autowisp.light_curves.correction module
Class Inheritance Diagram
Define base class for all LC de-trend algorithms.
- class autowisp.light_curves.correction.Correction(fit_datasets, mark_progress, **iterative_fit_config)[source]
Bases:
object
Functionality and interface shared by all LC de-trending corrections.
- fit_datasets
See __init__().
- iterative_fit_config
Configuration to use for iterative fitting. See iterative_fit() for details.
- Type:
- __init__(fit_datasets, mark_progress, **iterative_fit_config)[source]
Configure the fitting.
- Parameters:
fit_datasets ([]) – A list of 3-tuples of pipeline keys corresponding to each variable identifying a dataset to fit and correct, an associated dictionary of path substitutions, and a pipeline key for the output dataset. Configurations of how the fitting was done and the resulting residual and non-rejected points are added to configuration datasets generated by removing the tail of the destination and adding ‘.cfg.’ + <parameter name> for configurations and just ‘.’ + <parameter name> for fitting statistics. For example, if the output dataset key is
'shapefit.epd.magnitude'
look (the configuration datasets will)
'shapefit.epd.cfg.fit_terms' (like)
and
'shapefit.epd.residual'.
iterative_fit_config – Any other arguments to pass directly to iterative_fit().
- Returns:
None
- _fix_substitutions(*, light_curve, photometry_mode, fit_points, substitutions, in_place=False)[source]
Fix magfit iteration in substitutions if negative.
- static _get_config_key_prefix(fit_target)[source]
Return the prefix of the pipeline key for storing configuration.
- _get_fit_data(light_curve, get_fit_dataset, fit_target, fit_points)[source]
Return the lightcurve points to detrend.
- _get_io_iterative_fit_config(pipeline_key_prefix)[source]
Return the iterative fit portion of the configuration to save in the LC.
- Parameters:
pipeline_key_prefix (str) – The part of the pipeline key specifying which configuration is being defined (i.e. everything except the last item in the key).
- Returns:
A list of tuples of the configuration options contained in
iterative_fit_config.- Return type:
[()]
- static _process_fit(*, fit_results, raw_values, predictors, fit_index, result, num_extra_predictors)[source]
Incorporate the results of a single fit in final result of __call__().
- Parameters:
fit_results – The return value of the iterative_fit() used to calculate the correction
raw_values (1-D array) – The data to apply the correction to. Should already exclude any points not selected for correction.
predictors (2-D array) – The predictors to use for the correction (e.g. the templates for TFA fitting).
fit_index (int) – The index of the dataset being fit within the list of datasets that will be fit for this lightcurve.
result – The result variable for the parent update for this fit.
num_extra_predictors (int) – How many extra predictors are there.
- Returns:
The same structure as fit_results, but ready to pass to self._save_result().
- Return type:
- _save_result(*, fit_index, corrected_values, fit_residual, non_rejected_points, fit_points, configuration, light_curve)[source]
Stores the de-treneded results and configuration to the light curve.
- Parameters:
fit_index (int) – The index of the dataset for which a correction was applied in within the list of datasets specified at init.
corrected_values (array) – The corrected data to save.
fit_residual (float) – The residual from the fit, calculated as specified at init.
non_rejected_points (int) – The number of points used in the last iteration of the itaritive fit.
fit_points (bool array) – Flags indicating for each entry in the input (uncorrected) dataset, whether it is represented in corrected_values.
configuration ([]) – The configuration used for the fit, properly formatted to be converted to an entry in the configurations argument to LightCurveFile.add_configurations().
light_curve (LightCurveFile) – A light curve file opened for writing.
- Returns:
None