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Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/cycler/__init__.py

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+"""
+Cycler
+======
+
+Cycling through combinations of values, producing dictionaries.
+
+You can add cyclers::
+
+    from cycler import cycler
+    cc = (cycler(color=list('rgb')) +
+          cycler(linestyle=['-', '--', '-.']))
+    for d in cc:
+        print(d)
+
+Results in::
+
+    {'color': 'r', 'linestyle': '-'}
+    {'color': 'g', 'linestyle': '--'}
+    {'color': 'b', 'linestyle': '-.'}
+
+
+You can multiply cyclers::
+
+    from cycler import cycler
+    cc = (cycler(color=list('rgb')) *
+          cycler(linestyle=['-', '--', '-.']))
+    for d in cc:
+        print(d)
+
+Results in::
+
+    {'color': 'r', 'linestyle': '-'}
+    {'color': 'r', 'linestyle': '--'}
+    {'color': 'r', 'linestyle': '-.'}
+    {'color': 'g', 'linestyle': '-'}
+    {'color': 'g', 'linestyle': '--'}
+    {'color': 'g', 'linestyle': '-.'}
+    {'color': 'b', 'linestyle': '-'}
+    {'color': 'b', 'linestyle': '--'}
+    {'color': 'b', 'linestyle': '-.'}
+"""
+
+
+from __future__ import annotations
+
+from collections.abc import Hashable, Iterable, Generator
+import copy
+from functools import reduce
+from itertools import product, cycle
+from operator import mul, add
+# Dict, List, Union required for runtime cast calls
+from typing import TypeVar, Generic, Callable, Union, Dict, List, Any, overload, cast
+
+__version__ = "0.12.1"
+
+K = TypeVar("K", bound=Hashable)
+L = TypeVar("L", bound=Hashable)
+V = TypeVar("V")
+U = TypeVar("U")
+
+
+def _process_keys(
+    left: Cycler[K, V] | Iterable[dict[K, V]],
+    right: Cycler[K, V] | Iterable[dict[K, V]] | None,
+) -> set[K]:
+    """
+    Helper function to compose cycler keys.
+
+    Parameters
+    ----------
+    left, right : iterable of dictionaries or None
+        The cyclers to be composed.
+
+    Returns
+    -------
+    keys : set
+        The keys in the composition of the two cyclers.
+    """
+    l_peek: dict[K, V] = next(iter(left)) if left != [] else {}
+    r_peek: dict[K, V] = next(iter(right)) if right is not None else {}
+    l_key: set[K] = set(l_peek.keys())
+    r_key: set[K] = set(r_peek.keys())
+    if l_key & r_key:
+        raise ValueError("Can not compose overlapping cycles")
+    return l_key | r_key
+
+
+def concat(left: Cycler[K, V], right: Cycler[K, U]) -> Cycler[K, V | U]:
+    r"""
+    Concatenate `Cycler`\s, as if chained using `itertools.chain`.
+
+    The keys must match exactly.
+
+    Examples
+    --------
+    >>> num = cycler('a', range(3))
+    >>> let = cycler('a', 'abc')
+    >>> num.concat(let)
+    cycler('a', [0, 1, 2, 'a', 'b', 'c'])
+
+    Returns
+    -------
+    `Cycler`
+        The concatenated cycler.
+    """
+    if left.keys != right.keys:
+        raise ValueError(
+            "Keys do not match:\n"
+            "\tIntersection: {both!r}\n"
+            "\tDisjoint: {just_one!r}".format(
+                both=left.keys & right.keys, just_one=left.keys ^ right.keys
+            )
+        )
+    _l = cast(Dict[K, List[Union[V, U]]], left.by_key())
+    _r = cast(Dict[K, List[Union[V, U]]], right.by_key())
+    return reduce(add, (_cycler(k, _l[k] + _r[k]) for k in left.keys))
+
+
+class Cycler(Generic[K, V]):
+    """
+    Composable cycles.
+
+    This class has compositions methods:
+
+    ``+``
+      for 'inner' products (zip)
+
+    ``+=``
+      in-place ``+``
+
+    ``*``
+      for outer products (`itertools.product`) and integer multiplication
+
+    ``*=``
+      in-place ``*``
+
+    and supports basic slicing via ``[]``.
+
+    Parameters
+    ----------
+    left, right : Cycler or None
+        The 'left' and 'right' cyclers.
+    op : func or None
+        Function which composes the 'left' and 'right' cyclers.
+    """
+
+    def __call__(self):
+        return cycle(self)
+
+    def __init__(
+        self,
+        left: Cycler[K, V] | Iterable[dict[K, V]] | None,
+        right: Cycler[K, V] | None = None,
+        op: Any = None,
+    ):
+        """
+        Semi-private init.
+
+        Do not use this directly, use `cycler` function instead.
+        """
+        if isinstance(left, Cycler):
+            self._left: Cycler[K, V] | list[dict[K, V]] = Cycler(
+                left._left, left._right, left._op
+            )
+        elif left is not None:
+            # Need to copy the dictionary or else that will be a residual
+            # mutable that could lead to strange errors
+            self._left = [copy.copy(v) for v in left]
+        else:
+            self._left = []
+
+        if isinstance(right, Cycler):
+            self._right: Cycler[K, V] | None = Cycler(
+                right._left, right._right, right._op
+            )
+        else:
+            self._right = None
+
+        self._keys: set[K] = _process_keys(self._left, self._right)
+        self._op: Any = op
+
+    def __contains__(self, k):
+        return k in self._keys
+
+    @property
+    def keys(self) -> set[K]:
+        """The keys this Cycler knows about."""
+        return set(self._keys)
+
+    def change_key(self, old: K, new: K) -> None:
+        """
+        Change a key in this cycler to a new name.
+        Modification is performed in-place.
+
+        Does nothing if the old key is the same as the new key.
+        Raises a ValueError if the new key is already a key.
+        Raises a KeyError if the old key isn't a key.
+        """
+        if old == new:
+            return
+        if new in self._keys:
+            raise ValueError(
+                f"Can't replace {old} with {new}, {new} is already a key"
+            )
+        if old not in self._keys:
+            raise KeyError(
+                f"Can't replace {old} with {new}, {old} is not a key"
+            )
+
+        self._keys.remove(old)
+        self._keys.add(new)
+
+        if self._right is not None and old in self._right.keys:
+            self._right.change_key(old, new)
+
+        # self._left should always be non-None
+        # if self._keys is non-empty.
+        elif isinstance(self._left, Cycler):
+            self._left.change_key(old, new)
+        else:
+            # It should be completely safe at this point to
+            # assume that the old key can be found in each
+            # iteration.
+            self._left = [{new: entry[old]} for entry in self._left]
+
+    @classmethod
+    def _from_iter(cls, label: K, itr: Iterable[V]) -> Cycler[K, V]:
+        """
+        Class method to create 'base' Cycler objects
+        that do not have a 'right' or 'op' and for which
+        the 'left' object is not another Cycler.
+
+        Parameters
+        ----------
+        label : hashable
+            The property key.
+
+        itr : iterable
+            Finite length iterable of the property values.
+
+        Returns
+        -------
+        `Cycler`
+            New 'base' cycler.
+        """
+        ret: Cycler[K, V] = cls(None)
+        ret._left = list({label: v} for v in itr)
+        ret._keys = {label}
+        return ret
+
+    def __getitem__(self, key: slice) -> Cycler[K, V]:
+        # TODO : maybe add numpy style fancy slicing
+        if isinstance(key, slice):
+            trans = self.by_key()
+            return reduce(add, (_cycler(k, v[key]) for k, v in trans.items()))
+        else:
+            raise ValueError("Can only use slices with Cycler.__getitem__")
+
+    def __iter__(self) -> Generator[dict[K, V], None, None]:
+        if self._right is None:
+            for left in self._left:
+                yield dict(left)
+        else:
+            if self._op is None:
+                raise TypeError(
+                    "Operation cannot be None when both left and right are defined"
+                )
+            for a, b in self._op(self._left, self._right):
+                out = {}
+                out.update(a)
+                out.update(b)
+                yield out
+
+    def __add__(self, other: Cycler[L, U]) -> Cycler[K | L, V | U]:
+        """
+        Pair-wise combine two equal length cyclers (zip).
+
+        Parameters
+        ----------
+        other : Cycler
+        """
+        if len(self) != len(other):
+            raise ValueError(
+                f"Can only add equal length cycles, not {len(self)} and {len(other)}"
+            )
+        return Cycler(
+            cast(Cycler[Union[K, L], Union[V, U]], self),
+            cast(Cycler[Union[K, L], Union[V, U]], other),
+            zip
+        )
+
+    @overload
+    def __mul__(self, other: Cycler[L, U]) -> Cycler[K | L, V | U]:
+        ...
+
+    @overload
+    def __mul__(self, other: int) -> Cycler[K, V]:
+        ...
+
+    def __mul__(self, other):
+        """
+        Outer product of two cyclers (`itertools.product`) or integer
+        multiplication.
+
+        Parameters
+        ----------
+        other : Cycler or int
+        """
+        if isinstance(other, Cycler):
+            return Cycler(
+                cast(Cycler[Union[K, L], Union[V, U]], self),
+                cast(Cycler[Union[K, L], Union[V, U]], other),
+                product
+            )
+        elif isinstance(other, int):
+            trans = self.by_key()
+            return reduce(
+                add, (_cycler(k, v * other) for k, v in trans.items())
+            )
+        else:
+            return NotImplemented
+
+    @overload
+    def __rmul__(self, other: Cycler[L, U]) -> Cycler[K | L, V | U]:
+        ...
+
+    @overload
+    def __rmul__(self, other: int) -> Cycler[K, V]:
+        ...
+
+    def __rmul__(self, other):
+        return self * other
+
+    def __len__(self) -> int:
+        op_dict: dict[Callable, Callable[[int, int], int]] = {zip: min, product: mul}
+        if self._right is None:
+            return len(self._left)
+        l_len = len(self._left)
+        r_len = len(self._right)
+        return op_dict[self._op](l_len, r_len)
+
+    # iadd and imul do not exapand the the type as the returns must be consistent with
+    # self, thus they flag as inconsistent with add/mul
+    def __iadd__(self, other: Cycler[K, V]) -> Cycler[K, V]:  # type: ignore[misc]
+        """
+        In-place pair-wise combine two equal length cyclers (zip).
+
+        Parameters
+        ----------
+        other : Cycler
+        """
+        if not isinstance(other, Cycler):
+            raise TypeError("Cannot += with a non-Cycler object")
+        # True shallow copy of self is fine since this is in-place
+        old_self = copy.copy(self)
+        self._keys = _process_keys(old_self, other)
+        self._left = old_self
+        self._op = zip
+        self._right = Cycler(other._left, other._right, other._op)
+        return self
+
+    def __imul__(self, other: Cycler[K, V] | int) -> Cycler[K, V]:  # type: ignore[misc]
+        """
+        In-place outer product of two cyclers (`itertools.product`).
+
+        Parameters
+        ----------
+        other : Cycler
+        """
+        if not isinstance(other, Cycler):
+            raise TypeError("Cannot *= with a non-Cycler object")
+        # True shallow copy of self is fine since this is in-place
+        old_self = copy.copy(self)
+        self._keys = _process_keys(old_self, other)
+        self._left = old_self
+        self._op = product
+        self._right = Cycler(other._left, other._right, other._op)
+        return self
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Cycler):
+            return False
+        if len(self) != len(other):
+            return False
+        if self.keys ^ other.keys:
+            return False
+        return all(a == b for a, b in zip(self, other))
+
+    __hash__ = None  # type: ignore
+
+    def __repr__(self) -> str:
+        op_map = {zip: "+", product: "*"}
+        if self._right is None:
+            lab = self.keys.pop()
+            itr = list(v[lab] for v in self)
+            return f"cycler({lab!r}, {itr!r})"
+        else:
+            op = op_map.get(self._op, "?")
+            msg = "({left!r} {op} {right!r})"
+            return msg.format(left=self._left, op=op, right=self._right)
+
+    def _repr_html_(self) -> str:
+        # an table showing the value of each key through a full cycle
+        output = "<table>"
+        sorted_keys = sorted(self.keys, key=repr)
+        for key in sorted_keys:
+            output += f"<th>{key!r}</th>"
+        for d in iter(self):
+            output += "<tr>"
+            for k in sorted_keys:
+                output += f"<td>{d[k]!r}</td>"
+            output += "</tr>"
+        output += "</table>"
+        return output
+
+    def by_key(self) -> dict[K, list[V]]:
+        """
+        Values by key.
+
+        This returns the transposed values of the cycler.  Iterating
+        over a `Cycler` yields dicts with a single value for each key,
+        this method returns a `dict` of `list` which are the values
+        for the given key.
+
+        The returned value can be used to create an equivalent `Cycler`
+        using only `+`.
+
+        Returns
+        -------
+        transpose : dict
+            dict of lists of the values for each key.
+        """
+
+        # TODO : sort out if this is a bottle neck, if there is a better way
+        # and if we care.
+
+        keys = self.keys
+        out: dict[K, list[V]] = {k: list() for k in keys}
+
+        for d in self:
+            for k in keys:
+                out[k].append(d[k])
+        return out
+
+    # for back compatibility
+    _transpose = by_key
+
+    def simplify(self) -> Cycler[K, V]:
+        """
+        Simplify the cycler into a sum (but no products) of cyclers.
+
+        Returns
+        -------
+        simple : Cycler
+        """
+        # TODO: sort out if it is worth the effort to make sure this is
+        # balanced.  Currently it is is
+        # (((a + b) + c) + d) vs
+        # ((a + b) + (c + d))
+        # I would believe that there is some performance implications
+        trans = self.by_key()
+        return reduce(add, (_cycler(k, v) for k, v in trans.items()))
+
+    concat = concat
+
+
+@overload
+def cycler(arg: Cycler[K, V]) -> Cycler[K, V]:
+    ...
+
+
+@overload
+def cycler(**kwargs: Iterable[V]) -> Cycler[str, V]:
+    ...
+
+
+@overload
+def cycler(label: K, itr: Iterable[V]) -> Cycler[K, V]:
+    ...
+
+
+def cycler(*args, **kwargs):
+    """
+    Create a new `Cycler` object from a single positional argument,
+    a pair of positional arguments, or the combination of keyword arguments.
+
+    cycler(arg)
+    cycler(label1=itr1[, label2=iter2[, ...]])
+    cycler(label, itr)
+
+    Form 1 simply copies a given `Cycler` object.
+
+    Form 2 composes a `Cycler` as an inner product of the
+    pairs of keyword arguments. In other words, all of the
+    iterables are cycled simultaneously, as if through zip().
+
+    Form 3 creates a `Cycler` from a label and an iterable.
+    This is useful for when the label cannot be a keyword argument
+    (e.g., an integer or a name that has a space in it).
+
+    Parameters
+    ----------
+    arg : Cycler
+        Copy constructor for Cycler (does a shallow copy of iterables).
+    label : name
+        The property key. In the 2-arg form of the function,
+        the label can be any hashable object. In the keyword argument
+        form of the function, it must be a valid python identifier.
+    itr : iterable
+        Finite length iterable of the property values.
+        Can be a single-property `Cycler` that would
+        be like a key change, but as a shallow copy.
+
+    Returns
+    -------
+    cycler : Cycler
+        New `Cycler` for the given property
+
+    """
+    if args and kwargs:
+        raise TypeError(
+            "cycler() can only accept positional OR keyword arguments -- not both."
+        )
+
+    if len(args) == 1:
+        if not isinstance(args[0], Cycler):
+            raise TypeError(
+                "If only one positional argument given, it must "
+                "be a Cycler instance."
+            )
+        return Cycler(args[0])
+    elif len(args) == 2:
+        return _cycler(*args)
+    elif len(args) > 2:
+        raise TypeError(
+            "Only a single Cycler can be accepted as the lone "
+            "positional argument. Use keyword arguments instead."
+        )
+
+    if kwargs:
+        return reduce(add, (_cycler(k, v) for k, v in kwargs.items()))
+
+    raise TypeError("Must have at least a positional OR keyword arguments")
+
+
+def _cycler(label: K, itr: Iterable[V]) -> Cycler[K, V]:
+    """
+    Create a new `Cycler` object from a property name and iterable of values.
+
+    Parameters
+    ----------
+    label : hashable
+        The property key.
+    itr : iterable
+        Finite length iterable of the property values.
+
+    Returns
+    -------
+    cycler : Cycler
+        New `Cycler` for the given property
+    """
+    if isinstance(itr, Cycler):
+        keys = itr.keys
+        if len(keys) != 1:
+            msg = "Can not create Cycler from a multi-property Cycler"
+            raise ValueError(msg)
+
+        lab = keys.pop()
+        # Doesn't need to be a new list because
+        # _from_iter() will be creating that new list anyway.
+        itr = (v[lab] for v in itr)
+
+    return Cycler._from_iter(label, itr)

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/jinja2-3.1.6.dist-info/INSTALLER

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+uv

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/jinja2-3.1.6.dist-info/METADATA

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+Metadata-Version: 2.4
+Name: Jinja2
+Version: 3.1.6
+Summary: A very fast and expressive template engine.
+Maintainer-email: Pallets <contact@palletsprojects.com>
+Requires-Python: >=3.7
+Description-Content-Type: text/markdown
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
+Classifier: Topic :: Text Processing :: Markup :: HTML
+Classifier: Typing :: Typed
+License-File: LICENSE.txt
+Requires-Dist: MarkupSafe>=2.0
+Requires-Dist: Babel>=2.7 ; extra == "i18n"
+Project-URL: Changes, https://jinja.palletsprojects.com/changes/
+Project-URL: Chat, https://discord.gg/pallets
+Project-URL: Documentation, https://jinja.palletsprojects.com/
+Project-URL: Donate, https://palletsprojects.com/donate
+Project-URL: Source, https://github.com/pallets/jinja/
+Provides-Extra: i18n
+
+# Jinja
+
+Jinja is a fast, expressive, extensible templating engine. Special
+placeholders in the template allow writing code similar to Python
+syntax. Then the template is passed data to render the final document.
+
+It includes:
+
+-   Template inheritance and inclusion.
+-   Define and import macros within templates.
+-   HTML templates can use autoescaping to prevent XSS from untrusted
+    user input.
+-   A sandboxed environment can safely render untrusted templates.
+-   AsyncIO support for generating templates and calling async
+    functions.
+-   I18N support with Babel.
+-   Templates are compiled to optimized Python code just-in-time and
+    cached, or can be compiled ahead-of-time.
+-   Exceptions point to the correct line in templates to make debugging
+    easier.
+-   Extensible filters, tests, functions, and even syntax.
+
+Jinja's philosophy is that while application logic belongs in Python if
+possible, it shouldn't make the template designer's job difficult by
+restricting functionality too much.
+
+
+## In A Nutshell
+
+```jinja
+{% extends "base.html" %}
+{% block title %}Members{% endblock %}
+{% block content %}
+  <ul>
+  {% for user in users %}
+    <li><a href="{{ user.url }}">{{ user.username }}</a></li>
+  {% endfor %}
+  </ul>
+{% endblock %}
+```
+
+## Donate
+
+The Pallets organization develops and supports Jinja and other popular
+packages. In order to grow the community of contributors and users, and
+allow the maintainers to devote more time to the projects, [please
+donate today][].
+
+[please donate today]: https://palletsprojects.com/donate
+
+## Contributing
+
+See our [detailed contributing documentation][contrib] for many ways to
+contribute, including reporting issues, requesting features, asking or answering
+questions, and making PRs.
+
+[contrib]: https://palletsprojects.com/contributing/
+

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/jinja2-3.1.6.dist-info/RECORD

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Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/jinja2-3.1.6.dist-info/WHEEL

@@ -0,0 +1,4 @@
+Wheel-Version: 1.0
+Generator: flit 3.11.0
+Root-Is-Purelib: true
+Tag: py3-none-any

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/jinja2-3.1.6.dist-info/entry_points.txt

@@ -0,0 +1,3 @@
+[babel.extractors]
+jinja2=jinja2.ext:babel_extract[i18n]
+

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/jinja2-3.1.6.dist-info/licenses/LICENSE.txt

@@ -0,0 +1,28 @@
+Copyright 2007 Pallets
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+1.  Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+2.  Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in the
+    documentation and/or other materials provided with the distribution.
+
+3.  Neither the name of the copyright holder nor the names of its
+    contributors may be used to endorse or promote products derived from
+    this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
+PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
+TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mako-1.3.10.dist-info/INSTALLER

@@ -0,0 +1 @@
+uv

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mako-1.3.10.dist-info/METADATA

@@ -0,0 +1,88 @@
+Metadata-Version: 2.4
+Name: Mako
+Version: 1.3.10
+Summary: A super-fast templating language that borrows the best ideas from the existing templating languages.
+Home-page: https://www.makotemplates.org/
+Author: Mike Bayer
+Author-email: mike@zzzcomputing.com
+License: MIT
+Project-URL: Documentation, https://docs.makotemplates.org
+Project-URL: Issue Tracker, https://github.com/sqlalchemy/mako
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Environment :: Web Environment
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
+Requires-Python: >=3.8
+Description-Content-Type: text/x-rst
+License-File: LICENSE
+Requires-Dist: MarkupSafe>=0.9.2
+Provides-Extra: testing
+Requires-Dist: pytest; extra == "testing"
+Provides-Extra: babel
+Requires-Dist: Babel; extra == "babel"
+Provides-Extra: lingua
+Requires-Dist: lingua; extra == "lingua"
+Dynamic: license-file
+
+=========================
+Mako Templates for Python
+=========================
+
+Mako is a template library written in Python. It provides a familiar, non-XML 
+syntax which compiles into Python modules for maximum performance. Mako's 
+syntax and API borrows from the best ideas of many others, including Django
+templates, Cheetah, Myghty, and Genshi. Conceptually, Mako is an embedded 
+Python (i.e. Python Server Page) language, which refines the familiar ideas
+of componentized layout and inheritance to produce one of the most 
+straightforward and flexible models available, while also maintaining close 
+ties to Python calling and scoping semantics.
+
+Nutshell
+========
+
+::
+
+    <%inherit file="base.html"/>
+    <%
+        rows = [[v for v in range(0,10)] for row in range(0,10)]
+    %>
+    <table>
+        % for row in rows:
+            ${makerow(row)}
+        % endfor
+    </table>
+
+    <%def name="makerow(row)">
+        <tr>
+        % for name in row:
+            <td>${name}</td>\
+        % endfor
+        </tr>
+    </%def>
+
+Philosophy
+===========
+
+Python is a great scripting language. Don't reinvent the wheel...your templates can handle it !
+
+Documentation
+==============
+
+See documentation for Mako at https://docs.makotemplates.org/en/latest/
+
+License
+========
+
+Mako is licensed under an MIT-style license (see LICENSE).
+Other incorporated projects may be licensed under different licenses.
+All licenses allow for non-commercial and commercial use.

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mako-1.3.10.dist-info/RECORD

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+mako/testing/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+mako/testing/_config.py,sha256=k-qpnsnbXUoN-ykMN5BRpg84i1x0p6UsAddKQnrIytU,3566
+mako/testing/assertions.py,sha256=pfbGl84QlW7QWGg3_lo3wP8XnBAVo9AjzNp2ajmn7FA,5161
+mako/testing/config.py,sha256=wmYVZfzGvOK3mJUZpzmgO8-iIgvaCH41Woi4yDpxq6E,323
+mako/testing/exclusions.py,sha256=_t6ADKdatk3f18tOfHV_ZY6u_ZwQsKphZ2MXJVSAOcI,1553
+mako/testing/fixtures.py,sha256=nEp7wTusf7E0n3Q-BHJW2s_t1vx0KB9poadQ1BmIJzE,3044
+mako/testing/helpers.py,sha256=z4HAactwlht4ut1cbvxKt1QLb3yLPk1U7cnh5BwVUlc,1623
+mako/util.py,sha256=SNYeX2_PmajQJIR3-S1Yqxxylz8lwS65rC8YbCdTkUU,10638

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mako-1.3.10.dist-info/WHEEL

@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: setuptools (78.1.0)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mako-1.3.10.dist-info/entry_points.txt

@@ -0,0 +1,18 @@
+[babel.extractors]
+mako = mako.ext.babelplugin:extract [babel]
+
+[console_scripts]
+mako-render = mako.cmd:cmdline
+
+[lingua.extractors]
+mako = mako.ext.linguaplugin:LinguaMakoExtractor [lingua]
+
+[pygments.lexers]
+css+mako = mako.ext.pygmentplugin:MakoCssLexer
+html+mako = mako.ext.pygmentplugin:MakoHtmlLexer
+js+mako = mako.ext.pygmentplugin:MakoJavascriptLexer
+mako = mako.ext.pygmentplugin:MakoLexer
+xml+mako = mako.ext.pygmentplugin:MakoXmlLexer
+
+[python.templating.engines]
+mako = mako.ext.turbogears:TGPlugin

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mako-1.3.10.dist-info/licenses/LICENSE

@@ -0,0 +1,19 @@
+Copyright 2006-2025 the Mako authors and contributors <see AUTHORS file>.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/mako-1.3.10.dist-info/top_level.txt

@@ -0,0 +1 @@
+mako

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/networkx/__init__.py

@@ -0,0 +1,53 @@
+"""
+NetworkX
+========
+
+NetworkX is a Python package for the creation, manipulation, and study of the
+structure, dynamics, and functions of complex networks.
+
+See https://networkx.org for complete documentation.
+"""
+
+__version__ = "3.4.2"
+
+
+# These are imported in order as listed
+from networkx.lazy_imports import _lazy_import
+
+from networkx.exception import *
+
+from networkx import utils
+from networkx.utils import _clear_cache, _dispatchable
+
+# load_and_call entry_points, set configs
+config = utils.backends._set_configs_from_environment()
+utils.config = utils.configs.config = config  # type: ignore[attr-defined]
+
+from networkx import classes
+from networkx.classes import filters
+from networkx.classes import *
+
+from networkx import convert
+from networkx.convert import *
+
+from networkx import convert_matrix
+from networkx.convert_matrix import *
+
+from networkx import relabel
+from networkx.relabel import *
+
+from networkx import generators
+from networkx.generators import *
+
+from networkx import readwrite
+from networkx.readwrite import *
+
+# Need to test with SciPy, when available
+from networkx import algorithms
+from networkx.algorithms import *
+
+from networkx import linalg
+from networkx.linalg import *
+
+from networkx import drawing
+from networkx.drawing import *

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/networkx/algorithms/walks.py

@@ -0,0 +1,79 @@
+"""Function for computing walks in a graph."""
+
+import networkx as nx
+
+__all__ = ["number_of_walks"]
+
+
+@nx._dispatchable
+def number_of_walks(G, walk_length):
+    """Returns the number of walks connecting each pair of nodes in `G`
+
+    A *walk* is a sequence of nodes in which each adjacent pair of nodes
+    in the sequence is adjacent in the graph. A walk can repeat the same
+    edge and go in the opposite direction just as people can walk on a
+    set of paths, but standing still is not counted as part of the walk.
+
+    This function only counts the walks with `walk_length` edges. Note that
+    the number of nodes in the walk sequence is one more than `walk_length`.
+    The number of walks can grow very quickly on a larger graph
+    and with a larger walk length.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    walk_length : int
+        A nonnegative integer representing the length of a walk.
+
+    Returns
+    -------
+    dict
+        A dictionary of dictionaries in which outer keys are source
+        nodes, inner keys are target nodes, and inner values are the
+        number of walks of length `walk_length` connecting those nodes.
+
+    Raises
+    ------
+    ValueError
+        If `walk_length` is negative
+
+    Examples
+    --------
+
+    >>> G = nx.Graph([(0, 1), (1, 2)])
+    >>> walks = nx.number_of_walks(G, 2)
+    >>> walks
+    {0: {0: 1, 1: 0, 2: 1}, 1: {0: 0, 1: 2, 2: 0}, 2: {0: 1, 1: 0, 2: 1}}
+    >>> total_walks = sum(sum(tgts.values()) for _, tgts in walks.items())
+
+    You can also get the number of walks from a specific source node using the
+    returned dictionary. For example, number of walks of length 1 from node 0
+    can be found as follows:
+
+    >>> walks = nx.number_of_walks(G, 1)
+    >>> walks[0]
+    {0: 0, 1: 1, 2: 0}
+    >>> sum(walks[0].values())  # walks from 0 of length 1
+    1
+
+    Similarly, a target node can also be specified:
+
+    >>> walks[0][1]
+    1
+
+    """
+    import numpy as np
+
+    if walk_length < 0:
+        raise ValueError(f"`walk_length` cannot be negative: {walk_length}")
+
+    A = nx.adjacency_matrix(G, weight=None)
+    # TODO: Use matrix_power from scipy.sparse when available
+    # power = sp.sparse.linalg.matrix_power(A, walk_length)
+    power = np.linalg.matrix_power(A.toarray(), walk_length)
+    result = {
+        u: {v: power.item(u_idx, v_idx) for v_idx, v in enumerate(G)}
+        for u_idx, u in enumerate(G)
+    }
+    return result

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/networkx/conftest.py

@@ -0,0 +1,284 @@
+"""
+Testing
+=======
+
+General guidelines for writing good tests:
+
+- doctests always assume ``import networkx as nx`` so don't add that
+- prefer pytest fixtures over classes with setup methods.
+- use the ``@pytest.mark.parametrize``  decorator
+- use ``pytest.importorskip`` for numpy, scipy, pandas, and matplotlib b/c of PyPy.
+  and add the module to the relevant entries below.
+
+"""
+
+import os
+import sys
+import warnings
+from importlib.metadata import entry_points
+
+import pytest
+
+import networkx
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--runslow", action="store_true", default=False, help="run slow tests"
+    )
+    parser.addoption(
+        "--backend",
+        action="store",
+        default=None,
+        help="Run tests with a backend by auto-converting nx graphs to backend graphs",
+    )
+    parser.addoption(
+        "--fallback-to-nx",
+        action="store_true",
+        default=False,
+        help="Run nx function if a backend doesn't implement a dispatchable function"
+        " (use with --backend)",
+    )
+
+
+def pytest_configure(config):
+    config.addinivalue_line("markers", "slow: mark test as slow to run")
+    backend = config.getoption("--backend")
+    if backend is None:
+        backend = os.environ.get("NETWORKX_TEST_BACKEND")
+    # nx_loopback backend is only available when testing with a backend
+    loopback_ep = entry_points(name="nx_loopback", group="networkx.backends")
+    if not loopback_ep:
+        warnings.warn(
+            "\n\n             WARNING: Mixed NetworkX configuration! \n\n"
+            "        This environment has mixed configuration for networkx.\n"
+            "        The test object nx_loopback is not configured correctly.\n"
+            "        You should not be seeing this message.\n"
+            "        Try `pip install -e .`, or change your PYTHONPATH\n"
+            "        Make sure python finds the networkx repo you are testing\n\n"
+        )
+    config.backend = backend
+    if backend:
+        # We will update `networkx.config.backend_priority` below in `*_modify_items`
+        # to allow tests to get set up with normal networkx graphs.
+        networkx.utils.backends.backends["nx_loopback"] = loopback_ep["nx_loopback"]
+        networkx.utils.backends.backend_info["nx_loopback"] = {}
+        networkx.config.backends = networkx.utils.Config(
+            nx_loopback=networkx.utils.Config(),
+            **networkx.config.backends,
+        )
+        fallback_to_nx = config.getoption("--fallback-to-nx")
+        if not fallback_to_nx:
+            fallback_to_nx = os.environ.get("NETWORKX_FALLBACK_TO_NX")
+        networkx.config.fallback_to_nx = bool(fallback_to_nx)
+
+
+def pytest_collection_modifyitems(config, items):
+    # Setting this to True here allows tests to be set up before dispatching
+    # any function call to a backend.
+    if config.backend:
+        # Allow pluggable backends to add markers to tests (such as skip or xfail)
+        # when running in auto-conversion test mode
+        backend_name = config.backend
+        if backend_name != "networkx":
+            networkx.utils.backends._dispatchable._is_testing = True
+            networkx.config.backend_priority.algos = [backend_name]
+            networkx.config.backend_priority.generators = [backend_name]
+            backend = networkx.utils.backends.backends[backend_name].load()
+            if hasattr(backend, "on_start_tests"):
+                getattr(backend, "on_start_tests")(items)
+
+    if config.getoption("--runslow"):
+        # --runslow given in cli: do not skip slow tests
+        return
+    skip_slow = pytest.mark.skip(reason="need --runslow option to run")
+    for item in items:
+        if "slow" in item.keywords:
+            item.add_marker(skip_slow)
+
+
+# TODO: The warnings below need to be dealt with, but for now we silence them.
+@pytest.fixture(autouse=True)
+def set_warnings():
+    warnings.filterwarnings(
+        "ignore",
+        category=FutureWarning,
+        message="\n\nsingle_target_shortest_path_length",
+    )
+    warnings.filterwarnings(
+        "ignore",
+        category=FutureWarning,
+        message="\n\nshortest_path",
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="\n\nThe `normalized`"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="\n\nall_triplets"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="\n\nrandom_triad"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="minimal_d_separator"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="d_separated"
+    )
+    warnings.filterwarnings("ignore", category=DeprecationWarning, message="\n\nk_core")
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="\n\nk_shell"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="\n\nk_crust"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="\n\nk_corona"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="\n\ntotal_spanning_tree_weight"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message=r"\n\nThe 'create=matrix'"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="\n\n`compute_v_structures"
+    )
+    warnings.filterwarnings(
+        "ignore", category=DeprecationWarning, message="Keyword argument 'link'"
+    )
+
+
+@pytest.fixture(autouse=True)
+def add_nx(doctest_namespace):
+    doctest_namespace["nx"] = networkx
+
+
+# What dependencies are installed?
+
+try:
+    import numpy
+
+    has_numpy = True
+except ImportError:
+    has_numpy = False
+
+try:
+    import scipy
+
+    has_scipy = True
+except ImportError:
+    has_scipy = False
+
+try:
+    import matplotlib
+
+    has_matplotlib = True
+except ImportError:
+    has_matplotlib = False
+
+try:
+    import pandas
+
+    has_pandas = True
+except ImportError:
+    has_pandas = False
+
+try:
+    import pygraphviz
+
+    has_pygraphviz = True
+except ImportError:
+    has_pygraphviz = False
+
+try:
+    import pydot
+
+    has_pydot = True
+except ImportError:
+    has_pydot = False
+
+try:
+    import sympy
+
+    has_sympy = True
+except ImportError:
+    has_sympy = False
+
+
+# List of files that pytest should ignore
+
+collect_ignore = []
+
+needs_numpy = [
+    "algorithms/approximation/traveling_salesman.py",
+    "algorithms/centrality/current_flow_closeness.py",
+    "algorithms/centrality/laplacian.py",
+    "algorithms/node_classification.py",
+    "algorithms/non_randomness.py",
+    "algorithms/polynomials.py",
+    "algorithms/shortest_paths/dense.py",
+    "algorithms/tree/mst.py",
+    "drawing/nx_latex.py",
+    "generators/expanders.py",
+    "linalg/bethehessianmatrix.py",
+    "linalg/laplacianmatrix.py",
+    "utils/misc.py",
+]
+needs_scipy = [
+    "algorithms/approximation/traveling_salesman.py",
+    "algorithms/assortativity/correlation.py",
+    "algorithms/assortativity/mixing.py",
+    "algorithms/assortativity/pairs.py",
+    "algorithms/bipartite/matrix.py",
+    "algorithms/bipartite/spectral.py",
+    "algorithms/centrality/current_flow_betweenness.py",
+    "algorithms/centrality/current_flow_betweenness_subset.py",
+    "algorithms/centrality/eigenvector.py",
+    "algorithms/centrality/katz.py",
+    "algorithms/centrality/laplacian.py",
+    "algorithms/centrality/second_order.py",
+    "algorithms/centrality/subgraph_alg.py",
+    "algorithms/communicability_alg.py",
+    "algorithms/community/divisive.py",
+    "algorithms/distance_measures.py",
+    "algorithms/link_analysis/hits_alg.py",
+    "algorithms/link_analysis/pagerank_alg.py",
+    "algorithms/node_classification.py",
+    "algorithms/similarity.py",
+    "algorithms/tree/mst.py",
+    "algorithms/walks.py",
+    "convert_matrix.py",
+    "drawing/layout.py",
+    "drawing/nx_pylab.py",
+    "generators/spectral_graph_forge.py",
+    "generators/expanders.py",
+    "linalg/algebraicconnectivity.py",
+    "linalg/attrmatrix.py",
+    "linalg/bethehessianmatrix.py",
+    "linalg/graphmatrix.py",
+    "linalg/laplacianmatrix.py",
+    "linalg/modularitymatrix.py",
+    "linalg/spectrum.py",
+    "utils/rcm.py",
+]
+needs_matplotlib = ["drawing/nx_pylab.py", "generators/classic.py"]
+needs_pandas = ["convert_matrix.py"]
+needs_pygraphviz = ["drawing/nx_agraph.py"]
+needs_pydot = ["drawing/nx_pydot.py"]
+needs_sympy = ["algorithms/polynomials.py"]
+
+if not has_numpy:
+    collect_ignore += needs_numpy
+if not has_scipy:
+    collect_ignore += needs_scipy
+if not has_matplotlib:
+    collect_ignore += needs_matplotlib
+if not has_pandas:
+    collect_ignore += needs_pandas
+if not has_pygraphviz:
+    collect_ignore += needs_pygraphviz
+if not has_pydot:
+    collect_ignore += needs_pydot
+if not has_sympy:
+    collect_ignore += needs_sympy

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/networkx/convert.py

@@ -0,0 +1,502 @@
+"""Functions to convert NetworkX graphs to and from other formats.
+
+The preferred way of converting data to a NetworkX graph is through the
+graph constructor.  The constructor calls the to_networkx_graph() function
+which attempts to guess the input type and convert it automatically.
+
+Examples
+--------
+Create a graph with a single edge from a dictionary of dictionaries
+
+>>> d = {0: {1: 1}}  # dict-of-dicts single edge (0,1)
+>>> G = nx.Graph(d)
+
+See Also
+--------
+nx_agraph, nx_pydot
+"""
+
+import warnings
+from collections.abc import Collection, Generator, Iterator
+
+import networkx as nx
+
+__all__ = [
+    "to_networkx_graph",
+    "from_dict_of_dicts",
+    "to_dict_of_dicts",
+    "from_dict_of_lists",
+    "to_dict_of_lists",
+    "from_edgelist",
+    "to_edgelist",
+]
+
+
+def to_networkx_graph(data, create_using=None, multigraph_input=False):
+    """Make a NetworkX graph from a known data structure.
+
+    The preferred way to call this is automatically
+    from the class constructor
+
+    >>> d = {0: {1: {"weight": 1}}}  # dict-of-dicts single edge (0,1)
+    >>> G = nx.Graph(d)
+
+    instead of the equivalent
+
+    >>> G = nx.from_dict_of_dicts(d)
+
+    Parameters
+    ----------
+    data : object to be converted
+
+        Current known types are:
+         any NetworkX graph
+         dict-of-dicts
+         dict-of-lists
+         container (e.g. set, list, tuple) of edges
+         iterator (e.g. itertools.chain) that produces edges
+         generator of edges
+         Pandas DataFrame (row per edge)
+         2D numpy array
+         scipy sparse array
+         pygraphviz agraph
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    multigraph_input : bool (default False)
+        If True and  data is a dict_of_dicts,
+        try to create a multigraph assuming dict_of_dict_of_lists.
+        If data and create_using are both multigraphs then create
+        a multigraph from a multigraph.
+
+    """
+    # NX graph
+    if hasattr(data, "adj"):
+        try:
+            result = from_dict_of_dicts(
+                data.adj,
+                create_using=create_using,
+                multigraph_input=data.is_multigraph(),
+            )
+            # data.graph should be dict-like
+            result.graph.update(data.graph)
+            # data.nodes should be dict-like
+            # result.add_node_from(data.nodes.items()) possible but
+            # for custom node_attr_dict_factory which may be hashable
+            # will be unexpected behavior
+            for n, dd in data.nodes.items():
+                result._node[n].update(dd)
+            return result
+        except Exception as err:
+            raise nx.NetworkXError("Input is not a correct NetworkX graph.") from err
+
+    # dict of dicts/lists
+    if isinstance(data, dict):
+        try:
+            return from_dict_of_dicts(
+                data, create_using=create_using, multigraph_input=multigraph_input
+            )
+        except Exception as err1:
+            if multigraph_input is True:
+                raise nx.NetworkXError(
+                    f"converting multigraph_input raised:\n{type(err1)}: {err1}"
+                )
+            try:
+                return from_dict_of_lists(data, create_using=create_using)
+            except Exception as err2:
+                raise TypeError("Input is not known type.") from err2
+
+    # edgelists
+    if isinstance(data, list | tuple | nx.reportviews.EdgeViewABC | Iterator):
+        try:
+            return from_edgelist(data, create_using=create_using)
+        except:
+            pass
+
+    # pygraphviz  agraph
+    if hasattr(data, "is_strict"):
+        try:
+            return nx.nx_agraph.from_agraph(data, create_using=create_using)
+        except Exception as err:
+            raise nx.NetworkXError("Input is not a correct pygraphviz graph.") from err
+
+    # Pandas DataFrame
+    try:
+        import pandas as pd
+
+        if isinstance(data, pd.DataFrame):
+            if data.shape[0] == data.shape[1]:
+                try:
+                    return nx.from_pandas_adjacency(data, create_using=create_using)
+                except Exception as err:
+                    msg = "Input is not a correct Pandas DataFrame adjacency matrix."
+                    raise nx.NetworkXError(msg) from err
+            else:
+                try:
+                    return nx.from_pandas_edgelist(
+                        data, edge_attr=True, create_using=create_using
+                    )
+                except Exception as err:
+                    msg = "Input is not a correct Pandas DataFrame edge-list."
+                    raise nx.NetworkXError(msg) from err
+    except ImportError:
+        pass
+
+    # numpy array
+    try:
+        import numpy as np
+
+        if isinstance(data, np.ndarray):
+            try:
+                return nx.from_numpy_array(data, create_using=create_using)
+            except Exception as err:
+                raise nx.NetworkXError(
+                    f"Failed to interpret array as an adjacency matrix."
+                ) from err
+    except ImportError:
+        pass
+
+    # scipy sparse array - any format
+    try:
+        import scipy
+
+        if hasattr(data, "format"):
+            try:
+                return nx.from_scipy_sparse_array(data, create_using=create_using)
+            except Exception as err:
+                raise nx.NetworkXError(
+                    "Input is not a correct scipy sparse array type."
+                ) from err
+    except ImportError:
+        pass
+
+    # Note: most general check - should remain last in order of execution
+    # Includes containers (e.g. list, set, dict, etc.), generators, and
+    # iterators (e.g. itertools.chain) of edges
+
+    if isinstance(data, Collection | Generator | Iterator):
+        try:
+            return from_edgelist(data, create_using=create_using)
+        except Exception as err:
+            raise nx.NetworkXError("Input is not a valid edge list") from err
+
+    raise nx.NetworkXError("Input is not a known data type for conversion.")
+
+
+@nx._dispatchable
+def to_dict_of_lists(G, nodelist=None):
+    """Returns adjacency representation of graph as a dictionary of lists.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list
+       Use only nodes specified in nodelist
+
+    Notes
+    -----
+    Completely ignores edge data for MultiGraph and MultiDiGraph.
+
+    """
+    if nodelist is None:
+        nodelist = G
+
+    d = {}
+    for n in nodelist:
+        d[n] = [nbr for nbr in G.neighbors(n) if nbr in nodelist]
+    return d
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_dict_of_lists(d, create_using=None):
+    """Returns a graph from a dictionary of lists.
+
+    Parameters
+    ----------
+    d : dictionary of lists
+      A dictionary of lists adjacency representation.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Examples
+    --------
+    >>> dol = {0: [1]}  # single edge (0,1)
+    >>> G = nx.from_dict_of_lists(dol)
+
+    or
+
+    >>> G = nx.Graph(dol)  # use Graph constructor
+
+    """
+    G = nx.empty_graph(0, create_using)
+    G.add_nodes_from(d)
+    if G.is_multigraph() and not G.is_directed():
+        # a dict_of_lists can't show multiedges.  BUT for undirected graphs,
+        # each edge shows up twice in the dict_of_lists.
+        # So we need to treat this case separately.
+        seen = {}
+        for node, nbrlist in d.items():
+            for nbr in nbrlist:
+                if nbr not in seen:
+                    G.add_edge(node, nbr)
+            seen[node] = 1  # don't allow reverse edge to show up
+    else:
+        G.add_edges_from(
+            ((node, nbr) for node, nbrlist in d.items() for nbr in nbrlist)
+        )
+    return G
+
+
+def to_dict_of_dicts(G, nodelist=None, edge_data=None):
+    """Returns adjacency representation of graph as a dictionary of dictionaries.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list
+       Use only nodes specified in nodelist
+
+    edge_data : scalar, optional
+       If provided, the value of the dictionary will be set to `edge_data` for
+       all edges. Usual values could be `1` or `True`. If `edge_data` is
+       `None` (the default), the edgedata in `G` is used, resulting in a
+       dict-of-dict-of-dicts. If `G` is a MultiGraph, the result will be a
+       dict-of-dict-of-dict-of-dicts. See Notes for an approach to customize
+       handling edge data. `edge_data` should *not* be a container.
+
+    Returns
+    -------
+    dod : dict
+       A nested dictionary representation of `G`. Note that the level of
+       nesting depends on the type of `G` and the value of `edge_data`
+       (see Examples).
+
+    See Also
+    --------
+    from_dict_of_dicts, to_dict_of_lists
+
+    Notes
+    -----
+    For a more custom approach to handling edge data, try::
+
+        dod = {
+            n: {nbr: custom(n, nbr, dd) for nbr, dd in nbrdict.items()}
+            for n, nbrdict in G.adj.items()
+        }
+
+    where `custom` returns the desired edge data for each edge between `n` and
+    `nbr`, given existing edge data `dd`.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(3)
+    >>> nx.to_dict_of_dicts(G)
+    {0: {1: {}}, 1: {0: {}, 2: {}}, 2: {1: {}}}
+
+    Edge data is preserved by default (``edge_data=None``), resulting
+    in dict-of-dict-of-dicts where the innermost dictionary contains the
+    edge data:
+
+    >>> G = nx.Graph()
+    >>> G.add_edges_from(
+    ...     [
+    ...         (0, 1, {"weight": 1.0}),
+    ...         (1, 2, {"weight": 2.0}),
+    ...         (2, 0, {"weight": 1.0}),
+    ...     ]
+    ... )
+    >>> d = nx.to_dict_of_dicts(G)
+    >>> d  # doctest: +SKIP
+    {0: {1: {'weight': 1.0}, 2: {'weight': 1.0}},
+     1: {0: {'weight': 1.0}, 2: {'weight': 2.0}},
+     2: {1: {'weight': 2.0}, 0: {'weight': 1.0}}}
+    >>> d[1][2]["weight"]
+    2.0
+
+    If `edge_data` is not `None`, edge data in the original graph (if any) is
+    replaced:
+
+    >>> d = nx.to_dict_of_dicts(G, edge_data=1)
+    >>> d
+    {0: {1: 1, 2: 1}, 1: {0: 1, 2: 1}, 2: {1: 1, 0: 1}}
+    >>> d[1][2]
+    1
+
+    This also applies to MultiGraphs: edge data is preserved by default:
+
+    >>> G = nx.MultiGraph()
+    >>> G.add_edge(0, 1, key="a", weight=1.0)
+    'a'
+    >>> G.add_edge(0, 1, key="b", weight=5.0)
+    'b'
+    >>> d = nx.to_dict_of_dicts(G)
+    >>> d  # doctest: +SKIP
+    {0: {1: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}},
+     1: {0: {'a': {'weight': 1.0}, 'b': {'weight': 5.0}}}}
+    >>> d[0][1]["b"]["weight"]
+    5.0
+
+    But multi edge data is lost if `edge_data` is not `None`:
+
+    >>> d = nx.to_dict_of_dicts(G, edge_data=10)
+    >>> d
+    {0: {1: 10}, 1: {0: 10}}
+    """
+    dod = {}
+    if nodelist is None:
+        if edge_data is None:
+            for u, nbrdict in G.adjacency():
+                dod[u] = nbrdict.copy()
+        else:  # edge_data is not None
+            for u, nbrdict in G.adjacency():
+                dod[u] = dod.fromkeys(nbrdict, edge_data)
+    else:  # nodelist is not None
+        if edge_data is None:
+            for u in nodelist:
+                dod[u] = {}
+                for v, data in ((v, data) for v, data in G[u].items() if v in nodelist):
+                    dod[u][v] = data
+        else:  # nodelist and edge_data are not None
+            for u in nodelist:
+                dod[u] = {}
+                for v in (v for v in G[u] if v in nodelist):
+                    dod[u][v] = edge_data
+    return dod
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_dict_of_dicts(d, create_using=None, multigraph_input=False):
+    """Returns a graph from a dictionary of dictionaries.
+
+    Parameters
+    ----------
+    d : dictionary of dictionaries
+      A dictionary of dictionaries adjacency representation.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    multigraph_input : bool (default False)
+       When True, the dict `d` is assumed
+       to be a dict-of-dict-of-dict-of-dict structure keyed by
+       node to neighbor to edge keys to edge data for multi-edges.
+       Otherwise this routine assumes dict-of-dict-of-dict keyed by
+       node to neighbor to edge data.
+
+    Examples
+    --------
+    >>> dod = {0: {1: {"weight": 1}}}  # single edge (0,1)
+    >>> G = nx.from_dict_of_dicts(dod)
+
+    or
+
+    >>> G = nx.Graph(dod)  # use Graph constructor
+
+    """
+    G = nx.empty_graph(0, create_using)
+    G.add_nodes_from(d)
+    # does dict d represent a MultiGraph or MultiDiGraph?
+    if multigraph_input:
+        if G.is_directed():
+            if G.is_multigraph():
+                G.add_edges_from(
+                    (u, v, key, data)
+                    for u, nbrs in d.items()
+                    for v, datadict in nbrs.items()
+                    for key, data in datadict.items()
+                )
+            else:
+                G.add_edges_from(
+                    (u, v, data)
+                    for u, nbrs in d.items()
+                    for v, datadict in nbrs.items()
+                    for key, data in datadict.items()
+                )
+        else:  # Undirected
+            if G.is_multigraph():
+                seen = set()  # don't add both directions of undirected graph
+                for u, nbrs in d.items():
+                    for v, datadict in nbrs.items():
+                        if (u, v) not in seen:
+                            G.add_edges_from(
+                                (u, v, key, data) for key, data in datadict.items()
+                            )
+                            seen.add((v, u))
+            else:
+                seen = set()  # don't add both directions of undirected graph
+                for u, nbrs in d.items():
+                    for v, datadict in nbrs.items():
+                        if (u, v) not in seen:
+                            G.add_edges_from(
+                                (u, v, data) for key, data in datadict.items()
+                            )
+                            seen.add((v, u))
+
+    else:  # not a multigraph to multigraph transfer
+        if G.is_multigraph() and not G.is_directed():
+            # d can have both representations u-v, v-u in dict.  Only add one.
+            # We don't need this check for digraphs since we add both directions,
+            # or for Graph() since it is done implicitly (parallel edges not allowed)
+            seen = set()
+            for u, nbrs in d.items():
+                for v, data in nbrs.items():
+                    if (u, v) not in seen:
+                        G.add_edge(u, v, key=0)
+                        G[u][v][0].update(data)
+                    seen.add((v, u))
+        else:
+            G.add_edges_from(
+                ((u, v, data) for u, nbrs in d.items() for v, data in nbrs.items())
+            )
+    return G
+
+
+@nx._dispatchable(preserve_edge_attrs=True)
+def to_edgelist(G, nodelist=None):
+    """Returns a list of edges in the graph.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    nodelist : list
+       Use only nodes specified in nodelist
+
+    """
+    if nodelist is None:
+        return G.edges(data=True)
+    return G.edges(nodelist, data=True)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_edgelist(edgelist, create_using=None):
+    """Returns a graph from a list of edges.
+
+    Parameters
+    ----------
+    edgelist : list or iterator
+      Edge tuples
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Examples
+    --------
+    >>> edgelist = [(0, 1)]  # single edge (0,1)
+    >>> G = nx.from_edgelist(edgelist)
+
+    or
+
+    >>> G = nx.Graph(edgelist)  # use Graph constructor
+
+    """
+    G = nx.empty_graph(0, create_using)
+    G.add_edges_from(edgelist)
+    return G

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/networkx/convert_matrix.py

@@ -0,0 +1,1317 @@
+"""Functions to convert NetworkX graphs to and from common data containers
+like numpy arrays, scipy sparse arrays, and pandas DataFrames.
+
+The preferred way of converting data to a NetworkX graph is through the
+graph constructor.  The constructor calls the `~networkx.convert.to_networkx_graph`
+function which attempts to guess the input type and convert it automatically.
+
+Examples
+--------
+Create a 10 node random graph from a numpy array
+
+>>> import numpy as np
+>>> rng = np.random.default_rng()
+>>> a = rng.integers(low=0, high=2, size=(10, 10))
+>>> DG = nx.from_numpy_array(a, create_using=nx.DiGraph)
+
+or equivalently:
+
+>>> DG = nx.DiGraph(a)
+
+which calls `from_numpy_array` internally based on the type of ``a``.
+
+See Also
+--------
+nx_agraph, nx_pydot
+"""
+
+import itertools
+from collections import defaultdict
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = [
+    "from_pandas_adjacency",
+    "to_pandas_adjacency",
+    "from_pandas_edgelist",
+    "to_pandas_edgelist",
+    "from_scipy_sparse_array",
+    "to_scipy_sparse_array",
+    "from_numpy_array",
+    "to_numpy_array",
+]
+
+
+@nx._dispatchable(edge_attrs="weight")
+def to_pandas_adjacency(
+    G,
+    nodelist=None,
+    dtype=None,
+    order=None,
+    multigraph_weight=sum,
+    weight="weight",
+    nonedge=0.0,
+):
+    """Returns the graph adjacency matrix as a Pandas DataFrame.
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the Pandas DataFrame.
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in `nodelist`.
+       If `nodelist` is None, then the ordering is produced by G.nodes().
+
+    multigraph_weight : {sum, min, max}, optional
+        An operator that determines how weights in multigraphs are handled.
+        The default is to sum the weights of the multiple edges.
+
+    weight : string or None, optional
+        The edge attribute that holds the numerical value used for
+        the edge weight.  If an edge does not have that attribute, then the
+        value 1 is used instead.
+
+    nonedge : float, optional
+        The matrix values corresponding to nonedges are typically set to zero.
+        However, this could be undesirable if there are matrix values
+        corresponding to actual edges that also have the value zero. If so,
+        one might prefer nonedges to have some other value, such as nan.
+
+    Returns
+    -------
+    df : Pandas DataFrame
+       Graph adjacency matrix
+
+    Notes
+    -----
+    For directed graphs, entry i,j corresponds to an edge from i to j.
+
+    The DataFrame entries are assigned to the weight edge attribute. When
+    an edge does not have a weight attribute, the value of the entry is set to
+    the number 1.  For multiple (parallel) edges, the values of the entries
+    are determined by the 'multigraph_weight' parameter.  The default is to
+    sum the weight attributes for each of the parallel edges.
+
+    When `nodelist` does not contain every node in `G`, the matrix is built
+    from the subgraph of `G` that is induced by the nodes in `nodelist`.
+
+    The convention used for self-loop edges in graphs is to assign the
+    diagonal matrix entry value to the weight attribute of the edge
+    (or the number 1 if the edge has no weight attribute).  If the
+    alternate convention of doubling the edge weight is desired the
+    resulting Pandas DataFrame can be modified as follows::
+
+        >>> import pandas as pd
+        >>> G = nx.Graph([(1, 1), (2, 2)])
+        >>> df = nx.to_pandas_adjacency(G)
+        >>> df
+             1    2
+        1  1.0  0.0
+        2  0.0  1.0
+        >>> diag_idx = list(range(len(df)))
+        >>> df.iloc[diag_idx, diag_idx] *= 2
+        >>> df
+             1    2
+        1  2.0  0.0
+        2  0.0  2.0
+
+    Examples
+    --------
+    >>> G = nx.MultiDiGraph()
+    >>> G.add_edge(0, 1, weight=2)
+    0
+    >>> G.add_edge(1, 0)
+    0
+    >>> G.add_edge(2, 2, weight=3)
+    0
+    >>> G.add_edge(2, 2)
+    1
+    >>> nx.to_pandas_adjacency(G, nodelist=[0, 1, 2], dtype=int)
+       0  1  2
+    0  0  2  0
+    1  1  0  0
+    2  0  0  4
+
+    """
+    import pandas as pd
+
+    M = to_numpy_array(
+        G,
+        nodelist=nodelist,
+        dtype=dtype,
+        order=order,
+        multigraph_weight=multigraph_weight,
+        weight=weight,
+        nonedge=nonedge,
+    )
+    if nodelist is None:
+        nodelist = list(G)
+    return pd.DataFrame(data=M, index=nodelist, columns=nodelist)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_pandas_adjacency(df, create_using=None):
+    r"""Returns a graph from Pandas DataFrame.
+
+    The Pandas DataFrame is interpreted as an adjacency matrix for the graph.
+
+    Parameters
+    ----------
+    df : Pandas DataFrame
+      An adjacency matrix representation of a graph
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    For directed graphs, explicitly mention create_using=nx.DiGraph,
+    and entry i,j of df corresponds to an edge from i to j.
+
+    If `df` has a single data type for each entry it will be converted to an
+    appropriate Python data type.
+
+    If you have node attributes stored in a separate dataframe `df_nodes`,
+    you can load those attributes to the graph `G` using the following code:
+
+    ```
+    df_nodes = pd.DataFrame({"node_id": [1, 2, 3], "attribute1": ["A", "B", "C"]})
+    G.add_nodes_from((n, dict(d)) for n, d in df_nodes.iterrows())
+    ```
+
+    If `df` has a user-specified compound data type the names
+    of the data fields will be used as attribute keys in the resulting
+    NetworkX graph.
+
+    See Also
+    --------
+    to_pandas_adjacency
+
+    Examples
+    --------
+    Simple integer weights on edges:
+
+    >>> import pandas as pd
+    >>> pd.options.display.max_columns = 20
+    >>> df = pd.DataFrame([[1, 1], [2, 1]])
+    >>> df
+       0  1
+    0  1  1
+    1  2  1
+    >>> G = nx.from_pandas_adjacency(df)
+    >>> G.name = "Graph from pandas adjacency matrix"
+    >>> print(G)
+    Graph named 'Graph from pandas adjacency matrix' with 2 nodes and 3 edges
+    """
+
+    try:
+        df = df[df.index]
+    except Exception as err:
+        missing = list(set(df.index).difference(set(df.columns)))
+        msg = f"{missing} not in columns"
+        raise nx.NetworkXError("Columns must match Indices.", msg) from err
+
+    A = df.values
+    G = from_numpy_array(A, create_using=create_using, nodelist=df.columns)
+
+    return G
+
+
+@nx._dispatchable(preserve_edge_attrs=True)
+def to_pandas_edgelist(
+    G,
+    source="source",
+    target="target",
+    nodelist=None,
+    dtype=None,
+    edge_key=None,
+):
+    """Returns the graph edge list as a Pandas DataFrame.
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the Pandas DataFrame.
+
+    source : str or int, optional
+        A valid column name (string or integer) for the source nodes (for the
+        directed case).
+
+    target : str or int, optional
+        A valid column name (string or integer) for the target nodes (for the
+        directed case).
+
+    nodelist : list, optional
+       Use only nodes specified in nodelist
+
+    dtype : dtype, default None
+        Use to create the DataFrame. Data type to force.
+        Only a single dtype is allowed. If None, infer.
+
+    edge_key : str or int or None, optional (default=None)
+        A valid column name (string or integer) for the edge keys (for the
+        multigraph case). If None, edge keys are not stored in the DataFrame.
+
+    Returns
+    -------
+    df : Pandas DataFrame
+       Graph edge list
+
+    Examples
+    --------
+    >>> G = nx.Graph(
+    ...     [
+    ...         ("A", "B", {"cost": 1, "weight": 7}),
+    ...         ("C", "E", {"cost": 9, "weight": 10}),
+    ...     ]
+    ... )
+    >>> df = nx.to_pandas_edgelist(G, nodelist=["A", "C"])
+    >>> df[["source", "target", "cost", "weight"]]
+      source target  cost  weight
+    0      A      B     1       7
+    1      C      E     9      10
+
+    >>> G = nx.MultiGraph([("A", "B", {"cost": 1}), ("A", "B", {"cost": 9})])
+    >>> df = nx.to_pandas_edgelist(G, nodelist=["A", "C"], edge_key="ekey")
+    >>> df[["source", "target", "cost", "ekey"]]
+      source target  cost  ekey
+    0      A      B     1     0
+    1      A      B     9     1
+
+    """
+    import pandas as pd
+
+    if nodelist is None:
+        edgelist = G.edges(data=True)
+    else:
+        edgelist = G.edges(nodelist, data=True)
+    source_nodes = [s for s, _, _ in edgelist]
+    target_nodes = [t for _, t, _ in edgelist]
+
+    all_attrs = set().union(*(d.keys() for _, _, d in edgelist))
+    if source in all_attrs:
+        raise nx.NetworkXError(f"Source name {source!r} is an edge attr name")
+    if target in all_attrs:
+        raise nx.NetworkXError(f"Target name {target!r} is an edge attr name")
+
+    nan = float("nan")
+    edge_attr = {k: [d.get(k, nan) for _, _, d in edgelist] for k in all_attrs}
+
+    if G.is_multigraph() and edge_key is not None:
+        if edge_key in all_attrs:
+            raise nx.NetworkXError(f"Edge key name {edge_key!r} is an edge attr name")
+        edge_keys = [k for _, _, k in G.edges(keys=True)]
+        edgelistdict = {source: source_nodes, target: target_nodes, edge_key: edge_keys}
+    else:
+        edgelistdict = {source: source_nodes, target: target_nodes}
+
+    edgelistdict.update(edge_attr)
+    return pd.DataFrame(edgelistdict, dtype=dtype)
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_pandas_edgelist(
+    df,
+    source="source",
+    target="target",
+    edge_attr=None,
+    create_using=None,
+    edge_key=None,
+):
+    """Returns a graph from Pandas DataFrame containing an edge list.
+
+    The Pandas DataFrame should contain at least two columns of node names and
+    zero or more columns of edge attributes. Each row will be processed as one
+    edge instance.
+
+    Note: This function iterates over DataFrame.values, which is not
+    guaranteed to retain the data type across columns in the row. This is only
+    a problem if your row is entirely numeric and a mix of ints and floats. In
+    that case, all values will be returned as floats. See the
+    DataFrame.iterrows documentation for an example.
+
+    Parameters
+    ----------
+    df : Pandas DataFrame
+        An edge list representation of a graph
+
+    source : str or int
+        A valid column name (string or integer) for the source nodes (for the
+        directed case).
+
+    target : str or int
+        A valid column name (string or integer) for the target nodes (for the
+        directed case).
+
+    edge_attr : str or int, iterable, True, or None
+        A valid column name (str or int) or iterable of column names that are
+        used to retrieve items and add them to the graph as edge attributes.
+        If `True`, all columns will be added except `source`, `target` and `edge_key`.
+        If `None`, no edge attributes are added to the graph.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    edge_key : str or None, optional (default=None)
+        A valid column name for the edge keys (for a MultiGraph). The values in
+        this column are used for the edge keys when adding edges if create_using
+        is a multigraph.
+
+    If you have node attributes stored in a separate dataframe `df_nodes`,
+    you can load those attributes to the graph `G` using the following code:
+
+    ```
+    df_nodes = pd.DataFrame({"node_id": [1, 2, 3], "attribute1": ["A", "B", "C"]})
+    G.add_nodes_from((n, dict(d)) for n, d in df_nodes.iterrows())
+    ```
+
+    See Also
+    --------
+    to_pandas_edgelist
+
+    Examples
+    --------
+    Simple integer weights on edges:
+
+    >>> import pandas as pd
+    >>> pd.options.display.max_columns = 20
+    >>> import numpy as np
+    >>> rng = np.random.RandomState(seed=5)
+    >>> ints = rng.randint(1, 11, size=(3, 2))
+    >>> a = ["A", "B", "C"]
+    >>> b = ["D", "A", "E"]
+    >>> df = pd.DataFrame(ints, columns=["weight", "cost"])
+    >>> df[0] = a
+    >>> df["b"] = b
+    >>> df[["weight", "cost", 0, "b"]]
+       weight  cost  0  b
+    0       4     7  A  D
+    1       7     1  B  A
+    2      10     9  C  E
+    >>> G = nx.from_pandas_edgelist(df, 0, "b", ["weight", "cost"])
+    >>> G["E"]["C"]["weight"]
+    10
+    >>> G["E"]["C"]["cost"]
+    9
+    >>> edges = pd.DataFrame(
+    ...     {
+    ...         "source": [0, 1, 2],
+    ...         "target": [2, 2, 3],
+    ...         "weight": [3, 4, 5],
+    ...         "color": ["red", "blue", "blue"],
+    ...     }
+    ... )
+    >>> G = nx.from_pandas_edgelist(edges, edge_attr=True)
+    >>> G[0][2]["color"]
+    'red'
+
+    Build multigraph with custom keys:
+
+    >>> edges = pd.DataFrame(
+    ...     {
+    ...         "source": [0, 1, 2, 0],
+    ...         "target": [2, 2, 3, 2],
+    ...         "my_edge_key": ["A", "B", "C", "D"],
+    ...         "weight": [3, 4, 5, 6],
+    ...         "color": ["red", "blue", "blue", "blue"],
+    ...     }
+    ... )
+    >>> G = nx.from_pandas_edgelist(
+    ...     edges,
+    ...     edge_key="my_edge_key",
+    ...     edge_attr=["weight", "color"],
+    ...     create_using=nx.MultiGraph(),
+    ... )
+    >>> G[0][2]
+    AtlasView({'A': {'weight': 3, 'color': 'red'}, 'D': {'weight': 6, 'color': 'blue'}})
+
+
+    """
+    g = nx.empty_graph(0, create_using)
+
+    if edge_attr is None:
+        if g.is_multigraph() and edge_key is not None:
+            for u, v, k in zip(df[source], df[target], df[edge_key]):
+                g.add_edge(u, v, k)
+        else:
+            g.add_edges_from(zip(df[source], df[target]))
+        return g
+
+    reserved_columns = [source, target]
+    if g.is_multigraph() and edge_key is not None:
+        reserved_columns.append(edge_key)
+
+    # Additional columns requested
+    attr_col_headings = []
+    attribute_data = []
+    if edge_attr is True:
+        attr_col_headings = [c for c in df.columns if c not in reserved_columns]
+    elif isinstance(edge_attr, list | tuple):
+        attr_col_headings = edge_attr
+    else:
+        attr_col_headings = [edge_attr]
+    if len(attr_col_headings) == 0:
+        raise nx.NetworkXError(
+            f"Invalid edge_attr argument: No columns found with name: {attr_col_headings}"
+        )
+
+    try:
+        attribute_data = zip(*[df[col] for col in attr_col_headings])
+    except (KeyError, TypeError) as err:
+        msg = f"Invalid edge_attr argument: {edge_attr}"
+        raise nx.NetworkXError(msg) from err
+
+    if g.is_multigraph():
+        # => append the edge keys from the df to the bundled data
+        if edge_key is not None:
+            try:
+                multigraph_edge_keys = df[edge_key]
+                attribute_data = zip(attribute_data, multigraph_edge_keys)
+            except (KeyError, TypeError) as err:
+                msg = f"Invalid edge_key argument: {edge_key}"
+                raise nx.NetworkXError(msg) from err
+
+        for s, t, attrs in zip(df[source], df[target], attribute_data):
+            if edge_key is not None:
+                attrs, multigraph_edge_key = attrs
+                key = g.add_edge(s, t, key=multigraph_edge_key)
+            else:
+                key = g.add_edge(s, t)
+
+            g[s][t][key].update(zip(attr_col_headings, attrs))
+    else:
+        for s, t, attrs in zip(df[source], df[target], attribute_data):
+            g.add_edge(s, t)
+            g[s][t].update(zip(attr_col_headings, attrs))
+
+    return g
+
+
+@nx._dispatchable(edge_attrs="weight")
+def to_scipy_sparse_array(G, nodelist=None, dtype=None, weight="weight", format="csr"):
+    """Returns the graph adjacency matrix as a SciPy sparse array.
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the sparse array.
+
+    nodelist : list, optional
+       The rows and columns are ordered according to the nodes in `nodelist`.
+       If `nodelist` is None, then the ordering is produced by ``G.nodes()``.
+
+    dtype : NumPy data-type, optional
+        A valid NumPy dtype used to initialize the array. If None, then the
+        NumPy default is used.
+
+    weight : string or None, optional (default='weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight.  If None then all edge weights are 1.
+
+    format : str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}
+        The format of the sparse array to be returned (default 'csr').  For
+        some algorithms different implementations of sparse arrays
+        can perform better.  See [1]_ for details.
+
+    Returns
+    -------
+    A : SciPy sparse array
+       Graph adjacency matrix.
+
+    Notes
+    -----
+    For directed graphs, matrix entry ``i, j`` corresponds to an edge from
+    ``i`` to ``j``.
+
+    The values of the adjacency matrix are populated using the edge attribute held in
+    parameter `weight`. When an edge does not have that attribute, the
+    value of the entry is 1.
+
+    For multiple edges the matrix values are the sums of the edge weights.
+
+    When `nodelist` does not contain every node in `G`, the adjacency matrix
+    is built from the subgraph of `G` that is induced by the nodes in
+    `nodelist`.
+
+    The convention used for self-loop edges in graphs is to assign the
+    diagonal matrix entry value to the weight attribute of the edge
+    (or the number 1 if the edge has no weight attribute).  If the
+    alternate convention of doubling the edge weight is desired the
+    resulting array can be modified as follows::
+
+        >>> G = nx.Graph([(1, 1)])
+        >>> A = nx.to_scipy_sparse_array(G)
+        >>> A.toarray()
+        array([[1]])
+        >>> A.setdiag(A.diagonal() * 2)
+        >>> A.toarray()
+        array([[2]])
+
+    Examples
+    --------
+
+    Basic usage:
+
+    >>> G = nx.path_graph(4)
+    >>> A = nx.to_scipy_sparse_array(G)
+    >>> A  # doctest: +SKIP
+    <Compressed Sparse Row sparse array of dtype 'int64'
+        with 6 stored elements and shape (4, 4)>
+
+    >>> A.toarray()
+    array([[0, 1, 0, 0],
+           [1, 0, 1, 0],
+           [0, 1, 0, 1],
+           [0, 0, 1, 0]])
+
+    .. note:: The `toarray` method is used in these examples to better visualize
+       the adjacancy matrix. For a dense representation of the adjaceny matrix,
+       use `to_numpy_array` instead.
+
+    Directed graphs:
+
+    >>> G = nx.DiGraph([(0, 1), (1, 2), (2, 3)])
+    >>> nx.to_scipy_sparse_array(G).toarray()
+    array([[0, 1, 0, 0],
+           [0, 0, 1, 0],
+           [0, 0, 0, 1],
+           [0, 0, 0, 0]])
+
+    >>> H = G.reverse()
+    >>> H.edges
+    OutEdgeView([(1, 0), (2, 1), (3, 2)])
+    >>> nx.to_scipy_sparse_array(H).toarray()
+    array([[0, 0, 0, 0],
+           [1, 0, 0, 0],
+           [0, 1, 0, 0],
+           [0, 0, 1, 0]])
+
+    By default, the order of the rows/columns of the adjacency matrix is determined
+    by the ordering of the nodes in `G`:
+
+    >>> G = nx.Graph()
+    >>> G.add_nodes_from([3, 5, 0, 1])
+    >>> G.add_edges_from([(1, 3), (1, 5)])
+    >>> nx.to_scipy_sparse_array(G).toarray()
+    array([[0, 0, 0, 1],
+           [0, 0, 0, 1],
+           [0, 0, 0, 0],
+           [1, 1, 0, 0]])
+
+    The ordering of the rows can be changed with `nodelist`:
+
+    >>> ordered = [0, 1, 3, 5]
+    >>> nx.to_scipy_sparse_array(G, nodelist=ordered).toarray()
+    array([[0, 0, 0, 0],
+           [0, 0, 1, 1],
+           [0, 1, 0, 0],
+           [0, 1, 0, 0]])
+
+    If `nodelist` contains a subset of the nodes in `G`, the adjacency matrix
+    for the node-induced subgraph is produced:
+
+    >>> nx.to_scipy_sparse_array(G, nodelist=[1, 3, 5]).toarray()
+    array([[0, 1, 1],
+           [1, 0, 0],
+           [1, 0, 0]])
+
+    The values of the adjacency matrix are drawn from the edge attribute
+    specified by the `weight` parameter:
+
+    >>> G = nx.path_graph(4)
+    >>> nx.set_edge_attributes(
+    ...     G, values={(0, 1): 1, (1, 2): 10, (2, 3): 2}, name="weight"
+    ... )
+    >>> nx.set_edge_attributes(
+    ...     G, values={(0, 1): 50, (1, 2): 35, (2, 3): 10}, name="capacity"
+    ... )
+    >>> nx.to_scipy_sparse_array(G).toarray()  # Default weight="weight"
+    array([[ 0,  1,  0,  0],
+           [ 1,  0, 10,  0],
+           [ 0, 10,  0,  2],
+           [ 0,  0,  2,  0]])
+    >>> nx.to_scipy_sparse_array(G, weight="capacity").toarray()
+    array([[ 0, 50,  0,  0],
+           [50,  0, 35,  0],
+           [ 0, 35,  0, 10],
+           [ 0,  0, 10,  0]])
+
+    Any edges that don't have a `weight` attribute default to 1:
+
+    >>> G[1][2].pop("capacity")
+    35
+    >>> nx.to_scipy_sparse_array(G, weight="capacity").toarray()
+    array([[ 0, 50,  0,  0],
+           [50,  0,  1,  0],
+           [ 0,  1,  0, 10],
+           [ 0,  0, 10,  0]])
+
+    When `G` is a multigraph, the values in the adjacency matrix are given by
+    the sum of the `weight` edge attribute over each edge key:
+
+    >>> G = nx.MultiDiGraph([(0, 1), (0, 1), (0, 1), (2, 0)])
+    >>> nx.to_scipy_sparse_array(G).toarray()
+    array([[0, 3, 0],
+           [0, 0, 0],
+           [1, 0, 0]])
+
+    References
+    ----------
+    .. [1] Scipy Dev. References, "Sparse Arrays",
+       https://docs.scipy.org/doc/scipy/reference/sparse.html
+    """
+    import scipy as sp
+
+    if len(G) == 0:
+        raise nx.NetworkXError("Graph has no nodes or edges")
+
+    if nodelist is None:
+        nodelist = list(G)
+        nlen = len(G)
+    else:
+        nlen = len(nodelist)
+        if nlen == 0:
+            raise nx.NetworkXError("nodelist has no nodes")
+        nodeset = set(G.nbunch_iter(nodelist))
+        if nlen != len(nodeset):
+            for n in nodelist:
+                if n not in G:
+                    raise nx.NetworkXError(f"Node {n} in nodelist is not in G")
+            raise nx.NetworkXError("nodelist contains duplicates.")
+        if nlen < len(G):
+            G = G.subgraph(nodelist)
+
+    index = dict(zip(nodelist, range(nlen)))
+    coefficients = zip(
+        *((index[u], index[v], wt) for u, v, wt in G.edges(data=weight, default=1))
+    )
+    try:
+        row, col, data = coefficients
+    except ValueError:
+        # there is no edge in the subgraph
+        row, col, data = [], [], []
+
+    if G.is_directed():
+        A = sp.sparse.coo_array((data, (row, col)), shape=(nlen, nlen), dtype=dtype)
+    else:
+        # symmetrize matrix
+        d = data + data
+        r = row + col
+        c = col + row
+        # selfloop entries get double counted when symmetrizing
+        # so we subtract the data on the diagonal
+        selfloops = list(nx.selfloop_edges(G, data=weight, default=1))
+        if selfloops:
+            diag_index, diag_data = zip(*((index[u], -wt) for u, v, wt in selfloops))
+            d += diag_data
+            r += diag_index
+            c += diag_index
+        A = sp.sparse.coo_array((d, (r, c)), shape=(nlen, nlen), dtype=dtype)
+    try:
+        return A.asformat(format)
+    except ValueError as err:
+        raise nx.NetworkXError(f"Unknown sparse matrix format: {format}") from err
+
+
+def _csr_gen_triples(A):
+    """Converts a SciPy sparse array in **Compressed Sparse Row** format to
+    an iterable of weighted edge triples.
+
+    """
+    nrows = A.shape[0]
+    indptr, dst_indices, data = A.indptr, A.indices, A.data
+    import numpy as np
+
+    src_indices = np.repeat(np.arange(nrows), np.diff(indptr))
+    return zip(src_indices.tolist(), dst_indices.tolist(), A.data.tolist())
+
+
+def _csc_gen_triples(A):
+    """Converts a SciPy sparse array in **Compressed Sparse Column** format to
+    an iterable of weighted edge triples.
+
+    """
+    ncols = A.shape[1]
+    indptr, src_indices, data = A.indptr, A.indices, A.data
+    import numpy as np
+
+    dst_indices = np.repeat(np.arange(ncols), np.diff(indptr))
+    return zip(src_indices.tolist(), dst_indices.tolist(), A.data.tolist())
+
+
+def _coo_gen_triples(A):
+    """Converts a SciPy sparse array in **Coordinate** format to an iterable
+    of weighted edge triples.
+
+    """
+    return zip(A.row.tolist(), A.col.tolist(), A.data.tolist())
+
+
+def _dok_gen_triples(A):
+    """Converts a SciPy sparse array in **Dictionary of Keys** format to an
+    iterable of weighted edge triples.
+
+    """
+    for (r, c), v in A.items():
+        # Use `v.item()` to convert a NumPy scalar to the appropriate Python scalar
+        yield int(r), int(c), v.item()
+
+
+def _generate_weighted_edges(A):
+    """Returns an iterable over (u, v, w) triples, where u and v are adjacent
+    vertices and w is the weight of the edge joining u and v.
+
+    `A` is a SciPy sparse array (in any format).
+
+    """
+    if A.format == "csr":
+        return _csr_gen_triples(A)
+    if A.format == "csc":
+        return _csc_gen_triples(A)
+    if A.format == "dok":
+        return _dok_gen_triples(A)
+    # If A is in any other format (including COO), convert it to COO format.
+    return _coo_gen_triples(A.tocoo())
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_scipy_sparse_array(
+    A, parallel_edges=False, create_using=None, edge_attribute="weight"
+):
+    """Creates a new graph from an adjacency matrix given as a SciPy sparse
+    array.
+
+    Parameters
+    ----------
+    A: scipy.sparse array
+      An adjacency matrix representation of a graph
+
+    parallel_edges : Boolean
+      If this is True, `create_using` is a multigraph, and `A` is an
+      integer matrix, then entry *(i, j)* in the matrix is interpreted as the
+      number of parallel edges joining vertices *i* and *j* in the graph.
+      If it is False, then the entries in the matrix are interpreted as
+      the weight of a single edge joining the vertices.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    edge_attribute: string
+       Name of edge attribute to store matrix numeric value. The data will
+       have the same type as the matrix entry (int, float, (real,imag)).
+
+    Notes
+    -----
+    For directed graphs, explicitly mention create_using=nx.DiGraph,
+    and entry i,j of A corresponds to an edge from i to j.
+
+    If `create_using` is :class:`networkx.MultiGraph` or
+    :class:`networkx.MultiDiGraph`, `parallel_edges` is True, and the
+    entries of `A` are of type :class:`int`, then this function returns a
+    multigraph (constructed from `create_using`) with parallel edges.
+    In this case, `edge_attribute` will be ignored.
+
+    If `create_using` indicates an undirected multigraph, then only the edges
+    indicated by the upper triangle of the matrix `A` will be added to the
+    graph.
+
+    Examples
+    --------
+    >>> import scipy as sp
+    >>> A = sp.sparse.eye(2, 2, 1)
+    >>> G = nx.from_scipy_sparse_array(A)
+
+    If `create_using` indicates a multigraph and the matrix has only integer
+    entries and `parallel_edges` is False, then the entries will be treated
+    as weights for edges joining the nodes (without creating parallel edges):
+
+    >>> A = sp.sparse.csr_array([[1, 1], [1, 2]])
+    >>> G = nx.from_scipy_sparse_array(A, create_using=nx.MultiGraph)
+    >>> G[1][1]
+    AtlasView({0: {'weight': 2}})
+
+    If `create_using` indicates a multigraph and the matrix has only integer
+    entries and `parallel_edges` is True, then the entries will be treated
+    as the number of parallel edges joining those two vertices:
+
+    >>> A = sp.sparse.csr_array([[1, 1], [1, 2]])
+    >>> G = nx.from_scipy_sparse_array(
+    ...     A, parallel_edges=True, create_using=nx.MultiGraph
+    ... )
+    >>> G[1][1]
+    AtlasView({0: {'weight': 1}, 1: {'weight': 1}})
+
+    """
+    G = nx.empty_graph(0, create_using)
+    n, m = A.shape
+    if n != m:
+        raise nx.NetworkXError(f"Adjacency matrix not square: nx,ny={A.shape}")
+    # Make sure we get even the isolated nodes of the graph.
+    G.add_nodes_from(range(n))
+    # Create an iterable over (u, v, w) triples and for each triple, add an
+    # edge from u to v with weight w.
+    triples = _generate_weighted_edges(A)
+    # If the entries in the adjacency matrix are integers, the graph is a
+    # multigraph, and parallel_edges is True, then create parallel edges, each
+    # with weight 1, for each entry in the adjacency matrix. Otherwise, create
+    # one edge for each positive entry in the adjacency matrix and set the
+    # weight of that edge to be the entry in the matrix.
+    if A.dtype.kind in ("i", "u") and G.is_multigraph() and parallel_edges:
+        chain = itertools.chain.from_iterable
+        # The following line is equivalent to:
+        #
+        #     for (u, v) in edges:
+        #         for d in range(A[u, v]):
+        #             G.add_edge(u, v, weight=1)
+        #
+        triples = chain(((u, v, 1) for d in range(w)) for (u, v, w) in triples)
+    # If we are creating an undirected multigraph, only add the edges from the
+    # upper triangle of the matrix. Otherwise, add all the edges. This relies
+    # on the fact that the vertices created in the
+    # `_generated_weighted_edges()` function are actually the row/column
+    # indices for the matrix `A`.
+    #
+    # Without this check, we run into a problem where each edge is added twice
+    # when `G.add_weighted_edges_from()` is invoked below.
+    if G.is_multigraph() and not G.is_directed():
+        triples = ((u, v, d) for u, v, d in triples if u <= v)
+    G.add_weighted_edges_from(triples, weight=edge_attribute)
+    return G
+
+
+@nx._dispatchable(edge_attrs="weight")  # edge attrs may also be obtained from `dtype`
+def to_numpy_array(
+    G,
+    nodelist=None,
+    dtype=None,
+    order=None,
+    multigraph_weight=sum,
+    weight="weight",
+    nonedge=0.0,
+):
+    """Returns the graph adjacency matrix as a NumPy array.
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the NumPy array.
+
+    nodelist : list, optional
+        The rows and columns are ordered according to the nodes in `nodelist`.
+        If `nodelist` is ``None``, then the ordering is produced by ``G.nodes()``.
+
+    dtype : NumPy data type, optional
+        A NumPy data type used to initialize the array. If None, then the NumPy
+        default is used. The dtype can be structured if `weight=None`, in which
+        case the dtype field names are used to look up edge attributes. The
+        result is a structured array where each named field in the dtype
+        corresponds to the adjacency for that edge attribute. See examples for
+        details.
+
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. If None, then the NumPy default
+        is used.
+
+    multigraph_weight : callable, optional
+        An function that determines how weights in multigraphs are handled.
+        The function should accept a sequence of weights and return a single
+        value. The default is to sum the weights of the multiple edges.
+
+    weight : string or None optional (default = 'weight')
+        The edge attribute that holds the numerical value used for
+        the edge weight. If an edge does not have that attribute, then the
+        value 1 is used instead. `weight` must be ``None`` if a structured
+        dtype is used.
+
+    nonedge : array_like (default = 0.0)
+        The value used to represent non-edges in the adjacency matrix.
+        The array values corresponding to nonedges are typically set to zero.
+        However, this could be undesirable if there are array values
+        corresponding to actual edges that also have the value zero. If so,
+        one might prefer nonedges to have some other value, such as ``nan``.
+
+    Returns
+    -------
+    A : NumPy ndarray
+        Graph adjacency matrix
+
+    Raises
+    ------
+    NetworkXError
+        If `dtype` is a structured dtype and `G` is a multigraph
+    ValueError
+        If `dtype` is a structured dtype and `weight` is not `None`
+
+    See Also
+    --------
+    from_numpy_array
+
+    Notes
+    -----
+    For directed graphs, entry ``i, j`` corresponds to an edge from ``i`` to ``j``.
+
+    Entries in the adjacency matrix are given by the `weight` edge attribute.
+    When an edge does not have a weight attribute, the value of the entry is
+    set to the number 1.  For multiple (parallel) edges, the values of the
+    entries are determined by the `multigraph_weight` parameter. The default is
+    to sum the weight attributes for each of the parallel edges.
+
+    When `nodelist` does not contain every node in `G`, the adjacency matrix is
+    built from the subgraph of `G` that is induced by the nodes in `nodelist`.
+
+    The convention used for self-loop edges in graphs is to assign the
+    diagonal array entry value to the weight attribute of the edge
+    (or the number 1 if the edge has no weight attribute). If the
+    alternate convention of doubling the edge weight is desired the
+    resulting NumPy array can be modified as follows:
+
+    >>> import numpy as np
+    >>> G = nx.Graph([(1, 1)])
+    >>> A = nx.to_numpy_array(G)
+    >>> A
+    array([[1.]])
+    >>> A[np.diag_indices_from(A)] *= 2
+    >>> A
+    array([[2.]])
+
+    Examples
+    --------
+    >>> G = nx.MultiDiGraph()
+    >>> G.add_edge(0, 1, weight=2)
+    0
+    >>> G.add_edge(1, 0)
+    0
+    >>> G.add_edge(2, 2, weight=3)
+    0
+    >>> G.add_edge(2, 2)
+    1
+    >>> nx.to_numpy_array(G, nodelist=[0, 1, 2])
+    array([[0., 2., 0.],
+           [1., 0., 0.],
+           [0., 0., 4.]])
+
+    When `nodelist` argument is used, nodes of `G` which do not appear in the `nodelist`
+    and their edges are not included in the adjacency matrix. Here is an example:
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(3, 1)
+    >>> G.add_edge(2, 0)
+    >>> G.add_edge(2, 1)
+    >>> G.add_edge(3, 0)
+    >>> nx.to_numpy_array(G, nodelist=[1, 2, 3])
+    array([[0., 1., 1.],
+           [1., 0., 0.],
+           [1., 0., 0.]])
+
+    This function can also be used to create adjacency matrices for multiple
+    edge attributes with structured dtypes:
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(0, 1, weight=10)
+    >>> G.add_edge(1, 2, cost=5)
+    >>> G.add_edge(2, 3, weight=3, cost=-4.0)
+    >>> dtype = np.dtype([("weight", int), ("cost", float)])
+    >>> A = nx.to_numpy_array(G, dtype=dtype, weight=None)
+    >>> A["weight"]
+    array([[ 0, 10,  0,  0],
+           [10,  0,  1,  0],
+           [ 0,  1,  0,  3],
+           [ 0,  0,  3,  0]])
+    >>> A["cost"]
+    array([[ 0.,  1.,  0.,  0.],
+           [ 1.,  0.,  5.,  0.],
+           [ 0.,  5.,  0., -4.],
+           [ 0.,  0., -4.,  0.]])
+
+    As stated above, the argument "nonedge" is useful especially when there are
+    actually edges with weight 0 in the graph. Setting a nonedge value different than 0,
+    makes it much clearer to differentiate such 0-weighted edges and actual nonedge values.
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(3, 1, weight=2)
+    >>> G.add_edge(2, 0, weight=0)
+    >>> G.add_edge(2, 1, weight=0)
+    >>> G.add_edge(3, 0, weight=1)
+    >>> nx.to_numpy_array(G, nonedge=-1.0)
+    array([[-1.,  2., -1.,  1.],
+           [ 2., -1.,  0., -1.],
+           [-1.,  0., -1.,  0.],
+           [ 1., -1.,  0., -1.]])
+    """
+    import numpy as np
+
+    if nodelist is None:
+        nodelist = list(G)
+    nlen = len(nodelist)
+
+    # Input validation
+    nodeset = set(nodelist)
+    if nodeset - set(G):
+        raise nx.NetworkXError(f"Nodes {nodeset - set(G)} in nodelist is not in G")
+    if len(nodeset) < nlen:
+        raise nx.NetworkXError("nodelist contains duplicates.")
+
+    A = np.full((nlen, nlen), fill_value=nonedge, dtype=dtype, order=order)
+
+    # Corner cases: empty nodelist or graph without any edges
+    if nlen == 0 or G.number_of_edges() == 0:
+        return A
+
+    # If dtype is structured and weight is None, use dtype field names as
+    # edge attributes
+    edge_attrs = None  # Only single edge attribute by default
+    if A.dtype.names:
+        if weight is None:
+            edge_attrs = dtype.names
+        else:
+            raise ValueError(
+                "Specifying `weight` not supported for structured dtypes\n."
+                "To create adjacency matrices from structured dtypes, use `weight=None`."
+            )
+
+    # Map nodes to row/col in matrix
+    idx = dict(zip(nodelist, range(nlen)))
+    if len(nodelist) < len(G):
+        G = G.subgraph(nodelist).copy()
+
+    # Collect all edge weights and reduce with `multigraph_weights`
+    if G.is_multigraph():
+        if edge_attrs:
+            raise nx.NetworkXError(
+                "Structured arrays are not supported for MultiGraphs"
+            )
+        d = defaultdict(list)
+        for u, v, wt in G.edges(data=weight, default=1.0):
+            d[(idx[u], idx[v])].append(wt)
+        i, j = np.array(list(d.keys())).T  # indices
+        wts = [multigraph_weight(ws) for ws in d.values()]  # reduced weights
+    else:
+        i, j, wts = [], [], []
+
+        # Special branch: multi-attr adjacency from structured dtypes
+        if edge_attrs:
+            # Extract edges with all data
+            for u, v, data in G.edges(data=True):
+                i.append(idx[u])
+                j.append(idx[v])
+                wts.append(data)
+            # Map each attribute to the appropriate named field in the
+            # structured dtype
+            for attr in edge_attrs:
+                attr_data = [wt.get(attr, 1.0) for wt in wts]
+                A[attr][i, j] = attr_data
+                if not G.is_directed():
+                    A[attr][j, i] = attr_data
+            return A
+
+        for u, v, wt in G.edges(data=weight, default=1.0):
+            i.append(idx[u])
+            j.append(idx[v])
+            wts.append(wt)
+
+    # Set array values with advanced indexing
+    A[i, j] = wts
+    if not G.is_directed():
+        A[j, i] = wts
+
+    return A
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def from_numpy_array(
+    A, parallel_edges=False, create_using=None, edge_attr="weight", *, nodelist=None
+):
+    """Returns a graph from a 2D NumPy array.
+
+    The 2D NumPy array is interpreted as an adjacency matrix for the graph.
+
+    Parameters
+    ----------
+    A : a 2D numpy.ndarray
+        An adjacency matrix representation of a graph
+
+    parallel_edges : Boolean
+        If this is True, `create_using` is a multigraph, and `A` is an
+        integer array, then entry *(i, j)* in the array is interpreted as the
+        number of parallel edges joining vertices *i* and *j* in the graph.
+        If it is False, then the entries in the array are interpreted as
+        the weight of a single edge joining the vertices.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    edge_attr : String, optional (default="weight")
+        The attribute to which the array values are assigned on each edge. If
+        it is None, edge attributes will not be assigned.
+
+    nodelist : sequence of nodes, optional
+        A sequence of objects to use as the nodes in the graph. If provided, the
+        list of nodes must be the same length as the dimensions of `A`. The
+        default is `None`, in which case the nodes are drawn from ``range(n)``.
+
+    Notes
+    -----
+    For directed graphs, explicitly mention create_using=nx.DiGraph,
+    and entry i,j of A corresponds to an edge from i to j.
+
+    If `create_using` is :class:`networkx.MultiGraph` or
+    :class:`networkx.MultiDiGraph`, `parallel_edges` is True, and the
+    entries of `A` are of type :class:`int`, then this function returns a
+    multigraph (of the same type as `create_using`) with parallel edges.
+
+    If `create_using` indicates an undirected multigraph, then only the edges
+    indicated by the upper triangle of the array `A` will be added to the
+    graph.
+
+    If `edge_attr` is Falsy (False or None), edge attributes will not be
+    assigned, and the array data will be treated like a binary mask of
+    edge presence or absence. Otherwise, the attributes will be assigned
+    as follows:
+
+    If the NumPy array has a single data type for each array entry it
+    will be converted to an appropriate Python data type.
+
+    If the NumPy array has a user-specified compound data type the names
+    of the data fields will be used as attribute keys in the resulting
+    NetworkX graph.
+
+    See Also
+    --------
+    to_numpy_array
+
+    Examples
+    --------
+    Simple integer weights on edges:
+
+    >>> import numpy as np
+    >>> A = np.array([[1, 1], [2, 1]])
+    >>> G = nx.from_numpy_array(A)
+    >>> G.edges(data=True)
+    EdgeDataView([(0, 0, {'weight': 1}), (0, 1, {'weight': 2}), (1, 1, {'weight': 1})])
+
+    If `create_using` indicates a multigraph and the array has only integer
+    entries and `parallel_edges` is False, then the entries will be treated
+    as weights for edges joining the nodes (without creating parallel edges):
+
+    >>> A = np.array([[1, 1], [1, 2]])
+    >>> G = nx.from_numpy_array(A, create_using=nx.MultiGraph)
+    >>> G[1][1]
+    AtlasView({0: {'weight': 2}})
+
+    If `create_using` indicates a multigraph and the array has only integer
+    entries and `parallel_edges` is True, then the entries will be treated
+    as the number of parallel edges joining those two vertices:
+
+    >>> A = np.array([[1, 1], [1, 2]])
+    >>> temp = nx.MultiGraph()
+    >>> G = nx.from_numpy_array(A, parallel_edges=True, create_using=temp)
+    >>> G[1][1]
+    AtlasView({0: {'weight': 1}, 1: {'weight': 1}})
+
+    User defined compound data type on edges:
+
+    >>> dt = [("weight", float), ("cost", int)]
+    >>> A = np.array([[(1.0, 2)]], dtype=dt)
+    >>> G = nx.from_numpy_array(A)
+    >>> G.edges()
+    EdgeView([(0, 0)])
+    >>> G[0][0]["cost"]
+    2
+    >>> G[0][0]["weight"]
+    1.0
+
+    """
+    kind_to_python_type = {
+        "f": float,
+        "i": int,
+        "u": int,
+        "b": bool,
+        "c": complex,
+        "S": str,
+        "U": str,
+        "V": "void",
+    }
+    G = nx.empty_graph(0, create_using)
+    if A.ndim != 2:
+        raise nx.NetworkXError(f"Input array must be 2D, not {A.ndim}")
+    n, m = A.shape
+    if n != m:
+        raise nx.NetworkXError(f"Adjacency matrix not square: nx,ny={A.shape}")
+    dt = A.dtype
+    try:
+        python_type = kind_to_python_type[dt.kind]
+    except Exception as err:
+        raise TypeError(f"Unknown numpy data type: {dt}") from err
+    if _default_nodes := (nodelist is None):
+        nodelist = range(n)
+    else:
+        if len(nodelist) != n:
+            raise ValueError("nodelist must have the same length as A.shape[0]")
+
+    # Make sure we get even the isolated nodes of the graph.
+    G.add_nodes_from(nodelist)
+    # Get a list of all the entries in the array with nonzero entries. These
+    # coordinates become edges in the graph. (convert to int from np.int64)
+    edges = ((int(e[0]), int(e[1])) for e in zip(*A.nonzero()))
+    # handle numpy constructed data type
+    if python_type == "void":
+        # Sort the fields by their offset, then by dtype, then by name.
+        fields = sorted(
+            (offset, dtype, name) for name, (dtype, offset) in A.dtype.fields.items()
+        )
+        triples = (
+            (
+                u,
+                v,
+                {}
+                if edge_attr in [False, None]
+                else {
+                    name: kind_to_python_type[dtype.kind](val)
+                    for (_, dtype, name), val in zip(fields, A[u, v])
+                },
+            )
+            for u, v in edges
+        )
+    # If the entries in the adjacency matrix are integers, the graph is a
+    # multigraph, and parallel_edges is True, then create parallel edges, each
+    # with weight 1, for each entry in the adjacency matrix. Otherwise, create
+    # one edge for each positive entry in the adjacency matrix and set the
+    # weight of that edge to be the entry in the matrix.
+    elif python_type is int and G.is_multigraph() and parallel_edges:
+        chain = itertools.chain.from_iterable
+        # The following line is equivalent to:
+        #
+        #     for (u, v) in edges:
+        #         for d in range(A[u, v]):
+        #             G.add_edge(u, v, weight=1)
+        #
+        if edge_attr in [False, None]:
+            triples = chain(((u, v, {}) for d in range(A[u, v])) for (u, v) in edges)
+        else:
+            triples = chain(
+                ((u, v, {edge_attr: 1}) for d in range(A[u, v])) for (u, v) in edges
+            )
+    else:  # basic data type
+        if edge_attr in [False, None]:
+            triples = ((u, v, {}) for u, v in edges)
+        else:
+            triples = ((u, v, {edge_attr: python_type(A[u, v])}) for u, v in edges)
+    # If we are creating an undirected multigraph, only add the edges from the
+    # upper triangle of the matrix. Otherwise, add all the edges. This relies
+    # on the fact that the vertices created in the
+    # `_generated_weighted_edges()` function are actually the row/column
+    # indices for the matrix `A`.
+    #
+    # Without this check, we run into a problem where each edge is added twice
+    # when `G.add_edges_from()` is invoked below.
+    if G.is_multigraph() and not G.is_directed():
+        triples = ((u, v, d) for u, v, d in triples if u <= v)
+    # Remap nodes if user provided custom `nodelist`
+    if not _default_nodes:
+        idx_to_node = dict(enumerate(nodelist))
+        triples = ((idx_to_node[u], idx_to_node[v], d) for u, v, d in triples)
+    G.add_edges_from(triples)
+    return G

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/networkx/exception.py

@@ -0,0 +1,131 @@
+"""
+**********
+Exceptions
+**********
+
+Base exceptions and errors for NetworkX.
+"""
+
+__all__ = [
+    "HasACycle",
+    "NodeNotFound",
+    "PowerIterationFailedConvergence",
+    "ExceededMaxIterations",
+    "AmbiguousSolution",
+    "NetworkXAlgorithmError",
+    "NetworkXException",
+    "NetworkXError",
+    "NetworkXNoCycle",
+    "NetworkXNoPath",
+    "NetworkXNotImplemented",
+    "NetworkXPointlessConcept",
+    "NetworkXUnbounded",
+    "NetworkXUnfeasible",
+]
+
+
+class NetworkXException(Exception):
+    """Base class for exceptions in NetworkX."""
+
+
+class NetworkXError(NetworkXException):
+    """Exception for a serious error in NetworkX"""
+
+
+class NetworkXPointlessConcept(NetworkXException):
+    """Raised when a null graph is provided as input to an algorithm
+    that cannot use it.
+
+    The null graph is sometimes considered a pointless concept [1]_,
+    thus the name of the exception.
+
+    Notes
+    -----
+    Null graphs and empty graphs are often used interchangeably but they
+    are well defined in NetworkX. An ``empty_graph`` is a graph with ``n`` nodes
+    and 0 edges, and a ``null_graph`` is a graph with 0 nodes and 0 edges.
+
+    References
+    ----------
+    .. [1] Harary, F. and Read, R. "Is the Null Graph a Pointless
+       Concept?"  In Graphs and Combinatorics Conference, George
+       Washington University.  New York: Springer-Verlag, 1973.
+
+    """
+
+
+class NetworkXAlgorithmError(NetworkXException):
+    """Exception for unexpected termination of algorithms."""
+
+
+class NetworkXUnfeasible(NetworkXAlgorithmError):
+    """Exception raised by algorithms trying to solve a problem
+    instance that has no feasible solution."""
+
+
+class NetworkXNoPath(NetworkXUnfeasible):
+    """Exception for algorithms that should return a path when running
+    on graphs where such a path does not exist."""
+
+
+class NetworkXNoCycle(NetworkXUnfeasible):
+    """Exception for algorithms that should return a cycle when running
+    on graphs where such a cycle does not exist."""
+
+
+class HasACycle(NetworkXException):
+    """Raised if a graph has a cycle when an algorithm expects that it
+    will have no cycles.
+
+    """
+
+
+class NetworkXUnbounded(NetworkXAlgorithmError):
+    """Exception raised by algorithms trying to solve a maximization
+    or a minimization problem instance that is unbounded."""
+
+
+class NetworkXNotImplemented(NetworkXException):
+    """Exception raised by algorithms not implemented for a type of graph."""
+
+
+class NodeNotFound(NetworkXException):
+    """Exception raised if requested node is not present in the graph"""
+
+
+class AmbiguousSolution(NetworkXException):
+    """Raised if more than one valid solution exists for an intermediary step
+    of an algorithm.
+
+    In the face of ambiguity, refuse the temptation to guess.
+    This may occur, for example, when trying to determine the
+    bipartite node sets in a disconnected bipartite graph when
+    computing bipartite matchings.
+
+    """
+
+
+class ExceededMaxIterations(NetworkXException):
+    """Raised if a loop iterates too many times without breaking.
+
+    This may occur, for example, in an algorithm that computes
+    progressively better approximations to a value but exceeds an
+    iteration bound specified by the user.
+
+    """
+
+
+class PowerIterationFailedConvergence(ExceededMaxIterations):
+    """Raised when the power iteration method fails to converge within a
+    specified iteration limit.
+
+    `num_iterations` is the number of iterations that have been
+    completed when this exception was raised.
+
+    """
+
+    def __init__(self, num_iterations, *args, **kw):
+        msg = f"power iteration failed to converge within {num_iterations} iterations"
+        exception_message = msg
+        superinit = super().__init__
+        superinit(self, exception_message, *args, **kw)

Scripts_RSCM_sim_growth_n_climate_to_Yield/.venv/lib/python3.10/site-packages/networkx/generators/__init__.py

@@ -0,0 +1,34 @@
+"""
+A package for generating various graphs in networkx.
+
+"""
+
+from networkx.generators.atlas import *
+from networkx.generators.classic import *
+from networkx.generators.cographs import *
+from networkx.generators.community import *
+from networkx.generators.degree_seq import *
+from networkx.generators.directed import *
+from networkx.generators.duplication import *
+from networkx.generators.ego import *
+from networkx.generators.expanders import *
+from networkx.generators.geometric import *
+from networkx.generators.harary_graph import *
+from networkx.generators.internet_as_graphs import *
+from networkx.generators.intersection import *
+from networkx.generators.interval_graph import *
+from networkx.generators.joint_degree_seq import *
+from networkx.generators.lattice import *
+from networkx.generators.line import *
+from networkx.generators.mycielski import *
+from networkx.generators.nonisomorphic_trees import *
+from networkx.generators.random_clustered import *
+from networkx.generators.random_graphs import *
+from networkx.generators.small import *
+from networkx.generators.social import *
+from networkx.generators.spectral_graph_forge import *
+from networkx.generators.stochastic import *
+from networkx.generators.sudoku import *
+from networkx.generators.time_series import *
+from networkx.generators.trees import *
+from networkx.generators.triads import *