getting_started.rst



Getting Started
Welcome to Getting Started. This page is intended for users and will give a brief
introduction as well as tell you how to install and run the code. At the end it also shows
how to build this documentation you are currently reading.

Introduction
Assume you have a random variable x that you can measure, some known, non-linear
function f and the observable you are actually interested in is given by
y=f(x). Since f is non-linear, the transformed sample mean
f(\bar{x}) is a biased estimator for y and linear error propagation is not
well suited to estimate its error. Jackknife resampling allows to reduce the bias and give
a better estimate for the error of y. For more details see the source code
documentation of :doc:`jackknife`.
In the special case of the present software the observables of interest y are
self-energies and susceptibilities. It is possible to jackknife multiple of them at once.
The non-linear function f is the ADGA program and the random input samples
x are :abbr:`DMFT (dynamical mean field theory)` Green's functions obtained with
w2dynamics. Since multiple input samples are needed, you have to use worm sampling to
generate the desired number of 2-particle Green's functions. The more worm samples you
use, the better your statistics will get. For more details see the source code
documentation of :doc:`adga`.

Installation
jackknife is written in Python 3, but also works with Python 2.7 and requires the
following packages.


NumPy (1.16.2)
h5py (1.10.2)
configparser (3.7.4)


The numbers in parenthesis are not the minimum required version, but just one set of
versions that are tested and work. In addition


w2dynamics and
ADGA


must also be installed (instructions on how to do that are provided in their respective
documentation). ADGA is the program that actually calculates the self-energies and
susceptibilities. w2dynamics is a DMFT code used to calculate 1- and 2-particle Green's
functions, which are the input quantities of ADGA.

Usage
The actual jackknife code is relatively easy to use, but one needs to generate quite a few
input and config files with/for w2dynamics and ADGA beforehand.

Input
In order to estimate the standard error, covariance matrix, etc. of ADGA quantities the
following input and config files are necessary:


an ADGA config file
a file containing the momentum resolved values of the tight-binding hamiltonian
H(k)

a file containing the 1-particle Green's function from w2dynamics
a file containing multiple 2-particle Green's functions from w2dynamics using worm
sampling
a jackknife config file