[MAINT] Move to pyproject.toml (#68)

nbara · May 10, 2023 · 501ef5c · 501ef5c
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diff --git a/Makefile b/Makefile
@@ -60,23 +60,32 @@ pydocstyle:
 	@pydocstyle
 
 pep:
-	@$(MAKE) -k flake pydocstyle codespell-error
-
-flake:
-	@if command -v flake8 > /dev/null; then \
-		echo "Running flake8"; \
-		flake8 --count meegkit examples; \
-	else \
-		echo "flake8 not found, please install it!"; \
-		exit 1; \
-	fi;
-	@echo "flake8 passed"
+	@$(MAKE) -k ruff codespell
 
-# Tests
+ruff:
+	@ruff check $(CODESPELL_DIRS)
+
+ruff-fix:
+	@ruff check $(CODESPELL_DIRS) --fix
+
+# Build and install
 # =============================================================================
-# test:
-# 	py.test tests
+install-requirements:
+	@echo "Checking/Installing requirements..."
+	@pip install -q -r requirements.in
+
+install:
+	@echo "Installing  package..."
+	@pip install -q --no-deps .
+	@echo "\x1b[1m\x1b[32m * Package successfully installed! \x1b[0m"
+
+install-dev:
+	@echo "Installing  package in editable mode..."
+	@pip install -q -e ".[docs, tests]" --config-settings editable_mode=compat
+	@echo "\x1b[1m\x1b[32m * Package successfully installed! \x1b[0m"
 
+# Tests
+# =============================================================================
 test: in
 	rm -f .coverage
 	$(PYTESTS) -m 'not ultraslowtest' meegkit
@@ -94,3 +103,6 @@ test-full: in
 	$(PYTESTS) meegkit
 
 .PHONY: init test
+
+
+
diff --git a/README.md b/README.md
@@ -5,19 +5,23 @@
 [![DOI](https://zenodo.org/badge/117451752.svg)](https://zenodo.org/badge/latestdoi/117451752)
 [![twitter](https://img.shields.io/twitter/follow/lebababa?style=flat&logo=Twitter)](https://twitter.com/intent/follow?screen_name=lebababa)
 
-# MEEGkit
+# `MEEGkit`
 
-Denoising tools for M/EEG processing in Python 3.7+.
+Denoising tools for M/EEG processing in Python 3.8+.
 
 ![meegkit-ERP](https://user-images.githubusercontent.com/10333715/176754293-eaa35071-94f8-40dd-a487-9f8103c92571.png)
 
-> **Disclaimer:** The project mostly consists of development code, although some modules and functions are already working. Bugs and performance problems are to be expected, so use at your own risk. More tests and improvements will be added in the future. Comments and suggestions are welcome.
+> **Disclaimer:** The project mostly consists of development code, although some modules
+and functions are already working. Bugs and performance problems are to be expected, so
+use at your own risk. More tests and improvements will be added in the future. Comments
+and suggestions are welcome.
 
 ## Documentation
 
 Automatic documentation is [available online](https://nbara.github.io/python-meegkit/).
 
-This code can also be tested directly from your browser using [Binder](https://mybinder.org), by clicking on the binder badge above.
+This code can also be tested directly from your browser using
+[Binder](https://mybinder.org), by clicking on the binder badge above.
 
 ## Installation
 
@@ -27,18 +31,21 @@ This package can be installed easily using `pip`:
 pip install meegkit
 ```
 
-Or you can clone this repository and run the following commands inside the `python-meegkit` directory:
+Or you can clone this repository and run the following commands inside the
+`python-meegkit` directory:
 
 ```bash
 pip install -r requirements.txt
 pip install .
 ```
 
-*Note* : Use developer mode with the `-e` flag (`pip install -e .`) to be able to modify the sources even after install.
+*Note* : Use developer mode with the `-e` flag (`pip install -e .`) to be able to modify
+the sources even after install.
 
 ### Advanced installation instructions
 
-Some ASR variants require additional dependencies such as `pymanopt`. To install meegkit with these optional packages, use:
+Some ASR variants require additional dependencies such as `pymanopt`. To install meegkit
+with these optional packages, use:
 
 ```bash
 pip install -e '.[extra]'
@@ -50,90 +57,95 @@ or:
 pip install meegkit[extra]
 ```
 
-Other available options are `[docs]` (which installs dependencies required to build the documentation), or `[tests]` (which install dependencies to run unit tests).
+Other available options are `[docs]` (which installs dependencies required to build the
+documentation), or `[tests]` (which install dependencies to run unit tests).
 
 ## References
 
-### 1. CCA, STAR, SNS, DSS, ZapLine, and Robust Detrending
+If you use this code, you should cite the relevant methods from the original articles.
 
-This is mostly a translation of Matlab code from the [NoiseTools toolbox](http://audition.ens.fr/adc/NoiseTools/) by Alain de Cheveigné. It builds on an initial python implementation by [Pedro Alcocer](https://github.com/pealco).
+### 1. CCA, STAR, SNS, DSS, ZapLine, and Robust Detrending
 
-Only CCA, SNS, DSS, STAR, ZapLine and robust detrending have been properly tested so far. TSCPA may give inaccurate results due to insufficient testing (contributions welcome!)
+This is mostly a translation of Matlab code from the
+[NoiseTools toolbox](http://audition.ens.fr/adc/NoiseTools/) by Alain de Cheveigné.
+It builds on an initial python implementation by 
+[Pedro Alcocer](https://github.com/pealco).
 
-If you use this code, you should cite the relevant methods from the original articles:
+Only CCA, SNS, DSS, STAR, ZapLine and robust detrending have been properly tested so far.
+TSCPA may give inaccurate results due to insufficient testing (contributions welcome!)
 
 ```sql
-[1] de Cheveigné, A. (2019). ZapLine: A simple and effective method to remove power line artifacts.
-    NeuroImage, 116356. https://doi.org/10.1016/j.neuroimage.2019.116356
-[2] de Cheveigné, A. et al. (2019). Multiway canonical correlation analysis of brain data.
-    NeuroImage, 186, 728–740. https://doi.org/10.1016/j.neuroimage.2018.11.026
-[3] de Cheveigné, A. et al. (2018). Decoding the auditory brain with canonical component analysis.
-    NeuroImage, 172, 206–216. https://doi.org/10.1016/j.neuroimage.2018.01.033
-[4] de Cheveigné, A. (2016). Sparse time artifact removal.
-    Journal of Neuroscience Methods, 262, 14–20. https://doi.org/10.1016/j.jneumeth.2016.01.005
-[5] de Cheveigné, A., & Parra, L. C. (2014). Joint decorrelation, a versatile tool for multichannel
-    data analysis. NeuroImage, 98, 487–505. https://doi.org/10.1016/j.neuroimage.2014.05.068
-[6] de Cheveigné, A. (2012). Quadratic component analysis.
-    NeuroImage, 59(4), 3838–3844. https://doi.org/10.1016/j.neuroimage.2011.10.084
-[7] de Cheveigné, A. (2010). Time-shift denoising source separation.
-    Journal of Neuroscience Methods, 189(1), 113–120. https://doi.org/10.1016/j.jneumeth.2010.03.002
+[1] de Cheveigné, A. (2019). ZapLine: A simple and effective method to remove power line 
+    artifacts. NeuroImage, 116356. https://doi.org/10.1016/j.neuroimage.2019.116356
+[2] de Cheveigné, A. et al. (2019). Multiway canonical correlation analysis of brain 
+    data. NeuroImage, 186, 728–740. https://doi.org/10.1016/j.neuroimage.2018.11.026
+[3] de Cheveigné, A. et al. (2018). Decoding the auditory brain with canonical component 
+    analysis. NeuroImage, 172, 206–216. https://doi.org/10.1016/j.neuroimage.2018.01.033
+[4] de Cheveigné, A. (2016). Sparse time artifact removal. Journal of Neuroscience 
+    Methods, 262, 14–20. https://doi.org/10.1016/j.jneumeth.2016.01.005
+[5] de Cheveigné, A., & Parra, L. C. (2014). Joint decorrelation, a versatile tool for 
+    multichannel data analysis. NeuroImage, 98, 487–505. 
+    https://doi.org/10.1016/j.neuroimage.2014.05.068
+[6] de Cheveigné, A. (2012). Quadratic component analysis. NeuroImage, 59(4), 3838–3844. 
+    https://doi.org/10.1016/j.neuroimage.2011.10.084
+[7] de Cheveigné, A. (2010). Time-shift denoising source separation. Journal of 
+    Neuroscience Methods, 189(1), 113–120. https://doi.org/10.1016/j.jneumeth.2010.03.002
 [8] de Cheveigné, A., & Simon, J. Z. (2008a). Denoising based on spatial filtering.
-    Journal of Neuroscience Methods, 171(2), 331–339. https://doi.org/10.1016/j.jneumeth.2008.03.015
-[9] de Cheveigné, A., & Simon, J. Z. (2008b). Sensor noise suppression.
-    Journal of Neuroscience Methods, 168(1), 195–202. https://doi.org/10.1016/j.jneumeth.2007.09.012
+    Journal of Neuroscience Methods, 171(2), 331–339. 
+    https://doi.org/10.1016/j.jneumeth.2008.03.015
+[9] de Cheveigné, A., & Simon, J. Z. (2008b). Sensor noise suppression. Journal of 
+    Neuroscience Methods, 168(1), 195–202. https://doi.org/10.1016/j.jneumeth.2007.09.012
 [10] de Cheveigné, A., & Simon, J. Z. (2007). Denoising based on time-shift PCA.
-     Journal of Neuroscience Methods, 165(2), 297–305. https://doi.org/10.1016/j.jneumeth.2007.06.003
+     Journal of Neuroscience Methods, 165(2), 297–305. 
+     https://doi.org/10.1016/j.jneumeth.2007.06.003
 ```
 
 ### 2. Artifact Subspace Reconstruction (ASR)
 
-The base code is inspired from the original [EEGLAB inplementation](https://github.com/sccn/clean_rawdata) [1], while the riemannian variant [2] was adapted from the [rASR toolbox](https://github.com/s4rify/rASRMatlab) by Sarah Blum.
-
-If you use this code, you should cite the relevant methods from the original articles:
+The base code is inspired from the original 
+[EEGLAB inplementation](https://github.com/sccn/clean_rawdata) [1], while the Riemannian
+variant [2] was adapted from the [rASR toolbox](https://github.com/s4rify/rASRMatlab) by
+Sarah Blum.
 
 ```sql
-[1] Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., et al. (2015).
-    Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Bio-Med.
-    Eng. 62, 2553–2567. https://doi.org/10.1109/TBME.2015.2481482
-[2] Blum, S., Jacobsen, N., Bleichner, M. G., & Debener, S. (2019). A Riemannian modification of
-    artifact subspace reconstruction for EEG artifact handling. Frontiers in human neuroscience,
-    13, 141.
+[1] Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S., 
+    et al. (2015). Real-time neuroimaging and cognitive monitoring using wearable dry 
+    EEG. IEEE Trans. Bio-Med. Eng. 62, 2553–2567. 
+    https://doi.org/10.1109/TBME.2015.2481482
+[2] Blum, S., Jacobsen, N., Bleichner, M. G., & Debener, S. (2019). A Riemannian 
+    modification of artifact subspace reconstruction for EEG artifact handling. Frontiers 
+    in human neuroscience, 13, 141.
 ```
 
 ### 3. Rhythmic Entrainment Source Separation (RESS)
 
 The code is based on [Matlab code from Mike X. Cohen](https://mikexcohen.com/data/) [1]
 
-If you use this, you should cite the following article:
-
 ```sql
-[1] Cohen, M. X., & Gulbinaite, R. (2017). Rhythmic entrainment source separation: Optimizing analyses
-    of neural responses to rhythmic sensory stimulation. Neuroimage, 147, 43-56.
+[1] Cohen, M. X., & Gulbinaite, R. (2017). Rhythmic entrainment source separation: 
+    Optimizing analyses of neural responses to rhythmic sensory stimulation. Neuroimage, 
+    147, 43-56.
 ```
 
 ### 4. Task-Related Component Analysis (TRCA)
 
-This code is based on the [Matlab implementation from Masaki Nakanishi](https://github.com/mnakanishi/TRCA-SSVEP), and was adapted to python by [Giuseppe Ferraro](mailto:[email protected])
-
-If you use this, you should cite the following articles:
+This code is based on the [Matlab implementation from Masaki Nakanishi](https://github.com/mnakanishi/TRCA-SSVEP),
+and was adapted to python by [Giuseppe Ferraro](mailto:[email protected])
 
 ```sql
 [1] M. Nakanishi, Y. Wang, X. Chen, Y.-T. Wang, X. Gao, and T.-P. Jung,
-    "Enhancing detection of SSVEPs for a high-speed brain speller using
-    task-related component analysis", IEEE Trans. Biomed. Eng, 65(1): 104-112,
-    2018.
-[2] X. Chen, Y. Wang, S. Gao, T. -P. Jung and X. Gao, "Filter bank
-    canonical correlation analysis for implementing a high-speed SSVEP-based
-    brain-computer interface", J. Neural Eng., 12: 046008, 2015.
-[3] X. Chen, Y. Wang, M. Nakanishi, X. Gao, T. -P. Jung, S. Gao,
-    "High-speed spelling with a noninvasive brain-computer interface",
-    Proc. Int. Natl. Acad. Sci. U. S. A, 112(44): E6058-6067, 2015.
+    "Enhancing detection of SSVEPs for a high-speed brain speller using task-related 
+    component analysis", IEEE Trans. Biomed. Eng, 65(1): 104-112, 2018.
+[2] X. Chen, Y. Wang, S. Gao, T. -P. Jung and X. Gao, "Filter bank canonical correlation 
+    analysis for implementing a high-speed SSVEP-based brain-computer interface", 
+    J. Neural Eng., 12: 046008, 2015.
+[3] X. Chen, Y. Wang, M. Nakanishi, X. Gao, T. -P. Jung, S. Gao, "High-speed spelling 
+    with a noninvasive brain-computer interface", Proc. Int. Natl. Acad. Sci. U.S.A, 
+    112(44): E6058-6067, 2015.
 ```
 
 ### 5. Local Outlier Factor (LOF)
 
-If you use this, you should cite the following article:
-
 ```sql
 [1] Breunig M, Kriegel HP, Ng RT, Sander J. 2000. LOF: identifying density-based 
     local outliers. SIGMOD Rec. 29, 2, 93-104. https://doi.org/10.1145/335191.335388

diff --git a/citation.cff b/citation.cff
@@ -5,7 +5,7 @@ authors:
   given-names: "Nicolas"
   orcid: "https://orcid.org/0000-0003-1495-561X"
 title: "MEEGkit"
-version: 0.1.3
+version: 0.1.4
 doi: 10.5281/zenodo.5643659
 date-released: 2021-10-15
 url: "https://github.com/nbara/python-meegkit"
diff --git a/doc/_static/logo-dark.png b/doc/_static/logo-dark.png
diff --git a/doc/_static/logo.png b/doc/_static/logo.png
diff --git a/doc/conf.py b/doc/conf.py
@@ -12,20 +12,22 @@
 #
 import os
 import sys
-import matplotlib
-matplotlib.use('agg')
+
+import matplotlib as mpl
+
+mpl.use("agg")
 
 curdir = os.path.dirname(__file__)
-sys.path.append(os.path.abspath(os.path.join(curdir, '..')))
-sys.path.append(os.path.abspath(os.path.join(curdir, '..', 'meegkit')))
+sys.path.append(os.path.abspath(os.path.join(curdir, "..")))
+sys.path.append(os.path.abspath(os.path.join(curdir, "..", "meegkit")))
 
 import meegkit # noqa
 
 # -- Project information -----------------------------------------------------
 
-project = 'MEEGkit'
-copyright = '2022, Nicolas Barascud'
-author = 'Nicolas Barascud'
+project = "MEEGkit"
+copyright = "2023, Nicolas Barascud"
+author = "Nicolas Barascud"
 release = meegkit.__version__
 version = meegkit.__version__
 
@@ -35,31 +37,31 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    'sphinx.ext.autodoc',
-    'sphinx.ext.autosummary',
-    'sphinx.ext.napoleon',
-    'numpydoc',
-    'jupyter_sphinx',
-    'sphinx_gallery.gen_gallery',
-    'sphinxemoji.sphinxemoji',
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.napoleon",
+    "numpydoc",
+    "jupyter_sphinx",
+    "sphinx_gallery.gen_gallery",
+    "sphinxemoji.sphinxemoji",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'config.py']
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "config.py"]
 
 # generate autosummary even if no references
 # autosummary_generate = True
 autodoc_default_options = {
-    'members': True,
-    'special-members': '__init__',
-    'undoc-members': True,
-    'show-inheritance': True,
-    'exclude-members': '__weakref__'
+    "members": True,
+    "special-members": "__init__",
+    "undoc-members": True,
+    "show-inheritance": True,
+    "exclude-members": "__weakref__"
 }
 numpydoc_show_class_members = True
 
@@ -75,15 +77,19 @@
 # -- Options for HTML output -------------------------------------------------
 
 # The theme to use for HTML and HTML Help pages
-html_theme = 'pydata_sphinx_theme'
+html_theme = "pydata_sphinx_theme"
 
 # 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"]
 
 html_theme_options = {
+   "logo": {
+      "image_light": "_static/logo.png",
+      "image_dark": "_static/logo-dark.png",
+      "text": "meegkit",
+   },
     "show_toc_level": 1,
     "external_links": [
         {
@@ -95,12 +101,12 @@
         {
             "name": "GitHub",
             "url": "https://github.com/nbara/python-meegkit",
-            "icon": "fab fa-github-square",
+            "icon": "fa-brands fa-github",
         },
         {
             "name": "Twitter",
             "url": "https://twitter.com/lebababa",
-            "icon": "fab fa-twitter-square",
+            "icon": "fa-brands fa-twitter",
         },
     ],
     "use_edit_page_button": True,
@@ -116,10 +122,10 @@
 # -- Options for Sphinx-gallery HTML ------------------------------------------
 
 sphinx_gallery_conf = {
-    'doc_module': ('meegkit',),
-    'examples_dirs': '../examples',   # path to your example scripts
-    'gallery_dirs': 'auto_examples',  # path to where to save gallery generated output
-    'filename_pattern': '/example_',
-    'ignore_pattern': 'config.py',
-    'run_stale_examples': False,
+    "doc_module": ("meegkit",),
+    "examples_dirs": "../examples",   # path to your example scripts
+    "gallery_dirs": "auto_examples",  # path to where to save gallery generated output
+    "filename_pattern": "/example_",
+    "ignore_pattern": "config.py",
+    "run_stale_examples": False,
 }