Enable Automated Multi-Platform PyPI Releases & Fix Metadata

.github/workflows/release.yml

@@ -0,0 +1,82 @@
+name: Build and Publish to PyPI
+
+on:
+  push:
+    tags:
+      - 'v*'  # Triggers on tags like v0.0.1, v1.0.0
+
+jobs:
+  build_wheels:
+    name: Build wheels on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        # Mac (Intel & Silicon), Windows, and Linux
+        os: [ubuntu-latest, windows-latest, macos-13, macos-14]
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0  # Required for setuptools_scm versioning
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Install cibuildwheel
+        run: python -m pip install cibuildwheel==2.22.0
+
+      # This prints the plan to the logs before building
+      - name: List intended builds
+        run: python -m cibuildwheel --print-build-identifiers
+        env:
+          CIBW_PROJECT_REQUIRES_PYTHON: ">=3.11"
+          CIBW_SKIP: "pp* *-win32 *-manylinux_i686"
+
+      - name: Build wheels
+        run: python -m cibuildwheel --output-dir wheelhouse
+        env:
+          # Reads pyproject.toml to skip Python < 3.11 automatically
+          CIBW_PROJECT_REQUIRES_PYTHON: ">=3.11"
+          # Skip PyPy (pp*) and 32-bit architectures to speed up build
+          CIBW_SKIP: "pp* *-win32 *-manylinux_i686"
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
+          path: ./wheelhouse/*.whl
+
+  build_sdist:
+    name: Build Source Distribution
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Build sdist
+        run: pipx run build --sdist
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cibw-sdist
+          path: dist/*.tar.gz
+
+  publish_to_pypi:
+    needs: [build_wheels, build_sdist]
+    runs-on: ubuntu-latest
+    # Only run this job if the tag starts with 'v' (redundant safety check)
+    if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
+    steps:
+      - uses: actions/download-artifact@v4
+        with:
+          pattern: cibw-*
+          path: dist
+          merge-multiple: true
+
+      - name: Publish to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}

Manifest.in

@@ -0,0 +1,10 @@
+# MANIFEST.in
+prune scallops/tests
+prune tests
+prune docs/notebooks
+prune docs/_static/uncorrected.png
+prune docs/_static/corrected.png
+prune docs/_static/flatfield.png
+prune docs/_static/css
+global-exclude .git*
+global-exclude .DS_Store

README.md

@@ -1,9 +1,13 @@
 <h1 align="center">
-<img src="docs/_static/scallopsLogo.png" width="150" alt="logo">
+<img src="https://raw.githubusercontent.com/Genentech/scallops/62db13112dee13dc228bd2a458ada5a7a973d2bf/docs/_static/scallopsLogo.png" width="150" alt="logo">
 </h1><br>
 
 # SCALLOPS
 
+[![PyPI version](https://badge.fury.io/py/scallops.svg)](https://badge.fury.io/py/scallops)
+[![Python Versions](https://img.shields.io/pypi/pyversions/scallops.svg)](https://pypi.org/project/scallops/)
+[![License](https://img.shields.io/pypi/l/scallops.svg)](https://raw.githubusercontent.com/Genentech/scallops/refs/heads/main/LICENSE)
+
 ## Description
 SCALLOPS (Scalable Library for Optical Pooled Screens) is a comprehensive Python package designed to
 streamline and scale the analysis of Optical Pooled Screens (OPS) for biological data. With a focus on
@@ -12,25 +16,42 @@ analyzing, and interpreting OPS data, leveraging modern distributed computing fr
 
 ## Installation
 
+### Option 1: Install from PyPI (Recommended)
+For most users, the easiest way to install SCALLOPS is via pip. This will install the pre-compiled binary wheels for your operating system (Linux, Windows, or macOS).
+
+```bash
+pip install scallops
+
+```
+
+*Note: SCALLOPS requires Python 3.11 or newer.*
+
+### Option 2: Install from Source (For Development)
+
+If you wish to contribute to the codebase or need the latest unreleased changes:
+
 1. Clone the repository and change to the scallops directory:
+```bash
+git clone [https://github.com/Genentech/scallops.git](https://github.com/Genentech/scallops.git)
+cd scallops
+
+```
 
-    ```
-    git clone https://github.com/Genentech/scallops.git
-    cd scallops
-   ```
 
-1. Install SCALLOPS:
+2. Install SCALLOPS in editable mode with dependencies:
+```bash
+pip install -r requirements.txt -e .
 
-    ```pip install -r requirements.txt -e .```
+```
 
 
 
 ## Main Focus Areas:
 
-- **High-Throughput Data Processing**: SCALLOPS is built to manage massive datasets typical of OPS
-  experiments, allowing users to efficiently process and analyze data across multiple scales.
-- **Scalability and Performance**: The package is optimized for both local and cloud-based distributed
-  environments, making it ideal for scaling to large datasets without compromising performance.
+* **High-Throughput Data Processing**: SCALLOPS is built to manage massive datasets typical of OPS
+experiments, allowing users to efficiently process and analyze data across multiple scales.
+* **Scalability and Performance**: The package is optimized for both local and cloud-based distributed
+environments, making it ideal for scaling to large datasets without compromising performance.
 
 ### Modular Workflows:
 
@@ -39,30 +60,29 @@ their specific experimental needs.
 
 ## Key Features:
 
-- **Efficient Data Handling**: SCALLOPS utilizes advanced memory management and lazy evaluation
-    techniques, which minimize resource usage while handling large datasets.
-- **Command-Line Interface (CLI)**: Automates batch processing and simplifies integration into larger
-  pipelines.
-- **Customizable Outputs**: The package generates versatile outputs, including data visualizations and
-  summary statistics, which can be integrated into downstream analyses.
-- **Notebook Examples**: SCALLOPS includes practical Jupyter notebooks that walk users through typical
-  workflows, making it easy to get started with real-world datasets.
-- **Custom Features**: Advanced users can extend SCALLOPS with their own custom functions and workflows,
-  ensuring the package can grow with the complexity of the data.
-- **Comprehensive API**: SCALLOPS provides a rich API that exposes all the package functionalities,
-  allowing users to integrate it directly into their own Python scripts and workflows. This makes
-  SCALLOPS highly adaptable, enabling users to build fully customized data pipelines and analyses
-  tailored to their unique experimental needs.
+* **Efficient Data Handling**: SCALLOPS utilizes advanced memory management and lazy evaluation
+techniques, which minimize resource usage while handling large datasets.
+* **Command-Line Interface (CLI)**: Automates batch processing and simplifies integration into larger
+pipelines.
+* **Customizable Outputs**: The package generates versatile outputs, including data visualizations and
+summary statistics, which can be integrated into downstream analyses.
+* **Notebook Examples**: SCALLOPS includes practical Jupyter notebooks that walk users through typical
+workflows, making it easy to get started with real-world datasets.
+* **Custom Features**: Advanced users can extend SCALLOPS with their own custom functions and workflows,
+ensuring the package can grow with the complexity of the data.
+* **Comprehensive API**: SCALLOPS provides a rich API that exposes all the package functionalities,
+allowing users to integrate it directly into their own Python scripts and workflows. This makes
+SCALLOPS highly adaptable, enabling users to build fully customized data pipelines and analyses
+tailored to their unique experimental needs.
 
 ## Typical Use Cases:
 
-- **Large-Scale Screening Projects**: SCALLOPS is designed for handling the immense data loads of
-  genome-wide OPS projects, helping users efficiently identify and quantify biological perturbations.
-- **Data-Driven Insights**: SCALLOPS facilitates the discovery of patterns and trends in OPS data,
-  helping users extract and interpret complex biological systems' data.
-
-
+* **Large-Scale Screening Projects**: SCALLOPS is designed for handling the immense data loads of
+genome-wide OPS projects, helping users efficiently identify and quantify biological perturbations.
+* **Data-Driven Insights**: SCALLOPS facilitates the discovery of patterns and trends in OPS data,
+helping users extract and interpret complex biological systems' data.
 
 ## Contributing to SCALLOPS
+
 We welcome all forms of contributions, including bug reports, documentation improvements, and feature
 enhancements.

pyproject.toml

@@ -19,8 +19,9 @@ authors = [
 dynamic = ["version"]
 readme = { file = "README.md", content-type = "text/markdown" }
 requires-python = ">=3.11"
+license = { file = "LICENSE" }
 classifiers = [# https://pypi.python.org/pypi?%3Aaction=list_classifiers
-    "License :: OSI Approved :: BSD License",
+    "License :: OSI Approved :: Apache Software License",
     "Intended Audience :: Developers",
     "Intended Audience :: Science/Research",
     "Natural Language :: English",