deploy: braindecode/braindecode@aaebf80

Author: bruAristimunha

master/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -1,12 +1,17 @@
 [
     {
-        "name": "0.7 (experimental)",
+        "name": "0.8 (devel)",
         "version": "dev",
-        "url": "https://braindecode.org/master/"
+        "url": "https://braindecode.org/dev/"
     },
     {
-        "name": "0.6",
+        "name": "0.7 (stable)",
         "version": "stable",
+        "url": "https://braindecode.org/stable/"
+    },
+    {
+        "name": "0.6",
+        "version": "0.6",
         "url": "https://braindecode.org/0.6/"
     }
-]
+]

master/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -454,7 +454,7 @@
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
 
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7fc5c792d250&gt;
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f8c9ff57f50&gt;
 </pre></div>
 </div>
 <section id="create-model-and-compute-windowing-parameters">
@@ -623,10 +623,10 @@ <h2>Split the dataset<a class="headerlink" href="#split-the-dataset" title="Perm
   warnings.warn(warning_msg, SkorchWarning)
   epoch    train_accuracy    train_loss    valid_accuracy    valid_loss      lr      dur
 -------  ----------------  ------------  ----------------  ------------  ------  -------
-      1            0.2500        1.4486            0.2500        5.4385  0.0006  16.9464
-      2            0.2569        1.2315            0.2500        3.6823  0.0005  16.7438
-      3            0.3958        1.1476            0.3611        2.2702  0.0002  16.7334
-      4            0.4340        1.1225            0.4410        1.4375  0.0000  16.6981
+      1            0.2500        1.4486            0.2500        5.4385  0.0006  16.9399
+      2            0.2569        1.2315            0.2500        3.6823  0.0005  16.6699
+      3            0.3958        1.1476            0.3611        2.2702  0.0002  16.6633
+      4            0.4340        1.1225            0.4410        1.4375  0.0000  16.6739
 
 &lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
   module_=ShallowFBCSPNet(
@@ -723,8 +723,8 @@ <h2>Plot Confusion Matrix<a class="headerlink" href="#plot-confusion-matrix" tit
 <img src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_2a_moabb_cropped_002.png" alt="plot bcic iv 2a moabb cropped" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Figure size 800x550 with 1 Axes&gt;
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  37.668 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  876 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  35.288 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  815 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-bcic-iv-2a-moabb-cropped-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/995f58ef1b3d29dce64caee24e5bf457/plot_bcic_iv_2a_moabb_cropped.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_2a_moabb_cropped.py</span></code></a></p>

master/_images/sphx_glr_plot_benchmark_preprocessing_001.png

@@ -424,7 +424,7 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>/home/runner/work/braindecode/braindecode/braindecode/preprocessing/preprocess.py:55: UserWarning: Preprocessing choices with lambda functions cannot be saved.
   warn(&#39;Preprocessing choices with lambda functions cannot be saved.&#39;)
 
-&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7fc5d5086b90&gt;
+&lt;braindecode.datasets.moabb.MOABBDataset object at 0x7f8c9da5a190&gt;
 </pre></div>
 </div>
 </section>
@@ -557,10 +557,10 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_accuracy    train_loss    valid_accuracy    valid_loss      lr     dur
 -------  ----------------  ------------  ----------------  ------------  ------  ------
-      1            0.2535        1.6382            0.2500        5.2101  0.0006  6.4145
-      2            0.2708        1.2828            0.2465        5.6990  0.0005  5.8541
-      3            0.2604        1.1267            0.2500        5.0720  0.0002  5.8323
-      4            0.2708        1.1386            0.2500        4.2096  0.0000  5.8476
+      1            0.2535        1.6382            0.2500        5.2101  0.0006  5.8677
+      2            0.2708        1.2828            0.2465        5.6990  0.0005  5.7424
+      3            0.2604        1.1267            0.2500        5.0720  0.0002  5.7687
+      4            0.2708        1.1386            0.2500        4.2096  0.0000  5.7511
 
 &lt;class &#39;braindecode.classifier.EEGClassifier&#39;&gt;[initialized](
   module_=ShallowFBCSPNet(
@@ -651,8 +651,8 @@ <h2>Plot Confusion Matrix<a class="headerlink" href="#plot-confusion-matrix" tit
 <img src="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_2a_moabb_trial_002.png" alt="plot bcic iv 2a moabb trial" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;Figure size 800x550 with 1 Axes&gt;
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  39.581 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  709 MB</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  37.962 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  596 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-bcic-iv-2a-moabb-trial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/475edf2d5ed60b15717de3100f3551a5/plot_bcic_iv_2a_moabb_trial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_2a_moabb_trial.py</span></code></a></p>

master/_images/sphx_glr_plot_benchmark_preprocessing_thumb.png

@@ -464,7 +464,7 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 <a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BaseConcatDataset.html#braindecode.datasets.BaseConcatDataset" title="braindecode.datasets.BaseConcatDataset" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">test_set</span></a><span class="p">,</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">24</span><span class="p">)])</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7fc5d5d08090&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.base.BaseConcatDataset object at 0x7f8c9e409f50&gt;
 </pre></div>
 </div>
 <p>In time series targets setup, targets variables are stored in mne.Raw object as channels
@@ -691,14 +691,14 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
   warnings.warn(warning_msg, SkorchWarning)
   epoch    r2_train    r2_valid    train_loss    valid_loss      lr     dur
 -------  ----------  ----------  ------------  ------------  ------  ------
-      1    -16.9064     -8.3116        2.5378       20.7385  0.0006  1.7719
-      2    -13.7579     -7.1286        1.9596       18.0806  0.0006  1.5010
-      3    -12.7813     -6.7445        1.6787       17.1950  0.0005  1.4806
-      4    -11.8017     -6.3939        1.5638       16.4455  0.0004  1.4700
-      5    -11.2758     -6.1864        1.4237       16.0128  0.0002  1.4742
-      6    -10.5777     -5.9225        1.3549       15.4464  0.0001  1.4713
-      7     -9.8264     -5.6200        1.3132       14.7823  0.0000  1.4785
-      8     -9.0673     -5.3063        1.3316       14.0904  0.0000  1.4664
+      1    -16.9064     -8.3116        2.5378       20.7385  0.0006  1.6250
+      2    -13.7579     -7.1286        1.9596       18.0806  0.0006  1.4415
+      3    -12.7813     -6.7445        1.6787       17.1950  0.0005  1.4142
+      4    -11.8017     -6.3939        1.5638       16.4455  0.0004  1.4135
+      5    -11.2758     -6.1864        1.4237       16.0128  0.0002  1.4191
+      6    -10.5777     -5.9225        1.3549       15.4464  0.0001  1.4107
+      7     -9.8264     -5.6200        1.3132       14.7823  0.0000  1.4050
+      8     -9.0673     -5.3063        1.3316       14.0904  0.0000  1.4084
 </pre></div>
 </div>
 <p>Obtaining predictions and targets for the test, train, and validation dataset</p>
@@ -809,8 +809,8 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" alt="plot bcic iv 4 ecog cropped" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  23.856 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  1163 MB</p>
+<img src="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_4_ecog_cropped_002.png" alt="plot bcic iv 4 ecog cropped" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  21.765 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  1060 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-bcic-iv-4-ecog-cropped-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/dce0c540b9a673ff87c724934b4a0581/plot_bcic_iv_4_ecog_cropped.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_4_ecog_cropped.py</span></code></a></p>

master/_images/sphx_glr_plot_sleep_staging_usleep_001.png

@@ -446,7 +446,7 @@ <h3>Preprocessing<a class="headerlink" href="#preprocessing" title="Permalink to
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><a href="../generated/braindecode.preprocessing.preprocess.html#braindecode.preprocessing.preprocess" title="braindecode.preprocessing.preprocess" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-function"><span class="n">preprocess</span></a><span class="p">(</span><a href="../generated/braindecode.datasets.BCICompetitionIVDataset4.html#braindecode.datasets.BCICompetitionIVDataset4" title="braindecode.datasets.BCICompetitionIVDataset4" class="sphx-glr-backref-module-braindecode-datasets sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">dataset</span></a><span class="p">,</span> <span class="p">[</span><a href="../generated/braindecode.preprocessing.Preprocessor.html#braindecode.preprocessing.Preprocessor" title="braindecode.preprocessing.Preprocessor" class="sphx-glr-backref-module-braindecode-preprocessing sphx-glr-backref-type-py-class"><span class="n">Preprocessor</span></a><span class="p">(</span><span class="s1">&#39;crop&#39;</span><span class="p">,</span> <span class="n">tmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tmax</span><span class="o">=</span><span class="mi">30</span><span class="p">)])</span>
 </pre></div>
 </div>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.bcicomp.BCICompetitionIVDataset4 object at 0x7fc5d5f2f690&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;braindecode.datasets.bcicomp.BCICompetitionIVDataset4 object at 0x7f8c9e268690&gt;
 </pre></div>
 </div>
 <p>In time series targets setup, targets variables are stored in mne.Raw object as channels
@@ -644,14 +644,14 @@ <h2>Training<a class="headerlink" href="#training" title="Permalink to this head
 </div>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>  epoch    train_loss    train_pearson_r    train_r2    valid_loss    valid_pearson_r    valid_r2      lr      dur
 -------  ------------  -----------------  ----------  ------------  -----------------  ----------  ------  -------
-      1        2.5083             0.1251     -0.5575        5.6921            -0.1469     -1.6035  0.0006  28.5593
-      2        1.6500             0.3664      0.2126        3.2733            -0.0339     -0.4971  0.0006  27.6052
-      3        1.0988             0.4603      0.4332        2.9570             0.0646     -0.3525  0.0005  27.6161
-      4        1.0579             0.4546      0.5680        3.2952             0.1217     -0.5072  0.0004  28.4497
-      5        0.9256             0.4871      0.6518        3.6003             0.1068     -0.6467  0.0002  27.6639
-      6        0.8316             0.5715      0.7011        3.5173             0.0864     -0.6088  0.0001  27.6245
-      7        0.7715             0.5904      0.7115        3.4734             0.0879     -0.5887  0.0000  27.5925
-      8        0.7617             0.5930      0.7118        3.4683             0.0880     -0.5863  0.0000  27.6574
+      1        2.5083             0.1251     -0.5575        5.6921            -0.1469     -1.6035  0.0006  27.1166
+      2        1.6500             0.3664      0.2126        3.2733            -0.0339     -0.4971  0.0006  26.3199
+      3        1.0988             0.4603      0.4332        2.9570             0.0646     -0.3525  0.0005  26.1417
+      4        1.0579             0.4546      0.5680        3.2952             0.1217     -0.5072  0.0004  26.0801
+      5        0.9256             0.4871      0.6518        3.6003             0.1068     -0.6467  0.0002  26.2077
+      6        0.8316             0.5715      0.7011        3.5173             0.0864     -0.6088  0.0001  26.3798
+      7        0.7715             0.5904      0.7115        3.4734             0.0879     -0.5887  0.0000  26.1678
+      8        0.7617             0.5930      0.7118        3.4683             0.0880     -0.5863  0.0000  26.1309
 </pre></div>
 </div>
 <p>Obtaining predictions and targets for the test, train, and validation dataset</p>
@@ -748,8 +748,8 @@ <h2>Plot Results<a class="headerlink" href="#plot-results" title="Permalink to t
 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="matplotlib.pyplot.tight_layout" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
 </pre></div>
 </div>
-<img src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" alt="plot bcic iv 4 ecog trial" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  21.125 seconds)</p>
-<p><strong>Estimated memory usage:</strong>  1980 MB</p>
+<img src="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" srcset="../_images/sphx_glr_plot_bcic_iv_4_ecog_trial_002.png" alt="plot bcic iv 4 ecog trial" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  0.373 seconds)</p>
+<p><strong>Estimated memory usage:</strong>  1880 MB</p>
 <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-plot-bcic-iv-4-ecog-trial-py">
 <div class="sphx-glr-download sphx-glr-download-python docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/9dd4851efc4f3e6584054e947df0f447/plot_bcic_iv_4_ecog_trial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_bcic_iv_4_ecog_trial.py</span></code></a></p>