rbiswas4

# Required packages sqlachemy, pandas (both are part of anaconda distribution, or can be installed with a python installer)
# One step requires the LSST stack, can be skipped for a particular OPSIM database in question
import OpSimSummary.summarize_opsim as so
from sqlalchemy import create_engine
import pandas as pd
print so.__file__

# Change dbname to point at your own location of the opsim output
dbname = '/Users/rbiswas/data/LSST/OpSimData/enigma_1189_sqlite.db'

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time[sniacatsim_rebase]examples$ sshx $acc
Welcome to Ubuntu 14.04.3 LTS (GNU/Linux 3.13.0-63-generic x86_64)

 * Documentation:  https://help.ubuntu.com/

175 packages can be updated.
97 updates are security updates.

Last login: Thu Nov 12 19:02:59 2015 from d-173-250-158-216.dhcp4.washington.edu
rbiswas@accelerator:~$ cd .c

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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {

rbiswas4

SN Sprinkler pseudo code

Assumptions and discussion

• Cosmology is known. This cosmology should be whatever is used to compute time delays from the lenses. CatSim is cosmology dependent as well. This plays into the setup in two ways: Galaxy evolution is cosmology dependent, but this dependence is relatively weak, and smudged anyway in how we are populating SN. The second is that SN have distance moduli calculated according to the udnerlying cosmology, which will determine the sensitivity/discovery in images. This may need to be changed to match the time delay calculations.
• The OM10 cataloag provides us with an ensemble of lens-source systems. The number of lens source systems in a given area is a function of the number density of lens galaxies and the number density of sources. I do not understand the way we need to oversample these two quantities to make the procedure sensible. At this point, let us assume that we can find a catalog of lenses that would make sense in combination with the transients

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import numpy as np
import pandas as pd

# Note that the np.array(fits_rec) is essential for functionality like groupby to not fail
d = np.array(fits_rec).byteswap().newbyteorder()
df = pd.DataFrame(d)

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import pandas as pd

%matplotlib inline
import matplotlib.pyplot as plt