lalapps_power_likelihood_pipe.py

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##python
#
# Copyright (C) 2007  Kipp C. Cannon
#
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 2 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
 
 
#
# =============================================================================
#
#                                   Preamble
#
# =============================================================================
#
 
 
"""
Excess power offline pipeline's likelihood stage construction script.
"""
 
 
from __future__ import print_function
 
 
import itertools
from optparse import OptionParser
import sys
import tempfile
from configparser import ConfigParser
 
 
from igwn_segments import utils as segmentsUtils
import lal
from lal import pipeline
from lal.utils import CacheEntry
from lalburst import power
 
 
__author__ = "Kipp Cannon <kipp@gravity.phys.uwm.edu>"
__date__ = "$Date$"
__version__ = "$Revision$"
 
 
#
# =============================================================================
#
#                                 Command Line
#
# =============================================================================
#
 
 
def parse_command_line():
        parser = OptionParser(
                version = "%prog CVS $Id$",
                usage = "%prog [options]",
                description = "Constructs the likelihood-ratio based coincidence stage for an excess power analysis.  The input consists of one or more LAL caches listing the sqlite database trigger files, and a list of segments giving the time intervals that should be considered to be independent.  The LAL caches list all trigger files together, that is injections, time slides, and zero-lag.  The individual trigger files are self-describing, so the analysis codes can autodetect their type.  Each segment will be analyzed using the files that intersect it:  the likelihood ratios will be constructed from the injections and time-lag triggers contained in files that intersect the segment, and that data used to assign likelihoods to the injections, time-lag, and zero-lag coincs in all files that intersect the same segment."
        )
        parser.add_option("--input-cache", metavar = "filename", action = "append", default = [], help = "Add the contents of this cache file to the list of files from which to draw statistics.")
        parser.add_option("--round-robin-cache", metavar = "filename", action = "append", default = [], help = "Add the contents of this cache file to the list of files from which to draw injection statistics in a round-robin way.")
        parser.add_option("--condor-log-dir", metavar = "path", default = ".", help = "Set the directory for Condor log files (default = \".\").")
        parser.add_option("--config-file", metavar = "filename", default = "power.ini", help = "Set .ini configuration file name (default = \"power.ini\").")
        parser.add_option("--distribution-segments", metavar = "filename", help = "Read boundaries for distribution data intervals from this segwizard format segments file (required).")
        parser.add_option("-v", "--verbose", action = "store_true", help = "Be verbose.")
        options, filenames = parser.parse_args()
 
        if options.distribution_segments is None:
                raise ValueError("missing required argument --distribution-segments")
        options.distribution_segments = segmentsUtils.fromsegwizard(file(options.distribution_segments), coltype = lal.LIGOTimeGPS)
 
        options.input_cache = set([CacheEntry(line) for filename in options.input_cache for line in file(filename)])
        options.round_robin_cache = [set(map(CacheEntry, file(filename))) for filename in options.round_robin_cache]
 
        return options, (filenames or [])
 
 
#
# =============================================================================
#
#                                    Config
#
# =============================================================================
#
 
 
def parse_config_file(options):
        if options.verbose:
                print("reading %s ..." % options.config_file, file=sys.stderr)
        config = ConfigParser()
        config.read(options.config_file)
 
        options.tag = config.get("pipeline", "user_tag")
        options.likelihood_data_cache_base = config.get("pipeline", "likelihood_data_cache_base")
 
        return config
 
 
#
# =============================================================================
#
#                                 Place Holder
#
# =============================================================================
#
 
 
class PlaceHolder(object):
        def __init__(self):
                self.cache = set()
 
        def add_input_cache(self, cache):
                self.cache |= cache
 
        def get_output_cache(self):
                return self.cache
 
 
#
# =============================================================================
#
#                               DAG Construction
#
# =============================================================================
#
 
 
#
# Command line
#
 
 
options, filenames = parse_command_line()
 
 
#
# Parse .ini file, input cache(s), and segment list.
#
 
 
config_parser = parse_config_file(options)
 
 
#
# Define .sub files
#
 
 
power.init_job_types(config_parser)
 
 
#
# Start DAG
#
 
 
power.make_dag_directories(config_parser)
dag = pipeline.CondorDAG(tempfile.mkstemp(".log", "power_likelihood_", options.condor_log_dir)[1])
dag.set_dag_file("power_likelihood")
 
 
#
# Generate likelihood data
#
 
 
input_cache_nodes = set()
round_robin_cache_nodes = [set() for cache in options.round_robin_cache]
for seg in options.distribution_segments:
        if options.verbose:
                print("generating distribution measurement jobs for %s ..." % str(seg), file=sys.stderr)
        input_cache_nodes |= power.make_burca_tailor_fragment(dag, set([entry for entry in options.input_cache if entry.segmentlistdict.intersects_segment(seg)]), seg, "LIKELIHOOD_MAIN")
        for i, (nodes, cache) in enumerate(zip(round_robin_cache_nodes, options.round_robin_cache)):
                nodes |= power.make_burca_tailor_fragment(dag, set([entry for entry in cache if entry.segmentlistdict.intersects_segment(seg)]), seg, "LIKELIHOOD_RR%02d" % i)
 
 
#
# Compute likelihood ratios for coincs
#
 
 
if options.verbose:
        print("generating likelihood assignment jobs for main group ...", file=sys.stderr)
parents = reduce(lambda a, b: a | b, round_robin_cache_nodes, input_cache_nodes)
nodes = power.make_burca2_fragment(dag, options.input_cache, parents, "LIKELIHOOD_MAIN")
 
 
def round_robin(round_robin_cache_nodes, round_robin_cache):
        parents = list(itertools.combinations(round_robin_cache_nodes, len(round_robin_cache_nodes) - 1))
        parents.reverse()
        parents = [reduce(lambda a, b: a | b, seq) for seq in parents]
        return zip(parents, [cache for (cache,) in itertools.combinations(round_robin_cache, 1)])
 
for i, (parents, apply_to_cache) in enumerate(round_robin(round_robin_cache_nodes, options.round_robin_cache)):
        if options.verbose:
                print("generating likelihood assignment jobs for round-robin group %d ..." % i, file=sys.stderr)
        nodes |= power.make_burca2_fragment(dag, apply_to_cache, parents | input_cache_nodes, "LIKELIHOOD_RR%02d" % i)
 
 
#
# Output
#
 
 
if options.verbose:
        print("writing dag ...", file=sys.stderr)
dag.write_sub_files()
dag.write_dag()