unfoldr

unfolding and nearest-neighbor level spacings

Given the eigenvalues of an ensemble of random matrices, unfoldr calculates the nearest-neighbor level spacings of the unfolded spectrum, either as a whole or for slices of it. You can specify how you want to cut the spectrum into slices — linearly, logarithmically —, and unfoldr will calculate the level spacings for each slice individually. With this you can study how the level spacing statistics change with energy and whether or not there is a phase transition in the spectrum.

Check out this blog post for more details.

Current status

The current branch continuous integration status:

Installation

unfoldr has been tested with Python 2.7 on 64-bit Linux and Mac OS X. It requires the following packages:

Obtaining the source code

$ git clone git@github.com:tscholak/unfoldr.git

Setting up unfoldr

unfoldr uses the setuptools. From within the source code directory, run:

python setup.py install

Make sure your Python's bin directory is in your $PATH. On Mac OS X, if you use Macports and haven't done so already, add

export PATH=/opt/local/Library/Frameworks/Python.framework/Versions/2.7/bin:$PATH

to your ~/.bash_login or ~/.bash_profile file.

Usage

unfoldr reads input files one-by-one and processes them in parallel. The output file is written to once at the end of the program. The data in the output file can be analyzed statistically with histogramr.

Command line arguments

usage: unfoldr [-h] [-V] [-q] [-v] [-D] -d <dsname> -m <mname> [-b <size>]
               [-l <range>] [-L] [-p <size>] [-i] -o <outfile>
               <infile> [<infile> ...]

unfold spectra of random matrices and Hamiltonians and calculate the nearest-
neighbor level spacings.

positional arguments:
  <infile>              name(s) of input file(s)

optional arguments:
  -b <size>, --binning <size>
                        binning (default: inf)
  -l <range>, --limits <range>
                        limits (default: [-1.7976931348623157e+308,
                        1.7976931348623157e+308])
  -L, --l10             logarithmic binning (default: False)
  -p <size>, --poolsize <size>
                        relative size of the unfolding pool (default: 2)
  -i, --inmemory        keep all data in memory (default: False)

mandatory arguments:
  -d <dsname>, --dataset <dsname>
                        input data set (default: None)
  -m <mname>, --member <mname>
                        input data set member (default: None)
  -o <outfile>, --output <outfile>
                        name of output file (default: None)

other arguments:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -q, --quiet           quiet output (default: False)
  -v, --verbose         verbose output (default: False)
  -D, --debug           debug output (default: False)

Report bugs to: torsten.scholak+unfoldr@googlemail.com unfoldr home page:
<https://github.com/tscholak/unfoldr>

Impact

So far, unfoldr has unfolded spectra for the following publication(s):

Torsten Scholak, Thomas Wellens, Andreas Buchleitner, "Spectral Backbone of Excitation Transport in Ultra-Cold Rydberg Gases", Phys. Rev. A 90, 063415 (2014)

coppolachan / unfoldr