Modules.ProbabilisticMarchingSquares package

Submodules

Modules.ProbabilisticMarchingSquares.plot module

Modules.ProbabilisticMarchingSquares.probabilistic_marching_squares module

Modules.ProbabilisticMarchingSquares.probabilistic_marching_squares.probabilistic_marching_squares(ensemble_images, isovalue, ax=None, colormap='viridis', x_coords=None, y_coords=None)

Compute and visualize probabilistic marching squares.

This function implements the complete stats->mesh->vis pipeline for probabilistic marching squares visualization. It calculates the probability of isocontour presence in each cell and creates a matplotlib visualization.

Parameters:

ensemble_imagesnp.ndarray

3D array of shape (y_dim, x_dim, n_ensemble) representing the scalar field with ensemble members.

isovaluefloat

The isovalue for which to compute the isocontour.

axmatplotlib.axes.Axes, optional

The axis to draw on. If None, a new figure and axis will be created.

colormapstr, optional

Colormap for the visualization. Default is ‘viridis’.

x_coordsnp.ndarray, optional

1D array of x-axis coordinates defining the spatial domain. Length must match x_dim. If None, pixel indices are used.

y_coordsnp.ndarray, optional

1D array of y-axis coordinates defining the spatial domain. Length must match y_dim. If None, pixel indices are used.

Returns:

axmatplotlib.axes.Axes

The axis with the visualized probabilistic isocontour.

Modules.ProbabilisticMarchingSquares.probabilistic_marching_squares_stats module

Modules.ProbabilisticMarchingSquares.probabilistic_marching_squares_stats.probabilistic_marching_squares_summary_statistics(ensemble_images, isovalue)

Compute level crossing probability for probabilistic marching squares.

This function calculates the probability of an isocontour crossing through each cell in a 2D grid based on an ensemble of scalar fields.

Parameters:

ensemble_imagesnp.ndarray

3D array of shape (y_dim, x_dim, n_ensemble) representing the scalar field with ensemble members.

isovaluefloat

The isovalue for which to compute the contour crossing probability.

Returns:

dict

Dictionary containing: - ‘level_crossing_probability’: np.ndarray

2D array of shape (y_dim-1, x_dim-1) with probabilities of contour presence in each cell. Values range from 0 to 1.

Modules.ProbabilisticMarchingSquares.probabilistic_marching_squares_vis module

Modules.ProbabilisticMarchingSquares.probabilistic_marching_squares_vis.visualize_probabilistic_marching_squares(mesh_data, ax=None, colormap='viridis')

Visualize the probability map of isocontour presence using matplotlib.

Parameters:

mesh_datadict

Dictionary from probabilistic_marching_squares_mesh containing: - ‘level_crossing_probability’: np.ndarray

2D array of shape (y_dim-1, x_dim-1) with probabilities of contour presence in each cell.

• ‘extent’: tuple (x_min, x_max, y_min, y_max) or None

axmatplotlib.axes.Axes, optional

The axis to draw on. If None, a new figure and axis will be created.

colormapstr, optional

Colormap for the probability map. Default is ‘viridis’.

Returns:

axmatplotlib.axes.Axes

The axis with the visualized probabilistic isocontour.

Module contents

ProbabilisticMarchingSquares Module

This module provides functionality for performing probabilistic marching squares on 2D datasets with uncertainty. It includes methods for calculating cell crossing probabilities and visualizing the results using matplotlib.

Modules.ProbabilisticMarchingSquares.probabilistic_marching_squares(ensemble_images, isovalue, ax=None, colormap='viridis', x_coords=None, y_coords=None)

Compute and visualize probabilistic marching squares.

This function implements the complete stats->mesh->vis pipeline for probabilistic marching squares visualization. It calculates the probability of isocontour presence in each cell and creates a matplotlib visualization.

Parameters:

ensemble_imagesnp.ndarray

3D array of shape (y_dim, x_dim, n_ensemble) representing the scalar field with ensemble members.

isovaluefloat

The isovalue for which to compute the isocontour.

axmatplotlib.axes.Axes, optional

The axis to draw on. If None, a new figure and axis will be created.

colormapstr, optional

Colormap for the visualization. Default is ‘viridis’.

x_coordsnp.ndarray, optional

1D array of x-axis coordinates defining the spatial domain. Length must match x_dim. If None, pixel indices are used.

y_coordsnp.ndarray, optional

1D array of y-axis coordinates defining the spatial domain. Length must match y_dim. If None, pixel indices are used.

Returns:

axmatplotlib.axes.Axes

The axis with the visualized probabilistic isocontour.

Modules.ProbabilisticMarchingSquares.probabilistic_marching_squares_mesh(summary_statistics, x_coords=None, y_coords=None)

Process summary statistics and spatial coordinates for visualization.

This function exists to maintain consistency with the stats->mesh->vis pipeline architecture used in other modules, even though no mesh transformation is needed for probabilistic marching squares.

Parameters:

summary_statisticsdict

Dictionary containing: - ‘level_crossing_probability’: np.ndarray

2D array of shape (y_dim-1, x_dim-1) representing the probability of contour presence in each cell.

x_coordsnp.ndarray, optional

1D array of x-axis coordinates defining the spatial domain. Length must match x_dim (the number of grid vertices, which is one more than the probability image width). If None, pixel indices are used.

y_coordsnp.ndarray, optional

1D array of y-axis coordinates defining the spatial domain. Length must match y_dim (the number of grid vertices, which is one more than the probability image height). If None, pixel indices are used.

Returns:

dict

Dictionary containing: - ‘level_crossing_probability’: np.ndarray - ‘extent’: tuple (x_min, x_max, y_min, y_max) or None - ‘x_coords’: np.ndarray or None - ‘y_coords’: np.ndarray or None

Modules.ProbabilisticMarchingSquares.probabilistic_marching_squares_summary_statistics(ensemble_images, isovalue)

Compute level crossing probability for probabilistic marching squares.

This function calculates the probability of an isocontour crossing through each cell in a 2D grid based on an ensemble of scalar fields.

Parameters:

ensemble_imagesnp.ndarray

3D array of shape (y_dim, x_dim, n_ensemble) representing the scalar field with ensemble members.

isovaluefloat

The isovalue for which to compute the contour crossing probability.

Returns:

dict

Dictionary containing: - ‘level_crossing_probability’: np.ndarray

2D array of shape (y_dim-1, x_dim-1) with probabilities of contour presence in each cell. Values range from 0 to 1.

Modules.ProbabilisticMarchingSquares.visualize_probabilistic_marching_squares(mesh_data, ax=None, colormap='viridis')

Visualize the probability map of isocontour presence using matplotlib.

Parameters:

mesh_datadict

Dictionary from probabilistic_marching_squares_mesh containing: - ‘level_crossing_probability’: np.ndarray

2D array of shape (y_dim-1, x_dim-1) with probabilities of contour presence in each cell.

• ‘extent’: tuple (x_min, x_max, y_min, y_max) or None

axmatplotlib.axes.Axes, optional

The axis to draw on. If None, a new figure and axis will be created.

colormapstr, optional

Colormap for the probability map. Default is ‘viridis’.

Returns:

axmatplotlib.axes.Axes

The axis with the visualized probabilistic isocontour.