`pyLIMA.microlstats`¶

Module Contents¶

`normal_Kolmogorov_Smirnov`(sample)	The moon illumination expressed as a percentage.
`normal_Anderson_Darling`(sample)	Compute a Anderson-Darling test on the sample versus a normal distribution with mu = 0, sigma = 1
`normal_Shapiro_Wilk`(sample)	Compute a Shapiro-Wilk test on the sample versus a normal distribution with mu = 0, sigma = 1
`normalized_chi2`(chi2, n_data, n_parameters)	Compute the chi^2/dof
`Bayesian_Information_Criterion`(chi2, n_data, n_parameters)	Compute the BIC statistic.
`Akaike_Information_Criterion`(chi2, n_parameters)	Compute the BIC statistic.

pyLIMA.microlstats.normal_Kolmogorov_Smirnov(sample)[source]¶

The moon illumination expressed as a percentage.

Parameters:	sun (astropy) – the sun ephemeris moon (astropy) – the moon ephemeris
Returns:	a numpy array like indicated the moon illumination.
Return type:	array_like

pyLIMA.microlstats.normal_Anderson_Darling(sample)[source]¶

Compute a Anderson-Darling test on the sample versus a normal distribution with mu = 0, sigma = 1

Parameters:	sample (array_like) – the sample you want to check the “Gaussianity”
Returns:	the Anderson-Darling statistic, the Anderson-Darling critical values associated to the significance

level of 15 % and the Anderson-Darling judgement :rtype: float, array_like, array_like

pyLIMA.microlstats.normal_Shapiro_Wilk(sample)[source]¶

Compute a Shapiro-Wilk test on the sample versus a normal distribution with mu = 0, sigma = 1

Parameters:	sample (array_like) – the sample you want to check the “Gaussianity”
Returns:	the Shapiro-Wilk statistic and its related p_value
Return type:	float, float

pyLIMA.microlstats.normalized_chi2(chi2, n_data, n_parameters)[source]¶

Compute the chi^2/dof

Parameters:	chi2 (float) – the chi^2 n_data (int) – the number of data_points n_parameters (int) – the number of model parameters
Returns:	the chi^2/dof and the chi2dof_judgement
Return type:	float

pyLIMA.microlstats.Bayesian_Information_Criterion(chi2, n_data, n_parameters)[source]¶

Compute the BIC statistic.

Parameters:	chi2 (float) – the chi^2 n_data (int) – the number of data_points n_parameters (int) – the number of model parameters
Returns:	the chi^2/dof
Return type:	float

pyLIMA.microlstats.Akaike_Information_Criterion(chi2, n_parameters)[source]¶

Compute the BIC statistic.

Parameters:	chi2 (float) – the chi^2 n_parameters (int) – the number of model parameters
Returns:	the chi^2/dof
Return type:	float