# Copyright (C) 2013 Ian Harry
#
# 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 3 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
#
# =============================================================================
#
"""
This module is responsible for setting up the matched-filtering stage of
workflows. For details about this module and its capabilities see here:
https://ldas-jobs.ligo.caltech.edu/~cbc/docs/pycbc/NOTYETCREATED.html
"""
import os, logging
from math import radians
from pycbc.workflow.core import FileList, make_analysis_dir
from pycbc.workflow.jobsetup import (select_matchedfilter_class,
sngl_ifo_job_setup,
multi_ifo_coherent_job_setup)
[docs]def setup_matchedfltr_workflow(workflow, science_segs, datafind_outs,
tmplt_banks, output_dir=None,
injection_file=None, tags=None):
'''
This function aims to be the gateway for setting up a set of matched-filter
jobs in a workflow. This function is intended to support multiple
different ways/codes that could be used for doing this. For now the only
supported sub-module is one that runs the matched-filtering by setting up
a serious of matched-filtering jobs, from one executable, to create
matched-filter triggers covering the full range of science times for which
there is data and a template bank file.
Parameters
-----------
Workflow : pycbc.workflow.core.Workflow
The workflow instance that the coincidence jobs will be added to.
science_segs : ifo-keyed dictionary of ligo.segments.segmentlist instances
The list of times that are being analysed in this workflow.
datafind_outs : pycbc.workflow.core.FileList
An FileList of the datafind files that are needed to obtain the
data used in the analysis.
tmplt_banks : pycbc.workflow.core.FileList
An FileList of the template bank files that will serve as input
in this stage.
output_dir : path
The directory in which output will be stored.
injection_file : pycbc.workflow.core.File, optional (default=None)
If given the file containing the simulation file to be sent to these
jobs on the command line. If not given no file will be sent.
tags : list of strings (optional, default = [])
A list of the tagging strings that will be used for all jobs created
by this call to the workflow. An example might be ['BNSINJECTIONS'] or
['NOINJECTIONANALYSIS']. This will be used in output names.
Returns
-------
inspiral_outs : pycbc.workflow.core.FileList
A list of output files written by this stage. This *will not* contain
any intermediate products produced within this stage of the workflow.
If you require access to any intermediate products produced at this
stage you can call the various sub-functions directly.
'''
if tags is None:
tags = []
logging.info("Entering matched-filtering setup module.")
make_analysis_dir(output_dir)
cp = workflow.cp
# Parse for options in .ini file
mfltrMethod = cp.get_opt_tags("workflow-matchedfilter", "matchedfilter-method",
tags)
# Could have a number of choices here
if mfltrMethod == "WORKFLOW_INDEPENDENT_IFOS":
logging.info("Adding matched-filter jobs to workflow.")
inspiral_outs = setup_matchedfltr_dax_generated(workflow, science_segs,
datafind_outs, tmplt_banks, output_dir,
injection_file=injection_file,
tags=tags)
elif mfltrMethod == "WORKFLOW_MULTIPLE_IFOS":
logging.info("Adding matched-filter jobs to workflow.")
inspiral_outs = setup_matchedfltr_dax_generated_multi(workflow,
science_segs, datafind_outs, tmplt_banks,
output_dir, injection_file=injection_file,
tags=tags)
else:
errMsg = "Matched filter method not recognized. Must be one of "
errMsg += "WORKFLOW_INDEPENDENT_IFOS (currently only one option)."
raise ValueError(errMsg)
logging.info("Leaving matched-filtering setup module.")
return inspiral_outs
[docs]def setup_matchedfltr_dax_generated(workflow, science_segs, datafind_outs,
tmplt_banks, output_dir,
injection_file=None,
tags=None):
'''
Setup matched-filter jobs that are generated as part of the workflow.
This
module can support any matched-filter code that is similar in principle to
lalapps_inspiral, but for new codes some additions are needed to define
Executable and Job sub-classes (see jobutils.py).
Parameters
-----------
workflow : pycbc.workflow.core.Workflow
The Workflow instance that the coincidence jobs will be added to.
science_segs : ifo-keyed dictionary of ligo.segments.segmentlist instances
The list of times that are being analysed in this workflow.
datafind_outs : pycbc.workflow.core.FileList
An FileList of the datafind files that are needed to obtain the
data used in the analysis.
tmplt_banks : pycbc.workflow.core.FileList
An FileList of the template bank files that will serve as input
in this stage.
output_dir : path
The directory in which output will be stored.
injection_file : pycbc.workflow.core.File, optional (default=None)
If given the file containing the simulation file to be sent to these
jobs on the command line. If not given no file will be sent.
tags : list of strings (optional, default = [])
A list of the tagging strings that will be used for all jobs created
by this call to the workflow. An example might be ['BNSINJECTIONS'] or
['NOINJECTIONANALYSIS']. This will be used in output names.
Returns
-------
inspiral_outs : pycbc.workflow.core.FileList
A list of output files written by this stage. This *will not* contain
any intermediate products produced within this stage of the workflow.
If you require access to any intermediate products produced at this
stage you can call the various sub-functions directly.
'''
if tags is None:
tags = []
# Need to get the exe to figure out what sections are analysed, what is
# discarded etc. This should *not* be hardcoded, so using a new executable
# will require a bit of effort here ....
cp = workflow.cp
ifos = science_segs.keys()
match_fltr_exe = os.path.basename(cp.get('executables','inspiral'))
# Select the appropriate class
exe_class = select_matchedfilter_class(match_fltr_exe)
# Set up class for holding the banks
inspiral_outs = FileList([])
# Matched-filtering is done independently for different ifos, but might not be!
# If we want to use multi-detector matched-filtering or something similar to this
# it would probably require a new module
for ifo in ifos:
logging.info("Setting up matched-filtering for %s." %(ifo))
job_instance = exe_class(workflow.cp, 'inspiral', ifo=ifo,
out_dir=output_dir,
injection_file=injection_file,
tags=tags)
sngl_ifo_job_setup(workflow, ifo, inspiral_outs, job_instance,
science_segs[ifo], datafind_outs,
parents=tmplt_banks, allow_overlap=False)
return inspiral_outs
[docs]def setup_matchedfltr_dax_generated_multi(workflow, science_segs, datafind_outs,
tmplt_banks, output_dir,
injection_file=None,
tags=None):
'''
Setup matched-filter jobs that are generated as part of the workflow in
which a single job reads in and generates triggers over multiple ifos.
This
module can support any matched-filter code that is similar in principle to
pycbc_multi_inspiral or lalapps_coh_PTF_inspiral, but for new codes some
additions are needed to define Executable and Job sub-classes
(see jobutils.py).
Parameters
-----------
workflow : pycbc.workflow.core.Workflow
The Workflow instance that the coincidence jobs will be added to.
science_segs : ifo-keyed dictionary of ligo.segments.segmentlist instances
The list of times that are being analysed in this workflow.
datafind_outs : pycbc.workflow.core.FileList
An FileList of the datafind files that are needed to obtain the
data used in the analysis.
tmplt_banks : pycbc.workflow.core.FileList
An FileList of the template bank files that will serve as input
in this stage.
output_dir : path
The directory in which output will be stored.
injection_file : pycbc.workflow.core.File, optional (default=None)
If given the file containing the simulation file to be sent to these
jobs on the command line. If not given no file will be sent.
tags : list of strings (optional, default = [])
A list of the tagging strings that will be used for all jobs created
by this call to the workflow. An example might be ['BNSINJECTIONS'] or
['NOINJECTIONANALYSIS']. This will be used in output names.
Returns
-------
inspiral_outs : pycbc.workflow.core.FileList
A list of output files written by this stage. This *will not* contain
any intermediate products produced within this stage of the workflow.
If you require access to any intermediate products produced at this
stage you can call the various sub-functions directly.
'''
if tags is None:
tags = []
# Need to get the exe to figure out what sections are analysed, what is
# discarded etc. This should *not* be hardcoded, so using a new executable
# will require a bit of effort here ....
cp = workflow.cp
ifos = sorted(science_segs.keys())
match_fltr_exe = os.path.basename(cp.get('executables','inspiral'))
# List for holding the output
inspiral_outs = FileList([])
logging.info("Setting up matched-filtering for %s." %(' '.join(ifos),))
if match_fltr_exe == 'pycbc_multi_inspiral':
exe_class = select_matchedfilter_class(match_fltr_exe)
cp.set('inspiral', 'ra',
str(radians(float(cp.get('workflow', 'ra')))))
cp.set('inspiral', 'dec',
str(radians(float(cp.get('workflow', 'dec')))))
# At the moment we aren't using sky grids, but when we do this code
# might be used then.
# from pycbc.workflow.grb_utils import get_sky_grid_scale
# if cp.has_option("jitter_skyloc", "apply-fermi-error"):
# cp.set('inspiral', 'sky-error',
# str(get_sky_grid_scale(float(cp.get('workflow',
# 'sky-error')))))
# else:
# cp.set('inspiral', 'sky-error',
# str(get_sky_grid_scale(float(cp.get('workflow',
# 'sky-error')),
# sigma_sys=0.0)))
# cp.set('inspiral', 'trigger-time',\
# cp.get('workflow', 'trigger-time'))
# cp.set('inspiral', 'block-duration',
# str(abs(science_segs[ifos[0]][0]) - \
# 2 * int(cp.get('inspiral', 'pad-data'))))
job_instance = exe_class(workflow.cp, 'inspiral', ifo=ifos,
out_dir=output_dir,
injection_file=injection_file,
tags=tags)
if cp.has_option("workflow", "do-long-slides") and "slide" in tags[-1]:
slide_num = int(tags[-1].replace("slide", ""))
logging.info("Setting up matched-filtering for slide {}"
.format(slide_num))
slide_shift = int(cp.get("inspiral", "segment-length"))
time_slide_dict = {ifo: (slide_num + 1) * ix * slide_shift
for ix, ifo in enumerate(ifos)}
multi_ifo_coherent_job_setup(workflow, inspiral_outs, job_instance,
science_segs, datafind_outs,
output_dir, parents=tmplt_banks,
slide_dict=time_slide_dict)
else:
multi_ifo_coherent_job_setup(workflow, inspiral_outs, job_instance,
science_segs, datafind_outs,
output_dir, parents=tmplt_banks)
else:
# Select the appropriate class
raise ValueError("Not currently supported.")
return inspiral_outs