OD94Dust\n",
      "  wavelength range: [909.09, 33333.3] Angstroms\n",
      "parameters:\n",
      "  z       = -1.1599999999999999\n",
      "  t0      = 2.1500500000000002e-05\n",
      "  x0      = 0.39700000000000002\n",
      "  x1      = -0.03104283\n",
      "  c       = -1.104924\n",
      "  hostebv = 0.0\n",
      "  hostr_v = 3.1000000000000001\n",
      "  mwebv   = 0.0\n",
      "  mwr_v   = 3.1000000000000001\n"
     ]
    }
   ],
   "source": [
    "print _snlc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ff = np.unique(np.asarray(lc[['z', 'c', 'x1', 't0', 'snra', 'sndec']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sncosmodata = Table(lc.to_records())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sncosmodata['zp']=0.\n",
    "#sncosmodata['fluxerr'] = sncosmodata['flux_err']\n",
    "sncosmodata['zpsys'] = 'ab'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table id=\"table4659780176\"><thead><tr><th>index</th><th>snid</th><th>snra</th><th>sndec</th><th>z</th><th>t0</th><th>c</th><th>x1</th><th>x0</th><th>cosmologicalDistanceModulus</th><th>mwebv</th><th>time</th><th>band</th><th>flux</th><th>flux_err</th><th>mag</th><th>mag_err</th><th>zp</th><th>zpsys</th></tr></thead><tr><td>1</td><td>1777221</td><td>6.099567</td><td>-1.104924</td><td>0.397</td><td>49499.5548</td><td>-0.03104283</td><td>-1.16</td><td>2.15005e-05</td><td>41.6052</td><td>0.0249</td><td>49521.2338</td><td>y</td><td>5.963909e-10</td><td>6.196107e-10</td><td>23.0612</td><td>0.773524</td><td>0.0</td><td>ab</td></tr><tr><td>1</td><td>1777221</td><td>6.099567</td><td>-1.104924</td><td>0.397</td><td>49499.5548</td><td>-0.03104283</td><td>-1.16</td><td>2.15005e-05</td><td>41.6052</td><td>0.0249</td><td>49521.2527</td><td>y</td><td>5.963094e-10</td><td>8.517035e-10</td><td>23.0613</td><td>0.9632649</td><td>0.0</td><td>ab</td></tr></table>"
      ],
      "text/plain": [
       "<Table rows=2 names=('index','snid','snra','sndec','z','t0','c','x1','x0','cosmologicalDistanceModulus','mwebv','time','band','flux','flux_err','mag','mag_err','zp','zpsys')>\n",
       "array([ (1, 1777221, 6.0995669999999995, -1.104924, 0.397, 49499.5548, -0.03104283, -1.16, 2.1500500000000002e-05, 41.6052, 0.0249, 49521.2338, 'y', 5.963909e-10, 6.196107e-10, 23.0612, 0.773524, 0.0, 'ab'),\n",
       "       (1, 1777221, 6.0995669999999995, -1.104924, 0.397, 49499.5548, -0.03104283, -1.16, 2.1500500000000002e-05, 41.6052, 0.0249, 49521.2527, 'y', 5.963094e-10, 8.517035e-10, 23.0613, 0.9632649, 0.0, 'ab')], \n",
       "      dtype=[('index', '<i8'), ('snid', '<i8'), ('snra', '<f8'), ('sndec', '<f8'), ('z', '<f8'), ('t0', '<f8'), ('c', '<f8'), ('x1', '<f8'), ('x0', '<f8'), ('cosmologicalDistanceModulus', '<f8'), ('mwebv', '<f8'), ('time', '<f8'), ('band', 'O'), ('flux', '<f8'), ('flux_err', 'O'), ('mag', '<f8'), ('mag_err', 'O'), ('zp', '<f8'), ('zpsys', 'S2')])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sncosmodata[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Column name='zpsys' unit=None format=None description=None>\n",
       "array(['ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab',\n",
       "       'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab',\n",
       "       'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab',\n",
       "       'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab',\n",
       "       'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab',\n",
       "       'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab',\n",
       "       'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab',\n",
       "       'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab',\n",
       "       'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab', 'ab'], \n",
       "      dtype='|S2')"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sncosmodata['zpsys'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sncosmodata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('index', '<i8'), ('snid', '<i8'), ('snra', '<f8'), ('sndec', '<f8'), ('z', '<f8'), ('t0', '<f8'), ('c', '<f8'), ('x1', '<f8'), ('x0', '<f8'), ('cosmologicalDistanceModulus', '<f8'), ('mwebv', '<f8'), ('time', '<f8'), ('band', 'O'), ('flux', '<f8'), ('fluxerr', 'O'), ('mag', '<f8'), ('mag_err', 'O'), ('zp', '<f8'), ('zpsys', 'S2')])"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sncosmodata.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sncosmodata['fluxerr'] = sncosmodata['fluxerr'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sncosmodata['mag_err'] = sncosmodata['mag_err'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sncosmodata.rename_column('flux_err', 'fluxerr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/rbiswas/.local/lib/python2.7/site-packages/sncosmo-1.2.dev584-py2.7-macosx-10.5-x86_64.egg/sncosmo/__init__.pyc\n"
     ]
    }
   ],
   "source": [
    "print sncosmo.__file__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NJBjfBW4F3gi8D5gh6R+BG6gtdHZIy6KLiPHqLOAU4JwGZX5pe/82xRMRwzRkglH3reDK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\nSZLUnAmGJElqzgRDkiQ19//7raUYHbVdoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c3008d0>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xdPU8GI1I8lj92aI1nl88cdBz66yxtI2RdD5JuPM6eQ4o94KI1ml0L+jKGoyI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\nSZLUnAmGJElqzgRDkiQ19//7raUYHbVdoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c3008d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sncosmo.plot_lc(sncosmodata[['time', 'band', 'flux', 'fluxerr', 'zp', 'zpsys']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmp = sncosmo.photdata.standardize_data(sncosmodata[35:38])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(49531.2734, 'r', 2.447853e-10, 7.981129e-11, 0.0, 'ab'),\n",
       "       (49531.2738, 'r', 2.44779e-10, 7.972644e-11, 0.0, 'ab'),\n",
       "       (49531.2756, 'g', 5.931855e-11, '9.233251e-11', 0.0, 'ab')], \n",
       "      dtype=[('time', '<f8'), ('band', 'O'), ('flux', '<f8'), ('fluxerr', 'O'), ('zp', '<f8'), ('zpsys', 'S2')])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "_normmagsys = sncosmo.get_magsystem('ab')\n",
    "_factor = np.empty(len(tmp), dtype=np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r\n",
      "[  1.00000000e+10   1.00000000e+10]\n",
      "['ab' 'ab']\n",
      "g\n",
      "[  1.00000000e+10]\n",
      "['ab']\n"
     ]
    }
   ],
   "source": [
    "for b in set(tmp['band'].tolist()):\n",
    "    print(b)\n",
    "    idx = tmp['band'] == b\n",
    "    b = sncosmo.get_bandpass(b)\n",
    "    _bandfactor = 10.**(0.4 * (25 - tmp['zp'][idx]))\n",
    "    print _bandfactor\n",
    "    _bandzpsys = tmp['zpsys'][idx]\n",
    "    print _bandzpsys\n",
    "    #or ms in set(bandzpsys):\n",
    "    \n",
    "    for ms in set(_bandzpsys):\n",
    "        idx2 = _bandzpsys == ms\n",
    "        ms = sncosmo.get_magsystem(ms)\n",
    "        _bandfactor[idx2] *= (ms.zpbandflux(b) / _normmagsys.zpbandflux(b))\n",
    "    _factor[idx] = _bandfactor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e+10,   1.00000000e+10,   1.00000000e+10])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.981129e-11, 7.972644e-11, '9.233251e-11'], dtype=object)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp['fluxerr'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'float'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-189-83fadc817ee1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msncosmo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mphotdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/rbiswas/.local/lib/python2.7/site-packages/sncosmo-1.2.dev584-py2.7-macosx-10.5-x86_64.egg/sncosmo/photdata.pyc\u001b[0m in \u001b[0;36mnormalize_data\u001b[0;34m(data, zp, zpsys)\u001b[0m\n\u001b[1;32m    162\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'band'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'band'\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    163\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'flux'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'flux'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'fluxerr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fluxerr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\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    165\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'zp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m                              ('zpsys', zpsys)])\n",
      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
     ]
    }
   ],
   "source": [
    "sncosmo.photdata.normalize_data(tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'float'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-186-89beeb7ad9a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msncosmo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mphotdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msncosmodata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m35\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m38\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/rbiswas/.local/lib/python2.7/site-packages/sncosmo-1.2.dev584-py2.7-macosx-10.5-x86_64.egg/sncosmo/photdata.pyc\u001b[0m in \u001b[0;36mnormalize_data\u001b[0;34m(data, zp, zpsys)\u001b[0m\n\u001b[1;32m    162\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'band'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'band'\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    163\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'flux'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'flux'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'fluxerr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fluxerr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\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    165\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'zp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m                              ('zpsys', zpsys)])\n",
      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
     ]
    }
   ],
   "source": [
    "sncosmo.photdata.normalize_data(sncosmodata[35:38])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'float'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-177-da4122fefd67>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msncosmo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_lc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msncosmodata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m35\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m38\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'band'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'flux'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fluxerr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'zp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'zpsys'\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/rbiswas/.local/lib/python2.7/site-packages/sncosmo-1.2.dev584-py2.7-macosx-10.5-x86_64.egg/sncosmo/plotting.pyc\u001b[0m in \u001b[0;36mplot_lc\u001b[0;34m(data, model, bands, zp, zpsys, pulls, xfigsize, yfigsize, figtext, model_label, errors, ncol, figtextsize, show_model_params, tighten_ylim, color, cmap, cmap_lims, fname, **kwargs)\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstandardize_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnormalize_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mzp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzpsys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mzpsys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m     \u001b[0;31m# Bands to plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rbiswas/.local/lib/python2.7/site-packages/sncosmo-1.2.dev584-py2.7-macosx-10.5-x86_64.egg/sncosmo/photdata.pyc\u001b[0m in \u001b[0;36mnormalize_data\u001b[0;34m(data, zp, zpsys)\u001b[0m\n\u001b[1;32m    162\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'band'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'band'\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    163\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'flux'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'flux'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'fluxerr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fluxerr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\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    165\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'zp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m                              ('zpsys', zpsys)])\n",
      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
     ]
    }
   ],
   "source": [
    "sncosmo.plot_lc(a\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49525.4412</td>\n",
       "      <td>y</td>\n",
       "      <td>5.751316e-10</td>\n",
       "      <td>5.02191e-10</td>\n",
       "      <td>23.1006</td>\n",
       "      <td>0.681465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
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       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
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       "      <td>y</td>\n",
       "      <td>5.944512e-10</td>\n",
       "      <td>6.8582e-10</td>\n",
       "      <td>23.0647</td>\n",
       "      <td>0.83298</td>\n",
       "    </tr>\n",
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       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
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       "      <td>41.6052</td>\n",
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       "      <td>49530.3104</td>\n",
       "      <td>y</td>\n",
       "      <td>5.944408e-10</td>\n",
       "      <td>6.40106e-10</td>\n",
       "      <td>23.0647</td>\n",
       "      <td>0.793514</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49530.3153</td>\n",
       "      <td>y</td>\n",
       "      <td>5.944366e-10</td>\n",
       "      <td>6.8508e-10</td>\n",
       "      <td>23.0647</td>\n",
       "      <td>0.832366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49530.3215</td>\n",
       "      <td>y</td>\n",
       "      <td>5.944312e-10</td>\n",
       "      <td>7.60695e-10</td>\n",
       "      <td>23.0647</td>\n",
       "      <td>0.89471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49530.3443</td>\n",
       "      <td>y</td>\n",
       "      <td>5.944092e-10</td>\n",
       "      <td>6.11563e-10</td>\n",
       "      <td>23.0648</td>\n",
       "      <td>0.768146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49530.3526</td>\n",
       "      <td>y</td>\n",
       "      <td>5.944004e-10</td>\n",
       "      <td>6.14962e-10</td>\n",
       "      <td>23.0648</td>\n",
       "      <td>0.77121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49530.3715</td>\n",
       "      <td>y</td>\n",
       "      <td>5.943787e-10</td>\n",
       "      <td>6.0559e-10</td>\n",
       "      <td>23.0648</td>\n",
       "      <td>0.762783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2657</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448998e-10</td>\n",
       "      <td>9.00059e-11</td>\n",
       "      <td>24.0275</td>\n",
       "      <td>0.339872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2661</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448934e-10</td>\n",
       "      <td>9.60037e-11</td>\n",
       "      <td>24.0276</td>\n",
       "      <td>0.35915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2665</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448872e-10</td>\n",
       "      <td>9.58943e-11</td>\n",
       "      <td>24.0276</td>\n",
       "      <td>0.35881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2670</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448808e-10</td>\n",
       "      <td>9.57887e-11</td>\n",
       "      <td>24.0276</td>\n",
       "      <td>0.358481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2674</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448744e-10</td>\n",
       "      <td>9.56805e-11</td>\n",
       "      <td>24.0276</td>\n",
       "      <td>0.358144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2678</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448680e-10</td>\n",
       "      <td>9.55727e-11</td>\n",
       "      <td>24.0277</td>\n",
       "      <td>0.357808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2683</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448616e-10</td>\n",
       "      <td>9.54653e-11</td>\n",
       "      <td>24.0277</td>\n",
       "      <td>0.357474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2687</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448552e-10</td>\n",
       "      <td>9.53585e-11</td>\n",
       "      <td>24.0277</td>\n",
       "      <td>0.357141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2691</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448488e-10</td>\n",
       "      <td>9.5252e-11</td>\n",
       "      <td>24.0278</td>\n",
       "      <td>0.356809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2695</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448424e-10</td>\n",
       "      <td>8.39899e-11</td>\n",
       "      <td>24.0278</td>\n",
       "      <td>0.320259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2700</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448360e-10</td>\n",
       "      <td>8.38977e-11</td>\n",
       "      <td>24.0278</td>\n",
       "      <td>0.319961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2704</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448298e-10</td>\n",
       "      <td>8.38056e-11</td>\n",
       "      <td>24.0278</td>\n",
       "      <td>0.319664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2708</td>\n",
       "      <td>r</td>\n",
       "      <td>2.448234e-10</td>\n",
       "      <td>8.37168e-11</td>\n",
       "      <td>24.0279</td>\n",
       "      <td>0.319378</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2915</td>\n",
       "      <td>z</td>\n",
       "      <td>6.497216e-10</td>\n",
       "      <td>2.68757e-10</td>\n",
       "      <td>22.9682</td>\n",
       "      <td>0.375887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2919</td>\n",
       "      <td>z</td>\n",
       "      <td>6.497127e-10</td>\n",
       "      <td>2.68527e-10</td>\n",
       "      <td>22.9682</td>\n",
       "      <td>0.37562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2923</td>\n",
       "      <td>z</td>\n",
       "      <td>6.497035e-10</td>\n",
       "      <td>2.68298e-10</td>\n",
       "      <td>22.9682</td>\n",
       "      <td>0.375354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2927</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496944e-10</td>\n",
       "      <td>2.68064e-10</td>\n",
       "      <td>22.9682</td>\n",
       "      <td>0.375081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2932</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496852e-10</td>\n",
       "      <td>2.67831e-10</td>\n",
       "      <td>22.9682</td>\n",
       "      <td>0.37481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2936</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496761e-10</td>\n",
       "      <td>2.85605e-10</td>\n",
       "      <td>22.9683</td>\n",
       "      <td>0.395645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2940</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496672e-10</td>\n",
       "      <td>2.85357e-10</td>\n",
       "      <td>22.9683</td>\n",
       "      <td>0.395361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2944</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496580e-10</td>\n",
       "      <td>2.85117e-10</td>\n",
       "      <td>22.9683</td>\n",
       "      <td>0.395088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2949</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496489e-10</td>\n",
       "      <td>2.84872e-10</td>\n",
       "      <td>22.9683</td>\n",
       "      <td>0.394807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2953</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496398e-10</td>\n",
       "      <td>2.84628e-10</td>\n",
       "      <td>22.9683</td>\n",
       "      <td>0.394528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2957</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496306e-10</td>\n",
       "      <td>2.84384e-10</td>\n",
       "      <td>22.9683</td>\n",
       "      <td>0.39425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2962</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496215e-10</td>\n",
       "      <td>2.84142e-10</td>\n",
       "      <td>22.9683</td>\n",
       "      <td>0.393973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2966</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496123e-10</td>\n",
       "      <td>2.83901e-10</td>\n",
       "      <td>22.9684</td>\n",
       "      <td>0.393697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2970</td>\n",
       "      <td>z</td>\n",
       "      <td>6.496032e-10</td>\n",
       "      <td>2.70393e-10</td>\n",
       "      <td>22.9684</td>\n",
       "      <td>0.377878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2974</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495940e-10</td>\n",
       "      <td>2.70166e-10</td>\n",
       "      <td>22.9684</td>\n",
       "      <td>0.377615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2979</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495851e-10</td>\n",
       "      <td>2.6994e-10</td>\n",
       "      <td>22.9684</td>\n",
       "      <td>0.377352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2983</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495760e-10</td>\n",
       "      <td>2.69721e-10</td>\n",
       "      <td>22.9684</td>\n",
       "      <td>0.377099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2987</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495671e-10</td>\n",
       "      <td>2.69497e-10</td>\n",
       "      <td>22.9684</td>\n",
       "      <td>0.376838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2991</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495579e-10</td>\n",
       "      <td>2.69281e-10</td>\n",
       "      <td>22.9685</td>\n",
       "      <td>0.376587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.2996</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495488e-10</td>\n",
       "      <td>2.69059e-10</td>\n",
       "      <td>22.9685</td>\n",
       "      <td>0.37633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3000</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495396e-10</td>\n",
       "      <td>2.68839e-10</td>\n",
       "      <td>22.9685</td>\n",
       "      <td>0.376073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3004</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495305e-10</td>\n",
       "      <td>2.81964e-10</td>\n",
       "      <td>22.9685</td>\n",
       "      <td>0.391484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3008</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495213e-10</td>\n",
       "      <td>2.81733e-10</td>\n",
       "      <td>22.9685</td>\n",
       "      <td>0.39122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3013</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495121e-10</td>\n",
       "      <td>2.81504e-10</td>\n",
       "      <td>22.9685</td>\n",
       "      <td>0.390957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3017</td>\n",
       "      <td>z</td>\n",
       "      <td>6.495030e-10</td>\n",
       "      <td>2.81277e-10</td>\n",
       "      <td>22.9685</td>\n",
       "      <td>0.390696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3035</td>\n",
       "      <td>y</td>\n",
       "      <td>5.905820e-10</td>\n",
       "      <td>8.04301e-10</td>\n",
       "      <td>23.0718</td>\n",
       "      <td>0.933157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3035</td>\n",
       "      <td>y</td>\n",
       "      <td>5.905820e-10</td>\n",
       "      <td>8.04301e-10</td>\n",
       "      <td>23.0718</td>\n",
       "      <td>0.933157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3039</td>\n",
       "      <td>y</td>\n",
       "      <td>5.905791e-10</td>\n",
       "      <td>7.90695e-10</td>\n",
       "      <td>23.0718</td>\n",
       "      <td>0.922518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3043</td>\n",
       "      <td>y</td>\n",
       "      <td>5.905763e-10</td>\n",
       "      <td>7.90305e-10</td>\n",
       "      <td>23.0718</td>\n",
       "      <td>0.922215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1777221</td>\n",
       "      <td>6.099567</td>\n",
       "      <td>-1.104924</td>\n",
       "      <td>0.397</td>\n",
       "      <td>49499.5548</td>\n",
       "      <td>-0.031043</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>41.6052</td>\n",
       "      <td>0.0249</td>\n",
       "      <td>49531.3048</td>\n",
       "      <td>y</td>\n",
       "      <td>5.905734e-10</td>\n",
       "      <td>7.89917e-10</td>\n",
       "      <td>23.0718</td>\n",
       "      <td>0.921912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       snid      snra     sndec      z          t0         c    x1        x0  \\\n",
       "1   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "1   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "0   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "0   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "0   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "0   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "1   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "1   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "..      ...       ...       ...    ...         ...       ...   ...       ...   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "2   1777221  6.099567 -1.104924  0.397  49499.5548 -0.031043 -1.16  0.000022   \n",
       "\n",
       "    cosmologicalDistanceModulus   mwebv        time band          flux  \\\n",
       "1                       41.6052  0.0249  49521.2338    y  5.963909e-10   \n",
       "1                       41.6052  0.0249  49521.2527    y  5.963094e-10   \n",
       "2                       41.6052  0.0249  49521.2629    y  5.962654e-10   \n",
       "2                       41.6052  0.0249  49521.2633    y  5.962636e-10   \n",
       "2                       41.6052  0.0249  49525.3453    y  5.752254e-10   \n",
       "2                       41.6052  0.0249  49525.3466    y  5.752239e-10   \n",
       "0                       41.6052  0.0249  49525.4233    y  5.751469e-10   \n",
       "0                       41.6052  0.0249  49525.4331    y  5.751384e-10   \n",
       "0                       41.6052  0.0249  49525.4358    y  5.751361e-10   \n",
       "0                       41.6052  0.0249  49525.4412    y  5.751316e-10   \n",
       "2                       41.6052  0.0249  49530.2970    y  5.944512e-10   \n",
       "2                       41.6052  0.0249  49530.3104    y  5.944408e-10   \n",
       "2                       41.6052  0.0249  49530.3153    y  5.944366e-10   \n",
       "2                       41.6052  0.0249  49530.3215    y  5.944312e-10   \n",
       "2                       41.6052  0.0249  49530.3443    y  5.944092e-10   \n",
       "1                       41.6052  0.0249  49530.3526    y  5.944004e-10   \n",
       "1                       41.6052  0.0249  49530.3715    y  5.943787e-10   \n",
       "2                       41.6052  0.0249  49531.2657    r  2.448998e-10   \n",
       "2                       41.6052  0.0249  49531.2661    r  2.448934e-10   \n",
       "2                       41.6052  0.0249  49531.2665    r  2.448872e-10   \n",
       "2                       41.6052  0.0249  49531.2670    r  2.448808e-10   \n",
       "2                       41.6052  0.0249  49531.2674    r  2.448744e-10   \n",
       "2                       41.6052  0.0249  49531.2678    r  2.448680e-10   \n",
       "2                       41.6052  0.0249  49531.2683    r  2.448616e-10   \n",
       "2                       41.6052  0.0249  49531.2687    r  2.448552e-10   \n",
       "2                       41.6052  0.0249  49531.2691    r  2.448488e-10   \n",
       "2                       41.6052  0.0249  49531.2695    r  2.448424e-10   \n",
       "2                       41.6052  0.0249  49531.2700    r  2.448360e-10   \n",
       "2                       41.6052  0.0249  49531.2704    r  2.448298e-10   \n",
       "2                       41.6052  0.0249  49531.2708    r  2.448234e-10   \n",
       "..                          ...     ...         ...  ...           ...   \n",
       "2                       41.6052  0.0249  49531.2915    z  6.497216e-10   \n",
       "2                       41.6052  0.0249  49531.2919    z  6.497127e-10   \n",
       "2                       41.6052  0.0249  49531.2923    z  6.497035e-10   \n",
       "2                       41.6052  0.0249  49531.2927    z  6.496944e-10   \n",
       "2                       41.6052  0.0249  49531.2932    z  6.496852e-10   \n",
       "2                       41.6052  0.0249  49531.2936    z  6.496761e-10   \n",
       "2                       41.6052  0.0249  49531.2940    z  6.496672e-10   \n",
       "2                       41.6052  0.0249  49531.2944    z  6.496580e-10   \n",
       "2                       41.6052  0.0249  49531.2949    z  6.496489e-10   \n",
       "2                       41.6052  0.0249  49531.2953    z  6.496398e-10   \n",
       "2                       41.6052  0.0249  49531.2957    z  6.496306e-10   \n",
       "2                       41.6052  0.0249  49531.2962    z  6.496215e-10   \n",
       "2                       41.6052  0.0249  49531.2966    z  6.496123e-10   \n",
       "2                       41.6052  0.0249  49531.2970    z  6.496032e-10   \n",
       "2                       41.6052  0.0249  49531.2974    z  6.495940e-10   \n",
       "2                       41.6052  0.0249  49531.2979    z  6.495851e-10   \n",
       "2                       41.6052  0.0249  49531.2983    z  6.495760e-10   \n",
       "2                       41.6052  0.0249  49531.2987    z  6.495671e-10   \n",
       "2                       41.6052  0.0249  49531.2991    z  6.495579e-10   \n",
       "2                       41.6052  0.0249  49531.2996    z  6.495488e-10   \n",
       "2                       41.6052  0.0249  49531.3000    z  6.495396e-10   \n",
       "2                       41.6052  0.0249  49531.3004    z  6.495305e-10   \n",
       "2                       41.6052  0.0249  49531.3008    z  6.495213e-10   \n",
       "2                       41.6052  0.0249  49531.3013    z  6.495121e-10   \n",
       "2                       41.6052  0.0249  49531.3017    z  6.495030e-10   \n",
       "2                       41.6052  0.0249  49531.3035    y  5.905820e-10   \n",
       "2                       41.6052  0.0249  49531.3035    y  5.905820e-10   \n",
       "2                       41.6052  0.0249  49531.3039    y  5.905791e-10   \n",
       "2                       41.6052  0.0249  49531.3043    y  5.905763e-10   \n",
       "2                       41.6052  0.0249  49531.3048    y  5.905734e-10   \n",
       "\n",
       "       flux_err      mag   mag_err  \n",
       "1   6.19611e-10  23.0612  0.773524  \n",
       "1   8.51704e-10  23.0613  0.963265  \n",
       "2   7.53871e-10  23.0614  0.887359  \n",
       "2   6.94194e-10  23.0614  0.838279  \n",
       "2   6.78796e-10  23.1004  0.846182  \n",
       "2   6.53218e-10  23.1004   0.82381  \n",
       "0   4.36378e-10  23.1006  0.613015  \n",
       "0   5.10063e-10  23.1006  0.689364  \n",
       "0   5.16068e-10  23.1006  0.695356  \n",
       "0   5.02191e-10  23.1006  0.681465  \n",
       "2    6.8582e-10  23.0647   0.83298  \n",
       "2   6.40106e-10  23.0647  0.793514  \n",
       "2    6.8508e-10  23.0647  0.832366  \n",
       "2   7.60695e-10  23.0647   0.89471  \n",
       "2   6.11563e-10  23.0648  0.768146  \n",
       "1   6.14962e-10  23.0648   0.77121  \n",
       "1    6.0559e-10  23.0648  0.762783  \n",
       "2   9.00059e-11  24.0275  0.339872  \n",
       "2   9.60037e-11  24.0276   0.35915  \n",
       "2   9.58943e-11  24.0276   0.35881  \n",
       "2   9.57887e-11  24.0276  0.358481  \n",
       "2   9.56805e-11  24.0276  0.358144  \n",
       "2   9.55727e-11  24.0277  0.357808  \n",
       "2   9.54653e-11  24.0277  0.357474  \n",
       "2   9.53585e-11  24.0277  0.357141  \n",
       "2    9.5252e-11  24.0278  0.356809  \n",
       "2   8.39899e-11  24.0278  0.320259  \n",
       "2   8.38977e-11  24.0278  0.319961  \n",
       "2   8.38056e-11  24.0278  0.319664  \n",
       "2   8.37168e-11  24.0279  0.319378  \n",
       "..          ...      ...       ...  \n",
       "2   2.68757e-10  22.9682  0.375887  \n",
       "2   2.68527e-10  22.9682   0.37562  \n",
       "2   2.68298e-10  22.9682  0.375354  \n",
       "2   2.68064e-10  22.9682  0.375081  \n",
       "2   2.67831e-10  22.9682   0.37481  \n",
       "2   2.85605e-10  22.9683  0.395645  \n",
       "2   2.85357e-10  22.9683  0.395361  \n",
       "2   2.85117e-10  22.9683  0.395088  \n",
       "2   2.84872e-10  22.9683  0.394807  \n",
       "2   2.84628e-10  22.9683  0.394528  \n",
       "2   2.84384e-10  22.9683   0.39425  \n",
       "2   2.84142e-10  22.9683  0.393973  \n",
       "2   2.83901e-10  22.9684  0.393697  \n",
       "2   2.70393e-10  22.9684  0.377878  \n",
       "2   2.70166e-10  22.9684  0.377615  \n",
       "2    2.6994e-10  22.9684  0.377352  \n",
       "2   2.69721e-10  22.9684  0.377099  \n",
       "2   2.69497e-10  22.9684  0.376838  \n",
       "2   2.69281e-10  22.9685  0.376587  \n",
       "2   2.69059e-10  22.9685   0.37633  \n",
       "2   2.68839e-10  22.9685  0.376073  \n",
       "2   2.81964e-10  22.9685  0.391484  \n",
       "2   2.81733e-10  22.9685   0.39122  \n",
       "2   2.81504e-10  22.9685  0.390957  \n",
       "2   2.81277e-10  22.9685  0.390696  \n",
       "2   8.04301e-10  23.0718  0.933157  \n",
       "2   8.04301e-10  23.0718  0.933157  \n",
       "2   7.90695e-10  23.0718  0.922518  \n",
       "2   7.90305e-10  23.0718  0.922215  \n",
       "2   7.89917e-10  23.0718  0.921912  \n",
       "\n",
       "[98 rows x 16 columns]"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.query(\"band !='u'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't multiply sequence by non-int of type 'float'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-169-90862e3b19be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msncosmo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_lc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msncosmodata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m35\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m38\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/rbiswas/.local/lib/python2.7/site-packages/sncosmo-1.2.dev584-py2.7-macosx-10.5-x86_64.egg/sncosmo/plotting.pyc\u001b[0m in \u001b[0;36mplot_lc\u001b[0;34m(data, model, bands, zp, zpsys, pulls, xfigsize, yfigsize, figtext, model_label, errors, ncol, figtextsize, show_model_params, tighten_ylim, color, cmap, cmap_lims, fname, **kwargs)\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstandardize_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnormalize_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mzp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzpsys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mzpsys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m     \u001b[0;31m# Bands to plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rbiswas/.local/lib/python2.7/site-packages/sncosmo-1.2.dev584-py2.7-macosx-10.5-x86_64.egg/sncosmo/photdata.pyc\u001b[0m in \u001b[0;36mnormalize_data\u001b[0;34m(data, zp, zpsys)\u001b[0m\n\u001b[1;32m    162\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'band'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'band'\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    163\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'flux'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'flux'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'fluxerr'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'fluxerr'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactor\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    165\u001b[0m                              \u001b[0;34m(\u001b[0m\u001b[0;34m'zp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m                              ('zpsys', zpsys)])\n",
      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'float'"
     ]
    }
   ],
   "source": [
    "sncosmo.plot_lc(sncosmodata[35:38])a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1777221\n",
      "1249577\n",
      "1772714\n",
      "1772620\n",
      "1668879\n",
      "1339921\n",
      "1261272\n",
      "1944733\n"
     ]
    },
    {
     "data": {
      "image/png": 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H74E5qvq+e/wNmC1OZt0SqnrQT3aSqo5R1fdwDI9u2RlMVTcCM3G8vgfc5qM4hlOOMtIG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\n9FzNLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11546a750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QHejvtj/mO4c7pqUPNowwoqpBbc2kFfdlZQYwW1XfCKL/b0ANVf3Tr93WAsMI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Vuy2lHSIiKSZu7+irvvDg/nETy5BDCAYG7NTnf/jCCh6VZKFbG0sczdV7DnROSBiWojVLxIn7vv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\nIoWFhfTp0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LehpEVEoonl6aEt7v5zYCPwkpk1dvf57n4H0Cq54VWOu+8CrgZmAh8CU919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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c4da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1VfVpNzR5hqrWC9JvGY4PylBVHSoiuYCBOMrF33Ecb4vhJG2qiVOgrSGO8uLf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\n/AxgUUT0R8RWkiqPXcO8t3ZK9u/zylwvo32/WZk5H+zJ+SA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd83810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AHwMXmNmyKMDdVOBwM3u2hu/jSkLgvF5mdn0qeSRtBJxGOFCyjZldGZO3DfBHM7tEUiNC\nu7kaKDSzv0V5djCzuZK6AYui9mqDeigEAexOCNL3D8IodpXl1uRzcC4VaVuIK2kXSe2BnlGHZRDh\ni/w8UKBwtHxZ3u0l/T7B7dzof/yyfJdKGhU9LCR0LJDUJaboGYQREQhTUm8mSXuRECIcwtH1cxJd\nX9Jvga3MbCrQgfXhsQuiv3MJnZlEZcT7DSH8eKPY9x//0VXxXpxzib0FTDOz+6Lb5cDUaGT0F2a2\nPC7vE2Z2v5ndQ+h4HFSdwszsI2AK8GI0QguwgtBxqlFE2lQW3yfabEAY0Z5oZrcQovH2jXnJ0YQ1\nepBgE0GUPl3SYmB41GFJdfNCquU6lxHpnB4aShh9eF3ScGA5ULZFeSWhU/AlpDeMNimGzAaQtEd0\nrRIz+4+kj+OvD3QFtou2Nj9qZuskjQHOjr7kFq1xqTSMdjTi1NzMHo8enyTpWTNbEj1OKfx3Cp+R\ncw1VP6KRV0n7mdkUQkf/JOC2SvJ2IPygOLYGZX7F+h8/AD2BycDhwP/V4HoJF9+nkAegNXA3689s\nekvSNsDnhLOPICzaL9tEsCnrf4SdY2YTKykjfvPCddEhlwelWK5zGZHO6aFr49OiKRaANoSpj+pe\n83PCFwFgXPT3B9Z3WLAQ0vcP0cPHk6UlqmOS6y8A7ojL9wNhWDk2LWEZMc+/Eff4H3GPVwG3R7dY\nSa/pXL6TtB9wGXAdYR3c0ui2N+E7dghwJvB7YEx0f1vCj6JDrGII777AAkk3E34wTTGzRQqHJa6O\nK3oX4NOoTToWOMM2PPQwFYuia5WNgDwfLeA/lZp1Wipd0J8kTyFwPutHyn8F3Bnd70nodBwGYGav\nxm8iiPL1kfQDsF00apKoHok2L/w5lXKdy5RMb3l+VtLuhJGNLzNclnOujjOzKZLKplM+BW43s/0k\nbQYMMrOxkg4ws+clvQH8TFhn8UhchwXCP5qnEqYktlU4Abol66dyY7U3sycAohHWhwmjLdW1CNg8\nWiuySZIdjdXZbJDK4vsN8pQtMI6mk140s68k/YawYaAF0bRzgk0Ez5nZV8CFZmaSukbrVlLevJBK\nuc5lSkY7LTE7fV7KZDnOubzyXbSofQ3rR2DXsL6z8U70D+GbwHDgtWi0s5ykVsA6MyuVtJzwj+bu\n0d+N4vJuyvoF+hAWuPeMeb4FG44QCFhZ1tGJ8SWwOWE9zNMx6RWCPFZjCjyVxfcJ80RT5QPN7Ibo\nuW2BrQmduG6SdiV0nGI3ERwVfV6NgfuBIkLnL35jQdLNC6mU67sbXabU6eByzrl6SVXc/xdwC2FK\n6F5CRyReX8IIDGa2DkBSj2iEpjRB3vdiHp8GlHdGzOxn4KFUKm5mK6IFqha9rkyFEZKYkZYNLgE8\nFNMJm0FYBzI1qucL0eu7xExfJcwDHAX8JRphGmxm48peS9gQ8aakvQmdwZ8JmwgKCdNAb0XX6ApM\nj+7vElfGKtavn2kHzEmh3F7eYXGZ5J0W51zWSBpKWOi+N+Efx50k7UKIMG2SnrQQ6XmOma2SNIeo\ncxJzjV0Ia17WSDqJMC1xMOvXov0ck3cIcAbwlaSzCDsCC4Gza/E2XqPiKEuFMqFaIy2JNhKkstng\nVML6kmsJnb3BAJKaAecAfRW2Y99J4k0E50haQdju/GI1Ni9UVe4uknYzs1dSeO/OVZs2nCZ2zrn8\nJelC4P74KaUMlteNMOJQVfBI51wt1fUDE51zrrruA47IYnn7EUZGnHMZ5p0W51y9Eu3M+bCSgI5p\nI2kr4H0zK8p0Wc45nx5yzrkak9TUohPjnXOZV686LariDA8lOK8jSdo2hJX/95rZ2pjXVziHw8xW\nJyqzNmnOOeecS6zeTA8lOjsjQbb4czP6JUjrS4jDcBvwraSvJT2jBOdwJChzUC3SEtXXOeecc5H6\ntOU5lTM8tqXiuRmbEUZO4s/SWEM4N6g0OkPoW8LJzvHncPwpQZlWi7T4+jrnnHMukrZOi6TGhH/Y\ntyJEjewL3GxmC9JVRhVSOcMj0bkZU+LTojDXZVE3tzSzNySdz4bncCQqc10t0pxzzjmXRDpHWnYk\nHPJ3GNAUeAxYIulA4G0z+7osYzXO5aiOKs/wSHReR0ydNkgDzmP9abHx53Dsm6TM2qQ555xzLol0\nnvL8HkA0nXKrmS2QVAicALwTlzfVaJHVORcklTM8Ep3XkTAtsoeZXRfd/5oNz+FIdF4HNUxLWF/n\nXGKS/gicBNwA/IIw/Xt+bbYfS2pKGDFeBewPjIwL1x+bV8AtZnZBTFpaF+b7An7nKkrn9NAuhDUh\nPaMOy6DoWPTZCfKmei5Hdc4FSeUMD9jw3IwXEqVJ6k4YMSrzIuFANgjncLwPrGXD8zrW1SLNOZe6\ntwk7/u4HkPRPQrsyuRbX3AXY28yOk3Q0Ya3ZM/GZJLUl/CDbLSatfHG9pJ0kDSJqQzKYNjDmYFrn\n6r10Tg8NJYw8vC5pOLAsSt/gqPLqjLRUQ5VneCQ6NyPZWRqEQ8a+jKnzDEm7x53DscF5HbVJS/Pn\n4Vx914/osD9JHQk/JhIdrpgyM3tN0gfRw00IHaNE+b4HbpN0QExyos0AtVmY7wv4nYuTzumha+PT\nooakO+GL9XC6ykpSvgF/iB4+HqX9wPpDxzCzewmnxsaalyANM5sLjIhLuzbucaIya5zmXH0naT/g\nMuA6wjq4pdFtb+Am4BDgTMKBiGOi+9sSfhQdYhUDS/UhRL49E9gCGGpmq9NQzY0kXQCMM7OlVb2l\nmPvpXpjvC/idi5PRLc9m9g1wTCbLcM7lDzObImk0MA34FLjdzPaTtBkwyMzGSjrAzJ6X9Abh9OT3\ngUfiOiwA7czsnwCSXgaKJW1H6OwcAhQBowm7GJemugHAzJYBt0p6XNKn1Zh+SffCfF/A71yc+hSn\nxTmXH74zsxJJawijLBBiIxVE99+R9BvgTWA48Fr8ic2StiBMR5fZAigws48kLTSzFZJ2BK42s5VQ\no2np+YTR1so6LbEdoPjNALVZmO8L+J1LwDstzrlsUxX3/wXcQpgSupfE61T6EUZgynb8/DI6VqNj\nSFIBsFFZhyXKV+UGAEmXEjo/VwOFwJzotfEL+hPVP9FmgNoszPcF/M7F8U5LNUm6n7AVcqmZ/SoN\n15sK7Aq8amYHxqR3BSYRFhe+C/zOzNbVtjzncknSUGA7SXsT/tHdKdp5eCBgkp40s7ckzTGzVZLm\nEHVOYq6xG3AGsEjSJmb2raTJkg4njI6UAgPM7MXY16U40jIJ6B8tuF8NjI1f0B/VoSWhw7CLpMXA\n1iTeDFCdhfn3RduZvzazS2LynQkcCxxMaAsW+wJ+11DVqwMTsyEKQreS8MssHZ2W3YEWwOlxnZZH\ngMfN7DFJfwNmm9nfa1uec/WdpIuBxzIdjdvbAueyr94cmJgt0aK872PTJG0laaqktyW9HMV4SfV6\nLxEavnh7AGXB8x4k/MpyzlXBzP6S6Q5LVI63Bc5lmU8Ppcc9hF9H/1M4JfpvwJ41vZik9sD3ZlYa\nJS0CNq19NZ1zGeZtgXMZlJedFqUYxjqaE44Ps50obR9CPJlS4B/Rgr4KaYSthWXhvY+MLtWIEBVz\nEPCSpOXRJdtKOodwsOJvomt0JGzBbAbMMrNhtf4gnHN1RrTO5TfAY1E7A7BR9NzBwDVU3G0kYJG3\nBc6lLu86LdowVHbCMNZKHGY7UVo74DgzO0bS1UAPSQvj0whzzWXhvU8BWhF2N3xIiP57HuEYgy+A\nMWY2Jnrt7wiLaX9jZtdKGkUVK/7NbLmkNpIaRb+wOgNfVfYa51zONSKMiuwc/0QUT+af1b2gtwXO\nVZSPa1oGEMJXw/ow1hsws+/N7DZgU0lLo90IZWkrYrJeARws6T3gAGA/wkjKzOj50WY2y8xeA86J\n0toRRk1+Ai4FFgLDgAXRa8vC/19nZrNSqLPY8LiDl4DDo/vHE7aBOudqSFKBpJmSZkmaG/2ASJTv\nTkmfSJotqXdVl41umNlPwIJoZ0/Ztaq7QNfbAucqkdZOi6R9JZ0oaaKkzdN57RiJQmVX5mtgn7i0\n2EZhS8LpzVcQom5eRzjBubOkfYHzY/JuFHVutgW2AiYA7YHNCXEjXiaEJx8QvbZsCippnSW9AjwC\n7CHpi2grKITO0AWS/kvoJN1fxft0zlXCzIqB3c1sJ6A3YXty39g8koYB3cxsG+B04O5k15M0EXgd\n6B59d08kRAA/OerwfEDYyp0Sbwucq1o6T3nehjClcpSkCWa2JkpPKXR2NVQ3jPWPxK3wjyNgrZk9\nK2n7qLPRCPgxJm2YmU2NwnvvLOlx4HZCGPL9CGtWribMZ19OmKeOv17COptZ+VRVrGj3Q78q3ptz\nrhosnBoPIfpuEzZslw4iOlXezGZKai2pMNEZRGZ2dJJiarRGxdsC56qWzjUtJxBGHijrsET3Uw6d\nLakFcFh8MrDSzMq2/MWHyq5JGOvYhmo5YVRldlTWc4SRl8XR898RRl6mxrxmPnB0lOfPUUjysqmh\nRK9dkoY6O+dqKVo8/y7QDfirmcWf4rwZMae7E9aPbMb64wacczmUzk5LE8LajrLTnTGzb5RC6Ozy\nhPAr6KEqykkUKjvVMNuJ0iYBX5nZnyTdCxxF6HyUrTtpB8xR4vDebQi/2H4G5kbpi+Je+z6wFg+9\n7VzORYtZd5K0MfCUpO2jH1bOuTyQtoi4krYk/GP/AdDczB5Ly4U3LEeEI+zfBPqY2aUKYbanmFl8\nmO1TgUsIsRMOIYTLL0u7EbjHzH6OtlAvInRCzgV+DZxdlmZmtymE1c94wCrnGjIzS/QjIyOi7/0q\nM7s1Ju1u4CUzeyR6PB8YHD89JMlDiTuXQUnbAjOr9zegKzA3yXOFMff7Ap9Xch2rrlGjRlX7Neng\n5db/sutbudH3K5PtQAegdXS/OfAKsG9cnn0JP4Ag/Mh5M8m1qv3+6tt/r7pabi7L9nLTo7K2IO/i\ntFRXtMJ/CNBe0hfAKKAp4UO5BzhM4UCytYQD0o7MVV2dcxn1S+DBaF1LI8JuwWclnU7UHkSP95X0\nKWHH34m5rLBzrqJ632mx5Cv8y57/K/DXLFXHOZcjZjYXSBT47e9xj0dmrVLOuWrJx+ByeWXIkCFe\nbj0uN5dlN7Ry811D++/l30kvNxPSthC3IZBk/nk5lxmSsroQtza8LXAucyprC3ykxTnnnHN5wTst\nzjnnnMsL3mlxFXTq1AlJG9w6darqiCfnnHMus3xNSzU0tHnsaF4x19VwDYSvaXHOga9pcc45V00+\n6urqonofp8VVz/Tp05k+fXr546uuugoIW9t8q6tzDceSJUvK7/uoq0uVmRFO28kM77Q451J27bXX\nMmHCBDp27Ejnzp3p06cPF1xwQa6rlRJJnQkHshYCpcC9ZnZnXJ7BwL+Az6KkJ83suqxWtI7wHzAu\nFQsXLmSfffahX79+vPfeezz77LNsvvnmGSvP17RUQ0Obx/ZfVy7WO++8w2mnncbMmTMpLi5m5513\n5owzzkhbpyXTa1okdQI6mdlsSa2Ad4GDzGx+TJ7BwIVmdmAV1/K2wDlCp6Vbt2688cYb7LLLLmm5\npq9pcc7V2muvvcZBBx3ERhttRKtWrTjggANyXaVqMbMlZjY7ur8S+AjYLEHWvFgMnGmxa1oAX9Pi\nkurSpUvaOixV8U6Lc67BkdQV6A3MTPB0f0mzJU2RtH1WK1aHHHbYYXTp0oUuXboAlN8/7LDDclwz\nV9e0bNkya2X5mhbnXEoGDBjAGWecwaWXXsratWt55plnOP3003NdrWqLpoYeB34fjbjEehfYwsx+\nljQMeAronug6ZWs8oH6u8xg7dixjx44FwijL559/ntsKuTqrtlOH8eunKuNrWqrB57FdQ3fNNdcw\nceJECgsL6dixI0OHDuXkk09Oy7WzEadFUhPgGWCqmd2RQv4FwK/N7Lu4dG8LnCOsaTnggAOYM2dO\n2q5ZWVtQ7zstku4H9geWmtmvkuS5ExgGrAJOKJv3TpDPGyrXoK1atYqWLVuyevVqdtttN+699156\n9+6dlmtnqdPyELDMzBKuHpZUaGZLo/t9gUfNrGuCfA2yLahsK2tD+jxcZlXWFjSE6aFxwBjCVscN\nREPA3cxwRPR6AAAgAElEQVRsG0n9gLuBXbNYP+fyxmmnncaHH35IcXExJ5xwQto6LNkgaQBwDDBX\n0izAgMuALoCZ2T3AYZLOBNYCq4Ejc1XfuqiwsJClS5cmTHcuG6o90iKpJVBkZiWZqVL6SeoCTE40\n0iLpbuAlM3skevwRMKTs11Zc3gb568q5bPAw/nVXWVvQqVOnpJ2W2GB0ztVGrUZaJDUCjiL8QtkF\nKAYKJC0DpgB/N7NP01jfbNsM+DLm8VdR2obfTOeca8A8Sq7LtVS2PL8EdAP+SAjMtLmZdQQGAm8C\nN0o6NoN1dM4555xLaU3LXma2Nj4xWk3/BPCEpI3SXrPs+QqIjTncOUpLqL5vc3QuW6qzzdE55yDF\nNS2SBgJ7AJ2AEuBb4E0zm5bZ6qVHFEhqspntkOC5fYGzzWw/SbsCt5tZwoW4DXUe27ls8DUtdVei\ntsDbB5cptV3TchmwETALWAk0BjYG9pS0h5ldms7KppukicAQoL2kL4BRQFOi3QJm9qykfSV9Stjy\nfGLuauucc865ZKocaZF0oJk9neS5w8zs8YzUrA7yX1fOZY6PtNRdPtLisqm2cVp2lLQjYaRlFWF6\nqCXwK2ATQjhs55xzzrmMSnVNy57AAKAjYcfRUmAG8GJD+rnhv66cy5xMj7RI6kwIMlkIlAL3mtmd\nCfJVGSHb2wJvH1zmNOgw/unkDZVzmZOFTksnQtiG2dGhie8CB5nZ/Jg8w4CR0cL8fsAdiRbme1vg\n7YPLnMraglTitDjnXN4zsyVloybR6c4fEQJJxjqI6MgPM5sJtJbkMeqdqyNq3GmR1EXSa+msjHPO\nZUMUBqE3MDPuqWQRsp1zdUCNOy1mthDYL411cXXUwIEDadasGc2aNUNS+f2BAwfmumrOVVs0NfQ4\n8PtoxMU5lydSOuU534PLudqZMWNG+X1JFBUV5bA2ztWcpCaEDst4M/tXgiwpR8j26NjOpUd1omOn\nEqclWXC5voQAbXU6uFw6NdTFdwMHDuSdd94BoLi4mIKCAgD69OlToUPjXG1kI06LpIeAZWZ2QZLn\nU4qQ3VDbgqrSnEuHWu0e8uBy63lD5Q2Vy5ws7B4aALwCzAUsul0GdCGKkB3lGwsMJYqQbWbvJbiW\ntwXeFrgMqW2n5crobsLgcmb2hzTWtU7zhsobKpc5HhG37vK2wGVTrbY8m9m1wOvAzsChwFGEqaF3\ngIvSWE/nnHN13A8//ECzZs0AaNKkCddeey0fffRRjmvlGoqUdg+Z2Qtmdo2ZjTSzs8zs6ijNu9nO\nOdcAmBkPP/ww3bt3p7i4GICSkhK+//57hgwZwiWXXFKe7lymeETcavAhYR8Sdpnj00N1lyTOO+88\npk2bxvjx4xk8eDArV66kVatW/PTTTyxdupSTTz6ZRo0a8cQTT7DRRhvlusouj6U1jL+k1mb2o6Q2\nZvZDWmqYJxpiQ+WdFpct3mmpm3bYYQc++OADmjdvzuLFi2nTpg2wYVuwZs0aDj30UFq3bs348eOR\n8uI/pauD0h3G//jo73E1r5Jzzrl88MEHHwCwevXq8g5LIk2bNuWxxx5jzpw5PPDAA1mqnWtoanP2\nUF50oyUNlTRf0n8lXZLg+cGSfpD0XnS7Ihf1dM65uuaDDz6gcePGAPTq1avK/M2aNWPChAlcfPHF\nfPbZZ5munmuA6vWBiZIaAWOBfYCewAhJPRJkfcXMdo5u12W1ks45VweVlJRw0kkncffddwMwd+7c\nlF63ww47cNFFF3HWWWf5VLJLu3rdaSFszf7EzBaa2VpgEuEU13h5MWrknHPZctddd9G8eXNOPvnk\nar/2/PPP58svv+TJJ5/MQM1cQ1bfOy3xJ7YuIvGJrf0lzZY0RdL22amacy6bJN0vaamkOUme96ni\nyLfffsvVV1/N3//+9xotqN1oo4246667OP/881m50s+kdOmT0oGJcerbqMS7wBZm9rOkYcBTQPdk\nmf2QNOfSozqHpKXJOGAM8FAleV4xswOzVJ86a/To0YwYMYIePRLNpqdm8ODBDBkyhGuuuYa//OUv\naayda8hqsuV5ezP7sOxvhuqVFtGBZ1eZ2dDo8aWEM0ZurOQ1C4Bfm9l3CZ5rMNscwbc8u+zK0oGJ\nXYDJZvarBM8NBv5gZgekcJ162xYsWLCAPn368NFHH9GxY0eg5m3B0qVL6dWrFy+99FJKC3mdgzRv\neS7rqNT1DkvkbWBrSV0kNSUcQVDh8EdJhTH3+xI6cht0WJxzDUKDnyq+/vrrOfvss8s7LLVRWFjI\n1Vdf7YtyXdpUe3pI0uZASzObn4H6pJWZlUgaCUwjdNDuN7OPJJ3O+lNdD5N0JrAWWA0cmbsaO+dy\nqMFPFS9atIgnnniCTz75JG3XPP300xk3bhwPPfQQxx9/fNUvcA1OdaaKazI9dBvhH/cvgV2BCWY2\nrZp1zEv1eUg4EZ8ectmU6+mhBHkb3FTxBRdcAMCtt95aIb22bcE777zD/vvvz4cffki7du3SU1lX\nb6U7Iu4/zewy4AszOx6o/Riic85lh0iymaChTxX/8MMPjBs3rrzjkk59+vTh0EMP5fLLL0/7tV3D\nUpPdQxdK6gksjx5/kcb6OOdcRkiaCAwB2kv6AhgFNMWnigEYN24cw4YNo3Pnzhm5/nXXXcf222/P\niSeeSN++fTNShqv/ajI91A0oAAYSosxuYWYHZ6BudU59HRKOt2LFCk499VQeffRRjj76aB544IHy\nU1t9eshlih+YmDulpaV0796d8ePH079//w2eT9dU8fjx47n99tt56623yo8HcC5euncP/c/MPjSz\ne8zs94CP99UD3333HePGjWP//fenc+fOPProowBMnDiRX/7yl5xyyikcdFAIJnzOOefksqrOuTT7\nz3/+Q+vWrdl1110zWs6xxx5Lq1atyo8GcK66qj3S0pDVp19XpaWlzJ07lxdeeIF///vfzJw5k732\n2ovDDjuM/fbbj8svv5yxY8cycuRILrroIh577DH+8Ic/lL/+yCOPpFmzZhQXF1NUVETjxo1p3749\n3bt3p0+fPvTv35+mTZvm8B26fOMjLblz6KGHMmzYME455ZSEz6dzUf68efMYMmQI77//PptuummN\n6uvqt8ragppMD7UEOsXcBphZ+ldu1UH53lCZGS+88AITJ05k8uTJtG3blj333JO99tqLffbZh1at\nWlXIH98onXPOOYwdO5bhw4dzxBFHUFRURLNmzSgoKGDdunUsW7aMjz/+mNdff51PPvmEww8/nLPP\nPpvevXtn+626POSdltxYtmwZW2+9NV988QUbb7xxwjxlbUGnTp1YunTpBs8XFhayZMmSlMu88sor\nmTdvnp9N5BJKd6flL8CmwMtAG2CZmY2rdS3zQL42VGvXruWRRx7hpptuorS0lFNOOYXhw4fTpUuX\nSl9Xm19XX3/9NQ888ABjxoxh4MCB3HzzzWyxxRa1eh+ufvNOS26MGTOGmTNn8vDDDyfNk+5OS1FR\nEb179+b666/nkEMOqVG9Xf2V1k5LdMHuwE7ASjObUsv65Y18a6hKS0t55JFHuPzyy+natSsXXXQR\nQ4cOTfkAtHQMCa9atYpbbrmFMWPG8Je//IUTTjihRgewufrPOy258etf/5obb7yRvfbaK2mesu99\nZd/d6n4eM2bM4Mgjj2TevHm0adOmWq919VutOi2SWgEnAD8Dk8zs55jn9gZ2MrMGcRpWPjVUr776\nKhdeeCFmxk033VSjaJ3pnMeeO3cuRx99NDvuuCN/+9vf+MUvflHta7j6zTst2Tdnzhz2339/FixY\nUOlunkztGjzrrLNYt24d99xzT9qv7fJXbXcP3QxsDuwJPCupRdkTZvYc8EpaaunS4rvvvuPkk0/m\nmGOO4fzzz2fmzJl1Irz4DjvswMyZM2nWrBl9+vRhzpw5ua6Scw3egw8+yHHHHZez7cd//vOfmTp1\narZP+3Z5LJVOy1wzu8TMjiEcOFgh4JKZvZmRmrlqe+SRR+jZsyetWrVi3rx5jBgxgkaNahL0ODNa\ntGjBfffdx5VXXsmee+7JPffc4zFfnMuRtWvXMmHChJyeB9S6dWv++te/ctppp7F69eqc1cPlj1T+\nRSsqu2NmS4CfMlcdVxPFxcWcddZZXHnllTz11FPccccddXr65dhjj+XVV19l7NixHH300axYsSLX\nVXINgKT7JS2VlHSYT9Kdkj6JTnqu19ve/v3vf7P11luzzTbb5LQeBx54IL179+bKK6/MaT1cfkil\n0/JHSWMlnRR9ict/Gkvyc4dy7Msvv2S33XZjyZIlvP322/Tr1y/XVUpJjx49mDlzJhtvvDG9e/dm\n8uTJPuriMm0csE+yJ6OTnbuZ2TbA6UC9joD24IMP1plTl++66y4mTZrE888/n+uquDoulYW4VwDv\nAP2AvoRdQwuB14COZnZcpitZV9S1xXcvvPACxxxzDBdccAEXXXRR2nflZOuU52nTpnHuuefSqVMn\n/vSnP7H77rv7DqMGKNenPEu6G3jJzB6JHn8EDDGzDfb41rW2INb06dPL14hMnz69fE3bkCFDyu9/\n9913bLXVVixcuJDWrVtXec1sHN/x/PPPc8IJJzB79mw6dOiQ0bJc3ZaJLc9bEToxp5nZ7rWsX0ZJ\nGgrcThhVut/MbkyQ505gGLAKOMHMZie5Vp1oqMyMG2+8kTvuuIMJEyawxx57ZKScbHVaANatW8eE\nCRO44YYbaNKkCSeeeCKHH344m2++edrLcnVTHei0TAb+bGavR4+fBy42s/cS5K0TbUFVkn1f7777\nbqZPn86kSZNqdZ10u/jii/n444956qmn/IdLA1ZZW1DlKc+S2sUfz25mnwGfSfoqTXXMCEmNgLGE\nnU+Lgbcl/cvM5sfkKR8SltSPMCSc2QM4amHZsmWccsop5dNBmTqRNduaNGnC8ccfz3HHHceLL77I\nhAkTGD16NB06dKBPnz706NGDbbfdlh49erDNNtvQvHnzXFfZNXBXXXVV+f3YUYx88NBDD3HFFVfk\nuhobuO666+jfvz9//etfGTlyZK6r47IkdnSwKqlMDy0mjD5Mix4XAO3NbHEt65lxknYFRpnZsOjx\npYRj6G+MyZMXQ8JmxuOPP855553HiBEjGD16NAUFBRktM5sjLYmUlpYyZ84cZs+ezccff8zHH3/M\n/PnzWbBgAZttthm9evWiZ8+e9OrVix133JEePXpkbLdUVYG1qquoqIiFCxfy+eefs2TJEoqKisrP\ncSoqCmvfGzduXH5r0qQJTZs2paCgoPxvovtV1dHMMDNKS0srvSXLE1+PRPebNWtGy5YtKSgoqNZn\nVgdGWuLbgvnA4LrWFlRHou/rJ598wqBBg1i0aBFNmlT5uzXpdTLl008/ZcCAATz22GPstttuWSnT\n1S21GmkhxGk5TdJuwJVmVixpM0knENa0nJfGuqbbZsCXMY8XEdblVJbnqyhtw1jVhIWvksob47L7\n8Y+req5Jkya0bNmyyn9k161bx5QpU7jpppv46aefmDRpEoMGDar6ndcDjRo1onfv3hucXbR27Vo+\n/fRT5s2bxwcffMATTzzBFVdcwfLly+nTpw99+/alX79+9O/fn44dq7dW/JtvvuGNN97g9ddfZ/bs\n2Xz11VcsXryY77//niZNmlBQUEDLli3ZfPPN6dq1K126dKFLly507dqVzp0707RpUxo1aoSZ8d13\n37Fs2TK++eab8g7K559/zoIFC/j+++/Lr9GpUyeaN29Os2bNys9ygtCxKSkpoaSkhHXr1rFmzRrW\nrFlDcXFx+S328Zo1a1L+XCu7SUqaXlJSUl5WWdnx9SoqKuLnn39m7dq1tGjRghYtWtCyZcsN7sf/\nzRJFt0SeBs4GHol+8PyQqMOS78aPH8/RRx9dZYdl5MiRPPPMM+WPu3btCsD+++/P2LFjM1a/rbfe\nmvHjx3PkkUcyc+ZMP/7DVVT2yyvZjbBuBeB84D+EjkrZc/+s6vW5vAGHAvfEPD4WuDMuz2TgNzGP\nnwd2TnI9S3Rr2bKlFRYWWseOHW2TTTaxTTbZxDp06GDNmzdPmL+goMBatmxpjRo1sjZt2ljXrl2t\nT58+1q1bt4T5O3fubBMmTLB169ZZrFGjRiXMP2rUKEukuvmTvd90XT/d+b/55hubMmWK/elPf7J9\n9tnHCgoKEuY/7bTT7N1337W3337bpk6danfeeacdc8wx1rZt24T5L7roIispKbE1a9bYTz/9ZIsX\nL7aZM2faYYcdljB/hw4dbLvttrMBAwbYQQcdZKeeeqpde+21dvDBB+fV51nb/FdeeWXC/Mcdd5xN\nnjzZ/vSnP9mBBx5ow4YNs7322stCc5TR9mAiYZq4GPgCOJGwS+i0mDxjgU+B95O1A1G+hO+5romv\nZ0lJiXXp0sVmzZpV5WsLCwsT/vcrLCzMVHUruOmmm2znnXe2VatW1eo6w4cPt9atW1vr1q0NKL8/\nfPjwNNXUpVtlbUEq00P/MLOTovv9gDHApWb2oqSLzOymSi+QQ9GvpavMbGj0OJXpoawNCZeUlLBi\nxQq+//57li9fzrfffsuyZcv49ttvady4MZ07d2bgwIF06tQpbWVWR66nh2qrpKSkwq/JI444goUL\nF1YYkejQoQNbbrkl/fr1Y9ddd83oFJOrnIfxT7/47+vLL7/MOeecw/vvv1+TqbtMVDEpM+N3v/sd\nxcXFTJo0qcZRe9N1yKPLntpOD62RdD7wjJnNjHbjjJM0CPgxnRXNgLeBraN57K8JEX1HxOXJ2ZBw\n48aNadu2LW3btmWrrbbKRpENSuPGjRk5ciRjx45l5MiRjBkzJtdVci6n/vGPf3D88cen1GGJXxxZ\ntvA4W4uOJXHfffcxbNgwzj33XMaOHes7ilzqW54lNTez1TGPLwEuNLM6HWAu6mTdwfotzzdIOp0w\n4nJPlGcsMJSw5flES7DFMcqXF7+u0iXfR1pcfvGRlvSL/b5+8803bLvttnz66ae0b9++xtfJthUr\nVjB48GCGDx/OqFGjanUtb7/yQ9rjtMRceOdk/8DXR/nSUKWLd1pcNnmnJf1iv6+jR49mwYIF3Hff\nfSm9ti5NqyxdupQBAwZw3nnn1WortLdf+aG2cVqSfjvLOiyV5XHOOZdba9eu5W9/+xtTpkxJ+TV1\nab1HYWEhzz33HLvvvjulpaWce+65ua6Sy5FU1rS8JOkJ4F9m9kVZoqSmwEDgeOAl4IGM1NA551yt\n/POf/6Rbt27suOOOua5KjW255ZZMnz6dPfbYg7Vr13LhhRfmukouB1LptAwFTgL+T9KWwA9AM6Ax\nMA243cxmZa6KzjnnamPMmDH8/ve/z3U1aq1r1668/PLL7LHHHhQVFXHZZZf54twGplprWiRtBHQA\nVpvZDxmrVR3V0GbByuZ/Bw4cyDvvvANAcXFxefCzPn36MGPGjFxW0dUjvqYl/SQxa9YsDjjgABYs\nWJByBNy6bvHixQwbNoxBgwZxxx13pLwd2te05IeMLcRtaPKloUoXX4jrsinTnZaqDk+VNBj4F/BZ\nlPSkmV2X5Fp50RZI4vjjj6d79+5cdtllua5OWv34448cfPDBtG3blocffjil88i8/coPlbUFHkXL\nVWngwIHlIeaB8vsDBw7Mcc2cS03M4an7AD2BEZJ6JMj6ipntHN0SdljyzeTJkznrrLNyXY20a926\nNVOnTqVp06bstddedWrhsMsc77S4Ks2YMaP8ID8zK7/vU0Muj/QFPjGzhWa2FpgEHJQgX15MT1XH\nGWecQZs2bXJdjYwoKChgwoQJ7L333vTt27d8GtvVXyl3WiRtnyBtSFpr45xzmZHo8NTNEuTrL2m2\npCmJ2rx8smjRIgDOP//8HNcksxo1asRVV13FHXfcwbBhwxg/fnyuq+QyqDqrsh6VNB74C2H30F+A\nPkD/TFTMOeey7F1gCzP7WdIw4Cmge47rVGNXX311rquQVQcffDDbbLMNw4cPZ/bs2dx44431ZuGx\nW686/0X7ATcCrwO/ACYAAzJRKeecS7OvgC1iHneO0sqZ2cqY+1Ml3SWpnZl9l+iCZWfxQPbO40nV\nkiVLykccrrvuOm6//fYc1yg7evXqxVtvvcWIESPYfffdmTBhAltssUXVL3Q5FX/OVWWqc/ZQU2A0\nsDfQCrjCzCbVsI55KV92DKSLr7R32ZTJ3UOSGgMfA3sSDk99CxhhZh/F5CksOyxVUl/gUTPrmuR6\ndbotuPDCC1mxYgX33Xcf3377LR06dMh1lbKqtLSUm2++mVtuuYW77rqLQw89FPA2LV+kZcuzpPcJ\n2wGvJcRquRtYY2aHp6uidV1db6jSzb/gLpuytOU56eGpks4GzgTWAquB881sZpJr1dm24LPPPmOX\nXXbhgw8+YNNNN23Q3+GyUZe9996b2267jRYtWjTozyNfpKvT0sfM3olL+52ZNZhVT3W5ocoE77S4\nbPLgculxxBFH8Ktf/YorrrjCv8OEU6LPOOMMnnzySYqLi+nVqxdz587NdbVcJdLVaflTonQzu6YW\ndcsrdbmhygRv8Fw2eael9l577TVGjBjB/PnzadGihX+HI2ZGo0brN8suX76cdu3a5bBGrjLpCi63\nKuZWAgwDuta6dhkiqa2kaZI+lvQfSa2T5Ptc0vuSZkl6K9v1dM65dCgtLeWCCy5g9OjRtGjRItfV\nqVMk0atXLwDatWtHjx49+POf/8xPP/2U45q56kq502Jmt8TcRgNDgK0yVrPauxR43sy2BV4E/pgk\nXykwxMx2MrO+Waudc86l0SOPPEJJSQnHHHNMrqtSJ5VNCS1fvpzp06czd+5cunXrxrXXXlse08bV\nfbWJiNuCsG2wrjoIeDC6/yAwPEk+4ZGBnXN57Mcff+Siiy7i9ttvrzAN4hLbfvvtmThxIi+//DJf\nfvklO+64I3vuuSf3338/8+fPp7S0NNdVdElUZ03LXKAsc2NgE+AaMxubobrViqTvzKxdsscx6Z8B\nPxCmvO4xs3sruWadnMfOFJ8Pd9nka1pq7qyzzmLdunXcc889FdL9O1xRss+jqKiIZ555hieeeIKZ\nM2eyfPlydtppJ3r16kXPnj3Zfvvt6dmzZ4PbOp4r6VqI2yXm4TpgqZmtS0P9akzSc0BhbBKhY3UF\n8EBcp2W5mbVPcI1fmtnXkjYBngNGmlnCQ3XqWkOVad7guWzyTkvNvPLKKxx11FHMmzePtm3bVnjO\nv8MVpfp5LFu2jPfee48PP/yQefPmlf9t1aoVAwcOLL/tsMMONG7cOAs1b1jS0mnJN5I+IqxVWSqp\nE/CSmW1XxWtGAT+Z2a1JnrdRo0aVP65rUTDTzRs8l0nxUTCvvvpq77RU048//kjv3r0ZO3Ys++23\n3wbP+3e4otp8HmbGZ599xowZM3j11VeZMWMGS5YsYciQIfz2t79ln332oVu3bmmuccNUq06LpJ9Y\nPy1U4SlCUKaNa1/F9JN0I/Cdmd0o6RKgrZldGpenBdDIzFZKaglMA642s2lJrlknGqps8QbPZZOP\ntFSPmfG73/2OVq1acffddyfM49/hitL9eSxdupQXXniBadOmMW3aNJo3b84ee+xB37596du3Lz17\n9vTzj2qgtp2Wh83sWEnnmVneHGAhqR3wKLA5sBA4wsx+kPRL4F4z21/SlsA/CZ2yJsAEM7uhkmvm\nvKHKJm/wXDZlKSLu7ayPiHtjgjx3EsI5rAJOMLPZSa6V87bgjjvuYNy4cbz22mu0bNkyYR7/DleU\nyc/DzJg3bx7Tp0/n7bff5u233+azzz6ja9eudO/enS233JJNNtmEDh06lN823nhjWrRoUeHWrFmz\nBr+YuradlnmE84amErY5V7iQJTlMrD6qCw1VNnmD57Ipw2cPNQL+Szh7aDHwNnCUmc2PyTOMsKZt\nP0n9gDvMbNck18tpWzB16lROPPFE3njjDbbccsuk+fw7XFG2P4/Vq1fzv//9j08++YTPP/+cZcuW\nld++/fZbVq5cyc8//1zhVlRURLNmzTbozMTfmjdvTkFBAQUFBTRr1iwtfwsKCpByP9hZWVuQyrjV\n34EXCDFZ3qVip8Wo27FaXDXdfvvtPPXUU+WPy9bsDB8+nPPOOy9HtXKu1voCn5jZQgBJkwhhEebH\n5DkIeAjAzGZKah17iGJdMX36dI477jiefvrpSjssLveaN29Or169ygPbpaK0tJSioqINOjOJbsXF\nxRQXF1NUVERxcTHff/99hcfxfyt7rqioiDVr1tC0adMKnZjadISaNGlCo0aNkJTyrapRpursHvqb\nmZ2Z8idfD+X611W2+a80l00ZHmk5FNjHzE6LHh8L9DWzc2PyTAb+bGavR4+fBy42s/cSXM/eeuut\n8non+lvZc2V/W7RoQceOHfnFL36R0i/chx9+mPPPP59HH32U3XffPZX37d/hGP55VK60tJQ1a9ZU\n2blJpQNUXFzM2rVrMTNKS0sxs5RvDz30UPK2oDoXaui38HE1HA3t/brciv5/y9R391BCHKayx8cC\nd8blmQz8Jubx88DOSa5niW6dOnWynXbayXbaaSfr3bu39e7d23bccUcrLCxMmL9NmzbWqlUra9q0\nqXXv3t2OPPJIu+GGG+zYY49NmL9t27Y2d+7cDT67UaNGJcyf7DucLP+oUaM8v+fPev6XXnrJRo0a\nVX6rrC2ot1ueM6EhjLTETg+9/PLLDB48GPDpIZd5GR5p2RW4ysyGRo8vJTSMN8bkuZsQGuGR6PF8\nYLAlmB5Kd1tQtvZh1qxZzJo1i3fffZdZs2bRtGlTmjRpQmlpKSNHjuTCCy9Muug2ER9ZqMg/j/zQ\nIOO0ZEJD6LTssMMOfPTRRwCUlJSUB07abrvt/Dh3l1EZ7rQ0Bj4mLMT9GngLGGFmH8Xk2Rc428JC\n3F2B2y2HC3HNjG+++YY1a9bQuXPnlBdIxsa/mT59evm6tPoeVyoV3mnJD95pSZOG0GlxLleytOX5\nDgExABAAACAASURBVNZveb5B0umEEZd7ojxjgaGELc8nWoL1LFE+bwvykHda8oN3WtLEGyrnMseD\ny7lM6NSpE0uXbrgBrLCwkCVLluSgRq4qlbUFDTuCjXPOOefyhndanHPO1Vv9+/endevWtG7dGqD8\nfv/+/XNcM1cTPj1UDT4k7Fzm+PSQcw58esg555xz9YB3WpxzzjmXF7zT4pxzzrm84J0W55xzzuUF\n77Q455xzLi/U206LpMMkfSCpRNLOleQbKmm+pP9KuiTd9SgLp51tXm79L7uhlVsbktpKmibpY0n/\nkdQ6Sb7PJb0vaZakt9JZh4b238u/k15uJtTbTgswFzgYeDlZBkmNgLHAPkBPYISkHumsREP6n6kh\nlpvLshtaubV0KfC8mW0LvAj8MUm+UmCIme1kZn3TWYGG9t/Lv5NebibU206LmX1sZp8AlcV96At8\nYmYLzWwtMAk4KCsVdM5l00HAg9H9B4HhSfKJetwuOpfvGvqXczPgy5jHi6I051z90tHMlgKY2RKg\nY5J8Bjwn6W1Jp2atds65lOR1RFxJzwGFsUmERudyM5sc5XkJuDDRaa2SDgX2MbPTosfHAn3N7Nwk\n5eXvh+VcHqhNRNxK2oMrgAfMrF1M3uVm1j7BNX5pZl9L2gR4DhhpZjMS5PO2wLkMStYWNMl2RdLJ\nzPau5SW+AraIedw5SktWXl6EGHeuIaqsPZC0VFKhmS2V1An4Jsk1vo7+fivpn4Qp5A06Ld4WOJcb\nDWV6KFkD8zawtaQukpoCRwFPZ69azrkseRo4Ibp/PPCv+AySWkhqFd1vCfwW+CBbFXTOVa3edlok\nDZf0JbAr8IykqVH6LyU9A2BmJcBIYBowD5hkZh/lqs7OuYy5Edhb0sfAnsANULE9IEwtzZA0C3gT\nmGxm03JSW+dcQnm9psU555xzDUe9HWlxzjnnXP1S7zstku6PFuHNqSTPnZI+kTRbUu9s1s85lx3e\nFjiX/+p9pwUYR4h4m5CkYUA3M9sGOB24O1sVc85llbcFzuW5et9piWIsfF9JloOAh6K8M4HWkgor\nye+cy0PeFjiX/+p9pyUF8VFxv8Kj4jrXEHlb4Fwd550W55xzzuWFvI6ImyZfAZvHPE4aFddDdzuX\nWTmONOttgXN1RL0M418NInlU3KeBs4FHJO0K/FB2sFoiK1asqFbB119/PZdddlm1XpMOXm79L7u+\nlbvxxhun/ZoJeFtQz8vNZdlebnpU1hbU+06LpInAEKC9pC+AUUBTwMzsHjN7VtK+kj4FVgEn5q62\nzrlM8bbAufxX7zstZnZ0CnlGZqMuzrnc8bbAufxX7xfiShoqab6k/0q6JMHzgyX9IOm96HZFOssf\nNGhQOi/n5daxcv+fvTuPk6K6/v//OiwjirKpgIqgLJqAK+KICgIqEYw/wBXEBaNRY8Ql6keNmgzk\nY1CSTxLiFoMigmLcRUExqICCfhWUoCigRERFARcWZXWGOb8/umdshlm6Z7q6urrfz8ejH3RXV9c9\nDcyZU/feuhVm2/nWbl0pF+RHu2G2rXaDl9P3HjKzesBHxG6Q9iWxuzoPcfclCfv0Aq519wFJHM9T\nHccWkeQ0adIksIm4ygUi0VFdLsj1npZCYKm7f+ruxcCjxBaQqijMKxZEJHjKBSI5INeLloqLRa2g\n8sWijo7fa+R5M+ucmdBEJIOUC0RyQM5PxE3CO0Bbd98Uv/fIZOCAqnYeNWpU+fOePXtGdnxfJGyz\nZ89m9uzZYYeRSLlAJASp5IJcn9PSHRjh7v3ir28kdnnj6Go+8wlwhLuvqeQ9jWOLBCTgOS3KBSIR\nkc9zWuYBHc2snZkVAEOILSBVLvGGaGZWSKyQ2yFJiUikKReI5ICkh4fM7Jrq3nf3v9Y9nPRy921m\nNhyYTqxAG+fui83sUuILSgFnmNllQDGwGRgcXsQiEgTlApHckPTwkJkVVfe+u49MS0RZTF3CIsEJ\ncngo3ZQLRIJTXS5IuqclqkWJmfUDxvDj2dUOY9hmdgfQn9jS3Re4+4LMRikiQVMuEIm+VIaH7qju\nfXe/su7hpFd8Qam7SFhQysyerbCgVH+gg7t3MrOjgHuB7qEELCKBUC4QyQ2pXPL8TmBRBKd8QSkA\nMytbUGpJwj4DgYkA7v6WmTU1s1bV3d1VRCJHuUAkB6QyPDQhyEACUtmCUoU17PNFfFteJ6rEW4OX\njd1Xtk0kIvIuF1x++eXMnj2bnj17cvfdd9f6OLmSC6IYs+wo5cXlzGwmsMPsXXc/Pi0RZbnE//j5\norLvnI9/DyKJovIzsHz5ch566KG0HCtXckEUY5aY2qyIe13C80bA6UBJesJJuy+Atgmv28S3Vdxn\n3xr2KZcvFXqunF1JdAT8iyTvcsHll1/OnDlz6NGjh3paiGbM+aq6XJCWFXHNbK67V+xqDZ2Z1Qc+\nJDb5biUwFzjb3Rcn7HMycLm7/zy+auYYd6908p0ucxQJTsAr4ioXiEREWi55LmNmLRJe1gO6AU1r\nGVugkllQyt1fMLOTzey/xC5z/EWYMYtI+ikXiOSGlHta4vfjKPtQCbAc+IO7z0lvaHVjZs2Bx4B2\nxGI8y93XV7LfcmA9UAoUV9djpLMrkeAE3NOS1nygXCASnHTfe6gzcDfwLvA+MA14u/bhBeZG4GV3\nPxCYAfy2iv1Kgd7ufng2DnGJSFooH4jkgNoULROAnwJ3AHcSK2LSMzU9vQYSi5X4n4Oq2M/I/RtH\niuQ75QORHFCbq4cOcvfOCa9nmtmidAWURi3LFoVy91Vm1rKK/Rx4ycy2AWPd/b6MRSgimaJ8IJID\nalO0zDez7u7+JkB8uetQhofM7CWgVeImYknnlkp2r2ryzrHuvtLM9iSWrBZn2/wcEamZ8oFI7qtN\n0XIE8IaZfRZ/3Rb40MwWEpuFf0jaoquBu/et6j0zW122BLeZtQa+quIYK+N/fm1mzxBbJbPKJDVq\n1Kjy5z179qRnz561DV8kr82ePZvZs2en7XiZzgfKBSLpkUouqM3VQ+2qe7/s3h5hM7PRwBp3H21m\nNwDN3f3GCvvsAtRz9w1m1pjY5ZAj3X16FcfUFQMiAQn46qG05gPlApHgVJcL0rK4XDaKryfzOLEV\nLj8ldonjOjPbC7jP3U8xs/2BZ4h1FTcAJrn77dUcU4lKJCABFy1pzQfKBSLBycuiJQhKVCLBCbJo\nSTflApHgpHudlkgwszPM7H0z22ZmXavZr5+ZLTGzj+LdxiKSY5QPRHJDzhYtwELgVODVqnYws3rA\nXcBJQBfgbDP7STqDSOdEQ7Wbfe2G2Xa+tVtHoeeDfPv30s+k2g1CzhYt7v6huy8ldtljVQqBpe7+\nqbsXA48SW4QqbfLpP1M+thtm2/nWbl1kQz7It38v/Uyq3SDkbNGSpH2AzxNer4hvE5H8o3wgkuVq\ns05L1qhmMamb3X1KOFGJSBiUD0RyX85fPWRmM4Fr3X1+Je91B0a4e7/46xuJLZA3uopj5fZflkjI\ngr56KF35QLlAJFhV5YJI97SkoKpEOA/oGF8wbyUwBDi7qoNE5XJMEalWnfOBcoFIOHJ2TouZDTKz\nz4HuwFQzmxbfvpeZTQVw923AcGIrX34APOrui8OKWUSCoXwgkhtyfnhIREREckPO9rSIiIhIblHR\nIiIiIpGgokVEREQiQUWLiIiIRIKKFhEREYkEFS0iIiISCSpaREREJBJUtKTIzMaZ2Wozey9Nx5tm\nZmvN7LkK2/czszfN7CMz+5eZ5cvqxSKRoFwgknkqWlI3Hjgpjcf7E3BuJdtHA39x9wOAdcBFaWxT\nROpOuUAkw1S0pMjd5wBrE7eZWfv4WdI8M3vVzA5I4XgzgQ2VvHU88FT8+QTg1NrGLCLpp1wgknnq\nZkyPscCl7v6xmRUC/wBOqO3BzGx3YK27l8Y3rQD2rnuYIhIw5QKRAKloqSMzawwcAzxhZmV3fm0Y\nf+9U4A9A4g2eDFjh7v0zGqiIBEq5QCR4Klrqrh6xM6GuFd9w92eAZ1I9oLt/a2bNzKxe/AyrDfBF\n3UMVkQApF4gETHNa4sxsuZm9a2b/MbO5Ne0ef+Du3wOfmNkZCcc6JNXmy46XYCZwZvz5MODZFI8p\nIrVgZk3N7AkzW2xmH5jZUdXtjnKBSMaYu9e8Vx4ws2XAEe6+tob9HgF6A7sDq4EiYAZwL7AXsd6r\nR9391iTbfQ04ENgV+Ba4yN1fMrP9gUeB5sB/gHPdvbgWX01EUmBmDwKvuvv4+OXFu7j7d5Xsp1wg\nkmEqWuLM7BOgm7t/G3YsIhIOM2sC/MfdO4Qdi4jsSMNDP3LgpfiliheHHYyIhGJ/4BszG29m881s\nrJntHHZQIhKjnpY4M9vL3Vea2Z7AS8Dw+DoMifvoL0skQO5ecT5HRpnZEcCbwNHu/raZjQHWu3tR\nhf2UC0QCVFUuUE9LnLuvjP/5NbFZ/oVV7JfSo6ioKOXPpOOhdnO/7VxrN0usAD5397fjr58Edrga\nCJQLsrXdfPzOudZudVS0AGa2i5ntGn/eGPgZ8H64UYlIprn7auDzhJVsTwAWhRiSiCTQOi0xrYBn\n4l2+DYBJ7j495JhEJBxXApPMrCGwDPhFyPGISJyKFsDdPwEOC+LYvXv3DuKwajdL2g2z7XxrN1Pc\n/V3gyHQfN9/+vfQzqXaDoIm4KTAz19+XSDDMDA95Im6ylAtEglNdLtCcFhEREYkEFS0iIrKDgoIC\nzGyHR0FBQdihSR7TnBYREdnBDz/8UP483l0fYjQiMeppERGRHTRo0KC8dwUof96ggc51JTwqWkSk\nVnr06BF2CBKgkpKS7Rb7KnteUlIScmSSz3T1UAp0xYBIcHT1UPbS8JBkkq4eEpG022233cIOIRBm\nttzM3jWz/5jZ3LDjEclW//znPzn88MPp2rUr7du354QTTgi8TfW0pCDfzq5EqtOkSRO+++67tB0v\nW3pazGwZcIS7r61mn7zKBeppkeqUlJRwwgkncMMNN3DyySfX+XjV5QLNqBIR2Z6hXmhmzZrFrFmz\nyl+PGDECiK2CmuurIktqrrzySo4//vi0FCw1UU9LAjOrB7wNrHD3AZW8n1dnVyLVyfGelnXANmCs\nu99XyT55lQvU0yJVefDBB3nqqaeYMmVK2o6pnpbkXUXsjq5Nwg4kLGWXN1ZGSUsS5fD/h2PdfaWZ\n7Qm8ZGaL3X1O2EGJZJt33nmHv/zlL8yZk7kfDxUtcWbWBjgZ+CNwTcjhhCbxF5HOrqQ61RW4Uebu\nK+N/fm1mzwCFwA5ZuWy4BDRkIvnp7rvvZu3atfTp0weAbt26MXbs2JSPU3EosjoaHoozsyeIFSxN\ngWs1PKSiRTIrG4aHzGwXoJ67bzCzxsB0YKS7T6+wn3KBSEB0yXMNzOznwGp3X0BsEl5unkKKSE1a\nAXPM7D/Am8CUigWLiIQn5eGh+NnHFnffFkA8YTkWGGBmJwM7A7uZ2UR3P7/ijuoSFkmPVLqEM8Xd\nPwEOCzsOEalcjcND8StqhgDnAEcCW4GdgG+A54F/uvt/A44zY8ysFxoeAtQlLJmVDcNDyVIuEAlO\nXYeHZgIdgN8Crd19X3dvCfQg1n062szOTVu0IiIiIpVIpqelobsX13WfXKCzK5HgqKcleykXSCbV\naZ0Wdy82sx7A8UBrYgsufQ28WTZBLR8KlnymtVtERCQbJNPTchPQEPgPsAGoT2zxtULA3f3GoIPM\nFjq70hmXBEc9LdlLP/eSSXVdEfd9d3+uku1PmdkZdQtNREREJDnJFC2HmtmhxHpaNhIbHmoMHALs\nCTwZXHgiIpItEi9TnzVrVvmSD1r+QTIlqRVxzewEYmuZtCR2xdFqYstaz8inPlJ1CaubWIKj4aHs\npVwgmVRdLtAy/ilQolKikuBkU9GiO75vT7lAMknL+IuIpKbsju+SwMzKH5W9FglarYsWM2tnZq+n\nMxgRkbAl3PH9/rBjyTbuXv6o7LVI0GpdtLj7p8DP0xiLiEg2+BvwP4B+E4tkmaRumFjT4nIiIrkg\n8Y7vZtabau74rpuniqRHKjdP1eJyKdDkO02+k+Bkw0RcMxsFnAuUEL/jO/B0xTu+KxcoF0hw6nT1\nkJkNqGJxOczsDHeP/DotZrYT8BpQQKz36Ul3H1nJfkpUZqxbt45Jkybxwgsv8O6777J161ZatGjB\nkUceyWmnncbPf/5zCgoKQopaoiobipZEuuP7j1S0SCbVtWj5XfxppYvLuft1aYw1NGa2i7tvMrP6\nwOvAle4+t8I+eZ2otm7dSqNGjWjRogUnnHACgwcPpmvXrjRu3JhVq1bxxhtv8Mgjj7Bs2TJuueUW\nfvnLX9KgQVIjkCIqWrKYihbJpDqv05JPi8uZ2S7Eel0uc/d5Fd7Lta9brcSktHTpUg455BC2bNlC\np06d+Oijj6r83Ny5c7nxxhtZt24d48aN4/DDD89UyBJh2Va0VCefc0F120TSQYvLJSG+mNQ7QAfg\nbnf/bSX75GWimjlzJoMHD+brr78uf6+mvwd3Z+LEifzP//wPN998M1deeaXWcpBqqWjJXipaJJPq\nesPEvODupcDhZtYEmGxmnd19h8Wl8u2KgalTp3LhhRfy+OOPc8UVV/D+++9z0EEH1fg5M2PYsGH0\n6NGDs846i/nz53PfffdprouUS+WKARERqEVPi5k1dff1ZtbM3dcFFFeo4vN4Nrr7Xytsz7uzqz33\n3JPnn3+eI488snxbqn8HGzdu5LzzzuO7777jueeeY5dddgkiXIk49bRkL/W0SCalexn/YfE/z692\nrwgxsz3MrGn8+c5AX2BJuFGF64ADDgCgcePG5QVLbTVu3JgnnniCvffem4EDB7J58+Z0hCgiInmm\nLvceisQZUZL2Amaa2QLgLeDf7v5CyDGFZtOmTSxduhSA5cuXp+WY9evXZ/z48bRq1YpBgwaxZcuW\ntBxXRETyh26YCLj7Qnfv6u6Hufsh7v7HsGMK05VXXknTpk0Bkpq/kqz69evz4IMP0qJFC04//XSK\ni4vTdmwREcl9KlpkO1OnTmXGjBl8/vnnACxcuDCtx2/QoAEPPfQQAL/+9a81Ji4igWrduvV2d6Mu\ne7Ru3Trs0KQWVLRIubVr13LppZcyfvx4dtttt8DaadCgAY899hjz589n1KhRgbUjIplTUlLChx9+\nyIwZM3j99df57LPPdFIiaVebS55zaS6LJLjpppsYOHAgvXr1CrytXXfdlalTp3L00UfTrl07zj33\n3MDbFKlJsrf0kB8tXryYv/3tbzz11FM0a9aMtm3bsnXrVj7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kAg0PBWfr1q0cfPDBjBkz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FBeYit1TlrMLJtcZlpGRER9B6/9dUT834JElgeSKoANwHnAVmAZcHlErM/oMwe4ISI+JulM4NsR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\nmbZE95I3M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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cd46310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lEjcmgpK8F6hqhn/grPi5zH2eE2eZYaMA9TQc+vbtG5bzRFqsxKkaO7FanElz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\nZlYInrSYmZlZIXjSYmZmZoXw/5SoPS/+hQsFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d2aed10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9nH766Xz99decdtppfpuTYwYOHMiOHTuYPHmy36YYGRAV7SERqSwi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\n43ZJY4BbI