daplis 0.9.8

Data Analysis Package for LInoSpad2

Data Analysis Package for LInoSpad (DAPLIS)

Package for unpacking and analyzing the binary data from the timestamping mode of the LinoSPAD2 detector.

Introduction

This package was written for data analysis for LinoSPAD2, mainly for analysis of the timestamp output. The key functions are ones for unpacking the binary output of the detector that utilizes the numpy Python library for quick unpacking of .dat files to matrices, dictionaries, or data frames.

The "functions" folder holds all functions from unpacking to plotting numerous types of graphs (pixel population, histograms of timestamp differences, etc.)

The "params" folder holds masks (used to mask some of the noisiest pixels) and calibration data (compensating for TDC nonlinearities and offset) for LinoSPAD2 daughterboards.

The "archive" folder is a collection of scripts for debugging, tests, older versions of functions, etc.

The "examples" folder contains a few jupyter notebooks with examples on how to use the main functions, showcasing how to work with the most important function parameters.

Full documentation, including examples and full documentation of modules and functions, can be found here.

Some functions (mainly the plotting ones) save plots as pictures in the .png format, creating a folder for the output in the same folder that holds the data. Others (such as delta_t.py for collecting timestamp differences in the given time window) save .csv files with the processed data for easier and faster plotting.

Additionally, a standalone repo with an application for online plotting of the sensor population can be found here.

Installation and usage

The package can be installed using pip:

pip install daplis

Alternatively, to start using the package, one can download the whole repo. "requirements.txt" collects all packages required for this project to run. One can create an environment for this project either using conda or pip.

To install the necessary packages using pip (for creating virtual environments using pip see this):

pip install virtualenv
py -m venv PATH/TO/ENVIRONMENT/ENVIRONMENT_NAME
PATH/TO/ENVIRONMENT/ENVIRONMENT_NAME/Scripts/activate
cd PATH/TO/GITHUB/CODES/daplis
pip install -r requirements.txt

or, using conda:

conda create --name NEW_ENVIRONMENT_NAME --file /PATH/TO/requirements.txt -c conda-forge

To install the package, first, switch to the created environment:

conda activate NEW_ENVIRONMENT_NAME

or

PATH/TO/ENVIRONMENT/ENVIRONMENT_NAME/Scripts/activate

and run

pip install -e .

that will install the local package LinoSPAD2. After that, you can import all functions in your project:

from daplis.functions import sensor_plot, delta_t, fits

How to contribute

This repo consists of two branches: 'main' serves as the release version of the package, tested, proven to be functional, and ready to use, while the 'develop' branch serves as the main hub for testing new stuff. To contribute, the best way would be to fork the repository and use the 'develop' branch for new introductions, submitting the results via pull requests. Everyone willing to contribute is kindly asked to follow the PEP 8 and PEP 257 conventions.

License and contact info

This package is available under the MIT license. See LICENSE for more information. If you'd like to contact me, the author, feel free to write at [e-mail].