We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
UserWarning in decoding_rsa example
Running this example, I get the following warning 
    
    decoding_rsa.py:94: RuntimeWarning: More events than colors available. You should pass a list of unique colors.

--- 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/decoding/decoding_rsa.py`
Content:
```
1 """
2 
3 .. _rsa_noplot:
4 
5 ====================================
6 Representational Similarity Analysis
7 ====================================
8 
9 Representational Similarity Analysis is used to perform summary statistics
10 on supervised classifications where the number of classes is relatively high.
11 It consists in characterizing the structure of the confusion matrix to infer
12 the similarity between brain responses and serves as a proxy for characterizing
13 the space of mental representations [1]_ [2]_ [3]_.
14 
15 In this example, we perform RSA on responses to 24 object images (among
16 a list of 92 images). Subjects were presented with images of human, animal
17 and inanimate objects [4]_. Here we use the 24 unique images of faces
18 and body parts.
19 
20 .. note:: this example will download a very large (~6GB) file, so we will not
21           build the images below.
22 
23 References
24 ----------
25 
26 .. [1] Shepard, R. "Multidimensional scaling, tree-fitting, and clustering."
27        Science 210.4468 (1980): 390-398.
28 .. [2] Laakso, A. & Cottrell, G.. "Content and cluster analysis:
29        assessing representational similarity in neural systems." Philosophical
30        psychology 13.1 (2000): 47-76.
31 .. [3] Kriegeskorte, N., Marieke, M., & Bandettini.  P. "Representational
32        similarity analysis-connecting the branches of systems neuroscience."
33        Frontiers in systems neuroscience 2 (2008): 4.
34 .. [4] Cichy, R. M., Pantazis, D., & Oliva, A. "Resolving human object
35        recognition in space and time." Nature neuroscience (2014): 17(3),
36        455-462.
37 """
38 
39 # Authors: Jean-Remi King <jeanremi.king@gmail.com>
40 #          Jaakko Leppakangas <jaeilepp@student.jyu.fi>
41 #          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
42 #
43 # License: BSD (3-clause)
44 
45 import os.path as op
46 import numpy as np
47 from pandas import read_csv
48 import matplotlib.pyplot as plt
49 
50 from sklearn.model_selection import StratifiedKFold
51 from sklearn.pipeline import make_pipeline
52 from sklearn.preprocessing import StandardScaler
53 from sklearn.linear_model import LogisticRegression
54 from sklearn.metrics import roc_auc_score
55 from sklearn.manifold import MDS
56 
57 import mne
58 from mne.io import read_raw_fif, concatenate_raws
59 from mne.datasets import visual_92_categories
60 
61 print(__doc__)
62 
63 data_path = visual_92_categories.data_path()
64 
65 # Define stimulus - trigger mapping
66 fname = op.join(data_path, 'visual_stimuli.csv')
67 conds = read_csv(fname)
68 print(conds.head(5))
69 
70 ##############################################################################
71 # Let's restrict the number of conditions to speed up computation
72 max_trigger = 24
73 conds = conds[:max_trigger]  # take only the first 24 rows
74 
75 ##############################################################################
76 # Define stimulus - trigger mapping
77 conditions = []
78 for c in conds.values:
79     cond_tags = list(c[:2])
80     cond_tags += [('not-' if i == 0 else '') + conds.columns[k]
81                   for k, i in enumerate(c[2:], 2)]
82     conditions.append('/'.join(map(str, cond_tags)))
83 print(conditions[:10])
84 
85 ##############################################################################
86 # Let's make the event_id dictionary
87 event_id = dict(zip(conditions, conds.trigger + 1))
88 event_id['0/human bodypart/human/not-face/animal/natural']
89 
90 ##############################################################################
91 # Read MEG data
92 n_runs = 4  # 4 for full data (use less to speed up computations)
93 fname = op.join(data_path, 'sample_subject_%i_tsss_mc.fif')
94 raws = [read_raw_fif(fname % block) for block in range(n_runs)]
95 raw = concatenate_raws(raws)
96 
97 events = mne.find_events(raw, min_duration=.002)
98 
99 events = events[events[:, 2] <= max_trigger]
100 mne.viz.plot_events(events, sfreq=raw.info['sfreq'])
101 
102 ##############################################################################
103 # Epoch data
104 picks = mne.pick_types(raw.info, meg=True)
105 epochs = mne.Epochs(raw, events=events, event_id=event_id, baseline=None,
106                     picks=picks, tmin=-.1, tmax=.500, preload=True)
107 
108 ##############################################################################
109 # Let's plot some conditions
110 epochs['face'].average().plot()
111 epochs['not-face'].average().plot()
112 
113 ##############################################################################
114 # Representational Similarity Analysis (RSA) is a neuroimaging-specific
115 # appelation to refer to statistics applied to the confusion matrix
116 # also referred to as the representational dissimilarity matrices (RDM).
117 #
118 # Compared to the approach from Cichy et al. we'll use a multiclass
119 # classifier (Multinomial Logistic Regression) while the paper uses
120 # all pairwise binary classification task to make the RDM.
121 # Also we use here the ROC-AUC as performance metric while the
122 # paper uses accuracy. Finally here for the sake of time we use
123 # RSA on a window of data while Cichy et al. did it for all time
124 # instants separately.
125 
126 # Classify using the average signal in the window 50ms to 300ms
127 # to focus the classifier on the time interval with best SNR.
128 clf = make_pipeline(StandardScaler(),
129                     LogisticRegression(C=1, solver='lbfgs'))
130 X = epochs.copy().crop(0.05, 0.3).get_data().mean(axis=2)
131 y = epochs.events[:, 2]
132 
133 classes = set(y)
134 cv = StratifiedKFold(n_splits=5, random_state=0, shuffle=True)
135 
136 # Compute confusion matrix for each cross-validation fold
137 y_pred = np.zeros((len(y), len(classes)))
138 for train, test in cv.split(X, y):
139     # Fit
140     clf.fit(X[train], y[train])
141     # Probabilistic prediction (necessary for ROC-AUC scoring metric)
142     y_pred[test] = clf.predict_proba(X[test])
143 
144 ##############################################################################
145 # Compute confusion matrix using ROC-AUC
146 confusion = np.zeros((len(classes), len(classes)))
147 for ii, train_class in enumerate(classes):
148     for jj in range(ii, len(classes)):
149         confusion[ii, jj] = roc_auc_score(y == train_class, y_pred[:, jj])
150         confusion[jj, ii] = confusion[ii, jj]
151 
152 ##############################################################################
153 # Plot
154 labels = [''] * 5 + ['face'] + [''] * 11 + ['bodypart'] + [''] * 6
155 fig, ax = plt.subplots(1)
156 im = ax.matshow(confusion, cmap='RdBu_r', clim=[0.3, 0.7])
157 ax.set_yticks(range(len(classes)))
158 ax.set_yticklabels(labels)
159 ax.set_xticks(range(len(classes)))
160 ax.set_xticklabels(labels, rotation=40, ha='left')
161 ax.axhline(11.5, color='k')
162 ax.axvline(11.5, color='k')
163 plt.colorbar(im)
164 plt.tight_layout()
165 plt.show()
166 
167 ##############################################################################
168 # Confusion matrix related to mental representations have been historically
169 # summarized with dimensionality reduction using multi-dimensional scaling [1].
170 # See how the face samples cluster together.
171 fig, ax = plt.subplots(1)
172 mds = MDS(2, random_state=0, dissimilarity='precomputed')
173 chance = 0.5
174 summary = mds.fit_transform(chance - confusion)
175 cmap = plt.get_cmap('rainbow')
176 colors = ['r', 'b']
177 names = list(conds['condition'].values)
178 for color, name in zip(colors, set(names)):
179     sel = np.where([this_name == name for this_name in names])[0]
180     size = 500 if name == 'human face' else 100
181     ax.scatter(summary[sel, 0], summary[sel, 1], s=size,
182                facecolors=color, label=name, edgecolors='k')
183 ax.axis('off')
184 ax.legend(loc='lower right', scatterpoints=1, ncol=2)
185 plt.tight_layout()
186 plt.show()
187
```
--- 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/decoding/decoding_rsa.py b/examples/decoding/decoding_rsa.py
--- a/examples/decoding/decoding_rsa.py
+++ b/examples/decoding/decoding_rsa.py
@@ -97,7 +97,6 @@
 events = mne.find_events(raw, min_duration=.002)
 
 events = events[events[:, 2] <= max_trigger]
-mne.viz.plot_events(events, sfreq=raw.info['sfreq'])
 
 ##############################################################################
 # Epoch data

{"golden_diff": "diff --git a/examples/decoding/decoding_rsa.py b/examples/decoding/decoding_rsa.py\n--- a/examples/decoding/decoding_rsa.py\n+++ b/examples/decoding/decoding_rsa.py\n@@ -97,7 +97,6 @@\n events = mne.find_events(raw, min_duration=.002)\n \n events = events[events[:, 2] <= max_trigger]\n-mne.viz.plot_events(events, sfreq=raw.info['sfreq'])\n \n ##############################################################################\n # Epoch data\n", "issue": "UserWarning in decoding_rsa example\nRunning this example, I get the following warning \r\n    \r\n    decoding_rsa.py:94: RuntimeWarning: More events than colors available. You should pass a list of unique colors.\n", "before_files": [{"content": "\"\"\"\n\n.. _rsa_noplot:\n\n====================================\nRepresentational Similarity Analysis\n====================================\n\nRepresentational Similarity Analysis is used to perform summary statistics\non supervised classifications where the number of classes is relatively high.\nIt consists in characterizing the structure of the confusion matrix to infer\nthe similarity between brain responses and serves as a proxy for characterizing\nthe space of mental representations [1]_ [2]_ [3]_.\n\nIn this example, we perform RSA on responses to 24 object images (among\na list of 92 images). Subjects were presented with images of human, animal\nand inanimate objects [4]_. Here we use the 24 unique images of faces\nand body parts.\n\n.. note:: this example will download a very large (~6GB) file, so we will not\n          build the images below.\n\nReferences\n----------\n\n.. [1] Shepard, R. \"Multidimensional scaling, tree-fitting, and clustering.\"\n       Science 210.4468 (1980): 390-398.\n.. [2] Laakso, A. & Cottrell, G.. \"Content and cluster analysis:\n       assessing representational similarity in neural systems.\" Philosophical\n       psychology 13.1 (2000): 47-76.\n.. [3] Kriegeskorte, N., Marieke, M., & Bandettini.  P. \"Representational\n       similarity analysis-connecting the branches of systems neuroscience.\"\n       Frontiers in systems neuroscience 2 (2008): 4.\n.. [4] Cichy, R. M., Pantazis, D., & Oliva, A. \"Resolving human object\n       recognition in space and time.\" Nature neuroscience (2014): 17(3),\n       455-462.\n\"\"\"\n\n# Authors: Jean-Remi King <jeanremi.king@gmail.com>\n#          Jaakko Leppakangas <jaeilepp@student.jyu.fi>\n#          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n#\n# License: BSD (3-clause)\n\nimport os.path as op\nimport numpy as np\nfrom pandas import read_csv\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.manifold import MDS\n\nimport mne\nfrom mne.io import read_raw_fif, concatenate_raws\nfrom mne.datasets import visual_92_categories\n\nprint(__doc__)\n\ndata_path = visual_92_categories.data_path()\n\n# Define stimulus - trigger mapping\nfname = op.join(data_path, 'visual_stimuli.csv')\nconds = read_csv(fname)\nprint(conds.head(5))\n\n##############################################################################\n# Let's restrict the number of conditions to speed up computation\nmax_trigger = 24\nconds = conds[:max_trigger]  # take only the first 24 rows\n\n##############################################################################\n# Define stimulus - trigger mapping\nconditions = []\nfor c in conds.values:\n    cond_tags = list(c[:2])\n    cond_tags += [('not-' if i == 0 else '') + conds.columns[k]\n                  for k, i in enumerate(c[2:], 2)]\n    conditions.append('/'.join(map(str, cond_tags)))\nprint(conditions[:10])\n\n##############################################################################\n# Let's make the event_id dictionary\nevent_id = dict(zip(conditions, conds.trigger + 1))\nevent_id['0/human bodypart/human/not-face/animal/natural']\n\n##############################################################################\n# Read MEG data\nn_runs = 4  # 4 for full data (use less to speed up computations)\nfname = op.join(data_path, 'sample_subject_%i_tsss_mc.fif')\nraws = [read_raw_fif(fname % block) for block in range(n_runs)]\nraw = concatenate_raws(raws)\n\nevents = mne.find_events(raw, min_duration=.002)\n\nevents = events[events[:, 2] <= max_trigger]\nmne.viz.plot_events(events, sfreq=raw.info['sfreq'])\n\n##############################################################################\n# Epoch data\npicks = mne.pick_types(raw.info, meg=True)\nepochs = mne.Epochs(raw, events=events, event_id=event_id, baseline=None,\n                    picks=picks, tmin=-.1, tmax=.500, preload=True)\n\n##############################################################################\n# Let's plot some conditions\nepochs['face'].average().plot()\nepochs['not-face'].average().plot()\n\n##############################################################################\n# Representational Similarity Analysis (RSA) is a neuroimaging-specific\n# appelation to refer to statistics applied to the confusion matrix\n# also referred to as the representational dissimilarity matrices (RDM).\n#\n# Compared to the approach from Cichy et al. we'll use a multiclass\n# classifier (Multinomial Logistic Regression) while the paper uses\n# all pairwise binary classification task to make the RDM.\n# Also we use here the ROC-AUC as performance metric while the\n# paper uses accuracy. Finally here for the sake of time we use\n# RSA on a window of data while Cichy et al. did it for all time\n# instants separately.\n\n# Classify using the average signal in the window 50ms to 300ms\n# to focus the classifier on the time interval with best SNR.\nclf = make_pipeline(StandardScaler(),\n                    LogisticRegression(C=1, solver='lbfgs'))\nX = epochs.copy().crop(0.05, 0.3).get_data().mean(axis=2)\ny = epochs.events[:, 2]\n\nclasses = set(y)\ncv = StratifiedKFold(n_splits=5, random_state=0, shuffle=True)\n\n# Compute confusion matrix for each cross-validation fold\ny_pred = np.zeros((len(y), len(classes)))\nfor train, test in cv.split(X, y):\n    # Fit\n    clf.fit(X[train], y[train])\n    # Probabilistic prediction (necessary for ROC-AUC scoring metric)\n    y_pred[test] = clf.predict_proba(X[test])\n\n##############################################################################\n# Compute confusion matrix using ROC-AUC\nconfusion = np.zeros((len(classes), len(classes)))\nfor ii, train_class in enumerate(classes):\n    for jj in range(ii, len(classes)):\n        confusion[ii, jj] = roc_auc_score(y == train_class, y_pred[:, jj])\n        confusion[jj, ii] = confusion[ii, jj]\n\n##############################################################################\n# Plot\nlabels = [''] * 5 + ['face'] + [''] * 11 + ['bodypart'] + [''] * 6\nfig, ax = plt.subplots(1)\nim = ax.matshow(confusion, cmap='RdBu_r', clim=[0.3, 0.7])\nax.set_yticks(range(len(classes)))\nax.set_yticklabels(labels)\nax.set_xticks(range(len(classes)))\nax.set_xticklabels(labels, rotation=40, ha='left')\nax.axhline(11.5, color='k')\nax.axvline(11.5, color='k')\nplt.colorbar(im)\nplt.tight_layout()\nplt.show()\n\n##############################################################################\n# Confusion matrix related to mental representations have been historically\n# summarized with dimensionality reduction using multi-dimensional scaling [1].\n# See how the face samples cluster together.\nfig, ax = plt.subplots(1)\nmds = MDS(2, random_state=0, dissimilarity='precomputed')\nchance = 0.5\nsummary = mds.fit_transform(chance - confusion)\ncmap = plt.get_cmap('rainbow')\ncolors = ['r', 'b']\nnames = list(conds['condition'].values)\nfor color, name in zip(colors, set(names)):\n    sel = np.where([this_name == name for this_name in names])[0]\n    size = 500 if name == 'human face' else 100\n    ax.scatter(summary[sel, 0], summary[sel, 1], s=size,\n               facecolors=color, label=name, edgecolors='k')\nax.axis('off')\nax.legend(loc='lower right', scatterpoints=1, ncol=2)\nplt.tight_layout()\nplt.show()\n", "path": "examples/decoding/decoding_rsa.py"}], "after_files": [{"content": "\"\"\"\n\n.. _rsa_noplot:\n\n====================================\nRepresentational Similarity Analysis\n====================================\n\nRepresentational Similarity Analysis is used to perform summary statistics\non supervised classifications where the number of classes is relatively high.\nIt consists in characterizing the structure of the confusion matrix to infer\nthe similarity between brain responses and serves as a proxy for characterizing\nthe space of mental representations [1]_ [2]_ [3]_.\n\nIn this example, we perform RSA on responses to 24 object images (among\na list of 92 images). Subjects were presented with images of human, animal\nand inanimate objects [4]_. Here we use the 24 unique images of faces\nand body parts.\n\n.. note:: this example will download a very large (~6GB) file, so we will not\n          build the images below.\n\nReferences\n----------\n\n.. [1] Shepard, R. \"Multidimensional scaling, tree-fitting, and clustering.\"\n       Science 210.4468 (1980): 390-398.\n.. [2] Laakso, A. & Cottrell, G.. \"Content and cluster analysis:\n       assessing representational similarity in neural systems.\" Philosophical\n       psychology 13.1 (2000): 47-76.\n.. [3] Kriegeskorte, N., Marieke, M., & Bandettini.  P. \"Representational\n       similarity analysis-connecting the branches of systems neuroscience.\"\n       Frontiers in systems neuroscience 2 (2008): 4.\n.. [4] Cichy, R. M., Pantazis, D., & Oliva, A. \"Resolving human object\n       recognition in space and time.\" Nature neuroscience (2014): 17(3),\n       455-462.\n\"\"\"\n\n# Authors: Jean-Remi King <jeanremi.king@gmail.com>\n#          Jaakko Leppakangas <jaeilepp@student.jyu.fi>\n#          Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>\n#\n# License: BSD (3-clause)\n\nimport os.path as op\nimport numpy as np\nfrom pandas import read_csv\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.manifold import MDS\n\nimport mne\nfrom mne.io import read_raw_fif, concatenate_raws\nfrom mne.datasets import visual_92_categories\n\nprint(__doc__)\n\ndata_path = visual_92_categories.data_path()\n\n# Define stimulus - trigger mapping\nfname = op.join(data_path, 'visual_stimuli.csv')\nconds = read_csv(fname)\nprint(conds.head(5))\n\n##############################################################################\n# Let's restrict the number of conditions to speed up computation\nmax_trigger = 24\nconds = conds[:max_trigger]  # take only the first 24 rows\n\n##############################################################################\n# Define stimulus - trigger mapping\nconditions = []\nfor c in conds.values:\n    cond_tags = list(c[:2])\n    cond_tags += [('not-' if i == 0 else '') + conds.columns[k]\n                  for k, i in enumerate(c[2:], 2)]\n    conditions.append('/'.join(map(str, cond_tags)))\nprint(conditions[:10])\n\n##############################################################################\n# Let's make the event_id dictionary\nevent_id = dict(zip(conditions, conds.trigger + 1))\nevent_id['0/human bodypart/human/not-face/animal/natural']\n\n##############################################################################\n# Read MEG data\nn_runs = 4  # 4 for full data (use less to speed up computations)\nfname = op.join(data_path, 'sample_subject_%i_tsss_mc.fif')\nraws = [read_raw_fif(fname % block) for block in range(n_runs)]\nraw = concatenate_raws(raws)\n\nevents = mne.find_events(raw, min_duration=.002)\n\nevents = events[events[:, 2] <= max_trigger]\n\n##############################################################################\n# Epoch data\npicks = mne.pick_types(raw.info, meg=True)\nepochs = mne.Epochs(raw, events=events, event_id=event_id, baseline=None,\n                    picks=picks, tmin=-.1, tmax=.500, preload=True)\n\n##############################################################################\n# Let's plot some conditions\nepochs['face'].average().plot()\nepochs['not-face'].average().plot()\n\n##############################################################################\n# Representational Similarity Analysis (RSA) is a neuroimaging-specific\n# appelation to refer to statistics applied to the confusion matrix\n# also referred to as the representational dissimilarity matrices (RDM).\n#\n# Compared to the approach from Cichy et al. we'll use a multiclass\n# classifier (Multinomial Logistic Regression) while the paper uses\n# all pairwise binary classification task to make the RDM.\n# Also we use here the ROC-AUC as performance metric while the\n# paper uses accuracy. Finally here for the sake of time we use\n# RSA on a window of data while Cichy et al. did it for all time\n# instants separately.\n\n# Classify using the average signal in the window 50ms to 300ms\n# to focus the classifier on the time interval with best SNR.\nclf = make_pipeline(StandardScaler(),\n                    LogisticRegression(C=1, solver='lbfgs'))\nX = epochs.copy().crop(0.05, 0.3).get_data().mean(axis=2)\ny = epochs.events[:, 2]\n\nclasses = set(y)\ncv = StratifiedKFold(n_splits=5, random_state=0, shuffle=True)\n\n# Compute confusion matrix for each cross-validation fold\ny_pred = np.zeros((len(y), len(classes)))\nfor train, test in cv.split(X, y):\n    # Fit\n    clf.fit(X[train], y[train])\n    # Probabilistic prediction (necessary for ROC-AUC scoring metric)\n    y_pred[test] = clf.predict_proba(X[test])\n\n##############################################################################\n# Compute confusion matrix using ROC-AUC\nconfusion = np.zeros((len(classes), len(classes)))\nfor ii, train_class in enumerate(classes):\n    for jj in range(ii, len(classes)):\n        confusion[ii, jj] = roc_auc_score(y == train_class, y_pred[:, jj])\n        confusion[jj, ii] = confusion[ii, jj]\n\n##############################################################################\n# Plot\nlabels = [''] * 5 + ['face'] + [''] * 11 + ['bodypart'] + [''] * 6\nfig, ax = plt.subplots(1)\nim = ax.matshow(confusion, cmap='RdBu_r', clim=[0.3, 0.7])\nax.set_yticks(range(len(classes)))\nax.set_yticklabels(labels)\nax.set_xticks(range(len(classes)))\nax.set_xticklabels(labels, rotation=40, ha='left')\nax.axhline(11.5, color='k')\nax.axvline(11.5, color='k')\nplt.colorbar(im)\nplt.tight_layout()\nplt.show()\n\n##############################################################################\n# Confusion matrix related to mental representations have been historically\n# summarized with dimensionality reduction using multi-dimensional scaling [1].\n# See how the face samples cluster together.\nfig, ax = plt.subplots(1)\nmds = MDS(2, random_state=0, dissimilarity='precomputed')\nchance = 0.5\nsummary = mds.fit_transform(chance - confusion)\ncmap = plt.get_cmap('rainbow')\ncolors = ['r', 'b']\nnames = list(conds['condition'].values)\nfor color, name in zip(colors, set(names)):\n    sel = np.where([this_name == name for this_name in names])[0]\n    size = 500 if name == 'human face' else 100\n    ax.scatter(summary[sel, 0], summary[sel, 1], s=size,\n               facecolors=color, label=name, edgecolors='k')\nax.axis('off')\nax.legend(loc='lower right', scatterpoints=1, ncol=2)\nplt.tight_layout()\nplt.show()\n", "path": "examples/decoding/decoding_rsa.py"}]}