MLPY/Lib/site-packages/networkx/convert.py

"""Functions to convert NetworkX graphs to and from other formats.

The preferred way of converting data to a NetworkX graph is through the
graph constructor.  The constructor calls the to_networkx_graph() function
which attempts to guess the input type and convert it automatically.

Examples
--------
Create a graph with a single edge from a dictionary of dictionaries

>>> d = {0: {1: 1}}  # dict-of-dicts single edge (0,1)
>>> G = nx.Graph(d)

See Also
--------
nx_agraph, nx_pydot
"""
import warnings
from collections.abc import Collection, Generator, Iterator

import networkx as nx

__all__ = [
    "to_networkx_graph",
    "from_dict_of_dicts",
    "to_dict_of_dicts",
    "from_dict_of_lists",
    "to_dict_of_lists",
    "from_edgelist",
    "to_edgelist",
]


def to_networkx_graph(data, create_using=None, multigraph_input=False):
    """Make a NetworkX graph from a known data structure.

    The preferred way to call this is automatically
    from the class constructor

    >>> d = {0: {1: {"weight": 1}}}  # dict-of-dicts single edge (0,1)
    >>> G = nx.Graph(d)

    instead of the equivalent

    >>> G = nx.from_dict_of_dicts(d)

    Parameters
    ----------
    data : object to be converted

        Current known types are:
         any NetworkX graph
         dict-of-dicts
         dict-of-lists
         container (e.g. set, list, tuple) of edges
         iterator (e.g. itertools.chain) that produces edges
         generator of edges
         Pandas DataFrame (row per edge)
         2D numpy array
         scipy sparse array
         pygraphviz agraph

    create_using : NetworkX graph constructor, optional (default=nx.Graph)
        Graph type to create. If graph instance, then cleared before populated.

    multigraph_input : bool (default False)
        If True and  data is a dict_of_dicts,
        try to create a multigraph assuming dict_of_dict_of_lists.
        If data and create_using are both multigraphs then create
        a multigraph from a multigraph.

    """
    # NX graph
    if hasattr(data, "adj"):
        try:
            result = from_dict_of_dicts(
                data.adj,
                create_using=create_using,
                multigraph_input=data.is_multigraph(),
            )
            # data.graph should be dict-like
            result.graph.update(data.graph)
            # data.nodes should be dict-like
            # result.add_node_from(data.nodes.items()) possible but
            # for custom node_attr_dict_factory which may be hashable
            # will be unexpected behavior
            for n, dd in data.nodes.items():
                result._node[n].update(dd)
            return result
        except Exception as err:
            raise nx.NetworkXError("Input is not a correct NetworkX graph.") from err

    # pygraphviz  agraph
    if hasattr(data, "is_strict"):
        try:
            return nx.nx_agraph.from_agraph(data, create_using=create_using)
        except Exception as err:
            raise nx.NetworkXError("Input is not a correct pygraphviz graph.") from err

    # dict of dicts/lists
    if isinstance(data, dict):
        try:
            return from_dict_of_dicts(
                data, create_using=create_using, multigraph_input=multigraph_input
            )
        except Exception as err1:
            if multigraph_input is True:
                raise nx.NetworkXError(
                    f"converting multigraph_input raised:\n{type(err1)}: {err1}"
                )
            try:
                return from_dict_of_lists(data, create_using=create_using)
            except Exception as err2:
                raise TypeError("Input is not known type.") from err2

    # Pandas DataFrame
    try:
        import pandas as pd

        if isinstance(data, pd.DataFrame):
            if data.shape[0] == data.shape[1]:
                try:
                    return nx.from_pandas_adjacency(data, create_using=create_using)
                except Exception as err:
                    msg = "Input is not a correct Pandas DataFrame adjacency matrix."
                    raise nx.NetworkXError(msg) from err
            else:
                try:
                    return nx.from_pandas_edgelist(
                        data, edge_attr=True, create_using=create_using
                    )
                except Exception as err:
                    msg = "Input is not a correct Pandas DataFrame edge-list."
                    raise nx.NetworkXError(msg) from err
    except ImportError:
        warnings.warn("pandas not found, skipping conversion test.", ImportWarning)

    # numpy array
    try:
        import numpy as np

        if isinstance(data, np.ndarray):
            try:
                return nx.from_numpy_array(data, create_using=create_using)
            except Exception as err:
                raise nx.NetworkXError(
                    f"Failed to interpret array as an adjacency matrix."
                ) from err
    except ImportError:
        warnings.warn("numpy not found, skipping conversion test.", ImportWarning)

    # scipy sparse array - any format
    try:
        import scipy

        if hasattr(data, "format"):
            try:
                return nx.from_scipy_sparse_array(data, create_using=create_using)
            except Exception as err:
                raise nx.NetworkXError(
                    "Input is not a correct scipy sparse array type."
                ) from err
    except ImportError:
        warnings.warn("scipy not found, skipping conversion test.", ImportWarning)

    # Note: most general check - should remain last in order of execution
    # Includes containers (e.g. list, set, dict, etc.), generators, and
    # iterators (e.g. itertools.chain) of edges

    if isinstance(data, (Collection, Generator, Iterator)):
        try:
            return from_edgelist(data, create_using=create_using)
        except Exception as err:
            raise nx.NetworkXError("Input is not a valid edge list") from err

    raise nx.NetworkXError("Input is not a known data type for conversion.")


@nx._dispatch
def to_dict_of_lists(G, nodelist=None):
    """Returns adjacency representation of graph as a dictionary of lists.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list
       Use only nodes specified in nodelist

    Notes
    -----
    Completely ignores edge data for MultiGraph and MultiDiGraph.

    """
    if nodelist is None:
        nodelist = G

    d = {}
    for n in nodelist:
        d[n] = [nbr for nbr in G.neighbors(n) if nbr in nodelist]
    return d


@nx._dispatch(graphs=None)
def from_dict_of_lists(d, create_using=None):
    """Returns a graph from a dictionary of lists.

    Parameters
    ----------
    d : dictionary of lists
      A dictionary of lists adjacency representation.

    create_using : NetworkX graph constructor, optional (default=nx.Graph)
        Graph type to create. If graph instance, then cleared before populated.

    Examples
    --------
    >>> dol = {0: [1]}  # single edge (0,1)
    >>> G = nx.from_dict_of_lists(dol)

    or

    >>> G = nx.Graph(dol)  # use Graph constructor

    """
    G = nx.empty_graph(0, create_using)
    G.add_nodes_from(d)
    if G.is_multigraph() and not G.is_directed():
        # a dict_of_lists can't show multiedges.  BUT for undirected graphs,
        # each edge shows up twice in the dict_of_lists.
        # So we need to treat this case separately.
        seen = {}
        for node, nbrlist in d.items():
            for nbr in nbrlist:
                if nbr not in seen:
                    G.add_edge(node, nbr)
            seen[node] = 1  # don't allow reverse edge to show up
    else:
        G.add_edges_from(
            ((node, nbr) for node, nbrlist in d.items() for nbr in nbrlist)
        )
    return G


def to_dict_of_dicts(G, nodelist=None, edge_data=None):
    """Returns adjacency representation of graph as a dictionary of dictionaries.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list
       Use only nodes specified in nodelist

    edge_data : scalar, optional
       If provided, the value of the dictionary will be set to `edge_data` for
       all edges. Usual values could be `1` or `True`. If `edge_data` is
       `None` (the default), the edgedata in `G` is used, resulting in a
       dict-of-dict-of-dicts. If `G` is a MultiGraph, the result will be a
       dict-of-dict-of-dict-of-dicts. See Notes for an approach to customize
       handling edge data. `edge_data` should *not* be a container.

    Returns
    -------
    dod : dict
       A nested dictionary representation of `G`. Note that the level of
       nesting depends on the type of `G` and the value of `edge_data`
       (see Examples).

    See Also
    --------
    from_dict_of_dicts, to_dict_of_lists

    Notes
    -----
    For a more custom approach to handling edge data, try::

        dod = {
            n: {
                nbr: custom(n, nbr, dd) for nbr, dd in nbrdict.items()
            }
            for n, nbrdict in G.adj.items()
        }

    where `custom` returns the desired edge data for each edge between `n` and
    `nbr`, given existing edge data `dd`.

    Examples
    --------
    >>> G = nx.path_graph(3)
    >>> nx.to_dict_of_dicts(G)
    {0: {1: {}}, 1: {0: {}, 2: {}}, 2: {1: {}}}

    Edge data is preserved by default (``edge_data=None``), resulting
    in dict-of-dict-of-dicts where the innermost dictionary contains the
    edge data:

    >>> G = nx.Graph()
    >>> G.add_edges_from(
    ...     [
    ...         (0, 1, {'weight': 1.0}),
    ...         (1, 2, {'weight': 2.0}),
    ...         (2, 0, {'weight': 1.0}),
    ...     ]
    ... )
    >>> d = nx.to_dict_of_dicts(G)
    >>> d  # doctest: +SKIP
    {0: {1: {'weight': 1.0}, 2: {'weight': 1.0}},
     1: {0: {'weight': 1.0}, 2: {'weight': 2.0}},
     2: {1: {'weight': 2.0}, 0: {'weight': 1.0}}}
    >>> d[1][2]['weight']
    2.0

    If `edge_data` is not `None`, edge data in the original graph (if any) is
    replaced:

    >>> d = nx.to_dict_of_dicts(G, edge_data=1)
    >>> d
    {0: {1: 1, 2: 1}, 1: {0: 1, 2: 1}, 2: {1: 1, 0: 1}}
    >>> d[1][2]
    1

    This also applies to MultiGraphs: edge data is preserved by default:

    >>> G = nx.MultiGraph()
    >>> G.add_edge(0, 1, key='a', weight=1.0)
    'a'
    >>> G.add_edge(0, 1, key='b', weight=5.0)
    'b'
    >>> d = nx.to_dict_of_dicts(G)
    >>> d  # doctest: +SKIP
    {0: {1: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}},
     1: {0: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}}}
    >>> d[0][1]['b']['weight']
    5.0

    But multi edge data is lost if `edge_data` is not `None`:

    >>> d = nx.to_dict_of_dicts(G, edge_data=10)
    >>> d
    {0: {1: 10}, 1: {0: 10}}
    """
    dod = {}
    if nodelist is None:
        if edge_data is None:
            for u, nbrdict in G.adjacency():
                dod[u] = nbrdict.copy()
        else:  # edge_data is not None
            for u, nbrdict in G.adjacency():
                dod[u] = dod.fromkeys(nbrdict, edge_data)
    else:  # nodelist is not None
        if edge_data is None:
            for u in nodelist:
                dod[u] = {}
                for v, data in ((v, data) for v, data in G[u].items() if v in nodelist):
                    dod[u][v] = data
        else:  # nodelist and edge_data are not None
            for u in nodelist:
                dod[u] = {}
                for v in (v for v in G[u] if v in nodelist):
                    dod[u][v] = edge_data
    return dod


@nx._dispatch(graphs=None)
def from_dict_of_dicts(d, create_using=None, multigraph_input=False):
    """Returns a graph from a dictionary of dictionaries.

    Parameters
    ----------
    d : dictionary of dictionaries
      A dictionary of dictionaries adjacency representation.

    create_using : NetworkX graph constructor, optional (default=nx.Graph)
        Graph type to create. If graph instance, then cleared before populated.

    multigraph_input : bool (default False)
       When True, the dict `d` is assumed
       to be a dict-of-dict-of-dict-of-dict structure keyed by
       node to neighbor to edge keys to edge data for multi-edges.
       Otherwise this routine assumes dict-of-dict-of-dict keyed by
       node to neighbor to edge data.

    Examples
    --------
    >>> dod = {0: {1: {"weight": 1}}}  # single edge (0,1)
    >>> G = nx.from_dict_of_dicts(dod)

    or

    >>> G = nx.Graph(dod)  # use Graph constructor

    """
    G = nx.empty_graph(0, create_using)
    G.add_nodes_from(d)
    # does dict d represent a MultiGraph or MultiDiGraph?
    if multigraph_input:
        if G.is_directed():
            if G.is_multigraph():
                G.add_edges_from(
                    (u, v, key, data)
                    for u, nbrs in d.items()
                    for v, datadict in nbrs.items()
                    for key, data in datadict.items()
                )
            else:
                G.add_edges_from(
                    (u, v, data)
                    for u, nbrs in d.items()
                    for v, datadict in nbrs.items()
                    for key, data in datadict.items()
                )
        else:  # Undirected
            if G.is_multigraph():
                seen = set()  # don't add both directions of undirected graph
                for u, nbrs in d.items():
                    for v, datadict in nbrs.items():
                        if (u, v) not in seen:
                            G.add_edges_from(
                                (u, v, key, data) for key, data in datadict.items()
                            )
                            seen.add((v, u))
            else:
                seen = set()  # don't add both directions of undirected graph
                for u, nbrs in d.items():
                    for v, datadict in nbrs.items():
                        if (u, v) not in seen:
                            G.add_edges_from(
                                (u, v, data) for key, data in datadict.items()
                            )
                            seen.add((v, u))

    else:  # not a multigraph to multigraph transfer
        if G.is_multigraph() and not G.is_directed():
            # d can have both representations u-v, v-u in dict.  Only add one.
            # We don't need this check for digraphs since we add both directions,
            # or for Graph() since it is done implicitly (parallel edges not allowed)
            seen = set()
            for u, nbrs in d.items():
                for v, data in nbrs.items():
                    if (u, v) not in seen:
                        G.add_edge(u, v, key=0)
                        G[u][v][0].update(data)
                    seen.add((v, u))
        else:
            G.add_edges_from(
                ((u, v, data) for u, nbrs in d.items() for v, data in nbrs.items())
            )
    return G


@nx._dispatch(preserve_edge_attrs=True)
def to_edgelist(G, nodelist=None):
    """Returns a list of edges in the graph.

    Parameters
    ----------
    G : graph
       A NetworkX graph

    nodelist : list
       Use only nodes specified in nodelist

    """
    if nodelist is None:
        return G.edges(data=True)
    return G.edges(nodelist, data=True)


@nx._dispatch(graphs=None)
def from_edgelist(edgelist, create_using=None):
    """Returns a graph from a list of edges.

    Parameters
    ----------
    edgelist : list or iterator
      Edge tuples

    create_using : NetworkX graph constructor, optional (default=nx.Graph)
        Graph type to create. If graph instance, then cleared before populated.

    Examples
    --------
    >>> edgelist = [(0, 1)]  # single edge (0,1)
    >>> G = nx.from_edgelist(edgelist)

    or

    >>> G = nx.Graph(edgelist)  # use Graph constructor

    """
    G = nx.empty_graph(0, create_using)
    G.add_edges_from(edgelist)
    return G