hoops_ai.cadencoder.brep_encoder

Classes

BrepEncoder(brep_access[, storage_handler])

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

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.

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.

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.

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.

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.

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.

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_loops’ in storage or returns the computed data. - Requires ‘face_indices’ to already be pushed if using storage.

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_indices()

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

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.

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.

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)

push_facegrid(ugrid=5, vgrid=5)

Ensures ‘face_uv_grids’ is in storage or returns the face grid data directly. - Requires ‘face_indices’ if using storage.

Returns:

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

Return type:

If storage_handler is not None

Parameters:
class hoops_ai.cadencoder.brep_encoder.FacePairAngleComputation

Bases: object

Demonstrates sampling 25 normals per face, then computing fully broadcasted angle histograms (A3).

static compute_average_face_pair_angle_histograms(face_normals_list, num_bins, sample_size=25)

Returns a (F, F, num_bins) float32 array of normalized angle histograms between each pair of faces.

Parameters:
Return type:

numpy.ndarray

class hoops_ai.cadencoder.brep_encoder.FacePairDistanceComputation

Bases: object

Demonstrates: 1) Using float32 2) Sampling 25 points per face (or less, if P < 25) 3) Blocking faces into two chunks 4) Merging partial histograms in two threads

static compute_average_face_pair_distance_histograms(face_points_list, get_diagonal_length_func, num_bins, sample_size=25)

Returns a (F, F, num_bins) float32 array of normalized distance histograms between each pair of faces.

Parameters:
Return type:

numpy.ndarray