Merge branch 'main' of github.com:schmidtfa/pyrasa

.github/workflows/documentation.yml

@@ -1,8 +1,9 @@
 name: documentation
 
-on: [push, pull_request, workflow_dispatch]
-   branches:
-      - main 
+on:
+  push:
+    branches: 
+      - main
 
 permissions:
   contents: write
@@ -22,7 +23,7 @@ jobs:
           frozen: false
           environments: doc
       - run: pixi run -e doc build_docs
-      - uses: peaceiris/actions-gh-pages@v3
+      - uses: peaceiris/actions-gh-pages@v4
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
           publish_dir: ./doc/build/html

.github/workflows/draft-pdf.yml

@@ -0,0 +1,24 @@
+name: Draft PDF
+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v4
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf

README.md

@@ -1,6 +1,6 @@
 # PyRASA - Spectral parameterization in python based on IRASA
 
-[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
+[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
 [![License](https://img.shields.io/badge/License-BSD_2--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)
 [![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
 [![Coverage Status](https://coveralls.io/repos/github/schmidtfa/pyrasa/badge.svg?branch=main)](https://coveralls.io/github/schmidtfa/pyrasa?branch=main)
@@ -30,7 +30,7 @@ $ pip install pyrasa
 or conda
 
 ```bash
-$ conda install pyrasa 
+$ conda install -c conda-forge pyrasa 
 ```
 
 ### Dependencies
@@ -117,4 +117,4 @@ Wen, H., & Liu, Z. (2016). Separating fractal and oscillatory components in the
 
 If you are using PyRASA it would be nice, if you could additionally cite us (whenever the paper is finally ready):
 
-Schmidt F., Hartmann T., & Weisz, N. (2049). PyRASA - Spectral parameterization in python based on IRASA. SOME JOURNAL THAT LIKES US
+Schmidt F., Hartmann T., & Weisz, N. (2049). PyRASA - Spectral parameterization in python based on IRASA. SOME JOURNAL THAT LIKES US

doc/source/examples/irasa_mne.ipynb

@@ -17,23 +17,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
     "from neurodsp.sim import sim_combined\n",
     "import numpy as np\n",
     "import scipy.signal as dsp\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "import pandas as pd\n",
     "\n",
-    "sys.path.append('../')\n",
     "import seaborn as sns\n",
     "\n",
-    "import matplotlib as mpl\n",
-    "new_rc_params = {'text.usetex': False,\n",
-    "                 \"svg.fonttype\": 'none'\n",
-    "}\n",
-    "mpl.rcParams.update(new_rc_params)\n",
-    "\n",
     "import mne\n",
     "from mne.datasets import sample\n",
     "\n",

doc/source/examples/irasa_sprint.ipynb

@@ -6,8 +6,8 @@
    "source": [
     "# Run IRASA timeresolved\n",
     "\n",
-    "One of the original features of IRASA was its applicability in the time-frequency domain (Wen & Liu 2016).\n",
-    "The authors used this to investigate changes periodic and aperiodic activity over time and even computed broadband correlations of aperiodic activity over channels across time (see  [Wen & Liu, 2016](https://doi.org/10.1523/JNEUROSCI.0187-16.2016)). To make this form of analysis more accessible and track aperiodic and periodic changes over time we implemented the irasa_sprint function, that similarly to the SPRiNT package ([Wilson, da Silva Castanheira & Baillet, 2022](https://doi.org/10.7554/eLife.77348)), enables you to compute periodic and aperiodic spectrograms."
+    "In the original IRASA manuscript Wen & Liu ([2016](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4706469/)) show that the algorithm can be used in the time-frequency domain.\n",
+    "The authors used this in a subsequent manuscript to investigate changes periodic and aperiodic activity over time and even computed broadband correlations of aperiodic activity over channels across time (see [Wen & Liu, 2016](https://doi.org/10.1523/JNEUROSCI.0187-16.2016)). To make this form of analysis more accessible and track aperiodic and periodic changes over time we implemented the irasa_sprint function, that similarly to the SPRiNT package ([Wilson, da Silva Castanheira & Baillet, 2022](https://doi.org/10.7554/eLife.77348)), enables you to compute periodic and aperiodic spectrograms."
    ]
   },
   {
@@ -16,7 +16,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
+    "\n",
     "from neurodsp.sim import set_random_seed\n",
     "from neurodsp.sim import sim_powerlaw, sim_oscillation\n",
     "from neurodsp.utils import create_times\n",
@@ -28,12 +28,6 @@
     "#import seaborn as sns\n",
     "import pandas as pd\n",
     "\n",
-    "import matplotlib as mpl\n",
-    "new_rc_params = {'text.usetex': False,\n",
-    "                 \"svg.fonttype\": 'none'\n",
-    "}\n",
-    "mpl.rcParams.update(new_rc_params)\n",
-    "\n",
     "set_random_seed(84)\n",
     "\n",
     "from pyrasa.irasa import irasa_sprint"
@@ -320,20 +314,6 @@
     "f.tight_layout()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -344,7 +324,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "default",
    "language": "python",
    "name": "python3"
   },

doc/source/index.rst

@@ -1,9 +1,9 @@
 PyRASA - Spectral parameterization in python based on IRASA
 ===========================================================
 
-.. image:: https://www.repostatus.org/badges/latest/wip.svg
-   :target: https://www.repostatus.org/#wip
-   :alt: Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.
+.. image:: https://www.repostatus.org/badges/latest/active.svg
+   :target: https://www.repostatus.org/#active
+   :alt: Project Status: Active – The project has reached a stable, usable state and is being actively developed.
 
 .. image:: https://img.shields.io/badge/License-BSD_2--Clause-orange.svg
    :target: https://opensource.org/licenses/BSD-2-Clause

doc/source/install.rst

@@ -43,28 +43,40 @@ The required dependencies for installing PyRASA are:
 
 .. code::
 
- numpy>=1.26, <3 
- pandas>=2.1, <3 
- scipy>=1.12
+ numpy >= 1.26
+ pandas >= 2.1 
+ scipy >= 1.12
  attrs 
  
 and
 
 .. code::
 
-    python>=3.11
+    python >= 3.11
 
 
 Optional dependencies
 =====================
 
 Optionally you can combine PyRASA with MNE Python to better integrate spectral parametrization in your
-M/EEG analysis workflow.
+M/EEG analysis workflow. If you already have an MNE Python installation running you can try to install PyRASA in the respective environment.
+If you don't already have MNE Python in your environment you can install PyRASA including its optional dependency 
+`mne <https://mne.tools/stable/index.html>` using either pip or conda-forge. 
 
-.. code::
+Using pip
+---------
+.. code:: bash
+    
+    pip install "pyrasa[mne]"
+
+Using conda-forge
+-----------------
+.. code:: bash
+    
+    conda install -c conda-forge pyrasa mne
 
-    mne
 
+For a more detailed instruction on how to configure your MNE Python installation please refer to the `mne installation guide <https://mne.tools/stable/install/manual_install.html#manual-install>`.
 
 
 

doc/source/tutorials.rst

@@ -3,7 +3,25 @@
 Tutorials
 =========
 
-This section contains a number of tutorials, to get you started with PyRASA.
+This section contains a number of tutorials, to get you started with PyRASA. 
+In order to easily run the tutorials you need to add some additional python libraries to your current environment.
+You can do so using pip or conda-forge.
+
+Using pip
+---------
+.. code:: bash
+    
+    pip install neurodsp seaborn
+
+Using conda-forge
+-----------------
+.. code:: bash
+    
+    conda install -c conda-forge neurodsp seaborn 
+
+If you want to use PyRASA together with MNE Python (see Tutorial 4. IRASA MNE). 
+You also need MNE Python installed in your current enviroment (see `install <https://schmidtfa.github.io/pyrasa/install.html>` for further instructions).
+
 
 Introductory
 ------------

paper/paper.md

@@ -17,34 +17,35 @@ authors:
       corresponding: true
       orcid: 0000-0002-9839-1614
       affiliation: 1
-    - name: Thomas Hartmann
-      orcid: 0000-0002-8298-8125
-      affiliation: 1
     - name: Nathan Weisz
       orcid: 0000-0001-7816-0037
       affiliation: "1, 2"
+    - name: Thomas Hartmann
+      orcid: 0000-0002-8298-8125
+      affiliation: 1
 affiliations:
     - name: Paris-Lodron-University of Salzburg, Department of Psychology, Centre for Cognitive Neuroscience, Salzburg, Austria
       index: 1
     - name: Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
       index: 2
-date: XX August 2024
+date: 14 October 2024
 bibliography: paper.bib
 ---
 
 # Summary
-The electric signals generated by physiological activity exhibit both activity patterns that are regularly repeating over time (i.e. periodic) and activity patterns that are temporally irregular (i.e. aperiodic). In recent years several algorithms have been proposed to separate the periodic from the aperiodic parts of the signal, such as the irregular-resampling auto-spectral  analysis (IRASA; `@wen2016separating`). IRASA separates periodic and aperiodic components by up-/downsampling time domain signals and computing their respective auto-power spectra. Finally, the aperiodic component is isolated by averaging over the resampled auto-power spectra removing any frequency specific activity. The aperiodic component can then be subtracted from the original power spectrum yielding the residual periodic component. 
-`PyRASA` is a package that is built upon and extends the IRASA algorithm `[@wen2016separating]`. The package allows its users not only to separate power spectra, but also contains functionality to further parametrize the periodic and aperiodic spectra, by means of peak detection and several slope fitting options (eg. spectral knees). Furthermore, we extended the IRASA algorithm to the time-frequency domain allowing for a time-resolved spectral parameterization using IRASA.
+The electric signals generated by physiological activity exhibit both activity patterns that are regularly repeating over time (i.e. periodic) and activity patterns that are temporally irregular (i.e. aperiodic). In recent years several algorithms have been proposed to separate the periodic from the aperiodic parts of the signal, such as the irregular-resampling auto-spectral analysis (IRASA; [@wen2016separating]). IRASA separates periodic and aperiodic components by up-/downsampling time domain signals and computing their respective auto-power spectra. Finally, the aperiodic component is isolated by averaging over the resampled auto-power spectra removing any frequency specific activity. The aperiodic component can then be subtracted from the original power spectrum yielding the residual periodic component. 
+`PyRASA` is a package that is built upon and extends the IRASA algorithm [@wen2016separating]. The package allows its users not only to separate power spectra, but also contains functionality to further parametrize the periodic and aperiodic spectra, by means of peak detection and several slope fitting options (eg. spectral knees). Furthermore, we implemented a function to use the IRASA algorithm in the time-frequency domain allowing for a time-resolved spectral parameterization using IRASA.
 
 # Statement of Need
-`PyRASA` is an open-source Python package for the parametrization of (neural) power spectra. `PyRASA` has a lightweight architecture that allows users to directly apply the respective functions to separate power spectra to numpy arrays containing time series data `[@harris2020array]`. However, `PyRASA` can also be optionally extended with functionality to be used in conjunction with MNE Python (a popular beginner-friendly tool for the analysis of electrophysiological data, `@gramfort2014mne`). Thus offering both beginners in (neural) time series analysis and more advanced users a tool to easily analyze their data. The IRASA algorithm per se has been implemented in a couple other software packages `[@cole2019neurodsp; @vallat2021open; @oostenveld2011fieldtrip]`, but these implementations of IRASA largely lack functionality to further parametrize periodic and aperiodic spectra in their respective components. We close this gap by offering such functionality both for periodic and aperiodic spectra. For periodic spectra users can extract peak height, bandwidth and center frequency of putative oscillations. Aperiodic spectra can be further analyzed by means of several slope fitting options that allow not only for the assessment of Goodness of fit by several metrics (R2, mean squared error), but also allow for model comparison using information criteria (BIC/AIC). Furthermore, we extended the IRASA algorithm to the time-frequency domain, by computing IRASA over up/downsampled versions of spectrograms instead of power spectra thereby also allowing for a time-resolved spectral parametrization of (neural) time series data. 
+`PyRASA` is an open-source Python package for the parametrization of (neural) power spectra. `PyRASA` has a lightweight architecture that allows users to directly apply the respective functions to separate power spectra to numpy arrays containing time series data [@harris2020array]. However, `PyRASA` can also be optionally extended with functionality to be used in conjunction with `MNE Python` (a popular beginner-friendly tool for the analysis of electrophysiological data, [@gramfort2014mne]). Thus offering both beginners in (neural) time series analysis and more advanced users a tool to easily analyze their data. The IRASA algorithm per se has been implemented in a couple other software packages [@cole2019neurodsp; @vallat2021open; @oostenveld2011fieldtrip], but these implementations of IRASA largely lack functionality to further parametrize periodic and aperiodic spectra in their respective components. We close this gap by offering such functionality both for periodic and aperiodic spectra. For periodic spectra users can extract peak height, bandwidth and center frequency of putative oscillations. Aperiodic spectra can be further analyzed by means of several slope fitting options that allow not only for the assessment of Goodness of fit by several metrics (R2, mean squared error), but also allow for model comparison using information criteria (BIC/AIC). Additionally, users can easily implement their own custom functions to model aperiodic activity. Furthermore, we implemented a function to use the IRASA algorithm in the time-frequency domain, by computing IRASA over up/downsampled versions of spectrograms instead of power spectra thereby also allowing for a time-resolved spectral parametrization of (neural) time series data. 
 
 # Related Projects
-`PyRASA’s` functionality is inspired by specparam (formerly FOOOF, `@donoghue2020parameterizing`) a popular tool spectral parametrization built upon a different algorithm that seperates powers spectra by first flattening the spectrum and then sequentially modelling peaks as gaussians which is followed a final fit of the aperiodic component. Each algorithm (IRASA vs. Specparam) comes with their specific advantages and disadvantages that are in part also discussed herein `[@gerster2022separating]` and further eluded to in our `Documentation`.
+`PyRASA’s` functionality is inspired by specparam (formerly `FOOOF`, [@donoghue2020parameterizing]) a popular tool spectral parametrization built upon a different algorithm that seperates powers spectra by first flattening the spectrum and then sequentially modelling peaks as gaussians which is followed a final fit of the aperiodic component. Each algorithm (IRASA vs. Specparam) comes with their specific advantages and disadvantages that are also discussed herein [@gerster2022separating].
 
-The IRASA algorithm has also been implemented as part of other software packages NeuroDSP `[@cole2019neurodsp]`, YASA `[@vallat2021open]` and FieldTrip `[@oostenveld2011fieldtrip]`.
+The IRASA algorithm has also been implemented as part of other software packages `NeuroDSP` [@cole2019neurodsp], `YASA` [@vallat2021open] and `FieldTrip` [@oostenveld2011fieldtrip].
 
 # Acknowledgements
-We want to thank Gianpaolo Demarchi, Patrick Reisinger and Mohammed Ameen for beta testing PyRASA and helpful comments that improved its development.
+We want to thank Gianpaolo Demarchi, Patrick Reisinger and Mohammed Ameen for beta testing PyRASA and helpful comments that improved its development. 
+This project received financial support from the Land Salzburg through the BrainAge project (20102/F2400537-FPR).
 
 # References