CMOS Noise Map documentation

Contents:

• cmos-noise-map
  • Standard Deviation Noise Map [Default]
  • Model Pixel Noise with GMM
  • Getting pixel model params from GMM
  • Main wrapper function
  • Base functions to calculate the read noise from standard deviation
• Modeling RTS
  • What is Random Telegraph Signal?
  • Gaussian Mixture Models and RTS Properties
  • Error Propagation
• Future Directions
  • Overfitting in GMM
  • Error Propagation
• Playground for Parameters

About this project

This project was started in 2022, as the Las Cumbres Observatory Delta Rho upgrades were underway. We investigated the properties of random telegraph signal, which are further discussed in The Algorithm Section, and pondered how to deal with it in the new QHY600 cameras being installed at various LCO 0.4m telescopes. We decided to propagate noise as a 2D error map, similar to the bad pixel map maker. This document has information about why we chose to address the problem of RTS in the way that we did, documents the code, and contains suggestions for future directions and improvements on this work.

Installation

---

Note: Some features of this package use funpack and fpack. We recommend installing these with this package to ensure everything runs smoothly.

From PyPi

pip install cmos-noise-map

From GitHub

cd cmos-noise-map
pip install poetry
poetry install

Usage

Usage: rts-maker [OPTIONS] PATH [METHOD] [FILEPATH]

  This script builds a noise map with the chosen method.

  path: Path to input bias files

  filepath:OPTIONAL Path to write file. Default writes it in the current directory.

  method: Default method is std. Available methods are std, rts, and param.
  See docs for more information about each method.

Options:
  -r, --data_ext TEXT             Extension of fits file that contains the
                                  image data
  -uq, --upper_quantile FLOAT     Standard deviation cutoff for pixel noise
                                  evaluation
  -t, --tolerance FLOAT           The minimum difference between silhouette
                                  scores. See docs for more information.
  -m, --min_peak_separation FLOAT
                                  Minimum difference between pixel value
                                  cluster centers to be considered separate
                                  clusters
  -o, --out_hdu_name TEXT         Name for the header in which the data will
                                  be stored
  -f, --fpack                     Adding this option will fpack your output
                                  fits file
  -b, --bias_check                Adding this option will skip the check to
                                  see if files used are bias files  [default:
                                  True]
  --help                          Show this message and exit.

Tests

To run the unit tests, simply run:

pip install poetry
poetry run pytest

Indices and tables

• Index

• Search Page