def setup_matchedfltr_dax_generated(workflow,
science_segs,
datafind_outs,
tmplt_banks,
output_dir,
injection_file=None,
tags=None,
link_to_tmpltbank=False,
compatibility_mode=False):
'''
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 glue.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.
link_to_tmpltbank : boolean, optional (default=True)
If this option is given, the job valid_times will be altered so that there
will be one inspiral file for every template bank and they will cover the
same time span. Note that this option must also be given during template
bank generation to be meaningful.
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)
if link_to_tmpltbank:
# Use this to ensure that inspiral and tmpltbank jobs overlap. This
# means that there will be 1 inspiral job for every 1 tmpltbank and
# the data read in by both will overlap as much as possible. (If you
# ask the template bank jobs to use 2000s of data for PSD estimation
# and the matched-filter jobs to use 4000s, you will end up with
# twice as many matched-filter jobs that still use 4000s to estimate a
# PSD but then only generate triggers in the 2000s of data that the
# template bank jobs ran on.
tmpltbank_exe = os.path.basename(cp.get('executables', 'tmpltbank'))
link_exe_instance = select_tmpltbank_class(tmpltbank_exe)
else:
link_exe_instance = None
# 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)
if link_exe_instance:
link_job_instance = link_exe_instance(cp,
'tmpltbank',
ifo=ifo,
out_dir=output_dir,
tags=tags)
else:
link_job_instance = None
sngl_ifo_job_setup(workflow,
ifo,
inspiral_outs,
job_instance,
science_segs[ifo],
datafind_outs,
parents=tmplt_banks,
allow_overlap=False,
link_job_instance=link_job_instance,
compatibility_mode=compatibility_mode)
return inspiral_outs
def setup_matchedfltr_dax_generated(workflow, science_segs, datafind_outs,
tmplt_banks, output_dir,
injection_file=None,
tags=None, link_to_tmpltbank=False,
compatibility_mode=False):
'''
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 glue.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.
link_to_tmpltbank : boolean, optional (default=True)
If this option is given, the job valid_times will be altered so that there
will be one inspiral file for every template bank and they will cover the
same time span. Note that this option must also be given during template
bank generation to be meaningful.
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)
if link_to_tmpltbank:
# Use this to ensure that inspiral and tmpltbank jobs overlap. This
# means that there will be 1 inspiral job for every 1 tmpltbank and
# the data read in by both will overlap as much as possible. (If you
# ask the template bank jobs to use 2000s of data for PSD estimation
# and the matched-filter jobs to use 4000s, you will end up with
# twice as many matched-filter jobs that still use 4000s to estimate a
# PSD but then only generate triggers in the 2000s of data that the
# template bank jobs ran on.
tmpltbank_exe = os.path.basename(cp.get('executables', 'tmpltbank'))
link_exe_instance = select_tmpltbank_class(tmpltbank_exe)
else:
link_exe_instance = None
# 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)
if link_exe_instance:
link_job_instance = link_exe_instance(cp, 'tmpltbank', ifo=ifo,
out_dir=output_dir, tags=tags)
else:
link_job_instance = None
sngl_ifo_job_setup(workflow, ifo, inspiral_outs, job_instance,
science_segs[ifo], datafind_outs,
parents=tmplt_banks, allow_overlap=False,
link_job_instance=link_job_instance,
compatibility_mode=compatibility_mode)
return inspiral_outs
def setup_matchedfltr_dax_generated_multi(workflow, science_segs, datafind_outs,
tmplt_banks, output_dir,
injection_file=None, gate_files=None,
tags=[], link_to_tmpltbank=False,
compatibility_mode=False):
'''
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 glue.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.
'''
# 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)
# List for holding the output
inspiral_outs = FileList([])
logging.info("Setting up matched-filtering for %s." %(' '.join(ifos),))
if match_fltr_exe == 'lalapps_coh_PTF_inspiral':
cp.set('inspiral', 'right-ascension', cp.get('workflow', 'ra'))
cp.set('inspiral', 'declination', cp.get('workflow', 'dec'))
cp.set('inspiral', 'sky-error', cp.get('workflow', 'sky-error'))
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,
gate_files=gate_files, tags=tags)
multi_ifo_coherent_job_setup(workflow, inspiral_outs, job_instance,
science_segs, datafind_outs, output_dir,
parents=tmplt_banks)
else:
job_instance = exe_class(workflow.cp, 'inspiral', ifo=ifos,
out_dir=output_dir,
injection_file=injection_file,
gate_files=gate_files, tags=tags)
multi_ifo_job_setup(workflow, inspiral_outs, job_instance,
science_segs, datafind_outs, output_dir,
parents=tmplt_banks)
return inspiral_outs
def setup_tmpltbank_dax_generated(workflow,
science_segs,
datafind_outs,
output_dir,
tags=None,
link_to_matchedfltr=True,
compatibility_mode=False,
psd_files=None):
'''
Setup template bank jobs that are generated as part of the CBC workflow.
This function will add numerous jobs to the CBC workflow using
configuration options from the .ini file. The following executables are
currently supported:
* lalapps_tmpltbank
* pycbc_geom_nonspin_bank
Parameters
----------
workflow: pycbc.workflow.core.Workflow
An instanced class that manages the constructed workflow.
science_segs : Keyed dictionary of glue.segmentlist objects
scienceSegs[ifo] holds the science segments to be analysed for each
ifo.
datafind_outs : pycbc.workflow.core.FileList
The file list containing the datafind files.
output_dir : path string
The directory where data products will be placed.
tags : list of strings
If given these tags are used to uniquely name and identify output files
that would be produced in multiple calls to this function.
link_to_matchedfltr : boolean, optional (default=True)
If this option is given, the job valid_times will be altered so that
there will be one inspiral file for every template bank and they will
cover the same time span. Note that this option must also be given
during matched-filter generation to be meaningful.
psd_file : pycbc.workflow.core.FileList
The file list containing predefined PSDs, if provided.
Returns
--------
tmplt_banks : pycbc.workflow.core.FileList
The FileList holding the details of all the template bank jobs.
'''
if tags is None:
tags = []
cp = workflow.cp
# 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 ....
ifos = science_segs.keys()
tmplt_bank_exe = os.path.basename(cp.get('executables', 'tmpltbank'))
# Select the appropriate class
exe_class = select_tmpltbank_class(tmplt_bank_exe)
# The exe instance needs to know what data segments are analysed, what is
# discarded etc. This should *not* be hardcoded, so using a new executable
# will require a bit of effort here ....
if link_to_matchedfltr:
# Use this to ensure that inspiral and tmpltbank jobs overlap. This
# means that there will be 1 inspiral job for every 1 tmpltbank and
# the data read in by both will overlap as much as possible. (If you
# ask the template bank jobs to use 2000s of data for PSD estimation
# and the matched-filter jobs to use 4000s, you will end up with
# twice as many matched-filter jobs that still use 4000s to estimate a
# PSD but then only generate triggers in the 2000s of data that the
# template bank jobs ran on.
tmpltbank_exe = os.path.basename(cp.get('executables', 'inspiral'))
link_exe_instance = select_matchedfilter_class(tmpltbank_exe)
else:
link_exe_instance = None
# Set up class for holding the banks
tmplt_banks = FileList([])
# Template banks are independent for different ifos, but might not be!
# Begin with independent case and add after FIXME
for ifo in ifos:
job_instance = exe_class(workflow.cp,
'tmpltbank',
ifo=ifo,
out_dir=output_dir,
tags=tags)
# Check for the write_psd flag
if cp.has_option_tags("workflow-tmpltbank", "tmpltbank-write-psd-file",
tags):
job_instance.write_psd = True
else:
job_instance.write_psd = False
if link_exe_instance:
link_job_instance = link_exe_instance(cp,
'inspiral',
ifo=ifo,
out_dir=output_dir,
tags=tags)
else:
link_job_instance = None
sngl_ifo_job_setup(workflow,
ifo,
tmplt_banks,
job_instance,
science_segs[ifo],
datafind_outs,
link_job_instance=link_job_instance,
allow_overlap=True,
compatibility_mode=compatibility_mode)
return tmplt_banks
def setup_matchedfltr_dax_generated_multi(workflow,
science_segs,
datafind_outs,
tmplt_banks,
output_dir,
injection_file=None,
gate_files=None,
tags=[],
link_to_tmpltbank=False,
compatibility_mode=False):
'''
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 glue.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.
'''
# 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)
# List for holding the output
inspiral_outs = FileList([])
logging.info("Setting up matched-filtering for %s." % (' '.join(ifos), ))
if match_fltr_exe == 'lalapps_coh_PTF_inspiral':
cp.set('inspiral', 'right-ascension', cp.get('workflow', 'ra'))
cp.set('inspiral', 'declination', cp.get('workflow', 'dec'))
cp.set('inspiral', 'sky-error', cp.get('workflow', 'sky-error'))
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,
gate_files=gate_files,
tags=tags)
multi_ifo_coherent_job_setup(workflow,
inspiral_outs,
job_instance,
science_segs,
datafind_outs,
output_dir,
parents=tmplt_banks)
else:
job_instance = exe_class(workflow.cp,
'inspiral',
ifo=ifos,
out_dir=output_dir,
injection_file=injection_file,
gate_files=gate_files,
tags=tags)
multi_ifo_job_setup(workflow,
inspiral_outs,
job_instance,
science_segs,
datafind_outs,
output_dir,
parents=tmplt_banks)
return inspiral_outs
def setup_tmpltbank_dax_generated(workflow, science_segs, datafind_outs,
output_dir, tags=None,
link_to_matchedfltr=True,
compatibility_mode=False,
psd_files=None):
'''
Setup template bank jobs that are generated as part of the CBC workflow.
This function will add numerous jobs to the CBC workflow using
configuration options from the .ini file. The following executables are
currently supported:
* lalapps_tmpltbank
* pycbc_geom_nonspin_bank
Parameters
----------
workflow: pycbc.workflow.core.Workflow
An instanced class that manages the constructed workflow.
science_segs : Keyed dictionary of glue.segmentlist objects
scienceSegs[ifo] holds the science segments to be analysed for each
ifo.
datafind_outs : pycbc.workflow.core.FileList
The file list containing the datafind files.
output_dir : path string
The directory where data products will be placed.
tags : list of strings
If given these tags are used to uniquely name and identify output files
that would be produced in multiple calls to this function.
link_to_matchedfltr : boolean, optional (default=True)
If this option is given, the job valid_times will be altered so that
there will be one inspiral file for every template bank and they will
cover the same time span. Note that this option must also be given
during matched-filter generation to be meaningful.
psd_file : pycbc.workflow.core.FileList
The file list containing predefined PSDs, if provided.
Returns
--------
tmplt_banks : pycbc.workflow.core.FileList
The FileList holding the details of all the template bank jobs.
'''
if tags is None:
tags = []
cp = workflow.cp
# 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 ....
ifos = science_segs.keys()
tmplt_bank_exe = os.path.basename(cp.get('executables', 'tmpltbank'))
# Select the appropriate class
exe_class = select_tmpltbank_class(tmplt_bank_exe)
# The exe instance needs to know what data segments are analysed, what is
# discarded etc. This should *not* be hardcoded, so using a new executable
# will require a bit of effort here ....
if link_to_matchedfltr:
# Use this to ensure that inspiral and tmpltbank jobs overlap. This
# means that there will be 1 inspiral job for every 1 tmpltbank and
# the data read in by both will overlap as much as possible. (If you
# ask the template bank jobs to use 2000s of data for PSD estimation
# and the matched-filter jobs to use 4000s, you will end up with
# twice as many matched-filter jobs that still use 4000s to estimate a
# PSD but then only generate triggers in the 2000s of data that the
# template bank jobs ran on.
tmpltbank_exe = os.path.basename(cp.get('executables', 'inspiral'))
link_exe_instance = select_matchedfilter_class(tmpltbank_exe)
else:
link_exe_instance = None
# Set up class for holding the banks
tmplt_banks = FileList([])
# Template banks are independent for different ifos, but might not be!
# Begin with independent case and add after FIXME
for ifo in ifos:
job_instance = exe_class(workflow.cp, 'tmpltbank', ifo=ifo,
out_dir=output_dir,
tags=tags)
# Check for the write_psd flag
if cp.has_option_tags("workflow-tmpltbank", "tmpltbank-write-psd-file", tags):
job_instance.write_psd = True
else:
job_instance.write_psd = False
if link_exe_instance:
link_job_instance = link_exe_instance(cp, 'inspiral', ifo=ifo,
out_dir=output_dir, tags=tags)
else:
link_job_instance = None
sngl_ifo_job_setup(workflow, ifo, tmplt_banks, job_instance,
science_segs[ifo], datafind_outs,
link_job_instance=link_job_instance,
allow_overlap=True,
compatibility_mode=compatibility_mode)
return tmplt_banks
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', 'longitude',\
str(radians(float(cp.get('workflow', 'ra')))))
cp.set('inspiral', 'latitude',\
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
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
def setup_matchedfltr_dax_generated_multi(workflow, science_segs, datafind_outs,
tmplt_banks, output_dir,
injection_file=None,
tags=None, link_to_tmpltbank=False,
compatibility_mode=False):
'''
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', 'longitude',\
str(radians(float(cp.get('workflow', 'ra')))))
cp.set('inspiral', 'latitude',\
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
