Visualization (`radionets.plotting.visualization`)#

Visualization submodule of radionets.plotting.

Reference/API#

radionets.plotting.visualization Module#

Functions#

`calc_dr`(ifft_truth, ifft_pred)
`check_vmin_vmax`(inp)	Check wether the absolute of the maxmimum or the minimum is bigger.
`compute_area_ratio`(CS_pred, CS_truth)	Compute the ratio of the areas of truth and prediction.
`get_boxsize`(num_corners[, num_pixel])
`make_axes_locatable`(axes)
`make_axes_nice`(fig, ax, im, title[, phase, ...])	Create nice colorbars with bigger label size for every axis in a subplot.
`ms_ssim`(X, Y[, data_range, size_average, ...])	interface of ms-ssim Args: X (torch.Tensor): a batch of images, (N,C,[T,]H,W) Y (torch.Tensor): a batch of images, (N,C,[T,]H,W) data_range (float or int, optional): value range of input images. (usually 1.0 or 255) size_average (bool, optional): if size_average=True, ssim of all images will be averaged as a scalar win_size: (int, optional): the size of gauss kernel win_sigma: (float, optional): sigma of normal distribution win (torch.Tensor, optional): 1-D gauss kernel. if None, a new kernel will be created according to win_size and win_sigma weights (list, optional): weights for different levels K (list or tuple, optional): scalar constants (K1, K2). Try a larger K2 constant (e.g. 0.4) if you get a negative or NaN results. Returns: torch.Tensor: ms-ssim results.
`plot_box`(ax, num_boxes, corners)
`plot_contour`(ifft_pred, ifft_truth, out_path, i)
`plot_data`(x, path[, rows, cols, save, ...])	Plotting image of the dataset
`plot_fitgaussian`(data, fit_list, ...[, ...])	Plotting the sky image with the fitted gaussian distributian and the related parameters.
`plot_inp_tar`(h5_dataset[, fourier, amp_phase])
`plot_jet_components_results`(inp, pred, ...)	Plot input images, prediction and true image.
`plot_jet_results`(inp, pred, truth, path[, ...])	Plot input images, prediction, true and diff image of the overall prediction.
`plot_length_point`(length, vals, mask, out_path)
`plot_target`(h5_dataset[, log])
`reshape_2d`(array)	Reshape 1d arrays into 2d ones.
`visualize_sampled_unc`(i, mean, std, ...)
`visualize_source_reconstruction`(ifft_pred, ...)
`visualize_uncertainty`(i, img_pred, ...[, ...])
`visualize_with_fourier`(i, img_input, ...[, ...])	Visualizes how the target variables are displayed in fourier space.
`visualize_with_fourier_diff`(i, img_pred, ...)	Visualizing, if the target variables are displayed in fourier space.