autowisp.light_curves.apply_correction module
Class Inheritance Diagram
Unified interface to the detrending algorithms.
- autowisp.light_curves.apply_correction.apply_parallel_correction(lc_fnames, correct, num_parallel_processes, **config)[source]
Correct LCs running one of the detrending algorithms in parallel.
- Parameters:
lc_fnames ([str]) – The filenames of the light curves to correct.
correct (Correction) – The underlying correction to apply in parallel.
num_parallel_processes (int) – The maximum number of parallel processes to use.
statistics_fname (str) – Filename to use for saving the statistics.
- Returns:
The return values of correct.__call__() in the same order as lc_fnames.
- Return type:
numpy.array
- autowisp.light_curves.apply_correction.apply_reconstructive_correction_transit(lc_fname, correct, *, transit_model, transit_parameters, fit_parameter_flags, num_limbdark_coef)[source]
Perform a reconstructive correction on a LC assuming it contains a transit.
The corrected lightcurve, preserving the best-fit transit is saved in the lightcurve just like for non-reconstructive corrections.
- Parameters:
transit_model – Object which supports the transit model intefrace of pytransit.
transit_parameters (scipy float array) – The full array of parameters required by the transit model’s evaluate() method.
fit_parameter_flags (scipy bool array) – Flags indicating parameters whose values should be fit for (by having a corresponding entry of True). Must match exactly the shape of transit_parameters.
num_limbdark_coef (int) – How many of the transit parameters are limb darkening coefficinets? Those need to be passed to the model separately.
correct (Correction) – Instance of one of the correction algarithms to make adaptive.
- Returns:
The best fit transit parameters
The return value of ReconstructiveCorrectionTransit.__call__() for the best-fit transit parameters.
- Return type:
(scipy array, scipy array)
- autowisp.light_curves.apply_correction.calculate_iterative_rejection_scatter(values, calculate_average, calculate_scatter, outlier_threshold, max_outlier_rejections, *, return_average=False)[source]
Calculate the scatter for a dataset, with outlier rejectio iterations.
- Parameters:
values (numpy array like) – The data to calculate the scatter of.
calculate_average (callable) – A callable that returns the average of the data aroung which the scatter will be calculated.
calculate_scatter (callable) – The scatter is defined as the square root of whatever get_scatter calculates from the square deviations of the data from the average.
outlier_threshold (float) – In units of the scatter, how far away should a point be from the average to be considered an outlier.
max_outlier_rejections (int) – The maximum number of iterations between outlier rejection and re-calculating the scatter to perform.
return_average (bool) – Should the average of the poinst also be returned?
- Returns:
The scatter in values and the number of non-rejected points in the last scatter calculation.
- Return type:
- autowisp.light_curves.apply_correction.load_correction_statistics(filename, add_catalog=False)[source]
Read a previously stored statistics from a file.
- autowisp.light_curves.apply_correction.recalculate_correction_statistics(lc_fnames, fit_datasets, variables, lc_points_filter_expression, **calculate_scatter_config)[source]
Extract the performance metrics for a de-trending step directly from LCs.
- Parameters:
lc_fnames ([str]) – The filenames of the light curves that were corrected.
fit_datasets – See Correction.__init__().
extra_predictors – See EPDCorrection.__init__().
calculate__scatter_config – Arguments passed directly to calculate_iterative_rejection_scatter().
- Returns:
See apply_parallel_correction’s return value.