eunoia.EulerFit

class eunoia.EulerFit(shapes: tuple[~eunoia._models.S, ...], original_values: dict[str, float], fitted_values: dict[str, float], residuals: dict[str, float], region_error: dict[str, float], diag_error: float, stress: float, loss: float, plot_data: dict[str, ~typing.Any] = <factory>)

Result of fitting an area-proportional Euler diagram.

Variables:

• shapes (tuple[eunoia._models.S, ...]) – Tuple of fitted shapes (one per input set), in the order the sets were first encountered in the input.

• original_values (dict[str, float]) – The values originally passed in. Keys are canonical (sorted) form.

• fitted_values (dict[str, float]) – The fitted areas, expressed in the same scale as original_values (exclusive or inclusive, depending on input).

• residuals (dict[str, float]) – original_values - fitted_values per region.

• region_error (dict[str, float]) – Per-region error (eunoia core’s region_error metric, always in per-region exclusive form).

• diag_error (float) – Maximum region error (eulerAPE-style worst-case metric).

• stress (float) – venneuler-style stress metric.

• loss (float) – Final value of the objective the optimizer minimized.

__init__(shapes: tuple[~eunoia._models.S, ...], original_values: dict[str, float], fitted_values: dict[str, float], residuals: dict[str, float], region_error: dict[str, float], diag_error: float, stress: float, loss: float, plot_data: dict[str, ~typing.Any] = <factory>) → None

Methods

__init__(shapes, ...], original_values, ...)
plot(*[, ax, colors, fills, edges, labels, ...])	Render the fitted diagram with matplotlib.

Attributes

shapes
original_values
fitted_values
residuals
region_error
diag_error
stress
loss
plot_data