SpectrumObservation

class gammapy.spectrum.SpectrumObservation(on_vector, aeff=None, off_vector=None, edisp=None)

Bases: object

1D spectral analysis storage class.

This container holds the ingredients for 1D region based spectral analysis.

Meta data is stored in the on_vector attribute. This reflects the OGIP convention.

Parameters:

on_vector : PHACountsSpectrum

On vector

aeff : EffectiveAreaTable

Effective Area

off_vector : PHACountsSpectrum, optional

Off vector

edisp : EnergyDispersion, optional

Energy dispersion matrix

Examples

from gammapy.spectrum import SpectrumObservation
filename = '$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits'
obs = SpectrumObservation.read(filename)
print(obs)

Attributes Summary

alpha	Exposure ratio between signal and background regions
background_vector	Background CountsSpectrum.
e_reco	Reconstruced energy bounds array.
e_true	True energy bounds array.
excess_vector	Excess CountsSpectrum.
hi_threshold	High energy threshold
livetime	Dead-time corrected observation time
lo_threshold	Low energy threshold
meta	Meta information
nbins	Number of reconstruced energy bins
obs_id	Unique identifier
total_stats	Return total SpectrumStats
total_stats_safe_range	Return total SpectrumStats within the tresholds

Methods Summary

compute_energy_threshold([method_lo, …])	Compute and set the safe energy threshold.
copy()	A deep copy.
peek([figsize])	Quick-look summary plots.
predicted_counts(model)	Calculated number of predicted counts given a model.
read(filename)	Read SpectrumObservation from OGIP files.
reset_thresholds()	Reset energy thresholds (i.e.
stats(idx)	Compute stats for one energy bin.
stats_in_range(bin_min, bin_max)	Compute stats for a range of energy bins.
stats_table()	Per-bin stats as a table.
to_sherpa()	Convert to DataPHA.
write([outdir, use_sherpa, overwrite])	Write OGIP files.

Attributes Documentation

alpha

Exposure ratio between signal and background regions

background_vector

Background CountsSpectrum.

bkg = alpha * n_off

If alpha is a function of energy this will differ from self.on_vector * self.total_stats.alpha because the latter returns an average value for alpha.

e_reco

Reconstruced energy bounds array.

e_true

True energy bounds array.

excess_vector

Excess CountsSpectrum.

excess = n_on = alpha * n_off

hi_threshold

High energy threshold

livetime

Dead-time corrected observation time

lo_threshold

Low energy threshold

meta

Meta information

nbins

Number of reconstruced energy bins

obs_id

Unique identifier

total_stats

Return total SpectrumStats

total_stats_safe_range

Return total SpectrumStats within the tresholds

Methods Documentation

compute_energy_threshold(method_lo='none', method_hi='none', reset=False, **kwargs)

Compute and set the safe energy threshold.

Set the high and low energy threshold for each observation based on a chosen method.

Available methods for setting the low energy threshold:

• area_max : Set energy threshold at x percent of the maximum effective area (x given as kwargs[‘area_percent_lo’])
• energy_bias : Set energy threshold at energy where the energy bias exceeds a value of x percent (given as kwargs[‘bias_percent_lo’])
• none : Do not apply a lower threshold

Available methods for setting the high energy threshold:

• area_max : Set energy threshold at x percent of the maximum effective area (x given as kwargs[‘area_percent_hi’])
• energy_bias : Set energy threshold at energy where the energy bias exceeds a value of x percent (given as kwargs[‘bias_percent_hi’])
• none : Do not apply a higher energy threshold

Parameters:

method_lo : {‘area_max’, ‘energy_bias’, ‘none’}

Method for defining the low energy threshold

method_hi : {‘area_max’, ‘energy_bias’, ‘none’}

Method for defining the high energy threshold

reset : bool

Reset existing energy thresholds before setting the new ones (default is False)

copy()

A deep copy.

peek(figsize=(10, 10))

Quick-look summary plots.

predicted_counts(model)

Calculated number of predicted counts given a model.

Parameters:

model : SpectralModel

Spectral model

Returns:

npred : CountsSpectrum

Predicted counts

classmethod read(filename)

Read SpectrumObservation from OGIP files.

BKG file, ARF, and RMF must be set in the PHA header and be present in the same folder.

Parameters:

filename : str

OGIP PHA file to read

reset_thresholds()

Reset energy thresholds (i.e. declare all energy bins valid)

stats(idx)

Compute stats for one energy bin.

Parameters:

idx : int

Energy bin index

Returns:

stats : SpectrumStats

Stats

stats_in_range(bin_min, bin_max)

Compute stats for a range of energy bins.

Parameters:

bin_min, bin_max: int

Bins to include

Returns:

stats : SpectrumStats

Stacked stats

stats_table()

Per-bin stats as a table.

Returns:

table : Table

Table with stats for one energy bin in one row.

to_sherpa()

Convert to DataPHA.

Associated background vectors and IRFs are also translated to sherpa objects and appended to the PHA instance.

write(outdir=None, use_sherpa=False, overwrite=False)

Write OGIP files.

If you want to use the written files with Sherpa you have to set the use_sherpa flag. Then all files will be written in units ‘keV’ and ‘cm2’.

Parameters:

outdir : pathlib.Path

output directory, default: pwd

use_sherpa : bool, optional

Write Sherpa compliant files, default: False

overwrite : bool

Overwrite existing files?