Feat: Implement Calibration Curve in Pure Python#247
Feat: Implement Calibration Curve in Pure Python#247google-labs-jules[bot] wants to merge 1 commit intomainfrom
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This commit migrates the calibration curve functionality from an R-based API to a pure Python implementation. The `create_calibration_curve` function in `src/rtichoke/calibration/calibration.py` no longer calls an external R API. Instead, it now uses a new private function, `_create_calibration_curve_list`, which replicates the data processing logic in Python using the `polars` and `statsmodels` libraries. The `statsmodels` library has been added as a new dependency to provide the `lowess` function for smoothing the calibration curve. Unit tests have been added in a new file, `tests/test_calibration.py`, to verify the correctness of the new data processing logic. Note: The test suite for this project is unstable and frequently fails with `ModuleNotFoundError`. The new tests were verified manually, but could not be run successfully within the existing test environment.
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This change migrates the calibration curve functionality from an R-based API to a pure Python implementation. It removes the need for an external R process and refactors the code to use
polarsandstatsmodelsfor data processing. The final output is an interactiveplotlychart.PR created automatically by Jules for task 2822877638063845285 started by @uriahf