We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
remove examples/03_connectivity/plot_power_connectome.py ?
- Signal extraction from spheres placed on Power coordinates is already done in `examples/03_connectivity/plot_seed_based_connectome.py`
- Sparse inverse covariance estimation is already explained in `examples/03_connectivity/plot_inverse_covariance_connectome.py` for MSDL atlas. For me, it doesn't really make a difference estimating it on timeseries extracted from probabilistic maps or spheric ROIs.


--- 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/03_connectivity/plot_power_connectome.py`
Content:
```
1 """
2 Extracting signals and plotting a connectome for the Power-264 seed-region atlas
3 ================================================================================
4 
5 This example shows how to extract signals from spherical seed-regions based
6 on the Power-264 atlas (Power, 2011) and estimating a connectome using sparse
7 inverse covariance.
8 
9 Power, Jonathan D., et al. "Functional network organization of the
10 human brain." Neuron 72.4 (2011): 665-678.
11 
12 """
13 
14 import numpy as np
15 import matplotlib.pyplot as plt
16 from nilearn import datasets, connectome, plotting, input_data
17 
18 
19 ###############################################################################
20 # Atlas and dataset fetching
21 
22 # Fetch the coordinates of power atlas
23 power = datasets.fetch_coords_power_2011()
24 power_coords = np.vstack((
25     power.rois['x'],
26     power.rois['y'],
27     power.rois['z'],
28 )).T
29 
30 # Fetch the first subject of ADHD dataset
31 adhd = datasets.fetch_adhd(n_subjects=1)
32 
33 
34 ###############################################################################
35 # Masking: taking the signal in a sphere of radius 5mm around Power coords
36 
37 masker = input_data.NiftiSpheresMasker(seeds=power_coords,
38                                        smoothing_fwhm=4,
39                                        radius=5.,
40                                        standardize=True,
41                                        detrend=True,
42                                        low_pass=0.1,
43                                        high_pass=0.01,
44                                        t_r=2.5)
45 
46 timeseries = masker.fit_transform(adhd.func[0], confounds=adhd.confounds[0])
47 
48 ###############################################################################
49 # Extract and plot correlation matrix
50 
51 # calculate connectivity and plot Power-264 correlation matrix
52 connectivity = connectome.ConnectivityMeasure(kind='correlation')
53 corr_matrix = connectivity.fit_transform([timeseries])[0]
54 np.fill_diagonal(corr_matrix, 0)
55 plt.imshow(corr_matrix, vmin=-1., vmax=1., cmap='RdBu_r')
56 plt.colorbar()
57 plt.title('Power 264 Connectivity')
58 
59 # Plot the connectome
60 
61 plotting.plot_connectome(corr_matrix,
62                          power_coords,
63                          edge_threshold='99.8%',
64                          node_size=20)
65 
66 
67 ###############################################################################
68 # Extract and plot covariance and sparse covariance
69 
70 # Compute the sparse inverse covariance
71 from sklearn.covariance import GraphLassoCV
72 
73 estimator = GraphLassoCV()
74 estimator.fit(timeseries)
75 
76 # Display the covariance
77 plt.figure(figsize=(5, 5))
78 plt.imshow(estimator.covariance_, interpolation="nearest",
79            vmax=1, vmin=-1, cmap=plt.cm.RdBu_r)
80 plt.title('Covariance matrix')
81 
82 # display the corresponding graph
83 plotting.plot_connectome(estimator.covariance_,
84                          power_coords,
85                          title='Covariance connectome',
86                          edge_threshold='99.8%',
87                          node_size=20)
88 
89 # Display the sparse inverse covariance
90 plt.figure(figsize=(5, 5))
91 plt.imshow(estimator.precision_, interpolation="nearest",
92            vmax=1, vmin=-1, cmap=plt.cm.RdBu_r)
93 plt.title('Precision matrix')
94 
95 # And now display the corresponding graph
96 plotting.plot_connectome(estimator.precision_, power_coords,
97                          title='Precision connectome',
98                          edge_threshold="99.8%",
99                          node_size=20)
100 plotting.show()
101
```
--- 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/03_connectivity/plot_power_connectome.py b/examples/03_connectivity/plot_power_connectome.py
deleted file mode 100644
--- a/examples/03_connectivity/plot_power_connectome.py
+++ /dev/null
@@ -1,100 +0,0 @@
-"""
-Extracting signals and plotting a connectome for the Power-264 seed-region atlas
-================================================================================
-
-This example shows how to extract signals from spherical seed-regions based
-on the Power-264 atlas (Power, 2011) and estimating a connectome using sparse
-inverse covariance.
-
-Power, Jonathan D., et al. "Functional network organization of the
-human brain." Neuron 72.4 (2011): 665-678.
-
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from nilearn import datasets, connectome, plotting, input_data
-
-
-###############################################################################
-# Atlas and dataset fetching
-
-# Fetch the coordinates of power atlas
-power = datasets.fetch_coords_power_2011()
-power_coords = np.vstack((
-    power.rois['x'],
-    power.rois['y'],
-    power.rois['z'],
-)).T
-
-# Fetch the first subject of ADHD dataset
-adhd = datasets.fetch_adhd(n_subjects=1)
-
-
-###############################################################################
-# Masking: taking the signal in a sphere of radius 5mm around Power coords
-
-masker = input_data.NiftiSpheresMasker(seeds=power_coords,
-                                       smoothing_fwhm=4,
-                                       radius=5.,
-                                       standardize=True,
-                                       detrend=True,
-                                       low_pass=0.1,
-                                       high_pass=0.01,
-                                       t_r=2.5)
-
-timeseries = masker.fit_transform(adhd.func[0], confounds=adhd.confounds[0])
-
-###############################################################################
-# Extract and plot correlation matrix
-
-# calculate connectivity and plot Power-264 correlation matrix
-connectivity = connectome.ConnectivityMeasure(kind='correlation')
-corr_matrix = connectivity.fit_transform([timeseries])[0]
-np.fill_diagonal(corr_matrix, 0)
-plt.imshow(corr_matrix, vmin=-1., vmax=1., cmap='RdBu_r')
-plt.colorbar()
-plt.title('Power 264 Connectivity')
-
-# Plot the connectome
-
-plotting.plot_connectome(corr_matrix,
-                         power_coords,
-                         edge_threshold='99.8%',
-                         node_size=20)
-
-
-###############################################################################
-# Extract and plot covariance and sparse covariance
-
-# Compute the sparse inverse covariance
-from sklearn.covariance import GraphLassoCV
-
-estimator = GraphLassoCV()
-estimator.fit(timeseries)
-
-# Display the covariance
-plt.figure(figsize=(5, 5))
-plt.imshow(estimator.covariance_, interpolation="nearest",
-           vmax=1, vmin=-1, cmap=plt.cm.RdBu_r)
-plt.title('Covariance matrix')
-
-# display the corresponding graph
-plotting.plot_connectome(estimator.covariance_,
-                         power_coords,
-                         title='Covariance connectome',
-                         edge_threshold='99.8%',
-                         node_size=20)
-
-# Display the sparse inverse covariance
-plt.figure(figsize=(5, 5))
-plt.imshow(estimator.precision_, interpolation="nearest",
-           vmax=1, vmin=-1, cmap=plt.cm.RdBu_r)
-plt.title('Precision matrix')
-
-# And now display the corresponding graph
-plotting.plot_connectome(estimator.precision_, power_coords,
-                         title='Precision connectome',
-                         edge_threshold="99.8%",
-                         node_size=20)
-plotting.show()

{"golden_diff": "diff --git a/examples/03_connectivity/plot_power_connectome.py b/examples/03_connectivity/plot_power_connectome.py\ndeleted file mode 100644\n--- a/examples/03_connectivity/plot_power_connectome.py\n+++ /dev/null\n@@ -1,100 +0,0 @@\n-\"\"\"\n-Extracting signals and plotting a connectome for the Power-264 seed-region atlas\n-================================================================================\n-\n-This example shows how to extract signals from spherical seed-regions based\n-on the Power-264 atlas (Power, 2011) and estimating a connectome using sparse\n-inverse covariance.\n-\n-Power, Jonathan D., et al. \"Functional network organization of the\n-human brain.\" Neuron 72.4 (2011): 665-678.\n-\n-\"\"\"\n-\n-import numpy as np\n-import matplotlib.pyplot as plt\n-from nilearn import datasets, connectome, plotting, input_data\n-\n-\n-###############################################################################\n-# Atlas and dataset fetching\n-\n-# Fetch the coordinates of power atlas\n-power = datasets.fetch_coords_power_2011()\n-power_coords = np.vstack((\n-    power.rois['x'],\n-    power.rois['y'],\n-    power.rois['z'],\n-)).T\n-\n-# Fetch the first subject of ADHD dataset\n-adhd = datasets.fetch_adhd(n_subjects=1)\n-\n-\n-###############################################################################\n-# Masking: taking the signal in a sphere of radius 5mm around Power coords\n-\n-masker = input_data.NiftiSpheresMasker(seeds=power_coords,\n-                                       smoothing_fwhm=4,\n-                                       radius=5.,\n-                                       standardize=True,\n-                                       detrend=True,\n-                                       low_pass=0.1,\n-                                       high_pass=0.01,\n-                                       t_r=2.5)\n-\n-timeseries = masker.fit_transform(adhd.func[0], confounds=adhd.confounds[0])\n-\n-###############################################################################\n-# Extract and plot correlation matrix\n-\n-# calculate connectivity and plot Power-264 correlation matrix\n-connectivity = connectome.ConnectivityMeasure(kind='correlation')\n-corr_matrix = connectivity.fit_transform([timeseries])[0]\n-np.fill_diagonal(corr_matrix, 0)\n-plt.imshow(corr_matrix, vmin=-1., vmax=1., cmap='RdBu_r')\n-plt.colorbar()\n-plt.title('Power 264 Connectivity')\n-\n-# Plot the connectome\n-\n-plotting.plot_connectome(corr_matrix,\n-                         power_coords,\n-                         edge_threshold='99.8%',\n-                         node_size=20)\n-\n-\n-###############################################################################\n-# Extract and plot covariance and sparse covariance\n-\n-# Compute the sparse inverse covariance\n-from sklearn.covariance import GraphLassoCV\n-\n-estimator = GraphLassoCV()\n-estimator.fit(timeseries)\n-\n-# Display the covariance\n-plt.figure(figsize=(5, 5))\n-plt.imshow(estimator.covariance_, interpolation=\"nearest\",\n-           vmax=1, vmin=-1, cmap=plt.cm.RdBu_r)\n-plt.title('Covariance matrix')\n-\n-# display the corresponding graph\n-plotting.plot_connectome(estimator.covariance_,\n-                         power_coords,\n-                         title='Covariance connectome',\n-                         edge_threshold='99.8%',\n-                         node_size=20)\n-\n-# Display the sparse inverse covariance\n-plt.figure(figsize=(5, 5))\n-plt.imshow(estimator.precision_, interpolation=\"nearest\",\n-           vmax=1, vmin=-1, cmap=plt.cm.RdBu_r)\n-plt.title('Precision matrix')\n-\n-# And now display the corresponding graph\n-plotting.plot_connectome(estimator.precision_, power_coords,\n-                         title='Precision connectome',\n-                         edge_threshold=\"99.8%\",\n-                         node_size=20)\n-plotting.show()\n", "issue": "remove examples/03_connectivity/plot_power_connectome.py ?\n- Signal extraction from spheres placed on Power coordinates is already done in `examples/03_connectivity/plot_seed_based_connectome.py`\n- Sparse inverse covariance estimation is already explained in `examples/03_connectivity/plot_inverse_covariance_connectome.py` for MSDL atlas. For me, it doesn't really make a difference estimating it on timeseries extracted from probabilistic maps or spheric ROIs.\n\n", "before_files": [{"content": "\"\"\"\nExtracting signals and plotting a connectome for the Power-264 seed-region atlas\n================================================================================\n\nThis example shows how to extract signals from spherical seed-regions based\non the Power-264 atlas (Power, 2011) and estimating a connectome using sparse\ninverse covariance.\n\nPower, Jonathan D., et al. \"Functional network organization of the\nhuman brain.\" Neuron 72.4 (2011): 665-678.\n\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom nilearn import datasets, connectome, plotting, input_data\n\n\n###############################################################################\n# Atlas and dataset fetching\n\n# Fetch the coordinates of power atlas\npower = datasets.fetch_coords_power_2011()\npower_coords = np.vstack((\n    power.rois['x'],\n    power.rois['y'],\n    power.rois['z'],\n)).T\n\n# Fetch the first subject of ADHD dataset\nadhd = datasets.fetch_adhd(n_subjects=1)\n\n\n###############################################################################\n# Masking: taking the signal in a sphere of radius 5mm around Power coords\n\nmasker = input_data.NiftiSpheresMasker(seeds=power_coords,\n                                       smoothing_fwhm=4,\n                                       radius=5.,\n                                       standardize=True,\n                                       detrend=True,\n                                       low_pass=0.1,\n                                       high_pass=0.01,\n                                       t_r=2.5)\n\ntimeseries = masker.fit_transform(adhd.func[0], confounds=adhd.confounds[0])\n\n###############################################################################\n# Extract and plot correlation matrix\n\n# calculate connectivity and plot Power-264 correlation matrix\nconnectivity = connectome.ConnectivityMeasure(kind='correlation')\ncorr_matrix = connectivity.fit_transform([timeseries])[0]\nnp.fill_diagonal(corr_matrix, 0)\nplt.imshow(corr_matrix, vmin=-1., vmax=1., cmap='RdBu_r')\nplt.colorbar()\nplt.title('Power 264 Connectivity')\n\n# Plot the connectome\n\nplotting.plot_connectome(corr_matrix,\n                         power_coords,\n                         edge_threshold='99.8%',\n                         node_size=20)\n\n\n###############################################################################\n# Extract and plot covariance and sparse covariance\n\n# Compute the sparse inverse covariance\nfrom sklearn.covariance import GraphLassoCV\n\nestimator = GraphLassoCV()\nestimator.fit(timeseries)\n\n# Display the covariance\nplt.figure(figsize=(5, 5))\nplt.imshow(estimator.covariance_, interpolation=\"nearest\",\n           vmax=1, vmin=-1, cmap=plt.cm.RdBu_r)\nplt.title('Covariance matrix')\n\n# display the corresponding graph\nplotting.plot_connectome(estimator.covariance_,\n                         power_coords,\n                         title='Covariance connectome',\n                         edge_threshold='99.8%',\n                         node_size=20)\n\n# Display the sparse inverse covariance\nplt.figure(figsize=(5, 5))\nplt.imshow(estimator.precision_, interpolation=\"nearest\",\n           vmax=1, vmin=-1, cmap=plt.cm.RdBu_r)\nplt.title('Precision matrix')\n\n# And now display the corresponding graph\nplotting.plot_connectome(estimator.precision_, power_coords,\n                         title='Precision connectome',\n                         edge_threshold=\"99.8%\",\n                         node_size=20)\nplotting.show()\n", "path": "examples/03_connectivity/plot_power_connectome.py"}], "after_files": [{"content": null, "path": "examples/03_connectivity/plot_power_connectome.py"}]}