prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Strange comment in ICA example
  This is the comment:
  # XXX: must get the code to run for more than 1 subject
  And this is the link to the example:
  https://nilearn.github.io/auto_examples/05_advanced/plot_ica_resting_state.html
  
  The comment appears to relate to an internal todo rather than comment/instruction to users.
  
  --- 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/05_advanced/plot_ica_resting_state.py`
  Content:
  ```
  1 """
  2 Multivariate decompositions: Independent component analysis of fMRI
  3 ===================================================================
  4 
  5 
  6 This example is meant to demonstrate nilearn as a low-level tools used to
  7 combine feature extraction with a multivariate decomposition algorithm
  8 for movie-watching.
  9 
  10 This example is a toy. To apply ICA to fmri timeseries data, it is advised
  11 to look at the example
  12 :ref:`sphx_glr_auto_examples_03_connectivity_plot_compare_decomposition.py`.
  13 
  14 The example here applies the scikit-learn ICA to movie watching timeseries data.
  15 Note that following the code in the example, any unsupervised
  16 decomposition model, or other latent-factor models, can be applied to
  17 the data, as the scikit-learn API enables to exchange them as almost
  18 black box (though the relevant parameter for brain maps might no longer
  19 be given by a call to fit_transform).
  20 
  21 """
  22 
  23 #####################################################################
  24 # Load movie watching dataset
  25 from nilearn import datasets
  26 # Here we use only single subject to get faster-running code. For better
  27 # results, simply increase this number
  28 # XXX: must get the code to run for more than 1 subject
  29 dataset = datasets.fetch_development_fmri(n_subjects=1)
  30 func_filename = dataset.func[0]
  31 
  32 # print basic information on the dataset
  33 print('First subject functional nifti image (4D) is at: %s' %
  34       dataset.func[0])  # 4D data
  35 
  36 
  37 #####################################################################
  38 # Preprocess
  39 from nilearn.input_data import NiftiMasker
  40 
  41 # This is fmri timeseries data: the background has not been removed yet,
  42 # thus we need to use mask_strategy='epi' to compute the mask from the
  43 # EPI images
  44 masker = NiftiMasker(smoothing_fwhm=8, memory='nilearn_cache', memory_level=1,
  45                      mask_strategy='epi', standardize=True)
  46 data_masked = masker.fit_transform(func_filename)
  47 
  48 # Concatenate all the subjects
  49 # fmri_data = np.concatenate(data_masked, axis=1)
  50 fmri_data = data_masked
  51 
  52 
  53 #####################################################################
  54 # Apply ICA
  55 
  56 from sklearn.decomposition import FastICA
  57 n_components = 10
  58 ica = FastICA(n_components=n_components, random_state=42)
  59 components_masked = ica.fit_transform(data_masked.T).T
  60 
  61 # Normalize estimated components, for thresholding to make sense
  62 components_masked -= components_masked.mean(axis=0)
  63 components_masked /= components_masked.std(axis=0)
  64 # Threshold
  65 import numpy as np
  66 components_masked[np.abs(components_masked) < .8] = 0
  67 
  68 # Now invert the masking operation, going back to a full 3D
  69 # representation
  70 component_img = masker.inverse_transform(components_masked)
  71 
  72 #####################################################################
  73 # Visualize the results
  74 
  75 # Show some interesting components
  76 from nilearn import image
  77 from nilearn.plotting import plot_stat_map, show
  78 
  79 # Use the mean as a background
  80 mean_img = image.mean_img(func_filename)
  81 
  82 plot_stat_map(image.index_img(component_img, 0), mean_img)
  83 
  84 plot_stat_map(image.index_img(component_img, 1), mean_img)
  85 
  86 show()
  87
  ```
  --- 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()
  
  ```
  
golden_diff: diff --git a/examples/05_advanced/plot_ica_resting_state.py b/examples/05_advanced/plot_ica_resting_state.py
  --- a/examples/05_advanced/plot_ica_resting_state.py
  +++ b/examples/05_advanced/plot_ica_resting_state.py
  @@ -23,9 +23,7 @@
   #####################################################################
   # Load movie watching dataset
   from nilearn import datasets
  -# Here we use only single subject to get faster-running code. For better
  -# results, simply increase this number
  -# XXX: must get the code to run for more than 1 subject
  +# Here we use only single subject to get faster-running code.
   dataset = datasets.fetch_development_fmri(n_subjects=1)
   func_filename = dataset.func[0]
   
  @@ -45,10 +43,6 @@
                        mask_strategy='epi', standardize=True)
   data_masked = masker.fit_transform(func_filename)
   
  -# Concatenate all the subjects
  -# fmri_data = np.concatenate(data_masked, axis=1)
  -fmri_data = data_masked
  -
   
   #####################################################################
   # Apply ICA
  
verification_info: {"golden_diff": "diff --git a/examples/05_advanced/plot_ica_resting_state.py b/examples/05_advanced/plot_ica_resting_state.py\n--- a/examples/05_advanced/plot_ica_resting_state.py\n+++ b/examples/05_advanced/plot_ica_resting_state.py\n@@ -23,9 +23,7 @@\n #####################################################################\n # Load movie watching dataset\n from nilearn import datasets\n-# Here we use only single subject to get faster-running code. For better\n-# results, simply increase this number\n-# XXX: must get the code to run for more than 1 subject\n+# Here we use only single subject to get faster-running code.\n dataset = datasets.fetch_development_fmri(n_subjects=1)\n func_filename = dataset.func[0]\n \n@@ -45,10 +43,6 @@\n                      mask_strategy='epi', standardize=True)\n data_masked = masker.fit_transform(func_filename)\n \n-# Concatenate all the subjects\n-# fmri_data = np.concatenate(data_masked, axis=1)\n-fmri_data = data_masked\n-\n \n #####################################################################\n # Apply ICA\n", "issue": "Strange comment in ICA example\nThis is the comment:\r\n# XXX: must get the code to run for more than 1 subject\r\nAnd this is the link to the example:\r\nhttps://nilearn.github.io/auto_examples/05_advanced/plot_ica_resting_state.html\r\n\r\nThe comment appears to relate to an internal todo rather than comment/instruction to users.\n", "before_files": [{"content": "\"\"\"\nMultivariate decompositions: Independent component analysis of fMRI\n===================================================================\n\n\nThis example is meant to demonstrate nilearn as a low-level tools used to\ncombine feature extraction with a multivariate decomposition algorithm\nfor movie-watching.\n\nThis example is a toy. To apply ICA to fmri timeseries data, it is advised\nto look at the example\n:ref:`sphx_glr_auto_examples_03_connectivity_plot_compare_decomposition.py`.\n\nThe example here applies the scikit-learn ICA to movie watching timeseries data.\nNote that following the code in the example, any unsupervised\ndecomposition model, or other latent-factor models, can be applied to\nthe data, as the scikit-learn API enables to exchange them as almost\nblack box (though the relevant parameter for brain maps might no longer\nbe given by a call to fit_transform).\n\n\"\"\"\n\n#####################################################################\n# Load movie watching dataset\nfrom nilearn import datasets\n# Here we use only single subject to get faster-running code. For better\n# results, simply increase this number\n# XXX: must get the code to run for more than 1 subject\ndataset = datasets.fetch_development_fmri(n_subjects=1)\nfunc_filename = dataset.func[0]\n\n# print basic information on the dataset\nprint('First subject functional nifti image (4D) is at: %s' %\n      dataset.func[0])  # 4D data\n\n\n#####################################################################\n# Preprocess\nfrom nilearn.input_data import NiftiMasker\n\n# This is fmri timeseries data: the background has not been removed yet,\n# thus we need to use mask_strategy='epi' to compute the mask from the\n# EPI images\nmasker = NiftiMasker(smoothing_fwhm=8, memory='nilearn_cache', memory_level=1,\n                     mask_strategy='epi', standardize=True)\ndata_masked = masker.fit_transform(func_filename)\n\n# Concatenate all the subjects\n# fmri_data = np.concatenate(data_masked, axis=1)\nfmri_data = data_masked\n\n\n#####################################################################\n# Apply ICA\n\nfrom sklearn.decomposition import FastICA\nn_components = 10\nica = FastICA(n_components=n_components, random_state=42)\ncomponents_masked = ica.fit_transform(data_masked.T).T\n\n# Normalize estimated components, for thresholding to make sense\ncomponents_masked -= components_masked.mean(axis=0)\ncomponents_masked /= components_masked.std(axis=0)\n# Threshold\nimport numpy as np\ncomponents_masked[np.abs(components_masked) < .8] = 0\n\n# Now invert the masking operation, going back to a full 3D\n# representation\ncomponent_img = masker.inverse_transform(components_masked)\n\n#####################################################################\n# Visualize the results\n\n# Show some interesting components\nfrom nilearn import image\nfrom nilearn.plotting import plot_stat_map, show\n\n# Use the mean as a background\nmean_img = image.mean_img(func_filename)\n\nplot_stat_map(image.index_img(component_img, 0), mean_img)\n\nplot_stat_map(image.index_img(component_img, 1), mean_img)\n\nshow()\n", "path": "examples/05_advanced/plot_ica_resting_state.py"}], "after_files": [{"content": "\"\"\"\nMultivariate decompositions: Independent component analysis of fMRI\n===================================================================\n\n\nThis example is meant to demonstrate nilearn as a low-level tools used to\ncombine feature extraction with a multivariate decomposition algorithm\nfor movie-watching.\n\nThis example is a toy. To apply ICA to fmri timeseries data, it is advised\nto look at the example\n:ref:`sphx_glr_auto_examples_03_connectivity_plot_compare_decomposition.py`.\n\nThe example here applies the scikit-learn ICA to movie watching timeseries data.\nNote that following the code in the example, any unsupervised\ndecomposition model, or other latent-factor models, can be applied to\nthe data, as the scikit-learn API enables to exchange them as almost\nblack box (though the relevant parameter for brain maps might no longer\nbe given by a call to fit_transform).\n\n\"\"\"\n\n#####################################################################\n# Load movie watching dataset\nfrom nilearn import datasets\n# Here we use only single subject to get faster-running code.\ndataset = datasets.fetch_development_fmri(n_subjects=1)\nfunc_filename = dataset.func[0]\n\n# print basic information on the dataset\nprint('First subject functional nifti image (4D) is at: %s' %\n      dataset.func[0])  # 4D data\n\n\n#####################################################################\n# Preprocess\nfrom nilearn.input_data import NiftiMasker\n\n# This is fmri timeseries data: the background has not been removed yet,\n# thus we need to use mask_strategy='epi' to compute the mask from the\n# EPI images\nmasker = NiftiMasker(smoothing_fwhm=8, memory='nilearn_cache', memory_level=1,\n                     mask_strategy='epi', standardize=True)\ndata_masked = masker.fit_transform(func_filename)\n\n\n#####################################################################\n# Apply ICA\n\nfrom sklearn.decomposition import FastICA\nn_components = 10\nica = FastICA(n_components=n_components, random_state=42)\ncomponents_masked = ica.fit_transform(data_masked.T).T\n\n# Normalize estimated components, for thresholding to make sense\ncomponents_masked -= components_masked.mean(axis=0)\ncomponents_masked /= components_masked.std(axis=0)\n# Threshold\nimport numpy as np\ncomponents_masked[np.abs(components_masked) < .8] = 0\n\n# Now invert the masking operation, going back to a full 3D\n# representation\ncomponent_img = masker.inverse_transform(components_masked)\n\n#####################################################################\n# Visualize the results\n\n# Show some interesting components\nfrom nilearn import image\nfrom nilearn.plotting import plot_stat_map, show\n\n# Use the mean as a background\nmean_img = image.mean_img(func_filename)\n\nplot_stat_map(image.index_img(component_img, 0), mean_img)\n\nplot_stat_map(image.index_img(component_img, 1), mean_img)\n\nshow()\n", "path": "examples/05_advanced/plot_ica_resting_state.py"}]}