prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Use plot_event in a example
  The function `plot_event` has currently no example linked to its [doc](https://nilearn.github.io/modules/generated/nilearn.plotting.plot_event.html#nilearn.plotting.plot_event). 
  It wouldn't be too costly to use it in one example somewhere.
  
  --- 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/04_glm_first_level/write_events_file.py`
  Content:
  ```
  1 """Example of a events.tsv file generation: the neurospin/localizer events.
  2 =============================================================================
  3 
  4 The protocol described is the so-called "archi standard" localizer
  5 event sequence.  See Pinel et al., BMC neuroscience 2007 for reference.
  6 """
  7 
  8 print(__doc__)
  9 
  10 #########################################################################
  11 # Define the onset times in seconds. Those are typically extracted
  12 # from the stimulation software used.
  13 import numpy as np
  14 onset = np.array([
  15     0., 2.4, 8.7, 11.4, 15., 18., 20.7, 23.7, 26.7, 29.7, 33., 35.4, 39.,
  16     41.7, 44.7, 48., 56.4, 59.7, 62.4, 69., 71.4, 75., 83.4, 87., 89.7,
  17     96., 108., 116.7, 119.4, 122.7, 125.4, 131.4, 135., 137.7, 140.4,
  18     143.4, 146.7, 149.4, 153., 156., 159., 162., 164.4, 167.7, 170.4,
  19     173.7, 176.7, 188.4, 191.7, 195., 198., 201., 203.7, 207., 210.,
  20     212.7, 215.7, 218.7, 221.4, 224.7, 227.7, 230.7, 234., 236.7, 246.,
  21     248.4, 251.7, 254.7, 257.4, 260.4, 264., 266.7, 269.7, 275.4, 278.4,
  22     284.4, 288., 291., 293.4, 296.7])
  23 
  24 #########################################################################
  25 # Associated trial types: these are numbered between 0 and 9, hence
  26 # correspond to 10 different conditions.
  27 trial_idx = np.array(
  28     [7, 7, 0, 2, 9, 4, 9, 3, 5, 9, 1, 6, 8, 8, 6, 6, 8, 0, 3, 4, 5, 8, 6,
  29      2, 9, 1, 6, 5, 9, 1, 7, 8, 6, 6, 1, 2, 9, 0, 7, 1, 8, 2, 7, 8, 3, 6,
  30      0, 0, 6, 8, 7, 7, 1, 1, 1, 5, 5, 0, 7, 0, 4, 2, 7, 9, 8, 0, 6, 3, 3,
  31      7, 1, 0, 0, 4, 1, 9, 8, 4, 9, 9])
  32 
  33 #########################################################################
  34 # We may want to map these indices to explicit condition names.
  35 # For that, we define a list of 10 strings.
  36 condition_ids = ['horizontal checkerboard',
  37                  'vertical checkerboard',
  38                  'right button press, auditory instructions',
  39                  'left button press, auditory instructions',
  40                  'right button press, visual instructions',
  41                  'left button press, visual instructions',
  42                  'mental computation, auditory instructions',
  43                  'mental computation, visual instructions',
  44                  'visual sentence',
  45                  'auditory sentence']
  46 
  47 trial_type = np.array([condition_ids[i] for i in trial_idx])
  48 
  49 #########################################################################
  50 # We also define a duration (required by BIDS conventions).
  51 duration = np.ones_like(onset)
  52 
  53 
  54 #########################################################################
  55 # Form an event dataframe from these information.
  56 import pandas as pd
  57 events = pd.DataFrame({'trial_type': trial_type,
  58                        'onset': onset,
  59                        'duration': duration})
  60 
  61 #########################################################################
  62 # Export them to a tsv file.
  63 tsvfile = 'localizer_events.tsv'
  64 events.to_csv(tsvfile, sep='\t', index=False)
  65 print("Created the events file in %s " % tsvfile)
  66
  ```
  --- 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/04_glm_first_level/write_events_file.py b/examples/04_glm_first_level/write_events_file.py
  --- a/examples/04_glm_first_level/write_events_file.py
  +++ b/examples/04_glm_first_level/write_events_file.py
  @@ -63,3 +63,10 @@
   tsvfile = 'localizer_events.tsv'
   events.to_csv(tsvfile, sep='\t', index=False)
   print("Created the events file in %s " % tsvfile)
  +
  +#########################################################################
  +# Optionally, the events can be visualized using the plot_event function.
  +from matplotlib import pyplot as plt
  +from nilearn.plotting import plot_event
  +plot_event(events, figsize=(15, 5))
  +plt.show()
  
verification_info: {"golden_diff": "diff --git a/examples/04_glm_first_level/write_events_file.py b/examples/04_glm_first_level/write_events_file.py\n--- a/examples/04_glm_first_level/write_events_file.py\n+++ b/examples/04_glm_first_level/write_events_file.py\n@@ -63,3 +63,10 @@\n tsvfile = 'localizer_events.tsv'\n events.to_csv(tsvfile, sep='\\t', index=False)\n print(\"Created the events file in %s \" % tsvfile)\n+\n+#########################################################################\n+# Optionally, the events can be visualized using the plot_event function.\n+from matplotlib import pyplot as plt\n+from nilearn.plotting import plot_event\n+plot_event(events, figsize=(15, 5))\n+plt.show()\n", "issue": "Use plot_event in a example\nThe function `plot_event` has currently no example linked to its [doc](https://nilearn.github.io/modules/generated/nilearn.plotting.plot_event.html#nilearn.plotting.plot_event). \r\nIt wouldn't be too costly to use it in one example somewhere.\n", "before_files": [{"content": "\"\"\"Example of a events.tsv file generation: the neurospin/localizer events.\n=============================================================================\n\nThe protocol described is the so-called \"archi standard\" localizer\nevent sequence.  See Pinel et al., BMC neuroscience 2007 for reference.\n\"\"\"\n\nprint(__doc__)\n\n#########################################################################\n# Define the onset times in seconds. Those are typically extracted\n# from the stimulation software used.\nimport numpy as np\nonset = np.array([\n    0., 2.4, 8.7, 11.4, 15., 18., 20.7, 23.7, 26.7, 29.7, 33., 35.4, 39.,\n    41.7, 44.7, 48., 56.4, 59.7, 62.4, 69., 71.4, 75., 83.4, 87., 89.7,\n    96., 108., 116.7, 119.4, 122.7, 125.4, 131.4, 135., 137.7, 140.4,\n    143.4, 146.7, 149.4, 153., 156., 159., 162., 164.4, 167.7, 170.4,\n    173.7, 176.7, 188.4, 191.7, 195., 198., 201., 203.7, 207., 210.,\n    212.7, 215.7, 218.7, 221.4, 224.7, 227.7, 230.7, 234., 236.7, 246.,\n    248.4, 251.7, 254.7, 257.4, 260.4, 264., 266.7, 269.7, 275.4, 278.4,\n    284.4, 288., 291., 293.4, 296.7])\n\n#########################################################################\n# Associated trial types: these are numbered between 0 and 9, hence\n# correspond to 10 different conditions.\ntrial_idx = np.array(\n    [7, 7, 0, 2, 9, 4, 9, 3, 5, 9, 1, 6, 8, 8, 6, 6, 8, 0, 3, 4, 5, 8, 6,\n     2, 9, 1, 6, 5, 9, 1, 7, 8, 6, 6, 1, 2, 9, 0, 7, 1, 8, 2, 7, 8, 3, 6,\n     0, 0, 6, 8, 7, 7, 1, 1, 1, 5, 5, 0, 7, 0, 4, 2, 7, 9, 8, 0, 6, 3, 3,\n     7, 1, 0, 0, 4, 1, 9, 8, 4, 9, 9])\n\n#########################################################################\n# We may want to map these indices to explicit condition names.\n# For that, we define a list of 10 strings.\ncondition_ids = ['horizontal checkerboard',\n                 'vertical checkerboard',\n                 'right button press, auditory instructions',\n                 'left button press, auditory instructions',\n                 'right button press, visual instructions',\n                 'left button press, visual instructions',\n                 'mental computation, auditory instructions',\n                 'mental computation, visual instructions',\n                 'visual sentence',\n                 'auditory sentence']\n\ntrial_type = np.array([condition_ids[i] for i in trial_idx])\n\n#########################################################################\n# We also define a duration (required by BIDS conventions).\nduration = np.ones_like(onset)\n\n\n#########################################################################\n# Form an event dataframe from these information.\nimport pandas as pd\nevents = pd.DataFrame({'trial_type': trial_type,\n                       'onset': onset,\n                       'duration': duration})\n\n#########################################################################\n# Export them to a tsv file.\ntsvfile = 'localizer_events.tsv'\nevents.to_csv(tsvfile, sep='\\t', index=False)\nprint(\"Created the events file in %s \" % tsvfile)\n", "path": "examples/04_glm_first_level/write_events_file.py"}], "after_files": [{"content": "\"\"\"Example of a events.tsv file generation: the neurospin/localizer events.\n=============================================================================\n\nThe protocol described is the so-called \"archi standard\" localizer\nevent sequence.  See Pinel et al., BMC neuroscience 2007 for reference.\n\"\"\"\n\nprint(__doc__)\n\n#########################################################################\n# Define the onset times in seconds. Those are typically extracted\n# from the stimulation software used.\nimport numpy as np\nonset = np.array([\n    0., 2.4, 8.7, 11.4, 15., 18., 20.7, 23.7, 26.7, 29.7, 33., 35.4, 39.,\n    41.7, 44.7, 48., 56.4, 59.7, 62.4, 69., 71.4, 75., 83.4, 87., 89.7,\n    96., 108., 116.7, 119.4, 122.7, 125.4, 131.4, 135., 137.7, 140.4,\n    143.4, 146.7, 149.4, 153., 156., 159., 162., 164.4, 167.7, 170.4,\n    173.7, 176.7, 188.4, 191.7, 195., 198., 201., 203.7, 207., 210.,\n    212.7, 215.7, 218.7, 221.4, 224.7, 227.7, 230.7, 234., 236.7, 246.,\n    248.4, 251.7, 254.7, 257.4, 260.4, 264., 266.7, 269.7, 275.4, 278.4,\n    284.4, 288., 291., 293.4, 296.7])\n\n#########################################################################\n# Associated trial types: these are numbered between 0 and 9, hence\n# correspond to 10 different conditions.\ntrial_idx = np.array(\n    [7, 7, 0, 2, 9, 4, 9, 3, 5, 9, 1, 6, 8, 8, 6, 6, 8, 0, 3, 4, 5, 8, 6,\n     2, 9, 1, 6, 5, 9, 1, 7, 8, 6, 6, 1, 2, 9, 0, 7, 1, 8, 2, 7, 8, 3, 6,\n     0, 0, 6, 8, 7, 7, 1, 1, 1, 5, 5, 0, 7, 0, 4, 2, 7, 9, 8, 0, 6, 3, 3,\n     7, 1, 0, 0, 4, 1, 9, 8, 4, 9, 9])\n\n#########################################################################\n# We may want to map these indices to explicit condition names.\n# For that, we define a list of 10 strings.\ncondition_ids = ['horizontal checkerboard',\n                 'vertical checkerboard',\n                 'right button press, auditory instructions',\n                 'left button press, auditory instructions',\n                 'right button press, visual instructions',\n                 'left button press, visual instructions',\n                 'mental computation, auditory instructions',\n                 'mental computation, visual instructions',\n                 'visual sentence',\n                 'auditory sentence']\n\ntrial_type = np.array([condition_ids[i] for i in trial_idx])\n\n#########################################################################\n# We also define a duration (required by BIDS conventions).\nduration = np.ones_like(onset)\n\n\n#########################################################################\n# Form an event dataframe from these information.\nimport pandas as pd\nevents = pd.DataFrame({'trial_type': trial_type,\n                       'onset': onset,\n                       'duration': duration})\n\n#########################################################################\n# Export them to a tsv file.\ntsvfile = 'localizer_events.tsv'\nevents.to_csv(tsvfile, sep='\\t', index=False)\nprint(\"Created the events file in %s \" % tsvfile)\n\n#########################################################################\n# Optionally, the events can be visualized using the plot_event function.\nfrom matplotlib import pyplot as plt\nfrom nilearn.plotting import plot_event\nplot_event(events, figsize=(15, 5))\nplt.show()\n", "path": "examples/04_glm_first_level/write_events_file.py"}]}