Plotting functions¶
sekupy.plot.connectivity module¶
- sekupy.plot.connectivity.plot_connectivity_lines(matrix, node_names, kind='circle', node_position=None, node_colors=None, con_thresh=None, linewidth=None, facecolor='white', colormap='magma', font='Manjari', fontsize=14, colorbar=None, title=None, fig=None)[source]¶
- sekupy.plot.connectivity.plot_connectivity_matrix(matrix, networks, roi_names=None, a=None, threshold=None, **kwargs)[source]¶
This function is used to plot connections in square matrix form.
- Parameters:
matrix (numpy array (n x n) float) – The values of connectivity between each of n ROI
roi_names (list of n string) – The names of each of the n ROI
networks (list of p string) – List of names representing the networks subdivision
threshold (int) – Indicates the value of the most important connections
ticks_type ({'networks', 'roi'}, optional) – Indicates if the tick names should be ROI or networks
ticks_color (list of colors, optional) – The list in matplotlib formats of colors used to color the ticks names, this should be in line with the ticks_type choice: p colors if we choose ‘networks’
facecolor (string, optional) – As in matplotlib it indicates the background color of the plot
- Returns:
f – The figure just composed.
- Return type:
matplotlib figure
- sekupy.plot.connectivity.plot_connectome(matrix, coords, colors, size, threshold, fname, cmap=<matplotlib.colors.LinearSegmentedColormap object>, title='', max_=None, min_=None, display_='ortho')[source]¶
Wrapper of the plot_connectome function in nilearn with some fixed values
- sekupy.plot.connectivity.plot_connectomics(matrix, node_size, save_path, prename, save=False, **kwargs)[source]¶
- sekupy.plot.connectivity.plot_features_distribution(feature_set, feature_set_permutation, save_path, prename='features', n_features=90, n_bins=20)[source]¶