nifreeze.viz.signals module

Visualizing signals and intermediate aspects of models.

nifreeze.viz.signals.calculate_sphere_pts(points, center)

Calculate the location of each point when it is expanded out to the sphere.

nifreeze.viz.signals.compute_dmri_convex_hull(s, dirs, mask=None)

Compute the convex hull of the dMRI signal s.

nifreeze.viz.signals.plot_correlation(x, y, title)

nifreeze.viz.signals.plot_error(kfolds: list[int], mean: ndarray, std_dev: ndarray, xlabel: str, ylabel: str, title: str, color: str = 'orange', figsize: tuple[int, int] = (19.2, 10.8)) → matplotlib.pyplot.Figure

Plot the error and standard deviation.

Parameters:

• kfolds (list) – Number of k-folds.

• mean (ndarray) – Mean RMSE values.

• std_dev (ndarray) – Standard deviation values.

• xlabel (str) – X-axis label.

• ylabel (str) – Y-axis label.

• title (str) – Plot title.

• color (str, optional) – Plot color.

• figsize (tuple, optional) – Figure size.

Returns:

Matplotlib figure object.

Return type:

Figure

nifreeze.viz.signals.plot_estimation_carpet(gt_nii, gp_nii, gtab, suptitle, **kwargs)

nifreeze.viz.signals.plot_prediction_surface(y, y_pred, S0, y_dirs, y_pred_dirs, title, cmap)

Plot the prediction surface obtained by computing the convex hull of the predicted signal data, and plot the true data as a scatter plot.

nifreeze.viz.signals.plot_signal_data(y, ax)

Plot the data provided as a scatter plot

nifreeze.viz.signals.plot_surface(scaled_vecs, sphere_idx, triang_idx, title, cmap)

Plot a surface.