Hist

class radionets.plotting.hist.Hist(outpath, plot_format: str = 'png', hist_kwargs: dict | None = None, save_kwargs: dict | None = None)

Bases: object

Methods Summary

area(vals[, bins, return_fig])
dynamic_ranges(dr_truth, dr_pred[, return_fig])
gan_sources(ratio, num_zero, above_zero, ...)
jet_angles(vals[, return_fig])
jet_gaussian_distance(dist[, return_fig])	Plotting the distances between predicted and true component of several images. Parameters ---------- dist: 2d array array of shape (n, 2), where n is the number of distances.
mean_diff(vals[, return_fig])
ms_ssim(vals[, bins, return_fig])
peak_intensity(vals[, bins, return_fig])
point(vals, mask[, return_fig])
sum_intensity(vals[, bins, return_fig])
unc(vals[, return_fig])

Methods Documentation

area(vals: tensor, bins: int = 30, return_fig: bool = False)

dynamic_ranges(dr_truth: tensor, dr_pred: tensor, return_fig: bool = False)

gan_sources(ratio, num_zero, above_zero, below_zero, num_images)

jet_angles(vals: tensor, return_fig: bool = False)

jet_gaussian_distance(dist: tensor, return_fig: bool = False)

Plotting the distances between predicted and true component of several images. Parameters ———- dist: 2d array

array of shape (n, 2), where n is the number of distances

mean_diff(vals: tensor, return_fig: bool = False)

ms_ssim(vals: tensor, bins: int = 30, return_fig: bool = False)

peak_intensity(vals: tensor, bins: int = 30, return_fig: bool = False)

point(vals: tensor, mask: tensor, return_fig: bool = False)

sum_intensity(vals: tensor, bins: int = 30, return_fig: bool = False)

unc(vals: tensor, return_fig: bool = False)