Analysis

FermiAnalysis

class vtspy.analysis.FermiAnalysis(status_file='initial', config_file='config.yaml', overwrite=False, remove_weak_srcs=False, construct_dataset=False, verbosity=True, **kwargs)

Bases: object

This is to perform a simple Fermi-LAT analysis and to prepare the joint-fit analysis. All fermipy.GTanalysis functions and attributes can be accessed with the ‘gta’ arribute. e.g.,

fermi = FermiAnalysis() fermi.gta.optimize()

All information about the status of the analysis (See fermipy documentation for details) will be saved in the numpy array format (npy).

Parameters

  • status_file (str) – status filename (npy) Default: initial

  • config_file (str) – config filename (yaml) Default: config.yaml

  • overwrite (bool) – overwrite the status Default: False

  • remove_weak_srcs (bool) – remove sources with TS of nan or 0 Default: False

  • construct_dataset (bool) – construct dataset for the gammapy analysis Default: False

  • verbosity (int) –

  • **kwargs – passed to fermipy.GTAnalysis module

analysis(jobs=['ts', 'resid', 'sed'], status_file='analyzed', **kwargs)

Perform various analyses: TS map, Residual map, and SED.

Parameters

  • jobs (str or list) – list of jobs, ‘ts’, ‘resid’, and/or ‘sed’. Default: [‘ts’, ‘resid’, ‘sed’]

  • status_file (str) – output status filename (npy) Default: analyzed

  • **kwargs – passed to GTanalysis.sed

construct_dataset(psf_cut=10, min_energy_cut=None, fix_other_srcs=True, eventlist='ft1_00.fits', exposure='bexpmap_00.fits', psf='gtpsf_00.fits', **kwargs)

Construct a dataset for the gammapy analysis.

Parameters

  • psf_cut (float) – apply a psf cut. Ignore energy channels whose

  • number. (99.9% angular resolution is higher than an input) – Default: 10

  • min_energy_cut (float) – apply a minimum energy cut. Default: None

  • fix_other_srcs (bool) – fix source parameters except for the target Default: False

  • eventlist (str) – event list file (gtapp.maketime) Default: ft1_00.fits

  • exposure (str) – exposure map file (gtapp.gtexpmap)

  • psf (str) – psf file (gtapp.gtpsf)

find_sources(status_file='wt_new_srcs', re_fit=True, return_srcs=False, **kwargs)

Find sources within the ROI (using GTanalysis.find_sources).

Parameters

  • status_file (str) – output status filename (npy) Default: wt_new_srcs

  • re_fit (bool) – re fit the ROI with new sources Default: True

  • return_srcs (bool) – return source dictionaries Default: False

  • **kwargs – passed to fit.

Returns

output of GTanalysis.find_sources

Return type

dict

fit(status_file='simple', pre_status=None, free_all=False, free_target=True, remove_weak_srcs=False, fix_index=False, min_ts=5, distance=3.0, optimizer='NEWMINUIT', return_output=False, **kwargs)

Perform a simple fitting with various cuts

Parameters

  • status_file (str) – output status filename (npy) Default: simple

  • free_all (bool) – make the target’s all parameters free Default: True

  • free_all – make all sources parameters free Default: False

  • remove_weak_srcs (bool) – remove sources with TS of nan or 0. This setting will trigger another round of fit process after the first run. Default: False

  • fix_index (bool) – fix spectral shapes for sources for TS less than min_ts Default: False

  • min_ts (int) – minimum TS value for fixing a spectral shape Default: 5

  • distance (float) – parameters for sources outside of a certain distance from the center are fixed, except for the normalization Default: 3.0

  • optimizer (str) – either MINUIT or NEWMINUIT Default: NEWMINUIT

  • return_output (bool) – return the fitting result (dict) Default: False

  • pre_status (str, optional) – input status filename (npy). If not defined, starting from the current status. Default: None

  • **kwargs – passed to fermipy.GTAnalysis.free_sources function

Return

dict: the output of the fitting when return_output is True

load_status(status_file)

Load the status

Parameters

status_file (str) – passed to fermipy.write_roi

peek_events()

Show event information

peek_irfs()

Show instrument response function (irf) information

plot(output, status_file='analyzed', **kwargs)

Show various plots: TS map, Residual map, and SED.

Parameters

  • output (str or list) – list of plots to show Options: [“sqrt_ts”, “npred”, “ts_hist”, “data”, “model”, “sigma”, “excess”, “resid”, “sed”]

  • status_file (str) – read the output (from FermiAnalysis.analysis)

print_association()

Print sources within ROI and their associations.

print_model()

Print source models within ROI

print_params(full_output=False)

Print parameters of sources within ROI

print_target()

Print the target properties

remove_weak_srcs(ts_cut=1, npred_cut=0)

Remove sources within ROI if they are too weak. :param ts_cut: remove sources with a TS cut

Default: 1

Parameters

npred_cut (float) – remove sources with a npred cut Default: 0

save_status(status_file, init=False)

Save the status

Parameters

  • status_file (str) – passed to fermipy.write_roi

  • init (bool) – check whether this is the initial analysis. Default: False

set_target(target)

Set/change the target

Parameters

target (str or int) – target name or id

property target

Return: fermipy.roi_model.Source

property target_id

Return: int: target id

property target_name

Return: str: target name

property verbosity

Return: int

VeritasAnalysis

class vtspy.analysis.VeritasAnalysis(status_file='initial', config_file='config.yaml', overwrite=False, verbosity=1, **kwargs)

Bases: object

add_exclusion_region(coord=None, name=None, radius=0.3, update_dataset=False, **kwargs)

Add exclusion region manually

Parameters

  • coord (list, optioanl) – [ra, dec]

  • name (list, optional) – [ra, dec]

  • radius (float) – size of an exlusion region

  • update_dataset (bool) – update dataset with the new exclusion region

analysis(jobs=['sed'], status_file='analyzed', **kwargs)

Perform a simple analysis, e.g., SED, lightcurve

Parameters

  • jobs (list) – list of jobs, ‘sed’, and/or ‘lc’. Default: [‘sed’]

  • status_file (str) – status filename (pickle) Default: analyzed

  • **kwargs – passed to vtspy.utils.define_time_intervals

construct_dataset(**kwargs)

Construct dataset for the gammapy analysis

Parameters

**kwargs – e.g., eff_cut, bias_cut, max_region_number, or others

property energy_bins

Return: MapAxis.edges: energy bins used in flux points.

fit(model='PowerLaw', status_file='simple', save_status=True, **kwargs)

Perform a simple fitting with a given model: PowerLaw, LogParabola, …

Parameters

  • model (str or gammapy.models) – model name or function Default: “PowerLaw”

  • status_file (str) – status filename (pickle) Default: simple

  • save_status (bool) – Default: True

  • **kwargs – passed to vtspy.model.default_model

load_status(status_file)

Load the status

Args: status_file (str): the name of status

property obs_ids

Return: list: list of observation id

peek_dataset()

Show dataset information

plot(output, **kwargs)

Show various results: fit result, flux, SED, and lightcurve

Parameters

  • output (str) – a plot to show Options: [“roi”, “fit”, “flux”, “sed”, “lc”]

  • filename (str) – read the output (from FermiAnalysis.analysis)

print_flux()
Returns

flux points in SED

Return type

astropy.table

print_lightcurve(sed_type='eflux')
Returns

flux points in lightcurve

Return type

astropy.table

print_models()
Returns

fit parameters

Return type

astropy.table

save_status(status_file, init=False)

Save the status

Parameters

  • status_file (str) – the name of status

  • init (bool) – check whether this is the initial analysis. Default: False

setup(**kwargs)

This is to initialize the VERITAS analysis; e.g., construct datasets To change the setting for this setup, check config file.

VeritasAnalysis.config

Parameters

**kwargs – passed to VeritasAnalysis.construct_dataset

property target

Return: astropy.SkyCoord

property target_name

Return: str: target name

property verbosity

Return: int

JointAnalysis

class vtspy.analysis.JointAnalysis(veritas=None, fermi=None, config_file='config.yaml', verbosity=1, **kwargs)

Bases: object

This is to perform a joint VERITAS and Fermi-LAT analysis. This class exploits results from VeritasAnalysis and FermiAnalysis.

Parameters

  • veritas (str or vtspy.VeritasAnalysis) – status filename or class for VERITAS Default: initial

  • fermi (str or vtspy.FermiAnalysis) – status filename or class for Fermi-LAT Default: initial

  • config_file (str) – config filename (yaml) Default: config.yaml

  • verbosity (int) –

  • **kwargs – passed to JointAnalysis.construct_dataset

add_dataset(data, sync=True, model=None, **kwargs)

To add a new dataset.

Parameters

  • data (str or gammapy.estimators.FluxPoints) – new dataset

  • sync (bool) – synchronize with the target model Default: True

  • model (gammapy.modeling.models.SpectralModel) – a model for the new dataset

  • **kwargs – passed to gammapy.estimators.FluxPointsDataset

analysis(**kwargs)

Perform a SED analysis.

Parameters

**kwargs – passed to FluxPointsEstimator

change_model(model, optimize=False, **kwargs)

To change a target model

Parameters

  • model (str or gammapy.modeling.models.SpectralModel) – a new target model

  • optimize (bool) – perform optimization (JointAnalysis.optimize) Default: False

  • **kwargs – passed to JointAnalysis.optimize

fit(**kwargs)

Perform a joint-fit analysis

Parameters

**kwargs – passed to vtspy.JointAnalysis.optimize

load_status(status_file, reconstruct=False)

Load the status

Args: status_file (str): the name of status reconstruct (bool): re-construct the datasets

Default: False

optimize(method='flux', instrument='VERITAS', **kwargs)

To find good initial parameters for a given model.

Parameters

  • method (str) – either “flux”, “rough” or “inst”. The “flux” method will fit flux points from VeritasAnalysis.analysis(“sed”) and FermiAnalysis.analysis(“sed”). The “rough” method will fit the dataset with tol of 10 and strategy of 0 (fast). The “inst” method will fit the model with one of datasets (defined by instrument) Default: flux

  • model (gammapy.modeling.models.SpectralModel) – a model to fit Default: None (fit with the current target model)

  • instrument (str) – instrument used for the “inst” method Default: VERITAS

  • **kwargs –

plot(output, **kwargs)

Show various results: SED

Parameters

  • output (str) – a plot to show Options: [“sed”]

  • **kwargs – passed to vtspy.JointAnalysis.plot_sed

plot_sed(fermi=True, veritas=True, joint=True, show_model=True, show_flux_points=True, **kwargs)

Plot a spectral energy distribution (SED) with a model and flux points.

Parameters

  • fermi (bool) – show Fermi-LAT results Default: True

  • veritas (bool) – show VERITAS results Default: True

  • fermi – show Joint-fit results Default: True

  • show_flux_points (bool) – slow flux points Default: True

  • show_model (bool) – Default: True

  • **kwargs – passed to plotting.plot_sed

print_datasets()

Print datasets

Returns

astropy.table

print_models(full_output=False)

Print model and parameters

Parameters

full_output (bool) – return a target model or all models default: False

Returns

astropy.table

save_status(status_file)

Save the status

Parameters

status_file (str) – the name of status

property target_model

Return: gammapy.modeling.models.SkyModel

property target_name

Return: tuple: target name (veritas and fermi)

property verbosity

Return: int