ogstools.meshlib.data_processing module

ogstools.meshlib.data_processing.difference(mesh1, mesh2, mesh_property=None)

Compute the difference of properties between two meshes.

Parameters:

• mesh1 (UnstructuredGrid) – The first mesh to be subtracted from.

• mesh2 (UnstructuredGrid) – The second mesh whose data is subtracted from the first mesh.

• mesh_property (Property | str | None) – The property of interest. If not given, all point and cell_data will be processed raw.

Returns:

A new mesh containing the difference of mesh_property or all datasets between mesh1 and mesh2.

Return type:

UnstructuredGrid

ogstools.meshlib.data_processing.difference_pairwise(meshes_1, meshes_2, mesh_property=None)

Compute pairwise difference between meshes from two lists/arrays (they have to be of the same length).

Parameters:

• meshes_1 (list | ndarray) – The first list/array of meshes to be subtracted from.

• meshes_2 (list | ndarray) – The second list/array of meshes whose data is subtracted from the first list/array of meshes - meshes_1.

• mesh_property (Property | str | None) – The property of interest. If not given, all point and cell_data will be processed raw.

Returns:

An array of meshes containing the differences of mesh_property or all datasets between meshes_1 and meshes_2.

Return type:

ndarray

ogstools.meshlib.data_processing.difference_matrix(meshes_1, meshes_2=None, mesh_property=None)

Compute the difference between all combinations of two meshes from one or two arrays based on a specified property.

Parameters:

• meshes_1 (list | ndarray) – The first list/array of meshes to be subtracted from.

• meshes_2 (list | ndarray | None) – The second list/array of meshes, it is subtracted from the first list/array of meshes - meshes_1 (optional).

• mesh_property (Property | str | None) – The property of interest. If not given, all point and cell_data will be processed raw.

Returns:

An array of meshes containing the differences of mesh_property or all datasets between meshes_1 and meshes_2 for all possible combinations.

Return type:

ndarray

ogstools.meshlib.data_processing.interp_points(points, resolution=100)

Provides lists of points on every segment at a line profile between arbitrary number of points pairs.

Parameters:

• points (ndarray) – Numpy array of N points to sample between. Has to be of shape (N, 3).

• resolution (int) – Resolution of the sampled profile. Total number of points within all profile segments.

Returns:

Numpy array of shape (N, 3), without duplicated nodal points.

Return type:

ndarray

ogstools.meshlib.data_processing.distance_in_segments(profile_nodes, profile)

Calculate the distance within segments of a polyline profile.

Parameters:

• profile_nodes (ndarray) – 2D array of N points (profile nodes) of shape (N, 3)

• profile (ndarray) – output from interp_points function. 2D array of N points (profile nodes) of shape (N, 3)

Returns:

1D array of distances in each segment to its starting point of shape (N, 3), where N is the number of points in profile

Return type:

ndarray

ogstools.meshlib.data_processing.distance_in_profile(points)

Parameters:

points (ndarray) – 2D array of N points (profile nodes) of shape (N, 3)

Returns:

1D array of distances of each point to the beginning of the profile (first row in points), shape of (N,)

Return type:

ndarray

ogstools.meshlib.data_processing.sample_polyline(mesh, properties, profile_nodes, resolution=100)

Sample one or more properties along a polyline. Profiles created by user can be passed as profile_nodes parameter. In this case user should also set resolution to None in order to avoid further interpolation between the points.

Parameters:

• mesh (UnstructuredGrid) – Mesh from which properties will be sampled.

• properties (str | Property | list) – Name or list of names of properties to sample. :param profile_nodes: 2D array of N points (profile nodes) of shape (N, 3)

• resolution (int | None) – Total number of sampling points.

• profile_nodes (ndarray)

Returns:

tuple containing DataFrame with results of the profile sampling and Numpy array of distances from the beginning of the profile at points defined in profile_points.

Return type:

tuple[DataFrame, array]