internlm/SWE-Fixer-Train-110K huggingface dataset:
Repository: networkx/networkx

Issue:
Add examples to functions in shortest path folder. 
I wish to add examples to the following functions for better understanding.  :- 
1. def _dijkstra_multisource, def _bellman_ford, def _inner_bellman_ford  [here](https://github.com/networkx/networkx/blob/main/networkx/algorithms/shortest_paths/weighted.py) 
2. def bidirectional_shortest_path(G, source, target):,def _single_shortest_path(adj, firstlevel, paths, cutoff, join): [here](https://github.com/networkx/networkx/blob/main/networkx/algorithms/shortest_paths/unweighted.py)
3. def floyd_warshall_numpy(G, nodelist=None, weight="weight"):, def floyd_warshall(G, weight="weight"): [here](https://github.com/networkx/networkx/blob/main/networkx/algorithms/shortest_paths/dense.py)
4. def astar_path_length(G, source, target, heuristic=None, weight="weight"): [here](https://github.com/networkx/networkx/blob/main/networkx/algorithms/shortest_paths/astar.py )



Discussion:

Resolving patch:
diff --git a/networkx/algorithms/shortest_paths/unweighted.py b/networkx/algorithms/shortest_paths/unweighted.py
--- a/networkx/algorithms/shortest_paths/unweighted.py
+++ b/networkx/algorithms/shortest_paths/unweighted.py
@@ -216,6 +216,13 @@ def bidirectional_shortest_path(G, source, target):
     NetworkXNoPath
        If no path exists between source and target.
 
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2, 3, 0, 4, 5, 6, 7, 4])
+    >>> nx.bidirectional_shortest_path(G, 2, 6)
+    [2, 1, 0, 4, 5, 6]
+
     See Also
     --------
     shortest_path


Test patch:
Repository: networkx/networkx

Issue:
Error in method description in ismags.py
The docstring of `partition_to_color` method in ismags.py seems off to me. The description is not clear, and it's hard to understand what the method is supposed to do. 
```python
def partition_to_color(partitions):
     """
    Creates a dictionary with for every item in partition for every partition 
    in partitions the index of partition in partitions.
    
    Parameters
    ----------
    partitions: collections.abc.Sequence[collections.abc.Iterable]
        As returned by :func:`make_partitions`.
    Returns
    -------
    dict
    """
    colors = {}
    for color, keys in enumerate(partitions):
        for key in keys:
            colors[key] = color
    return colors

```
I think the following description explains the method better.

```python
def partition_to_color(partitions):
    """
    Creates a dictionary that maps each item in each partition to the index of 
    the partition it belongs to
    """
```
If the new description looks alright, I'll go ahead and make the changes.


Discussion:

Resolving patch:
diff --git a/networkx/algorithms/isomorphism/ismags.py b/networkx/algorithms/isomorphism/ismags.py
--- a/networkx/algorithms/isomorphism/ismags.py
+++ b/networkx/algorithms/isomorphism/ismags.py
@@ -184,8 +184,8 @@ def make_partitions(items, test):
 
 def partition_to_color(partitions):
     """
-    Creates a dictionary with for every item in partition for every partition
-    in partitions the index of partition in partitions.
+    Creates a dictionary that maps each item in each partition to the index of
+    the partition to which it belongs.
 
     Parameters
     ----------


Test patch:
Repository: networkx/networkx

Issue:
Layout of website page for ISMAGS needs fixing
The website documentation page for [ISMAGS algorithm](https://networkx.org/documentation/stable/reference/algorithms/isomorphism.ismags.html) is not rendering properly. The layout of components is messed up.

<img width="959" alt="image" src="https://user-images.githubusercontent.com/82928853/227500999-8a7f51a5-f04e-41b1-9c0c-b17e9be9b3dc.png">

<img width="960" alt="image" src="https://user-images.githubusercontent.com/82928853/227501167-c14302bd-9b80-4139-9a6a-95d1bd4e8b0b.png">



Discussion:
@dschult @MridulS I looked into the ismags.py file but I'm not able to figure out why this is happening, could you help out a bit?
This looks like a bad interaction between sphinx extensions and the pydata-sphinx-theme. My guess is that one or more of `numpydoc` or `sphinx-autodoc` is mis-identifying some sections from the module docstring which is screwing up the heading levels/object tags for the rest of the docstring.

This should likely be fixed upstream, though we'll need to figure out which sphinx extension is behaving badly. In the mean time we can probably use a simple workaround to fix the docs.

Resolving patch:
diff --git a/networkx/algorithms/isomorphism/ismags.py b/networkx/algorithms/isomorphism/ismags.py
--- a/networkx/algorithms/isomorphism/ismags.py
+++ b/networkx/algorithms/isomorphism/ismags.py
@@ -1,7 +1,6 @@
 """
-****************
 ISMAGS Algorithm
-****************
+================
 
 Provides a Python implementation of the ISMAGS algorithm. [1]_
 
@@ -90,21 +89,21 @@
 
 Notes
 -----
- - The current implementation works for undirected graphs only. The algorithm
-   in general should work for directed graphs as well though.
- - Node keys for both provided graphs need to be fully orderable as well as
-   hashable.
- - Node and edge equality is assumed to be transitive: if A is equal to B, and
-   B is equal to C, then A is equal to C.
+- The current implementation works for undirected graphs only. The algorithm
+  in general should work for directed graphs as well though.
+- Node keys for both provided graphs need to be fully orderable as well as
+  hashable.
+- Node and edge equality is assumed to be transitive: if A is equal to B, and
+  B is equal to C, then A is equal to C.
 
 References
 ----------
-    .. [1] M. Houbraken, S. Demeyer, T. Michoel, P. Audenaert, D. Colle,
-       M. Pickavet, "The Index-Based Subgraph Matching Algorithm with General
-       Symmetries (ISMAGS): Exploiting Symmetry for Faster Subgraph
-       Enumeration", PLoS One 9(5): e97896, 2014.
-       https://doi.org/10.1371/journal.pone.0097896
-    .. [2] https://en.wikipedia.org/wiki/Maximum_common_induced_subgraph
+.. [1] M. Houbraken, S. Demeyer, T. Michoel, P. Audenaert, D. Colle,
+   M. Pickavet, "The Index-Based Subgraph Matching Algorithm with General
+   Symmetries (ISMAGS): Exploiting Symmetry for Faster Subgraph
+   Enumeration", PLoS One 9(5): e97896, 2014.
+   https://doi.org/10.1371/journal.pone.0097896
+.. [2] https://en.wikipedia.org/wiki/Maximum_common_induced_subgraph
 """
 
 __all__ = ["ISMAGS"]


Test patch:
Repository: networkx/networkx

Issue:
equitable_coloring file is not properly documented
The functions in networkx/algorithms/coloring/equitable_coloring.py are not properly documented. It needs docstring improvement. @MridulS @rossbar is this something I can go ahead with?


Discussion:
The only function exported from this module is `equitable_color`, which does appear to be properly documented. Some of the other functions seem like they are intended for direct use (e.g. `is_coloring` and `is_equitable`), while others definitely seem like "internal" functions that should likely not be used directly in user code (`make_*_from_*`, `procedure_P`, etc.).

Docstring improvements are always welcome, though I'd start by focusing on `equitable_color` since that's the only function that's clearly user-facing. The docstring for `equitable_color` generally LGTM but I'm sure there are opportunities to improve it.

Resolving patch:
diff --git a/networkx/algorithms/coloring/equitable_coloring.py b/networkx/algorithms/coloring/equitable_coloring.py
--- a/networkx/algorithms/coloring/equitable_coloring.py
+++ b/networkx/algorithms/coloring/equitable_coloring.py
@@ -384,12 +384,13 @@ def procedure_P(V_minus, V_plus, N, H, F, C, L, excluded_colors=None):
 
 
 def equitable_color(G, num_colors):
-    """Provides equitable (r + 1)-coloring for nodes of G in O(r * n^2) time
-    if deg(G) <= r. The algorithm is described in [1]_.
+    """Provides an equitable coloring for nodes of `G`.
 
-    Attempts to color a graph using r colors, where no neighbors of a node
-    can have same color as the node itself and the number of nodes with each
-    color differ by at most 1.
+    Attempts to color a graph using `num_colors` colors, where no neighbors of
+    a node can have same color as the node itself and the number of nodes with
+    each color differ by at most 1. `num_colors` must be greater than the
+    maximum degree of `G`. The algorithm is described in [1]_ and has
+    complexity O(num_colors * n**2).
 
     Parameters
     ----------
@@ -408,15 +409,13 @@ def equitable_color(G, num_colors):
     Examples
     --------
     >>> G = nx.cycle_graph(4)
-    >>> d = nx.coloring.equitable_color(G, num_colors=3)
-    >>> nx.algorithms.coloring.equitable_coloring.is_equitable(G, d)
-    True
+    >>> nx.coloring.equitable_color(G, num_colors=3)
+    {0: 2, 1: 1, 2: 2, 3: 0}
 
     Raises
     ------
     NetworkXAlgorithmError
-        If the maximum degree of the graph ``G`` is greater than
-        ``num_colors``.
+        If `num_colors` is not at least the maximum degree of the graph `G`
 
     References
     ----------


Test patch:
Repository: networkx/networkx

Issue:
Improve test coverage for clique.py
There is a line left uncovered in https://app.codecov.io/gh/networkx/networkx/blob/main/networkx/algorithms/approximation/clique.py and I am looking into it. There is a bit of an issue though when I try G = None. An attribute error is raised not a value error. 

Steps to Reproduce

from networkx.algorithms.approximation.clique import maximum_independent_set,max_clique
G=nx.Graph()
G.add_nodes_from([(2,3),(5,6),(3,6)])
max_clique(G=None)

output:

AttributeError                            Traceback (most recent call last)
Cell In[84], line 1
----> 1 max_clique(G)

File <class 'networkx.utils.decorators.argmap'> compilation 32:3, in argmap_max_clique_28(G)
      1 import bz2
      2 import collections
----> 3 import gzip
      4 import inspect
      5 import itertools

File ~\anaconda3\lib\site-packages\networkx\utils\decorators.py:83, in not_implemented_for.<locals>._not_implemented_for(g)
     82 def _not_implemented_for(g):
---> 83     if (mval is None or mval == g.is_multigraph()) and (
     84         dval is None or dval == g.is_directed()
     85     ):
     86         raise nx.NetworkXNotImplemented(errmsg)
     88     return g

AttributeError: 'NoneType' object has no attribute 'is_multigraph'


Discussion:
@MridulS @rossbar @dschult 
In this case I think we can safely delete the check in L109-110. I don't think it's possible to hit that due to the `not_implemented_for` decorator. Even if it were hit-able, it's a pretty trivial input validation that I think can be safely removed.

Resolving patch:
diff --git a/networkx/algorithms/approximation/clique.py b/networkx/algorithms/approximation/clique.py
--- a/networkx/algorithms/approximation/clique.py
+++ b/networkx/algorithms/approximation/clique.py
@@ -118,9 +118,6 @@ def max_clique(G):
         BIT Numerical Mathematics, 32(2), 180–196. Springer.
         doi:10.1007/BF01994876
     """
-    if G is None:
-        raise ValueError("Expected NetworkX graph!")
-
     # finding the maximum clique in a graph is equivalent to finding
     # the independent set in the complementary graph
     cgraph = nx.complement(G)


Test patch:
Repository: networkx/networkx

Issue:
Improve test coverage for connected.py
There are a few red lines I want to work on in https://app.codecov.io/gh/networkx/networkx/blob/main/networkx/algorithms/approximation/connectivity.py.

My issue is I can't import the modified bidirectional_shortest_path function in the connectivity.py file. The one in the shortest_path/unweighted.py does not have the exclude argument. To write the test cases, I need to access the function somehow. What am I missing? @MridulS @dschult @rossbar Also I don't know if that is why the doc examples of the function are missing in the doc, but I will like to work on that too.


Discussion:
The functions that are prefixed with an underscore (`_`) are "private" by convention; i.e. they are not intended for direct use by users. Thus they are intentionally left out of the API documentation.

Internal functions like this are often intended only for use in the module in which they're defined, so they are typically only accessible by that module. For example, if you wanted to access `_bidirectional_pred_succ` you'd do something like `from networkx.algorithms.approximation.connectivity import _bidirectional_pred_succ`.

Resolving patch:
diff --git a/networkx/algorithms/approximation/connectivity.py b/networkx/algorithms/approximation/connectivity.py
--- a/networkx/algorithms/approximation/connectivity.py
+++ b/networkx/algorithms/approximation/connectivity.py
@@ -357,12 +357,6 @@ def _bidirectional_shortest_path(G, source, target, exclude):
 def _bidirectional_pred_succ(G, source, target, exclude):
     # does BFS from both source and target and meets in the middle
     # excludes nodes in the container "exclude" from the search
-    if source is None or target is None:
-        raise nx.NetworkXException(
-            "Bidirectional shortest path called without source or target"
-        )
-    if target == source:
-        return ({target: None}, {source: None}, source)
 
     # handle either directed or undirected
     if G.is_directed():


Test patch:
Repository: networkx/networkx

Issue:
NetworkX fails with NumPy 2.0dev - NEP 51
The `main` branch currently [fails](https://github.com/networkx/networkx/actions/runs/5845735581/job/15850137542) with the numpy 2.0 nightly release as numpy has changed the (printing) behavior of scalars [NEP 51](https://numpy.org/neps/nep-0051-scalar-representation.html). Our doctests will fail as it's expecting an exact string match (?).




Discussion:
This looks like another one of those changes that mess up our doc_strings. When numpy changed their output format we had to handle those differently.  I don't recall if we only tested doc_strings with specific numpy versions, but I think it was something like that. And we changed all the doc_strings format in one big commit I think.

So, we should talk about how best to handle it.  My initial thought is that we drop the `--doctest-modules` on much of the testing matrix -- At first that can be for whenever numpy >= 2.0. But then we'll need to switch it at some point from dropping it for newest numpy to dropping it for the older numpy -- and we make that CI change when we make the doc_string change. 

I guess the idea is that the results of doc_string tests/examples are rarely dependent on version of numpy. So as long as we're testing it on one version of numpy, it is likely working on the others and we test the others using the test modules.
> So, we should talk about how best to handle it

My initial thought was to investigate how many of the tests we could reformat so that the output didn't contain scalar repr's. From a quick glance it seems there may be a few that can be modified, but I don't think that will work in every case. Therefore investigating alternatives is worthwhile.

I think @dschult 's proposal is the most workable, even if it requires a bit of manual config/pinning in CI. Another option might be to use `np.printoptions` to set printing to "legacy" mode for the scalars, at least for the purposes of testing. I *think* this is doable, but we'll have to look into it more (if it is possible, it should probably be added to the NEP!)

The silver lining of these failures is that the new scalar repr identifies some places in the codebase where numpy scalars are returned implicitly. Kinda neat IMO :)

Resolving patch:
diff --git a/networkx/conftest.py b/networkx/conftest.py
--- a/networkx/conftest.py
+++ b/networkx/conftest.py
@@ -94,6 +94,7 @@ def set_warnings():
 @pytest.fixture(autouse=True)
 def add_nx(doctest_namespace):
     doctest_namespace["nx"] = networkx
+    # TODO: remove the try-except block when we require numpy >= 2
     try:
         import numpy as np
 


Test patch:
Repository: networkx/networkx

Issue:
confusing results generated by dfs_predecessors()
The dfs_predecessors(G, source, depth) and the dfs_successors(G, source, depth) generate the same results. 
They both return the successors of the source node. 




Discussion:
If the graph `G` is undirected, then this is expected. You can think of undirected edges as bi-directional, in which case there is no distinction between predecessors and successors:

```python
>>> G = nx.path_graph(4)
>>> nx.dfs_successors(G, source=1, depth_limit=1)
{1: [0, 2]}
>>> nx.dfs_predecessors(G, source=1, depth_limit=1)
{0: 1, 2: 1}
```
Please try the following code

```python
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3) , (3, 4) , (3, 5) , (0, 1), (9, 0)])
>>> nx.draw(G, with_labels=True)
>>> a = nx.dfs_predecessors(G, source=1)
{2: 1, 3: 1, 4: 3, 5: 3}
>>> b = nx.dfs_successors(G, source=1)
{1: [2, 3], 3: [4, 5]}
```
Thanks for the example, I see what you mean now: I assume you are expecting `nx.dfs_predecessors(G, source=1)` to give the predecessors of node `1`, so something like `{1: [0, 9]}`. The current result does seem incorrect to me.
But the docs state that the source is the starting node for the DFS, not the node for which you want the predecessors.  So, while I think the returned value is correct, I can see that there is a misunderstanding in the wording of the doc_string.

We could change the name of the optional keyword argument from `source` to `starting_node_of_DFS`. But I think that may be more than what is called for.  How about the description becomes:
```
    source : node, optional
       Specify starting node for depth-first search.
        Note that you will get predecessors for all nodes in the
        component containing `source`. This input only specifies
        where the DFS starts.
```
Thoughts? Is there a better way to convey this?

Resolving patch:
diff --git a/networkx/algorithms/traversal/depth_first_search.py b/networkx/algorithms/traversal/depth_first_search.py
--- a/networkx/algorithms/traversal/depth_first_search.py
+++ b/networkx/algorithms/traversal/depth_first_search.py
@@ -156,6 +156,9 @@ def dfs_predecessors(G, source=None, depth_limit=None):
 
     source : node, optional
        Specify starting node for depth-first search.
+       Note that you will get predecessors for all nodes in the
+       component containing `source`. This input only specifies
+       where the DFS starts.
 
     depth_limit : int, optional (default=len(G))
        Specify the maximum search depth.
@@ -207,6 +210,9 @@ def dfs_successors(G, source=None, depth_limit=None):
 
     source : node, optional
        Specify starting node for depth-first search.
+       Note that you will get successors for all nodes in the
+       component containing `source`. This input only specifies
+       where the DFS starts.
 
     depth_limit : int, optional (default=len(G))
        Specify the maximum search depth.


Test patch:
Repository: networkx/networkx

Issue:
Travelling Salesman Problem gives wrong results if triangle inequality is not fulfilled
<!-- If you have a general question about NetworkX, please use the discussions tab to create a new discussion -->

<!--- Provide a general summary of the issue in the Title above -->

### Current Behavior

<!--- Tell us what happens instead of the expected behavior -->

Calculating the TSP with a graph that does not fulfill the triangle inequality returns a tour that contains nodes multiple times. Then, the result is not a valid Hamilton cycle.

### Expected Behavior

<!--- Tell us what should happen -->

If the triangle inequality is not fulfilled, the method should throw an exception. Further, the fact should be mentioned in the documentation.

### Steps to Reproduce

<!--- Provide a minimal example that reproduces the bug -->

```
graph = nx.Graph()
graph.add_nodes_from(range(2))
graph.add_weighted_edges_from([(0, 1, 1), (0, 2, 3), (1, 2, 1)])
print(nx.approximation.traveling_salesman_problem(graph))  # output: [0, 1, 2, 1, 0]
```
### Environment

<!--- Please provide details about your local environment -->

Python version: 3.10
NetworkX version: 3.1

### Additional context

<!--- Add any other context about the problem here, screenshots, etc. -->

https://en.wikipedia.org/wiki/Christofides_algorithm


Discussion:

Resolving patch:
diff --git a/networkx/algorithms/approximation/traveling_salesman.py b/networkx/algorithms/approximation/traveling_salesman.py
--- a/networkx/algorithms/approximation/traveling_salesman.py
+++ b/networkx/algorithms/approximation/traveling_salesman.py
@@ -224,6 +224,10 @@ def traveling_salesman_problem(G, weight="weight", nodes=None, cycle=True, metho
     the biggest weight edge is removed to make a Hamiltonian path.
     Then each edge on the new complete graph used for that analysis is
     replaced by the shortest_path between those nodes on the original graph.
+    If the input graph `G` includes edges with weights that do not adhere to
+    the triangle inequality, such as when `G` is not a complete graph (i.e
+    length of non-existent edges is infinity), then the returned path may
+    contain some repeating nodes (other than the starting node).
 
     Parameters
     ----------
@@ -265,7 +269,6 @@ def traveling_salesman_problem(G, weight="weight", nodes=None, cycle=True, metho
         List of nodes in `G` along a path with an approximation of the minimal
         path through `nodes`.
 
-
     Raises
     ------
     NetworkXError


Test patch:
Repository: networkx/networkx

Issue:
```is_k_edge_connected``` incorrectly returns True for k=2 with multi-component graphs without bridges
### Current Behavior

The implementation of ```is_k_edge_connected``` currently defers to ```is_connected``` for k=1, to ```has_bridges``` for k=2, and runs the full check for k>2.

As a result, this returns True for a multi-component graph without bridges.

### Expected Behavior

Since the graph has multiple components, it is not connected in the first place and ```is_k_edge_connected``` should return False for all (positive) values of k.

### Steps to Reproduce

```
import networkx as nx

G = nx.compose(nx.complete_graph([0, 1, 2]), nx.complete_graph([3, 4, 5]))
nx.connectivity.is_k_edge_connected(G, 2)
```

This returns True but should return False.

### Environment

Python version: 3.11.2
NetworkX version: 3.1

### Additional context

As defined in the NetworkX documentation, a bridge is an edge whose removal increases the number of connected components. It appears to me, then, that a graph not having bridges is a necessary-but-not-sufficient condition for a graph being 2-edge-connected.



Discussion:

Resolving patch:
diff --git a/networkx/algorithms/connectivity/edge_augmentation.py b/networkx/algorithms/connectivity/edge_augmentation.py
--- a/networkx/algorithms/connectivity/edge_augmentation.py
+++ b/networkx/algorithms/connectivity/edge_augmentation.py
@@ -68,7 +68,7 @@ def is_k_edge_connected(G, k):
         if k == 1:
             return nx.is_connected(G)
         elif k == 2:
-            return not nx.has_bridges(G)
+            return nx.is_connected(G) and not nx.has_bridges(G)
         else:
             return nx.edge_connectivity(G, cutoff=k) >= k
 


Test patch:
diff --git a/networkx/algorithms/connectivity/tests/test_edge_augmentation.py b/networkx/algorithms/connectivity/tests/test_edge_augmentation.py
--- a/networkx/algorithms/connectivity/tests/test_edge_augmentation.py
+++ b/networkx/algorithms/connectivity/tests/test_edge_augmentation.py
@@ -75,6 +75,11 @@ def test_is_k_edge_connected():
     assert is_k_edge_connected(G, k=3)
     assert is_k_edge_connected(G, k=4)
 
+    G = nx.compose(nx.complete_graph([0, 1, 2]), nx.complete_graph([3, 4, 5]))
+    assert not is_k_edge_connected(G, k=1)
+    assert not is_k_edge_connected(G, k=2)
+    assert not is_k_edge_connected(G, k=3)
+
 
 def test_is_k_edge_connected_exceptions():
     pytest.raises(