Announcing GooFit 2.0

The next version of the CUDA/OpenMP fitting program for HEP analysis, GooFit 2.0, has been released. GooFit is now easy to build on a wide variety of Unix systems, and supports debuggers and IDEs. GooFit is faster, has unit tests, and working examples. More PDFs and examples have been added, as well as newly released example datasets that are downloaded automatically. GooFit now has built in support for MPI, and can use that to deploy to multiple graphics cards on the same machine. A new command line parser (CLI11) and drastically improved logging and errors have made code easier to write and debug. Usage of GooFit specific terminology is now reduced, using standard Thrust or CUDA terms when possible, lowering the barrier for new developers. A new Python script has been added to assist users converting from pre 2.0 code.

Announcing CLI11 Version 1.0

CLI11, a powerful library for writing command line interfaces in C++11, has just been released. There are no requirements beyond C++11 support (and even <regex> support not required). It works on Mac, Linux, and Windows, and has 100% test coverage on all three systems. You can simply drop in a single header file (CLI11.hpp available in releases) to use CLI11 in your own application. Other ways to integrate it into a build system are listed in the README.

Feynman Diagrams in Tikz

There is a package for making Feynman diagrams in LaTeX. Unfortunately, it is old and dvi latex only. If you are using pdflatex or lualatex, as you should be, it does not work. Even in regular LaTeX, it's a bit of a pain. Why is there not a new package for pdflatex? Turns out, you don't need one. Due to the powerful drawing library Tikz, you can create any diagram easily, and can customize it completely. For example:

Including CRY cosmic ray generator in CMake

I realized that CRY did not have a CMake based install option, so including it in a GEANT4 cmake project might not be obvious. This is how you would do it in your CMakeLists.txt:

University of Texas Doctoral Thesis Template

I have created a thesis class file for a UT Thesis in LaTeX. It has already been used for at least one passing thesis, so it does meet the current UT guidelines. (Please let me know if there are any issues!)

Since I use Bitbucket for all my private repositories (like my thesis itself), the code is in a Bitbucket repository rather than GitHub. Here is the link if you want to create a pull request or want to compile the class and documentation from the source .dtx file.

If all you want is a download of a working version, and if you don't want to compile the code but just want the class file, here are downloadable packages including the class file.

Simple Overloading in Python

This is intended as an example to demonstrate the use of overloading in object oriented programming. This was written as a Jupyter notebook (aka IPython) in Python 3. To run in Python 2, simply rename the variables that have unicode names, and replace truediv with div.

While there are several nice Python libraries that support uncertainty (for example, the powerful uncertainties package and the related units and uncertainties package pint), they usually use standard error combination rules. For a beginning physics class, often 'maximum error' combination is used. Here, instead of using a standard deviation based error and using combination rules based on uncorrelated statistical distributions, we assume a simple maximum error and simply add errors.

To implement this, let's build a Python class and use overloading to implement algebraic operations.

In [1]:

import unittest
import math

The main rules are listed below.

If $C=A+B$ or $C=A-B$, then $$ \delta C = \delta A + \delta B. \tag{1} $$

If $C=AB$ or $C=A/B$, then $$ \delta C = C_0 \left( \frac{\delta A}{A_0} + \frac{\delta B}{B_0} \right). \tag{2} $$

Following from that, if $C=A^n$ then $$ \delta C = A_0^n \left( n \frac{\delta A}{A_0} \right). \tag{3} $$