deploy: braindecode/braindecode@0c205e2

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 9ec5c1237f59370fcf4be3165728dd0a
+config: dfa3b204b4ec02ee979e4bf266ee2b9c
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/090305d06248840b75133975e5121f41/plot_sleep_staging_chambon2018.ipynb

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import preprocess, Preprocessor, scale\n\nhigh_cut_hz = 30\n\npreprocessors = [\n    Preprocessor(scale, factor=1e6, apply_on_array=True),\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
       ]
     },
     {

dev/_downloads/1f2407fc0459906c808c847bdcd84ccf/plot_sleep_staging_eldele2021.py

@@ -52,7 +52,7 @@
 factor = 1e6
 
 preprocessors = [
-    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV
+    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV
     Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)
 ]
 

dev/_downloads/408ca4ffd0ea1f76faa9ef602734ac94/plot_tuh_eeg_corpus.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Lukas Gemein <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport tempfile\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport mne\n\nfrom braindecode.datasets import TUH\nfrom braindecode.preprocessing import (\n    preprocess, Preprocessor, create_fixed_length_windows, scale as multiply)\n\nmne.set_log_level('ERROR')  # avoid messages every time a window is extracted"
+        "# Author: Lukas Gemein <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport tempfile\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport mne\n\nfrom braindecode.datasets import TUH\nfrom braindecode.preprocessing import (\n    preprocess, Preprocessor, create_fixed_length_windows)\nfrom numpy import multiply\n\nmne.set_log_level('ERROR')  # avoid messages every time a window is extracted"
       ]
     },
     {
@@ -123,7 +123,7 @@
       },
       "outputs": [],
       "source": [
-        "def custom_rename_channels(raw, mapping):\n    # rename channels which are dependent on referencing:\n    # le: EEG 01-LE, ar: EEG 01-REF\n    # mne fails if the mapping contains channels as keys that are not present\n    # in the raw\n    reference = raw.ch_names[0].split('-')[-1].lower()\n    assert reference in ['le', 'ref'], 'unexpected referencing'\n    reference = 'le' if reference == 'le' else 'ar'\n    raw.rename_channels(mapping[reference])\n\n\ndef custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):\n    # crop recordings to tmin \u2013 tmax. can be incomplete if recording\n    # has lower duration than tmax\n    # by default mne fails if tmax is bigger than duration\n    tmax = min((raw.n_times - 1) / raw.info['sfreq'], tmax)\n    raw.crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax)\n\n\ntmin = 1 * 60\ntmax = 6 * 60\nsfreq = 100\n\npreprocessors = [\n    Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,\n                 apply_on_array=False),\n    Preprocessor('set_eeg_reference', ref_channels='average', ch_type='eeg'),\n    Preprocessor(custom_rename_channels, mapping=ch_mapping,\n                 apply_on_array=False),\n    Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),\n    Preprocessor(multiply, factor=1e6, apply_on_array=True),\n    Preprocessor(np.clip, a_min=-800, a_max=800, apply_on_array=True),\n    Preprocessor('resample', sfreq=sfreq),\n]"
+        "def custom_rename_channels(raw, mapping):\n    # rename channels which are dependent on referencing:\n    # le: EEG 01-LE, ar: EEG 01-REF\n    # mne fails if the mapping contains channels as keys that are not present\n    # in the raw\n    reference = raw.ch_names[0].split('-')[-1].lower()\n    assert reference in ['le', 'ref'], 'unexpected referencing'\n    reference = 'le' if reference == 'le' else 'ar'\n    raw.rename_channels(mapping[reference])\n\n\ndef custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):\n    # crop recordings to tmin \u2013 tmax. can be incomplete if recording\n    # has lower duration than tmax\n    # by default mne fails if tmax is bigger than duration\n    tmax = min((raw.n_times - 1) / raw.info['sfreq'], tmax)\n    raw.crop(tmin=tmin, tmax=tmax, include_tmax=include_tmax)\n\n\ntmin = 1 * 60\ntmax = 6 * 60\nsfreq = 100\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,\n                 apply_on_array=False),\n    Preprocessor('set_eeg_reference', ref_channels='average', ch_type='eeg'),\n    Preprocessor(custom_rename_channels, mapping=ch_mapping,\n                 apply_on_array=False),\n    Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),\n    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV\n    Preprocessor(np.clip, a_min=-800, a_max=800, apply_on_array=True),\n    Preprocessor('resample', sfreq=sfreq),\n]"
       ]
     },
     {

dev/_downloads/84fbcd59a346e5e56758285122dc69e2/plot_sleep_staging_eldele2021.ipynb

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor)),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
+        "from braindecode.preprocessing import preprocess, Preprocessor\nfrom numpy import multiply\n\nhigh_cut_hz = 30\n# Factor to convert from V to uV\nfactor = 1e6\n\npreprocessors = [\n    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV\n    Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)\n]\n\n# Transform the data\npreprocess(dataset, preprocessors)"
       ]
     },
     {

dev/_downloads/d1f211cc350870be312e474c12c8cbcc/plot_sleep_staging_chambon2018.py

@@ -44,12 +44,14 @@
 # a lowpass filter. We omit the downsampling step of [1]_ as the Sleep
 # Physionet data is already sampled at a lower 100 Hz.
 
-from braindecode.preprocessing import preprocess, Preprocessor, scale
+from braindecode.preprocessing import preprocess, Preprocessor
+from numpy import multiply
 
 high_cut_hz = 30
+factor = 1e6
 
 preprocessors = [
-    Preprocessor(scale, factor=1e6, apply_on_array=True),
+    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV
     Preprocessor('filter', l_freq=None, h_freq=high_cut_hz)
 ]
 

dev/_downloads/f3ae4448a9b1ee7edbbfd64d7ec838fb/plot_tuh_eeg_corpus.py

@@ -25,7 +25,8 @@
 
 from braindecode.datasets import TUH
 from braindecode.preprocessing import (
-    preprocess, Preprocessor, create_fixed_length_windows, scale as multiply)
+    preprocess, Preprocessor, create_fixed_length_windows)
+from numpy import multiply
 
 mne.set_log_level('ERROR')  # avoid messages every time a window is extracted
 
@@ -243,6 +244,7 @@ def custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):
 tmin = 1 * 60
 tmax = 6 * 60
 sfreq = 100
+factor = 1e6
 
 preprocessors = [
     Preprocessor(custom_crop, tmin=tmin, tmax=tmax, include_tmax=False,
@@ -251,7 +253,7 @@ def custom_crop(raw, tmin=0.0, tmax=None, include_tmax=True):
     Preprocessor(custom_rename_channels, mapping=ch_mapping,
                  apply_on_array=False),
     Preprocessor('pick_channels', ch_names=short_ch_names, ordered=True),
-    Preprocessor(multiply, factor=1e6, apply_on_array=True),
+    Preprocessor(lambda data: multiply(data, factor), apply_on_array=True),  # Convert from V to uV
     Preprocessor(np.clip, a_min=-800, a_max=800, apply_on_array=True),
     Preprocessor('resample', sfreq=sfreq),
 ]

dev/_static/documentation_options.js

@@ -1,5 +1,5 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: '0.7',
+    VERSION: '0.8',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',

dev/_static/versions.json

@@ -1,14 +1,19 @@
 [
     {
-        "name": "0.8 (devel)",
+        "name": "0.9 (devel)",
         "version": "dev",
         "url": "https://braindecode.org/dev/"
     },
     {
-        "name": "0.7 (stable)",
-        "version": "stable",
+        "name": "0.8 (stable)",
+        "version": "dev",
         "url": "https://braindecode.org/stable/"
     },
+    {
+        "name": "0.7",
+        "version": "0.7",
+        "url": "https://braindecode.org/0.7/"
+    },
     {
         "name": "0.6",
         "version": "0.6",

dev/api.html

@@ -8,7 +8,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>API Reference &#8212; Braindecode 0.7 documentation</title>
+    <title>API Reference &#8212; Braindecode 0.8 documentation</title>
 
 
 
@@ -41,7 +41,7 @@
 <link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=365ca57ee442770a23c6" />
   <script src="_static/vendor/fontawesome/6.1.2/js/all.min.js?digest=365ca57ee442770a23c6"></script>
 
-    <script src="_static/documentation_options.js?v=df5ae754"></script>
+    <script src="_static/documentation_options.js?v=aad85e6b"></script>
     <script src="_static/doctools.js?v=888ff710"></script>
     <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/design-tabs.js?v=36754332"></script>
@@ -62,7 +62,7 @@
     <script>
         DOCUMENTATION_OPTIONS.theme_version = '0.14.3';
         DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://braindecode.org/stable/_static/versions.json';
-        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.7';
+        DOCUMENTATION_OPTIONS.theme_switcher_version_match = '0.8';
         DOCUMENTATION_OPTIONS.show_version_warning_banner = false;
         </script>
     <link rel="index" title="Index" href="genindex.html" />
@@ -469,7 +469,6 @@
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.create_windows_from_target_channels.html">braindecode.preprocessing.create_windows_from_target_channels</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.exponential_moving_demean.html">braindecode.preprocessing.exponential_moving_demean</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.exponential_moving_standardize.html">braindecode.preprocessing.exponential_moving_standardize</a></li>
-<li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.scale.html">braindecode.preprocessing.scale</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.filterbank.html">braindecode.preprocessing.filterbank</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.preprocess.html">braindecode.preprocessing.preprocess</a></li>
 <li class="toctree-l1"><a class="reference internal" href="generated/braindecode.preprocessing.Preprocessor.html">braindecode.preprocessing.Preprocessor</a></li>
@@ -801,34 +800,31 @@ <h2><a class="toc-backref" href="#id13" role="doc-backlink">Preprocessing</a><a
 <tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.exponential_moving_standardize.html#braindecode.preprocessing.exponential_moving_standardize" title="braindecode.preprocessing.exponential_moving_standardize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exponential_moving_standardize</span></code></a>(data[, ...])</p></td>
 <td><p>Perform exponential moving standardization.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.scale.html#braindecode.preprocessing.scale" title="braindecode.preprocessing.scale"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scale</span></code></a>(data, factor)</p></td>
-<td><p>Scale continuous or windowed data in-place</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.filterbank.html#braindecode.preprocessing.filterbank" title="braindecode.preprocessing.filterbank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">filterbank</span></code></a>(raw, frequency_bands[, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.filterbank.html#braindecode.preprocessing.filterbank" title="braindecode.preprocessing.filterbank"><code class="xref py py-obj docutils literal notranslate"><span class="pre">filterbank</span></code></a>(raw, frequency_bands[, ...])</p></td>
 <td><p>Applies multiple bandpass filters to the signals in raw.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code></a>(concat_ds, preprocessors[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess"><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code></a>(concat_ds, preprocessors[, ...])</p></td>
 <td><p>Apply preprocessors to a concat dataset.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Preprocessor</span></code></a>(fn, *[, apply_on_array])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Preprocessor</span></code></a>(fn, *[, apply_on_array])</p></td>
 <td><p>Preprocessor for an MNE Raw or Epochs object.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Resample.html#braindecode.preprocessing.Resample" title="braindecode.preprocessing.Resample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Resample</span></code></a>([up, down, npad, axis, window, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Resample.html#braindecode.preprocessing.Resample" title="braindecode.preprocessing.Resample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Resample</span></code></a>([up, down, npad, axis, window, ...])</p></td>
 <td><p>Resample an array.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.DropChannels.html#braindecode.preprocessing.DropChannels" title="braindecode.preprocessing.DropChannels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DropChannels</span></code></a>(ch_names[, on_missing])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.DropChannels.html#braindecode.preprocessing.DropChannels" title="braindecode.preprocessing.DropChannels"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DropChannels</span></code></a>(ch_names[, on_missing])</p></td>
 <td><p>Drop channel(s).</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.SetEEGReference.html#braindecode.preprocessing.SetEEGReference" title="braindecode.preprocessing.SetEEGReference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SetEEGReference</span></code></a>([ref_channels, projection, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.SetEEGReference.html#braindecode.preprocessing.SetEEGReference" title="braindecode.preprocessing.SetEEGReference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SetEEGReference</span></code></a>([ref_channels, projection, ...])</p></td>
 <td><p>Specify which reference to use for EEG data.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Filter.html#braindecode.preprocessing.Filter" title="braindecode.preprocessing.Filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Filter</span></code></a>(l_freq, h_freq[, picks, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Filter.html#braindecode.preprocessing.Filter" title="braindecode.preprocessing.Filter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Filter</span></code></a>(l_freq, h_freq[, picks, ...])</p></td>
 <td><p>Filter a subset of channels.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Pick.html#braindecode.preprocessing.Pick" title="braindecode.preprocessing.Pick"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Pick</span></code></a>(picks[, exclude, verbose])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Pick.html#braindecode.preprocessing.Pick" title="braindecode.preprocessing.Pick"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Pick</span></code></a>(picks[, exclude, verbose])</p></td>
 <td><p>Pick a subset of channels.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Crop.html#braindecode.preprocessing.Crop" title="braindecode.preprocessing.Crop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Crop</span></code></a>([tmin, tmax, include_tmax, verbose])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="generated/braindecode.preprocessing.Crop.html#braindecode.preprocessing.Crop" title="braindecode.preprocessing.Crop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Crop</span></code></a>([tmin, tmax, include_tmax, verbose])</p></td>
 <td><p>Crop raw data file.</p></td>
 </tr>
 </tbody>