Commit f758e17e authored by Tiago Peixoto's avatar Tiago Peixoto

Merge branch 'warnings' into 'master'

Fix deprecation warnings

See merge request count0/graph-tool!23
parents 12bdfb90 dfa0fa1d
Pipeline #604 passed with stage
in 460 minutes and 16 seconds
......@@ -129,7 +129,7 @@ import weakref
import copy
import textwrap
import io
import collections
import collections.abc
import itertools
import csv
......@@ -2356,7 +2356,7 @@ class Graph(object):
"""
back = self.__graph.get_num_vertices(False) - 1
is_iter = isinstance(vertex, collections.Iterable)
is_iter = isinstance(vertex, collections.abc.Iterable)
if is_iter:
try:
vs = numpy.asarray(vertex, dtype="int64")
......@@ -3496,7 +3496,7 @@ class GraphView(Graph):
if efilt is not None:
if not isinstance(efilt, PropertyMap):
emap = self.new_edge_property("bool")
if isinstance(efilt, collections.Iterable):
if isinstance(efilt, collections.abc.Iterable):
emap.fa = efilt
else:
for e in g.edges():
......@@ -3516,7 +3516,7 @@ class GraphView(Graph):
if vfilt is not None:
if not isinstance(vfilt, PropertyMap):
vmap = self.new_vertex_property("bool")
if isinstance(vfilt, collections.Iterable):
if isinstance(vfilt, collections.abc.Iterable):
vmap.fa = vfilt
else:
for v in g.vertices():
......
......@@ -52,7 +52,7 @@ else:
also preserves the function's argument signature. This uses eval, and is
thus a bit of a hack, but there no better way I know of to do this."""
def decorate(f):
argspec = inspect.getargspec(func)
argspec = inspect.getfullargspec(func)
___wrap_defaults = defaults = argspec[-1]
if defaults is not None:
def_string = ["___wrap_defaults[%d]" % d for
......
......@@ -119,7 +119,7 @@ def minimize_blockmodel_dl(g, B_min=None, B_max=None, b_min=None, b_max=None,
mcmc_args={}, anneal_args={},
mcmc_equilibrate_args={}, shrink_args={},
mcmc_multilevel_args={}, verbose=False):
"""Fit the stochastic block model, by minimizing its description length using an
r"""Fit the stochastic block model, by minimizing its description length using an
agglomerative heuristic.
Parameters
......@@ -286,7 +286,7 @@ def minimize_nested_blockmodel_dl(g, B_min=None, B_max=None, b_min=None,
mcmc_args={}, anneal_args={},
mcmc_equilibrate_args={}, shrink_args={},
mcmc_multilevel_args={}, verbose=False):
"""Fit the nested stochastic block model, by minimizing its description length
r"""Fit the nested stochastic block model, by minimizing its description length
using an agglomerative heuristic.
Parameters
......@@ -479,4 +479,4 @@ def minimize_nested_blockmodel_dl(g, B_min=None, B_max=None, b_min=None,
**dmask(hierarchy_minimize_args,
["B_max", "B_min", "bisection_args", "verbose"]))
return state
\ No newline at end of file
return state
......@@ -999,7 +999,7 @@ class NestedBlockState(object):
def hierarchy_minimize(state, B_min=None, B_max=None, b_min=None, b_max=None,
frozen_levels=None, bisection_args={},
epsilon=1e-8, verbose=False):
"""Attempt to find a fit of the nested stochastic block model that minimizes the
r"""Attempt to find a fit of the nested stochastic block model that minimizes the
description length.
Parameters
......
......@@ -849,7 +849,7 @@ def max_cliques(g):
yield c
def min_spanning_tree(g, weights=None, root=None, tree_map=None):
"""
r"""
Return the minimum spanning tree of a given graph.
Parameters
......@@ -1043,7 +1043,7 @@ def random_spanning_tree(g, weights=None, root=None, tree_map=None):
def dominator_tree(g, root, dom_map=None):
"""Return a vertex property map the dominator vertices for each vertex.
r"""Return a vertex property map the dominator vertices for each vertex.
Parameters
----------
......@@ -1777,7 +1777,7 @@ def shortest_distance(g, source=None, target=None, weights=None,
negative_weights=False, max_dist=None, directed=None,
dense=False, dist_map=None, pred_map=False,
return_reached=False, dag=False):
"""Calculate the distance from a source to a target vertex, or to of all
r"""Calculate the distance from a source to a target vertex, or to of all
vertices from a given source, or the all pairs shortest paths, if the source
is not specified.
......@@ -2034,7 +2034,7 @@ def shortest_distance(g, source=None, target=None, weights=None,
def shortest_path(g, source, target, weights=None, negative_weights=False,
pred_map=None, dag=False):
"""Return the shortest path from ``source`` to ``target``.
r"""Return the shortest path from ``source`` to ``target``.
Parameters
----------
......@@ -2552,7 +2552,7 @@ def all_circuits(g, unique=False):
def pseudo_diameter(g, source=None, weights=None):
"""
r"""
Compute the pseudo-diameter of the graph.
Parameters
......@@ -3180,7 +3180,7 @@ def edge_reciprocity(g):
def tsp_tour(g, src, weight=None):
"""Return a traveling salesman tour of the graph, which is guaranteed to be
r"""Return a traveling salesman tour of the graph, which is guaranteed to be
twice as long as the optimal tour in the worst case.
Parameters
......@@ -3277,4 +3277,4 @@ def sequential_vertex_coloring(g, order=None, color=None):
return color
from .. flow import libgraph_tool_flow
\ No newline at end of file
from .. flow import libgraph_tool_flow
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