graph_tool源码及其注释
#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# graph_tool -- a general graph manipulation python module
#
# Copyright (C) 2006-2016 Tiago de Paula Peixoto <tiago@skewed.de>
#
# 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/>.
"""
graph_tool - efficient graph analysis and manipulation
======================================================
Summary
-------
.. autosummary::
:nosignatures:
Graph
GraphView
Vertex
Edge
PropertyMap
PropertyArray
load_graph
group_vector_property
ungroup_vector_property
map_property_values
infect_vertex_property
edge_endpoint_property
incident_edges_op
perfect_prop_hash
value_types
show_config
This module provides:
1. A :class:`~graph_tool.Graph` class for graph representation and manipulation
2. Property maps for Vertex, Edge or Graph.
3. Fast algorithms implemented in C++.
How to use the documentation
----------------------------
Documentation is available in two forms: docstrings provided
with the code, and the full documentation available in
`the graph-tool homepage <http://graph-tool.skewed.de>`_.
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)
Contents
--------
"""
from __future__ import division, absolute_import, print_function
import sys
if sys.version_info < (3,):
range = xrange
else:
unicode = str
__author__ = "Tiago de Paula Peixoto <tiago@skewed.de>"
__copyright__ = "Copyright 2006-2016 Tiago de Paula Peixoto"
__license__ = "GPL version 3 or above"
__URL__ = "http://graph-tool.skewed.de"
# import numpy and scipy before everything to avoid weird segmentation faults
# depending on the order things are imported.
import numpy
import numpy.ma
import scipy
import scipy.stats
from .dl_import import *
dl_import("from . import libgraph_tool_core as libcore")
__version__ = libcore.mod_info().version
from . import gt_io # sets up libcore io routines
import sys
import os
import re
import gzip
import weakref
import copy
import textwrap
import io
import collections
if sys.version_info < (3,):
import StringIO
from .decorators import _wraps, _require, _attrs, _limit_args, _copy_func
from inspect import ismethod
__all__ = ["Graph", "GraphView", "Vertex", "Edge", "VertexBase", "EdgeBase",
"Vector_bool", "Vector_int16_t", "Vector_int32_t", "Vector_int64_t",
"Vector_double", "Vector_long_double", "Vector_string",
"Vector_size_t", "value_types", "load_graph", "PropertyMap",
"group_vector_property", "ungroup_vector_property",
"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__"]
# this is rather pointless, but it works around a sphinx bug
graph_tool = sys.modules[__name__]
################################################################################
# Utility functions
################################################################################
def _prop(t, g, prop):
"""Return either a property map, or an internal property map with a given
name."""
if isinstance(prop, (str, unicode)):
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
if pmap is None:
return libcore.any()
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()
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",
"short": "int16_t",
"int": "int32_t",
"unsigned int": "int32_t",
"long": "int64_t",
"long long": "int64_t",
"unsigned long": "int64_t",
"object": "python::object",
"float": "double"}
if type_name in alias:
return alias[type_name]
if type_name in value_types():
return type_name
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
if type_name == "string":
return str
return object
def _gt_type(obj):
if isinstance(obj, numpy.dtype):
t = obj.type
else:
t = type(obj)
if issubclass(t, (numpy.int16, numpy.uint16, numpy.int8, numpy.uint8)):
return "int16_t"
if issubclass(t, (int, numpy.int32, numpy.uint32)):
return "int32_t"
if issubclass(t, (numpy.longlong, numpy.uint64, numpy.int64)):
return "int64_t"
if issubclass(t, (float, numpy.float, numpy.float16, numpy.float32, numpy.float64)):
return "double"
if issubclass(t, numpy.float128):
return "long double"
if issubclass(t, (str, unicode)):
return "string"
if issubclass(t, bool):
return "bool"
if issubclass(t, (list, numpy.ndarray)):
return "vector<%s>" % _gt_type(obj[0])
return "object"
def _converter(val_type):
# attempt to convert to a compatible python type. This is useful,
# for instance, when dealing with numpy types.
vtype = _python_type(val_type)
if type(vtype) is tuple:
def convert(val):
return [vtype[1](x) for x in val]
elif vtype is object:
def convert(val):
return val
elif vtype is str:
return _c_str
else:
def convert(val):
return vtype(val)
return convert
[docs]
def show_config():
"""Show ``graph_tool`` build configuration."""
info = libcore.mod_info()
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()))
def terminal_size():
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
return w, h
try:
libcore.mod_info("wrong")
except BaseException as e:
ArgumentError = type(e)
# Python 2 vs 3 compatibility
if sys.version_info < (3,):
def _c_str(s):
if isinstance(s, unicode):
return s.encode("utf-8")
return str(s)
def _str_decode(s):
return s
else:
def _c_str(s):
return str(s)
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]
################################################################################
# Property Maps
################################################################################
[docs]
class PropertyArray(numpy.ndarray):
"""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.
"""
__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
# do a copy
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):
if not hasattr(self, "_prop_map") or self.prop_map is None:
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)
[docs]
class PropertyMap(object):
"""This class provides a mapping from vertices, edges or whole graphs to
arbitrary properties.
See :ref:`sec_property_maps` for more details.
The possible property value types are listed below.
.. table::
======================= ======================
Type name Alias
======================= ======================
``bool`` ``uint8_t``
``int16_t`` ``short``
``int32_t`` ``int``
``int64_t`` ``long``, ``long long``
``double`` ``float``
``long double``
``string``
``vector<bool>`` ``vector<uint8_t>``
``vector<int16_t>`` ``short``
``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``
======================= ======================
"""
def __init__(self, pmap, g, key_type):
self.__map = pmap
self.__g = weakref.ref(g)
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
self.__key_type = key_type
self.__convert = _converter(self.value_type())
self.__register_map()
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()
def __key_trans(self, key):
if self.key_type() == "g":
return key._Graph__graph
else:
return key
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
def __register_map(self):
for g in [self.__g(), self.__base_g()]:
if g is not None:
g._Graph__known_properties[id(self)] = weakref.ref(self)
def __unregister_map(self):
for g in [self.__g(), self.__base_g()]:
if g is not None and id(self) in g._Graph__known_properties:
del g._Graph__known_properties[id(self)]
def __del__(self):
self.__unregister_map()
def __getitem__(self, k):
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"
raise ValueError("invalid key '%s' of type '%s', wanted type: %s"
% (str(k), str(type(k)), kt) )
def __setitem__(self, k, v):
key = self.__key_trans(k)
try:
try:
self.__map[key] = v
except TypeError:
self.__map[key] = self.__convert(v)
except ArgumentError:
try:
key = self.__key_convert(key)
try:
self.__map[key] = v
except TypeError:
self.__map[key] = self.__convert(v)
except ArgumentError:
if self.key_type() == "e":
kt = "Edge"
elif self.key_type() == "v":
kt = "Vertex"
else:
kt = "Graph"
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))
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]
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"
g = self.get_graph()
if g is None:
g = "a non-existent graph"
else:
g = "Graph 0x%x" % id(g)
return ("<PropertyMap object with key type '%s' and value type '%s',"
+ " for %s, at 0x%x>") % (k, self.value_type(), g, id(self))
[docs]
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)
def __copy__(self):
return self.copy()
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
[docs]
def get_graph(self):
"""Get the graph class to which the map refers."""
g = self.__g()
if g is None:
g = self.__base_g()
return g
[docs]
def key_type(self):
"""Return the key type of the map. Either 'g', 'v' or 'e'."""
return self.__key_type
[docs]
def value_type(self):
"""Return the value type of the map."""
return self.__map.value_type()
[docs]
def python_value_type(self):
"""Return the python-compatible value type of the map."""
return _python_type(self.__map.value_type())
[docs]
def get_array(self):
"""Get a :class:`~graph_tool.PropertyArray` with the property values.
.. 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
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.
.. warning::
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:
raise ValueError("Cannot get array for value type: " + self.value_type())
return PropertyArray(a, prop_map=self)
def _get_data(self):
g = self.get_graph()
if g is None:
raise ValueError("Cannot get array for an orphaned property map")
if self.__key_type == 'v':
n = g._Graph__graph.get_num_vertices(False)
elif self.__key_type == 'e':
n = g.edge_index_range
else:
n = 1
a = self.__map.get_array(n)
return a
def __set_array(self, v):
a = self.get_array()
a[:] = v
a = property(get_array, __set_array,
doc=r"""Shortcut to the :meth:`~PropertyMap.get_array` method
as an attribute. This makes assignments more convenient, e.g.:
>>> g = gt.Graph()
>>> g.add_vertex(10)
<...>
>>> 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()
N = g.num_vertices()
elif self.__key_type == 'e':
filt = g.get_edge_filter()
if g.get_vertex_filter()[0] is not None:
filt = (g.new_edge_property("bool"), filt[1])
libcore.mark_edges(g._Graph__graph, _prop("e", g, filt[0]))
if filt[1]:
filt[0].a = numpy.logical_not(filt[0].a)
elif g.edge_index_range != g.num_edges():
filt = (g.new_edge_property("bool"), False)
libcore.mark_edges(g._Graph__graph, _prop("e", g, filt[0]))
if filt[0] is None:
N = g.edge_index_range
else:
N = (filt[0].a == (not filt[1])).sum()
if get:
if a is None:
return a
if filt[0] is None:
return a
return a[filt[0].a == (not filt[1])][:N]
else:
if a is None:
return
if filt[0] is None:
try:
a[:] = v
except ValueError:
a[:] = v[:len(a)]
else:
m = filt[0].a == (not filt[1])
m *= m.cumsum() <= N
try:
a[m] = v
except ValueError:
a[m] = v[:len(m)][m]
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()
if g.get_vertex_filter()[0] is not None:
filt = (g.new_edge_property("bool"), filt[1])
libcore.mark_edges(g._Graph__graph, _prop("e", g, filt[0]))
if filt[1]:
filt[0].a = 1 - filt[0].a
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.""")
[docs]
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()
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)
if self.key_type() == "v":
N = g.num_vertices()
idx = g.vertex_index
filt = vfilt
else:
N = g.edge_index_range
idx = g.edge_index
filt = efilt
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)):
a[i][idx[v]] = p[i][v]
if len(a) == 1:
a = a[0]
a = numpy.array(a)
if vfilt[0] is not None:
a = a[filt[0].a[:a.shape[0]] == (not filt[1])]
return a
try:
return numpy.array(self.fa)
except ValueError:
p = ungroup_vector_property(self, pos)
return numpy.array([x.fa for x in p])
[docs]
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:
iters = sorted(g.edges(), key=lambda e: g.edge_index[e])
for j, v in enumerate(iters):
self[v] = a[j]
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)
[docs]
def is_writable(self):
"""Return True if the property is writable."""
return self.__map.is_writable()
[docs]
def set_value(self, val):
"""Sets all values in the property map to ``val``."""
g = self.get_graph()
if self.key_type() == "v":
libcore.set_vertex_property(g._Graph__graph, _prop("v", g, self), val)
elif self.key_type() == "e":
libcore.set_edge_property(g._Graph__graph, _prop("e", g, self), val)
else:
self[g] = val
[docs]
def reserve(self, size):
"""Reserve enough space for ``size`` elements in underlying container. If the
original size is already equal or larger, nothing will happen."""
self.__map.reserve(size)
[docs]
def resize(self, size):
"""Resize the underlying container to contain exactly ``size`` elements."""
self.__map.resize(size)
[docs]
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":
size = g.edge_index_range
else:
size = 1
self.__map.resize(size)
self.__map.shrink_to_fit()
def __getstate__(self):
g = self.get_graph()
if g is None:
raise ValueError("cannot pickle orphaned property map")
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":
vals = [self.__convert(self[v]) for v in u.vertices()]
elif key_type == "e":
vals = [self.__convert(self[e]) for e in u.edges()]
else:
vals = self.__convert(self[g])
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):
conv_pickle_state(state)
g = state["g"]
key_type = _str_decode(state["key_type"])
value_type = _str_decode(state["value_type"])
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":
pmap = u.new_vertex_property(value_type, vals=vals)
elif key_type == "e":
pmap = u.new_edge_property(value_type, vals=vals)
else:
pmap = u.new_graph_property(value_type)
pmap[u] = vals
pmap = g.own_property(pmap)
self.__map = pmap.__map
self.__g = pmap.__g
self.__base_g = pmap.__base_g
self.__key_type = key_type
self.__convert = _converter(self.value_type())
self.__register_map()
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):
scalars = ["bool", "int16_t", "int32_t", "int64_t", "unsigned long",
"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):
scalars = ["bool", "int16_t", "int32_t", "int64_t", "unsigned long",
"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 "")))
[docs]
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``.
Examples
--------
>>> from numpy.random import seed, randint
>>> from numpy import array
>>> seed(42)
>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> props = [g.new_vertex_property("int") for i in range(3)]
>>> for i in range(3):
... props[i].a = randint(0, 100, g.num_vertices())
>>> gprop = gt.group_vector_property(props)
>>> print(gprop[g.vertex(0)].a)
[51 25 8]
>>> print(array([p[g.vertex(0)] for p in props]))
[51 25 8]
"""
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":
u = GraphView(g, directed=True, reversed=g.is_reversed(),
skip_properties=True)
libcore.group_vector_property(u._Graph__graph, _prop(k, g, vprop),
_prop(k, g, p),
i if pos == None else pos[i],
k == 'e')
else:
vprop[g][i if pos is None else pos[i]] = p[g]
return vprop
[docs]
def ungroup_vector_property(vprop, pos, props=None):
"""Ungroup vector property map ``vprop`` into a list of individual property maps.
Parameters
----------
vprop : :class:`~graph_tool.PropertyMap`
Vector property map to be ungrouped.
pos : list of ints
A list of indexes corresponding to where each element of ``vprop``
should be inserted into the ungrouped list.
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``.
Examples
--------
>>> from numpy.random import seed, randint
>>> from numpy import array
>>> seed(42)
>>> gt.seed_rng(42)
>>> 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])
>>> print(prop[g.vertex(0)].a)
[51 92 14]
>>> print(array([p[g.vertex(0)] for p in uprops]))
[51 92 14]
"""
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':
u = GraphView(g, directed=True, reversed=g.is_reversed(),
skip_properties=True)
libcore.ungroup_vector_property(u._Graph__graph,
_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
[docs]
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,
... lambda x: len(x))
>>> 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')
[docs]
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
-------
None : ``None``
Examples
--------
>>> from numpy.random import seed
>>> seed(42)
>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> prop = g.vertex_index.copy("int32_t")
>>> gt.infect_vertex_property(g, prop, [10])
>>> print(sum(prop.a == 10))
4
"""
libcore.infect_vertex_property(g._Graph__graph, _prop("v", g, prop),
vals)
@_limit_args({"endpoint": ["source", "target"]})
[docs]
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`.
Parameters
----------
prop : :class:`~graph_tool.PropertyMap`
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.
Returns
-------
eprop : :class:`~graph_tool.PropertyMap`
Propagated edge property.
Examples
--------
>>> gt.seed_rng(42)
>>> g = gt.random_graph(100, lambda: (3, 3))
>>> 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]
"""
val_t = prop.value_type()
if val_t == "unsigned long" or val_t == "unsigned int":
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': " +
val_t)
libcore.edge_endpoint(g._Graph__graph, _prop("v", g, prop),
_prop("e", g, eprop), endpoint)
return eprop
@_limit_args({"direction": ["in", "out"], "op": ["sum", "prod", "min", "max"]})
[docs]
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`.
Parameters
----------
direction : `"in"` or `"out"`
Direction of the incident edges.
op : `"sum"`, `"prod"`, `"min"` or `"max"`
Operation performed on incident edges.
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))
>>> vsum = gt.incident_edges_op(g, "out", "sum", g.edge_index)
>>> 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]
"""
val_t = eprop.value_type()
if val_t == "unsigned long" or val_t == "unsigned int":
val_t = "int64_t"
if vprop is None:
vprop = g.new_vertex_property(val_t)
orig_vprop = vprop
if vprop.value_type != val_t:
vprop = g.new_vertex_property(val_t)
if direction == "in" and not g.is_directed():
return orig_vprop
if direction == "in":
g = GraphView(g, reversed=True, skip_properties=True)
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
@_limit_args({"htype": ["int8_t", "int32_t", "int64_t"]})
[docs]
def perfect_prop_hash(props, htype="int32_t"):
"""Given a list of property maps `props` of the same type, a derived list of
property maps with integral type `htype` is returned, where each value is
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()
g = GraphView(g, directed=True, skip_properties=True)
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()
g = GraphView(g, directed=True, skip_properties=True)
libcore.perfect_vhash(g._Graph__graph, _prop('v', g, vprop),
_prop('v', g, hvprop), hdict)
return hprops
class InternalPropertyDict(dict):
"""Internal dictionary of property maps. It only accepts string keys and
:class:`PropertyMap` instances as values."""
def __init__(self, g):
self.g = weakref.ref(g)
dict.__init__(self)
@_require("key", tuple)
@_require("val", PropertyMap)
def __setitem__(self, key, val):
t, k = key
self.__set_property(t, k, val)
@_limit_args({"t": ["v", "e", "g"]})
@_require("key", str)
def __set_property(self, t, key, v):
dict.__setitem__(self, (t, key), v)
@_require("key", tuple)
def __delitem__(self, key):
dict.__delitem__(self, key)
@_require("key", tuple)
def setdefault(self, key, default=None):
if not isinstance(default, PropertyMap):
raise ValueError("default parameter must be of type PropertyMap, not: %s" % type(default))
v = self.get(key, None)
if v is None:
self[key] = v = default
return v
if sys.version_info < (3,):
def update(self, *args, **kwargs):
temp = dict(*args, **kwargs)
for k, v in temp.iteritems():
self[k] = v
else:
def update(self, *args, **kwargs):
temp = dict(*args, **kwargs)
for k, v in temp.items():
self[k] = v
class PropertyDict(object):
"""Wrapper for the dict of vertex, graph or edge properties, which sets the
value on the property map when changed in the dict.
For convenience, the dictionary entries are also available via attributes.
"""
def __init__(self, properties, t):
super(PropertyDict, self).__setattr__("properties", properties)
super(PropertyDict, self).__setattr__("t", t)
def __contains__(self, key):
return (self.t, key) in self.properties
def __getitem__(self, key):
if self.t == "g":
p = self.properties[(self.t, key)]
return p[p.get_graph()]
return self.properties[(self.t, key)]
def get(self, key, default=None):
try:
return self[key]
except KeyError:
return default
def __setitem__(self, key, val):
k = (self.t, key)
if self.t == "g" and not isinstance(val, PropertyMap) and k in self.properties:
p = self.properties[k]
p[p.get_graph()] = val
else:
if not isinstance(val, PropertyMap):
raise ValueError("value must be of type PropertyMap, not %s" % str(type(val)))
if val.key_type() != self.t:
def name(t):
if t == "e":
return "Edge"
if t == "v":
return "Vertex"
if t == "g":
return "Graph"
raise ValueError("wanted a property map of type '%s', not '%s'" %
(name(self.t), name(val.key_type())))
self.properties[k] = val
def setdefault(self, key, default=None):
self.properties.setdefault((self.t, key), default)
if sys.version_info < (3,):
def update(self, *args, **kwargs):
temp = dict(*args, **kwargs)
for k, v in temp.iteritems():
self.properties[(self.t, k)] = v
else:
def update(self, *args, **kwargs):
temp = dict(*args, **kwargs)
for k, v in temp.items():
self.properties[(self.t, k)] = v
def __delitem__(self, key):
del self.properties[(self.t, key)]
def clear(self):
keys = []
for k in self.properties.items():
if k[0] == self.t:
keys.append(k[1])
for k in keys:
del self.properties[(self.t, k)]
def __len__(self):
count = 0
for k in self.properties.iterkeys():
if k[0] == self.t:
count += 1
return count
def __iter__(self):
return self.iterkeys()
def iterkeys(self):
for k in self.properties.iterkeys():
if k[0] == self.t:
yield k[0]
def items(self):
for k, v in self.properties.items():
if k[0] == self.t:
yield k[1], v
if sys.version_info < (3,):
def has_key(self, key):
return self.properties.has_key((self.t, key))
def iteritems(self):
for k, v in self.properties.iteritems():
if k[0] == self.t:
yield k[1], v
def itervalues(self):
for k, v in self.properties.iteritems():
if k[0] == self.t:
yield v
def keys(self):
return [k[1] for k in self.properties.keys() if k[0] == self.t]
if sys.version_info < (3,):
def values(self):
return [v for k, v in self.properties.iteritems() if k[0] == self.t]
def __repr__(self):
temp = dict([(k[1], v) for k, v in self.properties.iteritems() if k[0] == self.t])
return repr(temp)
else:
def values(self):
return [v for k, v in self.properties.items() if k[0] == self.t]
def __repr__(self):
temp = dict([(k[1], v) for k, v in self.properties.items() if k[0] == self.t])
return repr(temp)
def __getattr__(self, attr):
return self.__getitem__(attr)
def __setattr__(self, attr, val):
return self.__setitem__(attr, val)
################################################################################
# Graph class
# The main graph interface
################################################################################
from .libgraph_tool_core import Vertex, EdgeBase, Vector_bool, Vector_int16_t, \
Vector_int32_t, Vector_int64_t, Vector_double, Vector_long_double, \
Vector_string, Vector_size_t, new_vertex_property, new_edge_property, \
new_graph_property
[docs]
class Graph(object):
"""Generic multigraph class.
This class encapsulates either a directed multigraph (default or if
``directed=True``) or an undirected multigraph (if ``directed=False``),
with optional internal edge, vertex or graph properties.
If ``g`` is specified, the graph (and its internal properties) will be
copied.
If ``prune`` is set to ``True``, and ``g`` is specified, only the filtered
graph will be copied, and the new graph object will not be
filtered. Optionally, a tuple of three booleans can be passed as value to
``prune``, to specify a different behavior to vertex, edge, and reversal
filters, respectively.
If ``vorder`` is specified, it should correspond to a vertex
:class:`~graph_tool.PropertyMap` specifying the ordering of the vertices in
the copied graph.
The graph is implemented as an `adjacency list`_, where both vertex and edge
lists are C++ STL vectors.
.. _adjacency list: http://en.wikipedia.org/wiki/Adjacency_list
"""
def __init__(self, g=None, directed=True, prune=False, vorder=None):
self.__properties = InternalPropertyDict(self)
self.__graph_properties = PropertyDict(self.__properties, "g")
self.__vertex_properties = PropertyDict(self.__properties, "v")
self.__edge_properties = PropertyDict(self.__properties, "e")
self.__known_properties = {}
self.__filter_state = {"reversed": False,
"edge_filter": (None, False),
"vertex_filter": (None, False),
"directed": True}
if g is None:
self.__graph = libcore.GraphInterface()
self.set_directed(directed)
# internal index maps
self.__vertex_index = \
PropertyMap(libcore.get_vertex_index(self.__graph), self, "v")
self.__edge_index = \
PropertyMap(libcore.get_edge_index(self.__graph), self, "e")
else:
if isinstance(prune, bool):
vprune = eprune = rprune = prune
else:
vprune, eprune, rprune = prune
if not (vprune or eprune or rprune):
gv = GraphView(g, skip_vfilt=True,
skip_efilt=True)
if not rprune:
gv.set_reversed(False)
else:
gv = g
# The filters may or may not not be in the internal property maps
vfilt = g.get_vertex_filter()[0]
efilt = g.get_edge_filter()[0]
if (vorder is None and ((vfilt is None and efilt is None) or
(not vprune and not eprune))):
# Do a simpler, faster copy.
self.__graph = libcore.GraphInterface(gv.__graph, False,
[], [], None)
# internal index maps
self.__vertex_index = \
PropertyMap(libcore.get_vertex_index(self.__graph), self, "v")
self.__edge_index = \
PropertyMap(libcore.get_edge_index(self.__graph), self, "e")
nvfilt = nefilt = None
for k, m in g.properties.items():
nmap = self.copy_property(m, g=gv)
self.properties[k] = nmap
if m is vfilt:
nvfilt = nmap
if m is efilt:
nefilt = nmap
if vfilt is not None:
if nvfilt is None:
nvfilt = self.copy_property(vfilt, g=gv)
if efilt is not None:
if nefilt is None:
nefilt = self.copy_property(efilt, g=gv)
self.set_filters(nefilt, nvfilt,
inverted_edges=g.get_edge_filter()[1],
inverted_vertices=g.get_vertex_filter()[1])
else:
# Copy all internal properties from original graph.
vprops = []
eprops = []
ef_pos = vf_pos = None
for k, m in gv.vertex_properties.items():
if not m.is_writable():
m = m.copy("int32_t")
if not vprune and m is vfilt:
vf_pos = len(vprops)
vprops.append([_prop("v", gv, m), libcore.any()])
for k, m in gv.edge_properties.items():
if not m.is_writable():
m = m.copy("int32_t")
if not eprune and m is efilt:
ef_pos = len(eprops)
eprops.append([_prop("e", gv, m), libcore.any()])
if not vprune and vf_pos is None and vfilt is not None:
vf_pos = len(vprops)
vprops.append([_prop("v", gv, vfilt), libcore.any()])
if not eprune and ef_pos is None and efilt is not None:
ef_pos = len(eprops)
eprops.append([_prop("e", gv, efilt), libcore.any()])
# The vertex ordering
if vorder is None:
vorder = gv.new_vertex_property("int")
vorder.fa = numpy.arange(gv.num_vertices())
# The actual copying of the graph and property maps
self.__graph = libcore.GraphInterface(gv.__graph, False,
vprops,
eprops,
_prop("v", gv, vorder))
# internal index maps
self.__vertex_index = \
PropertyMap(libcore.get_vertex_index(self.__graph), self, "v")
self.__edge_index = \
PropertyMap(libcore.get_edge_index(self.__graph), self, "e")
# Put the copied properties in the internal dictionary
for i, (k, m) in enumerate(gv.vertex_properties.items()):
pmap = new_vertex_property(m.value_type() if m.is_writable() else "int32_t",
self.__graph.get_vertex_index(),
vprops[i][1])
self.vertex_properties[k] = PropertyMap(pmap, self, "v")
for i, (k, m) in enumerate(gv.edge_properties.items()):
pmap = new_edge_property(m.value_type() if m.is_writable() else "int32_t",
self.__graph.get_edge_index(),
eprops[i][1])
self.edge_properties[k] = PropertyMap(pmap, self, "e")
for k, v in gv.graph_properties.items():
new_p = self.new_graph_property(v.value_type())
new_p[self] = v[gv]
self.graph_properties[k] = new_p
epmap = vpmap = None
if vf_pos is not None:
vpmap = new_vertex_property("bool",
self.__graph.get_vertex_index(),
vprops[vf_pos][1])
vpmap = PropertyMap(vpmap, self, "v")
if ef_pos is not None:
epmap = new_edge_property("bool",
self.__graph.get_edge_index(),
eprops[ef_pos][1])
epmap = PropertyMap(epmap, self, "e")
self.set_filters(epmap, vpmap,
inverted_edges=g.get_edge_filter()[1],
inverted_vertices=g.get_vertex_filter()[1])
if not rprune:
self.set_reversed(g.is_reversed())
# directedness is always a filter
self.set_directed(g.is_directed())
def _get_any(self):
return self.__graph.get_graph_view()
[docs]
def copy(self):
"""Return a deep copy of self. All :ref:`internal property maps <sec_internal_props>`
are also copied."""
return Graph(self)
def __copy__(self):
return self.copy()
def __deepcopy__(self, memo):
g = self.copy()
for k, prop in [x for x in g.properties
if x[1].value_type == "python::object"]:
g.properties[k] = copy.deepcopy(prop)
def __repr__(self):
# provide more useful information
d = "directed" if self.is_directed() else "undirected"
fr = ", reversed" if self.is_reversed() and self.is_directed() else ""
f = ""
if self.get_edge_filter()[0] is not None:
f += ", edges filtered by %s" % (str(self.get_edge_filter()))
if self.get_vertex_filter()[0] is not None:
f += ", vertices filtered by %s" % (str(self.get_vertex_filter()))
n = self.num_vertices()
e = self.num_edges()
return "<%s object, %s%s, with %d %s and %d edge%s%s at 0x%x>"\
% (type(self).__name__, d, fr, n,
"vertex" if n == 1 else "vertices", e, "" if e == 1 else "s",
f, id(self))
# Graph access
# ============
[docs]
def vertices(self):
"""Return an :meth:`iterator <iterator.__iter__>` over the vertices.
.. note::
The order of the vertices traversed by the iterator **always**
corresponds to the vertex index ordering, as given by the
:attr:`~graph_tool.Graph.vertex_index` property map.
Examples
--------
>>> g = gt.Graph()
>>> vlist = list(g.add_vertex(5))
>>> vlist2 = []
>>> for v in g.vertices():
... vlist2.append(v)
...
>>> assert(vlist == vlist2)
"""
return libcore.get_vertices(self.__graph)
[docs]
def vertex(self, i, use_index=True, add_missing=False):
"""Return the vertex with index ``i``. If ``use_index=False``, the
``i``-th vertex is returned (which can differ from the vertex with index
``i`` in case of filtered graphs).
If ``add_missing == True``, and the vertex does not exist in the graph,
the necessary number of missing vertices are inserted, and the new
vertex is returned.
"""
v = libcore.get_vertex(self.__graph, int(i), use_index)
if not v.is_valid():
if add_missing:
self.add_vertex(int(i) - self.num_vertices(use_index) + 1)
return self.vertex(int(i), use_index)
raise ValueError("Invalid vertex index: %d" % int(i))
return v
[docs]
def edge(self, s, t, all_edges=False, add_missing=False):
"""Return the edge from vertex ``s`` to ``t``, if it exists. If
``all_edges=True`` then a list is returned with all the parallel edges
from ``s`` to ``t``, otherwise only one edge is returned.
If ``add_missing == True``, a new edge is created and returned, if none
currently exists.
This operation will take :math:`O(min(k(s), k(t)))` time, where
:math:`k(s)` and :math:`k(t)` are the out-degree and in-degree (or
out-degree if undirected) of vertices :math:`s` and :math:`t`.
"""
s = self.vertex(int(s))
t = self.vertex(int(t))
edges = libcore.get_edge(self.__graph, int(s), int(t), all_edges)
if add_missing and len(edges) == 0:
edges.append(self.add_edge(s, t))
if all_edges:
return edges
elif len(edges) > 0:
return edges[0]
else:
return None
[docs]
def edges(self):
"""Return an :meth:`iterator <iterator.__iter__>` over the edges.
.. note::
The order of the edges traversed by the iterator **does not**
necessarily correspond to the edge index ordering, as given by the
:attr:`~graph_tool.Graph.edge_index` property map. This will only
happen after :meth:`~graph_tool.Graph.reindex_edges` is called, or in
certain situations such as just after a graph is loaded from a
file. However, further manipulation of the graph may destroy the
ordering.
"""
return libcore.get_edges(self.__graph)
[docs]
def add_vertex(self, n=1):
"""Add a vertex to the graph, and return it. If ``n != 1``, ``n``
vertices are inserted and an iterator over the new vertices is returned.
This operation is :math:`O(n)`.
"""
v = libcore.add_vertex(self.__graph, n)
if n == 1:
return v
else:
pos = self.num_vertices(True) - n
return (self.vertex(i) for i in range(pos, pos + n))
[docs]
def remove_vertex(self, vertex, fast=False):
r"""Remove a vertex from the graph. If ``vertex`` is an iterable, it
should correspond to a sequence of vertices to be removed.
.. note::
If the option ``fast == False`` is given, this operation is
:math:`O(V + E)` (this is the default). Otherwise it is
:math:`O(k + k_{\text{last}})`, where :math:`k` is the (total)
degree of the vertex being deleted, and :math:`k_{\text{last}}` is
the (total) degree of the vertex with the largest index.
.. warning::
This operation may invalidate vertex descriptors. Vertices are always
indexed contiguously in the range :math:`[0, N-1]`, hence vertex
descriptors with an index higher than ``vertex`` will be invalidated
after removal (if ``fast == False``, otherwise only descriptors
pointing to vertices with the largest index will be invalidated).
Because of this, the only safe way to remove more than one vertex at
once is to sort them in decreasing index order:
.. code::
# 'del_list' is a list of vertex descriptors
for v in reversed(sorted(del_list)):
g.remove_vertex(v)
Alternatively (and preferably), a list (or iterable) may be passed
directly as the ``vertex`` parameter, and the above is performed
internally (in C++).
.. warning::
If ``fast == True``, the vertex being deleted is 'swapped' with the
last vertex (i.e. with the largest index), which will in turn inherit
the index of the vertex being deleted. All property maps associated
with the graph will be properly updated, but the index ordering of
the graph will no longer be the same.
"""
back = self.__graph.get_num_vertices(False) - 1
is_iter = isinstance(vertex, collections.Iterable)
if is_iter:
try:
vs = numpy.asarray(vertex, dtype="int64")
except TypeError:
vs = numpy.asarray([int(v) for v in vertex], dtype="int64")
if len(vs) == 0:
return
vs = numpy.sort(vs)[::-1]
vmax = vs[0]
if vs[0] > back:
raise ValueError("Vertex index %d is invalid" % vs[0])
else:
vmax = int(vertex)
# move / shift all known property maps
if vmax != back:
if not is_iter:
vs = numpy.asarray((vertex,), dtype="int64")
for pmap in self.__known_properties.values():
if pmap() is not None and pmap().key_type() == "v" and pmap().is_writable():
if fast:
self.__graph.move_vertex_property(_prop("v", self, pmap()), vs)
else:
self.__graph.shift_vertex_property(_prop("v", self, pmap()), vs)
if is_iter:
libcore.remove_vertex_array(self.__graph, vs, fast)
else:
libcore.remove_vertex(self.__graph, vertex, fast)
[docs]
def clear_vertex(self, vertex):
"""Remove all in and out-edges from the given vertex."""
libcore.clear_vertex(self.__graph, int(vertex))
[docs]
def add_edge(self, source, target, add_missing=True):
"""Add a new edge from ``source`` to ``target`` to the graph, and return
it. This operation is :math:`O(1)`.
If ``add_missing == True``, the source and target vertices are included
in the graph if they don't yet exist.
"""
e = libcore.add_edge(self.__graph,
self.vertex(int(source), add_missing=add_missing),
self.vertex(int(target), add_missing=add_missing))
return e
[docs]
def remove_edge(self, edge):
r"""Remove an edge from the graph.
.. note::
This operation is normally :math:`O(k_s + k_t)`, where :math:`k_s`
and :math:`k_s` are the total degrees of the source and target
vertices, respectively. However, if :meth:`~Graph.set_fast_edge_removal`
is set to `True`, this operation becomes :math:`O(1)`.
.. warning::
The relative ordering of the remaining edges in the graph is kept
unchanged, unless :meth:`~Graph.set_fast_edge_removal` is set to
`True`, in which case it can change.
"""
return libcore.remove_edge(self.__graph, edge)
[docs]
def add_edge_list(self, edge_list, hashed=False, string_vals=False,
eprops=None):
"""Add a list of edges to the graph, given by ``edge_list``, which can
be an iterator of ``(source, target)`` pairs where both ``source`` and
``target`` are vertex indexes, or a :class:`~numpy.ndarray` of shape
``(E,2)``, where ``E`` is the number of edges, and each line specifies a
``(source, target)`` pair. If the list references vertices which do not
exist in the graph, they will be created.
Optionally, if ``hashed == True``, the vertex values in the edge list
are not assumed to correspond to vertex indices directly. In this case
they will be mapped to vertex indices according to the order in which
they are encountered, and a vertex property map with the vertex values
is returned. If ``string_vals == True``, the algorithm assumes that the
vertex values are strings. Otherwise, they will be assumed to be numeric
if ``edge_list`` is a :class:`~numpy.ndarray`, or arbitrary python
objects if it is not.
If given, ``eprops`` specifies edge property maps that will be filled
with the remaining values at each row, if there are more than two.
"""
if eprops is None:
eprops = ()
else:
convert = [_converter(x.value_type()) for x in eprops]
eprops = [_prop("e", self, x) for x in eprops]
if not isinstance(edge_list, numpy.ndarray):
def wrap(elist):
for row in elist:
yield (val if i < 2 else convert[i - 2](val)
for (i, val) in enumerate(row))
edge_list = wrap(edge_list)
if not hashed:
if isinstance(edge_list, numpy.ndarray):
libcore.add_edge_list(self.__graph, edge_list, eprops)
else:
libcore.add_edge_list_iter(self.__graph, edge_list, eprops)
else:
if isinstance(edge_list, numpy.ndarray):
vprop = self.new_vertex_property(_gt_type(edge_list.dtype))
elif string_vals:
vprop = self.new_vertex_property("string")
else:
vprop = self.new_vertex_property("object")
libcore.add_edge_list_hashed(self.__graph, edge_list,
_prop("v", self, vprop),
string_vals, eprops)
return vprop
[docs]
def set_fast_edge_removal(self, fast=True):
r"""If ``fast == True`` the fast :math:`O(1)` removal of edges will be
enabled. This requires an additional data structure of size :math:`O(E)`
to be kept at all times. If ``fast == False``, this data structure is
destroyed."""
self.__graph.set_keep_epos(fast)
[docs]
def get_fast_edge_removal(self):
r"""Return whether the fast :math:`O(1)` removal of edges is currently
enabled."""
return self.__graph.get_keep_epos()
[docs]
def clear(self):
"""Remove all vertices and edges from the graph."""
self.__graph.clear()
[docs]
def clear_edges(self):
"""Remove all edges from the graph."""
self.__graph.clear_edges()
# Internal property maps
# ======================
properties = property(lambda self: self.__properties,
doc=
"""Dictionary of internal properties. Keys must always be a tuple, where the
first element if a string from the set {'v', 'e', 'g'}, representing a
vertex, edge or graph property, respectively, and the second element is the
name of the property map.
Examples
--------
>>> g = gt.Graph()
>>> g.properties[("e", "foo")] = g.new_edge_property("vector<double>")
>>> del g.properties[("e", "foo")]
""")
# vertex properties
vertex_properties = property(lambda self: self.__vertex_properties,
doc="Dictionary of internal vertex properties. The keys are the property names.")
vp = property(lambda self: self.__vertex_properties,
doc="Alias to :attr:`~Graph.vertex_properties`.")
# edge properties
edge_properties = property(lambda self: self.__edge_properties,
doc="Dictionary of internal edge properties. The keys are the property names.")
ep = property(lambda self: self.__edge_properties,
doc="Alias to :attr:`~Graph.edge_properties`.")
# graph properties
graph_properties = property(lambda self: self.__graph_properties,
doc="Dictionary of internal graph properties. The keys are the property names.")
gp = property(lambda self: self.__graph_properties,
doc="Alias to :attr:`~Graph.graph_properties`.")
def own_property(self, prop):
"""Return a version of the property map 'prop' (possibly belonging to
another graph) which is owned by the current graph."""
return PropertyMap(prop._PropertyMap__map, self, prop.key_type())
[docs]
def list_properties(self):
"""Print a list of all internal properties.
Examples
--------
>>> g = gt.Graph()
>>> g.properties[("e", "foo")] = g.new_edge_property("vector<double>")
>>> g.vertex_properties["foo"] = g.new_vertex_property("double")
>>> g.vertex_properties["bar"] = g.new_vertex_property("python::object")
>>> g.graph_properties["gnat"] = g.new_graph_property("string", "hi there!")
>>> g.list_properties()
gnat (graph) (type: string, val: hi there!)
bar (vertex) (type: python::object)
foo (vertex) (type: double)
foo (edge) (type: vector<double>)
"""
if len(self.__properties) == 0:
return
w = max([len(x[0]) for x in list(self.__properties.keys())]) + 4
w = w if w > 14 else 14
for k, v in sorted(self.graph_properties.items(), key=lambda k: k[0]):
pref="%%-%ds (graph) (type: %%s, val: " % w % (k, v.value_type())
val = str(v[self])
if len(val) > 1000:
val = val[:1000] + "..."
tw = terminal_size()[0]
val = textwrap.fill(val,
width=max(tw - len(pref), 1))
val = val.replace("\n", "\n" + " " * len(pref))
print("%s%s)" % (pref, val))
for k, v in sorted(self.vertex_properties.items(), key=lambda k: k[0]):
print("%%-%ds (vertex) (type: %%s)" % w % (k, v.value_type()))
for k, v in sorted(self.edge_properties.items(), key=lambda k: k[0]):
print("%%-%ds (edge) (type: %%s)" % w % (k, v.value_type()))
# index properties
def _get_vertex_index(self):
return self.__vertex_index
vertex_index = property(_get_vertex_index,
doc="""Vertex index map.
It maps for each vertex in the graph an unique
integer in the range [0, :meth:`~graph_tool.Graph.num_vertices` - 1].
.. note::
Like :attr:`~graph_tool.Graph.edge_index`, this
is a special instance of a :class:`~graph_tool.PropertyMap`
class, which is **immutable**, and cannot be
accessed as an array.""")
def _get_edge_index(self):
return self.__edge_index
edge_index = property(_get_edge_index, doc="""Edge index map.
It maps for each edge in the graph an unique
integer.
.. note::
Like :attr:`~graph_tool.Graph.vertex_index`, this
is a special instance of a :class:`~graph_tool.PropertyMap`
class, which is **immutable**, and cannot be
accessed as an array.
Additionally, the indexes may not necessarily
lie in the range [0, :meth:`~graph_tool.Graph.num_edges` - 1].
However this will always happen whenever no
edges are deleted from the graph.""")
def _get_edge_index_range(self):
return self.__graph.get_edge_index_range()
edge_index_range = property(_get_edge_index_range,
doc="The size of the range of edge indexes.")
[docs]
def reindex_edges(self):
"""
Reset the edge indexes so that they lie in the [0, :meth:`~graph_tool.Graph.num_edges` - 1]
range. The index ordering will be compatible with the sequence returned
by the :meth:`~graph_tool.Graph.edges` function.
.. WARNING::
Calling this function will invalidate all existing edge property
maps, if the index ordering is modified! The property maps will still
be usable, but their contents will still be tied to the old indexes,
and thus may become scrambled.
"""
self.__graph.re_index_edges()
[docs]
def shrink_to_fit(self):
"""Force the physical capacity of the underlying containers to match the graph's
actual size, potentially freeing memory back to the system."""
self.__graph.shrink_to_fit()
# Property map creation
[docs]
def new_property(self, key_type, value_type, vals=None):
"""Create a new (uninitialized) vertex property map of key type
``key_type`` (``v``, ``e`` or ``g``), value type ``value_type``, and
return it. If provided, the values will be initialized by ``vals``,
which should be a sequence.
"""
if key_type == "v" or key_type == "vertex":
return self.new_vertex_property(value_type, vals)
if key_type == "e" or key_type == "edge":
return self.new_edge_property(value_type, vals)
if key_type == "g" or key_type == "graph":
return self.new_graph_property(value_type, vals)
raise ValueError("unknown key type: " + key_type)
[docs]
def new_vertex_property(self, value_type, vals=None, val=None):
"""Create a new vertex property map of type ``value_type``, and return it. If
provided, the values will be initialized by ``vals``, which should be
sequence or by ``val`` which should be a single value.
"""
prop = PropertyMap(new_vertex_property(_type_alias(value_type),
self.__graph.get_vertex_index(),
libcore.any()),
self, "v")
if vals is not None:
try:
prop.fa = vals
except ValueError:
for v, x in zip(self.vertices(), vals):
prop[v] = x
elif val is not None:
prop.set_value(val)
return prop
new_vp = _copy_func(new_vertex_property, "new_vp")
new_vp.__doc__ = "Alias to :func:`~graph_tool.Graph.new_vertex_property`."
[docs]
def new_edge_property(self, value_type, vals=None, val=None):
"""Create a new edge property map of type ``value_type``, and return it. If
provided, the values will be initialized by ``vals``, which should be
sequence or by ``val`` which should be a single value.
"""
prop = PropertyMap(new_edge_property(_c_str(_type_alias(value_type)),
self.__graph.get_edge_index(),
libcore.any()),
self, "e")
if vals is not None:
try:
prop.a = vals
except ValueError:
for e, x in zip(self.edges(), vals):
prop[e] = x
elif val is not None:
prop.set_value(val)
return prop
new_ep = _copy_func(new_edge_property, "new_ep")
new_ep.__doc__ = "Alias to :func:`~graph_tool.Graph.new_edge_property`."
[docs]
def new_graph_property(self, value_type, val=None):
"""Create a new graph property map of type ``value_type``, and return
it. If ``val`` is not None, the property is initialized to its value."""
prop = PropertyMap(new_graph_property(_c_str(_type_alias(value_type)),
self.__graph.get_graph_index(),
libcore.any()),
self, "g")
if val is not None:
prop[self] = val
return prop
new_gp = _copy_func(new_graph_property, "new_gp")
new_gp.__doc__ = "Alias to :func:`~graph_tool.Graph.new_graph_property`."
# property map copying
@_require("src", PropertyMap)
@_require("tgt", (PropertyMap, type(None)))
[docs]
def copy_property(self, src, tgt=None, value_type=None, g=None, full=True):
"""Copy contents of ``src`` property to ``tgt`` property. If ``tgt`` is None,
then a new property map of the same type (or with the type given by the
optional ``value_type`` parameter) is created, and returned. The
optional parameter ``g`` specifies the source graph to copy properties
from (defaults to self). 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).
"""
if tgt is None:
tgt = self.new_property(src.key_type(),
(src.value_type()
if value_type is None else value_type))
ret = tgt
else:
ret = None
if src.key_type() != tgt.key_type():
raise ValueError("source and target properties must have the same key type")
if g is None:
g = self
is_directed = g.is_directed()
efilt = g.get_edge_filter()
vfilt = g.get_vertex_filter()
if g is not self:
self_is_directed = self.is_directed()
self_efilt = self.get_edge_filter()
self_vfilt = self.get_vertex_filter()
try:
if full:
g.set_directed(True)
g.clear_filters()
if g is not self:
self.set_directed(True)
self.clear_filters()
if src.key_type() == "v":
if g.num_vertices() > self.num_vertices():
raise ValueError("graphs with incompatible sizes (%d, %d)" %
(g.num_vertices(), self.num_vertices()))
try:
self.__graph.copy_vertex_property(g.__graph,
_prop("v", g, src),
_prop("v", self, tgt))
except ValueError:
raise ValueError("property maps with the following types are"
" not convertible: %s, %s" %
(src.value_type(), tgt.value_type()))
elif src.key_type() == "e":
if g.num_edges() > self.num_edges():
raise ValueError("graphs with incompatible sizes (%d, %d)" %
(g.num_edges(), self.num_edges()))
try:
self.__graph.copy_edge_property(g.__graph,
_prop("e", g, src),
_prop("e", self, tgt))
except ValueError:
raise ValueError("property maps with the following types are"
" not convertible: %s, %s" %
(src.value_type(), tgt.value_type()))
else:
tgt[self] = src[g]
finally:
g.set_directed(is_directed)
g.set_edge_filter(efilt[0], efilt[1])
g.set_vertex_filter(vfilt[0], vfilt[1])
if g is not self:
self.set_directed(self_is_directed)
self.set_edge_filter(self_efilt[0], self_efilt[1])
self.set_vertex_filter(self_vfilt[0], self_vfilt[1])
return ret
# degree property map
@_limit_args({"deg": ["in", "out", "total"]})
[docs]
def degree_property_map(self, deg, weight=None):
"""Create and return a vertex property map containing the degree type
given by ``deg``, which can be any of ``"in"``, ``"out"``, or ``"total"``.
If provided, ``weight`` should be an edge :class:`~graph_tool.PropertyMap`
containing the edge weights which should be summed."""
pmap = self.__graph.degree_map(deg, _prop("e", self, weight))
return PropertyMap(pmap, self, "v")
# I/O operations
# ==============
def __get_file_format(self, file_name):
fmt = None
for f in ["gt", "graphml", "xml", "dot", "gml"]:
names = ["." + f, ".%s.gz" % f, ".%s.bz2" % f, ".%s.xz" % f]
for name in names:
if file_name.endswith(name):
fmt = f
break
if fmt is None:
raise ValueError("cannot determine file format of: " + file_name)
return fmt
[docs]
def load(self, file_name, fmt="auto", ignore_vp=None, ignore_ep=None,
ignore_gp=None):
"""Load graph from ``file_name`` (which can be either a string or a file-like
object). The format is guessed from ``file_name``, or can be specified
by ``fmt``, which can be either "gt", "graphml", "xml", "dot" or "gml".
(Note that "graphml" and "xml" are synonyms).
If provided, the parameters ``ignore_vp``, ``ignore_ep`` and
``ignore_gp``, should contain a list of property names (vertex, edge or
graph, respectively) which should be ignored when reading the file.
.. warning::
The only file formats which are capable of perfectly preserving the
internal property maps are "gt" and "graphml". Because of this,
they should be preferred over the other formats whenever possible.
"""
if isinstance(file_name, (str, unicode)):
file_name = os.path.expanduser(file_name)
f = open(file_name) # throw the appropriate exception, if not found
if fmt == 'auto' and isinstance(file_name, (str, unicode)):
fmt = self.__get_file_format(file_name)
elif fmt == "auto":
fmt = "gt"
if isinstance(file_name, (str, unicode)) and file_name.endswith(".xz"):
try:
import lzma
file_name = lzma.open(file_name, mode="rb")
except ImportError:
raise ValueError("lzma compression is only available in Python >= 3.3")
if fmt == "graphml":
fmt = "xml"
if ignore_vp is None:
ignore_vp = []
if ignore_ep is None:
ignore_ep = []
if ignore_gp is None:
ignore_gp = []
if isinstance(file_name, (str, unicode)):
props = self.__graph.read_from_file(_c_str(file_name), None,
_c_str(fmt), ignore_vp,
ignore_ep, ignore_gp)
else:
props = self.__graph.read_from_file("", file_name, _c_str(fmt),
ignore_vp, ignore_ep, ignore_gp)
for name, prop in props[0].items():
self.vertex_properties[name] = PropertyMap(prop, self, "v")
for name, prop in props[1].items():
self.edge_properties[name] = PropertyMap(prop, self, "e")
for name, prop in props[2].items():
self.graph_properties[name] = PropertyMap(prop, self, "g")
if "_Graph__save__vfilter" in self.graph_properties:
self.set_vertex_filter(self.vertex_properties["_Graph__save__vfilter"],
self.graph_properties["_Graph__save__vfilter"])
del self.vertex_properties["_Graph__save__vfilter"]
del self.graph_properties["_Graph__save__vfilter"]
if "_Graph__save__efilter" in self.graph_properties:
self.set_edge_filter(self.edge_properties["_Graph__save__efilter"],
self.graph_properties["_Graph__save__efilter"])
del self.edge_properties["_Graph__save__efilter"]
del self.graph_properties["_Graph__save__efilter"]
if "_Graph__reversed" in self.graph_properties:
self.set_reversed(True)
del self.graph_properties["_Graph__reversed"]
self.shrink_to_fit()
[docs]
def save(self, file_name, fmt="auto"):
"""Save graph to ``file_name`` (which can be either a string or a file-like
object). The format is guessed from the ``file_name``, or can be
specified by ``fmt``, which can be either "gt", "graphml", "xml", "dot"
or "gml". (Note that "graphml" and "xml" are synonyms).
.. warning::
The only file formats which are capable of perfectly preserving the
internal property maps are "gt" and "graphml". Because of this,
they should be preferred over the other formats whenever possible.
"""
u = GraphView(self, reversed=self.is_reversed(), skip_vfilt=True,
skip_efilt=True)
if self.get_vertex_filter()[0] is not None:
u.graph_properties["_Graph__save__vfilter"] = self.new_graph_property("bool")
u.vertex_properties["_Graph__save__vfilter"] = self.get_vertex_filter()[0]
u.graph_properties["_Graph__save__vfilter"] = self.get_vertex_filter()[1]
if self.get_edge_filter()[0] is not None:
u.graph_properties["_Graph__save__efilter"] = self.new_graph_property("bool")
u.edge_properties["_Graph__save__efilter"] = self.get_edge_filter()[0]
u.graph_properties["_Graph__save__efilter"] = self.get_edge_filter()[1]
if self.is_reversed():
u.graph_properties["_Graph__reversed"] = self.new_graph_property("bool")
u.graph_properties["_Graph__reversed"] = True
if isinstance(file_name, (str, unicode)):
file_name = os.path.expanduser(file_name)
if fmt == 'auto' and isinstance(file_name, (str, unicode)):
fmt = self.__get_file_format(file_name)
elif fmt == "auto":
fmt = "gt"
if fmt == "graphml":
fmt = "xml"
if isinstance(file_name, (str, unicode)) and file_name.endswith(".xz"):
try:
import lzma
file_name = lzma.open(file_name, mode="wb")
except ImportError:
raise ValueError("lzma compression is only available in Python >= 3.3")
props = [(_c_str(name[1]), prop._PropertyMap__map) for name, prop in \
u.__properties.items()]
if isinstance(file_name, (str, unicode)):
f = open(file_name, "w") # throw the appropriate exception, if
# unable to open
f.close()
u.__graph.write_to_file(_c_str(file_name), None, _c_str(fmt), props)
else:
u.__graph.write_to_file("", file_name, _c_str(fmt), props)
# Directedness
# ============
[docs]
def set_directed(self, is_directed):
"""Set the directedness of the graph."""
self.__graph.set_directed(is_directed)
[docs]
def is_directed(self):
"""Get the directedness of the graph."""
return self.__graph.get_directed()
# Reversedness
# ============
[docs]
def set_reversed(self, is_reversed):
"""Reverse the direction of the edges, if ``is_reversed`` is ``True``,
or maintain the original direction otherwise."""
self.__graph.set_reversed(is_reversed)
[docs]
def is_reversed(self):
"""Return ``True`` if the edges are reversed, and ``False`` otherwise.
"""
return self.__graph.get_reversed()
# Filtering
# =========
[docs]
def set_filters(self, eprop, vprop, inverted_edges=False, inverted_vertices=False):
"""Set the boolean properties for edge and vertex filters, respectively.
Only the vertices and edges with value different than ``True`` are kept in
the filtered graph. If either the ``inverted_edges`` or ``inverted_vertex``
options are supplied with the value ``True``, only the edges or vertices
with value ``False`` are kept. If any of the supplied property is ``None``,
an empty filter is constructed which allows all edges or vertices."""
if eprop is None and vprop is None:
return
if eprop is None:
eprop = self.new_edge_property("bool")
eprop.a = not inverted_edges
if vprop is None:
vprop = self.new_vertex_property("bool")
vprop.a = not inverted_vertices
self.__graph.set_vertex_filter_property(_prop("v", self, vprop),
inverted_vertices)
self.__filter_state["vertex_filter"] = (vprop, inverted_vertices)
self.__graph.set_edge_filter_property(_prop("e", self, eprop),
inverted_edges)
self.__filter_state["edge_filter"] = (eprop, inverted_edges)
[docs]
def set_vertex_filter(self, prop, inverted=False):
"""Set the vertex boolean filter property. Only the vertices with value
different than ``False`` are kept in the filtered graph. If the ``inverted``
option is supplied with value ``True``, only the vertices with value
``False`` are kept. If the supplied property is ``None``, the filter is
replaced by an uniform filter allowing all vertices."""
if prop is not None and prop.value_type() != "bool":
raise ValueError("filter property map must have 'bool' type")
vfilt = self.own_property(prop) if prop is not None else prop
efilt = None
eprop = self.get_edge_filter()
if eprop[0] is None and vfilt is not None:
efilt = self.new_edge_property("bool")
efilt.a = True
if eprop[0] is not None and vfilt is None:
vfilt = self.new_vertex_property("bool")
vfilt.a = not inverted
self.__graph.set_vertex_filter_property(_prop("v", self, vfilt),
inverted)
self.__filter_state["vertex_filter"] = (vfilt, inverted)
if efilt is not None:
self.set_edge_filter(efilt)
[docs]
def get_vertex_filter(self):
"""Return a tuple with the vertex filter property and bool value
indicating whether or not it is inverted."""
return self.__filter_state["vertex_filter"]
[docs]
def set_edge_filter(self, prop, inverted=False):
"""Set the edge boolean filter property. Only the edges with value
different than ``False`` are kept in the filtered graph. If the ``inverted``
option is supplied with value ``True``, only the edges with value ``False``
are kept. If the supplied property is ``None``, the filter is
replaced by an uniform filter allowing all edges."""
if prop is not None and prop.value_type() != "bool":
raise ValueError("filter property map must have 'bool' type")
efilt = self.own_property(prop) if prop is not None else prop
vfilt = None
vprop = self.get_vertex_filter()
if vprop[0] is None and efilt is not None:
vfilt = self.new_vertex_property("bool")
vfilt.a = True
if vprop[0] is not None and efilt is None:
efilt = self.new_edge_property("bool")
efilt.a = not inverted
self.__graph.set_edge_filter_property(_prop("e", self, efilt), inverted)
self.__filter_state["edge_filter"] = (efilt, inverted)
if vfilt is not None:
self.set_vertex_filter(vfilt)
[docs]
def get_edge_filter(self):
"""Return a tuple with the edge filter property and bool value
indicating whether or not it is inverted."""
return self.__filter_state["edge_filter"]
[docs]
def clear_filters(self):
"""Remove vertex and edge filters, and set the graph to the unfiltered
state."""
self.__graph.set_vertex_filter_property(_prop("v", self, None), False)
self.__filter_state["vertex_filter"] = (None, False)
self.__graph.set_edge_filter_property(_prop("e", self, None), False)
self.__filter_state["edge_filter"] = (None, False)
[docs]
def purge_vertices(self, in_place=False):
"""Remove all vertices of the graph which are currently being filtered out. This
operation is not reversible.
If the option ``in_place == True`` is given, the algorithm will remove
the filtered vertices and re-index all property maps which are tied with
the graph. This is a slow operation which has an :math:`O(V^2)`
complexity.
If ``in_place == False``, the graph and its vertex and edge property
maps are temporarily copied to a new unfiltered graph, which will
replace the contents of the original graph. This is a fast operation
with an :math:`O(V + E)` complexity. This is the default behaviour if no
option is given.
.. note :
The graph will remain in a filtered state after this operation, since
there might be edge filters present. To return the graph to an
unfiltered state, use :meth:`~graph_tool.Graph.clear_filters`.
"""
if in_place:
old_indexes = self.vertex_index.copy("int64_t")
self.__graph.purge_vertices(_prop("v", self, old_indexes))
self.set_vertex_filter(None)
for pmap in self.__known_properties.values():
if (pmap() is not None and pmap().key_type() == "v" and
pmap().is_writable() and
pmap() not in [self.vertex_index, self.edge_index]):
self.__graph.re_index_vertex_property(_prop("v", self, pmap()),
_prop("v", self, old_indexes))
else:
stamp = id(self)
pmaps = []
for pmap in self.__known_properties.values():
if (pmap() is not None and pmap().key_type() in ["v", "e"] and
pmap() not in [self.vertex_index, self.edge_index]):
pmaps.append(pmap())
pname = "__tmp_purge_vertices_%d_%d" % (stamp, id(pmaps[-1]))
self.properties[(pmaps[-1].key_type(), pname)] = pmaps[-1]
new_g = Graph(self, prune=(True, False, False))
self.__graph = new_g.__graph
self.set_vertex_filter(None)
for pmap in pmaps:
pname = "__tmp_purge_vertices_%d_%d" % (stamp, id(pmap))
new_pmap = new_g.properties[(pmap.key_type(), pname)]
pmap._PropertyMap__map = new_pmap._PropertyMap__map
del self.properties[(pmap.key_type(), pname)]
# update edge filter if set
efilt = self.get_edge_filter()
if efilt[0] is not None:
self.set_edge_filter(efilt[0], efilt[1])
[docs]
def purge_edges(self):
"""Remove all edges of the graph which are currently being filtered out. This
operation is not reversible.
.. note :
The graph will remain in a filtered state after this operation, since
there might be vertex filters present. To return the graph to an
unfiltered state, use :meth:`~graph_tool.Graph.clear_filters`.
"""
self.__graph.purge_edges()
self.set_edge_filter(None)
def get_filter_state(self):
"""Return a copy of the filter state of the graph."""
self.__filter_state["directed"] = self.is_directed()
self.__filter_state["reversed"] = self.is_reversed()
return copy.copy(self.__filter_state)
def set_filter_state(self, state):
"""Set the filter state of the graph."""
if libcore.graph_filtering_enabled():
self.set_vertex_filter(state["vertex_filter"][0],
state["vertex_filter"][1])
self.set_edge_filter(state["edge_filter"][0],
state["edge_filter"][1])
self.set_directed(state["directed"])
self.set_reversed(state["reversed"])
# Basic graph statistics
# ======================
[docs]
def num_vertices(self, ignore_filter=False):
"""Get the number of vertices.
If ``ignore_filter == True``, vertex filters are ignored.
.. note::
If the vertices are being filtered, and ``ignore_filter == False``,
this operation is :math:`O(V)`. Otherwise it is :math:`O(1)`.
"""
return self.__graph.get_num_vertices(not ignore_filter)
[docs]
def num_edges(self, ignore_filter=False):
"""Get the number of edges.
If ``ignore_filter == True``, edge filters are ignored.
.. note::
If the edges are being filtered, and ``ignore_filter == False``,
this operation is :math:`O(E)`. Otherwise it is :math:`O(1)`.
"""
return self.__graph.get_num_edges(not ignore_filter)
# Pickling support
# ================
def __getstate__(self):
state = dict()
sio = get_bytes_io()
self.save(sio, "gt")
state["blob"] = sio.getvalue()
return state
def __setstate__(self, state):
conv_pickle_state(state)
self.__init__()
blob = state["blob"]
if blob != "":
try:
try:
sio = get_bytes_io(blob)
self.load(sio, "gt")
except IOError:
sio = get_bytes_io(blob)
stream = gzip.GzipFile(fileobj=sio, mode="rb")
self.load(stream, "gt")
except IOError:
sio = get_bytes_io(blob)
stream = gzip.GzipFile(fileobj=sio, mode="rb")
self.load(stream, "xml")
[docs]
def load_graph(file_name, fmt="auto", ignore_vp=None, ignore_ep=None,
ignore_gp=None):
"""Load a graph from ``file_name`` (which can be either a string or a file-like object).
The format is guessed from ``file_name``, or can be specified by ``fmt``,
which can be either "gt", "graphml", "xml", "dot" or "gml". (Note that
"graphml" and "xml" are synonyms).
If provided, the parameters ``ignore_vp``, ``ignore_ep`` and
``ignore_gp``, should contain a list of property names (vertex, edge or
graph, respectively) which should be ignored when reading the file.
.. warning::
The only file formats which are capable of perfectly preserving the
internal property maps are "gt" and "graphml". Because of this,
they should be preferred over the other formats whenever possible.
"""
g = Graph()
g.load(file_name, fmt, ignore_vp, ignore_ep, ignore_gp)
return g
[docs]
class GraphView(Graph):
"""A view of selected vertices or edges of another graph.
This class uses shared data from another :class:`~graph_tool.Graph`
instance, but allows for local filtering of vertices and/or edges, edge
directionality or reversal. See :ref:`sec_graph_views` for more details and
examples.
The existence of a :class:`~graph_tool.GraphView` object does not affect the
original graph, except if the graph view is modified (addition or removal of
vertices or edges), in which case the modification is directly reflected in
the original graph (and vice-versa), since they both point to the same
underlying data. Because of this, instances of
:class:`~graph_tool.PropertyMap` can be used interchangeably with a graph
and its views.
The argument ``g`` must be an instance of a :class:`~graph_tool.Graph`
class. If specified, ``vfilt`` and ``efilt`` select which vertices and edges
are filtered, respectively. These parameters can either be a boolean-valued
:class:`~graph_tool.PropertyMap` or a :class:`~numpy.ndarray`, which specify
which vertices/edges are selected, or an unary function that returns
``True`` if a given vertex/edge is to be selected, or ``False`` otherwise.
The boolean parameter ``directed`` can be used to set the directionality of
the graph view. If ``directed == None``, the directionality is inherited
from ``g``.
If ``reversed == True``, the direction of the edges is reversed.
If ``vfilt`` or ``efilt`` is anything other than a
:class:`~graph_tool.PropertyMap` instance, the instantiation running time is
:math:`O(V)` and :math:`O(E)`, respectively. Otherwise, the running time is
:math:`O(1)`.
If either ``skip_properties``, ``skip_vfilt`` or ``skip_efilt`` is ``True``,
then the internal properties, vertex filter or edge filter of the original
graph are ignored, respectively.
"""
def __init__(self, g, vfilt=None, efilt=None, directed=None,
reversed=False, skip_properties=False, skip_vfilt=False,
skip_efilt=False):
self.__base = g if not isinstance(g, GraphView) else g.base
Graph.__init__(self)
# copy graph reference
self._Graph__graph = libcore.GraphInterface(g._Graph__graph, True,
[], [],
_prop("v", g, g.vertex_index))
if not skip_properties:
for k, v in g.properties.items():
self.properties[k] = self.own_property(v)
# set already existing filters
if not skip_efilt:
ef = list(g.get_edge_filter())
if ef[0] is not None:
ef[0] = ef[0].copy()
else:
ef = [None, False]
if not skip_vfilt:
vf = list(g.get_vertex_filter())
if vf[0] is not None:
vf[0] = vf[0].copy()
else:
vf = [None, False]
self.set_filters(ef[0], vf[0], ef[1], vf[1])
if efilt is not None:
if type(efilt) is not PropertyMap:
emap = self.new_edge_property("bool")
if isinstance(efilt, collections.Iterable):
emap.fa = efilt
else:
for e in g.edges():
emap[e] = efilt(e)
efilt = emap
efilt = self.own_property(efilt)
ef = self.get_edge_filter()
if ef[0] is not None:
if not ef[1]:
ef[0].fa = efilt.fa
else:
ef[0].fa = numpy.logical_not(efilt.fa)
self.set_edge_filter(ef[0], ef[1])
else:
self.set_edge_filter(efilt)
if vfilt is not None:
if type(vfilt) is not PropertyMap:
vmap = self.new_vertex_property("bool")
if isinstance(vfilt, collections.Iterable):
vmap.fa = vfilt
else:
for v in g.vertices():
vmap[v] = vfilt(v)
vfilt = vmap
vfilt = self.own_property(vfilt)
vf = self.get_vertex_filter()
if vf[0] is not None:
if not vf[1]:
vf[0].fa = vfilt.fa
else:
vf[0].fa = numpy.logical_not(vfilt.fa)
self.set_vertex_filter(vf[0], vf[1])
else:
self.set_vertex_filter(vfilt)
if directed is not None:
self.set_directed(directed)
if reversed:
self.set_reversed(not g.is_reversed())
def __get_base(self):
return self.__base
base = property(__get_base, doc="Base graph.")
# pickling support
def __getstate__(self):
return Graph.__getstate__(self)
def __setstate__(self, state):
g = Graph()
g.__setstate__(state)
self.__init__(g)
[docs]
def value_types():
"""Return a list of possible properties value types."""
return libcore.get_property_types()
# Vertex and Edge Types
# =====================
from .libgraph_tool_core import Vertex, Edge, VertexBase, EdgeBase
def _out_neighbours(self):
"""Return an iterator over the out-neighbours."""
for e in self.out_edges():
yield e.target()
def _in_neighbours(self):
"""Return an iterator over the in-neighbours."""
for e in self.in_edges():
yield e.source()
def _all_edges(self):
"""Return an iterator over all edges (both in or out)."""
for e in self.out_edges():
yield e
for e in self.in_edges():
yield e
def _all_neighbours(self):
"""Return an iterator over all neighbours (both in or out)."""
for v in self.out_neighbours():
yield v
for v in self.in_neighbours():
yield v
def _in_degree(self, weight=None):
"""Return the in-degree of the vertex. If provided, ``weight`` should be a
scalar edge :class:`~graph_tool.PropertyMap`, and the in-degree will
correspond to the sum of the weights of the in-edges.
"""
if weight is None:
return self.__in_degree()
else:
return self.__weighted_in_degree(_prop("e", weight.get_graph(), weight))
def _out_degree(self, weight=None):
"""Return the out-degree of the vertex. If provided, ``weight`` should be a
scalar edge :class:`~graph_tool.PropertyMap`, and the out-degree will
correspond to the sum of the weights of the out-edges.
"""
if weight is None:
return self.__out_degree()
else:
return self.__weighted_out_degree(_prop("e", weight.get_graph(), weight))
def _vertex_repr(self):
if not self.is_valid():
return "<invalid Vertex object at 0x%x>" % (id(self))
return "<Vertex object with index '%d' at 0x%x>" % (int(self), id(self))
_vertex_doc = """Vertex descriptor.
This class represents a vertex in a :class:`~graph_tool.Graph` instance.
:class:`~graph_tool.Vertex` instances are hashable, and are convertible to
integers, corresponding to its index (see :attr:`~graph_tool.Graph.vertex_index`).
"""
def _v_eq(v1, v2):
try:
return int(v1) == int(v2)
except TypeError:
return False
def _v_ne(v1, v2):
try:
return int(v1) != int(v2)
except TypeError:
return True
def _v_lt(v1, v2):
try:
return int(v1) < int(v2)
except TypeError:
return False
def _v_gt(v1, v2):
try:
return int(v1) > int(v2)
except TypeError:
return False
def _v_le(v1, v2):
try:
return int(v1) <= int(v2)
except TypeError:
return False
def _v_ge(v1, v2):
try:
return int(v1) >= int(v2)
except TypeError:
return False
if sys.version_info < (3,):
def _v_long(self):
return long(int(self))
for Vertex in libcore.get_vlist():
Vertex.__doc__ = _vertex_doc
Vertex.out_neighbours = _out_neighbours
Vertex.in_neighbours = _in_neighbours
Vertex.all_edges = _all_edges
Vertex.all_neighbours = _all_neighbours
Vertex.in_degree = _in_degree
Vertex.out_degree = _out_degree
try:
Vertex.is_valid.__doc__ = "Returns ``True`` if the descriptor corresponds to an existing vertex in the graph, ``False`` otherwise."
except AttributeError:
pass
Vertex.__repr__ = _vertex_repr
Vertex.__eq__ = _v_eq
Vertex.__ne__ = _v_ne
Vertex.__lt__ = _v_lt
Vertex.__gt__ = _v_gt
Vertex.__le__ = _v_le
Vertex.__ge__ = _v_ge
if sys.version_info < (3,):
Vertex.__long__ = _v_long
_edge_doc = """Edge descriptor.
This class represents an edge in a :class:`~graph_tool.Graph`.
:class:`~graph_tool.Edge` instances are hashable, iterable and thus are
convertible to a tuple, which contains the source and target vertices.
"""
def _edge_iter(self):
"""Iterate over the source and target"""
for v in (self.source(), self.target()):
yield v
def _edge_repr(self):
if not self.is_valid():
return "<invalid Edge object at 0x%x>" % (id(self))
return ("<Edge object with source '%d' and target '%d'" +
" at 0x%x>") % (int(self.source()), int(self.target()), id(self))
# There are several edge classes... me must cycle through them all to modify
# them.
for Edge in libcore.get_elist():
Edge.__repr__ = _edge_repr
Edge.__iter__ = _edge_iter
Edge.__doc__ = _edge_doc
try:
Edge.is_valid.__doc__ = "Returns ``True`` if the descriptor corresponds to an existing edge in the graph, ``False`` otherwise."
Edge.source.__doc__ = "Returns the source of the edge (a :class:`~graph_tool.Vertex` instance)."
Edge.target.__doc__ = "Returns the target of the edge (a :class:`~graph_tool.Vertex` instance)."
except AttributeError:
pass
# some shenanigans to make it seem there is only a single edge and vertex class
EdgeBase.__doc__ = Edge.__doc__
EdgeBase.source = Edge.source
EdgeBase.target = Edge.target
EdgeBase.is_valid = Edge.is_valid
Edge = EdgeBase
Edge.__name__ = "Edge"
VertexBase.__doc__ = Vertex.__doc__
VertexBase.out_neighbours = Vertex.out_neighbours
VertexBase.in_neighbours = Vertex.in_neighbours
VertexBase.all_edges = Vertex.all_edges
VertexBase.all_neighbours = Vertex.all_neighbours
VertexBase.in_degree = Vertex.in_degree
VertexBase.out_degree = Vertex.out_degree
VertexBase.is_valid = Vertex.is_valid
Vertex = VertexBase
Vertex.__name__ = "Vertex"
# Add convenience function to vector classes
def _get_array_view(self):
return self.get_array()[:]
def _set_array_view(self, v):
self.get_array()[:] = v
vector_types = [Vector_bool, Vector_int16_t, Vector_int32_t, Vector_int64_t,
Vector_double, Vector_long_double, Vector_size_t]
for vt in vector_types:
vt.a = property(_get_array_view, _set_array_view,
doc=r"""Shortcut to the `get_array` method as an attribute.""")
vt.__repr__ = lambda self: self.a.__repr__()
Vector_string.a = None
Vector_string.get_array = lambda self: None
Vector_string.__repr__ = lambda self: repr(list(self))
# Global RNG
_rng = libcore.get_rng((numpy.random.randint(0, sys.maxsize) + os.getpid()) % sys.maxsize)
def seed_rng(seed):
"Seed the random number generator used by graph-tool's algorithms."
import graph_tool
graph_tool._rng = libcore.get_rng(int(seed))
def _get_rng():
global _rng
return _rng
# OpenMP Setup
def openmp_enabled():
"""Return `True` if OpenMP was enabled during compilation."""
return libcore.openmp_enabled()
def openmp_get_num_threads():
"""Return the number of OpenMP threads."""
return libcore.openmp_get_num_threads()
def openmp_set_num_threads(n):
"""Set the number of OpenMP threads."""
return libcore.openmp_set_num_threads(n)
def openmp_get_schedule():
"""Return the runtime OpenMP schedule and chunk size. The schedule can by
any of: `"static"`, `"dynamic"`, `"guided"`, `"auto"`."""
return libcore.openmp_get_schedule()
def openmp_set_schedule(schedule, chunk=0):
"""Set the runtime OpenMP schedule and chunk size. The schedule can by
any of: `"static"`, `"dynamic"`, `"guided"`, `"auto"`."""
return libcore.openmp_set_schedule(schedule, chunk)
if openmp_enabled() and os.environ.get("OMP_SCHEDULE") is None:
openmp_set_schedule("static", 0)
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