CashCountsStatistic#
- class gammapy.stats.CashCountsStatistic(n_on, mu_bkg)[source]#
Bases:
gammapy.stats.counts_statistic.CountsStatistic
Class to compute statistics for Poisson distributed variable with known background.
- Parameters
- n_onint
Measured counts
- mu_bkgfloat
Known level of background
Attributes Summary
Approximate error from the covariance matrix.
Expected background counts
Excess
Return p_value of measured excess.
Return statistical significance of measured excess.
Stat value for best fit hypothesis, i.e. expected signal mu = n_on - mu_bkg.
Stat value for null hypothesis, i.e. 0 expected signal counts.
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.
sum
([axis])Return summed CountsStatistics.
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. Here the value accounts only for the positive excess significance (i.e. one-sided).
- sqrt_ts#
Return statistical significance of measured excess. The sign of the excess is applied to distinguish positive and negative fluctuations.
- 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_sig
numpy.ndarray
Excess
- n_sig