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

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.

This library greatly streamlines working with raw timestamps from the LinoSPAD2 camera. With this package, one can analyze dark current rate, cross-talk probability, plot sensor population, and analyze the raw data to look for photon coincidences.

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 or .feather 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.

A fresh, separate virtual environment is highly recommended before installing the package. This can be done using pip, see, e.g., this. This can help to avoid any dependency conflicts and ensure smooth operation of the package.

First, check if the virtualenv package is installed. To do this, one can run:

pip show virtualenv

If the package was not found, it can be installed using:

pip install virtualenv

To create a new environment, run the following:

virtualenv PATH/TO/NEW/ENVIRONMENT

To activate the environment (on Windows):

PATH/TO/NEW/ENVIRONMENT/Scripts/activate

and on Linux:

source PATH/TO/NEW/ENVIRONMENT/bin/activate

Then, package itself can be installed using pip inside the environment:

pip install daplis

Alternatively, to start using the package, one can download the whole repo. "requirements.txt" lists all packages required for this project to run. One can create an environment for this project either using conda or pip following the instruction above. Once the new environmnt is activated, run the following to install the required packages:

cd PATH/TO/GITHUB/CODES/daplis
pip install -r requirements.txt

Now, the package can be installed via

pip install -e .

where '-e' stands for editable: any changes introduced to the package will instantly become a part of the package and can be used without the need of reinstalling the whole thing. After that, one can import any function from the daplis package:

from daplis.functions import sensor_plot, delta_t, fits

For conda users, the new environment can be installed using the 'requirements' text file directly:

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

and run

pip install -e .

For a fast introduction on how to use the package, please see the jupyter notebooks with examples on the main functions at "daplis/examples/".

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.

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 [email protected].