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Memory-mapped file handling and streaming API #39

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@jgoedeke jgoedeke commented Dec 15, 2025 •
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This PR introduces memory-mapped file handling and a streaming API to enable efficient processing of large IMC files without loading the entire dataset into memory.

Key Changes:

  • Core Library: Replaced std::vector file buffering with a MemoryMappedFile implementation to significantly reduce memory usage.
  • Python Bindings:
    • Added iter_channel_numpy() to the imctermite class. This generator yields data in chunks as NumPy arrays, supporting both "scaled" (physical units) and "raw" (native integer) modes.
    • Added type stubs (.pyi) and package data for improved IDE support.
  • Performance: Implemented efficient memcpy transfer from C++ buffers to NumPy arrays.
  • Fixes: Improved text encoding/codepage handling for Windows.
  • Documentation: Added usage_numpy_chunks.py and usage_timerange.py to demonstrate new functionality.
  • CI: Added Windows target to CI tests.

Performance Improvements:

Benchmarks comparing the new memory-mapped implementation against the baseline show significant improvements in both memory efficiency and load times:

  • Memory Usage: Reduced memory footprint by ~50% for large files.
    • 100MB file: Overhead reduced from ~204MB to ~101MB.
    • Small file: Overhead reduced from ~1.3MB to ~92KB.
  • Loading Speed: File loading is approximately 5x faster for large files.
    • 100MB file: Load time decreased from ~1.25s to ~0.24s.
    • Small file: Load time decreased from ~2.6ms to ~0.2ms.

NOTE: This PR is based on #37

Closes #33
Closes #9

@jgoedeke
Add pytest and tests for CLI and Python module
c02027f 
- Add pytest configuration for test management
- Implement test suite for CLI functionality and Python module
- Update README with testing instructions and badge
- Fix Dockerfile
- Create .dockerignore to exclude unnecessary files from Docker builds
- Add GitHub Actions workflows for testing
- Clean up makefile to include test commands
@jgoedeke
Implement chunked NumPy export for large files and add corresponding …
574393c 
…example and tests
@jgoedeke jgoedeke force-pushed the numpy-streaming branch 3 times, most recently from 86df1f2 to d416f25 Compare December 16, 2025 20:17
jgoedeke and others added 5 commits December 16, 2025 21:29
@jgoedeke
Enhance channel data handling with raw mode support and update exampl…
393240b 
…es for chunked NumPy export
@jgoedeke
Refactor IMC library for improved data handling and performance
4cee020 
- Modified `component_group` and `channel` constructors to accept raw buffer pointers instead of vectors.
- Enhanced `load_all_data` and `init_metadata` methods for better data initialization and loading.
- Implemented `read_chunk` method in `channel` to facilitate chunked data reading with support for raw and scaled modes.
- Updated `convert_data_to_type` and `convert_chunk_to_double` functions to handle raw data more efficiently.
- Removed redundant `imc_result.hpp` file to streamline the codebase.
- Adjusted Python bindings in `imctermite.pyx` to manage C++ instance memory correctly.
@jgoedeke
Add windows target to CI tests and add tests for streaming functionality
fdc8ece 
- Update GitHub Actions workflow to support testing on multiple OS
- Refactor memory mapping in imc_buffer.hpp for Windows compatibility
- Improve makefile to handle .pyd files for Python builds
- Add comprehensive tests for streaming and chunking functionality in test_streaming.py
@jgoedeke
Fix platform detection in codepage conversion and update CLI path for…
0d93e03 
… Windows
@jgoedeke
Implement chunked streaming for channel printing to improve memory ef…
27d8215 
…ficiency (RecordEvolution#9)

Reduces memory usage by 90% for large datasets while maintaining comparable processing speed.
@jgoedeke jgoedeke changed the title Add memory mapping and NumPy streaming support Memory-mapped file handling and streaming API Dec 16, 2025
@jgoedeke jgoedeke marked this pull request as ready for review December 16, 2025 21:53
@jgoedeke jgoedeke marked this pull request as draft December 17, 2025 08:29
@jgoedeke
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jgoedeke commented Dec 17, 2025

I just realized the new numpy dependency was not correctly handled and that makes the PR a major change. So I took the opportunity to modernize the Python packaging infrastructure and CI workflows to follow current best practices and support Python 3.10-3.13.

Changes

Packaging

  • Migrated package metadata from setup.cfg to pyproject.toml (PEP 621)
  • Replaced deprecated setup.py commands with python -m build
  • Added numpy as explicit dependency (>=1.26.0)
  • Version bumped to 3.0.0
  • Simplified setup.py to minimal Cython extension builder

CI/CD

  • Updated GitHub Actions from v2 → v4
  • Replaced ubuntu-24.04 with ubuntu-latest for better maintainability
  • Removed obsolete cibuildwheel==2.1.2 version pinning
  • Configured cibuildwheel via pyproject.toml with proper platform targeting
  • Streamlined dependency installation in test workflows

Documentation

  • Added Python version support badge to README
  • Enhanced test documentation with pip editable install instructions
  • Added note about numpy dependency requirement

Build System

  • Updated makefiles to use python3 consistently
  • Simplified build targets using modern pip commands

Testing

All existing tests pass. CI workflows validated on Ubuntu and Windows with Python 3.10-3.13.

@jgoedeke jgoedeke marked this pull request as ready for review December 17, 2025 08:56
@jgoedeke
chore: modernize Python packaging and CI workflows
c2c9109 
- Migrate from setup.cfg to pyproject.toml with PEP 517/621 compliance
- Update to Python build tools (replace setup.py commands with python -m build)
- Upgrade all GitHub Actions to latest versions (@v4, ubuntu-latest)
- Remove outdated cibuildwheel version pinning
- Add numpy as explicit build and runtime dependency
- Bump package version to 3.0.0
- Improve test documentation with development install guidance
- Add Python version badge to README
- Standardize python3 usage across makefiles
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Loading single channels optimize file export

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