forked from 170010011/fr
416 lines
12 KiB
Python
416 lines
12 KiB
Python
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"""
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Miscellaneous Helpers for NetworkX.
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These are not imported into the base networkx namespace but
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can be accessed, for example, as
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>>> import networkx
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>>> networkx.utils.is_list_of_ints([1, 2, 3])
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True
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>>> networkx.utils.is_list_of_ints([1, 2, "spam"])
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False
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"""
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from collections import defaultdict
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from collections import deque
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import warnings
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import sys
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import uuid
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from itertools import tee, chain
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import networkx as nx
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# some cookbook stuff
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# used in deciding whether something is a bunch of nodes, edges, etc.
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# see G.add_nodes and others in Graph Class in networkx/base.py
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def is_string_like(obj): # from John Hunter, types-free version
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"""Check if obj is string."""
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msg = (
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"is_string_like is deprecated and will be removed in 3.0."
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"Use isinstance(obj, str) instead."
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)
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warnings.warn(msg, DeprecationWarning)
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return isinstance(obj, str)
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def iterable(obj):
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""" Return True if obj is iterable with a well-defined len()."""
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if hasattr(obj, "__iter__"):
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return True
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try:
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len(obj)
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except:
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return False
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return True
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def empty_generator():
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""" Return a generator with no members """
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yield from ()
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def flatten(obj, result=None):
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""" Return flattened version of (possibly nested) iterable object. """
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if not iterable(obj) or is_string_like(obj):
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return obj
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if result is None:
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result = []
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for item in obj:
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if not iterable(item) or is_string_like(item):
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result.append(item)
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else:
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flatten(item, result)
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return obj.__class__(result)
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def make_list_of_ints(sequence):
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"""Return list of ints from sequence of integral numbers.
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All elements of the sequence must satisfy int(element) == element
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or a ValueError is raised. Sequence is iterated through once.
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If sequence is a list, the non-int values are replaced with ints.
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So, no new list is created
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"""
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if not isinstance(sequence, list):
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result = []
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for i in sequence:
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errmsg = f"sequence is not all integers: {i}"
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try:
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ii = int(i)
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except ValueError:
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raise nx.NetworkXError(errmsg) from None
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if ii != i:
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raise nx.NetworkXError(errmsg)
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result.append(ii)
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return result
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# original sequence is a list... in-place conversion to ints
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for indx, i in enumerate(sequence):
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errmsg = f"sequence is not all integers: {i}"
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if isinstance(i, int):
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continue
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try:
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ii = int(i)
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except ValueError:
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raise nx.NetworkXError(errmsg) from None
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if ii != i:
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raise nx.NetworkXError(errmsg)
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sequence[indx] = ii
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return sequence
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def is_list_of_ints(intlist):
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""" Return True if list is a list of ints. """
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if not isinstance(intlist, list):
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return False
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for i in intlist:
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if not isinstance(i, int):
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return False
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return True
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def make_str(x):
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"""Returns the string representation of t."""
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msg = "make_str is deprecated and will be removed in 3.0. Use str instead."
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warnings.warn(msg, DeprecationWarning)
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return str(x)
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def generate_unique_node():
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""" Generate a unique node label."""
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return str(uuid.uuid1())
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def default_opener(filename):
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"""Opens `filename` using system's default program.
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Parameters
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----------
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filename : str
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The path of the file to be opened.
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"""
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from subprocess import call
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cmds = {
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"darwin": ["open"],
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"linux": ["xdg-open"],
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"linux2": ["xdg-open"],
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"win32": ["cmd.exe", "/C", "start", ""],
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}
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cmd = cmds[sys.platform] + [filename]
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call(cmd)
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def dict_to_numpy_array(d, mapping=None):
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"""Convert a dictionary of dictionaries to a numpy array
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with optional mapping."""
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try:
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return dict_to_numpy_array2(d, mapping)
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except (AttributeError, TypeError):
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# AttributeError is when no mapping was provided and v.keys() fails.
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# TypeError is when a mapping was provided and d[k1][k2] fails.
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return dict_to_numpy_array1(d, mapping)
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def dict_to_numpy_array2(d, mapping=None):
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"""Convert a dictionary of dictionaries to a 2d numpy array
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with optional mapping.
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"""
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import numpy
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if mapping is None:
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s = set(d.keys())
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for k, v in d.items():
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s.update(v.keys())
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mapping = dict(zip(s, range(len(s))))
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n = len(mapping)
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a = numpy.zeros((n, n))
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for k1, i in mapping.items():
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for k2, j in mapping.items():
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try:
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a[i, j] = d[k1][k2]
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except KeyError:
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pass
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return a
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def dict_to_numpy_array1(d, mapping=None):
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"""Convert a dictionary of numbers to a 1d numpy array
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with optional mapping.
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"""
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import numpy
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if mapping is None:
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s = set(d.keys())
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mapping = dict(zip(s, range(len(s))))
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n = len(mapping)
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a = numpy.zeros(n)
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for k1, i in mapping.items():
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i = mapping[k1]
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a[i] = d[k1]
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return a
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def is_iterator(obj):
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"""Returns True if and only if the given object is an iterator
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object.
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"""
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has_next_attr = hasattr(obj, "__next__") or hasattr(obj, "next")
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return iter(obj) is obj and has_next_attr
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def arbitrary_element(iterable):
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"""Returns an arbitrary element of `iterable` without removing it.
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This is most useful for "peeking" at an arbitrary element of a set,
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but can be used for any list, dictionary, etc., as well::
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>>> arbitrary_element({3, 2, 1})
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1
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>>> arbitrary_element("hello")
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'h'
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This function raises a :exc:`ValueError` if `iterable` is an
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iterator (because the current implementation of this function would
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consume an element from the iterator)::
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>>> iterator = iter([1, 2, 3])
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>>> arbitrary_element(iterator)
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Traceback (most recent call last):
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...
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ValueError: cannot return an arbitrary item from an iterator
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"""
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if is_iterator(iterable):
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raise ValueError("cannot return an arbitrary item from an iterator")
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# Another possible implementation is ``for x in iterable: return x``.
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return next(iter(iterable))
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# Recipe from the itertools documentation.
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def consume(iterator):
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"Consume the iterator entirely."
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# Feed the entire iterator into a zero-length deque.
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deque(iterator, maxlen=0)
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# Recipe from the itertools documentation.
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def pairwise(iterable, cyclic=False):
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"s -> (s0, s1), (s1, s2), (s2, s3), ..."
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a, b = tee(iterable)
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first = next(b, None)
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if cyclic is True:
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return zip(a, chain(b, (first,)))
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return zip(a, b)
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def groups(many_to_one):
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"""Converts a many-to-one mapping into a one-to-many mapping.
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`many_to_one` must be a dictionary whose keys and values are all
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:term:`hashable`.
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The return value is a dictionary mapping values from `many_to_one`
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to sets of keys from `many_to_one` that have that value.
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For example::
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>>> from networkx.utils import groups
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>>> many_to_one = {"a": 1, "b": 1, "c": 2, "d": 3, "e": 3}
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>>> groups(many_to_one) # doctest: +SKIP
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{1: {'a', 'b'}, 2: {'c'}, 3: {'d', 'e'}}
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"""
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one_to_many = defaultdict(set)
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for v, k in many_to_one.items():
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one_to_many[k].add(v)
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return dict(one_to_many)
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def to_tuple(x):
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"""Converts lists to tuples.
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For example::
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>>> from networkx.utils import to_tuple
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>>> a_list = [1, 2, [1, 4]]
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>>> to_tuple(a_list)
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(1, 2, (1, 4))
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"""
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if not isinstance(x, (tuple, list)):
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return x
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return tuple(map(to_tuple, x))
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def create_random_state(random_state=None):
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"""Returns a numpy.random.RandomState instance depending on input.
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Parameters
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----------
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random_state : int or RandomState instance or None optional (default=None)
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If int, return a numpy.random.RandomState instance set with seed=int.
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if numpy.random.RandomState instance, return it.
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if None or numpy.random, return the global random number generator used
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by numpy.random.
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"""
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import numpy as np
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if random_state is None or random_state is np.random:
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return np.random.mtrand._rand
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if isinstance(random_state, np.random.RandomState):
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return random_state
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if isinstance(random_state, int):
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return np.random.RandomState(random_state)
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msg = (
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f"{random_state} cannot be used to generate a numpy.random.RandomState instance"
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)
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raise ValueError(msg)
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class PythonRandomInterface:
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try:
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def __init__(self, rng=None):
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import numpy
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if rng is None:
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self._rng = numpy.random.mtrand._rand
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self._rng = rng
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except ImportError:
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msg = "numpy not found, only random.random available."
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warnings.warn(msg, ImportWarning)
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def random(self):
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return self._rng.random_sample()
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def uniform(self, a, b):
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return a + (b - a) * self._rng.random_sample()
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def randrange(self, a, b=None):
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return self._rng.randint(a, b)
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def choice(self, seq):
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return seq[self._rng.randint(0, len(seq))]
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def gauss(self, mu, sigma):
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return self._rng.normal(mu, sigma)
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def shuffle(self, seq):
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return self._rng.shuffle(seq)
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# Some methods don't match API for numpy RandomState.
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# Commented out versions are not used by NetworkX
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def sample(self, seq, k):
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return self._rng.choice(list(seq), size=(k,), replace=False)
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def randint(self, a, b):
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return self._rng.randint(a, b + 1)
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# exponential as expovariate with 1/argument,
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def expovariate(self, scale):
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return self._rng.exponential(1 / scale)
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# pareto as paretovariate with 1/argument,
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def paretovariate(self, shape):
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return self._rng.pareto(shape)
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# weibull as weibullvariate multiplied by beta,
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# def weibullvariate(self, alpha, beta):
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# return self._rng.weibull(alpha) * beta
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#
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# def triangular(self, low, high, mode):
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# return self._rng.triangular(low, mode, high)
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#
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# def choices(self, seq, weights=None, cum_weights=None, k=1):
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# return self._rng.choice(seq
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def create_py_random_state(random_state=None):
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"""Returns a random.Random instance depending on input.
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Parameters
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----------
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random_state : int or random number generator or None (default=None)
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If int, return a random.Random instance set with seed=int.
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if random.Random instance, return it.
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if None or the `random` package, return the global random number
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generator used by `random`.
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if np.random package, return the global numpy random number
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generator wrapped in a PythonRandomInterface class.
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if np.random.RandomState instance, return it wrapped in
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PythonRandomInterface
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if a PythonRandomInterface instance, return it
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"""
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import random
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try:
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import numpy as np
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if random_state is np.random:
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return PythonRandomInterface(np.random.mtrand._rand)
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if isinstance(random_state, np.random.RandomState):
|
||
|
|
return PythonRandomInterface(random_state)
|
||
|
|
if isinstance(random_state, PythonRandomInterface):
|
||
|
|
return random_state
|
||
|
|
except ImportError:
|
||
|
|
pass
|
||
|
|
|
||
|
|
if random_state is None or random_state is random:
|
||
|
|
return random._inst
|
||
|
|
if isinstance(random_state, random.Random):
|
||
|
|
return random_state
|
||
|
|
if isinstance(random_state, int):
|
||
|
|
return random.Random(random_state)
|
||
|
|
msg = f"{random_state} cannot be used to generate a random.Random instance"
|
||
|
|
raise ValueError(msg)
|