Bump mistune from 0.8.4 to 2.0.3 (#303)

* Bump mistune from 0.8.4 to 2.0.3

Bumps [mistune](https://github.com/lepture/mistune) from 0.8.4 to 2.0.3.
- [Release notes](https://github.com/lepture/mistune/releases)
- [Changelog](https://github.com/lepture/mistune/blob/master/docs/changes.rst)
- [Commits](lepture/mistune@v0.8.4...v2.0.3)

---
updated-dependencies:
- dependency-name: mistune
  dependency-type: direct:development
...

Signed-off-by: dependabot[bot] <[email protected]>

* correct lint error

* use new precommit action

* switch linting to 3.8 due to flake8 bug

* correct flake8 error on abstract class

Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Sylvain Chevallier <[email protected]>

.github/workflows/test-devel.yml

@@ -76,7 +76,7 @@ jobs:
       fail-fast: true
       matrix:
         os: [ubuntu-18.04]
-        python-version: ["3.7"]
+        python-version: ["3.8"]
     steps:
       - uses: actions/checkout@v2
 
@@ -85,4 +85,4 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - uses: pre-commit/action@v2.0.0
+      - uses: pre-commit/action@v3.0.0

moabb/paradigms/base.py

@@ -12,6 +12,7 @@
 class BaseParadigm(metaclass=ABCMeta):
     """Base Paradigm."""
 
+    @abstractmethod
     def __init__(self):
         pass
 
@@ -62,7 +63,8 @@ def prepare_process(self, dataset):
             The dataset corresponding to the raw file. mainly use to access
             dataset specific information.
         """
-        pass
+        if dataset is not None:
+            pass
 
     def process_raw(self, raw, dataset, return_epochs=False):  # noqa: C901
         """

moabb/pipelines/classification.py

@@ -68,7 +68,7 @@ def predict(self, X):
                 if f.replace(".", "", 1).isnumeric():
                     S_x, S_y = self.cca.fit_transform(x.T, self.Yf[f].T)
                     corr_f[f] = np.corrcoef(S_x.T, S_y.T)[0, 1]
-            y.append(self.one_hot[max(corr_f, key=lambda k: corr_f[k])])
+            y.append(self.one_hot[max(corr_f, key=corr_f.get)])
         return y
 
     def predict_proba(self, X):

pyproject.toml

@@ -33,7 +33,7 @@ sphinx-gallery = "^0.8.2"
 sphinx-bootstrap-theme = "^0.8"
 numpydoc = "^1.1.0"
 m2r2 = "^0.2.7"
-mistune = "<2"
+mistune = "<3"
 pre-commit = "^2.11.1"
 tdlda = {git = "https://github.com/jsosulski/tdlda.git", rev = "0.1.0"}