histogramr processes data from members (named columns) of HDF5 data sets of compound data type. It can use data from compound members spread over different data sets. histogramr produces a multivariate histogram, i.e. an approximate multivariate probability density function (PDF) discretized on a multidimensional rectangular regular grid of predefined shape. histogramr offers control over the histogram limits, the binning (grid spacing), and whether or not log-transformed data is used. histogramr creates an HDF5 output file with the PDF.
Check out this blog post for more details.
The current branch continuous integration status:
histogramr has been tested on 64-bit Linux and Mac OS X. It depends on the following libraries:
$ git clone git@github.com:tscholak/histogramr.git
histogramr uses the autotools. Run:
$ ./autogen.sh
$ ./configure
$ make
Should you prefer the Intel compiler, run:
$ env CC=icc make
You may also want to create a soft link to the executable:
$ ln -sf "`pwd`/src/histogramr" ~/bin
histogramr reads in the input files one-by-one and commits the data to the histogram data structure. The output file is written multiple times, whenever a predetermined number of input files has been processed.
histogramr: create multivariate histograms of continuous data
Usage: histogramr -d <dsname1> -m <mname1[:mname2...]>
-b <size1[:size2...]> -l <range1[:range2...]>
[-L <boolean1[:boolean2...]>] [-d <dsname2> ...] [-e <number>]
-o <outfile> <infile1> [<infile2> ...]
Mandatory options:
-d, --dataset <dsname> data set(s) must be specified first
-m, --member <mname> data set member(s)
-b, --binning <size> histogram binning(s)
-l, --limit <range> histogram limits
-o, --output <outfile> name the output file
Optional options:
-e, --save-every <number> save every <number> of files
(default: 1)
-L, --l10 <boolean> logarithmic transform (default: false)
Other options:
-h, --help print this help message and quit
-v, --version print version information and quit
Report bugs to: torsten.scholak+histogramr@googlemail.com
histogramr home page: <https://github.com/tscholak/histogramr>
So far, histogramr has processed data for the following publications:
- Torsten Scholak, Thomas Wellens, Andreas Buchleitner, "Spectral Backbone of Excitation Transport in Ultra-Cold Rydberg Gases", Phys. Rev. A 90, 063415 (2014)
- Tobias Zech, Mattia Walschaers, Torsten Scholak, Roberto Mulet, Thomas Wellens, Andreas Buchleitner, "Quantum transport in biological functional units: noise, disorder, structure", Fluct. Noise Lett. 12, 1340007 (2013)
- Torsten Scholak, Tobias Zech, Thomas Wellens and Andreas Buchleitner, "Disorder-assisted exciton transport", Acta Phys. Pol. A 120, 89 (2011)
- Torsten Scholak, Thomas Wellens, and Andreas Buchleitner, "Optimal networks for excitonic energy transport", J. Phys. B: At. Mol. Opt. Phys. 44, 184012 (2011)
- Torsten Scholak, Thomas Wellens, and Andreas Buchleitner, "The optimization topography of exciton transport", Europhys. Lett. 96, 10001 (2011)
- Torsten Scholak, Fernando de Melo, Thomas Wellens, Florian Mintert, and Andreas Buchleitner, "Efficient and coherent excitation transfer across disordered molecular networks", Phys. Rev. E 83, 021912 (2011)
- Torsten Scholak, Florian Mintert, Thomas Wellens and Andreas Buchleitner, "Transport and entanglement", Semiconductors and Semimetals 83, 1 (2010)