stats - Statistics#

gammapy.stats Package#

Statistics.

`cash`(n_on, mu_on[, truncation_value])	Cash statistic, for Poisson data.
`cstat`(n_on, mu_on[, truncation_value])	C statistic, for Poisson data.
`get_wstat_gof_terms`(n_on, n_off)	Goodness of fit terms for WSTAT.
`get_wstat_mu_bkg`(n_on, n_off, alpha, mu_sig)	Background estimate `mu_bkg` for WSTAT.
`wstat`(n_on, n_off, alpha, mu_sig[, mu_bkg, ...])	W statistic, for Poisson data with Poisson background.
`compute_fvar`(flux, flux_err[, axis])	Calculate the fractional excess variance.
`compute_fpp`(flux, flux_err[, axis])	Calculate the point-to-point excess variance.
`compute_flux_doubling`(flux, flux_err, coords)	Compute the minimum characteristic flux doubling and halving over a certain coordinate axis for a series of measurements.
`compute_chisq`(flux)	Calculate the chi-square test for `LightCurve`.
`structure_function`(flux, flux_err, time[, ...])	Compute the discrete structure function for a variable source.
`discrete_correlation`(flux1, flux_err1, ...)	Compute the discrete correlation function for a variable source.
`TimmerKonig_lightcurve_simulator`(...[, ...])	Implementation of the Timmer-Koenig algorithm to simulate a time series from a power spectrum.
`sigma_to_ts`(n_sigma[, df, n_sigma_asimov])	Convert number of sigma to delta ts.
`ts_to_sigma`(ts[, df, ts_asimov])	Convert delta ts to number of sigma.

`CashCountsStatistic`	Class to compute statistics for Poisson distributed variable with known background.
`Chi2FitStatistic`	Chi2 fit statistic class for measurements with gaussian symmetric errors.
`Chi2AsymmetricErrorFitStatistic`	Pseudo-Chi2 fit statistic class for measurements with gaussian asymmetric errors with upper limits.
`WStatCountsStatistic`	Class to compute statistics for Poisson distributed variable with unknown background.