prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Spelling Error
  <!--Describe your proposed enhancement in detail.-->
  I think the authors meant to describe ADHD but have written ADHD as AHDH. It is just a simple spelling or typographic error.
  <!--List any pages that would be impacted by the enhancement.-->
  ### Affected pages
  1. https://nilearn.github.io/dev/auto_examples/04_glm_first_level/plot_adhd_dmn.html#sphx-glr-auto-examples-04-glm-first-level-plot-adhd-dmn-py
   2. https://nilearn.github.io/dev/glm/first_level_model.html#fitting-a-first-level-model
  
  </issue>
  <code>
  [start of examples/04_glm_first_level/plot_adhd_dmn.py]
  1 """Default Mode Network extraction of AHDH dataset
  2 ===============================================
  3 
  4 This example shows a full step-by-step workflow of fitting a GLM to data
  5 extracted from a seed on the Posterior Cingulate Cortex and saving the results.
  6 
  7 More specifically:
  8 
  9 1. A sequence of fMRI volumes are loaded.
  10 2. A design matrix with the Posterior Cingulate Cortex seed is defined.
  11 3. A GLM is applied to the dataset (effect/covariance, then contrast estimation).
  12 4. The Default Mode Network is displayed.
  13 
  14 .. include:: ../../../examples/masker_note.rst
  15 
  16 """
  17 import numpy as np
  18 
  19 from nilearn import datasets, plotting
  20 from nilearn.maskers import NiftiSpheresMasker
  21 
  22 from nilearn.glm.first_level import FirstLevelModel
  23 from nilearn.glm.first_level import make_first_level_design_matrix
  24 
  25 #########################################################################
  26 # Prepare data and analysis parameters
  27 # -------------------------------------
  28 # Prepare the data.
  29 adhd_dataset = datasets.fetch_adhd(n_subjects=1)
  30 
  31 # Prepare timing
  32 t_r = 2.
  33 slice_time_ref = 0.
  34 n_scans = 176
  35 
  36 # Prepare seed
  37 pcc_coords = (0, -53, 26)
  38 
  39 #########################################################################
  40 # Estimate contrasts
  41 # ------------------
  42 # Specify the contrasts.
  43 seed_masker = NiftiSpheresMasker([pcc_coords], radius=10, detrend=True,
  44                                  standardize=True, low_pass=0.1,
  45                                  high_pass=0.01, t_r=2.,
  46                                  memory='nilearn_cache',
  47                                  memory_level=1, verbose=0)
  48 seed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])
  49 frametimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)
  50 design_matrix = make_first_level_design_matrix(frametimes, hrf_model='spm',
  51                                                add_regs=seed_time_series,
  52                                                add_reg_names=["pcc_seed"])
  53 dmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))
  54 contrasts = {'seed_based_glm': dmn_contrast}
  55 
  56 #########################################################################
  57 # Perform first level analysis
  58 # ----------------------------
  59 # Setup and fit GLM.
  60 first_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref)
  61 first_level_model = first_level_model.fit(run_imgs=adhd_dataset.func[0],
  62                                     design_matrices=design_matrix)
  63 
  64 #########################################################################
  65 # Estimate the contrast.
  66 print('Contrast seed_based_glm computed.')
  67 z_map = first_level_model.compute_contrast(contrasts['seed_based_glm'],
  68                                            output_type='z_score')
  69 
  70 # Saving snapshots of the contrasts
  71 filename = 'dmn_z_map.png'
  72 display = plotting.plot_stat_map(z_map, threshold=3.0, title='Seed based GLM',
  73                                  cut_coords=pcc_coords)
  74 display.add_markers(marker_coords=[pcc_coords], marker_color='g',
  75                     marker_size=300)
  76 display.savefig(filename)
  77 print("Save z-map in '{0}'.".format(filename))
  78 
  79 ###########################################################################
  80 # Generating a report
  81 # -------------------
  82 # It can be useful to quickly generate a
  83 # portable, ready-to-view report with most of the pertinent information.
  84 # This is easy to do if you have a fitted model and the list of contrasts,
  85 # which we do here.
  86 
  87 from nilearn.reporting import make_glm_report
  88 
  89 report = make_glm_report(first_level_model,
  90                          contrasts=contrasts,
  91                          title='ADHD DMN Report',
  92                          cluster_threshold=15,
  93                          min_distance=8.,
  94                          plot_type='glass',
  95                          )
  96 
  97 #########################################################################
  98 # We have several ways to access the report:
  99 
  100 # report  # This report can be viewed in a notebook
  101 # report.save_as_html('report.html')
  102 # report.open_in_browser()
  103
  [end of examples/04_glm_first_level/plot_adhd_dmn.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/examples/04_glm_first_level/plot_adhd_dmn.py b/examples/04_glm_first_level/plot_adhd_dmn.py
  --- a/examples/04_glm_first_level/plot_adhd_dmn.py
  +++ b/examples/04_glm_first_level/plot_adhd_dmn.py
  @@ -1,4 +1,4 @@
  -"""Default Mode Network extraction of AHDH dataset
  +"""Default Mode Network extraction of ADHD dataset
   ===============================================
   
   This example shows a full step-by-step workflow of fitting a GLM to data
  
verification_info: {"golden_diff": "diff --git a/examples/04_glm_first_level/plot_adhd_dmn.py b/examples/04_glm_first_level/plot_adhd_dmn.py\n--- a/examples/04_glm_first_level/plot_adhd_dmn.py\n+++ b/examples/04_glm_first_level/plot_adhd_dmn.py\n@@ -1,4 +1,4 @@\n-\"\"\"Default Mode Network extraction of AHDH dataset\n+\"\"\"Default Mode Network extraction of ADHD dataset\n ===============================================\n \n This example shows a full step-by-step workflow of fitting a GLM to data\n", "issue": "Spelling Error\n<!--Describe your proposed enhancement in detail.-->\r\nI think the authors meant to describe ADHD but have written ADHD as AHDH. It is just a simple spelling or typographic error.\r\n<!--List any pages that would be impacted by the enhancement.-->\r\n### Affected pages\r\n1. https://nilearn.github.io/dev/auto_examples/04_glm_first_level/plot_adhd_dmn.html#sphx-glr-auto-examples-04-glm-first-level-plot-adhd-dmn-py\r\n 2. https://nilearn.github.io/dev/glm/first_level_model.html#fitting-a-first-level-model\n", "before_files": [{"content": "\"\"\"Default Mode Network extraction of AHDH dataset\n===============================================\n\nThis example shows a full step-by-step workflow of fitting a GLM to data\nextracted from a seed on the Posterior Cingulate Cortex and saving the results.\n\nMore specifically:\n\n1. A sequence of fMRI volumes are loaded.\n2. A design matrix with the Posterior Cingulate Cortex seed is defined.\n3. A GLM is applied to the dataset (effect/covariance, then contrast estimation).\n4. The Default Mode Network is displayed.\n\n.. include:: ../../../examples/masker_note.rst\n\n\"\"\"\nimport numpy as np\n\nfrom nilearn import datasets, plotting\nfrom nilearn.maskers import NiftiSpheresMasker\n\nfrom nilearn.glm.first_level import FirstLevelModel\nfrom nilearn.glm.first_level import make_first_level_design_matrix\n\n#########################################################################\n# Prepare data and analysis parameters\n# -------------------------------------\n# Prepare the data.\nadhd_dataset = datasets.fetch_adhd(n_subjects=1)\n\n# Prepare timing\nt_r = 2.\nslice_time_ref = 0.\nn_scans = 176\n\n# Prepare seed\npcc_coords = (0, -53, 26)\n\n#########################################################################\n# Estimate contrasts\n# ------------------\n# Specify the contrasts.\nseed_masker = NiftiSpheresMasker([pcc_coords], radius=10, detrend=True,\n                                 standardize=True, low_pass=0.1,\n                                 high_pass=0.01, t_r=2.,\n                                 memory='nilearn_cache',\n                                 memory_level=1, verbose=0)\nseed_time_series = seed_masker.fit_transform(adhd_dataset.func[0])\nframetimes = np.linspace(0, (n_scans - 1) * t_r, n_scans)\ndesign_matrix = make_first_level_design_matrix(frametimes, hrf_model='spm',\n                                               add_regs=seed_time_series,\n                                               add_reg_names=[\"pcc_seed\"])\ndmn_contrast = np.array([1] + [0] * (design_matrix.shape[1] - 1))\ncontrasts = {'seed_based_glm': dmn_contrast}\n\n#########################################################################\n# Perform first level analysis\n# ----------------------------\n# Setup and fit GLM.\nfirst_level_model = FirstLevelModel(t_r=t_r, slice_time_ref=slice_time_ref)\nfirst_level_model = first_level_model.fit(run_imgs=adhd_dataset.func[0],\n                                    design_matrices=design_matrix)\n\n#########################################################################\n# Estimate the contrast.\nprint('Contrast seed_based_glm computed.')\nz_map = first_level_model.compute_contrast(contrasts['seed_based_glm'],\n                                           output_type='z_score')\n\n# Saving snapshots of the contrasts\nfilename = 'dmn_z_map.png'\ndisplay = plotting.plot_stat_map(z_map, threshold=3.0, title='Seed based GLM',\n                                 cut_coords=pcc_coords)\ndisplay.add_markers(marker_coords=[pcc_coords], marker_color='g',\n                    marker_size=300)\ndisplay.savefig(filename)\nprint(\"Save z-map in '{0}'.\".format(filename))\n\n###########################################################################\n# Generating a report\n# -------------------\n# It can be useful to quickly generate a\n# portable, ready-to-view report with most of the pertinent information.\n# This is easy to do if you have a fitted model and the list of contrasts,\n# which we do here.\n\nfrom nilearn.reporting import make_glm_report\n\nreport = make_glm_report(first_level_model,\n                         contrasts=contrasts,\n                         title='ADHD DMN Report',\n                         cluster_threshold=15,\n                         min_distance=8.,\n                         plot_type='glass',\n                         )\n\n#########################################################################\n# We have several ways to access the report:\n\n# report  # This report can be viewed in a notebook\n# report.save_as_html('report.html')\n# report.open_in_browser()\n", "path": "examples/04_glm_first_level/plot_adhd_dmn.py"}]}