CashCountsStatistic

class gammapy.stats.CashCountsStatistic(n_on, mu_bkg)[source]

Bases: gammapy.stats.counts_statistic.CountsStatistic

Class to compute statistics (significance, asymmetric errors , ul) for Poisson distributed variable with known background.

Parameters
n_onint

Measured counts

mu_bkgfloat

Known level of background

Attributes Summary

error

Approximate error from the covariance matrix.

n_bkg

Expected background counts

n_sig

Excess

p_value

Return p_value of measured excess.

sqrt_ts

Return statistical significance of measured excess.

stat_max

Stat value for best fit hypothesis, i.e.

stat_null

Stat value for null hypothesis, i.e.

ts

Return stat difference (TS) of measured excess versus no excess.

Methods Summary

compute_errn([n_sigma])

Compute downward excess uncertainties.

compute_errp([n_sigma])

Compute upward excess uncertainties.

compute_upper_limit([n_sigma])

Compute upper limit on the signal.

n_sig_matching_significance(significance)

Compute excess matching a given significance.

Attributes Documentation

error

Approximate error from the covariance matrix.

n_bkg

Expected background counts

n_sig

Excess

p_value

Return p_value of measured excess.

sqrt_ts

Return statistical significance of measured excess.

stat_max

Stat value for best fit hypothesis, i.e. expected signal mu = n_on - mu_bkg

stat_null

Stat value for null hypothesis, i.e. 0 expected signal counts

ts

Return stat difference (TS) of measured excess versus no excess.

Methods Documentation

compute_errn(n_sigma=1.0)

Compute downward excess uncertainties.

Searches the signal value for which the test statistics is n_sigma**2 away from the maximum.

Parameters
n_sigmafloat

Confidence level of the uncertainty expressed in number of sigma. Default is 1.

compute_errp(n_sigma=1)

Compute upward excess uncertainties.

Searches the signal value for which the test statistics is n_sigma**2 away from the maximum.

Parameters
n_sigmafloat

Confidence level of the uncertainty expressed in number of sigma. Default is 1.

compute_upper_limit(n_sigma=3)

Compute upper limit on the signal.

Searches the signal value for which the test statistics is n_sigma**2 away from the maximum or from 0 if the measured excess is negative.

Parameters
n_sigmafloat

Confidence level of the upper limit expressed in number of sigma. Default is 3.

n_sig_matching_significance(significance)

Compute excess matching a given significance.

This function is the inverse of significance.

Parameters
significancefloat

Significance

Returns
n_signumpy.ndarray

Excess