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do_bkg_llhs.py
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do_bkg_llhs.py
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import numpy as np
from astropy.io import fits
import os
import argparse
import logging, traceback
from bkg_rate_estimation import rate_obj_from_sqltab
from sqlite_funcs import get_conn
from dbread_funcs import get_rate_fits_tab, guess_dbfname
from config import EBINS0, EBINS1
# need to read rate fits from DB
# and read twinds
# and read/get event, dmask, and ebins
# then get bkg_llh_obj and a minimizer
# then loop over all time windows
# minimizing nllh and recording bf params
def cli():
parser = argparse.ArgumentParser()
parser.add_argument('--evfname', type=str,\
help="Event data file",
default=None)
parser.add_argument('--dmask', type=str,\
help="Detmask fname",
default=None)
parser.add_argument('--dbfname', type=str,\
help="Name to save the database to",\
default=None)
args = parser.parse_args()
return args
def do_bkg_analysis_mp(i_proc, nprocs):
if args.dbfname is None:
db_fname = guess_dbfname()
if isinstance(db_fname, list):
db_fname = db_fname[0]
else:
db_fname = args.dbfname
logging.info('Connecting to DB')
conn = get_conn(db_fname)
info_tab = get_info_tab(conn)
logging.info('Got info table')
files_tab = get_files_tab(conn)
logging.info('Got files table')
trigtime = info_tab['trigtimeMET'][0]
evfname = files_tab['evfname'][0]
ev_data = fits.open(evfname)[1].data
dmask = files_tab['detmask'][0].data
bl_dmask = (dmask==0.0)
logging.debug('Opened up event and detmask files')
ebins0 = np.array(EBINS0)
ebins1 = np.array(EBINS1)
logging.debug("ebins0")
logging.debug(ebins0)
logging.debug("ebins1")
logging.debug(ebins1)
# probably get times from twind table
rate_fits_df = get_rate_fits_tab(conn)
bkg_rates_obj = rate_obj_from_sqltab(rate_fits_df, 0, 1)
twind_df = get_twinds_tab(conn)
logging.info("Got TimeWindows table")
logging.info("Getting rate fits from DB")
min_bin_size = np.min(twind_df['duration'])
logging.info("Smallest duration to test is %.3fs" %(min_bin_size))
llh_bkg = get_bkg_llh_obj(ev_data, ebins0, ebins1, bl_dmask,\
bkg_rates_obj, twind_df['time'].values[0],\
min_bin_size)
miner.set_llh(llh_bkg)
t_bins0 = twind_df['time'].values
t_bins1 = twind_df['time_end'].values
t_bins0 = t_bins0[i_proc:][::nprocs]
t_bins1 = t_bins1[i_proc:][::nprocs]
ntbins = len(t_bins0)
logging.debug("There are %d time0 bins" %(len(t_bins0)))
logging.debug("There are %d time1 bins" %(len(t_bins1)))
logging.debug("min(t_bins0), max(t_bins0): %.3f, %.3f" %(np.min(t_bins0),np.max(t_bins0)))
logging.debug("min(t_bins1), max(t_bins1): %.3f, %.3f" %(np.min(t_bins1),np.max(t_bins1)))
for i in xrange(ntbins):
# pretty sure I don't have to do miner.set_llh() again
dt = t_bins1[i] - t_bins0[i]
llh_bkg.set_time(t_bins0[i], dt)
bf_vals, bf_nllhs, ress = miner.minimize()
# then write the result
def main(args):
logging.basicConfig(filename='bkg_llh_analysis.log', level=logging.DEBUG,\
format='%(asctime)s-' '%(levelname)s- %(message)s')
# probably want to move this entire thing to a seperate function
# so that they can be launched as seperate processes
if args.dbfname is None:
db_fname = guess_dbfname()
if isinstance(db_fname, list):
db_fname = db_fname[0]
else:
db_fname = args.dbfname
logging.info('Connecting to DB')
conn = get_conn(db_fname)
info_tab = get_info_tab(conn)
logging.info('Got info table')
files_tab = get_files_tab(conn)
logging.info('Got files table')
trigtime = info_tab['trigtimeMET'][0]
evfname = files_tab['evfname'][0]
ev_data = fits.open(evfname)[1].data
dmask = files_tab['detmask'][0].data
bl_dmask = (dmask==0.0)
logging.debug('Opened up event and detmask files')
ebins0 = np.array(EBINS0)
ebins1 = np.array(EBINS1)
logging.debug("ebins0")
logging.debug(ebins0)
logging.debug("ebins1")
logging.debug(ebins1)
# probably get times from twind table
rate_fits_df = get_rate_fits_tab(conn)
bkg_rates_obj = rate_obj_from_sqltab(rate_fits_df, 0, 1)
twind_df = get_twinds_tab(conn)
logging.info("Got TimeWindows table")
logging.info("Getting rate fits from DB")
min_bin_size = np.min(twind_df['duration'])
logging.info("Smallest duration to test is %.3fs" %(min_bin_size))
exp_groups = twind_df.groupby('duration')
nexps = len(exp_groups)
miner = NLLH_ScipyMinimize('')
llh_bkg = get_bkg_llh_obj(ev_data, ebins0, ebins1, bl_dmask,\
bkg_rates_obj, twind_df['time'].values[0],\
min_bin_size)
miner.set_llh(llh_bkg)
for ii, exp_group in enumerate(exp_groups):
logging.info("Starting duration size %d of %d" %(ii+1, nexps))
df_twind = exp_group[1]
t_bins0 = df_twind['time'].values
t_bins1 = df_twind['time_end'].values
dt = t_bins1[0] - t_bins0[0]
ntbins = len(t_bins0)
logging.debug("There are %d time0 bins" %(len(t_bins0)))
logging.debug("There are %d time1 bins" %(len(t_bins1)))
logging.debug("min(t_bins0), max(t_bins0): %.3f, %.3f" %(np.min(t_bins0),np.max(t_bins0)))
logging.debug("min(t_bins1), max(t_bins1): %.3f, %.3f" %(np.min(t_bins1),np.max(t_bins1)))
for i in xrange(ntbins):
# pretty sure I don't have to do miner.set_llh() again
llh_bkg.set_time(t_bins0[i], dt)
bf_vals, bf_nllhs, ress = miner.minimize()
# then write the result
if __name__ == "__main__":
args = cli()
main(args)