scatter_plot#

deepinv.utils.scatter_plot(xy_list, titles=None, save_dir=None, tight=True, show=True, return_fig=False, figsize=None, subtitles=None, suptitle=None, cmap='gray', fontsize=None, s=0.1, linewidths=1.5, color='b')[source]#

Plots a list of scatter plots.

Example usage:

import torch
from deepinv.utils import scatter_plot
xy = torch.randn(10, 2)
scatter_plot([xy, xy], titles=["scatter1", "scatter2"], save_dir="test.png")
Parameters:

  • xy_list (list[torch.Tensor], torch.Tensor) – list of scatter plots data, or single scatter plot data.

  • subtitles (list[list[str]], list[str], str, None) – list of subtitles for each image, can be either the same length or the same shape as img_list.

  • titles (list[str]) – list of titles for each image, has to be same length as img_list.

  • save_dir (None, str, pathlib.Path) – path to save the plot.

  • tight (bool) – use tight layout.

  • show (bool) – show the image plot.

  • return_fig (bool) – return the figure object.

  • figsize (tuple[int]) – size of the figure.

  • suptitle (str) – title of the figure.

  • cmap (str) – colormap to use for the images. Default: gray

  • fontsize (int) – fontsize for the plot. Default: 17

  • s (float) – size of the scattered points. Default: 0.1

  • linewidths (float) – width of the lines. Default: 1.5

  • color (str) – color of the points. Default: blue

Examples using scatter_plot:#

Radio interferometric imaging with deepinverse

Radio interferometric imaging with deepinverse