o hG@sNdZddlZddlmZddlZddlmZgdZej ddddde ddfd d Z ej dd d d+d dZ ej d d     d,ddZ ej dd d      d,ddZej ddd-ddZddZddZddZdd Zd!d"Zej dd d  d.d$d%Zej ddddde ddfd&d'Zej dd d  d.dd(d)d*ZdS)/aFunctions to convert NetworkX graphs to and from common data containers like numpy arrays, scipy sparse arrays, and pandas DataFrames. The preferred way of converting data to a NetworkX graph is through the graph constructor. The constructor calls the `~networkx.convert.to_networkx_graph` function which attempts to guess the input type and convert it automatically. Examples -------- Create a 10 node random graph from a numpy array >>> import numpy as np >>> rng = np.random.default_rng() >>> a = rng.integers(low=0, high=2, size=(10, 10)) >>> DG = nx.from_numpy_array(a, create_using=nx.DiGraph) or equivalently: >>> DG = nx.DiGraph(a) which calls `from_numpy_array` internally based on the type of ``a``. See Also -------- nx_agraph, nx_pydot N) defaultdict)not_implemented_for)from_pandas_adjacencyto_pandas_adjacencyfrom_pandas_edgelistto_pandas_edgelistfrom_scipy_sparse_arrayto_scipy_sparse_arrayfrom_numpy_arrayto_numpy_arrayweight) edge_attrsgc Cs>ddl}t|||||||d}|durt|}|j|||dS)a Returns the graph adjacency matrix as a Pandas DataFrame. Parameters ---------- G : graph The NetworkX graph used to construct the Pandas DataFrame. nodelist : list, optional The rows and columns are ordered according to the nodes in `nodelist`. If `nodelist` is None, then the ordering is produced by G.nodes(). multigraph_weight : {sum, min, max}, optional An operator that determines how weights in multigraphs are handled. The default is to sum the weights of the multiple edges. weight : string or None, optional The edge attribute that holds the numerical value used for the edge weight. If an edge does not have that attribute, then the value 1 is used instead. nonedge : float, optional The matrix values corresponding to nonedges are typically set to zero. However, this could be undesirable if there are matrix values corresponding to actual edges that also have the value zero. If so, one might prefer nonedges to have some other value, such as nan. Returns ------- df : Pandas DataFrame Graph adjacency matrix Notes ----- For directed graphs, entry i,j corresponds to an edge from i to j. The DataFrame entries are assigned to the weight edge attribute. When an edge does not have a weight attribute, the value of the entry is set to the number 1. For multiple (parallel) edges, the values of the entries are determined by the 'multigraph_weight' parameter. The default is to sum the weight attributes for each of the parallel edges. When `nodelist` does not contain every node in `G`, the matrix is built from the subgraph of `G` that is induced by the nodes in `nodelist`. The convention used for self-loop edges in graphs is to assign the diagonal matrix entry value to the weight attribute of the edge (or the number 1 if the edge has no weight attribute). If the alternate convention of doubling the edge weight is desired the resulting Pandas DataFrame can be modified as follows:: >>> import pandas as pd >>> G = nx.Graph([(1, 1), (2, 2)]) >>> df = nx.to_pandas_adjacency(G) >>> df 1 2 1 1.0 0.0 2 0.0 1.0 >>> diag_idx = list(range(len(df))) >>> df.iloc[diag_idx, diag_idx] *= 2 >>> df 1 2 1 2.0 0.0 2 0.0 2.0 Examples -------- >>> G = nx.MultiDiGraph() >>> G.add_edge(0, 1, weight=2) 0 >>> G.add_edge(1, 0) 0 >>> G.add_edge(2, 2, weight=3) 0 >>> G.add_edge(2, 2) 1 >>> nx.to_pandas_adjacency(G, nodelist=[0, 1, 2], dtype=int) 0 1 2 0 0 2 0 1 1 0 0 2 0 0 4 rN)nodelistdtypeordermultigraph_weightr nonedge)dataindexcolumns)pandasr list DataFrame) Grrrrr rpdMrk/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/networkx/convert_matrix.pyr.s\ rT)graphs returns_graphc Cstz||j}Wn%ty,}ztt|jt|j}|d}td||d}~ww|j}t |||jd}|S)a7Returns a graph from Pandas DataFrame. The Pandas DataFrame is interpreted as an adjacency matrix for the graph. Parameters ---------- df : Pandas DataFrame An adjacency matrix representation of a graph create_using : NetworkX graph constructor, optional (default=nx.Graph) Graph type to create. If graph instance, then cleared before populated. Notes ----- For directed graphs, explicitly mention create_using=nx.DiGraph, and entry i,j of df corresponds to an edge from i to j. If `df` has a single data type for each entry it will be converted to an appropriate Python data type. If you have node attributes stored in a separate dataframe `df_nodes`, you can load those attributes to the graph `G` using the following code: ``` df_nodes = pd.DataFrame({"node_id": [1, 2, 3], "attribute1": ["A", "B", "C"]}) G.add_nodes_from((n, dict(d)) for n, d in df_nodes.iterrows()) ``` If `df` has a user-specified compound data type the names of the data fields will be used as attribute keys in the resulting NetworkX graph. See Also -------- to_pandas_adjacency Examples -------- Simple integer weights on edges: >>> import pandas as pd >>> pd.options.display.max_columns = 20 >>> df = pd.DataFrame([[1, 1], [2, 1]]) >>> df 0 1 0 1 1 1 2 1 >>> G = nx.from_pandas_adjacency(df) >>> G.name = "Graph from pandas adjacency matrix" >>> print(G) Graph named 'Graph from pandas adjacency matrix' with 2 nodes and 3 edges z not in columnszColumns must match Indices.N) create_usingr) r Exceptionrset differencernx NetworkXErrorvaluesr )dfr errmissingmsgArrrrrs7 r)preserve_edge_attrssourcetargetc s$ddl}|dur|jddn|j|ddddD}ddD}tjdd D} || vr>> G = nx.Graph( ... [ ... ("A", "B", {"cost": 1, "weight": 7}), ... ("C", "E", {"cost": 9, "weight": 10}), ... ] ... ) >>> df = nx.to_pandas_edgelist(G, nodelist=["A", "C"]) >>> df[["source", "target", "cost", "weight"]] source target cost weight 0 A B 1 7 1 C E 9 10 >>> G = nx.MultiGraph([("A", "B", {"cost": 1}), ("A", "B", {"cost": 9})]) >>> df = nx.to_pandas_edgelist(G, nodelist=["A", "C"], edge_key="ekey") >>> df[["source", "target", "cost", "ekey"]] source target cost ekey 0 A B 1 0 1 A B 9 1 rNTrcSsg|]\}}}|qSrr).0s_rrr "z&to_pandas_edgelist..cSsg|]\}}}|qSrr)r0r2trrrr3#r4css|] \}}}|VqdSNkeysr0r2drrr %z%to_pandas_edgelist..z Source name z is an edge attr namez Target name nancs"i|] fddDqS)csg|] \}}}|qSrgetr9)kr=rrr3,sz1to_pandas_edgelist...rr0edgelistr=)r@r ,s"z&to_pandas_edgelist..zEdge key name cSsg|]\}}}|qSrr)r0r2r@rrrr31r4r7)r) redgesr"unionr$r%float is_multigraphupdater) rr-r.rredge_keyr source_nodes target_nodes all_attrs edge_attr edge_keys edgelistdictrrBrrs*>  rc sVtd|}|dur;|r-|dur-t|||D] \}}} |||| q|S|t|||S||g|rL|durL|g} g} |dur_fddjD} n t|t t Bri|} n|g} t | dkrzt d| z tfdd| D} Wnt tfy} z d|} t | | d} ~ ww|r|durz |}t| |} Wnt tfy} z d |} t | | d} ~ wwt||| D]*\}}}|dur|\}}|j|||d }n|||}||||t| |q|St||| D]\}}}||||||t| |q|S) aReturns a graph from Pandas DataFrame containing an edge list. The Pandas DataFrame should contain at least two columns of node names and zero or more columns of edge attributes. Each row will be processed as one edge instance. Note: This function iterates over DataFrame.values, which is not guaranteed to retain the data type across columns in the row. This is only a problem if your row is entirely numeric and a mix of ints and floats. In that case, all values will be returned as floats. See the DataFrame.iterrows documentation for an example. Parameters ---------- df : Pandas DataFrame An edge list representation of a graph source : str or int A valid column name (string or integer) for the source nodes (for the directed case). target : str or int A valid column name (string or integer) for the target nodes (for the directed case). edge_attr : str or int, iterable, True, or None A valid column name (str or int) or iterable of column names that are used to retrieve items and add them to the graph as edge attributes. If `True`, all columns will be added except `source`, `target` and `edge_key`. If `None`, no edge attributes are added to the graph. create_using : NetworkX graph constructor, optional (default=nx.Graph) Graph type to create. If graph instance, then cleared before populated. edge_key : str or None, optional (default=None) A valid column name for the edge keys (for a MultiGraph). The values in this column are used for the edge keys when adding edges if create_using is a multigraph. If you have node attributes stored in a separate dataframe `df_nodes`, you can load those attributes to the graph `G` using the following code: ``` df_nodes = pd.DataFrame({"node_id": [1, 2, 3], "attribute1": ["A", "B", "C"]}) G.add_nodes_from((n, dict(d)) for n, d in df_nodes.iterrows()) ``` See Also -------- to_pandas_edgelist Examples -------- Simple integer weights on edges: >>> import pandas as pd >>> pd.options.display.max_columns = 20 >>> import numpy as np >>> rng = np.random.RandomState(seed=5) >>> ints = rng.randint(1, 11, size=(3, 2)) >>> a = ["A", "B", "C"] >>> b = ["D", "A", "E"] >>> df = pd.DataFrame(ints, columns=["weight", "cost"]) >>> df[0] = a >>> df["b"] = b >>> df[["weight", "cost", 0, "b"]] weight cost 0 b 0 4 7 A D 1 7 1 B A 2 10 9 C E >>> G = nx.from_pandas_edgelist(df, 0, "b", ["weight", "cost"]) >>> G["E"]["C"]["weight"] 10 >>> G["E"]["C"]["cost"] 9 >>> edges = pd.DataFrame( ... { ... "source": [0, 1, 2], ... "target": [2, 2, 3], ... "weight": [3, 4, 5], ... "color": ["red", "blue", "blue"], ... } ... ) >>> G = nx.from_pandas_edgelist(edges, edge_attr=True) >>> G[0][2]["color"] 'red' Build multigraph with custom keys: >>> edges = pd.DataFrame( ... { ... "source": [0, 1, 2, 0], ... "target": [2, 2, 3, 2], ... "my_edge_key": ["A", "B", "C", "D"], ... "weight": [3, 4, 5, 6], ... "color": ["red", "blue", "blue", "blue"], ... } ... ) >>> G = nx.from_pandas_edgelist( ... edges, ... edge_key="my_edge_key", ... edge_attr=["weight", "color"], ... create_using=nx.MultiGraph(), ... ) >>> G[0][2] AtlasView({'A': {'weight': 3, 'color': 'red'}, 'D': {'weight': 6, 'color': 'blue'}}) rNTcsg|]}|vr|qSrr)r0c)reserved_columnsrrr3z(from_pandas_edgelist..z8Invalid edge_attr argument: No columns found with name: csg|]}|qSrr)r0col)r'rrr3zInvalid edge_attr argument: zInvalid edge_key argument: )key)r$ empty_graphrHzipadd_edgeadd_edges_fromappendr isinstancertuplelenr%KeyError TypeErrorrI)r'r-r.rNr rJguvr@attr_col_headingsattribute_datar(r*multigraph_edge_keysr1r5attrsmultigraph_edge_keyrVr)r'rRrr:sd v"         rcsrc sddl}t|dkrtd|durt|}t|}n>> G = nx.Graph([(1, 1)]) >>> A = nx.to_scipy_sparse_array(G) >>> A.toarray() array([[1]]) >>> A.setdiag(A.diagonal() * 2) >>> A.toarray() array([[2]]) Examples -------- Basic usage: >>> G = nx.path_graph(4) >>> A = nx.to_scipy_sparse_array(G) >>> A # doctest: +SKIP >>> A.toarray() array([[0, 1, 0, 0], [1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0]]) .. note:: The `toarray` method is used in these examples to better visualize the adjacancy matrix. For a dense representation of the adjaceny matrix, use `to_numpy_array` instead. Directed graphs: >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 3)]) >>> nx.to_scipy_sparse_array(G).toarray() array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]]) >>> H = G.reverse() >>> H.edges OutEdgeView([(1, 0), (2, 1), (3, 2)]) >>> nx.to_scipy_sparse_array(H).toarray() array([[0, 0, 0, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]) By default, the order of the rows/columns of the adjacency matrix is determined by the ordering of the nodes in `G`: >>> G = nx.Graph() >>> G.add_nodes_from([3, 5, 0, 1]) >>> G.add_edges_from([(1, 3), (1, 5)]) >>> nx.to_scipy_sparse_array(G).toarray() array([[0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 0], [1, 1, 0, 0]]) The ordering of the rows can be changed with `nodelist`: >>> ordered = [0, 1, 3, 5] >>> nx.to_scipy_sparse_array(G, nodelist=ordered).toarray() array([[0, 0, 0, 0], [0, 0, 1, 1], [0, 1, 0, 0], [0, 1, 0, 0]]) If `nodelist` contains a subset of the nodes in `G`, the adjacency matrix for the node-induced subgraph is produced: >>> nx.to_scipy_sparse_array(G, nodelist=[1, 3, 5]).toarray() array([[0, 1, 1], [1, 0, 0], [1, 0, 0]]) The values of the adjacency matrix are drawn from the edge attribute specified by the `weight` parameter: >>> G = nx.path_graph(4) >>> nx.set_edge_attributes( ... G, values={(0, 1): 1, (1, 2): 10, (2, 3): 2}, name="weight" ... ) >>> nx.set_edge_attributes( ... G, values={(0, 1): 50, (1, 2): 35, (2, 3): 10}, name="capacity" ... ) >>> nx.to_scipy_sparse_array(G).toarray() # Default weight="weight" array([[ 0, 1, 0, 0], [ 1, 0, 10, 0], [ 0, 10, 0, 2], [ 0, 0, 2, 0]]) >>> nx.to_scipy_sparse_array(G, weight="capacity").toarray() array([[ 0, 50, 0, 0], [50, 0, 35, 0], [ 0, 35, 0, 10], [ 0, 0, 10, 0]]) Any edges that don't have a `weight` attribute default to 1: >>> G[1][2].pop("capacity") 35 >>> nx.to_scipy_sparse_array(G, weight="capacity").toarray() array([[ 0, 50, 0, 0], [50, 0, 1, 0], [ 0, 1, 0, 10], [ 0, 0, 10, 0]]) When `G` is a multigraph, the values in the adjacency matrix are given by the sum of the `weight` edge attribute over each edge key: >>> G = nx.MultiDiGraph([(0, 1), (0, 1), (0, 1), (2, 0)]) >>> nx.to_scipy_sparse_array(G).toarray() array([[0, 3, 0], [0, 0, 0], [1, 0, 0]]) References ---------- .. [1] Scipy Dev. References, "Sparse Arrays", https://docs.scipy.org/doc/scipy/reference/sparse.html rNzGraph has no nodes or edgesznodelist has no nodeszNode  in nodelist is not in Gnodelist contains duplicates.c3(|]\}}}|||fVqdSr6rr0rbrcwtrrrr;&z(to_scipy_sparse_array..rdefault)shaperc3s$|] \}}}|| fVqdSr6rrmrorrr;s"zUnknown sparse matrix format: )scipyr^r$r%rr" nbunch_itersubgraphdictrXrangerE ValueError is_directedsparse coo_arrayselfloop_edgesasformat)rrrr formatspnlennodesetn coefficientsrowrTrr+r:rrQ selfloops diag_index diag_datar(rrorr s\.           r cCsZ|jd}|j|j|j}}}ddl}|||||}t| | |j S)ztConverts a SciPy sparse array in **Compressed Sparse Row** format to an iterable of weighted edge triples. rN rtindptrindicesrnumpyrepeatarangediffrXtolist)r+nrowsr dst_indicesrnp src_indicesrrr_csr_gen_triples rcCsZ|jd}|j|j|j}}}ddl}|||||}t| | |j S)zwConverts a SciPy sparse array in **Compressed Sparse Column** format to an iterable of weighted edge triples. rqrNr)r+ncolsrrrrrrrr_csc_gen_triplesrrcCst|j|j|jS)ziConverts a SciPy sparse array in **Coordinate** format to an iterable of weighted edge triples. )rXrrrTrr+rrr_coo_gen_triplessrccs4|D]\\}}}t|t||fVqdS)zqConverts a SciPy sparse array in **Dictionary of Keys** format to an iterable of weighted edge triples. N)itemsintitem)r+rrQrcrrr_dok_gen_triplessrcCsB|jdkr t|S|jdkrt|S|jdkrt|St|S)zReturns an iterable over (u, v, w) triples, where u and v are adjacent vertices and w is the weight of the edge joining u and v. `A` is a SciPy sparse array (in any format). ricscdok)rrrrrtocoorrrr_generate_weighted_edgess    rFc Cstd|}|j\}}||krtd|j|t|t|}|jjdvr<| r<|r>> import scipy as sp >>> A = sp.sparse.eye(2, 2, 1) >>> G = nx.from_scipy_sparse_array(A) If `create_using` indicates a multigraph and the matrix has only integer entries and `parallel_edges` is False, then the entries will be treated as weights for edges joining the nodes (without creating parallel edges): >>> A = sp.sparse.csr_array([[1, 1], [1, 2]]) >>> G = nx.from_scipy_sparse_array(A, create_using=nx.MultiGraph) >>> G[1][1] AtlasView({0: {'weight': 2}}) If `create_using` indicates a multigraph and the matrix has only integer entries and `parallel_edges` is True, then the entries will be treated as the number of parallel edges joining those two vertices: >>> A = sp.sparse.csr_array([[1, 1], [1, 2]]) >>> G = nx.from_scipy_sparse_array( ... A, parallel_edges=True, create_using=nx.MultiGraph ... ) >>> G[1][1] AtlasView({0: {'weight': 1}, 1: {'weight': 1}}) r#Adjacency matrix not square: nx,ny=)irbc3s.|]\}fddt|DVqdS)c3s|]}dfVqdSrqNrr0r:rbrcrrr;ez4from_scipy_sparse_array...Nry)r0wrrrr;e,z*from_scipy_sparse_array..cs(|]\}}}||kr|||fVqdSr6rr0rbrcr:rrrr;orp)r )r$rWrtr%add_nodes_fromryrrkindrH itertoolschain from_iterabler{add_weighted_edges_from) r+parallel_edgesr edge_attributerrmtriplesrrrrr s D  rcslddl}|dur t|}t|}t|} | t|r'td| t|dt| |kr2td|j||f|||d} |dksG|dkrI| Sd} | jj r[|durW|j } nt dt t |t |} t|t|krs||}|r| r~tdtt} |j|d d D]\}}}| | || |f|q|t| j\}}fd d | D}noggg}}}| r|jd dD]\}}}|| ||| |||q| D]fdd |D}|| ||f<|s|| ||f<q| S|j|d d D]\}}}|| ||| |||q || ||f<|s4|| ||f<| S)adReturns the graph adjacency matrix as a NumPy array. Parameters ---------- G : graph The NetworkX graph used to construct the NumPy array. nodelist : list, optional The rows and columns are ordered according to the nodes in `nodelist`. If `nodelist` is ``None``, then the ordering is produced by ``G.nodes()``. dtype : NumPy data type, optional A NumPy data type used to initialize the array. If None, then the NumPy default is used. The dtype can be structured if `weight=None`, in which case the dtype field names are used to look up edge attributes. The result is a structured array where each named field in the dtype corresponds to the adjacency for that edge attribute. See examples for details. order : {'C', 'F'}, optional Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory. If None, then the NumPy default is used. multigraph_weight : callable, optional An function that determines how weights in multigraphs are handled. The function should accept a sequence of weights and return a single value. The default is to sum the weights of the multiple edges. weight : string or None optional (default = 'weight') The edge attribute that holds the numerical value used for the edge weight. If an edge does not have that attribute, then the value 1 is used instead. `weight` must be ``None`` if a structured dtype is used. nonedge : array_like (default = 0.0) The value used to represent non-edges in the adjacency matrix. The array values corresponding to nonedges are typically set to zero. However, this could be undesirable if there are array values corresponding to actual edges that also have the value zero. If so, one might prefer nonedges to have some other value, such as ``nan``. Returns ------- A : NumPy ndarray Graph adjacency matrix Raises ------ NetworkXError If `dtype` is a structured dtype and `G` is a multigraph ValueError If `dtype` is a structured dtype and `weight` is not `None` See Also -------- from_numpy_array Notes ----- For directed graphs, entry ``i, j`` corresponds to an edge from ``i`` to ``j``. Entries in the adjacency matrix are given by the `weight` edge attribute. When an edge does not have a weight attribute, the value of the entry is set to the number 1. For multiple (parallel) edges, the values of the entries are determined by the `multigraph_weight` parameter. The default is to sum the weight attributes for each of the parallel edges. When `nodelist` does not contain every node in `G`, the adjacency matrix is built from the subgraph of `G` that is induced by the nodes in `nodelist`. The convention used for self-loop edges in graphs is to assign the diagonal array entry value to the weight attribute of the edge (or the number 1 if the edge has no weight attribute). If the alternate convention of doubling the edge weight is desired the resulting NumPy array can be modified as follows: >>> import numpy as np >>> G = nx.Graph([(1, 1)]) >>> A = nx.to_numpy_array(G) >>> A array([[1.]]) >>> A[np.diag_indices_from(A)] *= 2 >>> A array([[2.]]) Examples -------- >>> G = nx.MultiDiGraph() >>> G.add_edge(0, 1, weight=2) 0 >>> G.add_edge(1, 0) 0 >>> G.add_edge(2, 2, weight=3) 0 >>> G.add_edge(2, 2) 1 >>> nx.to_numpy_array(G, nodelist=[0, 1, 2]) array([[0., 2., 0.], [1., 0., 0.], [0., 0., 4.]]) When `nodelist` argument is used, nodes of `G` which do not appear in the `nodelist` and their edges are not included in the adjacency matrix. Here is an example: >>> G = nx.Graph() >>> G.add_edge(3, 1) >>> G.add_edge(2, 0) >>> G.add_edge(2, 1) >>> G.add_edge(3, 0) >>> nx.to_numpy_array(G, nodelist=[1, 2, 3]) array([[0., 1., 1.], [1., 0., 0.], [1., 0., 0.]]) This function can also be used to create adjacency matrices for multiple edge attributes with structured dtypes: >>> G = nx.Graph() >>> G.add_edge(0, 1, weight=10) >>> G.add_edge(1, 2, cost=5) >>> G.add_edge(2, 3, weight=3, cost=-4.0) >>> dtype = np.dtype([("weight", int), ("cost", float)]) >>> A = nx.to_numpy_array(G, dtype=dtype, weight=None) >>> A["weight"] array([[ 0, 10, 0, 0], [10, 0, 1, 0], [ 0, 1, 0, 3], [ 0, 0, 3, 0]]) >>> A["cost"] array([[ 0., 1., 0., 0.], [ 1., 0., 5., 0.], [ 0., 5., 0., -4.], [ 0., 0., -4., 0.]]) As stated above, the argument "nonedge" is useful especially when there are actually edges with weight 0 in the graph. Setting a nonedge value different than 0, makes it much clearer to differentiate such 0-weighted edges and actual nonedge values. >>> G = nx.Graph() >>> G.add_edge(3, 1, weight=2) >>> G.add_edge(2, 0, weight=0) >>> G.add_edge(2, 1, weight=0) >>> G.add_edge(3, 0, weight=1) >>> nx.to_numpy_array(G, nonedge=-1.0) array([[-1., 2., -1., 1.], [ 2., -1., 0., -1.], [-1., 0., -1., 0.], [ 1., -1., 0., -1.]]) rNzNodes rjrk) fill_valuerrzSpecifying `weight` not supported for structured dtypes .To create adjacency matrices from structured dtypes, use `weight=None`.z3Structured arrays are not supported for MultiGraphs?rrcsg|]}|qSrr)r0ws)rrrr3BrUz"to_numpy_array..Tr/csg|]}|dqS)rr>)r0rn)attrrrr3PrS)rrr^r"r$r%fullnumber_of_edgesrnamesrzrxrXryrwcopyrHrrEr[arrayr8Tr&r{)rrrrrr rrrrr+r idxr:rbrcrnrjwtsr attr_datar)rrrr tsl!       r )rc stttttttddtd|}jdkrtdjj \}}||kr1tdj j }z|j Wnt yO} zt d|| d} ~ ww|du} r[t|}n t||kretd ||d d tD} dkrtd d j jDfd d | D} nBtur|r|rtjj} dvr| fdd | D} n$| fdd | D} ndvrdd | D} n fdd | D} |r|sdd | D} | stt|fdd | D} || |S)aReturns a graph from a 2D NumPy array. The 2D NumPy array is interpreted as an adjacency matrix for the graph. Parameters ---------- A : a 2D numpy.ndarray An adjacency matrix representation of a graph parallel_edges : Boolean If this is True, `create_using` is a multigraph, and `A` is an integer array, then entry *(i, j)* in the array is interpreted as the number of parallel edges joining vertices *i* and *j* in the graph. If it is False, then the entries in the array are interpreted as the weight of a single edge joining the vertices. create_using : NetworkX graph constructor, optional (default=nx.Graph) Graph type to create. If graph instance, then cleared before populated. edge_attr : String, optional (default="weight") The attribute to which the array values are assigned on each edge. If it is None, edge attributes will not be assigned. nodelist : sequence of nodes, optional A sequence of objects to use as the nodes in the graph. If provided, the list of nodes must be the same length as the dimensions of `A`. The default is `None`, in which case the nodes are drawn from ``range(n)``. Notes ----- For directed graphs, explicitly mention create_using=nx.DiGraph, and entry i,j of A corresponds to an edge from i to j. If `create_using` is :class:`networkx.MultiGraph` or :class:`networkx.MultiDiGraph`, `parallel_edges` is True, and the entries of `A` are of type :class:`int`, then this function returns a multigraph (of the same type as `create_using`) with parallel edges. If `create_using` indicates an undirected multigraph, then only the edges indicated by the upper triangle of the array `A` will be added to the graph. If `edge_attr` is Falsy (False or None), edge attributes will not be assigned, and the array data will be treated like a binary mask of edge presence or absence. Otherwise, the attributes will be assigned as follows: If the NumPy array has a single data type for each array entry it will be converted to an appropriate Python data type. If the NumPy array has a user-specified compound data type the names of the data fields will be used as attribute keys in the resulting NetworkX graph. See Also -------- to_numpy_array Examples -------- Simple integer weights on edges: >>> import numpy as np >>> A = np.array([[1, 1], [2, 1]]) >>> G = nx.from_numpy_array(A) >>> G.edges(data=True) EdgeDataView([(0, 0, {'weight': 1}), (0, 1, {'weight': 2}), (1, 1, {'weight': 1})]) If `create_using` indicates a multigraph and the array has only integer entries and `parallel_edges` is False, then the entries will be treated as weights for edges joining the nodes (without creating parallel edges): >>> A = np.array([[1, 1], [1, 2]]) >>> G = nx.from_numpy_array(A, create_using=nx.MultiGraph) >>> G[1][1] AtlasView({0: {'weight': 2}}) If `create_using` indicates a multigraph and the array has only integer entries and `parallel_edges` is True, then the entries will be treated as the number of parallel edges joining those two vertices: >>> A = np.array([[1, 1], [1, 2]]) >>> temp = nx.MultiGraph() >>> G = nx.from_numpy_array(A, parallel_edges=True, create_using=temp) >>> G[1][1] AtlasView({0: {'weight': 1}, 1: {'weight': 1}}) User defined compound data type on edges: >>> dt = [("weight", float), ("cost", int)] >>> A = np.array([[(1.0, 2)]], dtype=dt) >>> G = nx.from_numpy_array(A) >>> G.edges() EdgeView([(0, 0)]) >>> G[0][0]["cost"] 2 >>> G[0][0]["weight"] 1.0 void)frrbbrQSUVrzInput array must be 2D, not rzUnknown numpy data type: Nz0nodelist must have the same length as A.shape[0]css(|]}t|dt|dfVqdS)rrqN)r)r0errrr;rpz#from_numpy_array..css"|] \}\}}|||fVqdSr6r)r0nameroffsetrrrr;s c 3sF|]\}}||dvrinfddt||fDfVqdS)FNcs&i|]\\}}}}||j|qSr)r)r0r2rrval)kind_to_python_typerrrDs z.from_numpy_array...N)rXr0rbrc)r+rNfieldsrrrr;s   rc3s4|]\fddtfDVqdS)c3s|]}ifVqdSr6rrrrrr; r-from_numpy_array...NrrArrrr; s2c3s6|]\fddtfDVqdS)c3s|] }difVqdSrrr)rNrbrcrrr;r<rNrrA)r+rNrrr;s& css|] \}}||ifVqdSr6rrrrrr;r<c3s.|]\}}||||fifVqdSr6rr)r+rN python_typerrr;rcsrr6rrrrrr;rpc3rlr6rr) idx_to_noderrr;#rp)rGrboolcomplexstrr$rWndimr%rtrrr!r`ryr^rzrrXnonzerosortedrrrHrrrr{rx enumeraterZ)r+rr rNrrrrdtr(_default_nodesrErrr)r+rNrrrrrr cshi            r r6)r-r.NNN)NNr ri)FNr )__doc__r collectionsrnetworkxr$networkx.utilsr__all__ _dispatchablesumrrrrr rrrrrrr r rrrrsp    k  C [  2 b    h  o