src.superphot_plus.plotting.utils

This module contains helper functions to access/manipulate data for plotting more concisely.

Module Contents

Functions

lighten_color(color[, amount])	Lightens the given color by multiplying (1-luminosity) by the given amount.
get_survey_fracs()	Return catalog with supernova fractions from existing
read_probs_csv(probs_fn)	Helper function to read in a probability csv file
get_alerce_pred_class(ztf_name, alerce[, superphot_style])	Get alerce probabilities corresponding to the four (no SN IIn)
create_alerce_pred_csv(probs_fn, save_fn)	Generate csv with ALeRCE's predicted
retrieve_four_class_info(probs_csv, probs_alerce_csv)	Extract Superphot+ and ALeRCE predictions and true class info.
gaussian(inputs, amp, mean, sigma)	Evaluate a gaussian with params A at the values in x.
histedges_equalN(vals, nbin)	Generate histogram bin edges, such that counts are equal in each bin.
add_snr_to_prob_csv(probs_csv, data_dir, new_csv)	Adds 10% SNR and num of SNR > 5 points columns
calc_precision_recall(y_true, y_score, folds)	Calculate purity recall values at
roc_curve_w_uncertainties(y_true, y_score, folds)	Incorporate K-fold uncertainties.
rebin_prec_recall(t, r, rerr, p, perr)	Turn completeness to the independent variable,
calc_calibration_curve(y_true, y_score, folds)	Return confidence vs. fraction of true positives.

lighten_color(color, amount=0.5)

Lightens the given color by multiplying (1-luminosity) by the given amount. Input can be matplotlib color string, hex string, or RGB tuple.

get_survey_fracs()

Return catalog with supernova fractions from existing catalogue datasets. referenced in papers.

read_probs_csv(probs_fn)

Helper function to read in a probability csv file and return the columns as numpy arrays.

get_alerce_pred_class(ztf_name, alerce, superphot_style=False)

Get alerce probabilities corresponding to the four (no SN IIn) classes in our ZTF classifier.

Parameters:

• ztf_name (str) – ZTF name of the object.

• superphot_style (bool, optional) – If True, change format of output labels. Default is False.

Returns:

Predicted class label.

Return type:

str

create_alerce_pred_csv(probs_fn, save_fn)

Generate csv with ALeRCE’s predicted classes for SN names in probs_csv.

retrieve_four_class_info(probs_csv, probs_alerce_csv, p07=False)

Extract Superphot+ and ALeRCE predictions and true class info.

gaussian(inputs, amp, mean, sigma)

Evaluate a gaussian with params A at the values in x.

Parameters:

• inputs (array-like or float) – Value(s) to evaluate gaussian at

• amp (float) – Amplitude of the Gaussian.

• mean (float) – Mean of Gaussian

• sigma (float) – Standard deviation of Gaussian

Returns:

Gaussian values evaluated at x

Return type:

array-like or float

histedges_equalN(vals, nbin)

Generate histogram bin edges, such that counts are equal in each bin.

Parameters:

• vals (array-like or float) – Value(s) to bin in histogram

• nbin (integer) – number of bins

add_snr_to_prob_csv(probs_csv, data_dir, new_csv)

Adds 10% SNR and num of SNR > 5 points columns to probability CSV. Useful for plots.

calc_precision_recall(y_true, y_score, folds)

Calculate purity recall values at multiple threshholds for plot. Assumes y_true only contains 0s and 1s (target), and y_score are probabilities of being class 1.

roc_curve_w_uncertainties(y_true, y_score, folds)

Incorporate K-fold uncertainties.

rebin_prec_recall(t, r, rerr, p, perr)

Turn completeness to the independent variable, and bin precision accordingly.

calc_calibration_curve(y_true, y_score, folds)

Return confidence vs. fraction of true positives.