dmpipe package

Module contents

Standalone Analysis Classes

class dmpipe.PrepareTargets(**kwargs)

Bases: fermipy.jobs.link.Link

Small class to preprare analysis pipeline.

Parameters:

• sims (<type 'list'>) – Names of the simulation scenario. [[]]
• alias_dict (<type 'str'>) – File to rename target version keys. [None]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• rosters (<type 'list'>) – Name of a dmsky target roster. [[]]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• config (<type 'str'>) – Path to fermipy config file. [None]
• spatial_models (<type 'list'>) – Types of spatial models to use [[]]

appname = 'dmpipe-prepare-targets'

default_options = {'alias_dict': (None, 'File to rename target version keys.', <type 'str'>), 'config': (None, 'Path to fermipy config file.', <type 'str'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'rosters': ([], 'Name of a dmsky target roster.', <type 'list'>), 'sims': ([], 'Names of the simulation scenario.', <type 'list'>), 'spatial_models': ([], 'Types of spatial models to use', <type 'list'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Prepare directories for target analyses'

linkname_default = 'prepare-targets'

run_analysis(argv)

Run this analysis

usage = 'dmpipe-prepare-targets [options]'

class dmpipe.SpecTable(**kwargs)

Bases: fermipy.jobs.link.Link

Small class to build a table with all the DM spectra for this analysis

Parameters:

• specconfig (<type 'str'>) – Path to DM yaml file defining DM spectra of interest. [None]
• specfile (<type 'str'>) – Path to DM spectrum file. [None]
• config (<type 'str'>) – Path to fermipy config file. [None]
• clobber (<type 'bool'>) – Overwrite existing files. [False]
• ttype (<type 'str'>) – Type of target being analyzed. [None]

appname = 'dmpipe-spec-table'

default_options = {'clobber': (False, 'Overwrite existing files.', <type 'bool'>), 'config': (None, 'Path to fermipy config file.', <type 'str'>), 'specconfig': (None, 'Path to DM yaml file defining DM spectra of interest.', <type 'str'>), 'specfile': (None, 'Path to DM spectrum file.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Build a table with the spectra for DM signals'

linkname_default = 'spec-table'

run_analysis(argv)

Run this analysis

usage = 'dmpipe-spec-table [options]'

class dmpipe.ConvertCastro(**kwargs)

Bases: fermipy.jobs.link.Link

Small class to convert SED to DM space.

Parameters:

• astro_value_file (<type 'str'>) – Path to yaml file with target j_value or d_value [None]
• limitfile (<type 'str'>) – Path to file with limits. [None]
• outfile (<type 'str'>) – Path to output file. [None]
• clobber (<type 'bool'>) – Overwrite existing files. [False]
• sed_file (<type 'str'>) – Path to SED file. [None]
• seed (<type 'int'>) – Seed number for first simulation. [0]
• nsims (<type 'int'>) – Number of simulations to run. [-1]
• astro_prior (<type 'str'>) – Types of Prior on J-factor or D-factor [None]
• specfile (<type 'str'>) – Path to DM spectrum file. [None]

appname = 'dmpipe-convert-castro'

static convert_sed(spec_table, channels, sed_file, outfile, limitfile, **kwargs)

Convert a single SED to DM space.

Parameters:

• spec_table (DMSpecTable) – Object with all the DM spectra
• channels (list) – List of the channels to convert
• sed_file (str) – Path to the SED file
• outfile (str) – Path to write the output DMCastroData object to
• limitfile (str) – Path to write the output limits to.

Keyword Arguments:  

• norm_type (str) – Normalization type to use
• j_factor (dict) – Dictionary with information about the J-factor
• d_factor (dict) – Dictionary with information about the J-factor
• clobber (bool) – Flag to overwrite existing files.

static convert_sed_to_dm(spec_table, sed, channels, **kwargs)

Convert an SED file to a DMCastroData object

Parameters:

• spec_table (DMSpecTable) – Object with all the DM spectra
• sed (CastroData) – Object with the SED data
• channels (list) – List of the channels to convert

Keyword Arguments:  

• norm_type (str) – Normalization type to use
• j_val (dict) – Dictionary with information about the J-factor
• d_val (dict) – Dictionary with information about the D-factor

Returns:

• castro_list (list) – List of the DMCastroData objects with the Likelihood data
• table_list (list) – List of astropy.table.Table objects with the Likelihood data
• name_list (list) – List of names

default_options = {'astro_prior': (None, 'Types of Prior on J-factor or D-factor', <type 'str'>), 'astro_value_file': (None, 'Path to yaml file with target j_value or d_value', <type 'str'>), 'clobber': (False, 'Overwrite existing files.', <type 'bool'>), 'limitfile': (None, 'Path to file with limits.', <type 'str'>), 'nsims': (-1, 'Number of simulations to run.', <type 'int'>), 'outfile': (None, 'Path to output file.', <type 'str'>), 'sed_file': (None, 'Path to SED file.', <type 'str'>), 'seed': (0, 'Seed number for first simulation.', <type 'int'>), 'specfile': (None, 'Path to DM spectrum file.', <type 'str'>)}

description = 'Convert SED to DMCastroData'

static extract_dm_limits(dm_castro_list, channels, alphas, mass_table)

Extract limits from a series of DMCastroData objects for a set of channels and masses

Parameters:

• dm_castro_lsit (list) – DMCastroData objects with all the DM spectra
• channels (list) – List of the channels to convert
• alphas (list) – List of the confidence level threshold to extract limits
• mass_table (astropy.table.Table) – Table with the masses. This just gets appended to the lists of output tables.

Returns:

• castro_list (list) – List of the DMCastroData objects with the Likelihood data
• table_list (list) – List of astropy.table.Table objects with the Likelihood data
• name_list (list) – List of names

static is_ann_sed(sedfile)

static is_decay_sed(sedfile)

linkname_default = 'convert-castro'

run_analysis(argv)

Run this analysis

static select_channels(channels, sedfile)

usage = 'dmpipe-convert-castro [options]'

class dmpipe.StackLikelihood(**kwargs)

Bases: fermipy.jobs.link.Link

Small class to stack likelihoods that were written to DMCastroData objects.

Parameters:

• specconfig (<type 'str'>) – Path to DM yaml file defining DM spectra of interest. [None]
• rosterlist (<type 'str'>) – Path to the roster list. [None]
• clobber (<type 'bool'>) – Overwrite existing files. [False]
• seed (<type 'int'>) – Seed number for first simulation. [0]
• nsims (<type 'int'>) – Number of simulations to run. [20]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• sim (<type 'str'>) – Name of the simulation scenario. [None]
• astro_prior (<type 'str'>) – Types of Prior on J-factor or D-factor [None]

appname = 'dmpipe-stack-likelihood'

default_options = {'astro_prior': (None, 'Types of Prior on J-factor or D-factor', <type 'str'>), 'clobber': (False, 'Overwrite existing files.', <type 'bool'>), 'nsims': (20, 'Number of simulations to run.', <type 'int'>), 'rosterlist': (None, 'Path to the roster list.', <type 'str'>), 'seed': (0, 'Seed number for first simulation.', <type 'int'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>), 'specconfig': (None, 'Path to DM yaml file defining DM spectra of interest.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Stack the likelihood from a set of targets'

static is_ann_roster(roster)

static is_decay_roster(roster)

linkname_default = 'stack-likelihood'

run_analysis(argv)

Run this analysis

static select_channels(channels, roster)

static stack_roster(rost, ttype, channels, astro_prior_key, sim, seed)

Stack all of the DMCastroData in a roster

Parameters:

• rost (list) – List of the targets
• ttype (str) – Type of target, used for bookkeeping and file names
• channels (list) – List of the channels to convert
• j_prior_key (str) – String that identifies the type of prior on the J-factor
• sim (str) – String that specifies the simulation scenario
• seed (int or None) – Key for the simulation instance, used for bookkeeping and file names

Returns:

output – Dictionary of DMCastroData objects, keyed by channel

Return type:

dict

static stack_rosters(roster_dict, ttype, channels, astro_prior_key, sim, seed, clobber)

Stack all of the DMCastroData in a dictionary of rosters

Parameters:

• roster_dict (dict) – Dictionary of all the roster being used.
• ttype (str) – Type of target, used for bookkeeping and file names
• channels (list) – List of the channels to convert
• astro_prior_key (str) – String that identifies the type of prior on the J-factor
• sim (str) – String that specifies the simulation scenario
• seed (int or None) – Key for the simulation instance, used for bookkeeping and file names
• clobber (bool) – Flag to overwrite existing files.

usage = 'dmpipe-stack-likelihood [options]'

static write_fits_files(stacked_dict, resultsfile, limitfile, clobber=False)

Write the stacked DMCastroData object and limits a FITS files

Parameters:

• stacked_dict (dict) – Dictionary of DMCastroData objects, keyed by channel
• resultsfile (str) – Path to the output file to write the DMCastroData objects to
• limitfile (str) – Path to write the upper limits to
• clobber (bool) – Overwrite existing files

static write_stacked(ttype, roster_name, stacked_dict, astro_prior_key, sim, seed, clobber)

Write the stacked DMCastroData object to a FITS file

Parameters:

• ttype (str) – Type of target, used for bookkeeping and file names
• roster_name (str) – Name of the roster, used for bookkeeping and file names
• stacked_dict (dict) – Dictionary of DMCastroData objects, keyed by channel
• astro_prior_key (str) – String that identifies the type of prior on the J-factor
• sim (str) – String that specifies the simulation scenario
• seed (int or None) – Key for the simulation instance, used for bookkeeping and file names
• clobber (bool) – Flag to overwrite existing files.

class dmpipe.CollectLimits(**kwargs)

Bases: fermipy.jobs.link.Link

Small class to collect limit results from a series of simulations.

Parameters:

• outfile (<type 'str'>) – Path to output file. [None]
• seed (<type 'int'>) – Seed number for first simulation. [0]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• limitfile (<type 'str'>) – Path to file with limits. [None]
• summaryfile (<type 'str'>) – Path to file with results summaries. [None]
• specconfig (<type 'str'>) – Path to DM yaml file defining DM spectra of interest. [None]
• nsims (<type 'int'>) – Number of simulations to run. [20]

appname = 'dmpipe-collect-limits'

default_options = {'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'limitfile': (None, 'Path to file with limits.', <type 'str'>), 'nsims': (20, 'Number of simulations to run.', <type 'int'>), 'outfile': (None, 'Path to output file.', <type 'str'>), 'seed': (0, 'Seed number for first simulation.', <type 'int'>), 'specconfig': (None, 'Path to DM yaml file defining DM spectra of interest.', <type 'str'>), 'summaryfile': (None, 'Path to file with results summaries.', <type 'str'>)}

description = 'Collect Limits from simulations'

static is_ann_limits(limitfile)

static is_decay_limits(limitfile)

linkname_default = 'collect-limits'

run_analysis(argv)

Run this analysis

static select_channels(channels, limitfile)

usage = 'dmpipe-collect-limits [options]'

Job-dispatch Analysis Classes

class dmpipe.ConvertCastro_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for the ConvertCastro script

This does a triple loop over targets, spatial profiles and J-factor priors

Parameters:

• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• clobber (<type 'bool'>) – Overwrite existing files. [False]
• seed (<type 'int'>) – Seed number for first simulation. [0]
• nsims (<type 'int'>) – Number of simulations to run. [20]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• targetlist (<type 'str'>) – Path to the target list. [None]
• sim (<type 'str'>) – Name of the simulation scenario. [None]
• specfile (<type 'str'>) – Path to DM spectrum file. [None]

appname = 'dmpipe-convert-castro-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of ConvertCastro

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'clobber': (False, 'Overwrite existing files.', <type 'bool'>), 'nsims': (20, 'Number of simulations to run.', <type 'int'>), 'seed': (0, 'Seed number for first simulation.', <type 'int'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>), 'specfile': (None, 'Path to DM spectrum file.', <type 'str'>), 'targetlist': (None, 'Path to the target list.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Run analyses on a series of ROIs'

job_time = 600

usage = 'dmpipe-convert-castro-sg [options]'

class dmpipe.StackLikelihood_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for StackLikelihood

This loops over the types of priors on the J-factor

Parameters:

• specconfig (<type 'str'>) – Path to DM spectrum file. [None]
• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• clobber (<type 'bool'>) – Overwrite existing files. [False]
• rosterlist (<type 'str'>) – Path to the roster list. [None]
• nsims (<type 'int'>) – Number of simulations to run. [20]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• seed (<type 'int'>) – Seed number for first simulation. [0]
• sim (<type 'str'>) – Name of the simulation scenario. [None]

appname = 'dmpipe-stack-likelihood-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of StackLikelihood

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'clobber': (False, 'Overwrite existing files.', <type 'bool'>), 'nsims': (20, 'Number of simulations to run.', <type 'int'>), 'rosterlist': (None, 'Path to the roster list.', <type 'str'>), 'seed': (0, 'Seed number for first simulation.', <type 'int'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>), 'specconfig': (None, 'Path to DM spectrum file.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Run analyses on a series of ROIs'

job_time = 120

usage = 'dmpipe-stack-likelihood-sg [options]'

class dmpipe.CollectLimits_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for CollectLimits

This does a triple loop over all targets, profiles and j-factor priors.

Parameters:

• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• nsims (<type 'int'>) – Number of simulations to run. [20]
• write_full (<type 'bool'>) – Write file with full collected results [False]
• seed (<type 'int'>) – Seed number for first simulation. [0]
• specconifg (<type 'str'>) – Path to DM yaml file defining DM spectra of interest. [None]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• targetlist (<type 'str'>) – Path to the target list. [None]
• sim (<type 'str'>) – Name of the simulation scenario. [None]

appname = 'dmpipe-collect-limits-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of CollectLimits

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'nsims': (20, 'Number of simulations to run.', <type 'int'>), 'seed': (0, 'Seed number for first simulation.', <type 'int'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>), 'specconifg': (None, 'Path to DM yaml file defining DM spectra of interest.', <type 'str'>), 'targetlist': (None, 'Path to the target list.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>), 'write_full': (False, 'Write file with full collected results', <type 'bool'>)}

description = 'Run analyses on a series of ROIs'

job_time = 120

usage = 'dmpipe-collect-limits-sg [options]'

class dmpipe.CollectStackedLimits_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for this script

This adds the following arguments:

Parameters:

• rosterlist (<type 'str'>) – Path to the target list. [None]
• write_summary (<type 'bool'>) – Write file with summary of collected results [False]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• write_full (<type 'bool'>) – Write file with full collected results [False]
• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• nsims (<type 'int'>) – Number of simulations to run. [20]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• seed (<type 'int'>) – Seed number for first simulation. [0]
• sim (<type 'str'>) – Name of the simulation scenario. [None]

appname = 'dmpipe-collect-stacked-limits-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of CollectLimits

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'nsims': (20, 'Number of simulations to run.', <type 'int'>), 'rosterlist': (None, 'Path to the target list.', <type 'str'>), 'seed': (0, 'Seed number for first simulation.', <type 'int'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>), 'write_full': (False, 'Write file with full collected results', <type 'bool'>), 'write_summary': (False, 'Write file with summary of collected results', <type 'bool'>)}

description = 'Run analyses on a series of ROIs'

job_time = 120

usage = 'dmpipe-collect-stacked-limits-sg [options]'

Standalone Plotting Classes

class dmpipe.PlotDMSpectra(**kwargs)

Bases: fermipy.jobs.link.Link

Small class to plot the DM spectra from pre-computed tables.

Parameters:

• mass (<type 'float'>) – DM particle mass [100]
• outfile (<type 'str'>) – Path to output file. [None]
• chan (<type 'str'>) – DM annihilation channel [bb]
• infile (<type 'str'>) – Path to input file. [None]
• spec_type (<type 'str'>) – Type of flux to consider [eflux]

appname = 'dmpipe-plot-dm-spectra'

default_options = {'chan': ('bb', 'DM annihilation channel', <type 'str'>), 'infile': (None, 'Path to input file.', <type 'str'>), 'mass': (100, 'DM particle mass', <type 'float'>), 'outfile': (None, 'Path to output file.', <type 'str'>), 'spec_type': ('eflux', 'Type of flux to consider', <type 'str'>)}

description = 'Plot the DM spectra stored in pre-computed tables'

linkname_default = 'plot-dm-spectra'

run_analysis(argv)

Run this analysis

usage = 'dmpipe-plot-dm-spectra [options]'

class dmpipe.PlotDM(**kwargs)

Bases: fermipy.jobs.link.Link

Small class to plot the likelihood vs <sigma v> and DM particle mass

Parameters:

• outfile (<type 'str'>) – Path to output file. [None]
• chan (<type 'str'>) – DM annihilation channel [bb]
• infile (<type 'str'>) – Path to input file. [None]
• global_min (<type 'bool'>) – Use global min for castro plots. [False]

appname = 'dmpipe-plot-dm'

default_options = {'chan': ('bb', 'DM annihilation channel', <type 'str'>), 'global_min': (False, 'Use global min for castro plots.', <type 'bool'>), 'infile': (None, 'Path to input file.', <type 'str'>), 'outfile': (None, 'Path to output file.', <type 'str'>)}

description = 'Plot the likelihood vs <sigma v> and DM particle mass'

linkname_default = 'plot-dm'

run_analysis(argv)

Run this analysis

usage = 'dmpipe-plot-dm [options]'

class dmpipe.PlotLimits(**kwargs)

Bases: fermipy.jobs.link.Link

Small class to Plot DM limits on <sigma v> versus mass.

Parameters:

• bands (<type 'str'>) – Name of file with expected limit bands. [None]
• chan (<type 'str'>) – DM annihilation channel [bb]
• infile (<type 'str'>) – Path to input file. [None]
• sim (<type 'str'>) – Name of the simulation scenario. [None]
• outfile (<type 'str'>) – Path to output file. [None]

appname = 'dmpipe-plot-limits'

default_options = {'bands': (None, 'Name of file with expected limit bands.', <type 'str'>), 'chan': ('bb', 'DM annihilation channel', <type 'str'>), 'infile': (None, 'Path to input file.', <type 'str'>), 'outfile': (None, 'Path to output file.', <type 'str'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>)}

description = 'Plot DM limits on <sigma v> versus mass'

linkname_default = 'plot-limits'

run_analysis(argv)

Run this analysis

usage = 'dmpipe-plot-limits [options]'

Job-dispatch Plotting Classes

class dmpipe.PlotLimits_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for PlotLimits

This does a triple nested loop over targets, profiles and j-factor priors

Parameters:

• channels (<type 'list'>) – DM annihilation channels [[]]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• targetlist (<type 'str'>) – Path to the target list. [None]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]

appname = 'dmpipe-plot-limits-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of PlotLimits

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'channels': ([], 'DM annihilation channels', <type 'list'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'targetlist': (None, 'Path to the target list.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Make castro plots for set of targets'

job_time = 60

usage = 'dmpipe-plot-limits-sg [options]'

class dmpipe.PlotStackedLimits_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for PlotStackedLimits

This does a double nested loop over rosters and j-factor priors

Parameters:

• rosterlist (<type 'str'>) – Path to the roster list. [None]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• bands (<type 'str'>) – Name of file with expected limit bands. [None]
• channels (<type 'list'>) – DM annihilation channels [[]]
• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• nsims (<type 'int'>) – Number of simulations to run. [20]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• seed (<type 'int'>) – Seed number for first simulation. [0]
• sim (<type 'str'>) – Name of the simulation scenario. [None]

appname = 'dmpipe-plot-stacked-limits-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of PlotLimits

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'bands': (None, 'Name of file with expected limit bands.', <type 'str'>), 'channels': ([], 'DM annihilation channels', <type 'list'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'nsims': (20, 'Number of simulations to run.', <type 'int'>), 'rosterlist': (None, 'Path to the roster list.', <type 'str'>), 'seed': (0, 'Seed number for first simulation.', <type 'int'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Make castro plots for set of targets'

job_time = 60

usage = 'dmpipe-plot-stacked-limits-sg [options]'

class dmpipe.PlotDM_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for PlotDM

This does a quadruple nested loop over targets, profiles, j-factor priors and channels

Parameters:

• channels (<type 'list'>) – DM annihilation channels [[]]
• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• global_min (<type 'bool'>) – Use global min for castro plots. [False]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• targetlist (<type 'str'>) – Path to the target list. [None]

appname = 'dmpipe-plot-dm-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of PlotDM

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'channels': ([], 'DM annihilation channels', <type 'list'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'global_min': (False, 'Use global min for castro plots.', <type 'bool'>), 'targetlist': (None, 'Path to the target list.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Make castro plots for set of targets'

job_time = 60

usage = 'dmpipe-plot-dm-sg [options]'

class dmpipe.PlotStackedDM_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for PlotDM

This does a triple loop over rosters, j-factor priors and channels

Parameters:

• rosterlist (<type 'str'>) – Path to the roster list. [None]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• channels (<type 'list'>) – DM annihilation channels [[]]
• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• global_min (<type 'bool'>) – Use global min for castro plots. [False]
• nsims (<type 'int'>) – Number of simulations to run. [20]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• seed (<type 'int'>) – Seed number for first simulation. [0]
• sim (<type 'str'>) – Name of the simulation scenario. [None]

appname = 'dmpipe-plot-stacked-dm-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of PlotDM

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'channels': ([], 'DM annihilation channels', <type 'list'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'global_min': (False, 'Use global min for castro plots.', <type 'bool'>), 'nsims': (20, 'Number of simulations to run.', <type 'int'>), 'rosterlist': (None, 'Path to the roster list.', <type 'str'>), 'seed': (0, 'Seed number for first simulation.', <type 'int'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Make castro plots for set of targets'

job_time = 60

usage = 'dmpipe-plot-stacked-dm-sg [options]'

class dmpipe.PlotControlLimits_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for PlotLimits

This does a quadruple loop over rosters, j-factor priors, channels, and expectation bands

Parameters:

• channels (<type 'list'>) – DM annihilation channels [[]]
• rosterlist (<type 'str'>) – Path to the target list. [None]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• ttype (<type 'str'>) – Type of target being analyzed. [None]
• sim (<type 'str'>) – Name of the simulation scenario. [None]

appname = 'dmpipe-plot-control-limits-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of PlotLimits

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'channels': ([], 'DM annihilation channels', <type 'list'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'rosterlist': (None, 'Path to the target list.', <type 'str'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Make limits plots for positve controls'

job_time = 60

usage = 'dmpipe-plot-control-limits-sg [options]'

class dmpipe.PlotFinalLimits_SG(link, **kwargs)

Bases: fermipy.jobs.scatter_gather.ScatterGather

Small class to generate configurations for PlotLimits

This does a quadruple loop over rosters, j-factor priors, channels, and expectation bands

Parameters:

• sims (<type 'list'>) – Names of the simulation scenario. [[]]
• channels (<type 'list'>) – DM annihilation channels [[]]
• rosterlist (<type 'str'>) – Path to the roster list. [None]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• astro_priors (<type 'list'>) – Types of Prior on J-factor or D-factor [[]]
• ttype (<type 'str'>) – Type of target being analyzed. [None]

appname = 'dmpipe-plot-final-limits-sg'

build_job_configs(args)

Hook to build job configurations

clientclass

alias of PlotLimits

default_options = {'astro_priors': ([], 'Types of Prior on J-factor or D-factor', <type 'list'>), 'channels': ([], 'DM annihilation channels', <type 'list'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'rosterlist': (None, 'Path to the roster list.', <type 'str'>), 'sims': ([], 'Names of the simulation scenario.', <type 'list'>), 'ttype': (None, 'Type of target being analyzed.', <type 'str'>)}

description = 'Make final limits plots'

job_time = 60

usage = 'dmpipe-plot-final-limits-sg [options]'

Pipeline Classes

class dmpipe.PipelineData(**kwargs)

Bases: fermipy.jobs.chain.Chain

Chain together the steps of the dSphs pipeline

This chain consists of:

analyze-roi : AnalyzeROI_SG

Do the baseline analysis for each target in the target list.

analyze-sed : AnalyzeSED_SG

Extract the SED for each profile for each target in the target list.

convert-castro : ConvertCastro_SG

Convert the SED to DM-space for each target, profile and J-factor prior type

stack-likelihood : StackLikelihood_SG

Stack the likelihoods for each roster in the analysis, for each J-factor prior type.

Optional plotting modules includde

plot-castro : PlotCastro

Make ‘Castro’ plots of the SEDs for each profile for each target in the target list.

plot-dm : PlotDM_SG

Make DM ‘Castro’ plots for each profile, target, J-factor prior type and channel.

plot-limits : PlotLimits_SG

Make DM ‘Castro’ plots for each profile, target, J-factor prior type and channel.

plot-stacked-dm : PlotStackedDM_SG

Make DM ‘Castro’ plots for each roster, J-factor prior type and channel.

plot-stacked-limits : PlotStackedLimits_SG

Make DM ‘Castro’ plots for each roster, J-factor prior type and channel.

Parameters:

• config (<type 'str'>) – Path to fermipy config file. [None]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• sim (<type 'str'>) – Name of the simulation scenario. [None]

appname = 'dmpipe-pipeline-data'

default_options = {'config': (None, 'Path to fermipy config file.', <type 'str'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>)}

description = 'Data analysis pipeline'

linkname_default = 'pipeline-data'

usage = 'dmpipe-pipeline-data [options]'

class dmpipe.PipelineSim(**kwargs)

Bases: fermipy.jobs.chain.Chain

Chain together the steps of the dSphs pipeline for simulations

This chain consists of:

copy-base-roi : CopyBaseROI_SG

Copy the baseline analysis directory files for each target.

simulate-roi : AnalyzeROI_SG

Simulate the SED analysis for each target and profile in the target list.

convert-castro : ConvertCastro_SG

Convert the SED to DM-space for each target, profile and J-factor prior type

stack-likelihood : StackLikelihood_SG

Stack the likelihoods for each roster in the analysis, for each J-factor prior type.

collect-sed : CollectSED_SG

Collect and summarize the SED results for all the simulations.

collect-limits : CollectLimits_SG

Collect and summarize the limits for all the targets for all the simulations.

collect-stacked-limits : CollectStackedLimits_SG

Collect and summarize the stacked imits for all the targets for all the simulations.

Optional plotting modules includde

plot-stacked-dm : PlotStackedDM_SG

Make DM ‘Castro’ plots for each roster, J-factor prior type and channel.

plot-stacked-limits : PlotStackedLimits_SG

Make DM ‘Castro’ plots for each roster, J-factor prior type and channel.

plot-control-limits : PlotControlLimits_SG

Make DM ‘Castro’ plots for each roster, J-factor prior type and channel.

plot-control-mles : PlotControlMLEs_SG

Make DM Maximum Likelihood estimate plots for each roster, J-factor prior type and channel.

Parameters:

• config (<type 'str'>) – Path to fermipy config file. [None]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]
• sim (<type 'str'>) – Name of the simulation scenario. [None]

appname = 'dmpipe-pipeline-sim'

default_options = {'config': (None, 'Path to fermipy config file.', <type 'str'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>), 'sim': (None, 'Name of the simulation scenario.', <type 'str'>)}

description = 'Run gtselect and gtbin together'

linkname_default = 'pipeline-sim'

usage = 'dmpipe-pipeline-sim [options]'

class dmpipe.PipelineRandom(**kwargs)

Bases: fermipy.jobs.chain.Chain

Chain together the steps of the dSphs pipeline for random

direction studies.

This chain consists of:

copy-base-roi : CopyBaseROI_SG

Copy the baseline analysis directory files for each target.

random-dir-gen : RandomDirGen_SG

Select random directions inside the ROI and generate approriate target files.

analyze-sed : AnalyzeSED_SG

Extract the SED for each profile for each target in the target list.

convert-castro : ConvertCastro_SG

Convert the SED to DM-space for each target, profile and J-factor prior type

stack-likelihood : StackLikelihood_SG

Stack the likelihoods for each roster in the analysis, for each J-factor prior type.

collect-sed : CollectSED_SG

Collect and summarize the SED results for all the simulations.

collect-limits : CollectLimits_SG

Collect and summarize the limits for all the targets for all the simulations.

collect-stacked-limits : CollectStackedLimits_SG

Collect and summarize the stacked imits for all the targets for all the simulations.

Optional plotting modules includde

plot-stacked-dm : PlotStackedDM_SG

Make DM ‘Castro’ plots for each roster, J-factor prior type and channel.

plot-stacked-limits : PlotStackedLimits_SG

Make DM ‘Castro’ plots for each roster, J-factor prior type and channel.

Parameters:

• config (<type 'str'>) – Path to fermipy config file. [None]
• dry_run (<type 'bool'>) – Print commands but do not run them. [False]

appname = 'dmpipe-pipeline-random'

default_options = {'config': (None, 'Path to fermipy config file.', <type 'str'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>)}

description = 'Data analysis pipeline for random directions'

linkname_default = 'pipeline-random'

usage = 'dmpipe-pipeline-random [options]'

class dmpipe.Pipeline(**kwargs)

Bases: fermipy.jobs.chain.Chain

Top level DM pipeline analysis chain.

This chain consists of:

prepare-targets : PrepareTargets

Make the input files need for all the targets in the analysis.

spec-table : SpecTable

Build the FITS table with the DM spectra for all the channels being analyzed.

data : PipelineData

Data analysis pipeline

sim_{sim_name} : PipelineSim

Simulation pipeline for each simulation scenario

random : PipelineRandom

Analysis pipeline for random direction control studies

final-plots : PlotFinalLimits_SG

Make the final analysis results plots

appname = 'dmpipe-pipeline'

default_options = {'config': (None, 'Path to fermipy config file.', <type 'str'>), 'dry_run': (False, 'Print commands but do not run them.', <type 'bool'>)}

description = 'Data analysis pipeline'

linkname_default = 'pipeline'

preconfigure(config_yaml)

Run any links needed to build files that are used in _map_arguments

usage = 'dmpipe-pipeline [options]'