Connectivity

plot3d.connectivity.combinations_of_nearest_blocks(blocks: List[Block], nearest_nblocks: int = 4)[source]

Returns the indices of the nearest 6 blocks based on their centroid

Parameters:

  • block (Block) – block you are interested in

  • blocks (List[Block]) – list of all your blocks

Returns:

combinations of nearest blocks

Return type:

List[Tuple[int,int]]

plot3d.connectivity.connectivity(blocks: List[Block])[source]

Returns a dictionary outlining the connectivity of the blocks along with any exterior surfaces

Parameters:

blocks (List[Block]) – List of all blocks in multi-block plot3d mesh

Returns:

All matching faces formatted as a list of { ‘block1’: {‘block_index’, ‘IMIN’, ‘JMIN’,’KMIN’, ‘IMAX’,’JMAX’,’KMAX’} } (List[Dict]): All exterior surfaces formatted as a list of { ‘block_index’, ‘surfaces’: [{‘IMIN’, ‘JMIN’,’KMIN’, ‘IMAX’,’JMAX’,’KMAX’, ‘ID’}] }

Return type:

(List[Dict])

plot3d.connectivity.connectivity_fast(blocks: List[Block])[source]

Reduces the size of the blocks by a factor of the minimum gcd. This speeds up finding the connectivity

Parameters:

blocks (List[Block]) – Lists of blocks you want to find the connectivity for

Returns:

All matching faces formatted as a list of { ‘block1’: {‘block_index’, ‘IMIN’, ‘JMIN’,’KMIN’, ‘IMAX’,’JMAX’,’KMAX’} } (List[Dict]): All exterior surfaces formatted as a list of { ‘block_index’, ‘surfaces’: [{‘IMIN’, ‘JMIN’,’KMIN’, ‘IMAX’,’JMAX’,’KMAX’, ‘ID’}] }

Return type:

(List[Dict])

plot3d.connectivity.face_matches_to_dict(face1: Face, face2: Face, block1: Block, block2: Block)[source]

Makes sure the diagonal of face 1 match the diagonal of face 2

Parameters:

  • face1 (Face) – Face 1 with block index

  • face2 (Face) – Face 2 with block index

  • block1 (Block) – Block 1

  • block2 (Block) – Block 2

Returns:

dictionary describing the corner matches

Return type:

(dict)

plot3d.connectivity.find_matching_blocks(block1: Block, block2: Block, block1_outer: List[Face], block2_outer: List[Face], tol: float = 1e-06)[source]

Takes two blocks and finds all matching pairs

Parameters:

  • block1 (Block) – Any plot3d Block that is not the same as block2

  • block2 (Block) – Any plot3d Block that is not the same as block1

  • block1_outer (List[Face]) – outer faces for block 1.

  • block2_outer (List[Face]) – Outer faces for block 2

  • tol (float, Optional) – tolerance to use. Defaults to 1E-6

Note

This function was changed to be given an input of outer faces for block 1 and block 2. Outer faces can change and we should use the updated value

Returns:

containing

  • df (pandas.DataFrame): corners of matching pair as block1_corners,block2_corners ([imin,jmin,kmin],[imax,jmax,kmax]), ([imin,jmin,kmin],[imax,jmax,kmax])

  • block1_outer (List[Face]):

  • block2_outer (List[Face]):

Return type:

(tuple)

plot3d.connectivity.get_face_intersection(face1: Face, face2: Face, block1: Block, block2: Block, tol: float = 1e-06)[source]
Get the index of the intersection between two faces located on two different blocks

Face1 needs to be the smaller face.

Parameters:

  • face1 (Face) – An exterior face

  • face2 (Face) – An exterior face from a different block

  • block1 (Block) – block containing face1

  • block2 (Block) – block containing face2

  • tol (float) – matching tolerance

Returns:

containing

  • (pandas.DataFrame): dataframe with matches. Columns = I1, J1, K1, I2, J2, K2

  • (List[Face]): any split faces from block 1

  • (List[Face]): any split faces from block 2

Return type:

(Tuple)

plot3d.connectivity.select_multi_dimensional(T: ndarray, dim1: tuple, dim2: tuple, dim3: tuple)[source]
Takes a block (T) and selects X,Y,Z from the block given a face’s dimensions

theres really no good way to do this in python

Parameters:

  • T (np.ndarray) – arbitrary array so say a full matrix containing X

  • dim1 (tuple) – 20,50 this selects X in the i direction from i=20 to 50

  • dim2 (tuple) – 40,60 this selects X in the j direction from j=40 to 60

  • dim3 (tuple) – 10,20 this selects X in the k direction from k=10 to 20

Returns:

returns X or Y or Z given some range of I,J,K

Return type:

np.ndarray

plot3d.connectivity.verify_connectivity(blocks: List[Block], face_matches: list, tol: float = 1e-06)[source]

Verifies that the diagonal corners of face_matches are spatially consistent.

For each face_match, checks that block1’s lower corner coordinates match block2’s lower corner coordinates (and similarly for upper corners) within the specified tolerance. If the stored diagonal doesn’t match, tries all permutations of block2’s face corners. If a valid permutation is found, the face_match is corrected and added to the verified list.

Uses GCD reduction (same as connectivity_fast) for efficient coordinate lookups.

Parameters:

  • blocks (List[Block]) – List of all blocks (original full-resolution)

  • face_matches (list) – List of face_match dicts from connectivity or periodicity

  • tol (float, optional) – Euclidean distance tolerance. Defaults to 1E-6.

Returns:

verified face_matches whose diagonals are confirmed or corrected (list): mismatched face_matches where no corner permutation matched

Return type:

(list)