WStatCountsStatistic#
- class gammapy.stats.WStatCountsStatistic(n_on, n_off, alpha, mu_sig=None)[source]#
Bases:
CountsStatistic
Class to compute statistics for Poisson distributed variable with unknown background.
- Parameters:
- n_onint
Measured counts in on region.
- n_offint
Measured counts in off region.
- alphafloat
Acceptance ratio of on and off measurements.
- mu_sigfloat
Expected signal counts in on region.
Attributes Summary
Approximate error from the covariance matrix.
Known background computed alpha * n_off.
Excess
Return p_value of measured excess.
Return statistical significance of measured excess.
Stat value for best fit hypothesis.
Stat value for null hypothesis, i.e. mu_sig 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.
A dictionary of the relevant quantities.
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#
Known background computed alpha * n_off.
- 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 - alpha * n_off - mu_sig.
- stat_null#
Stat value for null hypothesis, i.e. mu_sig 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.
- info_dict()[source]#
A dictionary of the relevant quantities.
- Returns:
- info_dictdict
Dictionary with summary info.
- 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