profe 0.1.2

Reduction Pipeline for OPTICAM Photometry of Exoplanets (PROFE in spanish)

PROFE: Pipeline de Reducción de OPTICAM para Fotometría de Exoplanetas

Reduction pipeline for OPTICAM photometry of exoplanets.

A Python-based pipeline to automate preprocessing and postprocessing of data acquired with the OPTICAM instrument on the 2.1 m telescope at OAN‑SPM, aimed at producing calibrated light curves and centroid analyses for transiting exoplanets implementing the data reduction methods proposed by Paez et. al (in prep.).

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Features

• Preprocessing (profe_pre):

  • Organize and standardize FITS files.
  • Update headers and compute Julian Date.
  • Apply median filter

• Postprocessing (profe_out):

  • Plot of altitude-azimuth trajectory and centroids and movement in pixels .
  • Generate binned light curves with RMS measurements.
  • Time-averaging curves with the red and white noise in the time-series.
  • Radial profile for target star
  • Field of View with apertures for target and comparison stars

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Requirements

Dependencies defined in pyproject.toml:

• Python ($\geq 3.8.20$, $\leq 3.12$)
• astropy ($5.3.2$)
• scipy ($\geq 1.10$, $<2.0$)
• matplotlib ($\geq3.10.3$, $<4.0.0$)
• tqdm ($\geq 4.67.1$, $<5.0.0$)
• pandas ($\geq 2.3.1$, $<3.0.0$)
• mc3 ($\geq 3.2.1$, $<4.0.0$)
• photutils ($\geq 2.2.0$, $<3.0.0$)
• numpy ($\geq 1.24$, $<2.0$)

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Installation

Install from the project root:

pip install profe

For a development environment (include testing and linting tools):

git clone https://github.com/s-paez/profe.git
cd profe
pip install .[dev]

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Usage

Preprocessing

Organize raw data from data/ directory into the organized_data/ directory, update time headers, perform median filter correction:

profe_pre

AstroImageJ

Once the data have been preprocessed with profe, it is time to perform data reduction and photometry with AstroImageJ and save the measurements tables in .tbl format.

Outputs

Generate light curves (plots and files), centroid movement plots, Alt-Az trajectory, Field of View apertures, radial profile and time-averaging curves for measurements.tbl files:

profe_out

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Development & Contribution

We welcome contributions to improve PROFE! Please follow these steps to ensure a smooth process:

1. Fork the repository on GitHub and clone your fork locally:

   git clone https://github.com/<username>/profe.git
   cd profe
   

2. Create a new branch for your feature of bugfix:

   git checkout -b feat/new-feature
   git checkout -b fix/issue-123
   

3. Install development dependencies:

   pip install -e ".[dev]"
   

4. Enable and run pre-commit hooks (for code style and quality checks):

   pre-commit install
   pre-commit run --all-files
   

5. Commit and push your changes to your fork
6. Open a Pull Request from your fork to the main repository. In your PR description:
   • Explain the what and why of the change
   • Reference related issues

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License

This project is licensed under the MIT License. See the LICENSE file for details.