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Merge pull request #179 from AustralianCancerDataNetwork/remove-cover…
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…age-badge

Removed coverage badge and workflow step
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dalmouiee authored Jan 23, 2025
2 parents 337d837 + 150b077 commit 54062d5
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30 changes: 16 additions & 14 deletions .github/workflows/pull-request.yml
Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,10 @@ jobs:
- name: Lint with Pylint
run: |
poetry run pylint pydicer
# Run tests with coverage & generate badge
- name: MyPy type checking
run: |
# TODO poetry run mypy
echo "Skipping MyPy type checking..."
- name: Conditional Pytest coverage
run: |
if [[ "${{ matrix.python-version }}" == "3.9" ]]; then
Expand All @@ -52,27 +55,26 @@ jobs:
echo "Running Pytest without coverage..."
poetry run pytest
fi
# Commit the coverage badge back to repo (only on main branch & for a specific Python version)
- name: Generate coverage and commit coverage badge
- name: Generate and commit coverage badge
if: github.ref == 'refs/heads/main' && matrix.python-version == '3.9'
run: |
poetry run pip install coverage-badge # These only work with python >=3.9
# Generate an SVG coverage badge
poetry run coverage-badge -o coverage.svg
# poetry run coverage-badge -o coverage.svg
# Configure git
git config user.name "github-actions"
git config user.email "github-actions@github.com"
# # Configure git
# git config user.name "github-actions"
# git config user.email "github-actions@github.com"
# Pull latest changes to avoid conflicts
git pull --rebase
# # Pull latest changes to avoid conflicts
# git pull --rebase
# Stage and commit coverage.svg
git add coverage.svg
git commit -m "Update coverage badge" || echo "No changes to commit"
# # Stage and commit coverage.svg
# git add coverage.svg
# git commit -m "Update coverage badge" || echo "No changes to commit"
# Push commit
git push
# # Push commit
# git push
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
1 change: 0 additions & 1 deletion README.md
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@@ -1,7 +1,6 @@
# PyDicer: PYthon Dicom Image ConvertER

[![SoftwareX](https://img.shields.io/badge/SoftwareX-10.1016/j.softx.2024.102010-green.svg)](https://doi.org/10.1016/j.softx.2024.102010)
![Coverage](./coverage.svg)

Welcome to PyDicer, a tool to ease the process of converting Radiotherapy DICOM data objects into a format typically used for research purposes. In addition to data conversion, functionality is provided to help analyse the data. This includes computing radiomic features, radiotherapy dose metrics and auto-segmentation metrics. PyDicer uses the NIfTI format to store data is a well defined file system structure. Tracking of these data objects in CSV files, also stored on the file system, provides an easy and flexible way to work with the converted data in your research.

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