We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Globbing should be advertised in doc/examples.


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/00_tutorials/plot_nilearn_101.py`
Content:
```
1 """
2 Basic nilearn example: manipulating and looking at data
3 =======================================================
4 
5 A simple example showing how to load an existing Nifti file and use
6 basic nilearn functionalities.
7 """
8 
9 # Let us use a Nifti file that is shipped with nilearn
10 from nilearn.datasets import MNI152_FILE_PATH
11 
12 # Note that the variable MNI152_FILE_PATH is just a path to a Nifti file
13 print(f"Path to MNI152 template: {MNI152_FILE_PATH!r}")
14 
15 # %%
16 # A first step: looking at our data
17 # ----------------------------------
18 #
19 # Let's quickly plot this file:
20 from nilearn import plotting
21 
22 plotting.plot_img(MNI152_FILE_PATH)
23 
24 # %%
25 # This is not a very pretty plot. We just used the simplest possible
26 # code. There is a whole :ref:`section of the documentation <plotting>`
27 # on making prettier code.
28 #
29 # **Exercise**: Try plotting one of your own files. In the above,
30 # MNI152_FILE_PATH is nothing more than a string with a path pointing to
31 # a nifti image. You can replace it with a string pointing to a file on
32 # your disk. Note that it should be a 3D volume, and not a 4D volume.
33 
34 # %%
35 # Simple image manipulation: smoothing
36 # ------------------------------------
37 #
38 # Let's use an image-smoothing function from nilearn:
39 # :func:`nilearn.image.smooth_img`
40 #
41 # Functions containing 'img' can take either a filename or an image as input.
42 #
43 # Here we give as inputs the image filename and the smoothing value in mm
44 from nilearn import image
45 
46 smooth_anat_img = image.smooth_img(MNI152_FILE_PATH, fwhm=3)
47 
48 # While we are giving a file name as input, the function returns
49 # an in-memory object:
50 smooth_anat_img
51 
52 # %%
53 # This is an in-memory object. We can pass it to nilearn function, for
54 # instance to look at it
55 plotting.plot_img(smooth_anat_img)
56 
57 # %%
58 # We could also pass it to the smoothing function
59 more_smooth_anat_img = image.smooth_img(smooth_anat_img, fwhm=3)
60 plotting.plot_img(more_smooth_anat_img)
61 
62 # %%
63 # Saving results to a file
64 # -------------------------
65 #
66 # We can save any in-memory object as follows:
67 from pathlib import Path
68 
69 output_dir = Path.cwd() / "results" / "plot_nilearn_101"
70 output_dir.mkdir(exist_ok=True, parents=True)
71 print(f"Output will be saved to: {output_dir}")
72 more_smooth_anat_img.to_filename(output_dir / "more_smooth_anat_img.nii.gz")
73 
74 # %%
75 # Finally, calling plotting.show() is necessary to display the figure
76 # when running as a script outside IPython
77 plotting.show()
78 
79 # %%
80 # |
81 #
82 # ______
83 #
84 # To recap, all the nilearn tools can take data as filenames or in-memory
85 # objects, and return brain volumes as in-memory objects. These can be
86 # passed on to other nilearn tools, or saved to disk.
87 
88 # sphinx_gallery_dummy_images=1
89
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/examples/00_tutorials/plot_nilearn_101.py b/examples/00_tutorials/plot_nilearn_101.py
--- a/examples/00_tutorials/plot_nilearn_101.py
+++ b/examples/00_tutorials/plot_nilearn_101.py
@@ -59,17 +59,49 @@
 more_smooth_anat_img = image.smooth_img(smooth_anat_img, fwhm=3)
 plotting.plot_img(more_smooth_anat_img)
 
+
+# %%
+# Globbing over multiple 3D volumes
+# ---------------------------------
+# Nilearn also supports reading multiple volumes at once,
+# using glob-style patterns.
+# For instance, we can smooth volumes from many subjects
+# at once and get a 4D image as output.
+
+# %%
+# First let's fetch Haxby dataset for subject 1 and 2
+from nilearn import datasets
+
+haxby = datasets.fetch_haxby(subjects=[1, 2])
+
+# %%
+# Now we can find the anatomical images from both
+# subjects using the `*` wildcard
+from pathlib import Path
+
+anats_all_subjects = (
+    Path(datasets.get_data_dirs()[0]) / "haxby2001" / "subj*" / "anat*"
+)
+
+# %%
+# Now we can smooth all the anatomical images at once
+anats_all_subjects_smooth = image.smooth_img(anats_all_subjects, fwhm=5)
+
+# %%
+# This is a 4D image containing one volume per subject
+print(anats_all_subjects_smooth.shape)
+
 # %%
 # Saving results to a file
 # -------------------------
 #
 # We can save any in-memory object as follows:
-from pathlib import Path
-
 output_dir = Path.cwd() / "results" / "plot_nilearn_101"
 output_dir.mkdir(exist_ok=True, parents=True)
 print(f"Output will be saved to: {output_dir}")
-more_smooth_anat_img.to_filename(output_dir / "more_smooth_anat_img.nii.gz")
+anats_all_subjects_smooth.to_filename(
+    output_dir / "anats_all_subjects_smooth.nii.gz"
+)
 
 # %%
 # Finally, calling plotting.show() is necessary to display the figure
@@ -81,8 +113,9 @@
 #
 # ______
 #
-# To recap, all the nilearn tools can take data as filenames or in-memory
-# objects, and return brain volumes as in-memory objects. These can be
+# To recap, all the nilearn tools can take data as filenames or
+# glob-style patterns or in-memory objects, and return brain
+# volumes as in-memory objects. These can be
 # passed on to other nilearn tools, or saved to disk.
 
 # sphinx_gallery_dummy_images=1

{"golden_diff": "diff --git a/examples/00_tutorials/plot_nilearn_101.py b/examples/00_tutorials/plot_nilearn_101.py\n--- a/examples/00_tutorials/plot_nilearn_101.py\n+++ b/examples/00_tutorials/plot_nilearn_101.py\n@@ -59,17 +59,49 @@\n more_smooth_anat_img = image.smooth_img(smooth_anat_img, fwhm=3)\n plotting.plot_img(more_smooth_anat_img)\n \n+\n+# %%\n+# Globbing over multiple 3D volumes\n+# ---------------------------------\n+# Nilearn also supports reading multiple volumes at once,\n+# using glob-style patterns.\n+# For instance, we can smooth volumes from many subjects\n+# at once and get a 4D image as output.\n+\n+# %%\n+# First let's fetch Haxby dataset for subject 1 and 2\n+from nilearn import datasets\n+\n+haxby = datasets.fetch_haxby(subjects=[1, 2])\n+\n+# %%\n+# Now we can find the anatomical images from both\n+# subjects using the `*` wildcard\n+from pathlib import Path\n+\n+anats_all_subjects = (\n+    Path(datasets.get_data_dirs()[0]) / \"haxby2001\" / \"subj*\" / \"anat*\"\n+)\n+\n+# %%\n+# Now we can smooth all the anatomical images at once\n+anats_all_subjects_smooth = image.smooth_img(anats_all_subjects, fwhm=5)\n+\n+# %%\n+# This is a 4D image containing one volume per subject\n+print(anats_all_subjects_smooth.shape)\n+\n # %%\n # Saving results to a file\n # -------------------------\n #\n # We can save any in-memory object as follows:\n-from pathlib import Path\n-\n output_dir = Path.cwd() / \"results\" / \"plot_nilearn_101\"\n output_dir.mkdir(exist_ok=True, parents=True)\n print(f\"Output will be saved to: {output_dir}\")\n-more_smooth_anat_img.to_filename(output_dir / \"more_smooth_anat_img.nii.gz\")\n+anats_all_subjects_smooth.to_filename(\n+    output_dir / \"anats_all_subjects_smooth.nii.gz\"\n+)\n \n # %%\n # Finally, calling plotting.show() is necessary to display the figure\n@@ -81,8 +113,9 @@\n #\n # ______\n #\n-# To recap, all the nilearn tools can take data as filenames or in-memory\n-# objects, and return brain volumes as in-memory objects. These can be\n+# To recap, all the nilearn tools can take data as filenames or\n+# glob-style patterns or in-memory objects, and return brain\n+# volumes as in-memory objects. These can be\n # passed on to other nilearn tools, or saved to disk.\n \n # sphinx_gallery_dummy_images=1\n", "issue": "Globbing should be advertised in doc/examples.\n\n", "before_files": [{"content": "\"\"\"\nBasic nilearn example: manipulating and looking at data\n=======================================================\n\nA simple example showing how to load an existing Nifti file and use\nbasic nilearn functionalities.\n\"\"\"\n\n# Let us use a Nifti file that is shipped with nilearn\nfrom nilearn.datasets import MNI152_FILE_PATH\n\n# Note that the variable MNI152_FILE_PATH is just a path to a Nifti file\nprint(f\"Path to MNI152 template: {MNI152_FILE_PATH!r}\")\n\n# %%\n# A first step: looking at our data\n# ----------------------------------\n#\n# Let's quickly plot this file:\nfrom nilearn import plotting\n\nplotting.plot_img(MNI152_FILE_PATH)\n\n# %%\n# This is not a very pretty plot. We just used the simplest possible\n# code. There is a whole :ref:`section of the documentation <plotting>`\n# on making prettier code.\n#\n# **Exercise**: Try plotting one of your own files. In the above,\n# MNI152_FILE_PATH is nothing more than a string with a path pointing to\n# a nifti image. You can replace it with a string pointing to a file on\n# your disk. Note that it should be a 3D volume, and not a 4D volume.\n\n# %%\n# Simple image manipulation: smoothing\n# ------------------------------------\n#\n# Let's use an image-smoothing function from nilearn:\n# :func:`nilearn.image.smooth_img`\n#\n# Functions containing 'img' can take either a filename or an image as input.\n#\n# Here we give as inputs the image filename and the smoothing value in mm\nfrom nilearn import image\n\nsmooth_anat_img = image.smooth_img(MNI152_FILE_PATH, fwhm=3)\n\n# While we are giving a file name as input, the function returns\n# an in-memory object:\nsmooth_anat_img\n\n# %%\n# This is an in-memory object. We can pass it to nilearn function, for\n# instance to look at it\nplotting.plot_img(smooth_anat_img)\n\n# %%\n# We could also pass it to the smoothing function\nmore_smooth_anat_img = image.smooth_img(smooth_anat_img, fwhm=3)\nplotting.plot_img(more_smooth_anat_img)\n\n# %%\n# Saving results to a file\n# -------------------------\n#\n# We can save any in-memory object as follows:\nfrom pathlib import Path\n\noutput_dir = Path.cwd() / \"results\" / \"plot_nilearn_101\"\noutput_dir.mkdir(exist_ok=True, parents=True)\nprint(f\"Output will be saved to: {output_dir}\")\nmore_smooth_anat_img.to_filename(output_dir / \"more_smooth_anat_img.nii.gz\")\n\n# %%\n# Finally, calling plotting.show() is necessary to display the figure\n# when running as a script outside IPython\nplotting.show()\n\n# %%\n# |\n#\n# ______\n#\n# To recap, all the nilearn tools can take data as filenames or in-memory\n# objects, and return brain volumes as in-memory objects. These can be\n# passed on to other nilearn tools, or saved to disk.\n\n# sphinx_gallery_dummy_images=1\n", "path": "examples/00_tutorials/plot_nilearn_101.py"}], "after_files": [{"content": "\"\"\"\nBasic nilearn example: manipulating and looking at data\n=======================================================\n\nA simple example showing how to load an existing Nifti file and use\nbasic nilearn functionalities.\n\"\"\"\n\n# Let us use a Nifti file that is shipped with nilearn\nfrom nilearn.datasets import MNI152_FILE_PATH\n\n# Note that the variable MNI152_FILE_PATH is just a path to a Nifti file\nprint(f\"Path to MNI152 template: {MNI152_FILE_PATH!r}\")\n\n# %%\n# A first step: looking at our data\n# ----------------------------------\n#\n# Let's quickly plot this file:\nfrom nilearn import plotting\n\nplotting.plot_img(MNI152_FILE_PATH)\n\n# %%\n# This is not a very pretty plot. We just used the simplest possible\n# code. There is a whole :ref:`section of the documentation <plotting>`\n# on making prettier code.\n#\n# **Exercise**: Try plotting one of your own files. In the above,\n# MNI152_FILE_PATH is nothing more than a string with a path pointing to\n# a nifti image. You can replace it with a string pointing to a file on\n# your disk. Note that it should be a 3D volume, and not a 4D volume.\n\n# %%\n# Simple image manipulation: smoothing\n# ------------------------------------\n#\n# Let's use an image-smoothing function from nilearn:\n# :func:`nilearn.image.smooth_img`\n#\n# Functions containing 'img' can take either a filename or an image as input.\n#\n# Here we give as inputs the image filename and the smoothing value in mm\nfrom nilearn import image\n\nsmooth_anat_img = image.smooth_img(MNI152_FILE_PATH, fwhm=3)\n\n# While we are giving a file name as input, the function returns\n# an in-memory object:\nsmooth_anat_img\n\n# %%\n# This is an in-memory object. We can pass it to nilearn function, for\n# instance to look at it\nplotting.plot_img(smooth_anat_img)\n\n# %%\n# We could also pass it to the smoothing function\nmore_smooth_anat_img = image.smooth_img(smooth_anat_img, fwhm=3)\nplotting.plot_img(more_smooth_anat_img)\n\n\n# %%\n# Globbing over multiple 3D volumes\n# ---------------------------------\n# Nilearn also supports reading multiple volumes at once,\n# using glob-style patterns.\n# For instance, we can smooth volumes from many subjects\n# at once and get a 4D image as output.\n\n# %%\n# First let's fetch Haxby dataset for subject 1 and 2\nfrom nilearn import datasets\n\nhaxby = datasets.fetch_haxby(subjects=[1, 2])\n\n# %%\n# Now we can find the anatomical images from both\n# subjects using the `*` wildcard\nfrom pathlib import Path\n\nanats_all_subjects = (\n    Path(datasets.get_data_dirs()[0]) / \"haxby2001\" / \"subj*\" / \"anat*\"\n)\n\n# %%\n# Now we can smooth all the anatomical images at once\nanats_all_subjects_smooth = image.smooth_img(anats_all_subjects, fwhm=5)\n\n# %%\n# This is a 4D image containing one volume per subject\nprint(anats_all_subjects_smooth.shape)\n\n# %%\n# Saving results to a file\n# -------------------------\n#\n# We can save any in-memory object as follows:\noutput_dir = Path.cwd() / \"results\" / \"plot_nilearn_101\"\noutput_dir.mkdir(exist_ok=True, parents=True)\nprint(f\"Output will be saved to: {output_dir}\")\nanats_all_subjects_smooth.to_filename(\n    output_dir / \"anats_all_subjects_smooth.nii.gz\"\n)\n\n# %%\n# Finally, calling plotting.show() is necessary to display the figure\n# when running as a script outside IPython\nplotting.show()\n\n# %%\n# |\n#\n# ______\n#\n# To recap, all the nilearn tools can take data as filenames or\n# glob-style patterns or in-memory objects, and return brain\n# volumes as in-memory objects. These can be\n# passed on to other nilearn tools, or saved to disk.\n\n# sphinx_gallery_dummy_images=1\n", "path": "examples/00_tutorials/plot_nilearn_101.py"}]}