EnergyBins

class gdt.core.data_primitives.EnergyBins(counts, lo_edges, hi_edges, exposure, **kwargs)

Bases: ExposureBins

A class defining a set of Energy (count spectra) bins.

Parameters:

• counts (np.array) – The array of counts in each bin

• lo_edges (np.array) – The low-value edges of the bins

• hi_edges (np.array) – The high-value edges of the bins

• exposure (np.array) – The exposure of each bin

• count_uncerts (np.array, optional) – An array the same length as counts if the uncertainty is not Poisson.

• precalc_good_segments (bool, optional) – If True, calculates contiguous bin segments on initialization. Default is True.

Attributes Summary

centroids	The centroids of the bins
count_uncertainty	The count uncertainty in each bin
counts	The counts in each bin
exposure	The exposure of each bin
hi_edges	The high-value edges of the bins
lo_edges	The low-value edges of the bins
range	The range of the bin edges
rate_uncertainty	The count rate uncertainty of each bin
rate_uncertainty_per_kev	Differential rate uncertainty
rates	counts/exposure
rates_per_kev	Differential count rate
size	Number of bins
widths	The widths of the bins

Methods Summary

closest_edge(val[, which])	Return the closest bin edge
contiguous_bins()	Return a list of ExposureBins, each one containing a continuous segment of data.
from_rates(rates, rate_uncerts, lo_edges, ...)	Create an ExposureBins object from count rates and uncertainties.
merge(histos, **kwargs)	Merge multiple ExposureBins together.
rebin(method, *args[, emin, emax])	Rebin the EnergyBins object in given a binning function and return a a new EnergyBins object
slice(tstart, tstop)	Perform a slice over a range and return a new ExposureBins object.
sum(histos)	Sum multiple EnergyBins together if they have the same energy range (support).

Attributes Documentation

centroids

The centroids of the bins

Type:

(np.array)

count_uncertainty

The count uncertainty in each bin

Type:

(np.array)

counts

The counts in each bin

Type:

(np.array)

exposure

The exposure of each bin

Type:

(np.array)

hi_edges

The high-value edges of the bins

Type:

(np.array)

lo_edges

The low-value edges of the bins

Type:

(np.array)

range

The range of the bin edges

Type:

(float, float)

rate_uncertainty

The count rate uncertainty of each bin

Type:

(np.array)

rate_uncertainty_per_kev

Differential rate uncertainty

Type:

(np.array)

rates

counts/exposure

Type:

(np.array)

Type:

The count rate of each bin

rates_per_kev

Differential count rate

Type:

(np.array)

size

Number of bins

Type:

(int)

widths

The widths of the bins

Type:

(np.array)

Methods Documentation

closest_edge(val, which='either')

Return the closest bin edge

Parameters:

• val (float) – Input value

• which (str, optional) –

  Options are:

  • ’either’ - closest edge to val;

  • ’low’ - closest edge lower than val; or

  • ’high’ - closest edge higher than val. Default is ‘either’

Returns:

(float)

contiguous_bins()

Return a list of ExposureBins, each one containing a continuous segment of data. This is done by comparing the edges of each bin, and if there is a gap between edges, the data is split into separate ExposureBin objects, each containing a contiguous set of data.

Returns

(list of ExposureBins)

classmethod from_rates(rates, rate_uncerts, lo_edges, hi_edges, exposure, **kwargs)

Create an ExposureBins object from count rates and uncertainties.

Parameters:

• rates (np.array) – The count rates

• rate_uncerts (np.array) – The count rate uncertainties

• lo_edges (np.array) – The low-value edges of the bins

• hi_edges (np.array) – The high-value edges of the bins

• exposure (np.array) – The exposure of each bin

• precalc_good_segments (bool, optional) – If True, calculates contiguous bin segments on initialization. Default is True.

Returns:

(ExposureBins)

classmethod merge(histos, **kwargs)

Merge multiple ExposureBins together. Note that the ExposureBins to merged must be strictly non-overlapping. The end of the range for one ExposureBins may be equal to the start of the range for another, or there may be a gap between the end of one or beginning of another, but there cannot be an overlap. If you need to sum the counts between multiple ExposureBins with the same bin edges, see the sum() method.

Parameters:

histos (list of ExposureBins) – A list containing the ExposureBins to be merged

Returns:

(ExposureBins)

rebin(method, *args, emin=None, emax=None)

Rebin the EnergyBins object in given a binning function and return a a new EnergyBins object

The binning function should take as input an array of counts, array of exposures, and an array of bin edges. Additional arguments specific to the function are allowed. The function should return an array of the new counts, new exposure, and new edges.

Parameters:

• method (<function>) – A binning function

• *args – Arguments to be passed to the binning function

• emin (float, optional) – If set, defines the starting energy of the EnergyBins to be binned, otherwise binning will begin at the first bin edge.

• emax (float, optional) – If set, defines the ending energy of the EnergyBins to be binned, otherwise binning will end at the last bin edge.

Returns:

(EnergyBins)

slice(tstart, tstop)

Perform a slice over a range and return a new ExposureBins object. Note that tstart and tstop values that fall inside a bin will result in that bin being included.

Parameters:

• tstart (float) – The start of the slice

• tstop (float) – The end of the slice

Returns:

(ExposureBins)

classmethod sum(histos)

Sum multiple EnergyBins together if they have the same energy range (support). Example use would be summing two count spectra.

Note

Count uncertainties are summed in quadrature.

Parameters:

histos (list of EnergyBins) – A list containing the EnergyBins to be summed

Returns:

(EnergyBins)