Version joss rev - integrate cython install and update readme

.github/workflows/go-rxr-tests.yml

@@ -25,20 +25,11 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: '3.10.12'
+        python-version: '3.10.14'
 
-    - name: Install dependencies
+    - name: Install app dependencies
       run: |
-        pip install --upgrade pip setuptools
-        pip install -r requirements.txt
-
-    - name: Install PyQt5 Dependencies
-      run: |
-        sudo apt-get install -y pyqt5-dev libqt5multimedia5-plugins
-
-    - name: Install Reflectivity Package
-      run: |
-        python setup_reflectivity.py install
+        pip install .
 
     - name: Run tests
       run: |
@@ -47,4 +38,4 @@ jobs:
         python -m unittest discover -s TESTS -p "test_density.py"
         python -m unittest discover -s TESTS -p "test_material_model.py"
         python -m unittest discover -s TESTS -p "test_material_structure.py"
-        python -m unittest discover -s TESTS -p "test_reflectivity.py"
+        python -m unittest discover -s TESTS -p "test_reflectivity.py"

.github/workflows/paper_joss.yml

@@ -1,5 +1,11 @@
-on: [push]
-
+name: Paper Draft - JOSS
+
+on:
+  push:
+    branches: ["main", "version-joss-rev"]
+  pull_request:
+    branches: ["main", "version-joss-rev"]
+
 jobs:
   paper:
     runs-on: ubuntu-latest
@@ -14,7 +20,7 @@ jobs:
           # This should be the path to the paper within your repo.
           paper-path: PAPER/paper.md
       - name: Upload
-        uses: actions/upload-artifact@v1
+        uses: actions/upload-artifact@v4
         with:
           name: paper
           # This is the output path where Pandoc will write the compiled

.github/.gitignore → .gitignore

@@ -4,14 +4,16 @@
 
 ## folder
 dist/
+.eggs/*
 __pycache__/
-build/
+**/__pycache__/
+/build/*
 venv-go-rxr/
 venv-go-rxr-paper/
 venv-gorxr310/
-**/*.h5
 .idea/
-TESTS/__pycache__/
+GO_RXR.egg-info/*
+UNKNOWN.egg-info/*
 
 ## Files - temporary files
-setup_new.py
+

PAPER/paper.bib

@@ -61,4 +61,96 @@ @misc{pythonreflectivity
     journal = {GitHub repository},
     howpublished = {\url{https://github.com/malaclypseII/PyXMRTool/tree/master}},
     commit = {30d1ed552cf807d3635d2e274dd89151c2a83090}
-}
+}
+
+@article{storn1997_de,
+    title={Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces},
+    author={Storn, R. and Price, K.},
+    journal={Journal of Global Optimization},
+    volume={11},
+    number={4},
+    pages={341--359},
+    year={1997},
+    doi={10.1023/A:1008202821328},
+    url={https://doi.org/10.1023/A:1008202821328}
+}
+
+@article{endres2018_shgo,
+    title={A simplicial homology algorithm for Lipschitz optimisation},
+    author={Endres, S.C. and Sandrock, C. and Focke, W.W.},
+    journal={Journal of Global Optimization},
+    volume={72},
+    number={2},
+    pages={181--217},
+    year={2018},
+    doi={10.1007/s10898-018-0627-y},
+    url={https://doi.org/10.1007/s10898-018-0627-y}
+}
+
+@article{xiang1997_dual_annealing,
+    title={Generalized Simulated Annealing Algorithm and Its Application to the Thomson Model},
+    author={Xiang, Y. and Sun, D.Y. and Fan, W. and Gong, X.G.},
+    journal={Physics Letters A},
+    volume={233},
+    number={3},
+    pages={216--220},
+    year={1997},
+    doi={10.1016/S0375-9601(97)00474-X},
+    url={https://doi.org/10.1016/S0375-9601(97)00474-X}
+}
+
+@article{virtanen2020_scipy,
+    title={SciPy 1.0: fundamental algorithms for scientific computing in Python},
+    author={Virtanen, P. and Gommers, R. and Oliphant, T.E. and others},
+    journal={Nature Methods},
+    volume={17},
+    pages={261--272},
+    year={2020},
+    doi={10.1038/s41592-019-0686-2},
+    url={https://doi.org/10.1038/s41592-019-0686-2}
+}
+
+@article{bjorck2007_genx,
+    title={GenX: an extensible X-ray reflectivity refinement program utilizing differential evolution},
+    author={Björck, M. and Andersson, G.},
+    journal={Journal of Applied Crystallography},
+    volume={40},
+    pages={1174--1178},
+    year={2007},
+    doi={10.1107/S0021889807045086},
+    url={https://doi.org/10.1107/S0021889807045086}
+}
+
+@article{nelson2006_motofit,
+    title={Co-refinement of multiple-contrast neutron/X-ray reflectivity data using MOTOFIT},
+    author={Nelson, A.},
+    journal={Journal of Applied Crystallography},
+    volume={39},
+    pages={273--276},
+    year={2006},
+    doi={10.1107/S0021889806005073},
+    url={https://doi.org/10.1107/S0021889806005073}
+}
+
+@misc{refl1d_github,
+    title={{Refl1D: Reflectometry Data Analysis}},
+    author={Kienzle, P.A. and Maranville, B.B. and O'Donovan, K.V. and Ankner, J.F. and Berk, N.F. and Majkrzak, C.F.},
+    howpublished={\url{https://github.com/reflectometry/refl1d}},
+    year={2017},
+    note={GitHub repository, Accessed: 2024-09-01},
+}
+
+@article{pospelov2020_bornagain,
+    title={{\it BornAgain}: software for simulating and fitting grazing-incidence small-angle scattering},
+    author={Pospelov, Gennady and Van Herck, Walter and Burle, Jan and Carmona Loaiza, Juan M. and Durniak, C{\'{e}}line and Fisher, Jonathan M. and Ganeva, Marina and Yurov, Dmitry and Wuttke, Joachim},
+    journal={Journal of Applied Crystallography},
+    volume={53},
+    number={1},
+    pages={262--276},
+    year={2020},
+    doi={10.1107/S1600576719016789},
+    url={https://doi.org/10.1107/S1600576719016789},
+}
+
+
+

PAPER/paper.md

@@ -43,6 +43,8 @@ The analysis of RXR presents multifaceted challenges, extending beyond computati
 
 # Comparison
 
+To the best of our knowledge, no existing software tool comprehensively addresses the general problem of RXR data analysis across a wide range of materials and conditions. Most of the currently available tools are designed for very specific tasks and cannot be extensively applied to the diverse challenges encountered in RXR studies. For example, tools like GenX [@bjorck2007_genx] and Motofit [@nelson2006_motofit] are widely used for reflectometry but are primarily focused on specific types of reflectivity data analysis, such as neutron or x-ray reflectometry, without the capability to handle the complex, element-specific interactions in RXR. Similarly, more modern tools like Refl1D [@refl1d_github] and BornAgain [@pospelov2020_bornagain] offer powerful features for modeling and fitting specific types of reflectometry data but still lack the flexibility and comprehensive approach needed for the broad range of challenges in RXR analysis.
+
 The development of GO-RXR is a response to the critical need for improved tools to analyze RXR data effectively. GO-RXR distinguishes itself from existing RXR data analysis tools through its comprehensive approach and user-friendly design. GO-RXR offers a graphical user interface (GUI) and intuitive plotting options, facilitating data interpretation and analysis. Furthermore, GO-RXR incorporates state-of-the-art global optimization algorithms, allowing for faster and more accurate data fitting compared to older methods. Its ability to model thin-film materials and customize objective functions makes it a versatile and valuable tool for researchers in the field of material science.
 
 # Functionality
@@ -51,12 +53,18 @@ GO-RXR is available for installation through its GitHub repository, providing us
 
 ## Basic concepts
 
-The primary goal of GO-RXR is to simplify and optimize the data analysis process for resonant x-ray reflectometry. Initially conceived as a command-line tool, it evolved into a GUI-based software with enhanced visualization capabilities. Developed using Python with PyQt5 for the interface, this software integrates the Pythonreflectivity [@pythonreflectivity] open-source package to carry out reflectivity calculations efficiently. GO-RXR has been tested extensively on Ubuntu 22.04 with Python 3.10, ensuring reliable performance on this operating system.
+The primary goal of GO-RXR is to simplify and optimize the data analysis process for resonant x-ray reflectometry. Initially conceived as a command-line tool, it evolved into a GUI-based software with enhanced visualization capabilities. Developed using Python with PyQt5 for the interface, this software integrates the Pythonreflectivity [@pythonreflectivity] open-source package to carry out reflectivity calculations efficiently. GO-RXR has been tested extensively on Ubuntu 22.04 with Python 3.10, ensuring reliable performance on this operating system. After running the GUI_GO.py file, the start screen in \autoref{fig:start-screen} will be displayed.
+
+![Start screen of GO-RXR. The interface is divided into three main sections: 1) Toolbar, 2) Workspace Navigator, and 3) Workspace Area. \label{fig:start-screen}](../FIGURES/go-rxr-start.png)
 
 \autoref{fig:flowchart} displays the main steps GO-RXR uses to go from input to output. The inputs of the software are the experimental data and are defined by the reflected intensity, incident grazing angle, photon energy, and polarization. The first step in the data analysis is the data selection step. In this step, the user selects the experimental datasets to include into the data analysis. The next step is the parameter selection. In this step, the user selects the model parameters to vary. The final step is the data fitting. In this step, the user selects the global optimization algorithm and its parameters and fits the data from the selected experimental and simulated datasets. The output of GO-RXR is the depth-dependent density profile, as defined by the model parameters.
 
 ![Flowchart of GO-RXR. The flowchart illustrates the path the software uses to take the experimental data and convert them into a depth-dependent density profile.\label{fig:flowchart}](../FIGURES/go-rxr-flowchart.png)
 
+GO-RXR incorporates three global optimization algorithms specifically designed to tackle the inherent complexities of RXR data analysis. These are Differential Evolution (DE) [@storn1997_de], Simplicial Homology Global Optimization (SHGO) [@endres2018_shgo], and Dual Annealing [@xiang1997_dual_annealing], each chosen for their effectiveness in handling the high-dimensional optimization challenges typical of RXR. A significant enhancement provided by GO-RXR is the integration of boundary and weight functions, which enable the selective emphasis of specific data regions, thereby improving the precision and relevance of the optimization results [@korol_MSc_2023]. Additionally, the objective function includes a total variation penalty, ensuring that the optimization not only fits the data but also preserves its physical trends, resulting in more accurate and reliable outcomes [@korol_MSc_2023]. These global optimization algorithms, combined with  local optimization nonlinear least-squares algorithms for post-processing refinement, allow GO-RXR to effectively model the structural and electronic properties of thin-film materials. The algorithms are implemented within GO-RXR using the SciPy library [@virtanen2020_scipy]. \autoref{fig:optimization} displays the Optimization workspace, which includes sections for parameter boundaries, objective function selection, and algorithm parameters, allowing users to fine-tune the optimization process to achieve the most accurate and physically meaningful results.
+
+![Optimization workspace. \label{fig:optimization}](../FIGURES/go-rxr-optimization.png)
+
 
 ## Example use-cases