threeML.utils.spectrum package

Submodules

threeML.utils.spectrum.binned_spectrum module

class threeML.utils.spectrum.binned_spectrum.BinnedSpectrum(counts, exposure, ebounds, count_errors=None, sys_errors=None, quality=None, scale_factor=1.0, is_poisson=False, mission=None, instrument=None, tstart=None, tstop=None)

Bases: threeML.utils.histogram.Histogram

INTERVAL_TYPE

alias of threeML.utils.spectrum.binned_spectrum.Channel

add_inverse_variance_weighted(other)

clone(new_counts=None, new_count_errors=None, new_exposure=None, new_scale_factor=None)

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

Parameters

• new_counts – new counts for the spectrum

• new_count_errors – new errors from the spectrum

Returns

property count_errors

Returns

count error per channel

property counts

Returns

counts per channel

property exposure

Exposure in seconds

Returns

exposure

classmethod from_pandas(pandas_dataframe, exposure, scale_factor=1.0, is_poisson=False, mission=None, instrument=None)

Build a spectrum from data contained within a pandas data frame.

The required columns are:

‘emin’: low energy bin edge ‘emax’: high energy bin edge ‘counts’: the counts in each bin

Optional column names are:

‘count_errors’: errors on the counts for non-Poisson data ‘sys_errors’: systematic error per channel ‘quality’ list of 3ML quality flags ‘good’, ‘warn’, ‘bad’

Parameters

• pandas_dataframe – data frame containing information to be read into spectrum

• exposure – the exposure of the spectrum

• scale_factor – the scale factor of the spectrum

• is_poisson – if the data are Poisson distributed

• mission – (optional) the mission name

• instrument – (optional) the instrument name

Returns

classmethod from_time_series(time_series, use_poly=False, from_model=False, **kwargs)

Parameters

• time_series –

• use_poly –

Returns

property instrument

property is_poisson

property mission

property n_channel

property n_channels

property quality

property rate_errors

If the spectrum has no Poisson error (POISSER is False in the header), this will return the STAT_ERR column :return: errors on the rates

property rates

Returns

rates per channel

property scale_factor

property sys_errors

Systematic errors per channel. This is nonzero only if the SYS_ERR column is present in the input file.

Returns

the systematic errors stored in the input spectrum

to_pandas(use_rate=True)

make a pandas table from the spectrum.

Parameters

use_rate – if the table should use rates or counts

Returns

property total_count

Returns

total counts

property total_count_error

Returns

total count error

property total_rate

Returns

total rate

property total_rate_error

Returns

total rate error

property tstart

property tstop

class threeML.utils.spectrum.binned_spectrum.BinnedSpectrumWithDispersion(counts, exposure, response, count_errors=None, sys_errors=None, quality=None, scale_factor=1.0, is_poisson=False, mission=None, instrument=None, tstart=None, tstop=None)

Bases: threeML.utils.spectrum.binned_spectrum.BinnedSpectrum

clone(new_counts=None, new_count_errors=None, new_sys_errors=None, new_exposure=None, new_scale_factor=None)

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

Parameters

• new_sys_errors –

• new_exposure –

• new_scale_factor –

• new_counts – new counts for the spectrum

• new_count_errors – new errors from the spectrum

Returns

classmethod from_time_series(time_series, response=None, use_poly=False, extract=False)

Parameters

• time_series –

• use_poly –

Returns

property response

class threeML.utils.spectrum.binned_spectrum.Channel(start: float, stop: float, swap_if_inverted: bool = False)

Bases: threeML.utils.interval.Interval

property channel_width

class threeML.utils.spectrum.binned_spectrum.ChannelSet(list_of_intervals=())

Bases: threeML.utils.interval.IntervalSet

INTERVAL_TYPE

alias of threeML.utils.spectrum.binned_spectrum.Channel

property channels_widths

classmethod from_instrument_response(instrument_response)

Build EBOUNDS interval from an instrument response

Parameters

instrument_response –

Returns

class threeML.utils.spectrum.binned_spectrum.Quality(quality)

Bases: object

property bad

classmethod create_all_good(n_channels)

construct a quality object with all good channels :param n_channels: :return:

classmethod from_ogip(ogip_quality)

get_slice(idx)

property good

property n_elements

to_ogip()

makes a quality array following the OGIP standards: 0 = good 2 = warn 5 = bad

Returns

property warn

threeML.utils.spectrum.binned_spectrum_set module

class threeML.utils.spectrum.binned_spectrum_set.BinnedSpectrumSet(binned_spectrum_list, reference_time=0.0, time_intervals=None)

Bases: object

property count_errors_per_bin

property counts_per_bin

property exposure_per_bin

property n_channels

property quality_per_bin

property rate_errors_per_bin

property rates_per_bin

property reference_time

sort()

sort the bin spectra in place according to time :return:

property sys_errors_per_bin

property time_intervals

time_to_index(time)

get the index of the input time

Parameters

time – time to search for

Returns

integer

threeML.utils.spectrum.pha_spectrum module

class threeML.utils.spectrum.pha_spectrum.PHASpectrum(pha_file_or_instance, spectrum_number=None, file_type='observed', rsp_file=None, arf_file=None)

Bases: threeML.utils.spectrum.binned_spectrum.BinnedSpectrumWithDispersion

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

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)

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

classmethod from_dispersion_spectrum(dispersion_spectrum: threeML.utils.spectrum.binned_spectrum.BinnedSpectrumWithDispersion, file_type: str = 'observed') → threeML.utils.spectrum.pha_spectrum.PHASpectrum

property grouping

property response_file

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

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

Returns

set_ogip_grouping(grouping)

If the counts are rebinned, this updates the grouping :param grouping:

class threeML.utils.spectrum.pha_spectrum.PHASpectrumSet(pha_file_or_instance, file_type='observed', rsp_file=None, arf_file=None)

Bases: threeML.utils.spectrum.binned_spectrum_set.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

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)

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

Parameters

• new_counts – new counts for the spectrum

• new_count_errors – new errors from the spectrum

Returns

new pha spectrum

property filename

classmethod from_dispersion_spectrum(dispersion_spectrum: threeML.utils.spectrum.binned_spectrum.BinnedSpectrumWithDispersion, file_type: str = 'observed') → threeML.utils.spectrum.pha_spectrum.PHASpectrum

property grouping

property response_file

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

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

Returns

set_ogip_grouping(grouping)

If the counts are rebinned, this updates the grouping :param grouping:

threeML.utils.spectrum.spectrum_likelihood module

class threeML.utils.spectrum.spectrum_likelihood.BinnedStatistic(spectrum_plugin)

Bases: object

get_current_value()

get_randomized_background_counts()

get_randomized_background_errors()

get_randomized_source_counts(source_model_counts)

get_randomized_source_errors()

class threeML.utils.spectrum.spectrum_likelihood.GaussianObservedStatistic(spectrum_plugin)

Bases: threeML.utils.spectrum.spectrum_likelihood.BinnedStatistic

get_current_value(precalc_fluxes: Optional[numpy.array] = None)

get_randomized_source_counts(source_model_counts)

get_randomized_source_errors()

class threeML.utils.spectrum.spectrum_likelihood.PoissonObservedGaussianBackgroundStatistic(spectrum_plugin)

Bases: threeML.utils.spectrum.spectrum_likelihood.BinnedStatistic

get_current_value(precalc_fluxes: Optional[numpy.array] = None)

get_randomized_background_counts()

get_randomized_background_errors()

get_randomized_source_counts(source_model_counts)

class threeML.utils.spectrum.spectrum_likelihood.PoissonObservedIdealBackgroundStatistic(spectrum_plugin)

Bases: threeML.utils.spectrum.spectrum_likelihood.BinnedStatistic

get_current_value(precalc_fluxes: Optional[numpy.array] = None)

get_randomized_background_counts()

get_randomized_source_counts(source_model_counts)

class threeML.utils.spectrum.spectrum_likelihood.PoissonObservedModeledBackgroundStatistic(spectrum_plugin)

Bases: threeML.utils.spectrum.spectrum_likelihood.BinnedStatistic

get_current_value(precalc_fluxes: Optional[numpy.array] = None)

get_randomized_background_errors()

get_randomized_source_counts(source_model_counts)

property synthetic_background_plugin

class threeML.utils.spectrum.spectrum_likelihood.PoissonObservedNoBackgroundStatistic(spectrum_plugin)

Bases: threeML.utils.spectrum.spectrum_likelihood.BinnedStatistic

get_current_value(precalc_fluxes: Optional[numpy.array] = None)

get_randomized_source_counts(source_model_counts)

class threeML.utils.spectrum.spectrum_likelihood.PoissonObservedPoissonBackgroundStatistic(spectrum_plugin)

Bases: threeML.utils.spectrum.spectrum_likelihood.BinnedStatistic

get_current_value(precalc_fluxes: Optional[numpy.array] = None)

get_randomized_background_counts()

get_randomized_source_counts(source_model_counts)

Module contents