Reference / API

Pre-processing

Light curve analysis

Performing feature extraction for 1 light curve

LightCurve()	Light Curve object, holding meta and photometric data.
LightCurve.load_snpcc_lc(path_to_data)	Reads one LC from SNPCC data.
LightCurve.fit_bazin(band)	Extract Bazin features for one filter.
LightCurve.fit_bazin_all()	Perform Bazin fit for all filters independently and concatenate results.
LightCurve.plot_bazin_fit([save, show, ...])	Plot data and Bazin fitted function.

Fitting an entire data set

fit_snpcc_bazin(path_to_data_dir, features_file)

Fit Bazin functions to all filters in training and test samples.

Basic light curve analysis tools

bazin.bazin(time, a, b, t0, tfall, trise)	Parametric light curve function proposed by Bazin et al., 2009.
bazin.errfunc(params, time, flux)	Absolute difference between theoretical and measured flux.
bazin.fit_scipy(time, flux)	Find best-fit parameters using scipy.least_squares.

Canonical sample

The Canonical object for holding the entire sample.

Canonical()	Canonical sample object.
Canonical.snpcc_get_canonical_info(...[, ...])	Load SNPCC metada data required to characterize objects.
Canonical.snpcc_identify_samples()	Identify training and test sample.
Canonical.find_neighbors()	Identify 1 nearest neighbor for each object in training.

Functions to populate the Canonical object

build_snpcc_canonical(path_to_raw_data, ...)	Build canonical sample for SNPCC data.
plot_snpcc_train_canonical(sample[, ...])	Plot comparison between training and canonical samples.

Build time domain data base

SNPCCPhotometry()	Handles photometric information for entire SNPCC data.
SNPCCPhotometry.get_lim_mjds(raw_data_dir)	Get minimum and maximum MJD for complete sample.
SNPCCPhotometry.create_daily_file(...[, header])	Create one file for a given day of the survey.
SNPCCPhotometry.build_one_epoch(...[, ...])	Fit bazin for all objects with enough points in a given day.

DataBase

Object upon which the learning process is performed

DataBase()	DataBase object, upon which the active learning loop is performed.
DataBase.load_bazin_features(path_to_bazin_file)	Load Bazin features from file.
DataBase.load_features(path_to_file[, ...])	Load features according to the chosen feature extraction method.
DataBase.build_samples(initial_training[, ...])	Separate train and test samples.
DataBase.classify([method, screen, n_est, ...])	Apply a machine learning classifier.
DataBase.evaluate_classification([...])	Evaluate results from classification.
DataBase.make_query([strategy, batch, seed, ...])	Identify new object to be added to the training sample.
DataBase.update_samples(query_indx, loop[, ...])	Add the queried obj(s) to training and remove them from test.
DataBase.save_metrics(loop, ...[, batch])	Save current metrics to file.
DataBase.save_queried_sample(...[, ...])	Save queried sample to file.

Classifiers

random_forest(train_features, train_labels, ...)

Random Forest classifier.

Query strategies

random_sampling(test_ids, queryable_ids[, ...])	Randomly choose an object from the test sample.
uncertainty_sampling(class_prob, test_ids, ...)	Search for the sample with highest uncertainty in predicted class.

Metrics

Individual metrics

accuracy(label_pred, label_true)	Calculate accuracy.
efficiency(label_pred, label_true[, ia_flag])	Calculate efficiency.
purity(label_pred, label_true[, ia_flag])	Calculate purity.
fom(label_pred, label_true[, ia_flag, penalty])	Calculate figure of merit.

Metrics agregated by category or use

get_snpcc_metric(label_pred, label_true[, ...])

Calculate the metric parameters used in the SNPCC.

Active Learning loop

Full light curve

learn_loop(nloops, strategy, ...[, ...])

Perform the active learning loop.

Time domain

get_original_training(path_to_features[, ...])	Read original full light curve training sample
time_domain_loop(days, output_metrics_file, ...)	Perform the active learning loop.

Plotting

Canvas()	Canvas object, handles and plot information from multiple strategies.
Canvas.load_metrics(path_to_files, ...[, ...])	Load and identify set of metrics.
Canvas.set_plot_dimensions()	Set directives for plot sizes.
Canvas.plot_metrics(output_plot_file, ...)	Generate plot for all metrics in files and strategies given as input.

Scripts

build_canonical(user_choices)	Build canonical sample for SNPCC data set fitted with Bazin features.
build_time_domain(user_choice)	Generates features files for a list of days of the survey.
fit_dataset(user_choices)	Fit the entire sample with the Bazin function.
make_metrics_plots(user_input)	Generate metric plots.
run_loop(args)	Command line interface to run the active learning loop.
run_time_domain(user_choice)	Command line interface to the Time Domain Active Learning scenario.