docs: reorganize tutorial 0 content for better logical flow

docs/source/whats_new.rst

@@ -40,6 +40,7 @@ Enhancements
 - Use NEMAR as the default download source for datasets with an assigned ``nemar_id``, while preserving existing dataset-specific downloaders as a fallback (by `Bruno Aristimunha`_).
 - Add :class:`moabb.datasets.preprocessing.EuclideanAlignment`, a trial-level Euclidean Alignment transformer (He & Wu 2020; Junqueira et al. 2024) that whitens each trial by the inverse square root of the Euclidean mean covariance to remove per-domain covariance shift before a (deep) model sees the data. Inductive and leakage-free by default (``fit`` learns the reference from training trials, ``transform`` re-applies it to unseen trials); ``fit_transform`` gives the transductive, per-recording form. Accepts an :class:`mne.BaseEpochs` or an ``(n_trials, n_channels, n_times)`` ndarray, uses a shrinkage covariance estimator (``"lwf"``) for robustness, and adds no new dependency (``pyriemann >= 0.11`` is already required). Distinct from :class:`pyriemann.transfer.TLCenter`, which recenters covariance *matrices* (:gh:`1108` by `Bruno Aristimunha`_).
 - Drive cross-validation folds with any stock scikit-learn cross-validator passed as ``cv_class``, controlled by a ``groups`` argument — a metadata column name, a list of column names (compound key, e.g. ``["subject", "session"]``), or a callable ``metadata -> array`` — together with callable ``cv_kwargs`` resolved against the metadata (e.g. ``cv_class=PredefinedSplit`` with a ``test_fold`` callable to target a single fold). ``groups`` is exposed on :class:`moabb.evaluations.WithinSessionEvaluation`, :class:`moabb.evaluations.WithinSubjectEvaluation`, :class:`moabb.evaluations.CrossSessionEvaluation` and :class:`moabb.evaluations.CrossSubjectEvaluation` and threaded to their splitters; each splitter keeps its default grouping (``"subject"`` / ``"session"`` / labels) when ``groups`` is ``None``. :class:`moabb.evaluations.splitters.CrossDatasetSplitter` gains ``groups`` (its ``group_column`` argument is now a deprecated alias) (:gh:`1104` by `Bruno Aristimunha`_).
+- Reorganize comments in the getting started tutorial to improve narrative flow by moving the logging setup and pipeline description to more logical positions within the script (:gh:`1114` by `Shamloor`).
 
 API changes
 ~~~~~~~~~~~

examples/tutorials/tutorial_0_plot_getting_started.py

@@ -29,9 +29,6 @@
 from sklearn.pipeline import make_pipeline
 from sklearn.svm import SVC
 
-##########################################################################
-# If you would like to specify the logging level when it is running, you can
-# use the standard python logging commands through the top-level moabb module
 import moabb
 from moabb.datasets import BNCI2014_001, utils
 from moabb.evaluations import CrossSessionEvaluation
@@ -40,17 +37,20 @@
 
 
 ##########################################################################
-# In order to create pipelines within a script, you will likely need at least
-# the make_pipeline function. They can also be specified via a .yml file. Here
-# we will make a couple pipelines just for convenience
-
+# If you would like to specify the logging level when it is running, you can
+# use the standard python logging commands through the top-level moabb module
 
 moabb.set_log_level("info")
 
+
 ##############################################################################
 # Create pipelines
 # ----------------
 #
+# In order to create pipelines within a script, you will likely need at least
+# the make_pipeline function. They can also be specified via a .yml file. Here
+# we will make a couple pipelines just for convenience.
+#
 # We create two pipelines: channel-wise log variance followed by LDA, and
 # channel-wise log variance followed by a cross-validated SVM (note that a
 # cross-validation via scikit-learn cannot be described in a .yml file). For