DynamicalCorrelators.jl is a frontend for calculating zero- and finite-temperature dynamical correlation functions and related observables based on time evolution matrix-product states methods.

RascalC: A Fast Code for Galaxy Covariance Matrix Estimation

Heavy ion Analysis Libraries

⚡️⚡️⚡️Blazing fast correlation functions on the CPU.

Cleaned repository focusing on running RascalC library for semi-analytical galaxy 2-point correlation function covariance matrices

encore: Efficient isotropic 2-, 3-, 4-, 5- and 6-point correlation functions in C++ and CUDA

Libraries to analyze numerical simulations (python3)

This repository contains the software written for the manuscript "When velocity autocorrelations mirror force autocorrelations: Exact noise-cancellation in interacting Brownian systems" by A. Lüders, S. Mandal, and T. Franosch. The NC Algorithm [Mandal et al. PRL 2019] is implemented using the FACF.

Tabulated Correlation Functions

Custom code (Python or Matlab) to compute centripetal propagation from astrocytic calcium recordings using pixel-wise correlation functions

🚀 analysis framework for constructing two-particle correlation in nuclear physics - applicable for experimental data and outputs from transport model

The public repository for the code COFFE

utilizuies to manage bath correlation function and related quantities including fitting of multi-exponential representations in time domain

Reconstruction of binary arrays using correlation functions and simulated annealing

Example on how to compute correlation functions of realistic experimental data for continuously measured quantum systems.

spatialstats is collection of statistical tools and utility routines used to analyze the multi-scale structure of 2D and 3D spatial fields and particle distributions.

⚡️⚡️⚡️Blazing fast correlation functions on the CPU.

A tutorial on using Python to extract the fringe contrast from an interferogram and calculate the temporal decay of the first-order correlation function.

StochasticA is a textbook / website for an “Introduction to Stochastic Signal Processing”. Materials for this website can be found here. Be sure to read the README.md document if you want to know more about the implementation.