API Reference

Welcome to the k-diagram API reference. This section provides detailed information on the functions, classes, and modules included in the package.

The documentation here is largely auto-generated from the docstrings within the k-diagram source code. Ensure you have installed the package (see Installation) for the documentation build process to find the modules correctly.

Plotting Functions (kdiagram.plot)

This is the core module containing the specialized visualization functions.

Uncertainty Visualization (kdiagram.plot.uncertainty)

Functions focused on visualizing prediction intervals, coverage, anomalies, drift, and other uncertainty-related diagnostics.

plot_actual_vs_predicted	Polar plot comparing actual observed vs.
plot_anomaly_magnitude	Visualize magnitude and type of prediction anomalies polar plot.
plot_coverage	Plot overall coverage scores for forecast intervals or points.
plot_coverage_diagnostic	Diagnose prediction interval coverage using a polar plot.
plot_interval_consistency	Polar plot showing consistency of prediction interval widths.
plot_interval_width	Polar scatter plot visualizing prediction interval width.
plot_model_drift	Visualize forecast drift across prediction horizons.
plot_temporal_uncertainty	Visualize multiple data series using polar scatter plots.
plot_uncertainty_drift	Polar plot visualizing temporal drift of uncertainty width.
plot_velocity	Polar plot visualizing average velocity across locations.

Model Evaluation (kdiagram.plot.evaluation)

Functions for evaluating model performance, primarily using Taylor Diagrams.

taylor_diagram	Plot a Taylor diagram to compare multiple predictions against a reference by visualizing their correlation and standard deviation.
plot_taylor_diagram_in	Plot Taylor Diagram with background color map.
plot_taylor_diagram	Plot a standard Taylor Diagram.

Model Comparison (kdiagram.plot.comparison)

Functions for comparing multi-model performances on a radar chart.

plot_model_comparison

Plot multi-metric model performance comparison on a radar chart.

Feature-Based Visualization (kdiagram.plot.feature_based)

Functions for visualizing feature importance and influence patterns.

plot_feature_fingerprint

Create a radar chart visualizing feature importance profiles.

Relationship Visualization (kdiagram.plot.relationship)

Functions for visualizing the relationship between true and predicted values using polar coordinates.

plot_relationship

Visualize the relationship between y_true and multiple y_preds using a circular or polar plot.

Utility Functions (kdiagram.utils)

Helper functions primarily focused on detecting, validating, and manipulating quantile-related data within pandas DataFrames, often used for preparing data for visualization functions.

build_q_column_names	Generate and validate quantile column names following naming conventions.
detect_quantiles_in	Detect quantile columns in a DataFrame using naming patterns and value validation.
melt_q_data	Reshape wide-format DataFrame with quantile columns to long format with explicit temporal and quantile dimensions.
pivot_q_data	Convert long-format DataFrame with quantile columns back to wide format with temporal quantile measurements.
reshape_quantile_data	Reshape a wide-format DataFrame with quantile columns into a DataFrame where the quantiles are separated into distinct columns for each quantile value.