pygeostats.utils¶
Utility functions and classes.
plot_anisotropy_rose(angles: Sequence[float], ranges: Sequence[float], backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Plot anisotropy range estimates as a rose diagram.
plot_directional_variograms(directional_results: Dict[float, Dict[str, ndarray]] | Iterable[Dict[str, ndarray]], backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Plot multiple directional variograms for anisotropy assessment.
plot_kriging_cross_section(distances: Sequence[float], predictions: Sequence[float], observations: Optional[Sequence[float]] = None, backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Plot a cross-section through a kriging prediction surface.
plot_kriging_results(grid_x: ndarray, grid_y: ndarray, predictions: ndarray, sample_coords: Optional[ndarray] = None, sample_values: Optional[ndarray] = None, backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Plot kriging prediction surfaces with optional sampling points.
plot_kriging_uncertainty(grid_x: ndarray, grid_y: ndarray, variance: ndarray, backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Visualize kriging uncertainty (variance) maps.
plot_prediction_comparison(predicted: Sequence[float], observed: Sequence[float], backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Scatter plot comparing predicted and observed values.
plot_residuals_qq(residuals: Sequence[float], backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Generate a QQ plot for kriging residuals.
plot_spatial_correlation(lags: Sequence[float], correlations: Sequence[float], backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Plot spatial correlation statistics against lag distance.
plot_variogram(distances: Sequence[float], gamma: Sequence[float], counts: Optional[Sequence[int]] = None, model_curves: Optional[Iterable[Tuple[str, Sequence[float], Sequence[float]]]] = None, confidence_interval: Optional[Tuple[Sequence[float], Sequence[float]]] = None, backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Plot an empirical variogram with optional model overlays and uncertainty.
plot_variogram_cloud(distances: Sequence[float], gamma: Sequence[float], backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Plot a variogram cloud scatter plot.
plot_variogram_rose(azimuths: Sequence[float], values: Sequence[float], backend: str = 'matplotlib', title: Optional[str] = None, save_path: Optional[str] = None, show: bool = True)
Plot a directional variogram rose diagram.
Generic array validation.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
array
|
array - like
|
Input array. |
required |
name
|
str
|
Name of the array for error messages. |
'array'
|
Returns:
| Name | Type | Description |
|---|---|---|
arr |
ndarray
|
Validated array. |
Validate and convert coordinates to numpy array.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
coordinates
|
array - like or GeoDataFrame
|
Input coordinates. |
required |
Returns:
| Name | Type | Description |
|---|---|---|
coords |
(ndarray, shape(n_samples, n_features))
|
Validated coordinate array. |
Validate and convert values to numpy array.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
values
|
array - like
|
Input values. |
required |
Returns:
| Name | Type | Description |
|---|---|---|
vals |
(ndarray, shape(n_samples))
|
Validated values array. |