Started drafting a paper for JOSS

.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: joss/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.pdf

joss/paper.bib

@@ -0,0 +1,49 @@
+@article{PelaezZapata_2024a,
+  title = {Ocean {{Wave Directional Distribution}} from {{GPS Buoy Observations}} off the {{West Coast}} of {{Ireland}}: {{Assessment}} of a {{Wavelet-Based Method}}},
+  shorttitle = {Ocean {{Wave Directional Distribution}} from {{GPS Buoy Observations}} off the {{West Coast}} of {{Ireland}}},
+  author = {{Pel{\'a}ez-Zapata}, Daniel and Pakrashi, Vikram and Dias, Fr{\'e}d{\'e}ric},
+  year = {2024},
+  month = aug,
+  doi = {10.1175/JTECH-D-23-0058.1},
+  urldate = {2024-09-16},
+  chapter = {Journal of Atmospheric and Oceanic Technology}
+}
+
+
+@inproceedings{Krogstad_etal_2006,
+  title = {Wavelet and Local Directional Analysis of Ocean Waves},
+  booktitle = {The Sixteenth International Offshore and Polar Engineering Conference},
+  author = {Krogstad, Harald Elias and Magnusson, Anne Karine and Donelan, Mark},
+  year = {2006},
+  volume = {16},
+  publisher = {{International Society of Offshore and Polar Engineers}}
+}
+
+
+@article{Donelan_etal_1996,
+  title = {Nonstationary Analysis of the Directional Properties of Propagating Waves},
+  author = {Donelan, M. A. and Drennan, W. M. and Magnusson, A. K.},
+  year = {1996},
+  month = sep,
+  journal = {Journal of Physical Oceanography},
+  volume = {26},
+  number = {9},
+  pages = {1901--1914},
+  publisher = {American Meteorological Society},
+  doi = {10.1175/1520-0485(1996)026<1901:naotdp>2.0.co;2}
+}
+
+@article{Donelan_etal_2015,
+  title = {A Comparison of Methods for Estimating Directional Spectra of Surface Waves},
+  shorttitle = {A Comparison of Methods for Estimating Directional Spectra of Surface Waves},
+  author = {Donelan, M. and Babanin, A. and Sanina, E. and Chalikov, D.},
+  year = {2015},
+  month = jul,
+  journal = {Journal of Geophysical Research: Oceans},
+  volume = {120},
+  number = {7},
+  pages = {5040--5053},
+  issn = {21699275},
+  doi = {10.1002/2015JC010808},
+  urldate = {2023-07-04}
+}

joss/paper.md

@@ -0,0 +1,53 @@
+---
+title: 'EWDM: Extended Wavelet Directional Method for estimating ocean waves directional spectra'
+tags:
+  - Python
+  - oceanography
+authors:
+  - name:
+        firstname: Daniel
+        surname:  Peláez-Zapata
+    orcid: 0000-0001-5862-6194
+    affiliation: 1
+  - name:
+        firstname: Frédéric
+        surname: Dias
+  - orcid: 0000-0002-5123-4929
+    affiliation: "1, 2"
+affiliations:
+ - name: École Normale Supérieure Paris-Saclay, France
+   index: 1
+   ror: 02hcn4061
+ - name: University College Dublin, Ireland
+   index: 2
+date: 1 December 2024
+bibliography: paper.bib
+
+---
+
+# Summary
+
+The research purpose of the Extended Wavelet Directional Method (EWDM) software is to address the limitations of conventional Fourier-based techniques in accurately capturing directional wave information in physical oceanography. It aims to provide a robust estimation of the directional wave spectrum from diverse sources of data such as GPS buoys, pitch-roll buoys, arrays of wave staffs, and ADCPs. The software implements wavelet-based algorithms, Kernel Density Estimation (KDE), and tools for processing and visualizing directional wave data, making it suitable for researchers, students, and engineers in physical oceanography.
+
+In the context of related work, the EWDM builds upon the traditional Wavelet Directional Method (WDM) by extending its capabilities to incorporate a wide range of data sources and configurations, thus offering an alternative and improved methodology for estimating directional wave spectra. Furthermore, the collaborative and open approach of the EWDM welcomes contributions, feedback, and collaboration from the community, aligning with the principles of transparency, reproducibility, and accessibility within the physical oceanography research community.
+
+Considering the significance of improved directional wave spectrum estimation and the unique features offered by the EWDM, its publication as an open-source software package in the Journal of Open Source Software (JOSS) would not only make the method more accessible but also promote transparency, reproducibility, and collaboration within the field of physical oceanography research.
+
+
+# Statement of need
+
+The estimation of directional wave spectra is paramount in the field of physical oceanography for understanding wave dynamics and coastal processes. Conventional Fourier-based techniques have inherent limitations in accurately capturing directional wave information, prompting the need for alternative methodologies. The wavelet-based approach has emerged as a promising alternative, offering improved directional wave spectrum estimation.
+
+The Extended Wavelet Directional Method (EWDM) is a Python toolkit designed to address the shortcomings of conventional Fourier-based techniques and provide a robust estimation of the directional wave spectrum from diverse sources of data, including GPS buoys, pitch-roll buoys, arrays of wave staffs, and ADCPs. With specific implementations for spatial arrays of wave staffs inspired by Donelan's WDM, as well as methods for single-point triplet data drawn from [@PelaezZapata_2024a] and Krogstad et al. (2006), the EWDM extends the capabilities of the original WDM to incorporate a wide range of data sources and configurations.
+
+Key features of the EWDM include the implementation of wavelet-based algorithms for extracting directional information from wave time series, improved estimation of wave directional distribution using Kernel Density Estimation (KDE), tools for processing and visualizing directional wave data, and compatibility with popular data sources such as SOFAR Spotter buoys and the CDIP database. The package is powered by xarray labelled multi-dimensional arrays, enhancing its efficiency and scalability.
+
+The utility of the EWDM extends to researchers, students, and engineers in physical oceanography, offering a powerful, user-friendly toolkit for estimating directional wave spectra. By fostering an open and collaborative approach, the EWDM welcomes contributions, feedback, and collaboration from the community to further enhance its capabilities and facilitate advancements in the field of directional wave spectrum estimation.
+
+In light of the significance of improved directional wave spectrum estimation and the unique features offered by the EWDM, its publication as an open-source software package in JOSS would not only enhance the accessibility of the method but also promote transparency, reproducibility, and collaboration within the physical oceanography research community.
+
+
+# Acknowledgements
+
+
+# References

notebooks/cdip_example.ipynb

@@ -29,7 +29,7 @@
     "import xarray as xr\n",
     "\n",
     "from matplotlib import pyplot as plt\n",
-    "from ewdm import BuoysEWDM\n",
+    "import ewdm\n",
     "from ewdm.plots import plot_directional_spectrum\n",
     "from ewdm.sources import CDIPDataSourceRealTime\n",
     "\n",