brainevent.csc_to_csr_index

brainevent.csc_to_csr_index#

brainevent.csc_to_csr_index(csc_indptr, csc_indices, *, shape, include_perm=True)[source]#

Convert CSC format index arrays to CSR format.

Inverse companion of csr_to_csc_index(). A Compressed Sparse Column layout of a matrix W with shape (n_rows, n_cols) is, array for array, the Compressed Sparse Row layout of W.T with shape (n_cols, n_rows). Building the CSR structure of W therefore reduces to calling csr_to_csc_index() on the transposed interpretation.

Parameters:

  • csc_indptr (Array | ndarray) – Column pointer array in CSC format. For a matrix with n_cols columns, this has length n_cols + 1.

  • csc_indices (Array | ndarray) – Row index array in CSC format. Contains the row index for each non-zero element, ordered by column.

  • shape (Tuple[int, int]) – A (n_rows, n_cols) tuple giving the dimensions of the matrix the CSC arrays describe. Keyword-only argument.

  • include_perm (bool) – If True (default), return the permutation that maps CSR slots back to CSC data positions. If False, return None for the third result while still constructing the CSR structure.

Returns:

  • csr_indptr (jax.Array or numpy.ndarray) – Row pointer array in CSR format. Length n_rows + 1.

  • csr_indices (jax.Array or numpy.ndarray) – Column index array in CSR format.

  • perm (jax.Array or numpy.ndarray) – Permutation array reordering data values from CSC order to CSR order. If data is the CSC data array, then data[perm] gives the values in CSR order.

Raises:

AssertionError – If shape is not a length-2 tuple/list of positive integers.

See also

csr_to_csc_index

The forward CSR-to-CSC companion (mutual inverse).

coo_to_csc_index

Convert COO indices to CSC indices.

Notes

Because the two helpers are mutual inverses on the same structure, the permutations they return compose to the identity: csr_perm[csc_perm] == arange(nse).

Examples

>>> import numpy as np
>>> from brainevent._misc import csr_to_csc_index, csc_to_csr_index
>>> indptr = np.array([0, 2, 3, 5])
>>> indices = np.array([0, 2, 1, 0, 3])
>>> csc_indptr, csc_indices, _ = csr_to_csc_index(indptr, indices, shape=(3, 4))
>>> csr_indptr, csr_indices, _ = csc_to_csr_index(
...     csc_indptr, csc_indices, shape=(3, 4)
... )