analistica/README.md

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# Statistical analysis
## Description
This repository contains the source code of two documents
- `lectures`: a summary of the lectures of the course
- `notes`: an explanation of the solutions of the exercises
and the programs written for each exercise (`ex-n` directories)
## Building the documents
The two documents `excercise.pdf` and `lectures.pdf` are written in Pandoc
markdown. XeTeX (with some standard LaTeX packages), the
[pandoc-crossref](https://github.com/lierdakil/pandoc-crossref) filter and a
Make program are required to build. Simply typing `make` in the respective
directory will build the document, provided the above dependencies are met.
## Building the programs
The programs used to solve the exercise are written in standard C99 (with the
only exception of the `#pragma once` clause) and require the following
libraries to build:
- [GMP](https://gmplib.org/)
- [GSL](https://www.gnu.org/software/gsl/)
* [pkg-config](https://www.freedesktop.org/wiki/Software/pkg-config/)
(build-time only)
Additionally Python (version 3) with `numpy` and `matplotlib` is required to
generate plots.
For convenience a `shell.nix` file is provided to set up the build environment.
See this [guide](https://nixos.org/nix/manual/#chap-quick-start) if you have
never used Nix before. Running `nix-shell` in the top-level will drop you into
the development shell.
Once ready, invoke `make` with the program you wishes to build. For example
$ make ex-1/bin/main
or, to build every program of an exercise
$ make ex-1
To clean up the build results run
$ make clean
## Running the programs
Notes:
- Many programs generate random numbers using a PRNG that is seeded with a
fixed value, for reproducibility. It's possible to test the program on
different samples by changing the seed via the environment variable
`GSL_RNG_SEED`.
### Exercise 1
`ex-1/bin/main` generate random numbers following the Landau distribution and
run a series of test to check if they really belong to such a distribution.
The size of the sample can be controlled with the argument `-n N`.
The program outputs the result of a Kolmogorov-Smirnov test and t-tests
comparing the sample mode, FWHM and median, in this order.
`ex-1/bin.pdf` prints a list of x-y points of the Landau PDF to the `stdout`.
The output can be redirected to `ex-1/pdf-plot.py` to generate a plot.
### Exercise 2
Every program in `ex-2` computes the best available approximation (with a given
method) to the Euler-Mascheroni γ constant and prints[1]:
1. the leading decimal digits of the approximate value found
2. the exact decimal digits of γ
3. the absolute difference between the 1. and 2.
[1]: Some program may also print additional debugging information.
`ex-2/bin/fancy`, `ex-2/bin/fancier` can compute γ to a variable precision and
take therefore the required number of decimal places as their only argument.
The exact γ digits (used in comparison) are limited to 50 and 500 places,
respectively.
### Exercise 3
`ex-3/bin/main` generates a sample of particle decay events and attempts to
recover the distribution parameters via both a MLE and a χ² method. In both
cases the best fit and the parameter covariance matrix are printed.
The program then performs a t-test to assert the compatibility of the data with
two hypothesis and print the results in a table.
To plot a 2D histogram of the generated sample do
$ ex-3/bin/main -i | ex-3/plot.py
In addition the program accepts a few more parameters to control the histogram
and number of events, run it with `-h` to see their usage.
Note: the histogram parameters affect the computation of the χ² and the
relative parameter estimation.