Module `connectome.visualization.viz_utils`

helper functions for the visualization of results

Functions

def ordered_regions() ‑> tuple

return list of indexes from yeo 7

def plot_coef_elastic_net(model, title='Elastic Net coefficients')

plot coefficients of elastic net model

plot

def plot_feature_map(heatmap, title, aggregated_network=False, ordered=False, cmap='gist_heat_r', center_0=False)

Plots a heatmap of the connectivity matrix

heatmap: The image data
title: A title for the plot
aggregated_network: Boolean, whether the matrices were aggregated based on yeo7
ordered: Boolean, whether to reorder the matrices based on the yeo7 network. (True is recommended when training with Brainnetome data. Only applicable to data based on the brainnetome atlas.
cmap: Choice of colormap from matplotlib
center_0: Boolean, whether to center the cmap around 0

Connectivity matrix plot

def plot_grouped_FI(df_importance, title='Grouped Permutation Feature Importance')

plot results grouped feature importance (groups based on yeo7 network)

df_importance: pd.DataFrame with results from calculation grouped FI. First column contains regions, second column contains importance values

plot