Visualization

Plotting utilities for DR results and chemical data analysis.

Charts & Plots

cdr_bench.visualization.visualization.create_radar_chart_subplots(data, attributes, methods, filename=None, fill=False, fontsize=15)

Create radar chart subplots for each descriptor value in the data.

Parameters: - data: DataFrame, data for the radar chart - attributes: list, list of attributes (measures) - methods: list, list of methods - filename: str, optional, file path to save the chart - fill: boolean, default False - fontsize: int, default 15

cdr_bench.visualization.visualization.visualize_networks(networks, similarity_matrix)

Visualize each network in a side-by-side layout to compare different thresholds.

Parameters:

Name	Type	Description	Default
networks	Dict[float, Graph]	Dictionary of networks where keys are thresholds and values are NetworkX graph objects.	required
similarity_matrix	ndarray	The similarity matrix used to display edge labels.	required

Returns:

Name	Type	Description
None	None	Displays a matplotlib figure showing the networks.

cdr_bench.visualization.visualization.plot_mean_accuracy_metric(summary_df)

Plot the Mean Accuracy Metric (MAM) with basis_width against num_basis_functions, reg_coeff, and num_nodes.

Parameters:

Name	Type	Description	Default
summary_df	DataFrame	DataFrame containing the mean and standard deviation of MAM for each group of parameters.	required

Returns:

Type	Description
None	None

3D Visualization

cdr_bench.visualization.visualization_3D.plot_3d_with_projections(x, y, z, values, highlight_indices=None)

Plots a 3D scatter plot along with its 2D projections onto the XY, XZ, and YZ planes. Highlights specified points across all plots.

Parameters:

Name	Type	Description	Default
x	array - like	X coordinates.	required
y	array - like	Y coordinates.	required
z	array - like	Z coordinates.	required
values	array - like	Values at each coordinate, used for coloring.	required
highlight_indices	list	Indices of points to highlight.	None

cdr_bench.visualization.visualization_3D.plot_custom_contour_density(gtm, grid_dim, values, cmap_start=0, vmin=None, vmax=None, figsize=(8, 6), plot_type='voxels')

Statistics Visualization

cdr_bench.visualization.visualize_stats.visualize_sim_id_stats(df, feature_columns, threshold=0.7)

Analyze and compare FisherS scores across features, and visualize the results.

Parameters:

Name	Type	Description	Default
df	DataFrame	The input DataFrame containing the data to analyze.	required
feature_columns	list	List of feature column names to analyze.	required
threshold	float	The threshold value for neighbors (default is 0.7).	0.7