__init__.py 118 KB
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#! /usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# graph_tool -- a general graph manipulation python module
#
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# Copyright (C) 2006-2016 Tiago de Paula Peixoto <tiago@skewed.de>
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#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

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"""
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graph_tool - efficient graph analysis and manipulation
======================================================

Summary
-------

.. autosummary::
   :nosignatures:

   Graph
   GraphView
   Vertex
   Edge
   PropertyMap
   PropertyArray
   load_graph
   group_vector_property
   ungroup_vector_property
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   map_property_values
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   infect_vertex_property
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   edge_endpoint_property
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   incident_edges_op
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   perfect_prop_hash
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   value_types
   show_config

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This module provides:
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   1. A :class:`~graph_tool.Graph` class for graph representation and manipulation
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   2. Property maps for Vertex, Edge or Graph.
   3. Fast algorithms implemented in C++.
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How to use the documentation
----------------------------
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Documentation is available in two forms: docstrings provided
with the code, and the full documentation available in
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`the graph-tool homepage <http://graph-tool.skewed.de>`_.
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We recommend exploring the docstrings using `IPython
<http://ipython.scipy.org>`_, an advanced Python shell with TAB-completion and
introspection capabilities.

The docstring examples assume that ``graph_tool.all`` has been imported as
``gt``::

   >>> import graph_tool.all as gt

Code snippets are indicated by three greater-than signs::

   >>> x = x + 1

Use the built-in ``help`` function to view a function's docstring::

   >>> help(gt.Graph)

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Contents
--------
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"""
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from __future__ import division, absolute_import, print_function
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import sys
if sys.version_info < (3,):
    range = xrange
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else:
    unicode = str
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__author__ = "Tiago de Paula Peixoto <tiago@skewed.de>"
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__copyright__ = "Copyright 2006-2016 Tiago de Paula Peixoto"
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__license__ = "GPL version 3 or above"
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__URL__ = "http://graph-tool.skewed.de"
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# import numpy and scipy before everything to avoid weird segmentation faults
# depending on the order things are imported.

import numpy
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import numpy.ma
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import scipy
import scipy.stats

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from .dl_import import *
dl_import("from . import libgraph_tool_core as libcore")
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__version__ = libcore.mod_info().version

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from . import gt_io  # sets up libcore io routines
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import sys
import os
import re
import gzip
import weakref
import copy
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import textwrap
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import io
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import collections
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if sys.version_info < (3,):
    import StringIO
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from .decorators import _wraps, _require, _attrs, _limit_args, _copy_func
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from inspect import ismethod
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__all__ = ["Graph", "GraphView", "Vertex", "Edge", "VertexBase", "EdgeBase",
           "Vector_bool", "Vector_int16_t", "Vector_int32_t", "Vector_int64_t",
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           "Vector_double", "Vector_long_double", "Vector_string",
           "Vector_size_t", "value_types", "load_graph", "PropertyMap",
           "group_vector_property", "ungroup_vector_property",
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           "map_property_values", "infect_vertex_property",
           "edge_endpoint_property", "incident_edges_op", "perfect_prop_hash",
           "seed_rng", "show_config", "PropertyArray", "openmp_enabled",
           "openmp_get_num_threads", "openmp_set_num_threads",
           "openmp_get_schedule", "openmp_set_schedule", "__author__",
           "__copyright__", "__URL__", "__version__"]
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# this is rather pointless, but it works around a sphinx bug
graph_tool = sys.modules[__name__]
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################################################################################
# Utility functions
################################################################################


def _prop(t, g, prop):
    """Return either a property map, or an internal property map with a given
    name."""
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    if isinstance(prop, (str, unicode)):
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        try:
            pmap = g.properties[(t, prop)]
        except KeyError:
            raise KeyError("no internal %s property named: %s" %\
                           ("vertex" if t == "v" else \
                            ("edge" if t == "e" else "graph"), prop))
    else:
        pmap = prop
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    if pmap is None:
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        return libcore.any()
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    if t != prop.key_type():
        names = {'e': 'edge', 'v': 'vertex', 'g': 'graph'}
        raise ValueError("Expected '%s' property map, got '%s'" %
                         (names[t], names[prop.key_type()]))
    return pmap._get_any()
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def _degree(g, name):
    """Retrieve the degree type from string, or returns the corresponding
    property map."""
    deg = name
    if name == "in-degree" or name == "in":
        deg = libcore.Degree.In
    elif name == "out-degree" or name == "out":
        deg = libcore.Degree.Out
    elif name == "total-degree" or name == "total":
        deg = libcore.Degree.Total
    else:
        deg = _prop("v", g, deg)
    return deg


def _type_alias(type_name):
    alias = {"int8_t": "bool",
             "boolean": "bool",
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             "short": "int16_t",
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             "int": "int32_t",
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             "unsigned int": "int32_t",
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             "long": "int64_t",
             "long long": "int64_t",
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             "unsigned long": "int64_t",
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             "object": "python::object",
             "float": "double"}
    if type_name in alias:
        return alias[type_name]
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    if type_name in value_types():
        return type_name
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    ma = re.compile(r"vector<(.*)>").match(type_name)
    if ma:
        t = ma.group(1)
        if t in alias:
            return "vector<%s>" % alias[t]
    raise ValueError("invalid property value type: " + type_name)


def _python_type(type_name):
    type_name = _type_alias(type_name)
    if "vector" in type_name:
        ma = re.compile(r"vector<(.*)>").match(type_name)
        t = ma.group(1)
        return list, _python_type(t)
    if "int" in type_name:
        return int
    if type_name == "bool":
        return bool
    if "double" in type_name:
        return float
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    if type_name == "string":
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        return str
    return object

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def _gt_type(obj):
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    if isinstance(obj, numpy.dtype):
        t = obj.type
    else:
        t = type(obj)
    if issubclass(t, (numpy.int16, numpy.uint16, numpy.int8, numpy.uint8)):
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        return "int16_t"
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    if issubclass(t, (int, numpy.int32, numpy.uint32)):
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        return "int32_t"
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    if issubclass(t, (numpy.longlong, numpy.uint64, numpy.int64)):
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        return "int64_t"
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    if issubclass(t, (float, numpy.float, numpy.float16, numpy.float32, numpy.float64)):
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        return "double"
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    if issubclass(t, numpy.float128):
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        return "long double"
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    if issubclass(t, (str, unicode)):
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        return "string"
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    if issubclass(t, bool):
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        return "bool"
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    if issubclass(t, (list, numpy.ndarray)):
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        return "vector<%s>" % _gt_type(obj[0])
    return "object"

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def _converter(val_type):
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    # attempt to convert to a compatible python type. This is useful,
    # for instance, when dealing with numpy types.
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    vtype = _python_type(val_type)
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    if type(vtype) is tuple:
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        def convert(val):
            return [vtype[1](x) for x in val]
    elif vtype is object:
        def convert(val):
            return val
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    elif vtype is str:
        return _c_str
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    else:
        def convert(val):
            return vtype(val)
    return convert
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def show_config():
    """Show ``graph_tool`` build configuration."""
    info = libcore.mod_info()
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    print("version:", info.version)
    print("gcc version:", info.gcc_version)
    print("compilation flags:", info.cxxflags)
    print("install prefix:", info.install_prefix)
    print("python dir:", info.python_dir)
    print("graph filtering:", libcore.graph_filtering_enabled())
    print("openmp:", libcore.openmp_enabled())
    print("uname:", " ".join(os.uname()))
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def terminal_size():
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    try:
        import fcntl, termios, struct
        h, w, hp, wp = struct.unpack('HHHH',
            fcntl.ioctl(0, termios.TIOCGWINSZ,
            struct.pack('HHHH', 0, 0, 0, 0)))
    except IOError:
        w, h = 80, 100
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    return w, h

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try:
    libcore.mod_info("wrong")
except BaseException as e:
    ArgumentError = type(e)

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# Python 2 vs 3 compatibility

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if sys.version_info < (3,):
    def _c_str(s):
        if isinstance(s, unicode):
            return s.encode("utf-8")
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        return str(s)
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    def _str_decode(s):
        return s
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else:
    def _c_str(s):
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        return str(s)
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    def _str_decode(s):
        if isinstance(s, bytes):
            return s.decode("utf-8")
        return s

def get_bytes_io(buf=None):
    """We want BytesIO for python 3, but StringIO for python 2."""
    if sys.version_info < (3,):
        return StringIO.StringIO(buf)
    else:
        return io.BytesIO(buf)

def conv_pickle_state(state):
    """State keys may be of type `bytes` if python 3 is being used, but state was
    pickled with python 2."""

    if sys.version_info >= (3,):
        keys = [k for k in state.keys() if type(k) is bytes]
        for k in keys:
            state[k.decode("utf-8")] = state[k]
            del state[k]

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################################################################################
# Property Maps
################################################################################


class PropertyArray(numpy.ndarray):
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    """This is a :class:`~numpy.ndarray` subclass which keeps a reference of its
    :class:`~graph_tool.PropertyMap` owner, and detects if the underlying data
    has been invalidated.
    """
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    __array_priority__ = -10

    def _get_pmap(self):
        return self._prop_map

    def _set_pmap(self, value):
        self._prop_map = value

    prop_map = property(_get_pmap, _set_pmap,
                        doc=":class:`~graph_tool.PropertyMap` owner instance.")

    def __new__(cls, input_array, prop_map):
        obj = numpy.asarray(input_array).view(cls)
        obj.prop_map = prop_map

        # check if data really belongs to property map
        if (prop_map._get_data().__array_interface__['data'][0] !=
            obj._get_base_data()):
            obj.prop_map = None
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            # do a copy
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            obj = numpy.asarray(obj)

        return obj

    def _get_base(self):
        base = self
        while base.base is not None:
            base = base.base
        return base

    def _get_base_data(self):
        return self._get_base().__array_interface__['data'][0]

    def _check_data(self):
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        if not hasattr(self, "_prop_map") or self.prop_map is None:
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            return

        data = self.prop_map._get_data()

        if (data is None or
            data.__array_interface__['data'][0] != self._get_base_data()):
            raise ValueError(("The graph correspondig to the underlying" +
                              " property map %s has changed. The" +
                              " PropertyArray at 0x%x is no longer valid!") %
                             (repr(self.prop_map), id(self)))

    def __array_finalize__(self, obj):
        if type(obj) is PropertyArray:
            obj._check_data()

        if obj is not None:
            # inherit prop_map only if the data is the same
            if (type(obj) is PropertyArray and
                self._get_base_data() == obj._get_base_data()):
                self.prop_map = getattr(obj, 'prop_map', None)
            else:
                self.prop_map = None
        self._check_data()

    def __array_prepare__(self, out_arr, context=None):
        self._check_data()
        return numpy.ndarray.__array_prepare__(self, out_arr, context)

    def __array_wrap__(self, out_arr, context=None):
        #demote to ndarray
        obj = numpy.ndarray.__array_wrap__(self, out_arr, context)
        return numpy.asarray(obj)

    # Overload members and operators to add data checking

    def _wrap_method(method):
        method = getattr(numpy.ndarray, method)

        def checked_method(self, *args, **kwargs):
            self._check_data()
            return method(self, *args, **kwargs)

        if ismethod(method):
            checked_method = _wraps(method)(checked_method)
        checked_method.__doc__ = getattr(method, "__doc__", None)
        return checked_method

    for method in ['all', 'any', 'argmax', 'argmin', 'argsort', 'astype',
                   'byteswap', 'choose', 'clip', 'compress', 'conj',
                   'conjugate', 'copy', 'cumprod', 'cumsum', 'diagonal', 'dot',
                   'dump', 'dumps', 'fill', 'flat', 'flatten', 'getfield',
                   'imag', 'item', 'itemset', 'itemsize', 'max', 'mean', 'min',
                   'newbyteorder', 'nonzero', 'prod', 'ptp', 'put', 'ravel',
                   'real', 'repeat', 'reshape', 'resize', 'round',
                   'searchsorted', 'setfield', 'setflags', 'sort', 'squeeze',
                   'std', 'sum', 'swapaxes', 'take', 'tofile', 'tolist',
                   'tostring', 'trace', 'transpose', 'var', 'view',
                   '__getitem__']:
        if hasattr(numpy.ndarray, method):
            locals()[method] = _wrap_method(method)


class PropertyMap(object):
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    """This class provides a mapping from vertices, edges or whole graphs to
    arbitrary properties.
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    See :ref:`sec_property_maps` for more details.

    The possible property value types are listed below.
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    .. table::

        =======================     ======================
         Type name                  Alias
        =======================     ======================
        ``bool``                    ``uint8_t``
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        ``int16_t``                 ``short``
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        ``int32_t``                 ``int``
        ``int64_t``                 ``long``, ``long long``
        ``double``                  ``float``
        ``long double``
        ``string``
        ``vector<bool>``            ``vector<uint8_t>``
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        ``vector<int16_t>``         ``short``
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        ``vector<int32_t>``         ``vector<int>``
        ``vector<int64_t>``         ``vector<long>``, ``vector<long long>``
        ``vector<double>``          ``vector<float>``
        ``vector<long double>``
        ``vector<string>``
        ``python::object``          ``object``
        =======================     ======================
    """
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    def __init__(self, pmap, g, key_type):
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        self.__map = pmap
        self.__g = weakref.ref(g)
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        self.__base_g = lambda: None
        try:
            if isinstance(g, GraphView):
                self.__base_g = weakref.ref(g.base)  # keep reference to the
                                                     # base graph, in case the
                                                     # graph view is deleted.
        except NameError:
            pass  # ignore if GraphView is yet undefined
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        self.__key_type = key_type
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        self.__convert = _converter(self.value_type())
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        self.__register_map()

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    def _get_any(self):
        t = self.key_type()
        g = self.get_graph()
        if t == "v":
            N = g.num_vertices(True)
        elif t == "e":
            N = g.edge_index_range
        else:
            N = 1
        self.reserve(N)
        return self.__map.get_map()

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    def __key_trans(self, key):
        if self.key_type() == "g":
            return key._Graph__graph
        else:
            return key

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    def __key_convert(self, k):
        if self.key_type() == "e":
            try:
                k = (int(k[0]), int(k[1]))
            except:
                raise ArgumentError
            key = self.__g().edge(k[0], k[1])
            if key is None:
                raise ValueError("Nonexistent edge: %s" % str(k))
        elif self.key_type() == "v":
            try:
                key = int(k)
            except:
                raise ArgumentError
            key = self.__g().vertex(key)
        return key
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    def __register_map(self):
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        for g in [self.__g(), self.__base_g()]:
            if g is not None:
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                g._Graph__known_properties[id(self)] = weakref.ref(self)
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    def __unregister_map(self):
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        for g in [self.__g(), self.__base_g()]:
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            if g is not None and id(self) in g._Graph__known_properties:
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                del g._Graph__known_properties[id(self)]
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    def __del__(self):
        self.__unregister_map()

    def __getitem__(self, k):
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        k = self.__key_trans(k)
        try:
            return self.__map[k]
        except ArgumentError:
            try:
                k = self.__key_convert(k)
                return self.__map[k]
            except ArgumentError:
                if self.key_type() == "e":
                    kt = "Edge"
                elif self.key_type() == "v":
                    kt = "Vertex"
                else:
                    kt = "Graph"
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                raise ValueError("invalid key '%s' of type '%s', wanted type: %s"
                                 % (str(k), str(type(k)), kt) )
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    def __setitem__(self, k, v):
        key = self.__key_trans(k)
        try:
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            try:
                self.__map[key] = v
            except TypeError:
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                self.__map[key] = self.__convert(v)
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        except ArgumentError:
            try:
                key = self.__key_convert(key)
                try:
                    self.__map[key] = v
                except TypeError:
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                    self.__map[key] = self.__convert(v)
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            except ArgumentError:
                if self.key_type() == "e":
                    kt = "Edge"
                elif self.key_type() == "v":
                    kt = "Vertex"
                else:
                    kt = "Graph"
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                vt = self.value_type()
                raise ValueError("invalid key value pair '(%s, %s)' of types "
                                 "'(%s, %s)', wanted types: (%s, %s)" %
                                 (str(k), str(v), str(type(k)),
                                  str(type(v)), kt, vt))
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    def __iter__(self):
        g = self.__g()
        if self.key_type() == "g":
            iters = [g]
        elif self.key_type() == "v":
            iters = g.vertices()
        else:
            iters = g.edges()
        for x in iters:
            yield self[x]
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    def __repr__(self):
        # provide some more useful information
        if self.key_type() == "e":
            k = "Edge"
        elif self.key_type() == "v":
            k = "Vertex"
        else:
            k = "Graph"
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        g = self.get_graph()
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        if g is None:
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            g = "a non-existent graph"
        else:
            g = "Graph 0x%x" % id(g)
        return ("<PropertyMap object with key type '%s' and value type '%s',"
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                + " for %s, at 0x%x>") % (k, self.value_type(), g, id(self))
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    def copy(self, value_type=None, full=True):
        """Return a copy of the property map. If ``value_type`` is specified, the value
        type is converted to the chosen type. If ``full == False``, in the case
        of filtered graphs only the unmasked values are copied (with the
        remaining ones taking the type-dependent default value).

        """
        return self.get_graph().copy_property(self, value_type=value_type,
                                              full=full)
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    def __copy__(self):
        return self.copy()

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    def __deepcopy__(self, memo):
        if self.value_type() != "python::object":
            return self.copy()
        else:
            pmap = self.copy()
            g = self.get_graph()
            if self.key_type() == "g":
                iters = [g]
            elif self.key_type() == "v":
                iters = g.vertices()
            else:
                iters = g.edges()
            for v in iters:
                pmap[v] = copy.deepcopy(self[v], memo)
            return pmap

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    def get_graph(self):
        """Get the graph class to which the map refers."""
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        g = self.__g()
        if g is None:
            g = self.__base_g()
        return g
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    def key_type(self):
        """Return the key type of the map. Either 'g', 'v' or 'e'."""
        return self.__key_type

    def value_type(self):
        """Return the value type of the map."""
        return self.__map.value_type()

    def python_value_type(self):
        """Return the python-compatible value type of the map."""
        return _python_type(self.__map.value_type())

    def get_array(self):
        """Get a :class:`~graph_tool.PropertyArray` with the property values.

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        .. note::

           An array is returned *only if* the value type of the property map is
           a scalar. For vector, string or object types, ``None`` is returned
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           instead. For vector and string objects, indirect array access is
           provided via the :func:`~graph_tool.PropertyMap.get_2d_array()` and
           :func:`~graph_tool.PropertyMap.set_2d_array()` member functions.
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        .. warning::
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           The returned array does not own the data, which belongs to the
           property map. Therefore, if the graph changes, the array may become
           *invalid* and any operation on it will fail with a
           :class:`ValueError` exception. Do **not** store the array if
           the graph is to be modified; store a **copy** instead.
        """
        a = self._get_data()
        if a is None:
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            raise ValueError("Cannot get array for value type: " + self.value_type())
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        return PropertyArray(a, prop_map=self)

    def _get_data(self):
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        g = self.get_graph()
        if g is None:
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            raise ValueError("Cannot get array for an orphaned property map")
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        if self.__key_type == 'v':
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            n = g._Graph__graph.get_num_vertices(False)
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        elif self.__key_type == 'e':
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            n = g.edge_index_range
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        else:
            n = 1
        a = self.__map.get_array(n)
        return a

    def __set_array(self, v):
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        a = self.get_array()
        a[:] = v
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    a = property(get_array, __set_array,
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                 doc=r"""Shortcut to the :meth:`~PropertyMap.get_array` method
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                 as an attribute. This makes assignments more convenient, e.g.:

                 >>> g = gt.Graph()
                 >>> g.add_vertex(10)
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                 <...>
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                 >>> prop = g.new_vertex_property("double")
                 >>> prop.a = np.random.random(10)           # Assignment from array
                 """)

    def __get_set_f_array(self, v=None, get=True):
        g = self.get_graph()
        if g is None:
            return None
        a = self.get_array()
        filt = [None]
        if self.__key_type == 'v':
            filt = g.get_vertex_filter()
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            N = g.num_vertices()
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        elif self.__key_type == 'e':
            filt = g.get_edge_filter()
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            if g.get_vertex_filter()[0] is not None:
                filt = (g.new_edge_property("bool"), filt[1])
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                libcore.mark_edges(g._Graph__graph, _prop("e", g, filt[0]))
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                if filt[1]:
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                    filt[0].a = numpy.logical_not(filt[0].a)
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            elif g.edge_index_range != g.num_edges():
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                filt = (g.new_edge_property("bool"), False)
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                libcore.mark_edges(g._Graph__graph, _prop("e", g, filt[0]))
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            if filt[0] is None:
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                N = g.edge_index_range
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            else:
                N = (filt[0].a == (not filt[1])).sum()
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        if get:
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            if a is None:
                return a
            if filt[0] is None:
                return a
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            return a[filt[0].a == (not filt[1])][:N]
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        else:
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            if a is None:
                return
            if filt[0] is None:
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                try:
                    a[:] = v
                except ValueError:
                    a[:] = v[:len(a)]
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            else:
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                m = filt[0].a == (not filt[1])
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                m *= m.cumsum() <= N
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                try:
                    a[m] = v
                except ValueError:
                    a[m] = v[:len(m)][m]
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    fa = property(__get_set_f_array,
                  lambda self, v: self.__get_set_f_array(v, False),
                  doc=r"""The same as the :attr:`~PropertyMap.a` attribute, but
                  instead an *indexed* array is returned, which contains only
                  entries for vertices/edges which are not filtered out. If
                  there are no filters in place, the array is not indexed, and
                  is identical to the :attr:`~PropertyMap.a` attribute.

                  Note that because advanced indexing is triggered, a **copy**
                  of the array is returned, not a view, as for the
                  :attr:`~PropertyMap.a` attribute. Nevertheless, the assignment
                  of values to the *whole* array at once works as expected.""")

    def __get_set_m_array(self, v=None, get=True):
        g = self.get_graph()
        if g is None:
            return None
        a = self.get_array()
        filt = [None]
        if self.__key_type == 'v':
            filt = g.get_vertex_filter()
        elif self.__key_type == 'e':
            filt = g.get_edge_filter()
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            if g.get_vertex_filter()[0] is not None:
                filt = (g.new_edge_property("bool"), filt[1])
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                libcore.mark_edges(g._Graph__graph, _prop("e", g, filt[0]))
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                if filt[1]:
                    filt[0].a = 1 - filt[0].a
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        if filt[0] is None or a is None:
            if get:
                return a
            else:
                return
        ma = numpy.ma.array(a, mask=(filt[0].a == False) if not filt[1] else (filt[0].a == True))
        if get:
            return ma
        else:
            ma[:] = v

    ma = property(__get_set_m_array,
                  lambda self, v: self.__get_set_m_array(v, False),
                  doc=r"""The same as the :attr:`~PropertyMap.a` attribute, but
                  instead a :class:`~numpy.ma.MaskedArray` object is returned,
                  which contains only entries for vertices/edges which are not
                  filtered out. If there are no filters in place, a regular
                  :class:`~graph_tool.PropertyArray` is returned, which is
                  identical to the :attr:`~PropertyMap.a` attribute.""")
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    def get_2d_array(self, pos):
        r"""Return a two-dimensional array with a copy of the entries of the
        vector-valued property map. The parameter ``pos`` must be a sequence of
        integers which specifies the indexes of the property values which will
        be used. """

        if self.key_type() == "g":
            raise ValueError("Cannot create multidimensional array for graph property maps.")
        if "vector" not in self.value_type() and (len(pos) > 1 or pos[0] != 0):
            raise ValueError("Cannot create array of dimension %d (indexes %s) from non-vector property map of type '%s'." \
                             % (len(pos), str(pos), self.value_type()))
        if "string" in self.value_type():
            if "vector" in self.value_type():
                p = ungroup_vector_property(self, pos)
            else:
                p = [self]
            g = self.get_graph()
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            vfilt = g.get_vertex_filter()
            efilt = g.get_edge_filter()
            if vfilt[0] is not None:
                g = GraphView(g, skip_vfilt=True, skip_efilt=True)
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            if self.key_type() == "v":
                N = g.num_vertices()
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                idx = g.vertex_index
                filt = vfilt
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            else:
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                N = g.edge_index_range
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                idx = g.edge_index
                filt = efilt
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            a = [["" for j in range(N)] for i in range(len(p))]
            if self.key_type() == "v":
                iters = g.vertices()
            else:
                iters = g.edges()
            for v in iters:
                for i in range(len(p)):
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                    a[i][idx[v]] = p[i][v]
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            if len(a) == 1:
                a = a[0]
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            a = numpy.array(a)
            if vfilt[0] is not None:
                a = a[filt[0].a[:a.shape[0]] == (not filt[1])]
            return a
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        try:
            return numpy.array(self.fa)
        except ValueError:
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            p = ungroup_vector_property(self, pos)
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            return numpy.array([x.fa for x in p])
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    def set_2d_array(self, a, pos=None):
        r"""Set the entries of the vector-valued property map from a
        two-dimensional array ``a``. If given, the parameter ``pos`` must be a
        sequence of integers which specifies the indexes of the property values
        which will be set."""

        if self.key_type() == "g":
            raise ValueError("Cannot set multidimensional array for graph property maps.")
        if "vector" not in self.value_type():
            if len(a.shape) != 1:
                raise ValueError("Cannot set array of shape %s to non-vector property map of type %s" % \
                                 (str(a.shape), self.value_type()))
            if self.value_type() != "string":
                self.fa = a
            else:
                g = self.get_graph()
                if self.key_type() == "v":
                    iters = g.vertices()
                else:
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                    iters = sorted(g.edges(), key=lambda e: g.edge_index[e])
                for j, v in enumerate(iters):
                    self[v] = a[j]
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            return

        val = self.value_type()[7:-1]
        ps = []
        for i in range(a.shape[0]):
            ps.append(self.get_graph().new_property(self.key_type(), val))
            if self.value_type() != "string":
                ps[-1].fa = a[i]
            else:
                g = self.get_graph()
                if self.key_type() == "v":
                    iters = g.vertices()
                else:
                    iters = g.edges()
                for j, v in enumerate(iters):
                    ps[-1][v] = a[i, j]
        group_vector_property(ps, val, self, pos)

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    def is_writable(self):
        """Return True if the property is writable."""
        return self.__map.is_writable()

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    def set_value(self, val):
        """Sets all values in the property map to ``val``."""
        g = self.get_graph()
        if self.key_type() == "v":
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            libcore.set_vertex_property(g._Graph__graph, _prop("v", g, self), val)
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        elif self.key_type() == "e":
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            libcore.set_edge_property(g._Graph__graph, _prop("e", g, self), val)
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        else:
            self[g] = val

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    def reserve(self, size):
        """Reserve enough space for ``size`` elements in underlying container. If the
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           original size is already equal or larger, nothing will happen."""
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        self.__map.reserve(size)

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    def resize(self, size):
        """Resize the underlying container to contain exactly ``size`` elements."""
        self.__map.resize(size)

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    def shrink_to_fit(self):
        """Shrink size of underlying container to accommodate only the necessary amount,
        and thus potentially freeing memory."""
        g = self.get_graph()
        if self.key_type() == "v":
            size = g.num_vertices(True)
        elif self.key_type() == "e":
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            size = g.edge_index_range
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        else:
            size = 1
        self.__map.resize(size)
        self.__map.shrink_to_fit()

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    def __getstate__(self):
        g = self.get_graph()
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        if g is None:
            raise ValueError("cannot pickle orphaned property map")
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        value_type = self.value_type()
        key_type = self.key_type()
        if not self.is_writable():
            vals = None
        else:
            u = GraphView(g, skip_vfilt=True, skip_efilt=True)
            if key_type == "v":
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                vals = [self.__convert(self[v]) for v in u.vertices()]
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            elif key_type == "e":
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                vals = [self.__convert(self[e]) for e in u.edges()]
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            else:
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                vals = self.__convert(self[g])
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        state = dict(g=g, value_type=value_type,
                     key_type=key_type, vals=vals,
                     is_vindex=self is g.vertex_index,
                     is_eindex=self is g.edge_index)

        return state

    def __setstate__(self, state):
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        conv_pickle_state(state)
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        g = state["g"]
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        key_type = _str_decode(state["key_type"])
        value_type = _str_decode(state["value_type"])
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        vals = state["vals"]

        if state["is_vindex"]:
            pmap = g.vertex_index
        elif state["is_eindex"]:
            pmap = g.edge_index
        else:
            u = GraphView(g, skip_vfilt=True, skip_efilt=True)
            if key_type == "v":
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                pmap = u.new_vertex_property(value_type, vals=vals)
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            elif key_type == "e":
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                pmap = u.new_edge_property(value_type, vals=vals)
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            else:
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                pmap = u.new_graph_property(value_type)
                pmap[u] = vals
            pmap = g.own_property(pmap)
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        self.__map = pmap.__map
        self.__g = pmap.__g
        self.__base_g = pmap.__base_g
        self.__key_type = key_type
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        self.__convert = _converter(self.value_type())
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        self.__register_map()

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def _check_prop_writable(prop, name=None):
    if not prop.is_writable():
        raise ValueError("property map%s is not writable." %\
                         ((" '%s'" % name) if name != None else ""))


def _check_prop_scalar(prop, name=None, floating=False):
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    scalars = ["bool", "int16_t", "int32_t", "int64_t", "unsigned long",
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               "double", "long double"]
    if floating:
        scalars = ["double", "long double"]

    if prop.value_type() not in scalars:
        raise ValueError("property map%s is not of scalar%s type." %\
                         (((" '%s'" % name) if name != None else ""),
                          (" floating" if floating else "")))


def _check_prop_vector(prop, name=None, scalar=True, floating=False):
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    scalars = ["bool", "int16_t", "int32_t", "int64_t", "unsigned long",
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               "double", "long double"]
    if not scalar:
        scalars += ["string"]
    if floating:
        scalars = ["double", "long double"]
    vals = ["vector<%s>" % v for v in scalars]
    if prop.value_type() not in vals:
        raise ValueError("property map%s is not of vector%s type." %\
                         (((" '%s'" % name) if name != None else ""),
                          (" floating" if floating else "")))


def group_vector_property(props, value_type=None, vprop=None, pos=None):
    """Group list of properties ``props`` into a vector property map of the same type.

    Parameters
    ----------
    props : list of :class:`~graph_tool.PropertyMap`
        Properties to be grouped.
    value_type : string (optional, default: None)
        If supplied, defines the value type of the grouped property.
    vprop : :class:`~graph_tool.PropertyMap` (optional, default: None)
        If supplied, the properties are grouped into this property map.
    pos : list of ints (optional, default: None)
        If supplied, should contain a list of indexes where each corresponding
        element of ``props`` should be inserted.

    Returns
    -------
    vprop : :class:`~graph_tool.PropertyMap`
       A vector property map with the grouped values of each property map in
       ``props``.
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    Examples
    --------
    >>> from numpy.random import seed, randint
    >>> from numpy import array
    >>> seed(42)
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    >>> gt.seed_rng(42)
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    >>> g = gt.random_graph(100, lambda: (3, 3))
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    >>> props = [g.new_vertex_property("int") for i in range(3)]
    >>> for i in range(3):
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    ...    props[i].a = randint(0, 100, g.num_vertices())
    >>> gprop = gt.group_vector_property(props)
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    >>> print(gprop[g.vertex(0)].a)
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    [51 25  8]
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    >>> print(array([p[g.vertex(0)] for p in props]))
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    [51 25  8]
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    """
    g = props[0].get_graph()
    vtypes = set()
    keys = set()
    for i, p in enumerate(props):
        if "vector" in p.value_type():
            raise ValueError("property map 'props[%d]' is a vector property." %
                             i)
        vtypes.add(p.value_type())
        keys.add(p.key_type())
    if len(keys) > 1:
        raise ValueError("'props' must be of the same key type.")
    k = keys.pop()

    if vprop == None:
        if value_type == None and len(vtypes) == 1:
            value_type = vtypes.pop()

        if value_type != None:
            value_type = "vector<%s>" % value_type
            if k == 'v':
                vprop = g.new_vertex_property(value_type)
            elif k == 'e':
                vprop = g.new_edge_property(value_type)
            else:
                vprop = g.new_graph_property(value_type)
        else:
            ValueError("Can't automatically determine property map value" +
                       " type. Please provide the 'value_type' parameter.")
    _check_prop_vector(vprop, name="vprop", scalar=False)

    for i, p in enumerate(props):
        if k != "g":
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            u = GraphView(g, directed=True, reversed=g.is_reversed(),
                          skip_properties=True)
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            libcore.group_vector_property(u._Graph__graph, _prop(k, g, vprop),
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                                          _prop(k, g, p),
                                          i if pos == None else pos[i],
                                          k == 'e')
        else:
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            vprop[g][i if pos is None else pos[i]] = p[g]
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    return vprop


def ungroup_vector_property(vprop, pos, props=None):
    """Ungroup vector property map ``vprop`` into a list of individual property maps.

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    Parameters
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    ----------
    vprop : :class:`~graph_tool.PropertyMap`
        Vector property map to be ungrouped.
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    pos : list of ints
        A list of indexes corresponding to where each element of ``vprop``
        should be inserted into the ungrouped list.
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    props : list of :class:`~graph_tool.PropertyMap`  (optional, default: None)
        If supplied, should contain a list of property maps to which ``vprop``
        should be ungroupped.

    Returns
    -------
    props : list of :class:`~graph_tool.PropertyMap`
       A list of property maps with the ungrouped values of ``vprop``.
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    Examples
    --------
    >>> from numpy.random import seed, randint
    >>> from numpy import array
    >>> seed(42)
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    >>> gt.seed_rng(42)
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    >>> g = gt.random_graph(100, lambda: (3, 3))
    >>> prop = g.new_vertex_property("vector<int>")
    >>> for v in g.vertices():
    ...    prop[v] = randint(0, 100, 3)
    >>> uprops = gt.ungroup_vector_property(prop, [0, 1, 2])
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    >>> print(prop[g.vertex(0)].a)
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    [51 92 14]
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    >>> print(array([p[g.vertex(0)] for p in uprops]))
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    [51 92 14]
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    """

    g = vprop.get_graph()
    _check_prop_vector(vprop, name="vprop", scalar=False)
    k = vprop.key_type()
    value_type = vprop.value_type().split("<")[1].split(">")[0]
    if props == None:
        if k == 'v':
            props = [g.new_vertex_property(value_type) for i in pos]
        elif k == 'e':
            props = [g.new_edge_property(value_type) for i in pos]
        else:
            props = [g.new_graph_property(value_type) for i in pos]

    for i, p in enumerate(pos):
        if props[i].key_type() != k:
            raise ValueError("'props' must be of the same key type as 'vprop'.")

        if k != 'g':
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            u = GraphView(g, directed=True, reversed=g.is_reversed(),
                          skip_properties=True)
            libcore.ungroup_vector_property(u._Graph__graph,
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                                            _prop(k, g, vprop),
                                            _prop(k, g, props[i]),
                                            p, k == 'e')
        else:
            if len(vprop[g]) <= pos[i]:
                vprop[g].resize(pos[i] + 1)
            props[i][g] = vprop[g][pos[i]]
    return props

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def map_property_values(src_prop, tgt_prop, map_func):
    """Map the values of ``src_prop`` to ``tgt_prop`` according to the mapping
    function ``map_func``.

    Parameters
    ----------
    src_prop : :class:`~graph_tool.PropertyMap`
        Source property map.
    tgt_prop : :class:`~graph_tool.PropertyMap`
        Target property map.
    map_func : function or callable object
        Function mapping values of ``src_prop`` to values of ``tgt_prop``.

    Returns
    -------
    None

    Examples
    --------
    >>> g = gt.collection.data["lesmis"]
    >>> label_len = g.new_vertex_property("int64_t")
    >>> gt.map_property_values(g.vp.label, label_len,
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    ...                        lambda x: len(x))
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    >>> print(label_len.a)
    [ 6  8 14 11 12  8 12  8  5  6  7  7 10  6  7  7  9  9  7 11  9  6  7  7 13
     10  7  6 12 10  8  8 11  6  5 12  6 10 11  9 12  7  7  6 14  7  9  9  8 12
      6 16 12 11 14  6  9  6  8 10  9  7 10  7  7  4  9 14  9  5 10 12  9  6  6
      6 12]
    """

    if src_prop.key_type() != tgt_prop.key_type():
        raise ValueError("src_prop and tgt_prop must be of the same key type")
    g = src_prop.get_graph()
    k = src_prop.key_type()
    if k == "g":
        tgt_prop[g] = map_func(src_prop[g])
        return
    u = GraphView(g, directed=True, reversed=g.is_reversed(),
                  skip_properties=True)
    libcore.property_map_values(u._Graph__graph,
                                _prop(k, g, src_prop),
                                _prop(k, g, tgt_prop),
                                map_func, k == 'e')
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def infect_vertex_property(g, prop, vals=None):
    """Propagate the `prop` values of vertices with value `val` to all their
    out-neighbours.

    Parameters
    ----------
    prop : :class:`~graph_tool.PropertyMap`
        Property map to be modified.
    vals : list (optional, default: `None`)
        List of values to be propagated. If not provided, all values
        will be propagated.

    Returns
    -------
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    None : ``None``
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    Examples
    --------
    >>> from numpy.random import seed
    >>> seed(42)
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    >>> gt.seed_rng(42)
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    >>> g = gt.random_graph(100, lambda: (3, 3))
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    >>> prop = g.vertex_index.copy("int32_t")
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    >>> gt.infect_vertex_property(g, prop, [10])
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    >>> print(sum(prop.a == 10))
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    """
    libcore.infect_vertex_property(g._Graph__graph, _prop("v", g, prop),
                                   vals)

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@_limit_args({"endpoint": ["source", "target"]})
def edge_endpoint_property(g, prop, endpoint, eprop=None):
    """Return an edge property map corresponding to the vertex property `prop` of
    either the target and source of the edge, according to `endpoint`.
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    Parameters
    ----------
    prop : :class:`~graph_tool.PropertyMap`
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        Vertex property map to be used to propagated to the edge.
    endpoint : `"source"` or `"target"`
        Edge endpoint considered. If the graph is undirected, the source is
        always the vertex with the lowest index.
    eprop : :class:`~graph_tool.PropertyMap` (optional, default: `None`)
        If provided, the resulting edge properties will be stored here.
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    Returns
    -------
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    eprop : :class:`~graph_tool.PropertyMap`
        Propagated edge property.
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    Examples
    --------
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    >>> gt.seed_rng(42)
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    >>> g = gt.random_graph(100, lambda: (3, 3))
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    >>> esource = gt.edge_endpoint_property(g, g.vertex_index, "source")
    >>> print(esource.a)
    [ 0  0  0 96 96 96 92 92 92 88 88 88 84 84 84 80 80 80 76 76 76 72 72 72 68
     68 68 64 64 64 60 60 60 56 56 56 52 52 52 48 48 48 44 44 44 40 40 40 36 36
     36 32 32 32 28 28 28 24 24 24 20 20 20 16 16 16 12 12 12  8  8  8  4  4  4
     99 99 99  1  1  1  2  2  2  3  3  3  5  5  5  6  6  6  7  7  7  9  9  9 10
     10 10 14 14 14 19 19 19 25 25 25 30 30 30 35 35 35 41 41 41 46 46 46 51 51
     51 57 57 57 62 62 62 67 67 67 73 73 73 78 78 78 83 83 83 89 89 89 94 94 94
     11 11 11 98 98 98 97 97 97 95 95 95 93 93 93 91 91 91 90 90 90 87 87 87 86
     86 86 85 85 85 82 82 82 81 81 81 79 79 79 77 77 77 75 75 75 74 74 74 71 71
     71 69 69 69 61 61 61 54 54 54 47 47 47 39 39 39 33 33 33 26 26 26 18 18 18
     70 70 70 13 13 13 15 15 15 17 17 17 21 21 21 22 22 22 23 23 23 27 27 27 29
     29 29 31 31 31 34 34 34 37 37 37 38 38 38 42 42 42 43 43 43 45 45 45 49 49
     49 50 50 50 53 53 53 55 55 55 58 58 58 59 59 59 63 63 63 65 65 65 66 66 66]
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    """
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    val_t = prop.value_type()
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    if val_t == "unsigned long" or val_t == "unsigned int":
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        val_t = "int64_t"
    if eprop is None:
        eprop = g.new_edge_property(val_t)
    if eprop.value_type() != val_t:
        raise ValueError("'eprop' must be of the same value type as 'prop': " +
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                         val_t)
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    libcore.edge_endpoint(g._Graph__graph, _prop("v", g, prop),
                          _prop("e", g, eprop), endpoint)
    return eprop
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@_limit_args({"direction": ["in", "out"], "op": ["sum", "prod", "min", "max"]})
def incident_edges_op(g, direction, op, eprop, vprop=None):
    """Return a vertex property map corresponding to a specific operation (sum,
    product, min or max) on the edge property `eprop` of incident edges on each
    vertex, following the direction given by `direction`.
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    Parameters
    ----------
    direction : `"in"` or `"out"`
        Direction of the incident edges.
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    op : `"sum"`, `"prod"`, `"min"` or `"max"`
        Operation performed on incident edges.
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    eprop : :class:`~graph_tool.PropertyMap`
        Edge property map to be summed.
    vprop : :class:`~graph_tool.PropertyMap` (optional, default: `None`)
        If provided, the resulting vertex properties will be stored here.

    Returns
    -------
    vprop : :class:`~graph_tool.PropertyMap`
        Summed vertex property.

    Examples
    --------
    >>> gt.seed_rng(42)
    >>> g = gt.random_graph(100, lambda: (3, 3))
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    >>> vsum = gt.incident_edges_op(g, "out", "sum", g.edge_index)
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    >>> print(vsum.a)
    [  3 237 246 255 219 264 273 282 210 291 300 453 201 687 309 696 192 705
     669 318 183 714 723 732 174 327 660 741 165 750 336 759 156 651 768 345
     147 777 786 642 138 354 795 804 129 813 363 633 120 822 831 372 111 840
     624 849 102 381 858 867  93 615 390 876  84 885 894 399  75 606 678 597
      66 408 588 579  57 570 417 561  48 552 543 426  39 534 525 516  30 435
     507 498  21 489 444 480  12 471 462 228]
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    """

    val_t = eprop.value_type()
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    if val_t == "unsigned long" or val_t == "unsigned int":
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        val_t = "int64_t"
    if vprop is None:
        vprop = g.new_vertex_property(val_t)
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    orig_vprop = vprop
    if vprop.value_type != val_t:
        vprop = g.new_vertex_property(val_t)
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    if direction == "in" and not g.is_directed():
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        return orig_vprop
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    if direction == "in":
        g = GraphView(g, reversed=True, skip_properties=True)
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    libcore.out_edges_op(g._Graph__graph, _prop("e", g, eprop),
                          _prop("v", g, vprop), op)
    if vprop is not orig_vprop:
        g.copy_property(vprop, orig_vprop)
    return orig_vprop
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@_limit_args({"htype": ["int8_t", "int32_t", "int64_t"]})
def perfect_prop_hash(props, htype="int32_t"):
    """Given a list of property maps `props` of the same type, a derived list of
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    property maps with integral type `htype` is returned, where each value is
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    replaced by a perfect (i.e. unique) hash value.

    .. note::
       The hash value is deterministic, but it will not be necessarily the same
       for different values of `props`.
    """

    val_types = set([p.value_type() for p in props])
    if len(val_types) > 1:
        raise ValueError("All properties must have the same value type")
    hprops = [p.get_graph().new_property(p.key_type(), htype) for p in props]

    eprops = [p for p in props if p.key_type() == "e"]
    heprops = [p for p in hprops if p.key_type() == "e"]

    vprops = [p for p in props if p.key_type() == "v"]
    hvprops = [p for p in hprops if p.key_type() == "v"]

    hdict = libcore.any()

    for eprop, heprop in zip(eprops, heprops):
        g = eprop.get_graph()
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        g = GraphView(g, directed=True, skip_properties=True)
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        libcore.perfect_ehash(g._Graph__graph, _prop('e', g, eprop),
                              _prop('e', g, heprop), hdict)

    for vprop, hvprop in zip(vprops, hvprops):
        g = vprop.get_graph()
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        g = GraphView(g, directed=True, skip_properties=True)
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        libcore.perfect_vhash(g._Graph__graph, _prop('v', g, vprop),
                              _prop('v', g, hvprop), hdict)

    return hprops