using for loop to install conda package

.CondaPkg/env/Lib/site-packages/networkx/utils/__init__.py

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+from networkx.utils.misc import *
+from networkx.utils.decorators import *
+from networkx.utils.random_sequence import *
+from networkx.utils.union_find import *
+from networkx.utils.rcm import *
+from networkx.utils.heaps import *

.CondaPkg/env/Lib/site-packages/networkx/utils/heaps.py

@@ -0,0 +1,340 @@
+"""
+Min-heaps.
+"""
+
+from heapq import heappop, heappush
+from itertools import count
+
+import networkx as nx
+
+__all__ = ["MinHeap", "PairingHeap", "BinaryHeap"]
+
+
+class MinHeap:
+    """Base class for min-heaps.
+
+    A MinHeap stores a collection of key-value pairs ordered by their values.
+    It supports querying the minimum pair, inserting a new pair, decreasing the
+    value in an existing pair and deleting the minimum pair.
+    """
+
+    class _Item:
+        """Used by subclassess to represent a key-value pair."""
+
+        __slots__ = ("key", "value")
+
+        def __init__(self, key, value):
+            self.key = key
+            self.value = value
+
+        def __repr__(self):
+            return repr((self.key, self.value))
+
+    def __init__(self):
+        """Initialize a new min-heap."""
+        self._dict = {}
+
+    def min(self):
+        """Query the minimum key-value pair.
+
+        Returns
+        -------
+        key, value : tuple
+            The key-value pair with the minimum value in the heap.
+
+        Raises
+        ------
+        NetworkXError
+            If the heap is empty.
+        """
+        raise NotImplementedError
+
+    def pop(self):
+        """Delete the minimum pair in the heap.
+
+        Returns
+        -------
+        key, value : tuple
+            The key-value pair with the minimum value in the heap.
+
+        Raises
+        ------
+        NetworkXError
+            If the heap is empty.
+        """
+        raise NotImplementedError
+
+    def get(self, key, default=None):
+        """Returns the value associated with a key.
+
+        Parameters
+        ----------
+        key : hashable object
+            The key to be looked up.
+
+        default : object
+            Default value to return if the key is not present in the heap.
+            Default value: None.
+
+        Returns
+        -------
+        value : object.
+            The value associated with the key.
+        """
+        raise NotImplementedError
+
+    def insert(self, key, value, allow_increase=False):
+        """Insert a new key-value pair or modify the value in an existing
+        pair.
+
+        Parameters
+        ----------
+        key : hashable object
+            The key.
+
+        value : object comparable with existing values.
+            The value.
+
+        allow_increase : bool
+            Whether the value is allowed to increase. If False, attempts to
+            increase an existing value have no effect. Default value: False.
+
+        Returns
+        -------
+        decreased : bool
+            True if a pair is inserted or the existing value is decreased.
+        """
+        raise NotImplementedError
+
+    def __nonzero__(self):
+        """Returns whether the heap if empty."""
+        return bool(self._dict)
+
+    def __bool__(self):
+        """Returns whether the heap if empty."""
+        return bool(self._dict)
+
+    def __len__(self):
+        """Returns the number of key-value pairs in the heap."""
+        return len(self._dict)
+
+    def __contains__(self, key):
+        """Returns whether a key exists in the heap.
+
+        Parameters
+        ----------
+        key : any hashable object.
+            The key to be looked up.
+        """
+        return key in self._dict
+
+
+class PairingHeap(MinHeap):
+    """A pairing heap."""
+
+    class _Node(MinHeap._Item):
+        """A node in a pairing heap.
+
+        A tree in a pairing heap is stored using the left-child, right-sibling
+        representation.
+        """
+
+        __slots__ = ("left", "next", "prev", "parent")
+
+        def __init__(self, key, value):
+            super().__init__(key, value)
+            # The leftmost child.
+            self.left = None
+            # The next sibling.
+            self.next = None
+            # The previous sibling.
+            self.prev = None
+            # The parent.
+            self.parent = None
+
+    def __init__(self):
+        """Initialize a pairing heap."""
+        super().__init__()
+        self._root = None
+
+    def min(self):
+        if self._root is None:
+            raise nx.NetworkXError("heap is empty.")
+        return (self._root.key, self._root.value)
+
+    def pop(self):
+        if self._root is None:
+            raise nx.NetworkXError("heap is empty.")
+        min_node = self._root
+        self._root = self._merge_children(self._root)
+        del self._dict[min_node.key]
+        return (min_node.key, min_node.value)
+
+    def get(self, key, default=None):
+        node = self._dict.get(key)
+        return node.value if node is not None else default
+
+    def insert(self, key, value, allow_increase=False):
+        node = self._dict.get(key)
+        root = self._root
+        if node is not None:
+            if value < node.value:
+                node.value = value
+                if node is not root and value < node.parent.value:
+                    self._cut(node)
+                    self._root = self._link(root, node)
+                return True
+            elif allow_increase and value > node.value:
+                node.value = value
+                child = self._merge_children(node)
+                # Nonstandard step: Link the merged subtree with the root. See
+                # below for the standard step.
+                if child is not None:
+                    self._root = self._link(self._root, child)
+                # Standard step: Perform a decrease followed by a pop as if the
+                # value were the smallest in the heap. Then insert the new
+                # value into the heap.
+                # if node is not root:
+                #     self._cut(node)
+                #     if child is not None:
+                #         root = self._link(root, child)
+                #     self._root = self._link(root, node)
+                # else:
+                #     self._root = (self._link(node, child)
+                #                   if child is not None else node)
+            return False
+        else:
+            # Insert a new key.
+            node = self._Node(key, value)
+            self._dict[key] = node
+            self._root = self._link(root, node) if root is not None else node
+            return True
+
+    def _link(self, root, other):
+        """Link two nodes, making the one with the smaller value the parent of
+        the other.
+        """
+        if other.value < root.value:
+            root, other = other, root
+        next = root.left
+        other.next = next
+        if next is not None:
+            next.prev = other
+        other.prev = None
+        root.left = other
+        other.parent = root
+        return root
+
+    def _merge_children(self, root):
+        """Merge the subtrees of the root using the standard two-pass method.
+        The resulting subtree is detached from the root.
+        """
+        node = root.left
+        root.left = None
+        if node is not None:
+            link = self._link
+            # Pass 1: Merge pairs of consecutive subtrees from left to right.
+            # At the end of the pass, only the prev pointers of the resulting
+            # subtrees have meaningful values. The other pointers will be fixed
+            # in pass 2.
+            prev = None
+            while True:
+                next = node.next
+                if next is None:
+                    node.prev = prev
+                    break
+                next_next = next.next
+                node = link(node, next)
+                node.prev = prev
+                prev = node
+                if next_next is None:
+                    break
+                node = next_next
+            # Pass 2: Successively merge the subtrees produced by pass 1 from
+            # right to left with the rightmost one.
+            prev = node.prev
+            while prev is not None:
+                prev_prev = prev.prev
+                node = link(prev, node)
+                prev = prev_prev
+            # Now node can become the new root. Its has no parent nor siblings.
+            node.prev = None
+            node.next = None
+            node.parent = None
+        return node
+
+    def _cut(self, node):
+        """Cut a node from its parent."""
+        prev = node.prev
+        next = node.next
+        if prev is not None:
+            prev.next = next
+        else:
+            node.parent.left = next
+        node.prev = None
+        if next is not None:
+            next.prev = prev
+            node.next = None
+        node.parent = None
+
+
+class BinaryHeap(MinHeap):
+    """A binary heap."""
+
+    def __init__(self):
+        """Initialize a binary heap."""
+        super().__init__()
+        self._heap = []
+        self._count = count()
+
+    def min(self):
+        dict = self._dict
+        if not dict:
+            raise nx.NetworkXError("heap is empty")
+        heap = self._heap
+        pop = heappop
+        # Repeatedly remove stale key-value pairs until a up-to-date one is
+        # met.
+        while True:
+            value, _, key = heap[0]
+            if key in dict and value == dict[key]:
+                break
+            pop(heap)
+        return (key, value)
+
+    def pop(self):
+        dict = self._dict
+        if not dict:
+            raise nx.NetworkXError("heap is empty")
+        heap = self._heap
+        pop = heappop
+        # Repeatedly remove stale key-value pairs until a up-to-date one is
+        # met.
+        while True:
+            value, _, key = heap[0]
+            pop(heap)
+            if key in dict and value == dict[key]:
+                break
+        del dict[key]
+        return (key, value)
+
+    def get(self, key, default=None):
+        return self._dict.get(key, default)
+
+    def insert(self, key, value, allow_increase=False):
+        dict = self._dict
+        if key in dict:
+            old_value = dict[key]
+            if value < old_value or (allow_increase and value > old_value):
+                # Since there is no way to efficiently obtain the location of a
+                # key-value pair in the heap, insert a new pair even if ones
+                # with the same key may already be present. Deem the old ones
+                # as stale and skip them when the minimum pair is queried.
+                dict[key] = value
+                heappush(self._heap, (value, next(self._count), key))
+                return value < old_value
+            return False
+        else:
+            dict[key] = value
+            heappush(self._heap, (value, next(self._count), key))
+            return True

.CondaPkg/env/Lib/site-packages/networkx/utils/mapped_queue.py

@@ -0,0 +1,298 @@
+"""Priority queue class with updatable priorities.
+"""
+
+import heapq
+
+__all__ = ["MappedQueue"]
+
+
+class _HeapElement:
+    """This proxy class separates the heap element from its priority.
+
+    The idea is that using a 2-tuple (priority, element) works
+    for sorting, but not for dict lookup because priorities are
+    often floating point values so round-off can mess up equality.
+
+    So, we need inequalities to look at the priority (for sorting)
+    and equality (and hash) to look at the element to enable
+    updates to the priority.
+
+    Unfortunately, this class can be tricky to work with if you forget that
+    `__lt__` compares the priority while `__eq__` compares the element.
+    In `greedy_modularity_communities()` the following code is
+    used to check that two _HeapElements differ in either element or priority:
+
+        if d_oldmax != row_max or d_oldmax.priority != row_max.priority:
+
+    If the priorities are the same, this implementation uses the element
+    as a tiebreaker. This provides compatibility with older systems that
+    use tuples to combine priority and elements.
+    """
+
+    __slots__ = ["priority", "element", "_hash"]
+
+    def __init__(self, priority, element):
+        self.priority = priority
+        self.element = element
+        self._hash = hash(element)
+
+    def __lt__(self, other):
+        try:
+            other_priority = other.priority
+        except AttributeError:
+            return self.priority < other
+        # assume comparing to another _HeapElement
+        if self.priority == other_priority:
+            try:
+                return self.element < other.element
+            except TypeError as err:
+                raise TypeError(
+                    "Consider using a tuple, with a priority value that can be compared."
+                )
+        return self.priority < other_priority
+
+    def __gt__(self, other):
+        try:
+            other_priority = other.priority
+        except AttributeError:
+            return self.priority > other
+        # assume comparing to another _HeapElement
+        if self.priority == other_priority:
+            try:
+                return self.element > other.element
+            except TypeError as err:
+                raise TypeError(
+                    "Consider using a tuple, with a priority value that can be compared."
+                )
+        return self.priority > other_priority
+
+    def __eq__(self, other):
+        try:
+            return self.element == other.element
+        except AttributeError:
+            return self.element == other
+
+    def __hash__(self):
+        return self._hash
+
+    def __getitem__(self, indx):
+        return self.priority if indx == 0 else self.element[indx - 1]
+
+    def __iter__(self):
+        yield self.priority
+        try:
+            yield from self.element
+        except TypeError:
+            yield self.element
+
+    def __repr__(self):
+        return f"_HeapElement({self.priority}, {self.element})"
+
+
+class MappedQueue:
+    """The MappedQueue class implements a min-heap with removal and update-priority.
+
+    The min heap uses heapq as well as custom written _siftup and _siftdown
+    methods to allow the heap positions to be tracked by an additional dict
+    keyed by element to position. The smallest element can be popped in O(1) time,
+    new elements can be pushed in O(log n) time, and any element can be removed
+    or updated in O(log n) time. The queue cannot contain duplicate elements
+    and an attempt to push an element already in the queue will have no effect.
+
+    MappedQueue complements the heapq package from the python standard
+    library. While MappedQueue is designed for maximum compatibility with
+    heapq, it adds element removal, lookup, and priority update.
+
+    Parameters
+    ----------
+    data : dict or iterable
+
+    Examples
+    --------
+
+    A `MappedQueue` can be created empty, or optionally, given a dictionary
+    of initial elements and priorities.  The methods `push`, `pop`,
+    `remove`, and `update` operate on the queue.
+
+    >>> colors_nm = {'red':665, 'blue': 470, 'green': 550}
+    >>> q = MappedQueue(colors_nm)
+    >>> q.remove('red')
+    >>> q.update('green', 'violet', 400)
+    >>> q.push('indigo', 425)
+    True
+    >>> [q.pop().element for i in range(len(q.heap))]
+    ['violet', 'indigo', 'blue']
+
+    A `MappedQueue` can also be initialized with a list or other iterable. The priority is assumed
+    to be the sort order of the items in the list.
+
+    >>> q = MappedQueue([916, 50, 4609, 493, 237])
+    >>> q.remove(493)
+    >>> q.update(237, 1117)
+    >>> [q.pop() for i in range(len(q.heap))]
+    [50, 916, 1117, 4609]
+
+    An exception is raised if the elements are not comparable.
+
+    >>> q = MappedQueue([100, 'a'])
+    Traceback (most recent call last):
+    ...
+    TypeError: '<' not supported between instances of 'int' and 'str'
+
+    To avoid the exception, use a dictionary to assign priorities to the elements.
+
+    >>> q = MappedQueue({100: 0, 'a': 1 })
+
+    References
+    ----------
+    .. [1] Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001).
+       Introduction to algorithms second edition.
+    .. [2] Knuth, D. E. (1997). The art of computer programming (Vol. 3).
+       Pearson Education.
+    """
+
+    def __init__(self, data=None):
+        """Priority queue class with updatable priorities."""
+        if data is None:
+            self.heap = []
+        elif isinstance(data, dict):
+            self.heap = [_HeapElement(v, k) for k, v in data.items()]
+        else:
+            self.heap = list(data)
+        self.position = {}
+        self._heapify()
+
+    def _heapify(self):
+        """Restore heap invariant and recalculate map."""
+        heapq.heapify(self.heap)
+        self.position = {elt: pos for pos, elt in enumerate(self.heap)}
+        if len(self.heap) != len(self.position):
+            raise AssertionError("Heap contains duplicate elements")
+
+    def __len__(self):
+        return len(self.heap)
+
+    def push(self, elt, priority=None):
+        """Add an element to the queue."""
+        if priority is not None:
+            elt = _HeapElement(priority, elt)
+        # If element is already in queue, do nothing
+        if elt in self.position:
+            return False
+        # Add element to heap and dict
+        pos = len(self.heap)
+        self.heap.append(elt)
+        self.position[elt] = pos
+        # Restore invariant by sifting down
+        self._siftdown(0, pos)
+        return True
+
+    def pop(self):
+        """Remove and return the smallest element in the queue."""
+        # Remove smallest element
+        elt = self.heap[0]
+        del self.position[elt]
+        # If elt is last item, remove and return
+        if len(self.heap) == 1:
+            self.heap.pop()
+            return elt
+        # Replace root with last element
+        last = self.heap.pop()
+        self.heap[0] = last
+        self.position[last] = 0
+        # Restore invariant by sifting up
+        self._siftup(0)
+        # Return smallest element
+        return elt
+
+    def update(self, elt, new, priority=None):
+        """Replace an element in the queue with a new one."""
+        if priority is not None:
+            new = _HeapElement(priority, new)
+        # Replace
+        pos = self.position[elt]
+        self.heap[pos] = new
+        del self.position[elt]
+        self.position[new] = pos
+        # Restore invariant by sifting up
+        self._siftup(pos)
+
+    def remove(self, elt):
+        """Remove an element from the queue."""
+        # Find and remove element
+        try:
+            pos = self.position[elt]
+            del self.position[elt]
+        except KeyError:
+            # Not in queue
+            raise
+        # If elt is last item, remove and return
+        if pos == len(self.heap) - 1:
+            self.heap.pop()
+            return
+        # Replace elt with last element
+        last = self.heap.pop()
+        self.heap[pos] = last
+        self.position[last] = pos
+        # Restore invariant by sifting up
+        self._siftup(pos)
+
+    def _siftup(self, pos):
+        """Move smaller child up until hitting a leaf.
+
+        Built to mimic code for heapq._siftup
+        only updating position dict too.
+        """
+        heap, position = self.heap, self.position
+        end_pos = len(heap)
+        startpos = pos
+        newitem = heap[pos]
+        # Shift up the smaller child until hitting a leaf
+        child_pos = (pos << 1) + 1  # start with leftmost child position
+        while child_pos < end_pos:
+            # Set child_pos to index of smaller child.
+            child = heap[child_pos]
+            right_pos = child_pos + 1
+            if right_pos < end_pos:
+                right = heap[right_pos]
+                if not child < right:
+                    child = right
+                    child_pos = right_pos
+            # Move the smaller child up.
+            heap[pos] = child
+            position[child] = pos
+            pos = child_pos
+            child_pos = (pos << 1) + 1
+        # pos is a leaf position. Put newitem there, and bubble it up
+        # to its final resting place (by sifting its parents down).
+        while pos > 0:
+            parent_pos = (pos - 1) >> 1
+            parent = heap[parent_pos]
+            if not newitem < parent:
+                break
+            heap[pos] = parent
+            position[parent] = pos
+            pos = parent_pos
+        heap[pos] = newitem
+        position[newitem] = pos
+
+    def _siftdown(self, start_pos, pos):
+        """Restore invariant. keep swapping with parent until smaller.
+
+        Built to mimic code for heapq._siftdown
+        only updating position dict too.
+        """
+        heap, position = self.heap, self.position
+        newitem = heap[pos]
+        # Follow the path to the root, moving parents down until finding a place
+        # newitem fits.
+        while pos > start_pos:
+            parent_pos = (pos - 1) >> 1
+            parent = heap[parent_pos]
+            if not newitem < parent:
+                break
+            heap[pos] = parent
+            position[parent] = pos
+            pos = parent_pos
+        heap[pos] = newitem
+        position[newitem] = pos

.CondaPkg/env/Lib/site-packages/networkx/utils/misc.py

@@ -0,0 +1,491 @@
+"""
+Miscellaneous Helpers for NetworkX.
+
+These are not imported into the base networkx namespace but
+can be accessed, for example, as
+
+>>> import networkx
+>>> networkx.utils.make_list_of_ints({1, 2, 3})
+[1, 2, 3]
+>>> networkx.utils.arbitrary_element({5, 1, 7})  # doctest: +SKIP
+1
+"""
+
+import sys
+import uuid
+import warnings
+from collections import defaultdict, deque
+from collections.abc import Iterable, Iterator, Sized
+from itertools import chain, tee
+
+import networkx as nx
+
+__all__ = [
+    "flatten",
+    "make_list_of_ints",
+    "dict_to_numpy_array",
+    "arbitrary_element",
+    "pairwise",
+    "groups",
+    "create_random_state",
+    "create_py_random_state",
+    "PythonRandomInterface",
+    "nodes_equal",
+    "edges_equal",
+    "graphs_equal",
+]
+
+
+# some cookbook stuff
+# used in deciding whether something is a bunch of nodes, edges, etc.
+# see G.add_nodes and others in Graph Class in networkx/base.py
+
+
+def flatten(obj, result=None):
+    """Return flattened version of (possibly nested) iterable object."""
+    if not isinstance(obj, (Iterable, Sized)) or isinstance(obj, str):
+        return obj
+    if result is None:
+        result = []
+    for item in obj:
+        if not isinstance(item, (Iterable, Sized)) or isinstance(item, str):
+            result.append(item)
+        else:
+            flatten(item, result)
+    return tuple(result)
+
+
+def make_list_of_ints(sequence):
+    """Return list of ints from sequence of integral numbers.
+
+    All elements of the sequence must satisfy int(element) == element
+    or a ValueError is raised. Sequence is iterated through once.
+
+    If sequence is a list, the non-int values are replaced with ints.
+    So, no new list is created
+    """
+    if not isinstance(sequence, list):
+        result = []
+        for i in sequence:
+            errmsg = f"sequence is not all integers: {i}"
+            try:
+                ii = int(i)
+            except ValueError:
+                raise nx.NetworkXError(errmsg) from None
+            if ii != i:
+                raise nx.NetworkXError(errmsg)
+            result.append(ii)
+        return result
+    # original sequence is a list... in-place conversion to ints
+    for indx, i in enumerate(sequence):
+        errmsg = f"sequence is not all integers: {i}"
+        if isinstance(i, int):
+            continue
+        try:
+            ii = int(i)
+        except ValueError:
+            raise nx.NetworkXError(errmsg) from None
+        if ii != i:
+            raise nx.NetworkXError(errmsg)
+        sequence[indx] = ii
+    return sequence
+
+
+def dict_to_numpy_array(d, mapping=None):
+    """Convert a dictionary of dictionaries to a numpy array
+    with optional mapping."""
+    try:
+        return _dict_to_numpy_array2(d, mapping)
+    except (AttributeError, TypeError):
+        # AttributeError is when no mapping was provided and v.keys() fails.
+        # TypeError is when a mapping was provided and d[k1][k2] fails.
+        return _dict_to_numpy_array1(d, mapping)
+
+
+def _dict_to_numpy_array2(d, mapping=None):
+    """Convert a dictionary of dictionaries to a 2d numpy array
+    with optional mapping.
+
+    """
+    import numpy as np
+
+    if mapping is None:
+        s = set(d.keys())
+        for k, v in d.items():
+            s.update(v.keys())
+        mapping = dict(zip(s, range(len(s))))
+    n = len(mapping)
+    a = np.zeros((n, n))
+    for k1, i in mapping.items():
+        for k2, j in mapping.items():
+            try:
+                a[i, j] = d[k1][k2]
+            except KeyError:
+                pass
+    return a
+
+
+def _dict_to_numpy_array1(d, mapping=None):
+    """Convert a dictionary of numbers to a 1d numpy array with optional mapping."""
+    import numpy as np
+
+    if mapping is None:
+        s = set(d.keys())
+        mapping = dict(zip(s, range(len(s))))
+    n = len(mapping)
+    a = np.zeros(n)
+    for k1, i in mapping.items():
+        i = mapping[k1]
+        a[i] = d[k1]
+    return a
+
+
+def arbitrary_element(iterable):
+    """Returns an arbitrary element of `iterable` without removing it.
+
+    This is most useful for "peeking" at an arbitrary element of a set,
+    but can be used for any list, dictionary, etc., as well.
+
+    Parameters
+    ----------
+    iterable : `abc.collections.Iterable` instance
+        Any object that implements ``__iter__``, e.g. set, dict, list, tuple,
+        etc.
+
+    Returns
+    -------
+    The object that results from ``next(iter(iterable))``
+
+    Raises
+    ------
+    ValueError
+        If `iterable` is an iterator (because the current implementation of
+        this function would consume an element from the iterator).
+
+    Examples
+    --------
+    Arbitrary elements from common Iterable objects:
+
+    >>> nx.utils.arbitrary_element([1, 2, 3])  # list
+    1
+    >>> nx.utils.arbitrary_element((1, 2, 3))  # tuple
+    1
+    >>> nx.utils.arbitrary_element({1, 2, 3})  # set
+    1
+    >>> d = {k: v for k, v in zip([1, 2, 3], [3, 2, 1])}
+    >>> nx.utils.arbitrary_element(d)  # dict_keys
+    1
+    >>> nx.utils.arbitrary_element(d.values())   # dict values
+    3
+
+    `str` is also an Iterable:
+
+    >>> nx.utils.arbitrary_element("hello")
+    'h'
+
+    :exc:`ValueError` is raised if `iterable` is an iterator:
+
+    >>> iterator = iter([1, 2, 3])  # Iterator, *not* Iterable
+    >>> nx.utils.arbitrary_element(iterator)
+    Traceback (most recent call last):
+        ...
+    ValueError: cannot return an arbitrary item from an iterator
+
+    Notes
+    -----
+    This function does not return a *random* element. If `iterable` is
+    ordered, sequential calls will return the same value::
+
+        >>> l = [1, 2, 3]
+        >>> nx.utils.arbitrary_element(l)
+        1
+        >>> nx.utils.arbitrary_element(l)
+        1
+
+    """
+    if isinstance(iterable, Iterator):
+        raise ValueError("cannot return an arbitrary item from an iterator")
+    # Another possible implementation is ``for x in iterable: return x``.
+    return next(iter(iterable))
+
+
+# Recipe from the itertools documentation.
+def pairwise(iterable, cyclic=False):
+    "s -> (s0, s1), (s1, s2), (s2, s3), ..."
+    a, b = tee(iterable)
+    first = next(b, None)
+    if cyclic is True:
+        return zip(a, chain(b, (first,)))
+    return zip(a, b)
+
+
+def groups(many_to_one):
+    """Converts a many-to-one mapping into a one-to-many mapping.
+
+    `many_to_one` must be a dictionary whose keys and values are all
+    :term:`hashable`.
+
+    The return value is a dictionary mapping values from `many_to_one`
+    to sets of keys from `many_to_one` that have that value.
+
+    Examples
+    --------
+    >>> from networkx.utils import groups
+    >>> many_to_one = {"a": 1, "b": 1, "c": 2, "d": 3, "e": 3}
+    >>> groups(many_to_one)  # doctest: +SKIP
+    {1: {'a', 'b'}, 2: {'c'}, 3: {'e', 'd'}}
+    """
+    one_to_many = defaultdict(set)
+    for v, k in many_to_one.items():
+        one_to_many[k].add(v)
+    return dict(one_to_many)
+
+
+def create_random_state(random_state=None):
+    """Returns a numpy.random.RandomState or numpy.random.Generator instance
+    depending on input.
+
+    Parameters
+    ----------
+    random_state : int or NumPy RandomState or Generator instance, optional (default=None)
+        If int, return a numpy.random.RandomState instance set with seed=int.
+        if `numpy.random.RandomState` instance, return it.
+        if `numpy.random.Generator` instance, return it.
+        if None or numpy.random, return the global random number generator used
+        by numpy.random.
+    """
+    import numpy as np
+
+    if random_state is None or random_state is np.random:
+        return np.random.mtrand._rand
+    if isinstance(random_state, np.random.RandomState):
+        return random_state
+    if isinstance(random_state, int):
+        return np.random.RandomState(random_state)
+    if isinstance(random_state, np.random.Generator):
+        return random_state
+    msg = (
+        f"{random_state} cannot be used to create a numpy.random.RandomState or\n"
+        "numpy.random.Generator instance"
+    )
+    raise ValueError(msg)
+
+
+class PythonRandomInterface:
+    def __init__(self, rng=None):
+        try:
+            import numpy as np
+        except ImportError:
+            msg = "numpy not found, only random.random available."
+            warnings.warn(msg, ImportWarning)
+
+        if rng is None:
+            self._rng = np.random.mtrand._rand
+        else:
+            self._rng = rng
+
+    def random(self):
+        return self._rng.random()
+
+    def uniform(self, a, b):
+        return a + (b - a) * self._rng.random()
+
+    def randrange(self, a, b=None):
+        import numpy as np
+
+        if isinstance(self._rng, np.random.Generator):
+            return self._rng.integers(a, b)
+        return self._rng.randint(a, b)
+
+    # NOTE: the numpy implementations of `choice` don't support strings, so
+    # this cannot be replaced with self._rng.choice
+    def choice(self, seq):
+        import numpy as np
+
+        if isinstance(self._rng, np.random.Generator):
+            idx = self._rng.integers(0, len(seq))
+        else:
+            idx = self._rng.randint(0, len(seq))
+        return seq[idx]
+
+    def gauss(self, mu, sigma):
+        return self._rng.normal(mu, sigma)
+
+    def shuffle(self, seq):
+        return self._rng.shuffle(seq)
+
+    #    Some methods don't match API for numpy RandomState.
+    #    Commented out versions are not used by NetworkX
+
+    def sample(self, seq, k):
+        return self._rng.choice(list(seq), size=(k,), replace=False)
+
+    def randint(self, a, b):
+        import numpy as np
+
+        if isinstance(self._rng, np.random.Generator):
+            return self._rng.integers(a, b + 1)
+        return self._rng.randint(a, b + 1)
+
+    #    exponential as expovariate with 1/argument,
+    def expovariate(self, scale):
+        return self._rng.exponential(1 / scale)
+
+    #    pareto as paretovariate with 1/argument,
+    def paretovariate(self, shape):
+        return self._rng.pareto(shape)
+
+
+#    weibull as weibullvariate multiplied by beta,
+#    def weibullvariate(self, alpha, beta):
+#        return self._rng.weibull(alpha) * beta
+#
+#    def triangular(self, low, high, mode):
+#        return self._rng.triangular(low, mode, high)
+#
+#    def choices(self, seq, weights=None, cum_weights=None, k=1):
+#        return self._rng.choice(seq
+
+
+def create_py_random_state(random_state=None):
+    """Returns a random.Random instance depending on input.
+
+    Parameters
+    ----------
+    random_state : int or random number generator or None (default=None)
+        If int, return a random.Random instance set with seed=int.
+        if random.Random instance, return it.
+        if None or the `random` package, return the global random number
+        generator used by `random`.
+        if np.random package, return the global numpy random number
+        generator wrapped in a PythonRandomInterface class.
+        if np.random.RandomState or np.random.Generator instance, return it
+        wrapped in PythonRandomInterface
+        if a PythonRandomInterface instance, return it
+    """
+    import random
+
+    try:
+        import numpy as np
+
+        if random_state is np.random:
+            return PythonRandomInterface(np.random.mtrand._rand)
+        if isinstance(random_state, (np.random.RandomState, np.random.Generator)):
+            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)
+
+
+def nodes_equal(nodes1, nodes2):
+    """Check if nodes are equal.
+
+    Equality here means equal as Python objects.
+    Node data must match if included.
+    The order of nodes is not relevant.
+
+    Parameters
+    ----------
+    nodes1, nodes2 : iterables of nodes, or (node, datadict) tuples
+
+    Returns
+    -------
+    bool
+        True if nodes are equal, False otherwise.
+    """
+    nlist1 = list(nodes1)
+    nlist2 = list(nodes2)
+    try:
+        d1 = dict(nlist1)
+        d2 = dict(nlist2)
+    except (ValueError, TypeError):
+        d1 = dict.fromkeys(nlist1)
+        d2 = dict.fromkeys(nlist2)
+    return d1 == d2
+
+
+def edges_equal(edges1, edges2):
+    """Check if edges are equal.
+
+    Equality here means equal as Python objects.
+    Edge data must match if included.
+    The order of the edges is not relevant.
+
+    Parameters
+    ----------
+    edges1, edges2 : iterables of with u, v nodes as
+        edge tuples (u, v), or
+        edge tuples with data dicts (u, v, d), or
+        edge tuples with keys and data dicts (u, v, k, d)
+
+    Returns
+    -------
+    bool
+        True if edges are equal, False otherwise.
+    """
+    from collections import defaultdict
+
+    d1 = defaultdict(dict)
+    d2 = defaultdict(dict)
+    c1 = 0
+    for c1, e in enumerate(edges1):
+        u, v = e[0], e[1]
+        data = [e[2:]]
+        if v in d1[u]:
+            data = d1[u][v] + data
+        d1[u][v] = data
+        d1[v][u] = data
+    c2 = 0
+    for c2, e in enumerate(edges2):
+        u, v = e[0], e[1]
+        data = [e[2:]]
+        if v in d2[u]:
+            data = d2[u][v] + data
+        d2[u][v] = data
+        d2[v][u] = data
+    if c1 != c2:
+        return False
+    # can check one direction because lengths are the same.
+    for n, nbrdict in d1.items():
+        for nbr, datalist in nbrdict.items():
+            if n not in d2:
+                return False
+            if nbr not in d2[n]:
+                return False
+            d2datalist = d2[n][nbr]
+            for data in datalist:
+                if datalist.count(data) != d2datalist.count(data):
+                    return False
+    return True
+
+
+def graphs_equal(graph1, graph2):
+    """Check if graphs are equal.
+
+    Equality here means equal as Python objects (not isomorphism).
+    Node, edge and graph data must match.
+
+    Parameters
+    ----------
+    graph1, graph2 : graph
+
+    Returns
+    -------
+    bool
+        True if graphs are equal, False otherwise.
+    """
+    return (
+        graph1.adj == graph2.adj
+        and graph1.nodes == graph2.nodes
+        and graph1.graph == graph2.graph
+    )

.CondaPkg/env/Lib/site-packages/networkx/utils/random_sequence.py

@@ -0,0 +1,164 @@
+"""
+Utilities for generating random numbers, random sequences, and
+random selections.
+"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "powerlaw_sequence",
+    "zipf_rv",
+    "cumulative_distribution",
+    "discrete_sequence",
+    "random_weighted_sample",
+    "weighted_choice",
+]
+
+
+# The same helpers for choosing random sequences from distributions
+# uses Python's random module
+# https://docs.python.org/3/library/random.html
+
+
+@py_random_state(2)
+def powerlaw_sequence(n, exponent=2.0, seed=None):
+    """
+    Return sample sequence of length n from a power law distribution.
+    """
+    return [seed.paretovariate(exponent - 1) for i in range(n)]
+
+
+@py_random_state(2)
+def zipf_rv(alpha, xmin=1, seed=None):
+    r"""Returns a random value chosen from the Zipf distribution.
+
+    The return value is an integer drawn from the probability distribution
+
+    .. math::
+
+        p(x)=\frac{x^{-\alpha}}{\zeta(\alpha, x_{\min})},
+
+    where $\zeta(\alpha, x_{\min})$ is the Hurwitz zeta function.
+
+    Parameters
+    ----------
+    alpha : float
+      Exponent value of the distribution
+    xmin : int
+      Minimum value
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    x : int
+      Random value from Zipf distribution
+
+    Raises
+    ------
+    ValueError:
+      If xmin < 1 or
+      If alpha <= 1
+
+    Notes
+    -----
+    The rejection algorithm generates random values for a the power-law
+    distribution in uniformly bounded expected time dependent on
+    parameters.  See [1]_ for details on its operation.
+
+    Examples
+    --------
+    >>> nx.utils.zipf_rv(alpha=2, xmin=3, seed=42)
+    8
+
+    References
+    ----------
+    .. [1] Luc Devroye, Non-Uniform Random Variate Generation,
+       Springer-Verlag, New York, 1986.
+    """
+    if xmin < 1:
+        raise ValueError("xmin < 1")
+    if alpha <= 1:
+        raise ValueError("a <= 1.0")
+    a1 = alpha - 1.0
+    b = 2**a1
+    while True:
+        u = 1.0 - seed.random()  # u in (0,1]
+        v = seed.random()  # v in [0,1)
+        x = int(xmin * u ** -(1.0 / a1))
+        t = (1.0 + (1.0 / x)) ** a1
+        if v * x * (t - 1.0) / (b - 1.0) <= t / b:
+            break
+    return x
+
+
+def cumulative_distribution(distribution):
+    """Returns normalized cumulative distribution from discrete distribution."""
+
+    cdf = [0.0]
+    psum = sum(distribution)
+    for i in range(0, len(distribution)):
+        cdf.append(cdf[i] + distribution[i] / psum)
+    return cdf
+
+
+@py_random_state(3)
+def discrete_sequence(n, distribution=None, cdistribution=None, seed=None):
+    """
+    Return sample sequence of length n from a given discrete distribution
+    or discrete cumulative distribution.
+
+    One of the following must be specified.
+
+    distribution = histogram of values, will be normalized
+
+    cdistribution = normalized discrete cumulative distribution
+
+    """
+    import bisect
+
+    if cdistribution is not None:
+        cdf = cdistribution
+    elif distribution is not None:
+        cdf = cumulative_distribution(distribution)
+    else:
+        raise nx.NetworkXError(
+            "discrete_sequence: distribution or cdistribution missing"
+        )
+
+    # get a uniform random number
+    inputseq = [seed.random() for i in range(n)]
+
+    # choose from CDF
+    seq = [bisect.bisect_left(cdf, s) - 1 for s in inputseq]
+    return seq
+
+
+@py_random_state(2)
+def random_weighted_sample(mapping, k, seed=None):
+    """Returns k items without replacement from a weighted sample.
+
+    The input is a dictionary of items with weights as values.
+    """
+    if k > len(mapping):
+        raise ValueError("sample larger than population")
+    sample = set()
+    while len(sample) < k:
+        sample.add(weighted_choice(mapping, seed))
+    return list(sample)
+
+
+@py_random_state(1)
+def weighted_choice(mapping, seed=None):
+    """Returns a single element from a weighted sample.
+
+    The input is a dictionary of items with weights as values.
+    """
+    # use roulette method
+    rnd = seed.random() * sum(mapping.values())
+    for k, w in mapping.items():
+        rnd -= w
+        if rnd < 0:
+            return k

.CondaPkg/env/Lib/site-packages/networkx/utils/rcm.py

@@ -0,0 +1,158 @@
+"""
+Cuthill-McKee ordering of graph nodes to produce sparse matrices
+"""
+from collections import deque
+from operator import itemgetter
+
+import networkx as nx
+
+from ..utils import arbitrary_element
+
+__all__ = ["cuthill_mckee_ordering", "reverse_cuthill_mckee_ordering"]
+
+
+def cuthill_mckee_ordering(G, heuristic=None):
+    """Generate an ordering (permutation) of the graph nodes to make
+    a sparse matrix.
+
+    Uses the Cuthill-McKee heuristic (based on breadth-first search) [1]_.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    heuristic : function, optional
+      Function to choose starting node for RCM algorithm.  If None
+      a node from a pseudo-peripheral pair is used.  A user-defined function
+      can be supplied that takes a graph object and returns a single node.
+
+    Returns
+    -------
+    nodes : generator
+       Generator of nodes in Cuthill-McKee ordering.
+
+    Examples
+    --------
+    >>> from networkx.utils import cuthill_mckee_ordering
+    >>> G = nx.path_graph(4)
+    >>> rcm = list(cuthill_mckee_ordering(G))
+    >>> A = nx.adjacency_matrix(G, nodelist=rcm)
+
+    Smallest degree node as heuristic function:
+
+    >>> def smallest_degree(G):
+    ...     return min(G, key=G.degree)
+    >>> rcm = list(cuthill_mckee_ordering(G, heuristic=smallest_degree))
+
+
+    See Also
+    --------
+    reverse_cuthill_mckee_ordering
+
+    Notes
+    -----
+    The optimal solution the bandwidth reduction is NP-complete [2]_.
+
+
+    References
+    ----------
+    .. [1] E. Cuthill and J. McKee.
+       Reducing the bandwidth of sparse symmetric matrices,
+       In Proc. 24th Nat. Conf. ACM, pages 157-172, 1969.
+       http://doi.acm.org/10.1145/800195.805928
+    .. [2]  Steven S. Skiena. 1997. The Algorithm Design Manual.
+       Springer-Verlag New York, Inc., New York, NY, USA.
+    """
+    for c in nx.connected_components(G):
+        yield from connected_cuthill_mckee_ordering(G.subgraph(c), heuristic)
+
+
+def reverse_cuthill_mckee_ordering(G, heuristic=None):
+    """Generate an ordering (permutation) of the graph nodes to make
+    a sparse matrix.
+
+    Uses the reverse Cuthill-McKee heuristic (based on breadth-first search)
+    [1]_.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    heuristic : function, optional
+      Function to choose starting node for RCM algorithm.  If None
+      a node from a pseudo-peripheral pair is used.  A user-defined function
+      can be supplied that takes a graph object and returns a single node.
+
+    Returns
+    -------
+    nodes : generator
+       Generator of nodes in reverse Cuthill-McKee ordering.
+
+    Examples
+    --------
+    >>> from networkx.utils import reverse_cuthill_mckee_ordering
+    >>> G = nx.path_graph(4)
+    >>> rcm = list(reverse_cuthill_mckee_ordering(G))
+    >>> A = nx.adjacency_matrix(G, nodelist=rcm)
+
+    Smallest degree node as heuristic function:
+
+    >>> def smallest_degree(G):
+    ...     return min(G, key=G.degree)
+    >>> rcm = list(reverse_cuthill_mckee_ordering(G, heuristic=smallest_degree))
+
+
+    See Also
+    --------
+    cuthill_mckee_ordering
+
+    Notes
+    -----
+    The optimal solution the bandwidth reduction is NP-complete [2]_.
+
+    References
+    ----------
+    .. [1] E. Cuthill and J. McKee.
+       Reducing the bandwidth of sparse symmetric matrices,
+       In Proc. 24th Nat. Conf. ACM, pages 157-72, 1969.
+       http://doi.acm.org/10.1145/800195.805928
+    .. [2]  Steven S. Skiena. 1997. The Algorithm Design Manual.
+       Springer-Verlag New York, Inc., New York, NY, USA.
+    """
+    return reversed(list(cuthill_mckee_ordering(G, heuristic=heuristic)))
+
+
+def connected_cuthill_mckee_ordering(G, heuristic=None):
+    # the cuthill mckee algorithm for connected graphs
+    if heuristic is None:
+        start = pseudo_peripheral_node(G)
+    else:
+        start = heuristic(G)
+    visited = {start}
+    queue = deque([start])
+    while queue:
+        parent = queue.popleft()
+        yield parent
+        nd = sorted(G.degree(set(G[parent]) - visited), key=itemgetter(1))
+        children = [n for n, d in nd]
+        visited.update(children)
+        queue.extend(children)
+
+
+def pseudo_peripheral_node(G):
+    # helper for cuthill-mckee to find a node in a "pseudo peripheral pair"
+    # to use as good starting node
+    u = arbitrary_element(G)
+    lp = 0
+    v = u
+    while True:
+        spl = dict(nx.shortest_path_length(G, v))
+        l = max(spl.values())
+        if l <= lp:
+            break
+        lp = l
+        farthest = (n for n, dist in spl.items() if dist == l)
+        v, deg = min(G.degree(farthest), key=itemgetter(1))
+    return v

.CondaPkg/env/Lib/site-packages/networkx/utils/tests/test__init.py

@@ -0,0 +1,11 @@
+import pytest
+
+
+def test_utils_namespace():
+    """Ensure objects are not unintentionally exposed in utils namespace."""
+    with pytest.raises(ImportError):
+        from networkx.utils import nx
+    with pytest.raises(ImportError):
+        from networkx.utils import sys
+    with pytest.raises(ImportError):
+        from networkx.utils import defaultdict, deque

.CondaPkg/env/Lib/site-packages/networkx/utils/tests/test_decorators.py

@@ -0,0 +1,491 @@
+import os
+import pathlib
+import random
+import tempfile
+
+import pytest
+
+import networkx as nx
+from networkx.utils.decorators import (
+    argmap,
+    not_implemented_for,
+    np_random_state,
+    open_file,
+    py_random_state,
+)
+from networkx.utils.misc import PythonRandomInterface
+
+
+def test_not_implemented_decorator():
+    @not_implemented_for("directed")
+    def test_d(G):
+        pass
+
+    test_d(nx.Graph())
+    with pytest.raises(nx.NetworkXNotImplemented):
+        test_d(nx.DiGraph())
+
+    @not_implemented_for("undirected")
+    def test_u(G):
+        pass
+
+    test_u(nx.DiGraph())
+    with pytest.raises(nx.NetworkXNotImplemented):
+        test_u(nx.Graph())
+
+    @not_implemented_for("multigraph")
+    def test_m(G):
+        pass
+
+    test_m(nx.Graph())
+    with pytest.raises(nx.NetworkXNotImplemented):
+        test_m(nx.MultiGraph())
+
+    @not_implemented_for("graph")
+    def test_g(G):
+        pass
+
+    test_g(nx.MultiGraph())
+    with pytest.raises(nx.NetworkXNotImplemented):
+        test_g(nx.Graph())
+
+    # not MultiDiGraph  (multiple arguments => AND)
+    @not_implemented_for("directed", "multigraph")
+    def test_not_md(G):
+        pass
+
+    test_not_md(nx.Graph())
+    test_not_md(nx.DiGraph())
+    test_not_md(nx.MultiGraph())
+    with pytest.raises(nx.NetworkXNotImplemented):
+        test_not_md(nx.MultiDiGraph())
+
+    # Graph only      (multiple decorators =>  OR)
+    @not_implemented_for("directed")
+    @not_implemented_for("multigraph")
+    def test_graph_only(G):
+        pass
+
+    test_graph_only(nx.Graph())
+    with pytest.raises(nx.NetworkXNotImplemented):
+        test_graph_only(nx.DiGraph())
+    with pytest.raises(nx.NetworkXNotImplemented):
+        test_graph_only(nx.MultiGraph())
+    with pytest.raises(nx.NetworkXNotImplemented):
+        test_graph_only(nx.MultiDiGraph())
+
+    with pytest.raises(ValueError):
+        not_implemented_for("directed", "undirected")
+
+    with pytest.raises(ValueError):
+        not_implemented_for("multigraph", "graph")
+
+
+def test_not_implemented_decorator_key():
+    with pytest.raises(KeyError):
+
+        @not_implemented_for("foo")
+        def test1(G):
+            pass
+
+        test1(nx.Graph())
+
+
+def test_not_implemented_decorator_raise():
+    with pytest.raises(nx.NetworkXNotImplemented):
+
+        @not_implemented_for("graph")
+        def test1(G):
+            pass
+
+        test1(nx.Graph())
+
+
+class TestOpenFileDecorator:
+    def setup_method(self):
+        self.text = ["Blah... ", "BLAH ", "BLAH!!!!"]
+        self.fobj = tempfile.NamedTemporaryFile("wb+", delete=False)
+        self.name = self.fobj.name
+
+    def teardown_method(self):
+        self.fobj.close()
+        os.unlink(self.name)
+
+    def write(self, path):
+        for text in self.text:
+            path.write(text.encode("ascii"))
+
+    @open_file(1, "r")
+    def read(self, path):
+        return path.readlines()[0]
+
+    @staticmethod
+    @open_file(0, "wb")
+    def writer_arg0(path):
+        path.write(b"demo")
+
+    @open_file(1, "wb+")
+    def writer_arg1(self, path):
+        self.write(path)
+
+    @open_file(2, "wb")
+    def writer_arg2default(self, x, path=None):
+        if path is None:
+            with tempfile.NamedTemporaryFile("wb+") as fh:
+                self.write(fh)
+        else:
+            self.write(path)
+
+    @open_file(4, "wb")
+    def writer_arg4default(self, x, y, other="hello", path=None, **kwargs):
+        if path is None:
+            with tempfile.NamedTemporaryFile("wb+") as fh:
+                self.write(fh)
+        else:
+            self.write(path)
+
+    @open_file("path", "wb")
+    def writer_kwarg(self, **kwargs):
+        path = kwargs.get("path", None)
+        if path is None:
+            with tempfile.NamedTemporaryFile("wb+") as fh:
+                self.write(fh)
+        else:
+            self.write(path)
+
+    def test_writer_arg0_str(self):
+        self.writer_arg0(self.name)
+
+    def test_writer_arg0_fobj(self):
+        self.writer_arg0(self.fobj)
+
+    def test_writer_arg0_pathlib(self):
+        self.writer_arg0(pathlib.Path(self.name))
+
+    def test_writer_arg1_str(self):
+        self.writer_arg1(self.name)
+        assert self.read(self.name) == "".join(self.text)
+
+    def test_writer_arg1_fobj(self):
+        self.writer_arg1(self.fobj)
+        assert not self.fobj.closed
+        self.fobj.close()
+        assert self.read(self.name) == "".join(self.text)
+
+    def test_writer_arg2default_str(self):
+        self.writer_arg2default(0, path=None)
+        self.writer_arg2default(0, path=self.name)
+        assert self.read(self.name) == "".join(self.text)
+
+    def test_writer_arg2default_fobj(self):
+        self.writer_arg2default(0, path=self.fobj)
+        assert not self.fobj.closed
+        self.fobj.close()
+        assert self.read(self.name) == "".join(self.text)
+
+    def test_writer_arg2default_fobj_path_none(self):
+        self.writer_arg2default(0, path=None)
+
+    def test_writer_arg4default_fobj(self):
+        self.writer_arg4default(0, 1, dog="dog", other="other")
+        self.writer_arg4default(0, 1, dog="dog", other="other", path=self.name)
+        assert self.read(self.name) == "".join(self.text)
+
+    def test_writer_kwarg_str(self):
+        self.writer_kwarg(path=self.name)
+        assert self.read(self.name) == "".join(self.text)
+
+    def test_writer_kwarg_fobj(self):
+        self.writer_kwarg(path=self.fobj)
+        self.fobj.close()
+        assert self.read(self.name) == "".join(self.text)
+
+    def test_writer_kwarg_path_none(self):
+        self.writer_kwarg(path=None)
+
+
+class TestRandomState:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+
+    @np_random_state(1)
+    def instantiate_np_random_state(self, random_state):
+        assert isinstance(random_state, np.random.RandomState)
+        return random_state.random_sample()
+
+    @py_random_state(1)
+    def instantiate_py_random_state(self, random_state):
+        assert isinstance(random_state, (random.Random, PythonRandomInterface))
+        return random_state.random()
+
+    def test_random_state_None(self):
+        np.random.seed(42)
+        rv = np.random.random_sample()
+        np.random.seed(42)
+        assert rv == self.instantiate_np_random_state(None)
+
+        random.seed(42)
+        rv = random.random()
+        random.seed(42)
+        assert rv == self.instantiate_py_random_state(None)
+
+    def test_random_state_np_random(self):
+        np.random.seed(42)
+        rv = np.random.random_sample()
+        np.random.seed(42)
+        assert rv == self.instantiate_np_random_state(np.random)
+        np.random.seed(42)
+        assert rv == self.instantiate_py_random_state(np.random)
+
+    def test_random_state_int(self):
+        np.random.seed(42)
+        np_rv = np.random.random_sample()
+        random.seed(42)
+        py_rv = random.random()
+
+        np.random.seed(42)
+        seed = 1
+        rval = self.instantiate_np_random_state(seed)
+        rval_expected = np.random.RandomState(seed).rand()
+        assert rval, rval_expected
+        # test that global seed wasn't changed in function
+        assert np_rv == np.random.random_sample()
+
+        random.seed(42)
+        rval = self.instantiate_py_random_state(seed)
+        rval_expected = random.Random(seed).random()
+        assert rval, rval_expected
+        # test that global seed wasn't changed in function
+        assert py_rv == random.random()
+
+    def test_random_state_np_random_RandomState(self):
+        np.random.seed(42)
+        np_rv = np.random.random_sample()
+
+        np.random.seed(42)
+        seed = 1
+        rng = np.random.RandomState(seed)
+        rval = self.instantiate_np_random_state(seed)
+        rval_expected = np.random.RandomState(seed).rand()
+        assert rval, rval_expected
+
+        rval = self.instantiate_py_random_state(seed)
+        rval_expected = np.random.RandomState(seed).rand()
+        assert rval, rval_expected
+        # test that global seed wasn't changed in function
+        assert np_rv == np.random.random_sample()
+
+    def test_random_state_py_random(self):
+        seed = 1
+        rng = random.Random(seed)
+        rv = self.instantiate_py_random_state(rng)
+        assert rv, random.Random(seed).random()
+
+        pytest.raises(ValueError, self.instantiate_np_random_state, rng)
+
+
+def test_random_state_string_arg_index():
+    with pytest.raises(nx.NetworkXError):
+
+        @np_random_state("a")
+        def make_random_state(rs):
+            pass
+
+        rstate = make_random_state(1)
+
+
+def test_py_random_state_string_arg_index():
+    with pytest.raises(nx.NetworkXError):
+
+        @py_random_state("a")
+        def make_random_state(rs):
+            pass
+
+        rstate = make_random_state(1)
+
+
+def test_random_state_invalid_arg_index():
+    with pytest.raises(nx.NetworkXError):
+
+        @np_random_state(2)
+        def make_random_state(rs):
+            pass
+
+        rstate = make_random_state(1)
+
+
+def test_py_random_state_invalid_arg_index():
+    with pytest.raises(nx.NetworkXError):
+
+        @py_random_state(2)
+        def make_random_state(rs):
+            pass
+
+        rstate = make_random_state(1)
+
+
+class TestArgmap:
+    class ArgmapError(RuntimeError):
+        pass
+
+    def test_trivial_function(self):
+        def do_not_call(x):
+            raise ArgmapError("do not call this function")
+
+        @argmap(do_not_call)
+        def trivial_argmap():
+            return 1
+
+        assert trivial_argmap() == 1
+
+    def test_trivial_iterator(self):
+        def do_not_call(x):
+            raise ArgmapError("do not call this function")
+
+        @argmap(do_not_call)
+        def trivial_argmap():
+            yield from (1, 2, 3)
+
+        assert tuple(trivial_argmap()) == (1, 2, 3)
+
+    def test_contextmanager(self):
+        container = []
+
+        def contextmanager(x):
+            nonlocal container
+            return x, lambda: container.append(x)
+
+        @argmap(contextmanager, 0, 1, 2, try_finally=True)
+        def foo(x, y, z):
+            return x, y, z
+
+        x, y, z = foo("a", "b", "c")
+
+        # context exits are called in reverse
+        assert container == ["c", "b", "a"]
+
+    def test_tryfinally_generator(self):
+        container = []
+
+        def singleton(x):
+            return (x,)
+
+        with pytest.raises(nx.NetworkXError):
+
+            @argmap(singleton, 0, 1, 2, try_finally=True)
+            def foo(x, y, z):
+                yield from (x, y, z)
+
+        @argmap(singleton, 0, 1, 2)
+        def foo(x, y, z):
+            return x + y + z
+
+        q = foo("a", "b", "c")
+
+        assert q == ("a", "b", "c")
+
+    def test_actual_vararg(self):
+        @argmap(lambda x: -x, 4)
+        def foo(x, y, *args):
+            return (x, y) + tuple(args)
+
+        assert foo(1, 2, 3, 4, 5, 6) == (1, 2, 3, 4, -5, 6)
+
+    def test_signature_destroying_intermediate_decorator(self):
+        def add_one_to_first_bad_decorator(f):
+            """Bad because it doesn't wrap the f signature (clobbers it)"""
+
+            def decorated(a, *args, **kwargs):
+                return f(a + 1, *args, **kwargs)
+
+            return decorated
+
+        add_two_to_second = argmap(lambda b: b + 2, 1)
+
+        @add_two_to_second
+        @add_one_to_first_bad_decorator
+        def add_one_and_two(a, b):
+            return a, b
+
+        assert add_one_and_two(5, 5) == (6, 7)
+
+    def test_actual_kwarg(self):
+        @argmap(lambda x: -x, "arg")
+        def foo(*, arg):
+            return arg
+
+        assert foo(arg=3) == -3
+
+    def test_nested_tuple(self):
+        def xform(x, y):
+            u, v = y
+            return x + u + v, (x + u, x + v)
+
+        # we're testing args and kwargs here, too
+        @argmap(xform, (0, ("t", 2)))
+        def foo(a, *args, **kwargs):
+            return a, args, kwargs
+
+        a, args, kwargs = foo(1, 2, 3, t=4)
+
+        assert a == 1 + 4 + 3
+        assert args == (2, 1 + 3)
+        assert kwargs == {"t": 1 + 4}
+
+    def test_flatten(self):
+        assert tuple(argmap._flatten([[[[[], []], [], []], [], [], []]], set())) == ()
+
+        rlist = ["a", ["b", "c"], [["d"], "e"], "f"]
+        assert "".join(argmap._flatten(rlist, set())) == "abcdef"
+
+    def test_indent(self):
+        code = "\n".join(
+            argmap._indent(
+                *[
+                    "try:",
+                    "try:",
+                    "pass#",
+                    "finally:",
+                    "pass#",
+                    "#",
+                    "finally:",
+                    "pass#",
+                ]
+            )
+        )
+        assert (
+            code
+            == """try:
+ try:
+  pass#
+ finally:
+  pass#
+ #
+finally:
+ pass#"""
+        )
+
+    def test_immediate_raise(self):
+        @not_implemented_for("directed")
+        def yield_nodes(G):
+            yield from G
+
+        G = nx.Graph([(1, 2)])
+        D = nx.DiGraph()
+
+        # test first call (argmap is compiled and executed)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            node_iter = yield_nodes(D)
+
+        # test second call (argmap is only executed)
+        with pytest.raises(nx.NetworkXNotImplemented):
+            node_iter = yield_nodes(D)
+
+        # ensure that generators still make generators
+        node_iter = yield_nodes(G)
+        next(node_iter)
+        next(node_iter)
+        with pytest.raises(StopIteration):
+            next(node_iter)

.CondaPkg/env/Lib/site-packages/networkx/utils/tests/test_heaps.py

@@ -0,0 +1,131 @@
+import pytest
+
+import networkx as nx
+from networkx.utils import BinaryHeap, PairingHeap
+
+
+class X:
+    def __eq__(self, other):
+        raise self is other
+
+    def __ne__(self, other):
+        raise self is not other
+
+    def __lt__(self, other):
+        raise TypeError("cannot compare")
+
+    def __le__(self, other):
+        raise TypeError("cannot compare")
+
+    def __ge__(self, other):
+        raise TypeError("cannot compare")
+
+    def __gt__(self, other):
+        raise TypeError("cannot compare")
+
+    def __hash__(self):
+        return hash(id(self))
+
+
+x = X()
+
+
+data = [  # min should not invent an element.
+    ("min", nx.NetworkXError),
+    # Popping an empty heap should fail.
+    ("pop", nx.NetworkXError),
+    # Getting nonexisting elements should return None.
+    ("get", 0, None),
+    ("get", x, None),
+    ("get", None, None),
+    # Inserting a new key should succeed.
+    ("insert", x, 1, True),
+    ("get", x, 1),
+    ("min", (x, 1)),
+    # min should not pop the top element.
+    ("min", (x, 1)),
+    # Inserting a new key of different type should succeed.
+    ("insert", 1, -2.0, True),
+    # int and float values should interop.
+    ("min", (1, -2.0)),
+    # pop removes minimum-valued element.
+    ("insert", 3, -(10**100), True),
+    ("insert", 4, 5, True),
+    ("pop", (3, -(10**100))),
+    ("pop", (1, -2.0)),
+    # Decrease-insert should succeed.
+    ("insert", 4, -50, True),
+    ("insert", 4, -60, False, True),
+    # Decrease-insert should not create duplicate keys.
+    ("pop", (4, -60)),
+    ("pop", (x, 1)),
+    # Popping all elements should empty the heap.
+    ("min", nx.NetworkXError),
+    ("pop", nx.NetworkXError),
+    # Non-value-changing insert should fail.
+    ("insert", x, 0, True),
+    ("insert", x, 0, False, False),
+    ("min", (x, 0)),
+    ("insert", x, 0, True, False),
+    ("min", (x, 0)),
+    # Failed insert should not create duplicate keys.
+    ("pop", (x, 0)),
+    ("pop", nx.NetworkXError),
+    # Increase-insert should succeed when allowed.
+    ("insert", None, 0, True),
+    ("insert", 2, -1, True),
+    ("min", (2, -1)),
+    ("insert", 2, 1, True, False),
+    ("min", (None, 0)),
+    # Increase-insert should fail when disallowed.
+    ("insert", None, 2, False, False),
+    ("min", (None, 0)),
+    # Failed increase-insert should not create duplicate keys.
+    ("pop", (None, 0)),
+    ("pop", (2, 1)),
+    ("min", nx.NetworkXError),
+    ("pop", nx.NetworkXError),
+]
+
+
+def _test_heap_class(cls, *args, **kwargs):
+    heap = cls(*args, **kwargs)
+    # Basic behavioral test
+    for op in data:
+        if op[-1] is not nx.NetworkXError:
+            assert op[-1] == getattr(heap, op[0])(*op[1:-1])
+        else:
+            pytest.raises(op[-1], getattr(heap, op[0]), *op[1:-1])
+    # Coverage test.
+    for i in range(99, -1, -1):
+        assert heap.insert(i, i)
+    for i in range(50):
+        assert heap.pop() == (i, i)
+    for i in range(100):
+        assert heap.insert(i, i) == (i < 50)
+    for i in range(100):
+        assert not heap.insert(i, i + 1)
+    for i in range(50):
+        assert heap.pop() == (i, i)
+    for i in range(100):
+        assert heap.insert(i, i + 1) == (i < 50)
+    for i in range(49):
+        assert heap.pop() == (i, i + 1)
+    assert sorted([heap.pop(), heap.pop()]) == [(49, 50), (50, 50)]
+    for i in range(51, 100):
+        assert not heap.insert(i, i + 1, True)
+    for i in range(51, 70):
+        assert heap.pop() == (i, i + 1)
+    for i in range(100):
+        assert heap.insert(i, i)
+    for i in range(100):
+        assert heap.pop() == (i, i)
+    pytest.raises(nx.NetworkXError, heap.pop)
+
+
+def test_PairingHeap():
+    _test_heap_class(PairingHeap)
+
+
+def test_BinaryHeap():
+    _test_heap_class(BinaryHeap)

.CondaPkg/env/Lib/site-packages/networkx/utils/tests/test_mapped_queue.py

@@ -0,0 +1,268 @@
+import pytest
+
+from networkx.utils.mapped_queue import MappedQueue, _HeapElement
+
+
+def test_HeapElement_gtlt():
+    bar = _HeapElement(1.1, "a")
+    foo = _HeapElement(1, "b")
+    assert foo < bar
+    assert bar > foo
+    assert foo < 1.1
+    assert 1 < bar
+
+
+def test_HeapElement_gtlt_tied_priority():
+    bar = _HeapElement(1, "a")
+    foo = _HeapElement(1, "b")
+    assert foo > bar
+    assert bar < foo
+
+
+def test_HeapElement_eq():
+    bar = _HeapElement(1.1, "a")
+    foo = _HeapElement(1, "a")
+    assert foo == bar
+    assert bar == foo
+    assert foo == "a"
+
+
+def test_HeapElement_iter():
+    foo = _HeapElement(1, "a")
+    bar = _HeapElement(1.1, (3, 2, 1))
+    assert list(foo) == [1, "a"]
+    assert list(bar) == [1.1, 3, 2, 1]
+
+
+def test_HeapElement_getitem():
+    foo = _HeapElement(1, "a")
+    bar = _HeapElement(1.1, (3, 2, 1))
+    assert foo[1] == "a"
+    assert foo[0] == 1
+    assert bar[0] == 1.1
+    assert bar[2] == 2
+    assert bar[3] == 1
+    pytest.raises(IndexError, bar.__getitem__, 4)
+    pytest.raises(IndexError, foo.__getitem__, 2)
+
+
+class TestMappedQueue:
+    def setup_method(self):
+        pass
+
+    def _check_map(self, q):
+        assert q.position == {elt: pos for pos, elt in enumerate(q.heap)}
+
+    def _make_mapped_queue(self, h):
+        q = MappedQueue()
+        q.heap = h
+        q.position = {elt: pos for pos, elt in enumerate(h)}
+        return q
+
+    def test_heapify(self):
+        h = [5, 4, 3, 2, 1, 0]
+        q = self._make_mapped_queue(h)
+        q._heapify()
+        self._check_map(q)
+
+    def test_init(self):
+        h = [5, 4, 3, 2, 1, 0]
+        q = MappedQueue(h)
+        self._check_map(q)
+
+    def test_incomparable(self):
+        h = [5, 4, "a", 2, 1, 0]
+        pytest.raises(TypeError, MappedQueue, h)
+
+    def test_len(self):
+        h = [5, 4, 3, 2, 1, 0]
+        q = MappedQueue(h)
+        self._check_map(q)
+        assert len(q) == 6
+
+    def test_siftup_leaf(self):
+        h = [2]
+        h_sifted = [2]
+        q = self._make_mapped_queue(h)
+        q._siftup(0)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_siftup_one_child(self):
+        h = [2, 0]
+        h_sifted = [0, 2]
+        q = self._make_mapped_queue(h)
+        q._siftup(0)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_siftup_left_child(self):
+        h = [2, 0, 1]
+        h_sifted = [0, 2, 1]
+        q = self._make_mapped_queue(h)
+        q._siftup(0)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_siftup_right_child(self):
+        h = [2, 1, 0]
+        h_sifted = [0, 1, 2]
+        q = self._make_mapped_queue(h)
+        q._siftup(0)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_siftup_multiple(self):
+        h = [0, 1, 2, 4, 3, 5, 6]
+        h_sifted = [0, 1, 2, 4, 3, 5, 6]
+        q = self._make_mapped_queue(h)
+        q._siftup(0)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_siftdown_leaf(self):
+        h = [2]
+        h_sifted = [2]
+        q = self._make_mapped_queue(h)
+        q._siftdown(0, 0)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_siftdown_single(self):
+        h = [1, 0]
+        h_sifted = [0, 1]
+        q = self._make_mapped_queue(h)
+        q._siftdown(0, len(h) - 1)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_siftdown_multiple(self):
+        h = [1, 2, 3, 4, 5, 6, 7, 0]
+        h_sifted = [0, 1, 3, 2, 5, 6, 7, 4]
+        q = self._make_mapped_queue(h)
+        q._siftdown(0, len(h) - 1)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_push(self):
+        to_push = [6, 1, 4, 3, 2, 5, 0]
+        h_sifted = [0, 2, 1, 6, 3, 5, 4]
+        q = MappedQueue()
+        for elt in to_push:
+            q.push(elt)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_push_duplicate(self):
+        to_push = [2, 1, 0]
+        h_sifted = [0, 2, 1]
+        q = MappedQueue()
+        for elt in to_push:
+            inserted = q.push(elt)
+            assert inserted
+        assert q.heap == h_sifted
+        self._check_map(q)
+        inserted = q.push(1)
+        assert not inserted
+
+    def test_pop(self):
+        h = [3, 4, 6, 0, 1, 2, 5]
+        h_sorted = sorted(h)
+        q = self._make_mapped_queue(h)
+        q._heapify()
+        popped = [q.pop() for _ in range(len(h))]
+        assert popped == h_sorted
+        self._check_map(q)
+
+    def test_remove_leaf(self):
+        h = [0, 2, 1, 6, 3, 5, 4]
+        h_removed = [0, 2, 1, 6, 4, 5]
+        q = self._make_mapped_queue(h)
+        removed = q.remove(3)
+        assert q.heap == h_removed
+
+    def test_remove_root(self):
+        h = [0, 2, 1, 6, 3, 5, 4]
+        h_removed = [1, 2, 4, 6, 3, 5]
+        q = self._make_mapped_queue(h)
+        removed = q.remove(0)
+        assert q.heap == h_removed
+
+    def test_update_leaf(self):
+        h = [0, 20, 10, 60, 30, 50, 40]
+        h_updated = [0, 15, 10, 60, 20, 50, 40]
+        q = self._make_mapped_queue(h)
+        removed = q.update(30, 15)
+        assert q.heap == h_updated
+
+    def test_update_root(self):
+        h = [0, 20, 10, 60, 30, 50, 40]
+        h_updated = [10, 20, 35, 60, 30, 50, 40]
+        q = self._make_mapped_queue(h)
+        removed = q.update(0, 35)
+        assert q.heap == h_updated
+
+
+class TestMappedDict(TestMappedQueue):
+    def _make_mapped_queue(self, h):
+        priority_dict = {elt: elt for elt in h}
+        return MappedQueue(priority_dict)
+
+    def test_init(self):
+        d = {5: 0, 4: 1, "a": 2, 2: 3, 1: 4}
+        q = MappedQueue(d)
+        assert q.position == d
+
+    def test_ties(self):
+        d = {5: 0, 4: 1, 3: 2, 2: 3, 1: 4}
+        q = MappedQueue(d)
+        assert q.position == {elt: pos for pos, elt in enumerate(q.heap)}
+
+    def test_pop(self):
+        d = {5: 0, 4: 1, 3: 2, 2: 3, 1: 4}
+        q = MappedQueue(d)
+        assert q.pop() == _HeapElement(0, 5)
+        assert q.position == {elt: pos for pos, elt in enumerate(q.heap)}
+
+    def test_empty_pop(self):
+        q = MappedQueue()
+        pytest.raises(IndexError, q.pop)
+
+    def test_incomparable_ties(self):
+        d = {5: 0, 4: 0, "a": 0, 2: 0, 1: 0}
+        pytest.raises(TypeError, MappedQueue, d)
+
+    def test_push(self):
+        to_push = [6, 1, 4, 3, 2, 5, 0]
+        h_sifted = [0, 2, 1, 6, 3, 5, 4]
+        q = MappedQueue()
+        for elt in to_push:
+            q.push(elt, priority=elt)
+        assert q.heap == h_sifted
+        self._check_map(q)
+
+    def test_push_duplicate(self):
+        to_push = [2, 1, 0]
+        h_sifted = [0, 2, 1]
+        q = MappedQueue()
+        for elt in to_push:
+            inserted = q.push(elt, priority=elt)
+            assert inserted
+        assert q.heap == h_sifted
+        self._check_map(q)
+        inserted = q.push(1, priority=1)
+        assert not inserted
+
+    def test_update_leaf(self):
+        h = [0, 20, 10, 60, 30, 50, 40]
+        h_updated = [0, 15, 10, 60, 20, 50, 40]
+        q = self._make_mapped_queue(h)
+        removed = q.update(30, 15, priority=15)
+        assert q.heap == h_updated
+
+    def test_update_root(self):
+        h = [0, 20, 10, 60, 30, 50, 40]
+        h_updated = [10, 20, 35, 60, 30, 50, 40]
+        q = self._make_mapped_queue(h)
+        removed = q.update(0, 35, priority=35)
+        assert q.heap == h_updated

.CondaPkg/env/Lib/site-packages/networkx/utils/tests/test_misc.py

@@ -0,0 +1,255 @@
+import random
+from copy import copy
+
+import pytest
+
+import networkx as nx
+from networkx.utils import (
+    PythonRandomInterface,
+    arbitrary_element,
+    create_py_random_state,
+    create_random_state,
+    dict_to_numpy_array,
+    discrete_sequence,
+    flatten,
+    groups,
+    make_list_of_ints,
+    pairwise,
+    powerlaw_sequence,
+)
+from networkx.utils.misc import _dict_to_numpy_array1, _dict_to_numpy_array2
+
+nested_depth = (
+    1,
+    2,
+    (3, 4, ((5, 6, (7,), (8, (9, 10), 11), (12, 13, (14, 15)), 16), 17), 18, 19),
+    20,
+)
+
+nested_set = {
+    (1, 2, 3, 4),
+    (5, 6, 7, 8, 9),
+    (10, 11, (12, 13, 14), (15, 16, 17, 18)),
+    19,
+    20,
+}
+
+nested_mixed = [
+    1,
+    (2, 3, {4, (5, 6), 7}, [8, 9]),
+    {10: "foo", 11: "bar", (12, 13): "baz"},
+    {(14, 15): "qwe", 16: "asd"},
+    (17, (18, "19"), 20),
+]
+
+
+@pytest.mark.parametrize("result", [None, [], ["existing"], ["existing1", "existing2"]])
+@pytest.mark.parametrize("nested", [nested_depth, nested_mixed, nested_set])
+def test_flatten(nested, result):
+    if result is None:
+        val = flatten(nested, result)
+        assert len(val) == 20
+    else:
+        _result = copy(result)  # because pytest passes parameters as is
+        nexisting = len(_result)
+        val = flatten(nested, _result)
+        assert len(val) == len(_result) == 20 + nexisting
+
+    assert issubclass(type(val), tuple)
+
+
+def test_make_list_of_ints():
+    mylist = [1, 2, 3.0, 42, -2]
+    assert make_list_of_ints(mylist) is mylist
+    assert make_list_of_ints(mylist) == mylist
+    assert type(make_list_of_ints(mylist)[2]) is int
+    pytest.raises(nx.NetworkXError, make_list_of_ints, [1, 2, 3, "kermit"])
+    pytest.raises(nx.NetworkXError, make_list_of_ints, [1, 2, 3.1])
+
+
+def test_random_number_distribution():
+    # smoke test only
+    z = powerlaw_sequence(20, exponent=2.5)
+    z = discrete_sequence(20, distribution=[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 3])
+
+
+class TestNumpyArray:
+    @classmethod
+    def setup_class(cls):
+        global np
+        np = pytest.importorskip("numpy")
+
+    def test_numpy_to_list_of_ints(self):
+        a = np.array([1, 2, 3], dtype=np.int64)
+        b = np.array([1.0, 2, 3])
+        c = np.array([1.1, 2, 3])
+        assert type(make_list_of_ints(a)) == list
+        assert make_list_of_ints(b) == list(b)
+        B = make_list_of_ints(b)
+        assert type(B[0]) == int
+        pytest.raises(nx.NetworkXError, make_list_of_ints, c)
+
+    def test__dict_to_numpy_array1(self):
+        d = {"a": 1, "b": 2}
+        a = _dict_to_numpy_array1(d, mapping={"a": 0, "b": 1})
+        np.testing.assert_allclose(a, np.array([1, 2]))
+        a = _dict_to_numpy_array1(d, mapping={"b": 0, "a": 1})
+        np.testing.assert_allclose(a, np.array([2, 1]))
+
+        a = _dict_to_numpy_array1(d)
+        np.testing.assert_allclose(a.sum(), 3)
+
+    def test__dict_to_numpy_array2(self):
+        d = {"a": {"a": 1, "b": 2}, "b": {"a": 10, "b": 20}}
+
+        mapping = {"a": 1, "b": 0}
+        a = _dict_to_numpy_array2(d, mapping=mapping)
+        np.testing.assert_allclose(a, np.array([[20, 10], [2, 1]]))
+
+        a = _dict_to_numpy_array2(d)
+        np.testing.assert_allclose(a.sum(), 33)
+
+    def test_dict_to_numpy_array_a(self):
+        d = {"a": {"a": 1, "b": 2}, "b": {"a": 10, "b": 20}}
+
+        mapping = {"a": 0, "b": 1}
+        a = dict_to_numpy_array(d, mapping=mapping)
+        np.testing.assert_allclose(a, np.array([[1, 2], [10, 20]]))
+
+        mapping = {"a": 1, "b": 0}
+        a = dict_to_numpy_array(d, mapping=mapping)
+        np.testing.assert_allclose(a, np.array([[20, 10], [2, 1]]))
+
+        a = _dict_to_numpy_array2(d)
+        np.testing.assert_allclose(a.sum(), 33)
+
+    def test_dict_to_numpy_array_b(self):
+        d = {"a": 1, "b": 2}
+
+        mapping = {"a": 0, "b": 1}
+        a = dict_to_numpy_array(d, mapping=mapping)
+        np.testing.assert_allclose(a, np.array([1, 2]))
+
+        a = _dict_to_numpy_array1(d)
+        np.testing.assert_allclose(a.sum(), 3)
+
+
+def test_pairwise():
+    nodes = range(4)
+    node_pairs = [(0, 1), (1, 2), (2, 3)]
+    node_pairs_cycle = node_pairs + [(3, 0)]
+    assert list(pairwise(nodes)) == node_pairs
+    assert list(pairwise(iter(nodes))) == node_pairs
+    assert list(pairwise(nodes, cyclic=True)) == node_pairs_cycle
+    empty_iter = iter(())
+    assert list(pairwise(empty_iter)) == []
+    empty_iter = iter(())
+    assert list(pairwise(empty_iter, cyclic=True)) == []
+
+
+def test_groups():
+    many_to_one = dict(zip("abcde", [0, 0, 1, 1, 2]))
+    actual = groups(many_to_one)
+    expected = {0: {"a", "b"}, 1: {"c", "d"}, 2: {"e"}}
+    assert actual == expected
+    assert {} == groups({})
+
+
+def test_create_random_state():
+    np = pytest.importorskip("numpy")
+    rs = np.random.RandomState
+
+    assert isinstance(create_random_state(1), rs)
+    assert isinstance(create_random_state(None), rs)
+    assert isinstance(create_random_state(np.random), rs)
+    assert isinstance(create_random_state(rs(1)), rs)
+    # Support for numpy.random.Generator
+    rng = np.random.default_rng()
+    assert isinstance(create_random_state(rng), np.random.Generator)
+    pytest.raises(ValueError, create_random_state, "a")
+
+    assert np.all(rs(1).rand(10) == create_random_state(1).rand(10))
+
+
+def test_create_py_random_state():
+    pyrs = random.Random
+
+    assert isinstance(create_py_random_state(1), pyrs)
+    assert isinstance(create_py_random_state(None), pyrs)
+    assert isinstance(create_py_random_state(pyrs(1)), pyrs)
+    pytest.raises(ValueError, create_py_random_state, "a")
+
+    np = pytest.importorskip("numpy")
+
+    rs = np.random.RandomState
+    rng = np.random.default_rng(1000)
+    rng_explicit = np.random.Generator(np.random.SFC64())
+    nprs = PythonRandomInterface
+    assert isinstance(create_py_random_state(np.random), nprs)
+    assert isinstance(create_py_random_state(rs(1)), nprs)
+    assert isinstance(create_py_random_state(rng), nprs)
+    assert isinstance(create_py_random_state(rng_explicit), nprs)
+    # test default rng input
+    assert isinstance(PythonRandomInterface(), nprs)
+
+
+def test_PythonRandomInterface_RandomState():
+    np = pytest.importorskip("numpy")
+
+    rs = np.random.RandomState
+    rng = PythonRandomInterface(rs(42))
+    rs42 = rs(42)
+
+    # make sure these functions are same as expected outcome
+    assert rng.randrange(3, 5) == rs42.randint(3, 5)
+    assert rng.choice([1, 2, 3]) == rs42.choice([1, 2, 3])
+    assert rng.gauss(0, 1) == rs42.normal(0, 1)
+    assert rng.expovariate(1.5) == rs42.exponential(1 / 1.5)
+    assert np.all(rng.shuffle([1, 2, 3]) == rs42.shuffle([1, 2, 3]))
+    assert np.all(
+        rng.sample([1, 2, 3], 2) == rs42.choice([1, 2, 3], (2,), replace=False)
+    )
+    assert np.all(
+        [rng.randint(3, 5) for _ in range(100)]
+        == [rs42.randint(3, 6) for _ in range(100)]
+    )
+    assert rng.random() == rs42.random_sample()
+
+
+def test_PythonRandomInterface_Generator():
+    np = pytest.importorskip("numpy")
+
+    rng = np.random.default_rng(42)
+    pri = PythonRandomInterface(np.random.default_rng(42))
+
+    # make sure these functions are same as expected outcome
+    assert pri.randrange(3, 5) == rng.integers(3, 5)
+    assert pri.choice([1, 2, 3]) == rng.choice([1, 2, 3])
+    assert pri.gauss(0, 1) == rng.normal(0, 1)
+    assert pri.expovariate(1.5) == rng.exponential(1 / 1.5)
+    assert np.all(pri.shuffle([1, 2, 3]) == rng.shuffle([1, 2, 3]))
+    assert np.all(
+        pri.sample([1, 2, 3], 2) == rng.choice([1, 2, 3], (2,), replace=False)
+    )
+    assert np.all(
+        [pri.randint(3, 5) for _ in range(100)]
+        == [rng.integers(3, 6) for _ in range(100)]
+    )
+    assert pri.random() == rng.random()
+
+
+@pytest.mark.parametrize(
+    ("iterable_type", "expected"), ((list, 1), (tuple, 1), (str, "["), (set, 1))
+)
+def test_arbitrary_element(iterable_type, expected):
+    iterable = iterable_type([1, 2, 3])
+    assert arbitrary_element(iterable) == expected
+
+
+@pytest.mark.parametrize(
+    "iterator", ((i for i in range(3)), iter([1, 2, 3]))  # generator
+)
+def test_arbitrary_element_raises(iterator):
+    """Value error is raised when input is an iterator."""
+    with pytest.raises(ValueError, match="from an iterator"):
+        arbitrary_element(iterator)

.CondaPkg/env/Lib/site-packages/networkx/utils/tests/test_random_sequence.py

@@ -0,0 +1,38 @@
+import pytest
+
+from networkx.utils import (
+    powerlaw_sequence,
+    random_weighted_sample,
+    weighted_choice,
+    zipf_rv,
+)
+
+
+def test_degree_sequences():
+    seq = powerlaw_sequence(10, seed=1)
+    seq = powerlaw_sequence(10)
+    assert len(seq) == 10
+
+
+def test_zipf_rv():
+    r = zipf_rv(2.3, xmin=2, seed=1)
+    r = zipf_rv(2.3, 2, 1)
+    r = zipf_rv(2.3)
+    assert type(r), int
+    pytest.raises(ValueError, zipf_rv, 0.5)
+    pytest.raises(ValueError, zipf_rv, 2, xmin=0)
+
+
+def test_random_weighted_sample():
+    mapping = {"a": 10, "b": 20}
+    s = random_weighted_sample(mapping, 2, seed=1)
+    s = random_weighted_sample(mapping, 2)
+    assert sorted(s) == sorted(mapping.keys())
+    pytest.raises(ValueError, random_weighted_sample, mapping, 3)
+
+
+def test_random_weighted_choice():
+    mapping = {"a": 10, "b": 0}
+    c = weighted_choice(mapping, seed=1)
+    c = weighted_choice(mapping)
+    assert c == "a"

.CondaPkg/env/Lib/site-packages/networkx/utils/tests/test_rcm.py

@@ -0,0 +1,63 @@
+import networkx as nx
+from networkx.utils import reverse_cuthill_mckee_ordering
+
+
+def test_reverse_cuthill_mckee():
+    # example graph from
+    # http://www.boost.org/doc/libs/1_37_0/libs/graph/example/cuthill_mckee_ordering.cpp
+    G = nx.Graph(
+        [
+            (0, 3),
+            (0, 5),
+            (1, 2),
+            (1, 4),
+            (1, 6),
+            (1, 9),
+            (2, 3),
+            (2, 4),
+            (3, 5),
+            (3, 8),
+            (4, 6),
+            (5, 6),
+            (5, 7),
+            (6, 7),
+        ]
+    )
+    rcm = list(reverse_cuthill_mckee_ordering(G))
+    assert rcm in [[0, 8, 5, 7, 3, 6, 2, 4, 1, 9], [0, 8, 5, 7, 3, 6, 4, 2, 1, 9]]
+
+
+def test_rcm_alternate_heuristic():
+    # example from
+    G = nx.Graph(
+        [
+            (0, 0),
+            (0, 4),
+            (1, 1),
+            (1, 2),
+            (1, 5),
+            (1, 7),
+            (2, 2),
+            (2, 4),
+            (3, 3),
+            (3, 6),
+            (4, 4),
+            (5, 5),
+            (5, 7),
+            (6, 6),
+            (7, 7),
+        ]
+    )
+
+    answers = [
+        [6, 3, 5, 7, 1, 2, 4, 0],
+        [6, 3, 7, 5, 1, 2, 4, 0],
+        [7, 5, 1, 2, 4, 0, 6, 3],
+    ]
+
+    def smallest_degree(G):
+        deg, node = min((d, n) for n, d in G.degree())
+        return node
+
+    rcm = list(reverse_cuthill_mckee_ordering(G, heuristic=smallest_degree))
+    assert rcm in answers

.CondaPkg/env/Lib/site-packages/networkx/utils/tests/test_unionfind.py

@@ -0,0 +1,55 @@
+import networkx as nx
+
+
+def test_unionfind():
+    # Fixed by: 2cddd5958689bdecdcd89b91ac9aaf6ce0e4f6b8
+    # Previously (in 2.x), the UnionFind class could handle mixed types.
+    # But in Python 3.x, this causes a TypeError such as:
+    #   TypeError: unorderable types: str() > int()
+    #
+    # Now we just make sure that no exception is raised.
+    x = nx.utils.UnionFind()
+    x.union(0, "a")
+
+
+def test_subtree_union():
+    # See https://github.com/networkx/networkx/pull/3224
+    # (35db1b551ee65780794a357794f521d8768d5049).
+    # Test if subtree unions hare handled correctly by to_sets().
+    uf = nx.utils.UnionFind()
+    uf.union(1, 2)
+    uf.union(3, 4)
+    uf.union(4, 5)
+    uf.union(1, 5)
+    assert list(uf.to_sets()) == [{1, 2, 3, 4, 5}]
+
+
+def test_unionfind_weights():
+    # Tests if weights are computed correctly with unions of many elements
+    uf = nx.utils.UnionFind()
+    uf.union(1, 4, 7)
+    uf.union(2, 5, 8)
+    uf.union(3, 6, 9)
+    uf.union(1, 2, 3, 4, 5, 6, 7, 8, 9)
+    assert uf.weights[uf[1]] == 9
+
+
+def test_unbalanced_merge_weights():
+    # Tests if the largest set's root is used as the new root when merging
+    uf = nx.utils.UnionFind()
+    uf.union(1, 2, 3)
+    uf.union(4, 5, 6, 7, 8, 9)
+    assert uf.weights[uf[1]] == 3
+    assert uf.weights[uf[4]] == 6
+    largest_root = uf[4]
+    uf.union(1, 4)
+    assert uf[1] == largest_root
+    assert uf.weights[largest_root] == 9
+
+
+def test_empty_union():
+    # Tests if a null-union does nothing.
+    uf = nx.utils.UnionFind((0, 1))
+    uf.union()
+    assert uf[0] == 0
+    assert uf[1] == 1

.CondaPkg/env/Lib/site-packages/networkx/utils/union_find.py

@@ -0,0 +1,106 @@
+"""
+Union-find data structure.
+"""
+
+from networkx.utils import groups
+
+
+class UnionFind:
+    """Union-find data structure.
+
+    Each unionFind instance X maintains a family of disjoint sets of
+    hashable objects, supporting the following two methods:
+
+    - X[item] returns a name for the set containing the given item.
+      Each set is named by an arbitrarily-chosen one of its members; as
+      long as the set remains unchanged it will keep the same name. If
+      the item is not yet part of a set in X, a new singleton set is
+      created for it.
+
+    - X.union(item1, item2, ...) merges the sets containing each item
+      into a single larger set.  If any item is not yet part of a set
+      in X, it is added to X as one of the members of the merged set.
+
+      Union-find data structure. Based on Josiah Carlson's code,
+      https://code.activestate.com/recipes/215912/
+      with significant additional changes by D. Eppstein.
+      http://www.ics.uci.edu/~eppstein/PADS/UnionFind.py
+
+    """
+
+    def __init__(self, elements=None):
+        """Create a new empty union-find structure.
+
+        If *elements* is an iterable, this structure will be initialized
+        with the discrete partition on the given set of elements.
+
+        """
+        if elements is None:
+            elements = ()
+        self.parents = {}
+        self.weights = {}
+        for x in elements:
+            self.weights[x] = 1
+            self.parents[x] = x
+
+    def __getitem__(self, object):
+        """Find and return the name of the set containing the object."""
+
+        # check for previously unknown object
+        if object not in self.parents:
+            self.parents[object] = object
+            self.weights[object] = 1
+            return object
+
+        # find path of objects leading to the root
+        path = []
+        root = self.parents[object]
+        while root != object:
+            path.append(object)
+            object = root
+            root = self.parents[object]
+
+        # compress the path and return
+        for ancestor in path:
+            self.parents[ancestor] = root
+        return root
+
+    def __iter__(self):
+        """Iterate through all items ever found or unioned by this structure."""
+        return iter(self.parents)
+
+    def to_sets(self):
+        """Iterates over the sets stored in this structure.
+
+        For example::
+
+            >>> partition = UnionFind("xyz")
+            >>> sorted(map(sorted, partition.to_sets()))
+            [['x'], ['y'], ['z']]
+            >>> partition.union("x", "y")
+            >>> sorted(map(sorted, partition.to_sets()))
+            [['x', 'y'], ['z']]
+
+        """
+        # Ensure fully pruned paths
+        for x in self.parents:
+            _ = self[x]  # Evaluated for side-effect only
+
+        yield from groups(self.parents).values()
+
+    def union(self, *objects):
+        """Find the sets containing the objects and merge them all."""
+        # Find the heaviest root according to its weight.
+        roots = iter(
+            sorted(
+                {self[x] for x in objects}, key=lambda r: self.weights[r], reverse=True
+            )
+        )
+        try:
+            root = next(roots)
+        except StopIteration:
+            return
+
+        for r in roots:
+            self.weights[root] += self.weights[r]
+            self.parents[r] = root