diff options
Diffstat (limited to 'networkx/convert_matrix.py')
| -rw-r--r-- | networkx/convert_matrix.py | 200 |
1 files changed, 132 insertions, 68 deletions
diff --git a/networkx/convert_matrix.py b/networkx/convert_matrix.py index 0746af7c..4a3dacb7 100644 --- a/networkx/convert_matrix.py +++ b/networkx/convert_matrix.py @@ -25,16 +25,30 @@ import itertools import networkx as nx from networkx.utils import not_implemented_for -__all__ = ['from_numpy_matrix', 'to_numpy_matrix', - 'from_pandas_adjacency', 'to_pandas_adjacency', - 'from_pandas_edgelist', 'to_pandas_edgelist', - 'to_numpy_recarray', - 'from_scipy_sparse_matrix', 'to_scipy_sparse_matrix', - 'from_numpy_array', 'to_numpy_array'] - - -def to_pandas_adjacency(G, nodelist=None, dtype=None, order=None, - multigraph_weight=sum, weight='weight', nonedge=0.0): +__all__ = [ + "from_numpy_matrix", + "to_numpy_matrix", + "from_pandas_adjacency", + "to_pandas_adjacency", + "from_pandas_edgelist", + "to_pandas_edgelist", + "to_numpy_recarray", + "from_scipy_sparse_matrix", + "to_scipy_sparse_matrix", + "from_numpy_array", + "to_numpy_array", +] + + +def to_pandas_adjacency( + G, + nodelist=None, + dtype=None, + order=None, + multigraph_weight=sum, + weight="weight", + nonedge=0.0, +): """Returns the graph adjacency matrix as a Pandas DataFrame. Parameters @@ -117,9 +131,16 @@ def to_pandas_adjacency(G, nodelist=None, dtype=None, order=None, """ import pandas as pd - M = to_numpy_array(G, nodelist=nodelist, dtype=dtype, order=order, - multigraph_weight=multigraph_weight, weight=weight, - nonedge=nonedge) + + M = to_numpy_array( + G, + nodelist=nodelist, + dtype=dtype, + order=order, + multigraph_weight=multigraph_weight, + weight=weight, + nonedge=nonedge, + ) if nodelist is None: nodelist = list(G) return pd.DataFrame(data=M, index=nodelist, columns=nodelist) @@ -190,8 +211,9 @@ def from_pandas_adjacency(df, create_using=None): return G -def to_pandas_edgelist(G, source='source', target='target', nodelist=None, - dtype=None, order=None): +def to_pandas_edgelist( + G, source="source", target="target", nodelist=None, dtype=None, order=None +): """Returns the graph edge list as a Pandas DataFrame. Parameters @@ -227,6 +249,7 @@ def to_pandas_edgelist(G, source='source', target='target', nodelist=None, """ import pandas as pd + if nodelist is None: edgelist = G.edges(data=True) else: @@ -234,15 +257,15 @@ def to_pandas_edgelist(G, source='source', target='target', nodelist=None, source_nodes = [s for s, t, d in edgelist] target_nodes = [t for s, t, d in edgelist] all_keys = set().union(*(d.keys() for s, t, d in edgelist)) - edge_attr = {k: [d.get(k, float("nan")) for s, t, d in edgelist] - for k in all_keys} + edge_attr = {k: [d.get(k, float("nan")) for s, t, d in edgelist] for k in all_keys} edgelistdict = {source: source_nodes, target: target_nodes} edgelistdict.update(edge_attr) return pd.DataFrame(edgelistdict) -def from_pandas_edgelist(df, source='source', target='target', edge_attr=None, - create_using=None): +def from_pandas_edgelist( + df, source="source", target="target", edge_attr=None, create_using=None +): """Returns a graph from Pandas DataFrame containing an edge list. The Pandas DataFrame should contain at least two columns of node names and @@ -347,8 +370,15 @@ def from_pandas_edgelist(df, source='source', target='target', edge_attr=None, return g -def to_numpy_matrix(G, nodelist=None, dtype=None, order=None, - multigraph_weight=sum, weight='weight', nonedge=0.0): +def to_numpy_matrix( + G, + nodelist=None, + dtype=None, + order=None, + multigraph_weight=sum, + weight="weight", + nonedge=0.0, +): """Returns the graph adjacency matrix as a NumPy matrix. Parameters @@ -442,9 +472,15 @@ def to_numpy_matrix(G, nodelist=None, dtype=None, order=None, """ import numpy as np - A = to_numpy_array(G, nodelist=nodelist, dtype=dtype, order=order, - multigraph_weight=multigraph_weight, weight=weight, - nonedge=nonedge) + A = to_numpy_array( + G, + nodelist=nodelist, + dtype=dtype, + order=order, + multigraph_weight=multigraph_weight, + weight=weight, + nonedge=nonedge, + ) M = np.asmatrix(A, dtype=dtype) return M @@ -536,18 +572,21 @@ def from_numpy_matrix(A, parallel_edges=False, create_using=None): """ # This should never fail if you have created a numpy matrix with numpy... import numpy as np - kind_to_python_type = {'f': float, - 'i': int, - 'u': int, - 'b': bool, - 'c': complex, - 'S': str, - 'V': 'void'} - kind_to_python_type['U'] = str + + kind_to_python_type = { + "f": float, + "i": int, + "u": int, + "b": bool, + "c": complex, + "S": str, + "V": "void", + } + kind_to_python_type["U"] = str G = nx.empty_graph(0, create_using) n, m = A.shape if n != m: - raise nx.NetworkXError('Adjacency matrix is not square.', f"nx,ny={A.shape}") + raise nx.NetworkXError("Adjacency matrix is not square.", f"nx,ny={A.shape}") dt = A.dtype try: python_type = kind_to_python_type[dt.kind] @@ -558,16 +597,24 @@ def from_numpy_matrix(A, parallel_edges=False, create_using=None): G.add_nodes_from(range(n)) # Get a list of all the entries in the matrix with nonzero entries. These # coordinates will become the edges in the graph. - edges = map(lambda e: (int(e[0]), int(e[1])), - zip(*(np.asarray(A).nonzero()))) + edges = map(lambda e: (int(e[0]), int(e[1])), zip(*(np.asarray(A).nonzero()))) # handle numpy constructed data type - if python_type == 'void': + if python_type == "void": # Sort the fields by their offset, then by dtype, then by name. - fields = sorted((offset, dtype, name) for name, (dtype, offset) in - A.dtype.fields.items()) - triples = ((u, v, {name: kind_to_python_type[dtype.kind](val) - for (_, dtype, name), val in zip(fields, A[u, v])}) - for u, v in edges) + fields = sorted( + (offset, dtype, name) for name, (dtype, offset) in A.dtype.fields.items() + ) + triples = ( + ( + u, + v, + { + name: kind_to_python_type[dtype.kind](val) + for (_, dtype, name), val in zip(fields, A[u, v]) + }, + ) + for u, v in edges + ) # If the entries in the adjacency matrix are integers, the graph is a # multigraph, and parallel_edges is True, then create parallel edges, each # with weight 1, for each entry in the adjacency matrix. Otherwise, create @@ -581,11 +628,11 @@ def from_numpy_matrix(A, parallel_edges=False, create_using=None): # for d in range(A[u, v]): # G.add_edge(u, v, weight=1) # - triples = chain(((u, v, dict(weight=1)) for d in range(A[u, v])) - for (u, v) in edges) + triples = chain( + ((u, v, dict(weight=1)) for d in range(A[u, v])) for (u, v) in edges + ) else: # basic data type - triples = ((u, v, dict(weight=python_type(A[u, v]))) - for u, v in edges) + triples = ((u, v, dict(weight=python_type(A[u, v]))) for u, v in edges) # If we are creating an undirected multigraph, only add the edges from the # upper triangle of the matrix. Otherwise, add all the edges. This relies # on the fact that the vertices created in the @@ -600,7 +647,7 @@ def from_numpy_matrix(A, parallel_edges=False, create_using=None): return G -@not_implemented_for('multigraph') +@not_implemented_for("multigraph") def to_numpy_recarray(G, nodelist=None, dtype=None, order=None): """Returns the graph adjacency matrix as a NumPy recarray. @@ -647,8 +694,9 @@ def to_numpy_recarray(G, nodelist=None, dtype=None, order=None): """ if dtype is None: - dtype = [('weight', float)] + dtype = [("weight", float)] import numpy as np + if nodelist is None: nodelist = list(G) nodeset = set(nodelist) @@ -672,8 +720,7 @@ def to_numpy_recarray(G, nodelist=None, dtype=None, order=None): return M.view(np.recarray) -def to_scipy_sparse_matrix(G, nodelist=None, dtype=None, - weight='weight', format='csr'): +def to_scipy_sparse_matrix(G, nodelist=None, dtype=None, weight="weight", format="csr"): """Returns the graph adjacency matrix as a SciPy sparse matrix. Parameters @@ -757,6 +804,7 @@ def to_scipy_sparse_matrix(G, nodelist=None, dtype=None, https://docs.scipy.org/doc/scipy/reference/sparse.html """ from scipy import sparse + if nodelist is None: nodelist = list(G) nlen = len(nodelist) @@ -768,9 +816,13 @@ def to_scipy_sparse_matrix(G, nodelist=None, dtype=None, raise nx.NetworkXError(msg) index = dict(zip(nodelist, range(nlen))) - coefficients = zip(*((index[u], index[v], d.get(weight, 1)) - for u, v, d in G.edges(nodelist, data=True) - if u in index and v in index)) + coefficients = zip( + *( + (index[u], index[v], d.get(weight, 1)) + for u, v, d in G.edges(nodelist, data=True) + if u in index and v in index + ) + ) try: row, col, data = coefficients except ValueError: @@ -778,8 +830,7 @@ def to_scipy_sparse_matrix(G, nodelist=None, dtype=None, row, col, data = [], [], [] if G.is_directed(): - M = sparse.coo_matrix((data, (row, col)), - shape=(nlen, nlen), dtype=dtype) + M = sparse.coo_matrix((data, (row, col)), shape=(nlen, nlen), dtype=dtype) else: # symmetrize matrix d = data + data @@ -789,9 +840,13 @@ def to_scipy_sparse_matrix(G, nodelist=None, dtype=None, # so we subtract the data on the diagonal selfloops = list(nx.selfloop_edges(G, data=True)) if selfloops: - diag_index, diag_data = zip(*((index[u], -d.get(weight, 1)) - for u, v, d in selfloops - if u in index and v in index)) + diag_index, diag_data = zip( + *( + (index[u], -d.get(weight, 1)) + for u, v, d in selfloops + if u in index and v in index + ) + ) d += diag_data r += diag_index c += diag_index @@ -853,18 +908,19 @@ def _generate_weighted_edges(A): `A` is a SciPy sparse matrix (in any format). """ - if A.format == 'csr': + if A.format == "csr": return _csr_gen_triples(A) - if A.format == 'csc': + if A.format == "csc": return _csc_gen_triples(A) - if A.format == 'dok': + if A.format == "dok": return _dok_gen_triples(A) # If A is in any other format (including COO), convert it to COO format. return _coo_gen_triples(A.tocoo()) -def from_scipy_sparse_matrix(A, parallel_edges=False, create_using=None, - edge_attribute='weight'): +def from_scipy_sparse_matrix( + A, parallel_edges=False, create_using=None, edge_attribute="weight" +): """Creates a new graph from an adjacency matrix given as a SciPy sparse matrix. @@ -942,7 +998,7 @@ def from_scipy_sparse_matrix(A, parallel_edges=False, create_using=None, # with weight 1, for each entry in the adjacency matrix. Otherwise, create # one edge for each positive entry in the adjacency matrix and set the # weight of that edge to be the entry in the matrix. - if A.dtype.kind in ('i', 'u') and G.is_multigraph() and parallel_edges: + if A.dtype.kind in ("i", "u") and G.is_multigraph() and parallel_edges: chain = itertools.chain.from_iterable # The following line is equivalent to: # @@ -965,8 +1021,15 @@ def from_scipy_sparse_matrix(A, parallel_edges=False, create_using=None, return G -def to_numpy_array(G, nodelist=None, dtype=None, order=None, - multigraph_weight=sum, weight='weight', nonedge=0.0): +def to_numpy_array( + G, + nodelist=None, + dtype=None, + order=None, + multigraph_weight=sum, + weight="weight", + nonedge=0.0, +): """Returns the graph adjacency matrix as a NumPy array. Parameters @@ -1111,7 +1174,7 @@ def to_numpy_array(G, nodelist=None, dtype=None, order=None, try: op = operator[multigraph_weight] except Exception: - raise ValueError('multigraph_weight must be sum, min, or max') + raise ValueError("multigraph_weight must be sum, min, or max") for u, v, attrs in G.edges(data=True): if (u in nodeset) and (v in nodeset): @@ -1225,5 +1288,6 @@ def from_numpy_array(A, parallel_edges=False, create_using=None): 1.0 """ - return from_numpy_matrix(A, parallel_edges=parallel_edges, - create_using=create_using) + return from_numpy_matrix( + A, parallel_edges=parallel_edges, create_using=create_using + ) |