threeML.utils.spectrum.pha_spectrum module

Bases: BinnedSpectrumWithDispersion

property ancillary_file: str | None

Returns the ancillary file definied in the header, or None if there is none defined

Returns:: a path to a file, or None

property background_file: None | str

Returns the background file definied in the header, or None if there is none defined p

return:

a path to a file, or None

clone(new_counts=None, new_count_errors=None, new_exposure=None, new_scale_factor=None) → PHASpectrum[source]

make a new spectrum with new counts and errors and all other parameters the same

Parameters:

new_exposure – the new exposure for the clone
new_scale_factor – the new scale factor for the clone
new_counts – new counts for the spectrum
new_count_errors – new errors from the spectrum

Returns:

new pha spectrum

property filename: str

classmethod from_dispersion_spectrum()[source]

property grouping: ndarray

property response_file: str | None

Returns the response file definied in the header, or None if there is none defined

Returns:: a path to a file, or None

property scale_factor: float

This is a scale factor (in the BACKSCAL keyword) which must be used to rescale background and source regions

Returns:

set_ogip_grouping(grouping) → None[source]: If the counts are rebinned, this updates the grouping :param grouping:

to_binned_spectrum() → BinnedSpectrumWithDispersion[source]: Convert directly to as Binned Spectrum :returns:

class threeML.utils.spectrum.pha_spectrum.PHASpectrumSet(pha_file_or_instance: str | Path | PHAII, file_type: str = 'observed', rsp_file: str | None = None, arf_file: str | None = None)[source]

Bases: BinnedSpectrumSet

property ancillary_file

Returns the ancillary file definied in the header, or None if there is none defined

Returns:: a path to a file, or None

property background_file