skyllh.analyses.i3.publicdata_ps package

Submodules

skyllh.analyses.i3.publicdata_ps.aeff module

skyllh.analyses.i3.publicdata_ps.aeff.load_effective_area_array(pathfilenames)

Loads the (nbins_decnu, nbins_log10enu)-shaped 2D effective area array from the given data file.

Parameters:

pathfilename (str | list of str) – The file name of the data file.

Returns:

  • aeff_decnu_log10enu ((nbins_decnu, nbins_log10enu)-shaped 2D ndarray) – The ndarray holding the effective area for each (dec_nu,log10(E_nu/GeV)) bin.

  • decnu_binedges_lower ((nbins_decnu,)-shaped ndarray) – The ndarray holding the lower bin edges of the dec_nu axis.

  • decnu_binedges_upper ((nbins_decnu,)-shaped ndarray) – The ndarray holding the upper bin edges of the dec_nu axis.

  • log10_enu_binedges_lower ((nbins_log10enu,)-shaped ndarray) – The ndarray holding the lower bin edges of the log10(E_nu/GeV) axis.

  • log10_enu_binedges_upper ((nbins_log10enu,)-shaped ndarray) – The ndarray holding the upper bin edges of the log10(E_nu/GeV) axis.

class skyllh.analyses.i3.publicdata_ps.aeff.PDAeff(pathfilenames, src_dec=None, min_log10enu=None, max_log10enu=None, **kwargs)

Bases: object

This class provides a representation of the effective area provided by the public data.

Creates an effective area instance by loading the effective area data from the given file.

Parameters:

  • pathfilenames (str | list of str) – The path file names of the effective area data file(s) which should be used for this public data effective area instance.

  • src_dec (float | None) – The source declination in radians for which detection probabilities should get pre-calculated using the get_detection_prob_for_decnu method.

  • min_log10enu (float | None) – The minimum log10(E_nu/GeV) value that should be used for calculating the detection probability. If None, the lowest available neutrino energy bin edge of the effective area is used.

  • max_log10enu (float | None) – The maximum log10(E_nu/GeV) value that should be used for calculating the detection probability. If None, the highest available neutrino energy bin edge of the effective area is used.

property decnu_binedges

(read-only) The bin edges of the neutrino declination axis in radians.

property sin_decnu_binedges

(read-only) The sin of the bin edges of the neutrino declination in radians.

property decnu_bincenters

(read-only) The bin center values of the neutrino declination axis in radians.

property n_decnu_bins

(read-only) The number of bins of the neutrino declination axis.

property log10_enu_binedges

(read-only) The bin edges of the log10(E_nu/GeV) neutrino energy axis.

property log10_enu_bincenters

(read-only) The bin center values of the log10(E_nu/GeV) neutrino energy axis.

property n_log10_enu_bins

(read-only) The number of bins of the log10 neutrino energy axis.

property aeff_decnu_log10enu

(read-only) The effective area in cm^2 as (n_decnu,n_log10enu)-shaped 2D numpy ndarray.

create_sin_decnu_log10_enu_spline()

DEPRECATED! Creates a FctSpline2D object representing a 2D spline of the effective area in sin(dec_nu)-log10(E_nu/GeV)-space.

Returns:

spl (FctSpline2D instance) – The FctSpline2D instance representing a spline in the sin(dec_nu)-log10(E_nu/GeV)-space.

get_aeff_for_decnu(decnu)

Retrieves the effective area as function of log10_enu.

Parameters:

decnu (float) – The true neutrino declination.

Returns:

aeff ((n,)-shaped numpy ndarray) – The effective area in cm^2 for the given true neutrino declination as a function of log10 true neutrino energy.

get_detection_prob_for_decnu(decnu, enu_min, enu_max, enu_range_min, enu_range_max)

Calculates the detection probability for given true neutrino energy ranges for a given neutrino declination.

Parameters:

  • decnu (float) – The neutrino declination in radians.

  • enu_min (float | ndarray of float) – The minimum energy in GeV.

  • enu_max (float | ndarray of float) – The maximum energy in GeV.

  • enu_range_min (float) – The minimum energy in GeV of the entire energy range.

  • enu_range_max (float) – The maximum energy in GeV of the entire energy range.

Returns:

det_prob (ndarray of float) – The neutrino energy detection probabilities for the given true enegry ranges.

skyllh.analyses.i3.publicdata_ps.backgroundpdf module

class skyllh.analyses.i3.publicdata_ps.backgroundpdf.PDEnergyPDF(data_logE, data_sinDec, data_mcweight, data_physicsweight, logE_binning, sinDec_binning, smoothing_filter, kde_smoothing=False)

Bases: EnergyPDF, UsesBinning

This is the base class for IceCube specific energy PDF models. IceCube energy PDFs depend solely on the energy and the zenith angle, and hence, on the declination of the event.

The IceCube energy PDF is modeled as a 1d histogram in energy, but for different sin(declination) bins, hence, stored as a 2d histogram.

Creates a new IceCube energy PDF object.

Parameters:

  • data_logE (1d ndarray) – The array holding the log10(E) values of the events.

  • data_sinDec (1d ndarray) – The array holding the sin(dec) values of the events.

  • data_mcweight (1d ndarray) – The array holding the monte-carlo weights of the events. The final data weight will be the product of data_mcweight and data_physicsweight.

  • data_physicsweight (1d ndarray) – The array holding the physics weights of the events. The final data weight will be the product of data_mcweight and data_physicsweight.

  • logE_binning (BinningDefinition) – The binning definition for the log(E) axis.

  • sinDec_binning (BinningDefinition) – The binning definition for the sin(declination) axis.

  • smoothing_filter (SmoothingFilter instance | None) – The smoothing filter to use for smoothing the energy histogram. If None, no smoothing will be applied.

  • kde_smoothing (bool) – Apply a kde smoothing to the energy pdf for each bin in sin(dec). This is useful for signal injections, because it ensures that the background is not zero when injecting high energy events. Default: False.

property hist_smoothing_method

The HistSmoothingMethod instance defining the smoothing filter of the energy PDF histogram.

property hist

(read-only) The 2D logE-sinDec histogram array.

property hist_mask_mc_covered

(read-only) The boolean ndarray holding the mask of the 2D histogram bins for which there is monte-carlo coverage.

property hist_mask_mc_covered_zero_physics

(read-only) The boolean ndarray holding the mask of the 2D histogram bins for which there is monte-carlo coverage but zero physics contribution.

property hist_mask_mc_covered_with_physics

(read-only) The boolean ndarray holding the mask of the 2D histogram bins for which there is monte-carlo coverage and has physics contribution.

get_prob(tdm, fitparams=None, tl=None)

Calculates the energy probability (in logE) of each event.

Parameters:

  • tdm (instance of TrialDataManager) –

    The TrialDataManager instance holding the data events for which the probability should be calculated for. The following data fields must exist:

    • ’log_energy’float

      The logarithm of the energy value of the event.

    • ’sin_dec’float

      The sin(declination) value of the event.

  • fitparams (None) – Unused interface parameter.

  • tl (TimeLord instance | None) – The optional TimeLord instance that should be used to measure timing information.

Returns:

prob (1D (N_events,) shaped ndarray) – The array with the energy probability for each event.

class skyllh.analyses.i3.publicdata_ps.backgroundpdf.PDDataBackgroundI3EnergyPDF(data_exp, logE_binning, sinDec_binning, smoothing_filter=None, kde_smoothing=False)

Bases: PDEnergyPDF, IsBackgroundPDF

This is the IceCube energy background PDF, which gets constructed from experimental data. This class is derived from I3EnergyPDF.

Constructs a new IceCube energy background PDF from experimental data.

Parameters:

  • data_exp (instance of DataFieldRecordArray) –

    The array holding the experimental data. The following data fields must exist:

    • ’log_energy’float

      The logarithm of the reconstructed energy value of the data event.

    • ’sin_dec’float

      The sine of the reconstructed declination of the data event.

  • logE_binning (BinningDefinition) – The binning definition for the binning in log10(E).

  • sinDec_binning (BinningDefinition) – The binning definition for the sin(declination).

  • smoothing_filter (SmoothingFilter instance | None) – The smoothing filter to use for smoothing the energy histogram. If None, no smoothing will be applied.

class skyllh.analyses.i3.publicdata_ps.backgroundpdf.PDMCBackgroundI3EnergyPDF(pdf_log10emu_sindecmu, log10emu_binning, sindecmu_binning, **kwargs)

Bases: EnergyPDF, IsBackgroundPDF, UsesBinning

This class provides a background energy PDF constructed from the public data and a monte-carlo background flux model.

Constructs a new background energy PDF with the given PDF data and binning.

Parameters:

  • pdf_log10emu_sindecmu (2D numpy ndarray) – The (n_log10emu, n_sindecmu)-shaped 2D numpy ndarray holding the PDF values in unit 1/log10(E_mu/GeV). A copy of this data will be created and held within this class instance.

  • log10emu_binning (BinningDefinition) – The binning definition for the binning in log10(E_mu/GeV).

  • sindecmu_binning (BinningDefinition) – The binning definition for the binning in sin(dec_mu).

assert_is_valid_for_trial_data(tdm)

Checks if this PDF covers the entire value range of the trail data events.

Parameters:

tdm (TrialDataManager instance) –

The TrialDataManager instance holding the data events. The following data fields need to exist:

’sin_dec’

’log_energy’

Raises:

ValueError – If parts of the trial data is outside the value range of this PDF.

get_prob(tdm, params=None, tl=None)

Gets the probability density for the given trial data events.

Parameters:

  • tdm (TrialDataManager instance) –

    The TrialDataManager instance holding the data events. The following data fields need to exist:

    ’sin_dec’

    ’log_energy’

  • params (dict | None) – The dictionary containing the parameter names and values for which the probability should get calculated. By definition of this PDF, this is Ǹone, because this PDF does not depend on any parameters.

  • tl (TimeLord instance | None) – The optional TimeLord instance that should be used to measure timing information.

Returns:

prob ((N_events,)-shaped numpy ndarray) – The 1D numpy ndarray with the probability density for each event.

skyllh.analyses.i3.publicdata_ps.bkg_flux module

skyllh.analyses.i3.publicdata_ps.bkg_flux.get_dOmega(dec_min, dec_max)

Calculates the solid angle given two declination angles.

Parameters:

  • dec_min (float | array of float) – The smaller declination angle.

  • dec_max (float | array of float) – The larger declination angle.

Returns:

solidangle (float | array of float) – The solid angle corresponding to the two given declination angles.

skyllh.analyses.i3.publicdata_ps.bkg_flux.southpole_zen2dec(zen)

Converts zenith angles at the South Pole to declination angles.

Parameters:

zen ((n,)-shaped 1d numpy ndarray) – The numpy ndarray holding the zenith angle values in radians.

Returns:

dec ((n,)-shaped 1d numpy ndarray) – The numpy ndarray holding the declination angle values in radians.

skyllh.analyses.i3.publicdata_ps.bkg_flux.get_flux_atmo_decnu_log10enu(flux_pathfilename, log10_enu_max=9)

Constructs the atmospheric flux map function f_atmo(log10(E_nu/GeV),dec_nu) in unit 1/(GeV cm^2 sr s).

Parameters:

  • flux_pathfilename (str) – The pathfilename of the file containing the MCEq fluxes.

  • log10_enu_max (float) – The log10(E/GeV) value of the maximum neutrino energy to be considered.

Returns:

  • flux_atmo ((n_dec, n_e_grid)-shaped 2D numpy ndarray) – The numpy ndarray holding the the atmospheric neutrino flux function in unit 1/(GeV cm^2 sr s).

  • decnu_binedges ((n_decnu+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the dec_nu bin edges.

  • log10_enu_binedges ((n_enu+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the neutrino energy bin edges in log10.

skyllh.analyses.i3.publicdata_ps.bkg_flux.get_flux_astro_decnu_log10enu(decnu_binedges, log10_enu_binedges)

Constructs the astrophysical neutrino flux function f_astro(log10(E_nu/GeV),dec_nu) in unit 1/(GeV cm^2 sr s).

It uses the best fit from the IceCube publication [1].

Parameters:

  • decnu_binedges ((n_decnu+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the dec_nu bin edges.

  • log10_enu_binedges ((n_enu+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the log10 values of the neutrino energy bin edges in GeV.

Returns:

f_astro ((n_decnu, n_log10enu)-shaped 2D numpy ndarray) – The numpy ndarray holding the astrophysical flux values in unit 1/(GeV cm^2 sr s).

References

[1] https://arxiv.org/pdf/2111.10299.pdf

skyllh.analyses.i3.publicdata_ps.bkg_flux.convert_flux_bkg_to_pdf_bkg(f_bkg, decnu_binedges, log10_enu_binedges)

Converts the given background flux function f_bkg into a background flux PDF in unit 1/(log10(E/GeV) rad).

Parameters:

  • f_bkg ((n_decnu, n_enu)-shaped 2D numpy ndarray) – The numpy ndarray holding the background flux values in unit 1/(GeV cm^2 s sr).

  • decnu_binedges ((n_decnu+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the dec_nu bin edges in radians.

  • log10_enu_binedges ((n_enu+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the log10 values of the neutrino energy bin edges in GeV.

Returns:

p_bkg ((n_decnu, n_enu)-shaped 2D numpy ndarray) – The numpy ndarray holding the background flux pdf values.

skyllh.analyses.i3.publicdata_ps.bkg_flux.get_pd_atmo_decnu_Enu(flux_pathfilename, log10_true_e_max=9)

Constructs the atmospheric neutrino PDF p_atmo(E_nu,dec_nu) in unit 1/(GeV rad).

Parameters:

  • flux_pathfilename (str) – The pathfilename of the file containing the MCEq flux.

  • log10_true_e_max (float) – The log10(E/GeV) value of the maximum true energy to be considered.

Returns:

  • pd_atmo ((n_dec, n_e_grid)-shaped 2D numpy ndarray) – The numpy ndarray holding the the atmospheric neutrino PDF in unit 1/(GeV rad).

  • decnu_binedges ((n_decnu+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the dec_nu bin edges.

  • log10_e_grid_edges ((n_e_grid+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the energy bin edges in log10.

skyllh.analyses.i3.publicdata_ps.bkg_flux.get_pd_atmo_E_nu_sin_dec_nu(flux_pathfilename)

Constructs the atmospheric energy PDF p_atmo(E_nu|sin(dec_nu)) in unit 1/GeV.

Parameters:

flux_pathfilename (str) – The pathfilename of the file containing the MCEq flux.

Returns:

  • pd_atmo ((n_sin_dec, n_e_grid)-shaped 2D numpy ndarray) – The numpy ndarray holding the the atmospheric energy PDF in unit 1/GeV.

  • sin_dec_binedges (numpy ndarray) – The (n_sin_dec+1,)-shaped 1D numpy ndarray holding the sin(dec) bin edges.

  • log10_e_grid_edges (numpy ndarray) – The (n_e_grid+1,)-shaped 1D numpy ndarray holding the energy bin edges in log10.

skyllh.analyses.i3.publicdata_ps.bkg_flux.get_pd_astro_E_nu_sin_dec_nu(sin_dec_binedges, log10_e_grid_edges)

Constructs the astrophysical energy PDF p_astro(E_nu|sin(dec_nu)) in unit 1/GeV. It uses the best fit from the IceCube publication [1].

Parameters:

  • sin_dec_binedges ((n_sin_dec+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the sin(dec) bin edges.

  • log10_e_grid_edges ((n_e_grid+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the log10 values of the energy bin edges in GeV of the energy grid.

Returns:

pd_astro ((n_sin_dec, n_e_grid)-shaped 2D numpy ndarray) – The numpy ndarray holding the energy probability density values p(E_nu|sin_dec_nu) in unit 1/GeV.

References

[1] https://arxiv.org/pdf/2111.10299.pdf

skyllh.analyses.i3.publicdata_ps.bkg_flux.get_pd_bkg_E_nu_sin_dec_nu(pd_atmo, pd_astro, log10_e_grid_edges)

Constructs the total background flux probability density p_bkg(E_nu|sin(dec_nu)) in unit 1/GeV.

Parameters:

  • pd_atmo ((n_sin_dec, n_e_grid)-shaped 2D numpy ndarray) – The numpy ndarray holding the probability density values p(E_nu|sin(dec_nu)) in 1/GeV of the atmospheric flux.

  • pd_astro ((n_sin_dec, n_e_grid)-shaped 2D numpy ndarray) – The numpy ndarray holding the probability density values p(E_nu|sin(dec_nu)) in 1/GeV of the astrophysical flux.

  • log10_e_grid_edges ((n_e_grid+1,)-shaped numpy ndarray) – The numpy ndarray holding the log10 values of the energy grid bin edges in GeV.

Returns:

pd_bkg ((n_sin_dec, n_e_grid)-shaped 2D numpy ndarray) – The numpy ndarray holding total background probability density values p_bkg(E_nu|sin(dec_nu)) in unit 1/GeV.

skyllh.analyses.i3.publicdata_ps.detsigyield module

class skyllh.analyses.i3.publicdata_ps.detsigyield.PublicDataPowerLawFluxPointLikeSourceI3DetSigYieldImplMethod(gamma_grid, spline_order_sinDec=2, spline_order_gamma=2, ncpu=None)

Bases: PowerLawFluxPointLikeSourceI3DetSigYieldImplMethod, IsParallelizable

This detector signal yield constructor class constructs a detector signal yield instance for a variable power law flux model, which has the spectral index gamma as fit parameter, assuming a point-like source. It constructs a two-dimensional spline function in sin(dec) and gamma, using a scipy.interpolate.RectBivariateSpline. Hence, the detector signal yield can vary with the declination and the spectral index, gamma, of the source.

This detector signal yield implementation method works with a PowerLawFlux flux model.

It is tailored to the IceCube detector at the South Pole, where the effective area depends solely on the zenith angle, and hence on the declination, of the source.

It takes the effective area for the detector signal yield from the auxilary detector effective area data file given by the public data.

Creates a new IceCube detector signal yield constructor instance for a power law flux model. It requires the effective area from the public data, and a gamma parameter grid to compute the gamma dependency of the detector signal yield.

Parameters:

  • gamma_grid (ParameterGrid instance) – The ParameterGrid instance which defines the grid of gamma values.

  • spline_order_sinDec (int) – The order of the spline function for the logarithmic values of the detector signal yield along the sin(dec) axis. The default is 2.

  • spline_order_gamma (int) – The order of the spline function for the logarithmic values of the detector signal yield along the gamma axis. The default is 2.

  • ncpu (int | None) – The number of CPUs to utilize. Global setting will take place if not specified, i.e. set to None.

construct_detsigyield(dataset, data, fluxmodel, livetime, ppbar=None)

Constructs a detector signal yield 2-dimensional log spline function for the given power law flux model with varying gamma values.

Parameters:

  • dataset (Dataset instance) – The Dataset instance holding the sin(dec) binning definition.

  • data (DatasetData instance) – The DatasetData instance holding the monte-carlo event data. This implementation loads the effective area from the provided public data and hence does not need monte-carlo data.

  • fluxmodel (FluxModel) – The flux model instance. Must be an instance of PowerLawFlux.

  • livetime (float | Livetime instance) – The live-time in days or an instance of Livetime to use for the detector signal yield.

  • ppbar (ProgressBar instance | None) – The instance of ProgressBar of the optional parent progress bar.

Returns:

detsigyield (PowerLawFluxPointLikeSourceI3DetSigYield instance) – The DetSigYield instance for a point-like source with a power law flux with variable gamma parameter.

skyllh.analyses.i3.publicdata_ps.mcbkg_ps module

The mcbkg_ps analysis is a multi-dataset time-integrated single source analysis with a two-component likelihood function using a spacial and an energy event PDF. It initializes the background energy pdf using auxiliary fluxes and pdfs, which are generated by running scripts/mceq_atm_bkg.py script.

skyllh.analyses.i3.publicdata_ps.mcbkg_ps.psi_func(tdm, src_hypo_group_manager, fitparams)

Function to calculate the opening angle between the source position and the event’s reconstructed position.

skyllh.analyses.i3.publicdata_ps.mcbkg_ps.TXS_location()
skyllh.analyses.i3.publicdata_ps.mcbkg_ps.create_analysis(rss, datasets, source, refplflux_Phi0=1, refplflux_E0=1000.0, refplflux_gamma=2, ns_seed=10.0, gamma_seed=3, cache_dir='.', cap_ratio=False, compress_data=False, keep_data_fields=None, optimize_delta_angle=10, efficiency_mode=None, tl=None, ppbar=None)

Creates the Analysis instance for this particular analysis.

Parameters:

datasetslist of Dataset instances

The list of Dataset instances, which should be used in the analysis.

sourcePointLikeSource instance

The PointLikeSource instance defining the point source position.

refplflux_Phi0float

The flux normalization to use for the reference power law flux model.

refplflux_E0float

The reference energy to use for the reference power law flux model.

refplflux_gammafloat

The spectral index to use for the reference power law flux model.

ns_seedfloat

Value to seed the minimizer with for the ns fit.

gamma_seedfloat | None

Value to seed the minimizer with for the gamma fit. If set to None, the refplflux_gamma value will be set as gamma_seed.

cache_dirstr

The cache directory where to look for cached data, e.g. signal PDFs.

compress_databool

Flag if the data should get converted from float64 into float32.

keep_data_fieldslist of str | None

List of additional data field names that should get kept when loading the data.

optimize_delta_anglefloat

The delta angle in degrees for the event selection optimization methods.

efficiency_modestr | None

The efficiency mode the data should get loaded with. Possible values are:

  • ‘memory’:

    The data will be load in a memory efficient way. This will require more time, because all data records of a file will be loaded sequentially.

  • ‘time’:

    The data will be loaded in a time efficient way. This will require more memory, because each data file gets loaded in memory at once.

The default value is 'time'. If set to None, the default value will be used.

tlTimeLord instance | None

The TimeLord instance to use to time the creation of the analysis.

ppbarProgressBar instance | None

The instance of ProgressBar for the optional parent progress bar.

returns:

analysis (SpatialEnergyTimeIntegratedMultiDatasetSingleSourceAnalysis) – The Analysis instance for this analysis.

skyllh.analyses.i3.publicdata_ps.pdfratio module

class skyllh.analyses.i3.publicdata_ps.pdfratio.PDPDFRatio(sig_pdf_set, bkg_pdf, cap_ratio=False, **kwargs)

Bases: SigSetOverBkgPDFRatio

Creates a PDFRatio instance for the public data. It takes a signal PDF set for different discrete gamma values.

Parameters:

  • sig_pdf_set (instance of PDSignalEnergyPDFSet) – The PDSignalEnergyPDFSet instance holding the set of signal energy PDFs.

  • bkg_pdf (instance of PDDataBackgroundI3EnergyPDF) – The PDDataBackgroundI3EnergyPDF instance holding the background energy PDF.

  • cap_ratio (bool) – Switch whether the S/B PDF ratio should get capped where no background is available. Default is False.

property cap_ratio

Boolean switch whether to cap the ratio where no background information is available (True) or use the smallest possible floating point number greater than zero as background pdf value (False).

get_ratio(tdm, fitparams=None, tl=None)

Calculates the PDF ratio values for all the events.

Parameters:

  • tdm (instance of TrialDataManager) – The TrialDataManager instance holding the trial data events for which the PDF ratio values should get calculated.

  • fitparams (dict | None) – The dictionary with the parameter name-value pairs. It can be None, if the PDF ratio does not depend on any parameters.

  • tl (TimeLord instance | None) – The optional TimeLord instance that should be used to measure timing information.

Returns:

ratios ((N_events,)-shaped 1d numpy ndarray of float) – The PDF ratio value for each trial event.

get_gradient(tdm, fitparams, fitparam_name)

Retrieves the PDF ratio gradient for the pidx’th fit parameter.

Parameters:

  • tdm (instance of TrialDataManager) – The TrialDataManager instance holding the trial event data for which the PDF ratio gradient values should get calculated.

  • fitparams (dict) – The dictionary with the fit parameter values.

  • fitparam_name (str) – The name of the fit parameter for which the gradient should get calculated.

skyllh.analyses.i3.publicdata_ps.signal_generator module

class skyllh.analyses.i3.publicdata_ps.signal_generator.PDDatasetSignalGenerator(ds, src_dec, effA=None, sm=None, **kwargs)

Bases: object

This class provides a signal generation method for a point-like source seen in the IceCube detector using one dataset of the 10 years public data release. It is used by the PDSignalGenerator class in a loop over all the datasets that have been added to the analysis.

Creates a new instance of the signal generator for generating signal events from a specific public data dataset.

Parameters:

dsDataset instance

Dataset instance for which signal events should get generated for.

src_decfloat

The declination of the source in radians.

effAPDAeff | None

Representation of the effective area provided by the public data.

smPDSmearingMatrix | None

Representation of the smearing matrix provided by the public data.

energy_filter = <numpy.vectorize object>
generate_signal_events(rss, src_dec, src_ra, flux_model, n_events, energy_cut_spline=None, cut_sindec=None)

Generates n_events signal events for the given source location and flux model.

Paramters

rss : RandomStateService src_dec : float

Declination coordinate of the injection point.

src_rafloat

Right ascension coordinate of the injection point.

flux_modelFluxModel

Instance of the FluxModel class.

n_eventsint

Number of signal events to be generated.

energy_cut_splinesscipy.interpolate.UnivariateSpline

A spline of E(sin_dec) that defines the declination dependent energy cut in the IceCube southern sky.

cut_sindecfloat

The sine of the declination to start applying the energy cut. The cut will be applied from this declination down.

returns:

events (numpy record array) – The numpy record array holding the event data. It contains the following data fields:

  • ‘isvalid’

  • ‘log_true_energy’

  • ‘log_energy’

  • ‘dec’

  • ‘ra’

  • ‘ang_err’

class skyllh.analyses.i3.publicdata_ps.signal_generator.PDSignalGenerator(src_hypo_group_manager, dataset_list, data_list=None, llhratio=None, energy_cut_splines=None, cut_sindec=None)

Bases: SignalGeneratorBase

This class provides a signal generation method for a point-like source seen in the IceCube detector using the 10 years public data release.

Constructs a new signal generator instance.

Parameters:

  • src_hypo_group_manager (SourceHypoGroupManager instance) – The SourceHypoGroupManager instance defining the source hypothesis groups.

  • dataset_list (list of Dataset instances) – The list of Dataset instances for which signal events should get generated for.

  • data_list (list of DatasetData instances) – The list of DatasetData instances holding the actual data of each dataset. The order must match the order of dataset_list.

  • llhratio (LLHRatio) – The likelihood ratio object contains the datasets signal weights needed for distributing the event generation among the different datasets.

  • energy_cut_splines (list of UnivariateSpline) – A list of splines of E(sin_dec) used to define the declination dependent energy cut in the IceCube southern sky.

  • cut_sindec (list of float) – The sine of the declination to start applying the energy cut. The cut will be applied from this declination down.

property src_hypo_group_manager

The SourceHypoGroupManager instance defining the source groups with their spectra.

property dataset_list

The list of Dataset instances for which signal events should get generated for.

property llhratio

The log-likelihood ratio function for the analysis.

generate_signal_events(rss, mean, poisson=True)

This abstract method must be implemented by the derived class to generate a given number of signal events.

Parameters:

  • rss (instance of RandomStateService) – The instance of RandomStateService providing the random number generator state.

  • mean (float) – The mean number of signal events. If the poisson argument is set to True, the actual number of generated signal events will be drawn from a Poisson distribution with this given mean value of signal events.

  • poisson (bool) – If set to True, the actual number of generated signal events will be drawn from a Poisson distribution with the given mean value of signal events. If set to False, the argument mean specifies the actual number of generated signal events.

Returns:

  • n_signal (int) – The number of generated signal events.

  • signal_events_dict (dict of DataFieldRecordArray) – The dictionary holding the DataFieldRecordArray instancs with the generated signal events. Each key of this dictionary represents the dataset index for which the signal events have been generated.

class skyllh.analyses.i3.publicdata_ps.signal_generator.PDTimeDependentSignalGenerator(src_hypo_group_manager, dataset_list, data_list=None, llhratio=None, energy_cut_splines=None, cut_sindec=None, gauss=None, box=None)

Bases: PDSignalGenerator

The time dependent signal generator works so far only for one single dataset. For multi datasets one needs to adjust the dataset weights accordingly (scaling of the effective area with livetime of the flare in the dataset).

Parameters:

  • src_hypo_group_manager (SourceHypoGroupManager instance) – The instance of SourceHypoGroupManager that defines the list of sources, i.e. the list of SourceModel instances.

  • dataset_list (list of Dataset instances) – The list of Dataset instances for which signal events should get generated for.

  • data_list (list of DatasetData instances) – The list of DatasetData instances holding the actual data of each dataset. The order must match the order of dataset_list.

  • llhratio (LLHRatio) – The likelihood ratio object contains the datasets signal weights needed for distributing the event generation among the different datsets.

  • energy_cut_splines (list of UnivariateSpline) –

  • cut_sindec (float) –

  • gauss (dict | None) – None or dictionary with {“mu”: float, “sigma”: float}.

  • box (dict | None) – None or dictionary with {“start”: float, “end”: float}.

set_flare(gauss=None, box=None)

Set the neutrino flare given parameters.

Parameters:

  • gauss (dict | None) – None or dictionary with {“mu”: float, “sigma”: float}.

  • box (dict | None) – None or dictionary with {“start”: float, “end”: float}.

is_in_grl(time, grl)

Helper function to check if given times are in the grl ontime.

Parameters:

  • time (1d ndarray) – Time values.

  • grl (ndarray) – Array of the detector good run list.

Returns:

is_in_grl (1d ndarray) – Boolean mask of time in grl ontime.

generate_signal_events(rss, mean, poisson=True)

Same as in PDSignalGenerator, but we assign times here.

skyllh.analyses.i3.publicdata_ps.signalpdf module

class skyllh.analyses.i3.publicdata_ps.signalpdf.PDSignalEnergyPDF(f_e_spl, **kwargs)

Bases: PDF, IsSignalPDF

This class provides a signal energy PDF for a spectrial index value.

Creates a new signal energy PDF instance for a particular spectral index value.

Parameters:

f_e_spl (FctSpline1D instance) – The FctSpline1D instance representing the spline of the energy PDF.

assert_is_valid_for_trial_data(tdm)

This method is supposed to check if this PDF is valid for all the given trial data. This means, it needs to check if there is a PDF value for each trial data event that will be used in the likelihood evaluation. This is just a seatbelt. The method must raise a ValueError if the PDF is not valid for the given trial data.

get_pd_by_log10_reco_e(log10_reco_e, tl=None)

Calculates the probability density for the given log10(E_reco/GeV) values using the spline representation of the PDF.

Parameters:

  • log10_reco_e ((n_log10_reco_e,)-shaped 1D numpy ndarray) – The numpy ndarray holding the log10(E_reco/GeV) values for which the energy PDF should get evaluated.

  • tl (TimeLord instance | None) – The optional TimeLord instance that should be used to measure timing information.

Returns:

pd ((N_events,)-shaped numpy ndarray) – The 1D numpy ndarray with the probability density for each event.

get_prob(tdm, params=None, tl=None)

Calculates the probability density for the events given by the TrialDataManager.

Parameters:

  • tdm (TrialDataManager instance) –

    The TrialDataManager instance holding the data events for which the probability should be looked up. The following data fields are required:

    • ’log_energy’

      The log10 of the reconstructed energy.

  • params (dict | None) – The dictionary containing the parameter names and values for which the probability should get calculated. By definition this PDF does not depend on parameters.

  • tl (TimeLord instance | None) – The optional TimeLord instance that should be used to measure timing information.

Returns:

  • pd ((N_events,)-shaped numpy ndarray) – The 1D numpy ndarray with the probability density for each event.

  • grads ((N_fitparams,N_events)-shaped ndarray | None) – The 2D numpy ndarray holding the gradients of the PDF w.r.t. each fit parameter for each event. The order of the gradients is the same as the order of floating parameters specified through the param_set property. It is None, if this PDF does not depend on any parameters.

class skyllh.analyses.i3.publicdata_ps.signalpdf.PDSignalEnergyPDFSet(ds, src_dec, flux_model, fitparam_grid_set, ncpu=None, ppbar=None, **kwargs)

Bases: PDFSet, IsSignalPDF, IsParallelizable

This class provides a signal energy PDF set for the public data. It creates a set of PDSignalEnergyPDF instances, one for each spectral index value on a grid.

Creates a new PDSignalEnergyPDFSet instance for the public data.

Parameters:

  • ds (I3Dataset instance) – The I3Dataset instance that defines the dataset of the public data.

  • src_dec (float) – The declination of the source in radians.

  • flux_model (FluxModel instance) – The FluxModel instance that defines the source’s flux model.

  • fitparam_grid_set (ParameterGrid | ParameterGridSet instance) – The parameter grid set defining the grids of the fit parameters.

  • ncpu (int | None) – The number of CPUs to utilize. Global setting will take place if not specified, i.e. set to None.

  • ppbar (ProgressBar instance | None) – The instance of ProgressBar for the optional parent progress bar.

get_prob(tdm, gridfitparams, tl=None)

Calculates the signal probability density of each event for the given set of signal fit parameters on a grid.

Parameters:

  • tdm (instance of TrialDataManager) –

    The TrialDataManager instance holding the data events for which the probability should be calculated for. The following data fields must exist:

    • ’log_energy’

      The log10 of the reconstructed energy.

    • ’psi’

      The opening angle from the source to the event in radians.

    • ’ang_err’

      The angular error of the event in radians.

  • gridfitparams (dict) – The dictionary holding the signal parameter values for which the signal energy probability should be calculated. Note, that the parameter values must match a set of parameter grid values for which a PDSignalPDF object has been created at construction time of this PDSignalPDFSet object.

  • tl (TimeLord instance | None) – The optional TimeLord instance that should be used to measure time.

Returns:

  • prob (1d ndarray) – The array with the signal energy probability for each event.

  • grads ((N_fitparams,N_events)-shaped ndarray | None) – The 2D numpy ndarray holding the gradients of the PDF w.r.t. each fit parameter for each event. The order of the gradients is the same as the order of floating parameters specified through the param_set property. It is None, if this PDF does not depend on any parameters.

Raises:

KeyError – If no energy PDF can be found for the given signal parameter values.

skyllh.analyses.i3.publicdata_ps.smearing_matrix module

skyllh.analyses.i3.publicdata_ps.smearing_matrix.load_smearing_histogram(pathfilenames)

Loads the 5D smearing histogram from the given data file.

Parameters:

pathfilenames (str | list of str) – The file name of the data file.

Returns:

  • histogram (5d ndarray) – The 5d histogram array holding the probability values of the smearing matrix. The axes are (true_e, true_dec, reco_e, psi, ang_err).

  • true_e_bin_edges (1d ndarray) – The ndarray holding the bin edges of the true energy axis.

  • true_dec_bin_edges (1d ndarray) – The ndarray holding the bin edges of the true declination axis in radians.

  • reco_e_lower_edges (3d ndarray) – The 3d ndarray holding the lower bin edges of the reco energy axis. For each pair of true_e and true_dec different reco energy bin edges are provided. The shape is (n_true_e, n_true_dec, n_reco_e).

  • reco_e_upper_edges (3d ndarray) – The 3d ndarray holding the upper bin edges of the reco energy axis. For each pair of true_e and true_dec different reco energy bin edges are provided. The shape is (n_true_e, n_true_dec, n_reco_e).

  • psi_lower_edges (4d ndarray) – The 4d ndarray holding the lower bin edges of the psi axis in radians. The shape is (n_true_e, n_true_dec, n_reco_e, n_psi).

  • psi_upper_edges (4d ndarray) – The 4d ndarray holding the upper bin edges of the psi axis in radians. The shape is (n_true_e, n_true_dec, n_reco_e, n_psi).

  • ang_err_lower_edges (5d ndarray) – The 5d ndarray holding the lower bin edges of the angular error axis in radians. The shape is (n_true_e, n_true_dec, n_reco_e, n_psi, n_ang_err).

  • ang_err_upper_edges (5d ndarray) – The 5d ndarray holding the upper bin edges of the angular error axis in radians. The shape is (n_true_e, n_true_dec, n_reco_e, n_psi, n_ang_err).

class skyllh.analyses.i3.publicdata_ps.smearing_matrix.PDSmearingMatrix(pathfilenames, **kwargs)

Bases: object

This class is a helper class for dealing with the smearing matrix provided by the public data.

Creates a smearing matrix instance by loading the smearing matrix from the given file.

property n_log10_true_e_bins

(read-only) The number of log10 true energy bins.

property true_e_bin_edges

(read-only) The (n_true_e+1,)-shaped 1D numpy ndarray holding the bin edges of the true energy.

Depricated! Use log10_true_enu_binedges instead!

property true_e_bin_centers

(read-only) The (n_true_e,)-shaped 1D numpy ndarray holding the bin center values of the true energy.

property log10_true_enu_binedges

(read-only) The (n_log10_true_enu+1,)-shaped 1D numpy ndarray holding the bin edges of the log10 true neutrino energy.

property n_true_dec_bins

(read-only) The number of true declination bins.

property true_dec_bin_edges

(read-only) The (n_true_dec+1,)-shaped 1D numpy ndarray holding the bin edges of the true declination.

property true_dec_bin_centers

(read-only) The (n_true_dec,)-shaped 1D ndarray holding the bin center values of the true declination.

property log10_reco_e_binedges_lower

(read-only) The upper bin edges of the log10 reco energy axes.

property log10_reco_e_binedges_upper

(read-only) The upper bin edges of the log10 reco energy axes.

property min_log10_reco_e

(read-only) The minimum value of the reconstructed energy axis.

property max_log10_reco_e

(read-only) The maximum value of the reconstructed energy axis.

property min_log10_psi

(read-only) The minimum log10 value of the psi axis.

property max_log10_psi

(read-only) The maximum log10 value of the psi axis.

property pdf

(read-only) The probability-density-function P(E_reco,psi,ang_err|E_nu,dec_nu), which, by definition, is the histogram property divided by the 3D bin volumes for E_reco, psi, and ang_err.

get_true_dec_idx(true_dec)

Returns the true declination index for the given true declination value.

Parameters:

true_dec (float) – The true declination value in radians.

Returns:

true_dec_idx (int) – The index of the declination bin for the given declination value.

get_log10_true_e_idx(log10_true_e)

Returns the bin index for the given true log10 energy value.

Parameters:

log10_true_e (float) – The log10 value of the true energy.

Returns:

log10_true_e_idx (int) – The index of the true log10 energy bin for the given log10 true energy value.

get_reco_e_idx(true_e_idx, true_dec_idx, reco_e)

Returns the bin index for the given reco energy value given the given true energy and true declination bin indices.

Parameters:

  • true_e_idx (int) – The index of the true energy bin.

  • true_dec_idx (int) – The index of the true declination bin.

  • reco_e (float) – The reco energy value for which the bin index should get returned.

Returns:

reco_e_idx (int | None) – The index of the reco energy bin the given reco energy value falls into. It returns None if the value is out of range.

get_psi_idx(true_e_idx, true_dec_idx, reco_e_idx, psi)

Returns the bin index for the given psi value given the true energy, true declination and reco energy bin indices.

Parameters:

  • true_e_idx (int) – The index of the true energy bin.

  • true_dec_idx (int) – The index of the true declination bin.

  • reco_e_idx (int) – The index of the reco energy bin.

  • psi (float) – The psi value in radians for which the bin index should get returned.

Returns:

psi_idx (int | None) – The index of the psi bin the given psi value falls into. It returns None if the value is out of range.

get_ang_err_idx(true_e_idx, true_dec_idx, reco_e_idx, psi_idx, ang_err)

Returns the bin index for the given angular error value given the true energy, true declination, reco energy, and psi bin indices.

Parameters:

  • true_e_idx (int) – The index of the true energy bin.

  • true_dec_idx (int) – The index of the true declination bin.

  • reco_e_idx (int) – The index of the reco energy bin.

  • psi_idx (int) – The index of the psi bin.

  • ang_err (float) – The angular error value in radians for which the bin index should get returned.

Returns:

ang_err_idx (int | None) – The index of the angular error bin the given angular error value falls into. It returns None if the value is out of range.

get_true_log_e_range_with_valid_log_e_pdfs(dec_idx)

Determines the true log energy range for which log_e PDFs are available for the given declination bin.

Parameters:

dec_idx (int) – The declination bin index.

Returns:

  • min_log_true_e (float) – The minimum true log energy value.

  • max_log_true_e (float) – The maximum true log energy value.

get_log_e_pdf(log_true_e_idx, dec_idx)

Retrieves the log_e PDF from the given true energy bin index and source bin index. Returns (None, None, None, None) if any of the bin indices are less then zero, or if the sum of all pdf bins is zero.

Parameters:

  • log_true_e_idx (int) – The index of the true energy bin.

  • dec_idx (int) – The index of the declination bin.

Returns:

  • pdf (1d ndarray) – The log_e pdf values.

  • lower_bin_edges (1d ndarray) – The lower bin edges of the energy pdf histogram.

  • upper_bin_edges (1d ndarray) – The upper bin edges of the energy pdf histogram.

  • bin_widths (1d ndarray) – The bin widths of the energy pdf histogram.

get_psi_pdf(log_true_e_idx, dec_idx, log_e_idx)

Retrieves the psi PDF from the given true energy bin index, the source bin index, and the log_e bin index. Returns (None, None, None, None) if any of the bin indices are less then zero, or if the sum of all pdf bins is zero.

Parameters:

  • log_true_e_idx (int) – The index of the true energy bin.

  • dec_idx (int) – The index of the declination bin.

  • log_e_idx (int) – The index of the log_e bin.

Returns:

  • pdf (1d ndarray) – The psi pdf values.

  • lower_bin_edges (1d ndarray) – The lower bin edges of the psi pdf histogram.

  • upper_bin_edges (1d ndarray) – The upper bin edges of the psi pdf histogram.

  • bin_widths (1d ndarray) – The bin widths of the psi pdf histogram.

get_ang_err_pdf(log_true_e_idx, dec_idx, log_e_idx, psi_idx)

Retrieves the angular error PDF from the given true energy bin index, the source bin index, the log_e bin index, and the psi bin index. Returns (None, None, None, None) if any of the bin indices are less then zero, or if the sum of all pdf bins is zero.

Parameters:

  • log_true_e_idx (int) – The index of the true energy bin.

  • dec_idx (int) – The index of the declination bin.

  • log_e_idx (int) – The index of the log_e bin.

  • psi_idx (int) – The index of the psi bin.

Returns:

  • pdf (1d ndarray) – The ang_err pdf values.

  • lower_bin_edges (1d ndarray) – The lower bin edges of the ang_err pdf histogram.

  • upper_bin_edges (1d ndarray) – The upper bin edges of the ang_err pdf histogram.

  • bin_widths (1d ndarray) – The bin widths of the ang_err pdf histogram.

sample_log_e(rss, dec_idx, log_true_e_idxs)

Samples log energy values for the given source declination and true energy bins.

Parameters:

  • rss (instance of RandomStateService) – The RandomStateService which should be used for drawing random numbers from.

  • dec_idx (int) – The index of the source declination bin.

  • log_true_e_idxs (1d ndarray of int) – The bin indices of the true energy bins.

Returns:

  • log_e_idx (1d ndarray of int) – The bin indices of the log_e pdf corresponding to the sampled log_e values.

  • log_e (1d ndarray of float) – The sampled log_e values.

sample_psi(rss, dec_idx, log_true_e_idxs, log_e_idxs)

Samples psi values for the given source declination, true energy bins, and log_e bins.

Parameters:

  • rss (instance of RandomStateService) – The RandomStateService which should be used for drawing random numbers from.

  • dec_idx (int) – The index of the source declination bin.

  • log_true_e_idxs (1d ndarray of int) – The bin indices of the true energy bins.

  • log_e_idxs (1d ndarray of int) – The bin indices of the log_e bins.

Returns:

  • psi_idx (1d ndarray of int) – The bin indices of the psi pdf corresponding to the sampled psi values.

  • psi (1d ndarray of float) – The sampled psi values in radians.

sample_ang_err(rss, dec_idx, log_true_e_idxs, log_e_idxs, psi_idxs)

Samples ang_err values for the given source declination, true energy bins, log_e bins, and psi bins.

Parameters:

  • rss (instance of RandomStateService) – The RandomStateService which should be used for drawing random numbers from.

  • dec_idx (int) – The index of the source declination bin.

  • log_true_e_idxs (1d ndarray of int) – The bin indices of the true energy bins.

  • log_e_idxs (1d ndarray of int) – The bin indices of the log_e bins.

  • psi_idxs (1d ndarray of int) – The bin indices of the psi bins.

Returns:

  • ang_err_idx (1d ndarray of int) – The bin indices of the angular error pdf corresponding to the sampled angular error values.

  • ang_err (1d ndarray of float) – The sampled angular error values in radians.

skyllh.analyses.i3.publicdata_ps.time_dependent_ps module

Setup the time-dependent analysis. For now this works on a single dataset.

skyllh.analyses.i3.publicdata_ps.time_dependent_ps.change_time_pdf(analysis, gauss=None, box=None)

Changes the time pdf.

Parameters:

  • gauss (dict | None) – None or dictionary with {“mu”: float, “sigma”: float}.

  • box (dict | None) – None or dictionary with {“start”: float, “end”: float}.

skyllh.analyses.i3.publicdata_ps.time_dependent_ps.get_energy_spatial_signal_over_background(analysis, fitparams)

Returns the signal over background ratio for (spatial_signal * energy_signal) / (spatial_background * energy_background).

Parameter

fitparamsdict

Dictionary with {“gamma”: float} for energy pdf.

returns:

ratio (1d ndarray) – Product of spatial and energy signal over background pdfs.

skyllh.analyses.i3.publicdata_ps.time_dependent_ps.change_fluxmodel_gamma(analysis, gamma)

Set new gamma for the flux model.

Parameter

gammafloat

Spectral index for flux model.

skyllh.analyses.i3.publicdata_ps.time_dependent_ps.change_signal_time(analysis, gauss=None, box=None)

Change the signal injection to gauss or box.

Parameters:

  • gauss (dict | None) – None or dictionary {“mu”: float, “sigma”: float}.

  • box (dict | None) – None or dictionary {“start” : float, “end” : float}.

skyllh.analyses.i3.publicdata_ps.time_dependent_ps.calculate_TS(analysis, em_results, rss)

Calculate the best TS value for the expectation maximization gamma scan.

Parameters:

  • em_results (1d ndarray of tuples) – Gamma scan result.

  • rss (instance of RandomStateService) – The instance of RandomStateService that should be used to generate random numbers from.

Returns:

  • float maximized TS value

  • tuple(gamma from em scan [float], best fit mean time [float], best fit width [float])

  • (float ns, float gamma) fitparams from TS optimization

skyllh.analyses.i3.publicdata_ps.time_dependent_ps.run_gamma_scan_single_flare(analysis, remove_time=None, gamma_min=1, gamma_max=5, n_gamma=51)

Run em for different gammas in the signal energy pdf

Parameters:

  • remove_time (float) – Time information of event that should be removed.

  • gamma_min (float) – Lower bound for gamma scan.

  • gamma_max (float) – Upper bound for gamma scan.

  • n_gamma (int) – Number of steps for gamma scan.

Returns:

array with “gamma”, “mu”, “sigma”, and scaling factor for flare “ns_em”

skyllh.analyses.i3.publicdata_ps.time_dependent_ps.unblind_flare(analysis, remove_time=None)

Run EM on unscrambled data. Similar to the original analysis, remove the alert event.

Parameters:

remove_time (float) – Time information of event that should be removed. In the case of the TXS analysis: remove_time=58018.8711856

Returns:

array with “gamma”, “mu”, “sigma”, and scaling factor for flare “ns_em”

skyllh.analyses.i3.publicdata_ps.time_dependent_ps.create_analysis(datasets, source, gauss=None, box=None, refplflux_Phi0=1, refplflux_E0=1000.0, refplflux_gamma=2.0, ns_seed=100.0, ns_min=0.0, ns_max=1000.0, gamma_seed=3.0, gamma_min=1.0, gamma_max=5.0, kde_smoothing=False, minimizer_impl='LBFGS', cut_sindec=None, spl_smooth=None, cap_ratio=False, compress_data=False, keep_data_fields=None, optimize_delta_angle=10, tl=None, ppbar=None)

Creates the Analysis instance for this particular analysis.

Parameters:

datasetslist of Dataset instances

The list of Dataset instances, which should be used in the analysis.

sourcePointLikeSource instance

The PointLikeSource instance defining the point source position.

gaussNone or dictionary with mu, sigma

None if no Gaussian time pdf. Else dictionary with {“mu”: float, “sigma”: float} of Gauss

boxNone or dictionary with start, end

None if no Box shaped time pdf. Else dictionary with {“start”: float, “end”: float} of box.

refplflux_Phi0float

The flux normalization to use for the reference power law flux model.

refplflux_E0float

The reference energy to use for the reference power law flux model.

refplflux_gammafloat

The spectral index to use for the reference power law flux model.

ns_seedfloat

Value to seed the minimizer with for the ns fit.

ns_minfloat

Lower bound for ns fit.

ns_maxfloat

Upper bound for ns fit.

gamma_seedfloat | None

Value to seed the minimizer with for the gamma fit. If set to None, the refplflux_gamma value will be set as gamma_seed.

gamma_minfloat

Lower bound for gamma fit.

gamma_maxfloat

Upper bound for gamma fit.

kde_smoothingbool

Apply a KDE-based smoothing to the data-driven background pdf. Default: False.

minimizer_implstr | “LBFGS”

Minimizer implementation to be used. Supported options are “LBFGS” (L-BFG-S minimizer used from the scipy.optimize module), or “minuit” (Minuit minimizer used by the iminuit module). Default: “LBFGS”.

cut_sindeclist of float | None

sin(dec) values at which the energy cut in the southern sky should start. If None, np.sin(np.radians([-2, 0, -3, 0, 0])) is used.

spl_smoothlist of float

Smoothing parameters for the 1D spline for the energy cut. If None, [0., 0.005, 0.05, 0.2, 0.3] is used.

cap_ratiobool

If set to True, the energy PDF ratio will be capped to a finite value where no background energy PDF information is available. This will ensure that an energy PDF ratio is available for high energies where no background is available from the experimental data. If kde_smoothing is set to True, cap_ratio should be set to False! Default is False.

compress_databool

Flag if the data should get converted from float64 into float32.

keep_data_fieldslist of str | None

List of additional data field names that should get kept when loading the data.

optimize_delta_anglefloat

The delta angle in degrees for the event selection optimization methods.

tlTimeLord instance | None

The TimeLord instance to use to time the creation of the analysis.

ppbarProgressBar instance | None

The instance of ProgressBar for the optional parent progress bar.

returns:

analysis (TimeIntegratedMultiDatasetSingleSourceAnalysis) – The Analysis instance for this analysis.

skyllh.analyses.i3.publicdata_ps.time_integrated_ps module

The time_integrated_ps analysis is a multi-dataset time-integrated single source analysis with a two-component likelihood function using a spacial and an energy event PDF.

skyllh.analyses.i3.publicdata_ps.time_integrated_ps.psi_func(tdm, src_hypo_group_manager, fitparams)

Function to calculate the opening angle between the source position and the event’s reconstructed position.

skyllh.analyses.i3.publicdata_ps.time_integrated_ps.TXS_location()
skyllh.analyses.i3.publicdata_ps.time_integrated_ps.create_analysis(datasets, source, refplflux_Phi0=1, refplflux_E0=1000.0, refplflux_gamma=2.0, ns_seed=100.0, ns_min=0.0, ns_max=1000.0, gamma_seed=3.0, gamma_min=1.0, gamma_max=5.0, kde_smoothing=False, minimizer_impl='LBFGS', cut_sindec=None, spl_smooth=None, cap_ratio=False, compress_data=False, keep_data_fields=None, optimize_delta_angle=10, tl=None, ppbar=None)

Creates the Analysis instance for this particular analysis.

Parameters:

datasetslist of Dataset instances

The list of Dataset instances, which should be used in the analysis.

sourcePointLikeSource instance

The PointLikeSource instance defining the point source position.

refplflux_Phi0float

The flux normalization to use for the reference power law flux model.

refplflux_E0float

The reference energy to use for the reference power law flux model.

refplflux_gammafloat

The spectral index to use for the reference power law flux model.

ns_seedfloat

Value to seed the minimizer with for the ns fit.

ns_minfloat

Lower bound for ns fit.

ns_maxfloat

Upper bound for ns fit.

gamma_seedfloat | None

Value to seed the minimizer with for the gamma fit. If set to None, the refplflux_gamma value will be set as gamma_seed.

gamma_minfloat

Lower bound for gamma fit.

gamma_maxfloat

Upper bound for gamma fit.

kde_smoothingbool

Apply a KDE-based smoothing to the data-driven background pdf. Default: False.

minimizer_implstr | “LBFGS”

Minimizer implementation to be used. Supported options are “LBFGS” (L-BFG-S minimizer used from the scipy.optimize module), or “minuit” (Minuit minimizer used by the iminuit module). Default: “LBFGS”.

cut_sindeclist of float | None

sin(dec) values at which the energy cut in the southern sky should start. If None, np.sin(np.radians([-2, 0, -3, 0, 0])) is used.

spl_smoothlist of float

Smoothing parameters for the 1D spline for the energy cut. If None, [0., 0.005, 0.05, 0.2, 0.3] is used.

cap_ratiobool

If set to True, the energy PDF ratio will be capped to a finite value where no background energy PDF information is available. This will ensure that an energy PDF ratio is available for high energies where no background is available from the experimental data. If kde_smoothing is set to True, cap_ratio should be set to False! Default is False.

compress_databool

Flag if the data should get converted from float64 into float32.

keep_data_fieldslist of str | None

List of additional data field names that should get kept when loading the data.

optimize_delta_anglefloat

The delta angle in degrees for the event selection optimization methods.

tlTimeLord instance | None

The TimeLord instance to use to time the creation of the analysis.

ppbarProgressBar instance | None

The instance of ProgressBar for the optional parent progress bar.

returns:

analysis (TimeIntegratedMultiDatasetSingleSourceAnalysis) – The Analysis instance for this analysis.

skyllh.analyses.i3.publicdata_ps.utils module

class skyllh.analyses.i3.publicdata_ps.utils.FctSpline1D(f, x_binedges, norm=False, **kwargs)

Bases: object

Class to represent a 1D function spline using the PchipInterpolator class from scipy.

The evaluate the spline, use the __call__ method.

Creates a new 1D function spline using the PchipInterpolator class from scipy.

Parameters:

  • f ((n_x,)-shaped 1D numpy ndarray) – The numpy ndarray holding the function values at the bin centers.

  • x_binedges ((n_x+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the bin edges of the x-axis.

  • norm (bool) – Whether to precalculate and save normalization internally.

__call__(x, oor_value=0)

Evaluates the spline at the given x values. For x-values outside the spline’s range, the oor_value is returned.

Parameters:

  • x ((n_x,)-shaped 1D numpy ndarray) – The numpy ndarray holding the x values at which the spline should get evaluated.

  • oor_value (float) – The value for out-of-range (oor) coordinates.

Returns:

f ((n_x,)-shaped 1D numpy ndarray) – The numpy ndarray holding the evaluated values of the spline.

evaluate(*args, **kwargs)

Alias for the __call__ method.

class skyllh.analyses.i3.publicdata_ps.utils.FctSpline2D(f, x_binedges, y_binedges, **kwargs)

Bases: object

Class to represent a 2D function spline using the RectBivariateSpline class from scipy.

The spline is constructed in the log10 space of the function value to ensure a smooth spline.

The evaluate the spline, use the __call__ method.

Creates a new 2D function spline using the RectBivariateSpline class from scipy.

Parameters:

  • f ((n_x, n_y)-shaped 2D numpy ndarray) – he numpy ndarray holding the function values at the bin centers.

  • x_binedges ((n_x+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the bin edges of the x-axis.

  • y_binedges ((n_y+1,)-shaped 1D numpy ndarray) – The numpy ndarray holding the bin edges of the y-axis.

__call__(x, y, oor_value=0)

Evaluates the spline at the given coordinates. For coordinates outside the spline’s range, the oor_value is returned.

Parameters:

  • x ((n_x,)-shaped 1D numpy ndarray) – The numpy ndarray holding the x values at which the spline should get evaluated.

  • y ((n_y,)-shaped 1D numpy ndarray) – The numpy ndarray holding the y values at which the spline should get evaluated.

  • oor_value (float) – The value for out-of-range (oor) coordinates.

Returns:

f ((n_x, n_y)-shaped 2D numpy ndarray) – The numpy ndarray holding the evaluated values of the spline.

skyllh.analyses.i3.publicdata_ps.utils.psi_to_dec_and_ra(rss, src_dec, src_ra, psi)

Generates random declinations and right-ascension coordinates for the given source location and opening angle psi.

Parameters:

  • rss (instance of RandomStateService) – The instance of RandomStateService to use for drawing random numbers.

  • src_dec (float) – The declination of the source in radians.

  • src_ra (float) – The right-ascension of the source in radians.

  • psi (1d ndarray of float) – The opening-angle values in radians.

Returns:

  • dec (1d ndarray of float) – The declination values.

  • ra (1d ndarray of float) – The right-ascension values.

skyllh.analyses.i3.publicdata_ps.utils.create_energy_cut_spline(ds, exp_data, spl_smooth)

Create the spline for the declination-dependent energy cut that the signal generator needs for injection in the southern sky Some special conditions are needed for IC79 and IC86_I, because their experimental dataset shows events that should probably have been cut by the IceCube selection.