Python networkx.dfs_successors方法代码示例

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def packagesNeedingX(packages):
    """
    Return a set of all packages needing X. Packages needing X are defines as those that directly or indirectly require rgl.
    """
    depTree = nx.DiGraph()
    for p, meta in packages.items():
        p = p.lower()
        if p not in depTree:
            depTree.add_node(p)
        for field in ["Imports", "Depends", "LinkingTo"]:
            if field in meta:
                for dep in meta[field].split(","):
                    # This is a simplified form for BioCProjectPage._parse_dependencies
                    dep = dep.strip().split("(")[0].strip().lower()
                    if dep not in depTree:
                        depTree.add_node(dep)
                    depTree.add_edge(dep, p)
    Xset = set()
    for cxp in CRAN_X_PACKAGES:
        if cxp in depTree:
            for xp in itertools.chain(*nx.dfs_successors(depTree, cxp).values()):
                Xset.add(xp)
    return Xset 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def get_subdags(dag, n_workers, worker_offset):
    if n_workers > 1 and worker_offset >= n_workers:
        raise ValueError(
            "n-workers is less than the worker-offset given! "
            "Either decrease --n-workers or decrease --worker-offset!")

    # Get connected subdags and sort by nodes
    if n_workers > 1:
        root_nodes = sorted([k for k, v in dag.in_degree().items() if v == 0])
        nodes = set()
        found = set()
        for idx, root_node in enumerate(root_nodes):
            # Flatten the nested list
            children = itertools.chain(*nx.dfs_successors(dag, root_node).values())
            # This is the only obvious way of ensuring that all nodes are included
            # in exactly 1 subgraph
            found.add(root_node)
            if idx % n_workers == worker_offset:
                nodes.add(root_node)
                for child in children:
                    if child not in found:
                        nodes.add(child)
                        found.add(child)
            else:
                for child in children:
                    found.add(child)
        subdags = dag.subgraph(list(nodes))
        logger.info("Building and testing sub-DAGs %i in each group of %i, which is %i packages", worker_offset, n_workers, len(subdags.nodes()))
    else:
        subdags = dag

    return subdags 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def get_all_predecessors(self, cfgnode, depth_limit=None):
        """
        Get all predecessors of a specific node on the control flow graph.

        :param CFGNode cfgnode: The CFGNode object
        :param int depth_limit: Optional depth limit for the depth-first search
        :return: A list of predecessors in the CFG
        :rtype: list
        """
        # use the reverse graph and query for successors (networkx.dfs_predecessors is misleading)
        # dfs_successors returns a dict of (node, [predecessors]). We ignore the keyset and use the values
        predecessors = set().union(*networkx.dfs_successors(self.graph.reverse(), cfgnode, depth_limit).values())
        return list(predecessors) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def get_all_successors(self, cfgnode, depth_limit=None):
        """
        Get all successors of a specific node on the control flow graph.

        :param CFGNode cfgnode: The CFGNode object
        :param int depth_limit: Optional depth limit for the depth-first search
        :return: A list of successors in the CFG
        :rtype: list
        """
        # dfs_successors returns a dict of (node, [predecessors]). We ignore the keyset and use the values
        successors = set().union(*networkx.dfs_successors(self.graph, cfgnode, depth_limit).values())
        return list(successors) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def _leaves_below(self, node):
        leaves = sum(([vv for vv in v if self.tree.out_degree(vv) == 0]
                      for k, v in nx.dfs_successors(self.tree, node).items()),
                     [])
        return sorted(leaves) or [node] 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def leaves_below(tree, node):
    return set(sum(([vv for vv in v if tree.out_degree(vv) == 0]
                    for k, v in nx.dfs_successors(tree, node).items()), [])) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def descendants(self, node) -> frozenset:
        return frozenset(nx.dfs_successors(self, node).keys()) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def dfs_successors(G, source=None):
    """Return dictionary of successors in depth-first-search from source.

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

    source : node, optional
       Specify starting node for depth-first search and return edges in
       the component reachable from source.

    Returns
    -------
    succ: dict
       A dictionary with nodes as keys and list of successor nodes as values.

    Examples
    --------
    >>> G = nx.Graph()
    >>> G.add_path([0,1,2])
    >>> print(nx.dfs_successors(G,0))
    {0: [1], 1: [2]}

    Notes
    -----
    Based on http://www.ics.uci.edu/~eppstein/PADS/DFS.py
    by D. Eppstein, July 2004.

    If a source is not specified then a source is chosen arbitrarily and
    repeatedly until all components in the graph are searched.
    """
    d = defaultdict(list)
    for s,t in dfs_edges(G,source=source):
        d[s].append(t)
    return dict(d) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def test_successor(self):
        assert_equal(nx.dfs_successors(self.G,source=0),
                     {0: [1], 1: [2], 2: [4], 4: [3]})
        assert_equal(nx.dfs_successors(self.D), {0: [1], 2: [3]}) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def test_successor(self):
        assert_equal(nx.dfs_successors(self.G, source=0),
                     {0: [1], 1: [2], 2: [4], 4: [3]})
        assert_equal(nx.dfs_successors(self.D), {0: [1], 2: [3]}) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def dls_test_successor(self):
        result = nx.dfs_successors(self.G, source=4, depth_limit=3)
        assert_equal({n: set(v) for n, v in result.items()},
                     {2: {1, 7}, 3: {2}, 4: {3, 5}, 5: {6}})
        result = nx.dfs_successors(self.D, source=7, depth_limit=2)
        assert_equal({n: set(v) for n, v in result.items()},
                     {8: {9}, 2: {3}, 7: {8, 2}}) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def get_3D_positions(atoms, bond_index=None):
    """Return an estimation of the 3D structure of a Gratoms object
    based on its graph.

    WARNING: This function operates on the atoms object in-place.

    Parameters
    ----------
    atoms : Gratoms object
        Structure with connectivity matrix to provide a 3D structure.
    bond_index : int
        Index of the atoms to consider as the origin of a surface
        bonding site.

    Returns
    -------
    atoms : Gratoms object
        Structure with updated 3D positions.
    """
    branches = nx.dfs_successors(atoms.graph, bond_index)

    complete = []
    for i, branch in enumerate(branches.items()):
        root, nodes = branch

        if len(nodes) == 0:
            continue

        c0 = atoms[root].position
        if i == 0:
            basis = catkit.gen.utils.get_basis_vectors([c0, [0, 0, -1]])
        else:
            bond_index = None
            for j, base_root in enumerate(complete):
                if root in branches[base_root]:
                    c1 = atoms[base_root].position
                    # Flip the basis for every alternate step down the chain.
                    basis = catkit.gen.utils.get_basis_vectors([c0, c1])
                    if (i - j) % 2 != 0:
                        basis[2] *= -1
                    break
        complete.insert(0, root)

        positions = _branch_molecule(atoms, branch, basis, bond_index)
        atoms.positions[nodes] = positions

    return atoms 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def get_all_elements_downstream(self, model, source):
        """Returns all the DiTTo objects which location is downstream of a given node.
        This might be handy when trying to find all the objects below a substation such that the network can be properly seperated in different feeders for analysis.
        """
        _elts = set()
        model.set_names()

        # Checking that the network is already built
        # TODO: Log instead of printing...
        if not self.is_built:
            logger.debug(
                "Warning. Trying to use Network model without building the network."
            )
            logger.debug("Calling build() with source={}".format(source))
            self.build(model, source=source)

        # Checking that the attributes have been set
        # TODO: Log instead of printing...
        if not self.attributes_set:
            logger.debug(
                "Warning. Trying to use Network model without setting the attributes first."
            )
            logger.debug("Setting the attributes...")
            self.set_attributes(model)

        # Run the dfs or die trying...
        try:
            childrens = nx.dfs_successors(self.digraph, source)
        except:
            traceback.print_exc()
            raise ValueError("dfs failed with source={}".format(source))

        # Following two lines extract the data stored in the edges
        # More precisely, the type of equipment and the name of the equipment
        # that makes the connection (usually a line or a transformer)
        #
        edge_equipment = nx.get_edge_attributes(self.graph, "equipment")
        edge_equipment_name = nx.get_edge_attributes(self.graph, "equipment_name")

        # Build the list of equipment names downstream
        for source, destinations in childrens.items():
            _elts.add(source)
            for destination in destinations:
                _elts.add(destination)
                if (source, destination) in edge_equipment_name:
                    _elts.add(edge_equipment_name[(source, destination)])
                elif (destination, source) in edge_equipment_name:
                    _elts.add(edge_equipment_name[(destination, source)])

        # Get the corresponding DiTTo objects
        # Warning: This will fail if set_names() has not been called before.
        _obj = []
        for x in _elts:
            try:
                _obj.append(model[x])
            except:
                raise ValueError("Unable to get DiTTo object with name {}".format(x))

        return _obj 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def dfs_successors(G, source=None, depth_limit=None):
    """Returns dictionary of successors in depth-first-search from source.

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

    source : node, optional
       Specify starting node for depth-first search and return edges in
       the component reachable from source.

    depth_limit : int, optional (default=len(G))
       Specify the maximum search depth.

    Returns
    -------
    succ: dict
       A dictionary with nodes as keys and list of successor nodes as values.

    Examples
    --------
    >>> G = nx.path_graph(5)
    >>> nx.dfs_successors(G, source=0)
    {0: [1], 1: [2], 2: [3], 3: [4]}
    >>> nx.dfs_successors(G, source=0, depth_limit=2)
    {0: [1], 1: [2]}

    Notes
    -----
    If a source is not specified then a source is chosen arbitrarily and
    repeatedly until all components in the graph are searched.

    The implementation of this function is adapted from David Eppstein's
    depth-first search function in `PADS`_, with modifications
    to allow depth limits based on the Wikipedia article
    "`Depth-limited search`_".

    .. _PADS: http://www.ics.uci.edu/~eppstein/PADS
    .. _Depth-limited search: https://en.wikipedia.org/wiki/Depth-limited_search
    """
    d = defaultdict(list)
    for s, t in dfs_edges(G, source=source, depth_limit=depth_limit):
        d[s].append(t)
    return dict(d) 

# 需要导入模块: import networkx [as 别名]
# 或者: from networkx import dfs_successors [as 别名]
def dfs_successors(G, source=None, depth_limit=None):
    """Return dictionary of successors in depth-first-search from source.

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

    source : node, optional
       Specify starting node for depth-first search and return edges in
       the component reachable from source.

    depth_limit : int, optional (default=len(G))
       Specify the maximum search depth.

    Returns
    -------
    succ: dict
       A dictionary with nodes as keys and list of successor nodes as values.

    Examples
    --------
    >>> G = nx.path_graph(5)
    >>> nx.dfs_successors(G, source=0)
    {0: [1], 1: [2], 2: [3], 3: [4]}
    >>> nx.dfs_successors(G, source=0, depth_limit=2)
    {0: [1], 1: [2]}

    Notes
    -----
    If a source is not specified then a source is chosen arbitrarily and
    repeatedly until all components in the graph are searched.

    The implementation of this function is adapted from David Eppstein's
    depth-first search function in `PADS`_, with modifications
    to allow depth limits based on the Wikipedia article
    "`Depth-limited search`_".

    .. _PADS: http://www.ics.uci.edu/~eppstein/PADS
    .. _Depth-limited search: https://en.wikipedia.org/wiki/Depth-limited_search
    """
    d = defaultdict(list)
    for s, t in dfs_edges(G, source=source, depth_limit=depth_limit):
        d[s].append(t)
    return dict(d)