diff --git a/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip index a7e9cd0..0df3e86 100644 Binary files a/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip and b/docs/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip differ diff --git a/docs/_downloads/63b412797a45350b72c090ea95e36ee8/plot_Hinss2021_classification.py b/docs/_downloads/63b412797a45350b72c090ea95e36ee8/plot_Hinss2021_classification.py index 76c15d7..a8d8d45 100644 --- a/docs/_downloads/63b412797a45350b72c090ea95e36ee8/plot_Hinss2021_classification.py +++ b/docs/_downloads/63b412797a45350b72c090ea95e36ee8/plot_Hinss2021_classification.py @@ -43,6 +43,7 @@ set_log_level("info") + ############################################################################## # Create util transformer # ---------------------- @@ -103,12 +104,11 @@ def transform(self, X): # To reduce the computation time in the example, we will only use the # first two subjects. -start_subject = 1 -stop_subject = 2 +n__subjects = 2 title = "Datasets: " for dataset in datasets: title = title + " " + dataset.code - dataset.subject_list = dataset.subject_list[start_subject:stop_subject] + dataset.subject_list = dataset.subject_list[:n__subjects] ############################################################################## # Create Pipelines diff --git a/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip index 6842242..69dae6d 100644 Binary files a/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip and b/docs/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip differ diff --git a/docs/_downloads/97a1de59bce682890841bb846e3dd09c/auto_tutorials_jupyter.zip b/docs/_downloads/97a1de59bce682890841bb846e3dd09c/auto_tutorials_jupyter.zip index 121e8a2..c19b132 100644 Binary files a/docs/_downloads/97a1de59bce682890841bb846e3dd09c/auto_tutorials_jupyter.zip and b/docs/_downloads/97a1de59bce682890841bb846e3dd09c/auto_tutorials_jupyter.zip differ diff --git a/docs/_downloads/c0b6836dfec75ec67ce644b1417286cf/plot_Hinss2021_classification.ipynb b/docs/_downloads/c0b6836dfec75ec67ce644b1417286cf/plot_Hinss2021_classification.ipynb index f877593..75f628b 100644 --- a/docs/_downloads/c0b6836dfec75ec67ce644b1417286cf/plot_Hinss2021_classification.ipynb +++ b/docs/_downloads/c0b6836dfec75ec67ce644b1417286cf/plot_Hinss2021_classification.ipynb @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "# Here we define the mne events for the RestingState paradigm.\nevents = dict(easy=2, diff=3)\n# The paradigm is adapted to the P300 paradigm.\nparadigm = RestingStateToP300Adapter(events=events, tmin=0, tmax=0.5)\n# We define a list with the dataset to use\ndatasets = [Hinss2021()]\n\n# To reduce the computation time in the example, we will only use the\n# first two subjects.\nstart_subject = 1\nstop_subject = 2\ntitle = \"Datasets: \"\nfor dataset in datasets:\n title = title + \" \" + dataset.code\n dataset.subject_list = dataset.subject_list[start_subject:stop_subject]" + "# Here we define the mne events for the RestingState paradigm.\nevents = dict(easy=2, diff=3)\n# The paradigm is adapted to the P300 paradigm.\nparadigm = RestingStateToP300Adapter(events=events, tmin=0, tmax=0.5)\n# We define a list with the dataset to use\ndatasets = [Hinss2021()]\n\n# To reduce the computation time in the example, we will only use the\n# first two subjects.\nn__subjects = 2\ntitle = \"Datasets: \"\nfor dataset in datasets:\n title = title + \" \" + dataset.code\n dataset.subject_list = dataset.subject_list[:n__subjects]" ] }, { diff --git a/docs/_downloads/cab7a090c4183ca69dc0cd84d3b04413/auto_tutorials_python.zip b/docs/_downloads/cab7a090c4183ca69dc0cd84d3b04413/auto_tutorials_python.zip index cd76a20..402bc05 100644 Binary files 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a/docs/_images/sphx_glr_plot_within_session_ssvep_002.png b/docs/_images/sphx_glr_plot_within_session_ssvep_002.png index df78fd5..892fa4a 100644 Binary files a/docs/_images/sphx_glr_plot_within_session_ssvep_002.png and b/docs/_images/sphx_glr_plot_within_session_ssvep_002.png differ diff --git a/docs/auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html b/docs/auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html index 6c2ac60..b5faefa 100644 --- a/docs/auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html +++ b/docs/auto_examples/advanced_examples/plot_filterbank_csp_vs_csp.html @@ -826,7 +826,7 @@

Evaluationplt.show() -plot filterbank csp vs csp

Total running time of the script: ( 0 minutes 15.909 seconds)

-

Estimated memory usage: 373 MB

+plot filterbank csp vs csp

Total running time of the script: ( 0 minutes 16.350 seconds)

+

Estimated memory usage: 220 MB

@@ -882,8 +882,8 @@

Load Best Model ParameterTotal running time of the script: ( 0 minutes 16.409 seconds)

-

Estimated memory usage: 252 MB

+

Total running time of the script: ( 0 minutes 16.766 seconds)

+

Estimated memory usage: 124 MB

 @@ -905,7 +903,7 @@

Advanced MNE PipelineAdvanced MNE PipelineAdvanced MNE Pipeline @@ -969,7 +967,7 @@

Numpy-based PipelineNumpy-based PipelineNumpy-based Pipeline @@ -1023,8 +1021,8 @@

Combining Resultsplt.show() -Algorithm comparison

Total running time of the script: ( 0 minutes 45.729 seconds)

-

Estimated memory usage: 365 MB

+Algorithm comparison

Total running time of the script: ( 0 minutes 47.674 seconds)

+

Estimated memory usage: 191 MB

-plot select electrodes resample

Total running time of the script: ( 0 minutes 46.328 seconds)

-

Estimated memory usage: 501 MB

+plot select electrodes resample

Total running time of the script: ( 0 minutes 47.674 seconds)

+

Estimated memory usage: 281 MB

-Algorithm comparison

Total running time of the script: ( 0 minutes 42.594 seconds)

-

Estimated memory usage: 324 MB

+Algorithm comparison

Total running time of the script: ( 0 minutes 47.497 seconds)

+

Estimated memory usage: 182 MB

-Errorbar shows Mean-CI across permutations

Total running time of the script: ( 1 minutes 58.757 seconds)

+Errorbar shows Mean-CI across permutations

Total running time of the script: ( 1 minutes 58.092 seconds)

Estimated memory usage: 11 MB

-Errorbar shows Mean-CI across permutations

Total running time of the script: ( 0 minutes 38.549 seconds)

-

Estimated memory usage: 264 MB

+Errorbar shows Mean-CI across permutations

Total running time of the script: ( 0 minutes 39.205 seconds)

+

Estimated memory usage: 124 MB

-Errorbar shows Mean-CI across permutations

Total running time of the script: ( 1 minutes 8.588 seconds)

+Errorbar shows Mean-CI across permutations

Total running time of the script: ( 1 minutes 9.178 seconds)

Estimated memory usage: 9 MB

@@ -787,9 +786,9 @@

Run evaluationresults = evaluation.process(pipelines) -
2024-07-05 10:38:36,381 INFO MainThread moabb.evaluations.base Processing dataset: Hinss2021
+
2024-07-06 10:47:23,988 INFO MainThread moabb.evaluations.base Processing dataset: Hinss2021
 
-Hinss2021-CrossSession:   0%|          | 0/1 [00:00<?, ?it/s]MNE_DATA is not already configured. It will be set to default location in the home directory - /home/runner/mne_data
+Hinss2021-CrossSession:   0%|          | 0/2 [00:00<?, ?it/s]MNE_DATA is not already configured. It will be set to default location in the home directory - /home/runner/mne_data
 All datasets will be downloaded to this location, if anything is already downloaded, please move manually to this location
 /home/runner/work/moabb/moabb/.venv/lib/python3.9/site-packages/urllib3/connectionpool.py:1061: InsecureRequestWarning: Unverified HTTPS request is being made to host 'zenodo.org'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
   warnings.warn(
@@ -799,136 +798,335 @@ 
Run evaluation
Averaging the session performance:
                      score      time
 pipeline
-Cov+ElSel+TS+LDA  0.846403  1.829310
-ElSel+Cov+TS+LDA  0.687131  0.425413
-Xdawn+Cov+TS+LDA  0.724900  0.382856
+Cov+ElSel+TS+LDA  0.770303  1.785038
+ElSel+Cov+TS+LDA  0.736622  0.421907
+Xdawn+Cov+TS+LDA  0.658810  0.365691
 

 

 
@@ -996,8 +1194,8 @@ 
Key Observations:Total running time of the script: ( 0 minutes  21.424 seconds)

-
Estimated memory usage:  459 MB

+
Total running time of the script: ( 0 minutes  48.987 seconds)

+
Estimated memory usage:  438 MB

 
-
Total running time of the script: ( 0 minutes  27.997 seconds)

-
Estimated memory usage:  274 MB

+
Total running time of the script: ( 0 minutes  30.318 seconds)

+
Estimated memory usage:  286 MB
-plot cross session motor imagery

Total running time of the script: ( 0 minutes 12.174 seconds)

-

Estimated memory usage: 323 MB

+plot cross session motor imagery

Total running time of the script: ( 0 minutes 12.625 seconds)

+

Estimated memory usage: 161 MB

-dataset = Zhou2016, dataset = BNCI2014-001

Total running time of the script: ( 0 minutes 19.101 seconds)

-

Estimated memory usage: 322 MB

+dataset = Zhou2016, dataset = BNCI2014-001

Total running time of the script: ( 0 minutes 19.762 seconds)

+

Estimated memory usage: 153 MB

-plot cross subject ssvep

Total running time of the script: ( 0 minutes 18.534 seconds)

+plot cross subject ssvep

Total running time of the script: ( 0 minutes 23.672 seconds)

Estimated memory usage: 16 MB

-
Using array cache: 0.31 seconds
-Using epochs cache: 0.51 seconds
-Using raw cache: 0.78 seconds
-Without cache: 2.32 seconds
+
Using array cache: 0.35 seconds
+Using epochs cache: 0.50 seconds
+Using raw cache: 0.82 seconds
+Without cache: 2.40 seconds
 

 

 
As you can see, using a raw cache is more than 5 times faster than
@@ -979,8 +979,8 @@ 
Cleanup
shutil.rmtree(temp_dir)
-

Total running time of the script: ( 0 minutes 16.601 seconds)

-

Estimated memory usage: 806 MB

+

Total running time of the script: ( 0 minutes 17.290 seconds)

+

Estimated memory usage: 668 MB

-

Total running time of the script: ( 0 minutes 32.575 seconds)

-

Estimated memory usage: 801 MB

+

Total running time of the script: ( 0 minutes 33.957 seconds)

+

Estimated memory usage: 605 MB