We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Ugly side plots in doc
They mask part of the code they are supposed to illustrate. See an example [here](http://nilearn.github.io/building_blocks/data_preparation.html#computing-the-mask).

![ugly-side-plots](https://cloud.githubusercontent.com/assets/1680079/5974579/266c30d8-a876-11e4-8830-febff99ed1dc.png)


--- 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/connectivity/plot_adhd_covariance.py`
Content:
```
1 """
2 Computation of covariance matrix between brain regions
3 ======================================================
4 
5 This example shows how to extract signals from regions defined by an atlas,
6 and to estimate a covariance matrix based on these signals.
7 """
8  
9 plotted_subject = 0  # subject to plot
10 
11 
12 import matplotlib.pyplot as plt
13 import matplotlib
14 # Copied from matplotlib 1.2.0 for matplotlib 0.99 compatibility.
15 _bwr_data = ((0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0))
16 plt.cm.register_cmap(cmap=matplotlib.colors.LinearSegmentedColormap.from_list(
17     "bwr", _bwr_data))
18 
19 
20 def plot_matrices(cov, prec, title):
21     """Plot covariance and precision matrices, for a given processing. """
22 
23     prec = prec.copy()  # avoid side effects
24 
25     # Display sparsity pattern
26     sparsity = prec == 0
27     plt.figure()
28     plt.imshow(sparsity, interpolation="nearest")
29     plt.title("%s / sparsity" % title)
30 
31     # Put zeros on the diagonal, for graph clarity.
32     size = prec.shape[0]
33     prec[range(size), range(size)] = 0
34     span = max(abs(prec.min()), abs(prec.max()))
35 
36     # Display covariance matrix
37     plt.figure()
38     plt.imshow(cov, interpolation="nearest",
39               vmin=-1, vmax=1, cmap=plt.cm.get_cmap("bwr"))
40     plt.colorbar()
41     plt.title("%s / covariance" % title)
42 
43     # Display precision matrix
44     plt.figure()
45     plt.imshow(prec, interpolation="nearest",
46               vmin=-span, vmax=span,
47               cmap=plt.cm.get_cmap("bwr"))
48     plt.colorbar()
49     plt.title("%s / precision" % title)
50 
51 
52 # Fetching datasets ###########################################################
53 print("-- Fetching datasets ...")
54 from nilearn import datasets
55 msdl_atlas_dataset = datasets.fetch_msdl_atlas()
56 adhd_dataset = datasets.fetch_adhd()
57 
58 # Extracting region signals ###################################################
59 import nilearn.image
60 import nilearn.input_data
61 
62 from sklearn.externals.joblib import Memory
63 mem = Memory(".")
64 
65 # Number of subjects to consider for group-sparse covariance
66 n_subjects = 10
67 subjects = []
68 
69 func_filenames = adhd_dataset.func
70 confound_filenames = adhd_dataset.confounds
71 for func_filename, confound_filename in zip(func_filenames,
72                                             confound_filenames):
73     print("Processing file %s" % func_filename)
74 
75     print("-- Computing confounds ...")
76     hv_confounds = mem.cache(nilearn.image.high_variance_confounds)(
77         func_filename)
78 
79     print("-- Computing region signals ...")
80     masker = nilearn.input_data.NiftiMapsMasker(
81         msdl_atlas_dataset.maps, resampling_target="maps", detrend=True,
82         low_pass=None, high_pass=0.01, t_r=2.5, standardize=True,
83         memory=mem, memory_level=1, verbose=1)
84     region_ts = masker.fit_transform(func_filename,
85                                      confounds=[hv_confounds,
86                                                 confound_filename])
87     subjects.append(region_ts)
88 
89 # Computing group-sparse precision matrices ###################################
90 print("-- Computing group-sparse precision matrices ...")
91 from nilearn.group_sparse_covariance import GroupSparseCovarianceCV
92 gsc = GroupSparseCovarianceCV(verbose=2, n_jobs=3)
93 gsc.fit(subjects)
94 
95 print("-- Computing graph-lasso precision matrices ...")
96 from sklearn import covariance
97 gl = covariance.GraphLassoCV(n_jobs=3)
98 gl.fit(subjects[plotted_subject])
99 
100 # Displaying results ##########################################################
101 print("-- Displaying results")
102 title = "{0:d} GroupSparseCovariance $\\alpha={1:.2e}$".format(plotted_subject,
103                                                      gsc.alpha_)
104 plot_matrices(gsc.covariances_[..., plotted_subject],
105               gsc.precisions_[..., plotted_subject], title)
106 
107 title = "{0:d} GraphLasso $\\alpha={1:.2e}$".format(plotted_subject,
108                                                      gl.alpha_)
109 plot_matrices(gl.covariance_, gl.precision_, title)
110 
111 plt.show()
112
```
--- 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/connectivity/plot_adhd_covariance.py b/examples/connectivity/plot_adhd_covariance.py
--- a/examples/connectivity/plot_adhd_covariance.py
+++ b/examples/connectivity/plot_adhd_covariance.py
@@ -20,13 +20,11 @@
 def plot_matrices(cov, prec, title):
     """Plot covariance and precision matrices, for a given processing. """
 
+    # Compute sparsity pattern
+    sparsity = (prec == 0)
+    
     prec = prec.copy()  # avoid side effects
 
-    # Display sparsity pattern
-    sparsity = prec == 0
-    plt.figure()
-    plt.imshow(sparsity, interpolation="nearest")
-    plt.title("%s / sparsity" % title)
 
     # Put zeros on the diagonal, for graph clarity.
     size = prec.shape[0]
@@ -39,6 +37,11 @@
               vmin=-1, vmax=1, cmap=plt.cm.get_cmap("bwr"))
     plt.colorbar()
     plt.title("%s / covariance" % title)
+    
+    # Display sparsity pattern
+    plt.figure()
+    plt.imshow(sparsity, interpolation="nearest")
+    plt.title("%s / sparsity" % title)
 
     # Display precision matrix
     plt.figure()

{"golden_diff": "diff --git a/examples/connectivity/plot_adhd_covariance.py b/examples/connectivity/plot_adhd_covariance.py\n--- a/examples/connectivity/plot_adhd_covariance.py\n+++ b/examples/connectivity/plot_adhd_covariance.py\n@@ -20,13 +20,11 @@\n def plot_matrices(cov, prec, title):\n     \"\"\"Plot covariance and precision matrices, for a given processing. \"\"\"\n \n+    # Compute sparsity pattern\n+    sparsity = (prec == 0)\n+    \n     prec = prec.copy()  # avoid side effects\n \n-    # Display sparsity pattern\n-    sparsity = prec == 0\n-    plt.figure()\n-    plt.imshow(sparsity, interpolation=\"nearest\")\n-    plt.title(\"%s / sparsity\" % title)\n \n     # Put zeros on the diagonal, for graph clarity.\n     size = prec.shape[0]\n@@ -39,6 +37,11 @@\n               vmin=-1, vmax=1, cmap=plt.cm.get_cmap(\"bwr\"))\n     plt.colorbar()\n     plt.title(\"%s / covariance\" % title)\n+    \n+    # Display sparsity pattern\n+    plt.figure()\n+    plt.imshow(sparsity, interpolation=\"nearest\")\n+    plt.title(\"%s / sparsity\" % title)\n \n     # Display precision matrix\n     plt.figure()\n", "issue": "Ugly side plots in doc\nThey mask part of the code they are supposed to illustrate. See an example [here](http://nilearn.github.io/building_blocks/data_preparation.html#computing-the-mask).\n\n![ugly-side-plots](https://cloud.githubusercontent.com/assets/1680079/5974579/266c30d8-a876-11e4-8830-febff99ed1dc.png)\n\n", "before_files": [{"content": "\"\"\"\nComputation of covariance matrix between brain regions\n======================================================\n\nThis example shows how to extract signals from regions defined by an atlas,\nand to estimate a covariance matrix based on these signals.\n\"\"\"\n \nplotted_subject = 0  # subject to plot\n\n\nimport matplotlib.pyplot as plt\nimport matplotlib\n# Copied from matplotlib 1.2.0 for matplotlib 0.99 compatibility.\n_bwr_data = ((0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0))\nplt.cm.register_cmap(cmap=matplotlib.colors.LinearSegmentedColormap.from_list(\n    \"bwr\", _bwr_data))\n\n\ndef plot_matrices(cov, prec, title):\n    \"\"\"Plot covariance and precision matrices, for a given processing. \"\"\"\n\n    prec = prec.copy()  # avoid side effects\n\n    # Display sparsity pattern\n    sparsity = prec == 0\n    plt.figure()\n    plt.imshow(sparsity, interpolation=\"nearest\")\n    plt.title(\"%s / sparsity\" % title)\n\n    # Put zeros on the diagonal, for graph clarity.\n    size = prec.shape[0]\n    prec[range(size), range(size)] = 0\n    span = max(abs(prec.min()), abs(prec.max()))\n\n    # Display covariance matrix\n    plt.figure()\n    plt.imshow(cov, interpolation=\"nearest\",\n              vmin=-1, vmax=1, cmap=plt.cm.get_cmap(\"bwr\"))\n    plt.colorbar()\n    plt.title(\"%s / covariance\" % title)\n\n    # Display precision matrix\n    plt.figure()\n    plt.imshow(prec, interpolation=\"nearest\",\n              vmin=-span, vmax=span,\n              cmap=plt.cm.get_cmap(\"bwr\"))\n    plt.colorbar()\n    plt.title(\"%s / precision\" % title)\n\n\n# Fetching datasets ###########################################################\nprint(\"-- Fetching datasets ...\")\nfrom nilearn import datasets\nmsdl_atlas_dataset = datasets.fetch_msdl_atlas()\nadhd_dataset = datasets.fetch_adhd()\n\n# Extracting region signals ###################################################\nimport nilearn.image\nimport nilearn.input_data\n\nfrom sklearn.externals.joblib import Memory\nmem = Memory(\".\")\n\n# Number of subjects to consider for group-sparse covariance\nn_subjects = 10\nsubjects = []\n\nfunc_filenames = adhd_dataset.func\nconfound_filenames = adhd_dataset.confounds\nfor func_filename, confound_filename in zip(func_filenames,\n                                            confound_filenames):\n    print(\"Processing file %s\" % func_filename)\n\n    print(\"-- Computing confounds ...\")\n    hv_confounds = mem.cache(nilearn.image.high_variance_confounds)(\n        func_filename)\n\n    print(\"-- Computing region signals ...\")\n    masker = nilearn.input_data.NiftiMapsMasker(\n        msdl_atlas_dataset.maps, resampling_target=\"maps\", detrend=True,\n        low_pass=None, high_pass=0.01, t_r=2.5, standardize=True,\n        memory=mem, memory_level=1, verbose=1)\n    region_ts = masker.fit_transform(func_filename,\n                                     confounds=[hv_confounds,\n                                                confound_filename])\n    subjects.append(region_ts)\n\n# Computing group-sparse precision matrices ###################################\nprint(\"-- Computing group-sparse precision matrices ...\")\nfrom nilearn.group_sparse_covariance import GroupSparseCovarianceCV\ngsc = GroupSparseCovarianceCV(verbose=2, n_jobs=3)\ngsc.fit(subjects)\n\nprint(\"-- Computing graph-lasso precision matrices ...\")\nfrom sklearn import covariance\ngl = covariance.GraphLassoCV(n_jobs=3)\ngl.fit(subjects[plotted_subject])\n\n# Displaying results ##########################################################\nprint(\"-- Displaying results\")\ntitle = \"{0:d} GroupSparseCovariance $\\\\alpha={1:.2e}$\".format(plotted_subject,\n                                                     gsc.alpha_)\nplot_matrices(gsc.covariances_[..., plotted_subject],\n              gsc.precisions_[..., plotted_subject], title)\n\ntitle = \"{0:d} GraphLasso $\\\\alpha={1:.2e}$\".format(plotted_subject,\n                                                     gl.alpha_)\nplot_matrices(gl.covariance_, gl.precision_, title)\n\nplt.show()\n", "path": "examples/connectivity/plot_adhd_covariance.py"}], "after_files": [{"content": "\"\"\"\nComputation of covariance matrix between brain regions\n======================================================\n\nThis example shows how to extract signals from regions defined by an atlas,\nand to estimate a covariance matrix based on these signals.\n\"\"\"\n \nplotted_subject = 0  # subject to plot\n\n\nimport matplotlib.pyplot as plt\nimport matplotlib\n# Copied from matplotlib 1.2.0 for matplotlib 0.99 compatibility.\n_bwr_data = ((0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0))\nplt.cm.register_cmap(cmap=matplotlib.colors.LinearSegmentedColormap.from_list(\n    \"bwr\", _bwr_data))\n\n\ndef plot_matrices(cov, prec, title):\n    \"\"\"Plot covariance and precision matrices, for a given processing. \"\"\"\n\n    # Compute sparsity pattern\n    sparsity = (prec == 0)\n    \n    prec = prec.copy()  # avoid side effects\n\n\n    # Put zeros on the diagonal, for graph clarity.\n    size = prec.shape[0]\n    prec[range(size), range(size)] = 0\n    span = max(abs(prec.min()), abs(prec.max()))\n\n    # Display covariance matrix\n    plt.figure()\n    plt.imshow(cov, interpolation=\"nearest\",\n              vmin=-1, vmax=1, cmap=plt.cm.get_cmap(\"bwr\"))\n    plt.colorbar()\n    plt.title(\"%s / covariance\" % title)\n    \n    # Display sparsity pattern\n    plt.figure()\n    plt.imshow(sparsity, interpolation=\"nearest\")\n    plt.title(\"%s / sparsity\" % title)\n\n    # Display precision matrix\n    plt.figure()\n    plt.imshow(prec, interpolation=\"nearest\",\n              vmin=-span, vmax=span,\n              cmap=plt.cm.get_cmap(\"bwr\"))\n    plt.colorbar()\n    plt.title(\"%s / precision\" % title)\n\n\n# Fetching datasets ###########################################################\nprint(\"-- Fetching datasets ...\")\nfrom nilearn import datasets\nmsdl_atlas_dataset = datasets.fetch_msdl_atlas()\nadhd_dataset = datasets.fetch_adhd()\n\n# Extracting region signals ###################################################\nimport nilearn.image\nimport nilearn.input_data\n\nfrom sklearn.externals.joblib import Memory\nmem = Memory(\".\")\n\n# Number of subjects to consider for group-sparse covariance\nn_subjects = 10\nsubjects = []\n\nfunc_filenames = adhd_dataset.func\nconfound_filenames = adhd_dataset.confounds\nfor func_filename, confound_filename in zip(func_filenames,\n                                            confound_filenames):\n    print(\"Processing file %s\" % func_filename)\n\n    print(\"-- Computing confounds ...\")\n    hv_confounds = mem.cache(nilearn.image.high_variance_confounds)(\n        func_filename)\n\n    print(\"-- Computing region signals ...\")\n    masker = nilearn.input_data.NiftiMapsMasker(\n        msdl_atlas_dataset.maps, resampling_target=\"maps\", detrend=True,\n        low_pass=None, high_pass=0.01, t_r=2.5, standardize=True,\n        memory=mem, memory_level=1, verbose=1)\n    region_ts = masker.fit_transform(func_filename,\n                                     confounds=[hv_confounds,\n                                                confound_filename])\n    subjects.append(region_ts)\n\n# Computing group-sparse precision matrices ###################################\nprint(\"-- Computing group-sparse precision matrices ...\")\nfrom nilearn.group_sparse_covariance import GroupSparseCovarianceCV\ngsc = GroupSparseCovarianceCV(verbose=2, n_jobs=3)\ngsc.fit(subjects)\n\nprint(\"-- Computing graph-lasso precision matrices ...\")\nfrom sklearn import covariance\ngl = covariance.GraphLassoCV(n_jobs=3)\ngl.fit(subjects[plotted_subject])\n\n# Displaying results ##########################################################\nprint(\"-- Displaying results\")\ntitle = \"{0:d} GroupSparseCovariance $\\\\alpha={1:.2e}$\".format(plotted_subject,\n                                                     gsc.alpha_)\nplot_matrices(gsc.covariances_[..., plotted_subject],\n              gsc.precisions_[..., plotted_subject], title)\n\ntitle = \"{0:d} GraphLasso $\\\\alpha={1:.2e}$\".format(plotted_subject,\n                                                     gl.alpha_)\nplot_matrices(gl.covariance_, gl.precision_, title)\n\nplt.show()\n", "path": "examples/connectivity/plot_adhd_covariance.py"}]}