prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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.
  
  
  </issue>
  <code>
  [start of examples/03_connectivity/plot_power_connectome.py]
  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 of examples/03_connectivity/plot_power_connectome.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/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()
  
verification_info: {"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"}]}