internlm/SWE-Fixer-Train-110K huggingface dataset:
Repository: mne-tools/mne-python

Issue:
using picks with ica.plot_sources breaks interactive properties plot
If i specify `picks` to `ica.plot_sources` If I then click on the ica_name to see the properties the indexing is incorrect and the properties for the wrong IC is plotted

For example in the image below I am not plotting **ICA001**, now if I right click on the **ICA004** to plot it's properties I get a window with the correct IC name however the topo and presumably the rest of the the properties plot is for the wrong IC (ICA003) in this case.

<img width="849" alt="image" src="https://user-images.githubusercontent.com/5585821/110039739-b3d51600-7d0f-11eb-9dd8-3a9b93ae7793.png">

cc @drammock 


Discussion:
I can reproduce, and yes, the data is for the wrong IC (i.e., it's not just the subplot title that is wrong)

Resolving patch:
diff --git a/mne/viz/_figure.py b/mne/viz/_figure.py
--- a/mne/viz/_figure.py
+++ b/mne/viz/_figure.py
@@ -862,9 +862,12 @@ def _create_ch_location_fig(self, pick):
         fig.lasso.style_sensors(inds)
         plt_show(fig=fig)
 
-    def _create_ica_properties_fig(self, pick):
+    def _create_ica_properties_fig(self, idx):
         """Show ICA properties for the selected component."""
-        ch_name = self.mne.ch_names[pick]
+        ch_name = self.mne.ch_names[idx]
+        if ch_name not in self.mne.ica._ica_names:  # for EOG chans: do nothing
+            return
+        pick = self.mne.ica._ica_names.index(ch_name)
         fig = self._new_child_figure(figsize=(7, 6), fig_name=None,
                                      window_title=f'{ch_name} properties')
         fig, axes = _create_properties_layout(fig=fig)


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in plot_parcellation.py
convert references in `examples/visualization/plot_parcellation.py` to use footcite / footbibliography



Discussion:

Resolving patch:
diff --git a/examples/visualization/plot_parcellation.py b/examples/visualization/plot_parcellation.py
--- a/examples/visualization/plot_parcellation.py
+++ b/examples/visualization/plot_parcellation.py
@@ -4,9 +4,10 @@
 Plot a cortical parcellation
 ============================
 
-In this example, we download the HCP-MMP1.0 parcellation [1]_ and show it
-on ``fsaverage``.
-We will also download the customized 448-label aparc parcellation from [2]_
+In this example, we download the HCP-MMP1.0 parcellation
+:footcite:`GlasserEtAl2016` and show it on ``fsaverage``.
+We will also download the customized 448-label aparc
+parcellation from :footcite:`KhanEtAl2018`.
 
 .. note:: The HCP-MMP dataset has license terms restricting its use.
           Of particular relevance:
@@ -15,14 +16,6 @@
               derived from WU-Minn HCP data when publicly presenting any
               results or algorithms that benefitted from their use."
 
-References
-----------
-.. [1] Glasser MF et al. (2016) A multi-modal parcellation of human
-       cerebral cortex. Nature 536:171-178.
-.. [2] Khan S et al. (2018) Maturation trajectories of cortical
-       resting-state networks depend on the mediating frequency band.
-       Neuroimage 174 57-68.
-
 """
 # Author: Eric Larson <larson.eric.d@gmail.com>
 #         Denis Engemann <denis.engemann@gmail.com>
@@ -61,3 +54,8 @@
 brain = Brain('fsaverage', 'lh', 'inflated', subjects_dir=subjects_dir,
               cortex='low_contrast', background='white', size=(800, 600))
 brain.add_annotation('aparc_sub')
+
+###############################################################################
+# References
+# ----------
+# .. footbibliography::


Test patch:
Repository: mne-tools/mne-python

Issue:
sidebar directive in the new doc theme not producing a sidebar
It appears `.. sidebar::` will not actually create a sidebar anymore – which **does** make sense IMHO with the new layout; however maybe we should replace "sidebars" with "info boxes" now?

Source:
<img width="500" alt="Screen Shot 2021-03-12 at 12 24 06" src="https://user-images.githubusercontent.com/2046265/110934250-0f199080-832e-11eb-9e97-a7a30bd0f4a8.png">

Rendering:
<img width="500" alt="Screen Shot 2021-03-12 at 12 23 42" src="https://user-images.githubusercontent.com/2046265/110934271-15a80800-832e-11eb-8b69-87ded7515050.png">



Discussion:
Can you look to see what needs to be changed in https://github.com/larsoner/sphinx-bootstrap-divs which we vendor in https://github.com/mne-tools/mne-python/tree/main/doc/sphinxext/sphinx_bootstrap_divs ?
... actually we shouldn't even really use that anymore since it's for sphinx-bootstrap-theme, which uses BS3, and pydata-sphinx-theme uses BS4 I think.

I'd propose adding whatever the correct thing is (if it is info boxes) to the upstream pydata-sphinx-theme to see if they'd want it. If they do then let's implement it there, if they don't then we can vendor in our own `doc/sphinxext/*` somewhere.

Resolving patch:
diff --git a/doc/conf.py b/doc/conf.py
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -475,8 +475,21 @@ def append_attr_meth_examples(app, what, name, obj, options, lines):
 # further.  For a list of options available for each theme, see the
 # documentation.
 html_theme_options = {
-    'github_url': 'https://github.com/mne-tools/mne-python',
-    'twitter_url': 'https://twitter.com/mne_python',
+    'icon_links': [
+        dict(name='GitHub',
+             url='https://github.com/mne-tools/mne-python',
+             icon='fab fa-github-square'),
+        dict(name='Twitter',
+             url='https://twitter.com/mne_python',
+             icon='fab fa-twitter-square'),
+        dict(name='Discourse',
+             url='https://mne.discourse.group/',
+             icon='fab fa-discourse'),
+        dict(name='Discord',
+             url='https://discord.gg/rKfvxTuATa',
+             icon='fab fa-discord')
+    ],
+    'icon_links_label': 'Quick Links',  # for screen reader
     'use_edit_page_button': False,
     'navigation_with_keys': False,
     'show_toc_level': 1,
@@ -499,8 +512,6 @@ def append_attr_meth_examples(app, what, name, obj, options, lines):
 html_static_path = ['_static']
 html_css_files = [
     'style.css',
-    'font-source-sans-pro.css',
-    'font-source-code-pro.css',
 ]
 
 # Add any extra paths that contain custom files (such as robots.txt or


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in plot_evoked_whitening.py
convert references in `examples/visualization/plot_evoked_whitening.py` to use footcite / footbibliography



Discussion:
I would take this one. 

Resolving patch:
diff --git a/examples/visualization/plot_evoked_whitening.py b/examples/visualization/plot_evoked_whitening.py
--- a/examples/visualization/plot_evoked_whitening.py
+++ b/examples/visualization/plot_evoked_whitening.py
@@ -7,13 +7,12 @@
 matrix. It's an excellent quality check to see if baseline signals match
 the assumption of Gaussian white noise during the baseline period.
 
-Covariance estimation and diagnostic plots are based on [1]_.
+Covariance estimation and diagnostic plots are based on
+:footcite:`EngemannGramfort2015`.
 
 References
 ----------
-.. [1] Engemann D. and Gramfort A. (2015) Automated model selection in
-    covariance estimation and spatial whitening of MEG and EEG signals, vol.
-    108, 328-342, NeuroImage.
+.. footbibliography::
 
 """
 # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in plot_sleep.py
convert references in `tutorials/sample-datasets/plot_sleep.py` to use footcite / footbibliography



Discussion:
Hi, I'll take that one :)

Resolving patch:
diff --git a/tutorials/sample-datasets/plot_sleep.py b/tutorials/sample-datasets/plot_sleep.py
--- a/tutorials/sample-datasets/plot_sleep.py
+++ b/tutorials/sample-datasets/plot_sleep.py
@@ -5,15 +5,17 @@
 Sleep stage classification from polysomnography (PSG) data
 ==========================================================
 
-.. note:: This code is taken from the analysis code used in [3]_. If you reuse
-          this code please consider citing this work.
+.. note:: This code is taken from the analysis code used in
+          :footcite:`ChambonEtAl2018`. If you reuse this code please consider
+          citing this work.
 
 This tutorial explains how to perform a toy polysomnography analysis that
 answers the following question:
 
-.. important:: Given two subjects from the Sleep Physionet dataset [1]_ [2]_,
-               namely *Alice* and *Bob*, how well can we predict the sleep
-               stages of *Bob* from *Alice's* data?
+.. important:: Given two subjects from the Sleep Physionet dataset
+               :footcite:`KempEtAl2000,GoldbergerEtAl2000`, namely
+               *Alice* and *Bob*, how well can we predict the sleep stages of
+               *Bob* from *Alice's* data?
 
 This problem is tackled as supervised multiclass classification task. The aim
 is to predict the sleep stage from 5 possible stages for each chunk of 30
@@ -53,7 +55,8 @@
 #
 # MNE-Python provides us with
 # :func:`mne.datasets.sleep_physionet.age.fetch_data` to conveniently download
-# data from the Sleep Physionet dataset [1]_ [2]_.
+# data from the Sleep Physionet dataset
+# :footcite:`KempEtAl2000,GoldbergerEtAl2000`.
 # Given a list of subjects and records, the fetcher downloads the data and
 # provides us for each subject, a pair of files:
 #
@@ -303,20 +306,4 @@ def eeg_power_band(epochs):
 #
 # References
 # ----------
-#
-# .. [1] B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of
-#        a sleep-dependent neuronal feedback loop: the slow-wave
-#        microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000).
-#
-# .. [2] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
-#        Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000)
-#        PhysioBank, PhysioToolkit, and PhysioNet: Components of a New
-#        Research Resource for Complex Physiologic Signals.
-#        Circulation 101(23):e215-e220
-#
-# .. [3] Chambon, S., Galtier, M., Arnal, P., Wainrib, G. and Gramfort, A.
-#       (2018)A Deep Learning Architecture for Temporal Sleep Stage
-#       Classification Using Multivariate and Multimodal Time Series.
-#       IEEE Trans. on Neural Systems and Rehabilitation Engineering 26:
-#       (758-769).
-#
+# .. footbibliography::


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in regression.py
convert references in `mne/stats/regression.py` to use footcite / footbibliography



Discussion:
I'll take this one.

Resolving patch:
diff --git a/mne/stats/regression.py b/mne/stats/regression.py
--- a/mne/stats/regression.py
+++ b/mne/stats/regression.py
@@ -158,7 +158,7 @@ def linear_regression_raw(raw, events, event_id=None, tmin=-.1, tmax=1,
     Internally, this constructs a predictor matrix X of size
     n_samples * (n_conds * window length), solving the linear system
     ``Y = bX`` and returning ``b`` as evoked-like time series split by
-    condition. See [1]_.
+    condition. See :footcite:`SmithKutas2015`.
 
     Parameters
     ----------
@@ -237,9 +237,7 @@ def linear_regression_raw(raw, events, event_id=None, tmin=-.1, tmax=1,
 
     References
     ----------
-    .. [1] Smith, N. J., & Kutas, M. (2015). Regression-based estimation of ERP
-           waveforms: II. Non-linear effects, overlap correction, and practical
-           considerations. Psychophysiology, 52(2), 169-189.
+    .. footbibliography::
     """
     from scipy import linalg
     if isinstance(solver, str):


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in raw.py
convert references in `mne/simulation/raw.py` to use footcite / footbibliography



Discussion:
Hey, I'm working on this issue !

Resolving patch:
diff --git a/mne/simulation/raw.py b/mne/simulation/raw.py
--- a/mne/simulation/raw.py
+++ b/mne/simulation/raw.py
@@ -166,7 +166,7 @@ def simulate_raw(info, stc=None, trans=None, src=None, bem=None, head_pos=None,
         solution filename (e.g., "sample-5120-5120-5120-bem-sol.fif").
         Can be None if ``forward`` is provided.
     %(head_pos)s
-        See for example [1]_.
+        See for example :footcite:`LarsonTaulu2017`.
     mindist : float
         Minimum distance between sources and the inner skull boundary
         to use during forward calculation.
@@ -237,9 +237,7 @@ def simulate_raw(info, stc=None, trans=None, src=None, bem=None, head_pos=None,
 
     References
     ----------
-    .. [1] Larson E, Taulu S (2017). "The Importance of Properly Compensating
-           for Head Movements During MEG Acquisition Across Different Age
-           Groups." Brain Topogr 30:172–181
+    .. footbibliography::
     """  # noqa: E501
     _validate_type(info, Info, 'info')
     raw_verbose = verbose
@@ -369,7 +367,7 @@ def add_eog(raw, head_pos=None, interp='cos2', n_jobs=1, random_state=None,
     1. Random activation times are drawn from an inhomogeneous poisson
        process whose blink rate oscillates between 4.5 blinks/minute
        and 17 blinks/minute based on the low (reading) and high (resting)
-       blink rates from [1]_.
+       blink rates from :footcite:`BentivoglioEtAl1997`.
     2. The activation kernel is a 250 ms Hanning window.
     3. Two activated dipoles are located in the z=0 plane (in head
        coordinates) at ±30 degrees away from the y axis (nasion).
@@ -382,8 +380,7 @@ def add_eog(raw, head_pos=None, interp='cos2', n_jobs=1, random_state=None,
 
     References
     ----------
-    .. [1] Bentivoglio et al. "Analysis of blink rate patterns in normal
-           subjects" Movement Disorders, 1997 Nov;12(6):1028-34.
+    .. footbibliography::
     """
     return _add_exg(raw, 'blink', head_pos, interp, n_jobs, random_state)
 


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in plot_otp.py
convert references in `examples/preprocessing/plot_otp.py` to use footcite / footbibliography


Discussion:
I will take it

Resolving patch:
diff --git a/examples/preprocessing/plot_otp.py b/examples/preprocessing/plot_otp.py
--- a/examples/preprocessing/plot_otp.py
+++ b/examples/preprocessing/plot_otp.py
@@ -3,8 +3,8 @@
 Plot sensor denoising using oversampled temporal projection
 ===========================================================
 
-This demonstrates denoising using the OTP algorithm [1]_ on data with
-with sensor artifacts (flux jumps) and random noise.
+This demonstrates denoising using the OTP algorithm :footcite:`LarsonTaulu2018`
+on data with with sensor artifacts (flux jumps) and random noise.
 """
 # Author: Eric Larson <larson.eric.d@gmail.com>
 #
@@ -79,6 +79,4 @@ def compute_bias(raw):
 ###############################################################################
 # References
 # ----------
-# .. [1] Larson E, Taulu S (2017). Reducing Sensor Noise in MEG and EEG
-#        Recordings Using Oversampled Temporal Projection.
-#        IEEE Transactions on Biomedical Engineering.
+# .. footbibliography::


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in source_estimate.py
convert references in `mne/source_estimate.py` to use footcite / footbibliography



Discussion:
Hello! I will pick this one for my issue.

Resolving patch:
diff --git a/mne/source_estimate.py b/mne/source_estimate.py
--- a/mne/source_estimate.py
+++ b/mne/source_estimate.py
@@ -1636,6 +1636,7 @@ def estimate_snr(self, info, fwd, cov, verbose=None):
 
         This function should only be used with source estimates with units
         nanoAmperes (i.e., MNE-like solutions, *not* dSPM or sLORETA).
+        See also :footcite:`GoldenholzEtAl2009`.
 
         .. warning:: This function currently only works properly for fixed
                      orientation.
@@ -1672,11 +1673,7 @@ def estimate_snr(self, info, fwd, cov, verbose=None):
 
         References
         ----------
-        .. [1] Goldenholz, D. M., Ahlfors, S. P., Hämäläinen, M. S., Sharon,
-               D., Ishitobi, M., Vaina, L. M., & Stufflebeam, S. M. (2009).
-               Mapping the Signal-To-Noise-Ratios of Cortical Sources in
-               Magnetoencephalography and Electroencephalography.
-               Human Brain Mapping, 30(4), 1077–1086. doi:10.1002/hbm.20571
+        .. footbibliography::
         """
         from .forward import convert_forward_solution, Forward
         from .minimum_norm.inverse import _prepare_forward
@@ -1710,7 +1707,7 @@ def center_of_mass(self, subject=None, hemi=None, restrict_vertices=False,
         """Compute the center of mass of activity.
 
         This function computes the spatial center of mass on the surface
-        as well as the temporal center of mass as in [1]_.
+        as well as the temporal center of mass as in :footcite:`LarsonLee2013`.
 
         .. note:: All activity must occur in a single hemisphere, otherwise
                   an error is raised. The "mass" of each point in space for
@@ -1762,8 +1759,7 @@ def center_of_mass(self, subject=None, hemi=None, restrict_vertices=False,
 
         References
         ----------
-        .. [1] Larson and Lee, "The cortical dynamics underlying effective
-               switching of auditory spatial attention", NeuroImage 2012.
+        .. footbibliography::
         """
         if not isinstance(surf, str):
             raise TypeError('surf must be a string, got %s' % (type(surf),))


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in plot_time_frequency_global_field_power.py
convert references in `examples/time_frequency/plot_time_frequency_global_field_power.py` to use footcite / footbibliography



Discussion:
Hi, can I be assigned to this one? I'll work on this

Resolving patch:
diff --git a/examples/time_frequency/plot_time_frequency_global_field_power.py b/examples/time_frequency/plot_time_frequency_global_field_power.py
--- a/examples/time_frequency/plot_time_frequency_global_field_power.py
+++ b/examples/time_frequency/plot_time_frequency_global_field_power.py
@@ -6,21 +6,24 @@
 ===========================================================
 
 The objective is to show you how to explore spectrally localized
-effects. For this purpose we adapt the method described in [1]_ and use it on
-the somato dataset. The idea is to track the band-limited temporal evolution
+effects. For this purpose we adapt the method described in
+:footcite:`HariSalmelin1997` and use it on the somato dataset.
+The idea is to track the band-limited temporal evolution
 of spatial patterns by using the :term:`global field power` (GFP).
 
 We first bandpass filter the signals and then apply a Hilbert transform. To
 reveal oscillatory activity the evoked response is then subtracted from every
 single trial. Finally, we rectify the signals prior to averaging across trials
 by taking the magniude of the Hilbert.
-Then the :term:`GFP` is computed as described in [2]_, using the sum of the
+Then the :term:`GFP` is computed as described in
+:footcite:`EngemannGramfort2015`, using the sum of the
 squares but without normalization by the rank.
 Baselining is subsequently applied to make the :term:`GFP` comparable
 between frequencies.
 The procedure is then repeated for each frequency band of interest and
 all :term:`GFPs<GFP>` are visualized. To estimate uncertainty, non-parametric
-confidence intervals are computed as described in [3]_ across channels.
+confidence intervals are computed as described in :footcite:`EfronHastie2016`
+across channels.
 
 The advantage of this method over summarizing the Space x Time x Frequency
 output of a Morlet Wavelet in frequency bands is relative speed and, more
@@ -31,15 +34,8 @@
 
 References
 ----------
+.. footbibliography::
 
-.. [1] Hari R. and Salmelin R. Human cortical oscillations: a neuromagnetic
-       view through the skull (1997). Trends in Neuroscience 20 (1),
-       pp. 44-49.
-.. [2] Engemann D. and Gramfort A. (2015) Automated model selection in
-       covariance estimation and spatial whitening of MEG and EEG signals,
-       vol. 108, 328-342, NeuroImage.
-.. [3] Efron B. and Hastie T. Computer Age Statistical Inference (2016).
-       Cambrdige University Press, Chapter 11.2.
 """  # noqa: E501
 # Authors: Denis A. Engemann <denis.engemann@gmail.com>
 #          Stefan Appelhoff <stefan.appelhoff@mailbox.org>


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in plot_time_frequency_erds.py
convert references in `examples/time_frequency/plot_time_frequency_erds.py` to use footcite / footbibliography



Discussion:
I can take care of this one. 

Resolving patch:
diff --git a/examples/time_frequency/plot_time_frequency_erds.py b/examples/time_frequency/plot_time_frequency_erds.py
--- a/examples/time_frequency/plot_time_frequency_erds.py
+++ b/examples/time_frequency/plot_time_frequency_erds.py
@@ -5,12 +5,13 @@
 
 This example calculates and displays ERDS maps of event-related EEG data. ERDS
 (sometimes also written as ERD/ERS) is short for event-related
-desynchronization (ERD) and event-related synchronization (ERS) [1]_.
+desynchronization (ERD) and event-related synchronization (ERS)
+:footcite:`PfurtschellerLopesdaSilva1999`.
 Conceptually, ERD corresponds to a decrease in power in a specific frequency
 band relative to a baseline. Similarly, ERS corresponds to an increase in
 power. An ERDS map is a time/frequency representation of ERD/ERS over a range
-of frequencies [2]_. ERDS maps are also known as ERSP (event-related spectral
-perturbation) [3]_.
+of frequencies :footcite:`GraimannEtAl2002`. ERDS maps are also known as ERSP
+(event-related spectral perturbation) :footcite:`Makeig1993`.
 
 We use a public EEG BCI data set containing two different motor imagery tasks
 available at PhysioNet. The two tasks are imagined hand and feet movement. Our
@@ -26,15 +27,7 @@
 References
 ----------
 
-.. [1] G. Pfurtscheller, F. H. Lopes da Silva. Event-related EEG/MEG
-       synchronization and desynchronization: basic principles. Clinical
-       Neurophysiology 110(11), 1842-1857, 1999.
-.. [2] B. Graimann, J. E. Huggins, S. P. Levine, G. Pfurtscheller.
-       Visualization of significant ERD/ERS patterns in multichannel EEG and
-       ECoG data. Clinical Neurophysiology 113(1), 43-47, 2002.
-.. [3] S. Makeig. Auditory event-related dynamics of the EEG spectrum and
-       effects of exposure to tones. Electroencephalography and Clinical
-       Neurophysiology 86(4), 283-293, 1993.
+.. footbibliography::
 """
 # Authors: Clemens Brunner <clemens.brunner@gmail.com>
 #


Test patch:
Repository: mne-tools/mne-python

Issue:
use bibtex in multi_comp.py
convert references in `mne/stats/multi_comp.py` to use footcite / footbibliography



Discussion:
I'm taking this one
You can add this to the bibtex file.  Try to insert it roughly in the right spot for alphabetical order by first author surname (not strictly required, but makes it easier to scan through)

```
@article{GenoveseEtAl2002,
  title = {Thresholding of {{Statistical Maps}} in {{Functional Neuroimaging Using}} the {{False Discovery Rate}}},
  author = {Genovese, Christopher R. and Lazar, Nicole A. and Nichols, Thomas},
  year = {2002},
  volume = {15},
  pages = {870--878},
  doi = {10.1006/nimg.2001.1037},
  journal = {NeuroImage},
  number = {4}
}
```

Resolving patch:
diff --git a/mne/stats/multi_comp.py b/mne/stats/multi_comp.py
--- a/mne/stats/multi_comp.py
+++ b/mne/stats/multi_comp.py
@@ -17,7 +17,7 @@ def _ecdf(x):
 def fdr_correction(pvals, alpha=0.05, method='indep'):
     """P-value correction with False Discovery Rate (FDR).
 
-    Correction for multiple comparison using FDR [1]_.
+    Correction for multiple comparison using FDR :footcite:`GenoveseEtAl2002`.
 
     This covers Benjamini/Hochberg for independent or positively correlated and
     Benjamini/Yekutieli for general or negatively correlated tests.
@@ -41,9 +41,7 @@ def fdr_correction(pvals, alpha=0.05, method='indep'):
 
     References
     ----------
-    .. [1] Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps
-           in functional neuroimaging using the false discovery rate.
-           Neuroimage. 2002 Apr;15(4):870-8.
+    .. footbibliography::
     """
     pvals = np.asarray(pvals)
     shape_init = pvals.shape


Test patch: