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We apparently already had `G.reverse()`, which made that function extra easy :) Authors: - Erik Welch (https://github.com/eriknw) Approvers: - Rick Ratzel (https://github.com/rlratzel) URL: #4103
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python/nx-cugraph/nx_cugraph/algorithms/operators/__init__.py
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# Copyright (c) 2024, NVIDIA CORPORATION. | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
from .unary import * |
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python/nx-cugraph/nx_cugraph/algorithms/operators/unary.py
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# Copyright (c) 2024, NVIDIA CORPORATION. | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
import cupy as cp | ||
import networkx as nx | ||
import numpy as np | ||
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import nx_cugraph as nxcg | ||
from nx_cugraph.convert import _to_graph | ||
from nx_cugraph.utils import index_dtype, networkx_algorithm | ||
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__all__ = ["complement", "reverse"] | ||
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@networkx_algorithm(version_added="24.02") | ||
def complement(G): | ||
G = _to_graph(G) | ||
N = G._N | ||
# Upcast to int64 so indices don't overflow. | ||
edges_a_b = N * G.src_indices.astype(np.int64) + G.dst_indices | ||
# Now compute flattened indices for all edges except self-loops | ||
# Alt (slower): | ||
# edges_full = np.arange(N * N) | ||
# edges_full = edges_full[(edges_full % (N + 1)).astype(bool)] | ||
edges_full = cp.arange(1, N * (N - 1) + 1) + cp.repeat(cp.arange(N - 1), N) | ||
edges_comp = cp.setdiff1d( | ||
edges_full, | ||
edges_a_b, | ||
assume_unique=not G.is_multigraph(), | ||
) | ||
src_indices, dst_indices = cp.divmod(edges_comp, N) | ||
return G.__class__.from_coo( | ||
N, | ||
src_indices.astype(index_dtype), | ||
dst_indices.astype(index_dtype), | ||
key_to_id=G.key_to_id, | ||
) | ||
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@networkx_algorithm(version_added="24.02") | ||
def reverse(G, copy=True): | ||
if not G.is_directed(): | ||
raise nx.NetworkXError("Cannot reverse an undirected graph.") | ||
if isinstance(G, nx.Graph): | ||
G = nxcg.from_networkx(G, preserve_all_attrs=True) | ||
return G.reverse(copy=copy) |