hoops_ai.cadencoder

Quick Overview

Classes

BrepEncoder(brep_access[, storage_handler])

A single class to compute + persist geometry and topology data.

CAD Encoding Module

The CAD Encoding module transforms CAD geometric data into machine learning-ready representations. It bridges the gap between CAD geometry and ML algorithms by converting B-Rep (Boundary Representation) data into graph structures and numerical features that can be processed by neural networks.

This module is essential for preparing CAD data for machine learning workflows, providing standardized encoding schemes that preserve geometric relationships while making the data compatible with ML frameworks like PyTorch and DGL (Deep Graph Library).

For detailed usage examples and encoding strategies, see the CAD Data Encoding Programming Guide.

class hoops_ai.cadencoder.BrepEncoder(brep_access, storage_handler=None)

Bases: object

A single class to compute + persist geometry and topology data.

Each ‘push_*’ method:

  1. Looks at storage_handler.get_keys().

  2. Ensures schema definition exists for the data.

  3. Computes the result, saves to storage.

  4. Returns 0, discarding any data from memory after storage.

Parameters:

  • brep_access (BrepAccess)

  • storage_handler (DataStorage)

push_average_face_pair_angle_histograms(grid=5, num_bins=64)

Ensures ‘a3_distance’ is in storage or returns the face pair angle histograms directly.

  • Requires ‘face_indices’ if using storage.

See full documentation: BrepEncoder.push_average_face_pair_angle_histograms(grid=5, num_bins=64)

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - the face pair angle histograms

Return type:

If storage_handler is not None

Parameters:
push_average_face_pair_distance_histograms(grid=5, num_bins=64)

Ensures ‘d2_distance’ is in storage or returns the face pair distance histograms directly.

  • Requires ‘face_indices’ if using storage.

See full documentation: BrepEncoder.push_average_face_pair_distance_histograms(grid=5, num_bins=64)

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - the face pair distance histograms

Return type:

If storage_handler is not None

Parameters:
push_curvegrid(ugrid=5)

Ensures ‘edge_u_grids’ is in storage or returns the edge grid data directly.

  • Requires ‘edge_indices’ if using storage.

See full documentation: BrepEncoder.push_curvegrid(ugrid=5)

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - the edge U grids

Return type:

If storage_handler is not None

Parameters:

ugrid (int)

push_edge_attributes()

Ensures ‘edge_types’, ‘edge_lengths’, ‘edge_dihedral_angles’, ‘edge_convexities’ are in storage or returns the computed data directly.

  • Requires ‘edge_indices’ if using storage.

See full documentation: BrepEncoder.push_edge_attributes()

Returns:

Tuple[List[str], Dict] - (list of stored key names, edge_type_descrip dictionary) If storage_handler is None: Tuple[List[np.ndarray], Dict] - (list of numpy arrays, edge_type_descrip dictionary)

where edge_type_descrip is a dictionary mapping edge type IDs to descriptions

Return type:

If storage_handler is not None

push_edge_indices()

Ensures ‘edge_indices’ is in storage or returns the edge indices directly.

See full documentation: BrepEncoder.push_edge_indices()

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - the edge indices

Return type:

If storage_handler is not None

push_extended_adjacency()

Ensures ‘extended_adjacency’ is in storage or returns the extended adjacency data directly.

  • Requires ‘graph’ if using storage.

See full documentation: BrepEncoder.push_extended_adjacency()

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - the extended adjacency matrix

Return type:

If storage_handler is not None

push_face_adjacency_graph()

Ensures ‘graph’ is in storage or returns the face adjacency graph directly.

See full documentation: BrepEncoder.push_face_adjacency_graph()

Returns:

str - the key name of stored data If storage_handler is None: nx.Graph - the face adjacency graph

Return type:

If storage_handler is not None

push_face_attributes()

Ensures ‘face_types’, ‘face_areas’, ‘face_centroids’, ‘face_loops’ in storage or returns the computed data.

  • Requires ‘face_indices’ to already be pushed if using storage.

See full documentation: BrepEncoder.push_face_attributes()

Returns:

Tuple[List[str], Dict] - (list of stored key names, face_types_descr dictionary) If storage_handler is None: Tuple[List[np.ndarray], Dict] - (list of numpy arrays, face_types_descr dictionary)

where face_types_descr is a dictionary mapping face type IDs to descriptions

Return type:

If storage_handler is not None

push_face_centroids()

Ensures ‘face_centroids’ is in storage or returns the face centroid data directly.

See full documentation: BrepEncoder.push_face_centroids()

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - the face centroid data

Return type:

If storage_handler is not None

push_face_discretization(pointsamples=25)

Ensures ‘face_discretization’ is in storage or returns the face discretization data directly.

  • Requires ‘face_indices’ if using storage.

See full documentation: BrepEncoder.push_face_discretization(pointsamples=25)

Parameters:

pointsamples (int) – Number of points to sample per face (default 25)

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - the face discretization

Return type:

If storage_handler is not None

push_face_indices()

Ensures ‘face_indices’ is in storage or returns the face indices directly.

See full documentation: BrepEncoder.push_face_indices()

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - the face indices

Return type:

If storage_handler is not None

push_face_neighbors_count()

Ensures ‘face_neighborscount’ is in storage or returns the neighbor count data directly.

  • Requires ‘graph’ if using storage.

See full documentation: BrepEncoder.push_face_neighbors_count()

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - array of neighbor counts for each face

Return type:

If storage_handler is not None

push_face_pair_edges_path(max_allow_edge_length=16)

Ensures ‘face_pair_edges_path’ is in storage or returns the edge path data directly.

  • Requires ‘graph’ if using storage.

See full documentation: BrepEncoder.push_face_pair_edges_path(max_allow_edge_length=16)

Returns:

str - the key name of stored data If storage_handler is None: np.ndarray - the face pair edges path array

Return type:

If storage_handler is not None

Parameters:

max_allow_edge_length (int)