pyLIMA.microlstats module

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
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.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_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_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