Merge pull request #1014 from DerAndereJohannes/fix-documentation-error-warnings

[Fix] All docs-build warnings and errors

Author: DominiqueMakowski

.github/workflows/docs-check.yml

@@ -38,6 +38,7 @@ jobs:
                   pip install EMD-signal
                   pip install cvxopt
                   pip install ts2vg
+                  pip install pickleshare
                   pip install https://github.com/neuropsychology/neurokit/zipball/dev
 
             - name: Build documentation 📜

AUTHORS.rst

@@ -52,6 +52,7 @@ Contributors
 * `Jannik Gut <https://github.com/rostro36>`_
 * `Nattapong Thammasan <https://github.com/Nattapong-OnePlanet>`_ *(OnePlanet, Netherlands)*
 * `Marek Sokol <https://github.com/sokolmarek>`_ *(Faculty of Biomedical Engineering of the CTU in Prague, Czech Republic)*
+* `Johannes Herforth <https://github.com/DerAndereJohannes>`_ *(University of Luxembourg, Luxembourg)*
 
 
 Thanks also to `Chuan-Peng Hu <https://github.com/hcp4715>`_, `@ucohen <https://github.com/ucohen>`_, `Anthony Gatti <https://github.com/gattia>`_, `Julien Lamour <https://github.com/lamourj>`_, `@renatosc <https://github.com/renatosc>`_, `Nicolas Beaudoin-Gagnon <https://github.com/Fegalf>`_ and `@rubinovitz <https://github.com/rubinovitz>`_ for their contribution in `NeuroKit 1 <https://github.com/neuropsychology/NeuroKit.py>`_.

docs/conf.py

@@ -131,7 +131,7 @@ def find_version():
     "use_issues_button": True,
     "path_to_docs": "docs/",
     "use_edit_page_button": True,
-    "logo_only": True,
+    # "logo_only": True,
     "show_toc_level": 1,
     "navigation_with_keys": False,
 }
@@ -140,4 +140,4 @@ def find_version():
 # 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_static_path = ["_static"]

docs/examples/eeg_microstates/eeg_microstates.ipynb

@@ -589,7 +589,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Several different clustering algorithms can be used to segment your EEG recordings into microstates. These algorithms mainly differ in how they define cluster membership and the cost functionals to be optimized ([Xu & Tian, 2015](10.1007/s40745-015-0040-1)). The method to use hence depends on your data and the underlying assumptions of the methods (e.g., some methods ignore polarity). There is no one true method that gives the best results but you can refer to [Poulsen et al., 2018](https://www.researchgate.net/publication/331367421_Microstate_EEGlab_toolbox_An_introductory_guide#pf6) if you would like a more detailed review of the different clustering methods."
+    "Several different clustering algorithms can be used to segment your EEG recordings into microstates. These algorithms mainly differ in how they define cluster membership and the cost functionals to be optimized ([Xu & Tian, 2015](https://doi.org/10.1007/s40745-015-0040-1)). The method to use hence depends on your data and the underlying assumptions of the methods (e.g., some methods ignore polarity). There is no one true method that gives the best results but you can refer to [Poulsen et al., 2018](https://www.researchgate.net/publication/331367421_Microstate_EEGlab_toolbox_An_introductory_guide#pf6) if you would like a more detailed review of the different clustering methods."
    ]
   },
   {

docs/make.bat

@@ -7,7 +7,7 @@ REM Command file for Sphinx documentation
 @REM SPHINXBUILD="D:\Python3\Scripts\sphinx-build.exe"
 
 if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD==python -m sphinx
+	set SPHINXBUILD=python -m sphinx
 )
 set SOURCEDIR="."
 set BUILDDIR="_build"
@@ -34,4 +34,4 @@ goto end
 %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
 
 :end
-popd
+popd

neurokit2/eda/eda_intervalrelated.py

@@ -17,6 +17,7 @@ def eda_intervalrelated(data, sampling_rate=1000, **kwargs):
 
     Parameters
     ----------
+
     data : Union[dict, pd.DataFrame]
         A DataFrame containing the different processed signal(s) as different columns, typically
         generated by :func:`eda_process` or :func:`bio_process`. Can also take a dict containing
@@ -31,6 +32,7 @@ def eda_intervalrelated(data, sampling_rate=1000, **kwargs):
     DataFrame
         A dataframe containing the analyzed EDA features. The analyzed
         features consist of the following:
+
         * ``"SCR_Peaks_N"``: the number of occurrences of Skin Conductance Response (SCR).
         * ``"SCR_Peaks_Amplitude_Mean"``: the mean amplitude of the SCR peak occurrences.
         * ``"EDA_Tonic_SD"``: the mean amplitude of the SCR peak occurrences.
@@ -44,22 +46,22 @@ def eda_intervalrelated(data, sampling_rate=1000, **kwargs):
     .bio_process, eda_eventrelated
 
     Examples
-    ----------
+    --------
     .. ipython:: python
 
-      import neurokit2 as nk
+        import neurokit2 as nk
 
-      # Download data
-      data = nk.data("bio_resting_8min_100hz")
+        # Download data
+        data = nk.data("bio_resting_8min_100hz")
 
-      # Process the data
-      df, info = nk.eda_process(data["EDA"], sampling_rate=100)
+        # Process the data
+        df, info = nk.eda_process(data["EDA"], sampling_rate=100)
 
-      # Single dataframe is passed
-      nk.eda_intervalrelated(df, sampling_rate=100)
+        # Single dataframe is passed
+        nk.eda_intervalrelated(df, sampling_rate=100)
 
-      epochs = nk.epochs_create(df, events=[0, 25300], sampling_rate=100, epochs_end=20)
-      nk.eda_intervalrelated(epochs, sampling_rate=100)
+        epochs = nk.epochs_create(df, events=[0, 25300], sampling_rate=100, epochs_end=20)
+        nk.eda_intervalrelated(epochs, sampling_rate=100)
 
     """
 

neurokit2/eeg/mne_to_df.py

@@ -83,15 +83,26 @@ def mne_to_dict(eeg):
 
       # Raw objects
       eeg = nk.mne_data("filt-0-40_raw")
-      nk.mne_to_dict(eeg)
+      eeg_dict = nk.mne_to_dict(eeg)
+
+      # Print function result summary
+      eeg_dict_view = {k: f"Signal with length: {len(v)}" for k, v in eeg_dict.items()}
+      eeg_dict_view
+
 
       # Epochs objects
       eeg = nk.mne_data("epochs")
-      nk.mne_to_dict(eeg)
+      eeg_epoch_dict = nk.mne_to_dict(eeg)
+
+      # Print function result summary
+      list(eeg_epoch_dict.items())[:2]
 
       # Evoked objects
       eeg = nk.mne_data("evoked")
-      nk.mne_to_dict(eeg)
+      eeg_evoked_dict = nk.mne_to_dict(eeg)
+
+      # Print function result summary
+      eeg_evoked_dict
 
     """
     return _mne_convert(eeg, to_what="dict")

neurokit2/events/events_find.py

@@ -137,6 +137,7 @@ def events_find(
     Convert the event condition results its human readable representation
 
     .. ipython:: python
+
       value_to_condition = {1: "Stimulus 1", 2: "Stimulus 2", 3: "Stimulus 3"}
       events["condition"] = [value_to_condition[id] for id in events["condition"]]
       events

neurokit2/misc/progress_bar.py

@@ -19,7 +19,7 @@ def progress_bar(it, prefix="", size=40, verbose=True):
 
     Examples
     --------
-    ..ipython:: python
+    .. ipython:: python
 
       import neurokit2 as nk
 

neurokit2/rsp/rsp_process.py

@@ -68,7 +68,8 @@ def rsp_process(
         * ``"RSP_Phase"``: breathing phase, marked by "1" for inspiration and "0" for expiration.
         * ``"RSP_Phase_Completion"``: breathing phase completion, expressed in percentage (from 0 to
           1), representing the stage of the current respiratory phase.
-         * ``"RSP_RVT"``: respiratory volume per time (RVT).
+        * ``"RSP_RVT"``: respiratory volume per time (RVT).
+
     info : dict
         A dictionary containing the samples at which inhalation peaks and exhalation troughs occur,
         accessible with the keys ``"RSP_Peaks"``, and ``"RSP_Troughs"`` respectively, as well as the

neurokit2/signal/signal_detrend.py

@@ -18,10 +18,12 @@ def signal_detrend(
     sampling_rate=1000,
 ):
     """**Signal Detrending**
+
     Apply a baseline (order = 0), linear (order = 1), or polynomial (order > 1) detrending to the
     signal (i.e., removing a general trend). One can also use other methods, such as smoothness
     priors approach described by Tarvainen (2002) or LOESS regression, but these scale badly for
     long signals.
+
     Parameters
     ----------
     signal : Union[list, np.array, pd.Series]

neurokit2/signal/signal_interpolate.py

@@ -32,13 +32,13 @@ def signal_interpolate(
     method : str
         Method of interpolation. Can be ``"linear"``, ``"nearest"``, ``"zero"``, ``"slinear"``,
         ``"quadratic"``, ``"cubic"``, ``"previous"``, ``"next"``, ``"monotone_cubic"``, or ``"akima"``.
-        The methods ``"zero"``, ``"slinear"``,``"quadratic"`` and ``"cubic"`` refer to a spline
+        The methods ``"zero"``, ``"slinear"``, ``"quadratic"`` and ``"cubic"`` refer to a spline
         interpolation of zeroth, first, second or third order; whereas ``"previous"`` and
         ``"next"`` simply return the previous or next value of the point. An integer specifying the
         order of the spline interpolator to use.
-        See `here <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.
+        See `monotone cubic method <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.
         PchipInterpolator.html>`_ for details on the ``"monotone_cubic"`` method.
-        See `here <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.
+        See `akima method <https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.
         Akima1DInterpolator.html>`_ for details on the ``"akima"`` method.
     fill_value : float or tuple or str
         If a ndarray (or float), this value will be used to fill in for
@@ -87,6 +87,7 @@ def signal_interpolate(
       plt.scatter(x_values, signal, label="original datapoints", zorder=3)
       @suppress
       plt.close()
+
     """
     # Sanity checks
     if x_values is None:

studies/complexity_eeg/README.md

@@ -71,7 +71,7 @@ data_delay <- read.csv("data_delay.csv") |>
 #   facet_wrap(~Metric, scales = "free_y")
 ```
 
-#### Per Channel
+#### Per Channel Delay Optimization
 
 ``` r
 delay_perchannel <- function(data_delay, dataset="Lemon") {
@@ -247,7 +247,7 @@ data_dim <- read.csv("data_dimension.csv") |>
          Area = fct_relevel(Area, c("F", "C", "T", "P", "O")))
 ```
 
-#### Per Channel
+#### Per Channel Dimension Optimization
 
 ``` r
 dim_perchannel <- function(data_dim, dataset="Lemon") {