Update github pages deployment workflows

.github/workflows/manual-pages-deploy.yml

@@ -12,6 +12,8 @@ jobs:
     runs-on: ubuntu-latest
     permissions:
       contents: write
+      pages: write
+      id-token: write
     steps:
     - name: Checkout repository
       uses: actions/checkout@v4
@@ -21,7 +23,7 @@ jobs:
         sed -i "s/PACKAGE_VERSION/0.0.1/" setup.py
         sed -i "s/PACKAGE_VERSION/0.0.1/" CHAP/__init__.py
         sed -i "s/PACKAGE_VERSION/0.0.1/" docs/conf.py
-        sed -i "s/PACKAGE_VERSION/0.0.1/" docs/installation.md
+        cat setup.py
 
     - name: Setup Miniconda
       uses: conda-incubator/setup-miniconda@v3

.github/workflows/pages-deploy-from-docs.yml

@@ -0,0 +1,75 @@
+name: Deploy Sphinx documentation to Pages from docs branch push
+
+on:
+  push:
+    branches:
+    - docs
+
+defaults:
+  run:
+    shell: bash -l {0}
+
+jobs:
+  build_and_deploy:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      pages: write
+      id-token: write
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    - name: Update docs configuration with version (tag) name
+      run: |
+        sed -i "s/PACKAGE_VERSION/0.0.1/" setup.py
+        sed -i "s/PACKAGE_VERSION/0.0.1/" CHAP/__init__.py
+        sed -i "s/PACKAGE_VERSION/0.0.1/" docs/conf.py
+        cat setup.py
+
+    - name: Setup Miniconda
+      uses: conda-incubator/setup-miniconda@v3
+      with:
+        python-version: '3.10'
+        environment-file: docs/environment.yml
+        auto-activate: true
+        activate-environment: sphinx
+        conda-remove-defaults: true
+        use-mamba: true
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install sphinx
+        pip install sphinx-math-dollar
+        pip install sphinx-rtd-theme
+        pip install myst-parser
+        #pip install autodoc_pydantic
+
+    - name: Install package
+      run: |
+        pip install -e .
+        conda list
+
+    - name: Generate .rst files with sphinx-apidoc
+      run: |
+        sphinx-apidoc -M --tocfile api_documentation --module-first -o docs CHAP CHAP/hdrm CHAP/inference CHAP/sin2psi CHAP/test
+
+    - name: Build Sphinx HTML documentation
+      run: |
+        make clean
+        make html
+      working-directory: ./docs
+
+    - name: List generated files
+      run: |
+        pwd
+        ls -lha
+        ls -lha ./docs/_build/html
+        cat ./docs/_build/html/index.html
+
+    - name: Deploy to GitHub Pages
+      uses: peaceiris/actions-gh-pages@v3
+      with:
+        github_token: ${{ secrets.GITHUB_TOKEN }}
+        publish_dir: ./docs/_build/html

.github/workflows/pages-deploy.yml

@@ -4,10 +4,6 @@ on:
   push:
     tags:
     - v*.*.*
-#on:
-#  push:
-#    branches:
-#    - docs
 
 defaults:
   run:
@@ -18,6 +14,8 @@ jobs:
     runs-on: ubuntu-latest
     permissions:
       contents: write
+      pages: write
+      id-token: write
     steps:
     - name: Checkout repository
       uses: actions/checkout@v4
@@ -27,7 +25,7 @@ jobs:
         sed -i "s/PACKAGE_VERSION/${{ github.ref_name }}/" setup.py
         sed -i "s/PACKAGE_VERSION/${{ github.ref_name }}/" CHAP/__init__.py
         sed -i "s/PACKAGE_VERSION/${{ github.ref_name }}/" docs/conf.py
-        sed -i "s/PACKAGE_VERSION/${{ github.ref_name }}/" docs/installation.md
+        cat setup.py
 
     - name: Setup Miniconda
       uses: conda-incubator/setup-miniconda@v3

AUTHORS

@@ -0,0 +1,8 @@
+AUTHORS
+
+# The official list of Cornell High Energy Synchrotron Source (CHESS) authors of  the CHESSComputing organization for copyright purposes
+
+- Valentin Kuznetsov, Cornell University
+- Keara L Soloway, Cornell University
+- Werner Sun, Cornell University
+- Rolf Verberg, Cornell University

CHAP/edd/models.py

@@ -64,7 +64,7 @@ class BaselineConfig(CHAPBaseModel):
 
 # Fit configuration class
 
-class _FitConfig(CHAPBaseModel):
+class FitConfig(CHAPBaseModel):
     """Fit parameters configuration class for peak fitting.
 
     :ivar background: Background model for peak fitting, defaults
@@ -237,9 +237,9 @@ def validate_materialconfig_after(self):
 # Avoid Pydantic "Class not fully defined" in sphinx autodoc as a
 # result of lazy importing by using in an _exclude pydantic instance
 # variable
-_FitConfig.model_rebuild(_types_namespace=vars(typing))
+FitConfig.model_rebuild(_types_namespace=vars(typing))
 
-class MCADetectorCalibration(Detector, _FitConfig):
+class MCADetectorCalibration(Detector, FitConfig):
     """Class representing the configuration for a single MCA detector
     element to perform detector calibration.
 
@@ -547,7 +547,7 @@ def get_tth_map(self, map_shape):
 ]
 
 
-class MCADetectorConfig(_FitConfig):
+class MCADetectorConfig(FitConfig):
     """Class representing metadata required to configure a full MCA
     detector.
 
@@ -559,7 +559,7 @@ class MCADetectorConfig(_FitConfig):
         'calibration', 'diffractionvolumelength', 'strainanalysis']
     detectors: Optional[conlist(min_length=1, item_type=MCADetector)] = []
 
-    _exclude = set(vars(_FitConfig()).keys())
+    _exclude = set(vars(FitConfig()).keys())
 
     @model_validator(mode='before')
     @classmethod
@@ -611,7 +611,7 @@ def update_detectors(self):
 
 # Processor configuration classes
 
-class DiffractionVolumeLengthConfig(_FitConfig):
+class DiffractionVolumeLengthConfig(FitConfig):
     """Configuration for the differential volume length processor
     :class:`~CHAP.edd.processor.DiffractionVolumeLengthProcessor`
     for an EDD setup using a steel-foil raster scan.
@@ -639,7 +639,7 @@ class DiffractionVolumeLengthConfig(_FitConfig):
     sample_thickness: Optional[confloat(gt=0, allow_inf_nan=False)] = None
     sigma_to_dvl_factor: Optional[Literal[2.0, 3.5, 4.0]] = 3.5
 
-    _exclude = set(vars(_FitConfig()).keys())
+    _exclude = set(vars(FitConfig()).keys())
 
     @model_validator(mode='after')
     def validate_diffractionvolumelengthconfig_after(self):

CHAP/edd/processor.py

@@ -1015,7 +1015,7 @@ def process(self, data):
         if len(sskipped_detectors) == 1:
             self.logger.warning(
                 f'Skipping detector {sskipped_detectors[0]} '
-                '(no raw data)')
+                '(no calibration data)')
         elif sskipped_detectors:
             skipped_detectors = [int(d) for d in sskipped_detectors]
             self.logger.warning(
@@ -2747,7 +2747,7 @@ def process(self, data):
         if len(sskipped_detectors) == 1:
             self.logger.warning(
                 f'Skipping detector {sskipped_detectors[0]} '
-                '(no raw data)')
+                '(no calibration data)')
         elif sskipped_detectors:
             skipped_detectors = [int(d) for d in sskipped_detectors]
             self.logger.warning(

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023 Valentin Kuznetsov
+Copyright (c) 2023 The ChessAnalysisPipeline (CHAP) Authors (see AUTHORS file)
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -3,10 +3,23 @@
 ![unittests](https://github.com/CHESSComputing/ChessAnalysisPipeline/actions/workflows/unittests.yml/badge.svg)
 
 # ChessAnalysisPipeline (CHAP)
-CHAP is a package that provides a framework for executing data anlaysis pipelines. The package can be found on PyPI and conda-forge.
 
-## Subpackages
-There are several subpackages within CHAP that contain specialized items to handle specific types of data processing in the CHAP framework. Dependencies for these subpackages can be found in `CHAP/<subpackage_name>/environment.yml`.
+## :bulb: About
+The CHESS Analysis Pipeline (CHAP) package is an object-oriented python framework for executing data reduction and analysis workflows by refactoring workflows into modular pipelines composed of interchangeable, reusable code components where each basic pipeline block consists of a reader, processor, and/or writer. Please check the CHAP [introduction](https://chesscomputing.github.io/ChessAnalysisPipeline/introduction.html) for a broad outline of its basic framework.
 
-## Documentation
-Documentation for the latest version can be found on [this project's github pages site](https://chesscomputing.github.io/ChessAnalysisPipeline/).
+## :hammer_and_wrench: Installation
+The package is available over PyPI and conda-forge. See the [installation instructions](https://chesscomputing.github.io/ChessAnalysisPipeline/installation.html) for installing CHAP on any Linux system.
+
+#### :package: Subpackages
+There are several subpackages within CHAP that contain specialized items to handle specific types of data processing workflows in the CHAP framework. Dependencies for these subpackages can be found in `CHAP/<subpackage_name>/environment.yml`. Details on how to install a conda environment for each of these subpackages are also included in the [installation instructions](https://chesscomputing.github.io/ChessAnalysisPipeline/installation.html) 
+
+## :books: Documentation
+Documentation for the latest release version can be found on [this project's github pages site](https://chesscomputing.github.io/ChessAnalysisPipeline/).
+
+## :handshake: Contributing
+We welcome feedback, suggestions, issues and bug reports. 
+
+Feel free to fork the [repository](https://github.com/CHESSComputing/ChessAnalysisPipeline) on GitHub and send a pull request, or to contribute by [submitting an issue](https://github.com/CHESSComputing/ChessAnalysisPipeline/issues). Each contribution helps CHAP grow and improve.
+When submitting an issue, please have a look at the [contribution guidelines](https://chesscomputing.github.io/ChessAnalysisPipeline/CONTRIBUTIONS.html). Following these guidelines grately helps us to address each issue promptly and effectively.
+
+We appreciate your support!