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qttools.datastructures.dsdbcsr#

source module qttools.datastructures.dsdbcsr

Classes

  • DSDBCSR — A Distributed Stack of Distributed Block-accessible CSR matrices.

source class DSDBCSR(data: NDArray, cols: NDArray, rowptr_map: dict, block_sizes: NDArray, global_stack_shape: tuple, return_dense: bool = True, symmetry: bool | None = False, symmetry_op: Callable = xp.conj)

Bases : DSDBSparse

A Distributed Stack of Distributed Block-accessible CSR matrices.

This DSDBSparse implementation uses a block-compressed sparse row format to store the sparsity pattern of the matrix. The data is sorted by block-row and -column. We use a row pointer map together with the column indices to access the blocks efficiently.

Note

It is the caller's responsibility to ensure that the data is distributed correctly across the ranks.

Initializes the DBCSR matrix.

Parameters

  • data : NDArray — The local slice of the data. This should be an array of shape (*local_stack_shape, nnz). It is the caller's responsibility to ensure that the data is distributed correctly across the ranks.

  • cols : NDArray — The column indices.

  • rowptr_map : dict — The row pointer map.

  • block_sizes : NDArray — The size of each block in the sparse matrix.

  • global_stack_shape : tuple or int — The global shape of the stack. If this is an integer, it is interpreted as a one-dimensional stack.

  • return_dense : bool, optional — Whether to return dense arrays when accessing the blocks. Default is True.

  • symmetry : bool, optional — Whether the matrix is symmetric. Default is False.

  • symmetry_op : callable, optional — The operation to use for the symmetry. Default is xp.conj.

Attributes

  • block_offsets : ArrayLike — Returns the block sizes.

  • blocks : _DSDBlockIndexer — Returns a block indexer.

  • sparse_blocks : _DSDBlockIndexer — Returns a block indexer.

  • stack : _DStackIndexer — Returns a stack indexer.

  • data : NDArray — Returns the local slice of the data, masking the padding.

Methods

  • block_sizes — Sets new block sizes for the matrix.

  • ltranspose — Performs a local transposition of the matrix.

  • symmetrize — Symmetrizes the matrix.

  • spy — Returns the row and column indices of the non-zero elements.

  • zeros_like — Creates a new DSDBSparse matrix with the same shape and dtype.

  • from_sparray — Creates a new DSDBSparse matrix from a scipy.sparse array.

  • to_dense — Converts the local data to a dense array.

source method DSDBCSR.block_sizes(block_sizes: NDArray) → None

Sets new block sizes for the matrix.

Parameters

  • block_sizes : NDArray — The new block sizes.

Raises

  • NotImplementedError

  • ValueError

source method DSDBCSR.ltranspose(copy=False) → None | DSDBCSR

Performs a local transposition of the matrix.

Parameters

  • copy : bool, optional — Whether to return a new object. Default is False.

Returns

  • None | DSDBCSR — The transposed matrix. If copy is False, this is None.

Raises

  • NotImplementedError

source method DSDBCSR.symmetrize(op: Callable[[NDArray, NDArray], NDArray] = xp.add) → None

Symmetrizes the matrix.

NOTE: Assumes that the matrix's sparsity pattern is symmetric.

Parameters

  • op : callable, optional — The operation to perform on the symmetric elements. Default is addition.

Raises

  • NotImplementedError

source method DSDBCSR.spy() → tuple[NDArray, NDArray]

Returns the row and column indices of the non-zero elements.

This is essentially the same as converting the sparsity pattern to coordinate format. The returned sparsity pattern is not sorted.

Returns

  • rows : NDArray — Row indices of the non-zero elements.

  • cols : NDArray — Column indices of the non-zero elements.

source classmethod DSDBCSR.zeros_like(dsdbsparse: DSDBSparse) → DSDBSparse

Creates a new DSDBSparse matrix with the same shape and dtype.

All non-zero elements are set to zero, but the sparsity pattern is preserved.

Parameters

  • dsdbsparse : DSDBSparse — The matrix to copy the shape and dtype from.

Returns

source classmethod DSDBCSR.from_sparray(arr: sparse.spmatrix, block_sizes: NDArray, global_stack_shape: tuple, symmetry: bool | None = False, symmetry_op: Callable = xp.conj) → DSDBCSR

Creates a new DSDBSparse matrix from a scipy.sparse array.

Parameters

  • arr : sparse.spmatrix — The sparse array to convert.

  • block_sizes : NDArray — The size of all the blocks in the matrix.

  • global_stack_shape : tuple — The global shape of the stack of matrices. The provided sparse matrix is replicated across the stack.

  • symmetry : bool, optional — Whether to enforce symmetry in the matrix. Default is False.

  • symmetry_op : callable, optional — The operation to use for the symmetry. Default is xp.conj.

Returns

  • DSDBCSR — The new DSDBCSR matrix.

source method DSDBCSR.to_dense() → NDArray

Converts the local data to a dense array.

This is dumb, unless used for testing and debugging.

Returns

  • arr : NDArray — The dense array of shape (*local_stack_shape, *shape).

Raises

  • ValueError