Merge 0.4.5 into master

.github/workflows/test-devel.yml

@@ -27,7 +27,7 @@ jobs:
           python-version: ${{ matrix.python-version }}
 
       - name: Install Poetry
-        uses: snok/install-poetry@v1.1.6
+        uses: snok/install-poetry@v1
         with:
           virtualenvs-create: true
           virtualenvs-in-project: true

.github/workflows/test.yml

@@ -26,10 +26,8 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - uses: pre-commit/[email protected]
-
       - name: Install Poetry
-        uses: snok/install-poetry@v1.1.6
+        uses: snok/install-poetry@v1
         with:
           virtualenvs-create: true
           virtualenvs-in-project: true
@@ -62,3 +60,11 @@ jobs:
         run: |
           source $VENV
           poetry run python -m moabb.run --pipelines=./moabb/tests/test_pipelines/ --verbose
+
+      - name: Upload Coverage to Codecov
+        uses: codecov/codecov-action@v2
+        if: success()
+        with:
+          verbose: true
+          directory: /home/runner/work/moabb/moabb
+          files: ./.coverage

README.md

@@ -129,7 +129,7 @@ can upgrade your pip version using: `pip install -U pip` before installing `moab
 ## Supported datasets
 
 The list of supported datasets can be found here :
-http://moabb.neurotechx.com/docs/datasets.html
+https://neurotechx.github.io/moabb/datasets.html
 
 ### Submit a new dataset
 
@@ -256,6 +256,6 @@ BCI algorithms applied on an extensive list of freely available EEG datasets.
 [link_sylvain]: https://sylvchev.github.io/
 [link_neurotechx_signup]: https://neurotechx.com/
 [link_gitter]: https://gitter.im/moabb_dev/community
-[link_moabb_docs]: http://moabb.neurotechx.com/docs/index.html
+[link_moabb_docs]: https://neurotechx.github.io/moabb/
 [link_arxiv]: https://arxiv.org/abs/1805.06427
 [link_jne]: http://iopscience.iop.org/article/10.1088/1741-2552/aadea0/meta

docs/source/README.md

@@ -128,8 +128,7 @@ can upgrade your pip version using: `pip install -U pip` before installing `moab
 
 ## Supported datasets
 
-The list of supported datasets can be found here :
-http://moabb.neurotechx.com/docs/datasets.html
+The list of supported datasets can be found here : https://neurotechx.github.io/moabb/
 
 ### Submit a new dataset
 
@@ -258,6 +257,6 @@ BCI algorithms applied on an extensive list of freely available EEG datasets.
 [link_sylvain]: https://sylvchev.github.io/
 [link_neurotechx_signup]: https://neurotechx.com/
 [link_gitter]: https://gitter.im/moabb_dev/community
-[link_moabb_docs]: http://moabb.neurotechx.com/docs/index.html
+[link_moabb_docs]: https://neurotechx.github.io/moabb/
 [link_arxiv]: https://arxiv.org/abs/1805.06427
 [link_jne]: http://iopscience.iop.org/article/10.1088/1741-2552/aadea0/meta

docs/source/whats_new.rst

@@ -31,7 +31,29 @@ API changes
 - None
 
 
-Version - 0.4.4  (Stable - PyPi)
+Version - 0.4.5  (Stable - PyPi)
+---------------------------------
+
+Enhancements
+~~~~~~~~~~~~
+
+- Progress bars, pooch, tqdm (:gh:`258` by `Divyesh Narayanan`_ and `Sylvain Chevallier`_)
+- Adding test and example for set_download_dir (:gh:`249` by `Divyesh Narayanan`_)
+- Update to newer version of Schirrmeister2017 dataset (:gh:`265` by `Robin Schirrmeister`_)
+- Adding Huebner2017 and Huebner2018 P300 datasets (:gh:`260`  by `Jan Sosulski`_)
+- Adding Sosulski2019 auditory P300 datasets (:gh:`266`  by `Jan Sosulski`_)
+- New script to visualize ERP on all datasets, as a sanity check (:gh:`261`  by `Jan Sosulski`_)
+
+Bugs
+~~~~
+
+- Removing dependency on mne method for PhysionetMI data downloading, renaming runs (:gh:`257` by `Divyesh Narayanan`_)
+- Correcting events management in Schirrmeister2017, renaming session and run (:gh:`255` by `Pierre Guetschel`_ and `Sylvain Chevallier`_)
+- Switch session and runs in MAMEM1, 2 and 3 to avoid error in WithinSessionEvaluation (:gh:`256` by `Sylvain Chevallier`_)
+- Correct doctstrings for the documentation, incuding Lee2017 (:gh:`256` by `Sylvain Chevallier`_)
+
+
+Version - 0.4.4
 ---------------
 
 Enhancements

examples/advanced_examples/plot_filterbank_csp_vs_csp.py

@@ -3,7 +3,7 @@
 FilterBank CSP versus CSP
 =========================
 
-This Example show a comparison of CSP versus FilterBank CSP on the
+This example show a comparison of CSP versus FilterBank CSP on the
 very popular dataset 2a from the BCI competition IV.
 """
 # Authors: Alexandre Barachant <[email protected]>
@@ -27,7 +27,7 @@
 moabb.set_log_level("info")
 
 ##############################################################################
-# Create pipelines
+# Create Pipelines
 # ----------------
 #
 # The CSP implementation from MNE is used. We selected 8 CSP components, as
@@ -51,7 +51,7 @@
 # ----------
 #
 # Since two different preprocessing will be applied, we have two different
-# paradigm objects. We have to make sure their filter matchs so the comparison
+# paradigm objects. We have to make sure their filter matches so the comparison
 # will be fair.
 #
 # The first one is a standard `LeftRightImagery` with a 8 to 35 Hz broadband
@@ -75,7 +75,7 @@
 )
 results = evaluation.process(pipelines)
 
-# bank of 6 filter, by 4 Hz increment
+# Bank of 6 filters, by 4 Hz increment
 filters = [[8, 12], [12, 16], [16, 20], [20, 24], [24, 28], [28, 35]]
 paradigm = FilterBankLeftRightImagery(filters=filters)
 evaluation = CrossSessionEvaluation(
@@ -93,10 +93,10 @@
 # Plot Results
 # ----------------
 #
-# Here we plot the results via normal methods. We the first plot is a pointplot
+# Here we plot the results via seaborn. We first display a pointplot
 # with the average performance of each pipeline across session and subjects.
 # The second plot is a paired scatter plot. Each point representing the score
-# of a single session. An algorithm will outperforms another is most of the
+# of a single session. An algorithm will outperform another is most of the
 # points are in its quadrant.
 
 fig, axes = plt.subplots(1, 2, figsize=[8, 4], sharey=True)

examples/advanced_examples/plot_mne_and_scikit_estimators.py

@@ -1,16 +1,16 @@
 """
-=========================
-MNE Epochs-based piplines
-=========================
+==========================
+MNE Epochs-based pipelines
+==========================
 
 This example shows how to use machine learning pipeline based on MNE Epochs
-instead of numpy arrays. This is useful to make the most of the MNE code base
+instead of Numpy arrays. This is useful to make the most of the MNE code base
 and to embed EEG specific code inside sklearn pipelines.
 
-We will compare compare different pipelines for P300:
-- Logistic Regression, based on MNE Epochs
+We will compare different pipelines for P300:
+- Logistic regression, based on MNE Epochs
 - XDAWN and Logistic Regression (LR), based on MNE Epochs
-- XDAWN extended covariance and LR on tangent space, based on numpy
+- XDAWN extended covariance and LR on tangent space, based on Numpy
 
 """
 # Authors: Sylvain Chevallier
@@ -47,7 +47,7 @@
 moabb.set_log_level("info")
 
 ###############################################################################
-# Loading dataset
+# Loading Dataset
 # ---------------
 #
 # Load 2 subjects of BNCI 2014-009 dataset, with 3 session each
@@ -58,15 +58,15 @@
 paradigm = P300()
 
 ##############################################################################
-# Get data (optional)
+# Get Data (optional)
 # -------------------
 #
 # To get access to the EEG signals downloaded from the dataset, you could
 # use ``dataset.get_data([subject_id)`` to obtain the EEG as MNE Epochs, stored
 # in a dictionary of sessions and runs.
 # The ``paradigm.get_data(dataset=dataset, subjects=[subject_id])`` allows to
 # obtain the preprocessed EEG data, the labels and the meta information. By
-# default, the EEG is return as a numpy array. With ``return_epochs=True``, MNE
+# default, the EEG is return as a Numpy array. With ``return_epochs=True``, MNE
 # Epochs are returned.
 
 subject_list = [1]
@@ -77,14 +77,14 @@
 )
 
 ##############################################################################
-# A simple MNE pipeline
+# A Simple MNE Pipeline
 # ---------------------
 #
 # Using ``return_epochs=True`` in the evaluation, it is possible to design a
 # pipeline based on MNE Epochs input. Let's create a simple one, that
 # reshape the input data from epochs, rescale the data and uses a logistic
 # regression to classify the data. We will need to write a basic Transformer
-# estimator, that comply with
+# estimator, that complies with
 # `sklearn convention <https://scikit-learn.org/stable/developers/develop.html>`_.
 # This transformer will extract the data from an input Epoch, and reshapes into
 # 2D array.
@@ -124,13 +124,13 @@ def transform(self, X, y=None):
 mne_res = mne_eval.process(mne_ppl)
 
 ##############################################################################
-# Advanced MNE pipeline
+# Advanced MNE Pipeline
 # ---------------------
 #
 # In some case, the MNE pipeline should have access to the original labels from
 # the dataset. This is the case for the XDAWN code of MNE. One could pass
 # `mne_labels` to evaluation in order to keep this label.
-# As an example, we will define a pipeline that compute an XDAWN filter, rescale,
+# As an example, we will define a pipeline that computes an XDAWN filter, rescale,
 # then apply a logistic regression.
 
 mne_adv = {}
@@ -151,10 +151,10 @@ def transform(self, X, y=None):
 adv_res = mne_eval.process(mne_adv)
 
 ###############################################################################
-# Numpy-based pipeline
+# Numpy-based Pipeline
 # --------------------
 #
-# For the comparison, we will define a numpy-based pipeline that relies on
+# For the comparison, we will define a Numpy-based pipeline that relies on
 # pyriemann to estimate XDAWN-extended covariance matrices that are projected
 # on the tangent space and classified with a logistic regression.
 
@@ -173,11 +173,12 @@ def transform(self, X, y=None):
 sk_res = sk_eval.process(sk_ppl)
 
 ###############################################################################
-# Combining results
+# Combining Results
 # -----------------
 #
 # Even if the results have been obtained by different evaluation processes, it
-# possible to combine the resulting dataframes to analyze and plot the results.
+# is possible to combine the resulting DataFrames to analyze and plot the
+# results.
 
 all_res = pd.concat([mne_res, adv_res, sk_res])
 

examples/advanced_examples/plot_select_electrodes_resample.py

@@ -1,6 +1,6 @@
 """
 ================================
-Select electrodes and resampling
+Select Electrodes and Resampling
 ================================
 
 Within paradigm, it is possible to restrict analysis only to a subset of
@@ -30,7 +30,7 @@
 # Datasets
 # --------
 #
-# Load 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 session each
+# Load 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 sessions each
 
 subj = [1, 2]
 datasets = [Zhou2016(), BNCI2014001()]
@@ -63,7 +63,7 @@
 print(results.head())
 
 ##############################################################################
-# Electrode selection
+# Electrode Selection
 # -------------------
 #
 # It is possible to select the electrodes that are shared by all datasets
@@ -79,7 +79,7 @@
 print(results.head())
 
 ##############################################################################
-# Plot results
+# Plot Results
 # ------------
 #
 # Compare the obtained results with the two pipelines, CSP+LDA and logistic

examples/advanced_examples/plot_statistical_analysis.py

@@ -1,4 +1,5 @@
-"""=======================
+"""
+=======================
 Statistical Analysis
 =======================
 
@@ -40,20 +41,20 @@
 # ---------------------
 #
 # First we need to set up a paradigm, dataset list, and some pipelines to
-# test. This is explored more in the examples -- we choose a left vs right
+# test. This is explored more in the examples -- we choose left vs right
 # imagery paradigm with a single bandpass. There is only one dataset here but
 # any number can be added without changing this workflow.
 #
-# Create pipelines
+# Create Pipelines
 # ----------------
 #
 # Pipelines must be a dict of sklearn pipeline transformer.
 #
-# The csp implementation from MNE is used. We selected 8 CSP components, as
-# usually done in the litterature.
+# The CSP implementation from MNE is used. We selected 8 CSP components, as
+# usually done in the literature.
 #
-# The riemannian geometry pipeline consists in covariance estimation, tangent
-# space mapping and finaly a logistic regression for the classification.
+# The Riemannian geometry pipeline consists in covariance estimation, tangent
+# space mapping and finally a logistic regression for the classification.
 
 pipelines = {}
 
@@ -70,7 +71,7 @@
 # ----------
 #
 # We define the paradigm (LeftRightImagery) and the dataset (BNCI2014001).
-# The evaluation will return a dataframe containing a single AUC score for
+# The evaluation will return a DataFrame containing a single AUC score for
 # each subject / session of the dataset, and for each pipeline.
 #
 # Results are saved into the database, so that if you add a new pipeline, it
@@ -89,7 +90,7 @@
 results = evaluation.process(pipelines)
 
 ##############################################################################
-# MOABB plotting
+# MOABB Plotting
 # ----------------
 #
 # Here we plot the results using some of the convenience methods within the
@@ -109,7 +110,7 @@
 plt.show()
 
 ###############################################################################
-# Statistical testing and further plots
+# Statistical Testing and Further Plots
 # ----------------------------------------
 #
 # If the statistical significance of results is of interest, the method
@@ -124,13 +125,13 @@
 ###############################################################################
 # The meta-analysis style plot shows the standardized mean difference within
 # each tested dataset for the two algorithms in question, in addition to a
-# meta-effect and significances both per-dataset and overall.
+# meta-effect and significance both per-dataset and overall.
 fig = moabb_plt.meta_analysis_plot(stats, "CSP+LDA", "RG+LDA")
 plt.show()
 
 ###############################################################################
 # The summary plot shows the effect and significance related to the hypothesis
-# that the algorithm on the y-axis significantly out-performed the algorithm on
+# that the algorithm on the y-axis significantly outperformed the algorithm on
 # the x-axis over all datasets
 moabb_plt.summary_plot(P, T)
 plt.show()