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|
# Copyright (C) 2021-2024 Garth N. Wells and Paul T. Kühner
#
# This file is part of DOLFINx (https://www.fenicsproject.org)
#
# SPDX-License-Identifier: LGPL-3.0-or-later
"""Graph representations and operations on graphs."""
import numpy as np
import numpy.typing as npt
from dolfinx import cpp as _cpp
from dolfinx.cpp.graph import partitioner
# Import graph partitioners, which may or may not be available
# (dependent on build configuration)
try:
from dolfinx.cpp.graph import partitioner_scotch # noqa
except ImportError:
pass
try:
from dolfinx.cpp.graph import partitioner_parmetis # noqa
except ImportError:
pass
try:
from dolfinx.cpp.graph import partitioner_kahip # noqa
except ImportError:
pass
__all__ = [
"AdjacencyList",
"adjacencylist",
"comm_graph",
"comm_graph_data",
"comm_to_json",
"partitioner",
]
class AdjacencyList:
_cpp_object: (
_cpp.graph.AdjacencyList_int32
| _cpp.graph.AdjacencyList_int64
| _cpp.graph.AdjacencyList_int_sizet_int8__int32_int32
)
def __init__(
self,
cpp_object: (
_cpp.graph.AdjacencyList_int32
| _cpp.graph.AdjacencyList_int64
| _cpp.graph.AdjacencyList_int_sizet_int8__int32_int32
),
):
"""Creates a Python wrapper for the exported adjacency list class.
Note:
Do not use this constructor directly. Instead use
:func:`adjacencylist`.
Args:
The underlying cpp instance that this object will wrap.
"""
self._cpp_object = cpp_object
def __repr__(self):
return self._cpp_object.__repr__
def links(self, node: np.int32 | np.int64) -> npt.NDArray[np.int32 | np.int64]:
"""Retrieve the links of a node.
Note:
This is available only for adjacency lists with no
additional link (edge) data.
Args:
Node to retrieve the connectivity of.
Returns:
Neighbors of the node.
"""
return self._cpp_object.links(node)
@property
def array(self) -> npt.NDArray[np.int32 | np.int64]:
"""Array representation of the adjacency list.
Note:
This is available only for adjacency lists with no
additional link (edge) data.
Returns:
Flattened array representation of the adjacency list.
"""
return self._cpp_object.array
@property
def offsets(self) -> npt.NDArray[np.int32]:
"""Offsets for each node in the :func:`array`.
Returns:
Array of indices with shape `(num_nodes+1)`.
"""
return self._cpp_object.offsets
@property
def num_nodes(self) -> np.int32:
"""Number of nodes in the adjacency list.
Returns:
Number of nodes.
"""
return self._cpp_object.num_nodes
def adjacencylist(
data: npt.NDArray[np.int32 | np.int64], offsets: npt.NDArray[np.int32] | None = None
) -> AdjacencyList:
"""Create an :class:`AdjacencyList` for `int32` or `int64` datasets.
Args:
data: The adjacency array. If the array is one-dimensional,
offsets should be supplied. If the array is two-dimensional
the number of edges per node is the second dimension.
offsets: The offsets array with the number of edges per node.
Returns:
An adjacency list.
"""
# TODO: Switch to np.isdtype(data.dtype, np.int32) once numpy >= 2.0 is
# enforced
if data.dtype == np.int32:
cpp_t = _cpp.graph.AdjacencyList_int32
elif data.dtype == np.int64:
cpp_t = _cpp.graph.AdjacencyList_int64
else:
raise TypeError("Data type for adjacency list not supported.")
cpp_object = cpp_t(data, offsets) if offsets is not None else cpp_t(data)
return AdjacencyList(cpp_object)
def comm_graph(map: _cpp.common.IndexMap, root: int = 0) -> AdjacencyList:
"""Build a parallel communication graph from an index map.
The communication graph is a directed graph that represents the
communication pattern for a distributed array, and specifically the
forward scatter operation where the values for owned indices are
sent to ghosting ranks. The graph is built from an index map, which
describes the local and ghosted indices of the array.
Edges in the graph represent communication from the owning rank to
ranks that ghost the data. The edge data holds the (0) target node,
(1) edge weight, and (2) an indicator for whether the sending and
receiving ranks share memory (``local==1``) or if the ranks do not
share memory (``local==0``). The node data holds the local size
(number of owned indices) and the number of ghost indices.
The graph can be processed using :func:`comm_graph` to build data
structures that can be used to build a `NetworkX
<https://networkx.org/>`_ directed graph.
Note:
This function is collective across all MPI ranks. The
communication graph is returned on the `root` rank. All other
ranks return an empty graph
Args:
map: Index map to build the communication graph from.
root: Rank that will return the communication graph. Other ranks
return an empty graph.
Returns:
An adjacency list representing the communication graph.
"""
return AdjacencyList(_cpp.graph.comm_graph(map))
def comm_graph_data(
graph: AdjacencyList,
) -> tuple[list[tuple[int, int, dict[str, int]]], list[tuple[int, dict[str, int]]]]:
"""Build from a communication graph data structures for use with
`NetworkX <https://networkx.org/>`_.
Args:
graph: Communication graph to build data from. Normally created
by :func:`comm_graph`.
Returns:
A tuple of two lists. The first list contains the edge data,
where an edge is a `(nodeID_0, nodeID_1, dict)` tuple, where
`dict` holds edge data. The second list hold node data, where a
node is a `(nodeID, dict)` tuple, where `dict` holds node data.
"""
return _cpp.graph.comm_graph_data(graph._cpp_object)
def comm_to_json(graph: AdjacencyList) -> str:
"""Build and JSON string from a communication graph.
The JSON string can be used to construct a `NetworkX
<https://networkx.org/>`_ graph. This is helpful for cases where a
simulation is executed and the graph data is written to file as a
JSON string for later analysis.
Args:
graph: The communication graph to convert. Normally created by
calling :meth:`comm_graph`.
Returns:
A JSON string representing the communication graph.
"""
return _cpp.graph.comm_to_json(graph._cpp_object)
|
