Clean up all docs warnings!

alejoe91 · alejoe91 · commit 6f4271311dbf · 2025-03-06T12:32:07.000+01:00
diff --git a/doc/_static/spikeinterface.ico b/doc/_static/spikeinterface.ico
diff --git a/doc/_static/spikeinterface.png b/doc/_static/spikeinterface.png
diff --git a/doc/api.rst b/doc/api.rst
@@ -22,6 +22,8 @@ spikeinterface.core
     .. autofunction:: estimate_sparsity
     .. autoclass:: ChannelSparsity
         :members:
+    .. autoclass:: Motion
+        :members:
     .. autoclass:: BinaryRecordingExtractor
     .. autoclass:: ZarrRecordingExtractor
     .. autoclass:: BinaryFolderRecording
@@ -447,7 +449,6 @@ Motion Correction
 ~~~~~~~~~~~~~~~~~
 .. automodule:: spikeinterface.sortingcomponents.motion
 
-    .. autoclass:: Motion
     .. autofunction:: estimate_motion
     .. autofunction:: interpolate_motion
     .. autofunction:: correct_motion_on_peaks
diff --git a/doc/conf.py b/doc/conf.py
@@ -31,14 +31,12 @@
 folders =  [
     '../examples/tutorials/core/my_recording',
     '../examples/tutorials/core/my_sorting',
-    '../examples/tutorials/core/waveform_folder',
-    '../examples/tutorials/core/waveform_folder_parallel',
-    '../examples/tutorials/core/waveform_folder_sparse',
-    '../examples/tutorials/core/waveform_folder_sparse_direct',
-    '../examples/tutorials/core/waveform_folder2',
-    '../examples/tutorials/core/waveform_folder',
-    '../examples/tutorials/qualitymetrics/waveforms_mearec',
-    '../examples/tutorials/qualitymetrics/wfs_mearec',
+    '../examples/tutorials/core/analyzer_folder',
+    '../examples/tutorials/core/analyzer_some_units',
+    '../examples/tutorials/core/analyzer.zarr',
+    '../examples/tutorials/curation/my_folder',
+    '../examples/tutorials/qualitymetrics/curated_sorting',
+    '../examples/tutorials/qualitymetrics/clean_analyzer.zarr',
     '../examples/tutorials/widgets/waveforms_mearec',
 
 ]
@@ -101,17 +99,17 @@
     import sphinx_rtd_theme
 
     html_theme = "sphinx_rtd_theme"
-    html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
 except ImportError:
     print("RTD theme not installed, using default")
     html_theme = 'alabaster'
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
-html_favicon = "_static/spikeinterface.ico"
-html_logo = "_static/spikeinterface.png"
+# html_static_path = ['_static']
+# html_css_files = ['custom.css']
+html_favicon = "images/logo.png"
+html_logo = "images/logo.png"
 
 
 from sphinx_gallery.sorting import ExplicitOrder
diff --git a/doc/index.rst b/doc/index.rst
@@ -2,11 +2,6 @@ Welcome to SpikeInterface's documentation!
 ==========================================
 
 
-.. image:: images/logo.png
-  :scale: 100 %
-  :align: center
-
-
 SpikeInterface is a Python module to analyze extracellular electrophysiology data.
 
 With a few lines of code, SpikeInterface enables you to load and pre-process the recording, run several
diff --git a/doc/modules/comparison.rst b/doc/modules/comparison.rst
@@ -26,7 +26,7 @@ For spike train comparison, there are three use cases:
 
 A ground-truth dataset can be a paired recording, in which a neuron is recorded both extracellularly and with
 a patch or juxtacellular electrode (either **in vitro** or **in vivo**), or it can be a simulated dataset
-(**in silico**) using spiking activity simulators such as `MEArec`_.
+(**in silico**) using spiking activity simulators such as `MEArec <https://github.com/SpikeInterface/MEArec>`_.
 
 The comparison to ground-truth datasets is useful to benchmark spike sorting algorithms.
 
diff --git a/doc/modules/index.rst b/doc/modules/index.rst
@@ -17,3 +17,4 @@ Modules documentation
     sortingcomponents
     motion_correction
     generation
+    benchmark
diff --git a/doc/modules/sorters.rst b/doc/modules/sorters.rst
@@ -473,8 +473,6 @@ Here is the list of external sorters accessible using the run_sorter wrapper:
 * **HDSort** :code:`run_sorter(sorter_name='hdsort')`
 * **YASS** :code:`run_sorter(sorter_name='yass')`
 
-Internal Sorters
-----------------
 
 Here a list of internal sorter based on `spikeinterface.sortingcomponents`; they are totally
 experimental for now:
diff --git a/examples/tutorials/curation/plot_2_train_a_model.py b/examples/tutorials/curation/plot_2_train_a_model.py
@@ -1,6 +1,6 @@
 """
 Training a model for automated curation
-=============================
+=======================================
 
 If the pretrained models do not give satisfactory performance on your data, it is easy to train your own classifier using SpikeInterface.
 """
@@ -57,7 +57,7 @@
 sw.plot_unit_templates(analyzer, unit_ids=["0", "5"])
 
 ##############################################################################
-# This is as expected: great! (Find out more about plotting using widgets `here <https://spikeinterface.readthedocs.io/en/latest/modules/widgets.html>`_.)
+# This is as expected: great! (Find out more about plotting `using widgets <https://spikeinterface.readthedocs.io/en/latest/modules/widgets.html>`_.)
 # We've set up our system so that the first five units are 'good' and the next five are 'bad'.
 # So we can make a list of labels which contain this information. For real data, you could
 # use a manual curation tool to make your own list.
@@ -129,8 +129,8 @@
 # half were pure noise and half were not.
 #
 # The model also contains some more information, such as which features are "important",
-# as defined by sklearn (learn about feature importance of a Random Forest Classifier
-# `here <https://scikit-learn.org/1.5/auto_examples/ensemble/plot_forest_importances.html>`_.)
+# as defined by sklearn (learn about feature importance of a
+# `Random Forest Classifier <https://scikit-learn.org/1.5/auto_examples/ensemble/plot_forest_importances.html>`_.)
 # We can plot these:
 
 # Plot feature importances
diff --git a/examples/tutorials/curation/plot_3_upload_a_model.py b/examples/tutorials/curation/plot_3_upload_a_model.py
@@ -136,4 +136,4 @@
 #
 #     Chris Halcrow
 #
-# You can see the repo with this Model card `here <https://huggingface.co/SpikeInterface/toy_tetrode_model>`_.
+# You can see the repo with this `Model card <https://huggingface.co/SpikeInterface/toy_tetrode_model>`_.
diff --git a/src/spikeinterface/benchmark/benchmark_peak_localization.py b/src/spikeinterface/benchmark/benchmark_peak_localization.py
@@ -403,7 +403,7 @@ def plot_comparison_positions(self, case_keys=None):
 #     ax2.spines["top"].set_visible(False)
 #     ax2.spines["right"].set_visible(False)
 #     ax2.set_xlim(xmin, xmax)
-#     ax2.set_xlabel(r"x ($\mu$m)")
+#     ax2.set_xlabel(r"x ($\\mu$m)")
 #     ax2.set_ylabel("# spikes")
 
 
diff --git a/src/spikeinterface/core/__init__.py b/src/spikeinterface/core/__init__.py
@@ -172,6 +172,8 @@
     compute_noise_levels,
 )
 
+from .motion import Motion
+
 # Important not for compatibility!!
 # This wil be uncommented after 0.100
 from .waveforms_extractor_backwards_compatibility import (
diff --git a/src/spikeinterface/core/sortinganalyzer.py b/src/spikeinterface/core/sortinganalyzer.py
@@ -88,9 +88,10 @@ def create_sorting_analyzer(
         If True, overwrite the folder if it already exists.
     backend_options : dict | None, default: None
         Keyword arguments for the backend specified by format. It can contain the:
-        - storage_options: dict | None (fsspec storage options)
-        - saving_options: dict | None (additional saving options for creating and saving datasets,
-                                       e.g. compression/filters for zarr)
+
+            * storage_options: dict | None (fsspec storage options)
+            * saving_options: dict | None (additional saving options for creating and saving datasets, e.g. compression/filters for zarr)
+
     sparsity_kwargs : keyword arguments
 
     Returns
@@ -187,8 +188,9 @@ def load_sorting_analyzer(folder, load_extensions=True, format="auto", backend_o
     backend_options : dict | None, default: None
         The backend options for the backend.
         The dictionary can contain the following keys:
-        - storage_options: dict | None (fsspec storage options)
-        - saving_options: dict | None (additional saving options for creating and saving datasets)
+
+            * storage_options: dict | None (fsspec storage options)
+            * saving_options: dict | None (additional saving options for creating and saving datasets)
 
     Returns
     -------
@@ -908,9 +910,9 @@ def _save_or_select_or_merge(
             If True, output is verbose.
         backend_options : dict | None, default: None
             Keyword arguments for the backend specified by format. It can contain the:
-            - storage_options: dict | None (fsspec storage options)
-            - saving_options: dict | None (additional saving options for creating and saving datasets,
-                                           e.g. compression/filters for zarr)
+
+                * storage_options: dict | None (fsspec storage options)
+                * saving_options: dict | None (additional saving options for creating and saving datasets, e.g. compression/filters for zarr)
         job_kwargs : keyword arguments
             Keyword arguments for the job parallelization.
 
@@ -1073,9 +1075,9 @@ def save_as(self, format="memory", folder=None, backend_options=None) -> "Sortin
             The new backend format to use
         backend_options : dict | None, default: None
             Keyword arguments for the backend specified by format. It can contain the:
-            - storage_options: dict | None (fsspec storage options)
-            - saving_options: dict | None (additional saving options for creating and saving datasets,
-                                           e.g. compression/filters for zarr)
+
+                * storage_options: dict | None (fsspec storage options)
+                * saving_options: dict | None (additional saving options for creating and saving datasets, e.g. compression/filters for zarr)
         """
         if format == "zarr":
             folder = clean_zarr_folder_name(folder)
@@ -1151,7 +1153,7 @@ def merge_units(
         **job_kwargs,
     ) -> "SortingAnalyzer":
         """
-        This method is equivalent to `save_as()`but with a list of merges that have to be achieved.
+        This method is equivalent to `save_as()` but with a list of merges that have to be achieved.
         Merges units by creating a new SortingAnalyzer object with the appropriate merges
 
         Extensions are also updated to display the merged `unit_ids`.
@@ -1175,6 +1177,7 @@ def merge_units(
             achieved, soft merging will not be possible and an error will be raised
         new_id_strategy : "append" | "take_first", default: "append"
             The strategy that should be used, if `new_unit_ids` is None, to create new unit_ids.
+
                 * "append" : new_units_ids will be added at the end of max(sorting.unit_ids)
                 * "take_first" : new_unit_ids will be the first unit_id of every list of merges
         return_new_unit_ids : bool, default False
@@ -1978,6 +1981,7 @@ class AnalyzerExtension:
     It also enables any custom computation on top of the SortingAnalyzer to be implemented by the user.
 
     An extension needs to inherit from this class and implement some attributes and abstract methods:
+
       * extension_name
       * depend_on
       * need_recording
diff --git a/src/spikeinterface/curation/train_manual_curation.py b/src/spikeinterface/curation/train_manual_curation.py
@@ -83,7 +83,7 @@ class CurationModelTrainer:
     metric_names : list of str, default: None
         A list of metrics to use for training. If None, default metrics will be used.
     imputation_strategies : list of str | None, default: None
-        A list of imputation strategies to try. Can be "knn”, "iterative" or any allowed
+        A list of imputation strategies to try. Can be "knn", "iterative" or any allowed
         strategy passable to the sklearn `SimpleImputer`. If None, the default strategies
         `["median", "most_frequent", "knn", "iterative"]` will be used.
     scaling_techniques : list of str | None, default: None
@@ -110,7 +110,7 @@ class CurationModelTrainer:
     labels : list of lists, default: None
         List of curated labels for each `sorting_analyzer` and each unit; must be in the same order as the metrics data.
     imputation_strategies : list of str | None, default: None
-        A list of imputation strategies to try. Can be "knn”, "iterative" or any allowed
+        A list of imputation strategies to try. Can be "knn", "iterative" or any allowed
         strategy passable to the sklearn `SimpleImputer`. If None, the default strategies
         `["median", "most_frequent", "knn", "iterative"]` will be used.
     scaling_techniques : list of str | None, default: None
@@ -665,53 +665,55 @@ def train_model(
     """
     Trains and evaluates machine learning models for spike sorting curation.
 
-    This function initializes a `CurationModelTrainer` object, loads and preprocesses the data,
+    This function initializes a ``CurationModelTrainer`` object, loads and preprocesses the data,
     and evaluates the specified combinations of imputation strategies, scaling techniques, and classifiers.
     The evaluation results, including the best model and its parameters, are saved to the output folder.
 
     Parameters
     ----------
-    mode : "analyzers" | "csv", default: "analyzers"
+    mode : ``"analyzers"`` | ``"csv"``, default: ``"analyzers"``
         Mode to use for training.
-    analyzers : list of SortingAnalyzer | None, default: None
-         List of SortingAnalyzer objects containing the quality metrics and labels to use for training, if using 'analyzers' mode.
+    analyzers : list of ``SortingAnalyzer`` | None, default: None
+        List of ``SortingAnalyzer`` objects containing the quality metrics and labels to use for training,
+        if using ``"analyzers"`` mode.
     labels : list of list | None, default: None
         List of curated labels for each unit; must be in the same order as the metrics data.
     metrics_paths : list of str or None, default: None
-        List of paths to the CSV files containing the metrics data if using 'csv' mode.
+        List of paths to the CSV files containing the metrics data if using ``"csv"`` mode.
     folder : str | None, default: None
         The folder where outputs such as models and evaluation metrics will be saved.
     metric_names : list of str | None, default: None
         A list of metrics to use for training. If None, default metrics will be used.
     imputation_strategies : list of str | None, default: None
-        A list of imputation strategies to try. Can be "knn”, "iterative" or any allowed
-        strategy passable to the sklearn `SimpleImputer`. If None, the default strategies
-        `["median", "most_frequent", "knn", "iterative"]` will be used.
+        A list of imputation strategies to try. Can be ``"knn"``, ``"iterative"``, or any allowed
+        strategy passable to the ``sklearn.SimpleImputer``. If None, the default strategies
+        ``["median", "most_frequent", "knn", "iterative"]`` will be used.
     scaling_techniques : list of str | None, default: None
-        A list of scaling techniques to try. Can be "standard_scaler", "min_max_scaler",
-        or "robust_scaler", If None, all techniques will be used.
+        A list of scaling techniques to try. Can be ``"standard_scaler"``, ``"min_max_scaler"``,
+        or ``"robust_scaler"``. If None, all techniques will be used.
     classifiers : list of str | dict | None, default: None
-        A list of classifiers to evaluate. Optionally, a dictionary of classifiers and their hyperparameter search spaces can be provided. If None, default classifiers will be used. Check the `get_classifier_search_space` method for the default search spaces & format for custom spaces.
+        A list of classifiers to evaluate. Optionally, a dictionary of classifiers and their
+        hyperparameter search spaces can be provided. If None, default classifiers will be used.
+        Check the ``get_classifier_search_space`` method for the default search spaces & format for custom spaces.
     test_size : float, default: 0.2
-        Proportion of the dataset to include in the test split, passed to `train_test_split` from `sklear`.
+        Proportion of the dataset to include in the test split, passed to ``train_test_split`` from ``sklearn``.
     overwrite : bool, default: False
-        Overwrites the `folder` if it already exists
+        Overwrites the ``folder`` if it already exists.
     seed : int | None, default: None
         Random seed for reproducibility. If None, a random seed will be generated.
     search_kwargs : dict or None, default: None
-        Keyword arguments passed to `BayesSearchCV` or `RandomizedSearchCV` from `sklearn`. If None, use
-        `search_kwargs = {'cv': 3, 'scoring': 'balanced_accuracy', 'n_iter': 25}`.
+        Keyword arguments passed to ``BayesSearchCV`` or ``RandomizedSearchCV`` from ``sklearn``. If None, use
+        ``search_kwargs = {'cv': 3, 'scoring': 'balanced_accuracy', 'n_iter': 25}``.
     verbose : bool, default: True
         If True, useful information is printed during training.
     enforce_metric_params : bool, default: False
-        If True and metric parameters used to calculate metrics for different `sorting_analyzer`s are
+        If True and metric parameters used to calculate metrics for different ``sorting_analyzer`` objects are
         different, an error will be raised.
 
-
     Returns
     -------
     CurationModelTrainer
-        The `CurationModelTrainer` object used for training and evaluation.
+        The ``CurationModelTrainer`` object used for training and evaluation.
 
     Notes
     -----
diff --git a/src/spikeinterface/widgets/unit_summary.py b/src/spikeinterface/widgets/unit_summary.py
@@ -33,11 +33,13 @@ class UnitSummaryWidget(BaseWidget):
         If SortingAnalyzer is already sparse, the argument is ignored
     subwidget_kwargs : dict or None, default: None
         Parameters for the subwidgets in a nested dictionary
-            unit_locations : UnitLocationsWidget (see UnitLocationsWidget for details)
-            unit_waveforms : UnitWaveformsWidget (see UnitWaveformsWidget for details)
-            unit_waveform_density_map : UnitWaveformDensityMapWidget (see UnitWaveformDensityMapWidget for details)
-            autocorrelograms : AutoCorrelogramsWidget (see AutoCorrelogramsWidget for details)
-            amplitudes : AmplitudesWidget (see AmplitudesWidget for details)
+
+            * unit_locations : UnitLocationsWidget (see UnitLocationsWidget for details)
+            * unit_waveforms : UnitWaveformsWidget (see UnitWaveformsWidget for details)
+            * unit_waveform_density_map : UnitWaveformDensityMapWidget (see UnitWaveformDensityMapWidget for details)
+            * autocorrelograms : AutoCorrelogramsWidget (see AutoCorrelogramsWidget for details)
+            * amplitudes : AmplitudesWidget (see AmplitudesWidget for details)
+
         Please note that the unit_colors should not be set in subwidget_kwargs, but directly as a parameter of plot_unit_summary.
     """
 
diff --git a/src/spikeinterface/widgets/unit_waveforms.py b/src/spikeinterface/widgets/unit_waveforms.py
@@ -307,7 +307,7 @@ def plot_matplotlib(self, data_plot, **backend_kwargs):
                     length_uv = int(np.ptp(wfs_for_scale) // 5)
                     x_offset = xscale_bar[0] - np.ptp(xscale_bar) // 2
                     ax.plot([xscale_bar[0], xscale_bar[0]], [min_wfs - offset, min_wfs - offset + length], color="k")
-                    ax.text(x_offset, min_wfs - offset + length // 3, f"{length_uv} $\mu$V", fontsize=8, rotation=90)
+                    ax.text(x_offset, min_wfs - offset + length // 3, f"{length_uv} $\\mu$V", fontsize=8, rotation=90)
 
             # plot template
             if dp.plot_templates:
@@ -379,7 +379,7 @@ def plot_matplotlib(self, data_plot, **backend_kwargs):
                     length_uv = int(np.ptp(template_for_scale) // 5)
                     x_offset = xscale_bar[0] - np.ptp(xscale_bar) // 2
                     ax.plot([xscale_bar[0], xscale_bar[0]], [min_wfs - offset, min_wfs - offset + length], color="k")
-                    ax.text(x_offset, min_wfs - offset + length // 3, f"{length_uv} $\mu$V", fontsize=8, rotation=90)
+                    ax.text(x_offset, min_wfs - offset + length // 3, f"{length_uv} $\\mu$V", fontsize=8, rotation=90)
 
             # plot channels
             if dp.plot_channels:
diff --git a/src/spikeinterface/widgets/widget_list.py b/src/spikeinterface/widgets/widget_list.py

Original file line number	Diff line number	Diff line change
`@@ -136,4 +136,4 @@`
`136`	`136`	`#`
`137`	`137`	`# Chris Halcrow`
`138`	`138`	`#`
`139`		-# You can see the repo with this Model card `here <https://huggingface.co/SpikeInterface/toy_tetrode_model>`_.
	`139`	+# You can see the repo with this `Model card <https://huggingface.co/SpikeInterface/toy_tetrode_model>`_.