photcalib 0.4

calibration tool for photometric data

PhotCalib

Calibration tool for Prisitine CaHK data. For more details see Martin, Starkenburg & Yuan 2023

Installation

Before installation, make sure you have pytorch or install essential dependencies :

pip install -r requirements.txt

• Installation from PyPI

pip install photcalib

• Manual installation

download the repository and run the setup

python setup.py install

Getting started

cd examples

To Calibrate CaHK data using the run model trained by the main survey: .. code:

python calib_raw.py 17Am05 combined_catalogue_17Am05_sel.fits -D cpu

Substitute 17Am05 to the run you need and make sure the input data for calibration has the same format as that in the examples/data/.fits

• D (str) is the device (default:cpu, gpu) to run pytorch. If gpu is chosen, it will use the first graphic card cuda:0 by default.

To train calibration run model: .. code:

python calib_mod.py 17Am05

Substitute 17Am05 to the run for calibration with the input file as examples/data/inputs_run.npy

Specify the device and training parameters (the followings are the default settings) .. code:

python calib_mod.py 17Am05 -D cpu -lr 1e-6 -n 400 -mom 0.9 -thr 1e-2

• D (str) is the device (default:cpu, mps, gpu) to run pytorch.

• n (int) is the number of training epochs.

• lr (float) is the initial learning rate, which will decrease using scheduler (see details at https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau).

• thr (float) is the threshold of the scheduler.

• mom (float) is the momentum of gradient descent.

Citing this work

Martin, F.N., Starkenburg, E., & Yuan, Z. et al. (A&A 2024) https://www.aanda.org/articles/aa/full_html/2024/12/aa47633-23/aa47633-23.html