threeML.plugins.OGIPLike module

class threeML.plugins.OGIPLike.OGIPLike(name: str, observation: Union[str, pathlib.Path, threeML.utils.spectrum.pha_spectrum.PHASpectrum, threeML.utils.OGIP.pha.PHAII], background: Optional[Union[str, pathlib.Path, threeML.utils.spectrum.pha_spectrum.PHASpectrum, threeML.utils.OGIP.pha.PHAII, threeML.plugins.SpectrumLike.SpectrumLike, threeML.plugins.XYLike.XYLike]] = None, response: Optional[str] = None, arf_file: Optional[str] = None, spectrum_number: Optional[int] = None, verbose: bool = True)

Bases: threeML.plugins.DispersionSpectrumLike.DispersionSpectrumLike

classmethod from_general_dispersion_spectrum(dispersion_like: threeML.plugins.DispersionSpectrumLike.DispersionSpectrumLike) → threeML.plugins.OGIPLike.OGIPLike

Build on OGIPLike from a dispersion like. This makes it easy to write a dispersion like to a pha file

Parameters

dispersion_like –

Returns

get_simulated_dataset(new_name: Optional[str] = None, **kwargs: dict) → threeML.plugins.OGIPLike.OGIPLike

Returns another OGIPLike instance where data have been obtained by randomizing the current expectation from the model, as well as from the background (depending on the respective noise models)

Parameters

• new_name – name of the simulated plugin

• kwargs – keywords to pass back up to parents

Returns

a DispersionSpectrumLike simulated instance

property grouping

write_pha(file_name: str, overwrite: bool = False, force_rsp_write: bool = False) → None

Create a pha file of the current pha selections

Parameters

• file_name – output file name (excluding extension)

• overwrite – overwrite the files

• force_rsp_write – for an rsp to be saved

Returns

None