Important python scritps used in my doctoral thesis. Some of them were included in jupyter notebooks (Thesis/notebooks)
buildCt.py computes the matrix of covariances C(t).
computeLambda.py computes the matrix of correlations, C(t), the matrix lambda and the predicted matrix of correlations with its error.
covariance_mm.py computes the covariance between two input matrix. The output is a matrix.
covariance_mv.py computes the covariance between a matrix and a vector. The output is a vector.
covariance_vm.py computes the covariance between a vector and a matrix. The output is a vector.
covariance_vv.py computes the covariance between two input vectors. The output is a vector.
lambda.py computes the matrix
laplaceTransform.py computes the laplace transform of the matrix of correlations C(t).
movie-correlations.py generates a movie of correlations
movie-matrices.py generates a movie from an input matrix
pbc.py transforms a correlations files from real to Fourier space.
reshapes.py changes format (vector/matrix) of an input file.
running-integral.py computes the cumulative integral of an input matrix.
symmetrizer.py takes advange of the simmetries of an input matrix of correlations in order to increase the statistics.