Cleanup docstrings and add basic docs-linter

.gitignore

@@ -10,3 +10,4 @@ __pycache__
 build/
 dist/
 docs/src/examples
+sg_execution_times.rst

examples/reconstruction/PlotGFRE.py

@@ -4,20 +4,19 @@
 """
 Global Feature Reconstruction Error (GFRE) and Distortion (GFRD)
 ================================================================
-
-Example for the usage of the :class:`skmatter.metrics.global_reconstruction_error`
-as global feature reconstruction error (GFRE) and
+Example for the usage of the :class:`skmatter.metrics.global_reconstruction_error` as
+global feature reconstruction error (GFRE) and
 :class:`skmatter.metrics.global_reconstruction_distortion` global feature reconstruction
-distortion (GFRD). We apply the global reconstruction measures on the degenerate
-CH4 manifold dataset. This dataset was specifically constructed to be
-representable by a 4-body features (bispectrum) but not by a 3-body features
-(power spectrum). In other words the dataset contains environments which are
-different, but have the same 3-body features. For more details about the dataset
-please refer to `Pozdnyakov 2020 <https://doi.org/10.1103/PhysRevLett.125.166001>`_.
+distortion (GFRD). We apply the global reconstruction measures on the degenerate CH4
+manifold dataset. This dataset was specifically constructed to be representable by a
+4-body features (bispectrum) but not by a 3-body features (power spectrum). In other
+words the dataset contains environments which are different, but have the same 3-body
+features. For more details about the dataset please refer to `Pozdnyakov 2020
+<https://doi.org/10.1103/PhysRevLett.125.166001>`_.
 
 The ``skmatter`` dataset already contains the 3 and 4-body features computed with
-`librascal <https://github.com/lab-cosmo/librascal>`_ so we can load it and
-compare it with the GFRE/GFRD.
+`librascal <https://github.com/lab-cosmo/librascal>`_ so we can load it and compare it
+with the GFRE/GFRD.
 """
 # %%
 #

examples/reconstruction/PlotLFRE.py

@@ -3,7 +3,6 @@
 """
 Pointwise Local Reconstruction Error
 ====================================
-
 Example for the usage of the
 :class:`skmatter.metrics.pointwise_local_reconstruction_error` as pointwise local
 reconstruction error (LFRE) on the degenerate CH4 manifold. We apply the local
@@ -14,9 +13,9 @@
 dataset please refer to `Pozdnyakov 2020
 <https://doi.org/10.1103/PhysRevLett.125.166001>`_.
 
-The skmatter dataset already contains the 3 and 4-body features computed with
-`librascal <https://github.com/lab-cosmo/librascal>`_ so we can load it and compare it
-with the LFRE.
+The skmatter dataset already contains the 3 and 4-body features computed with `librascal
+<https://github.com/lab-cosmo/librascal>`_ so we can load it and compare it with the
+LFRE.
 """
 # %%
 #

examples/reconstruction/PlotPointwiseGFRE.py

@@ -3,8 +3,7 @@
 
 """
 Pointwise GFRE applied on RKHS features
-================================================================
-
+=======================================
 Example for the usage of the
 :class:`skmatter.metrics.pointwise_global_reconstruction_error` as the pointwise global
 feature reconstruction error (pointwise GFRE). We apply the pointwise global feature

examples/regression/OrthogonalRegressionNonAnalytic.py

@@ -3,7 +3,6 @@
 r"""
 Regression with orthogonal projector/matrices
 =============================================
-
 In this example, we explain how when using
 :class:`skmatter.linear_model.OrthogonalRegression` the option
 ``use_orthogonal_projector`` can result in non-analytic behavior. In

examples/regression/Ridge2FoldCVRegularization.py

@@ -1,16 +1,14 @@
 # %%
 
 r"""
- Ridge2FoldCV for data with low effective rank
- =======================================================
- In this notebook we explain in more detail how
- :class:`skmatter.linear_model.Ridge2FoldCV` speeds up the
- cross-validation optimizing the regularitzation parameter :param alpha: and
- compare it with existing solution for that in scikit-learn
- :class:`slearn.linear_model.RidgeCV`.
- :class:`skmatter.linear_model.Ridge2FoldCV` was designed to predict
- efficiently feature matrices, but it can be also useful for the prediction
- single targets.
+Ridge2FoldCV for data with low effective rank
+=============================================
+In this notebook we explain in more detail how
+:class:`skmatter.linear_model.Ridge2FoldCV` speeds up the cross-validation optimizing
+the regularitzation parameter :param alpha: and compare it with existing solution for
+that in scikit-learn :class:`slearn.linear_model.RidgeCV`.
+:class:`skmatter.linear_model.Ridge2FoldCV` was designed to predict efficiently feature
+matrices, but it can be also useful for the prediction single targets.
 """
 # %%
 #
@@ -128,6 +126,7 @@
 
 
 def micro_bench(ridge):
+    """A small benchmark function."""
     global N_REPEAT_MICRO_BENCH, X, y
     timings = []
     train_mse = []
@@ -177,6 +176,7 @@ def micro_bench(ridge):
 
 
 def get_train_test_error(estimator):
+    """The train tets error based on the estimator."""
     global X_train, y_train, X_test, y_test
     estimator = estimator.fit(X_train, y_train)
     return (