Make arbitrarily structured, arbitrary-dimension boxes and log-normal mocks.
powerbox is a pure-python code for creating density grids (or boxes) that have an arbitrary two-point distribution
(i.e. power spectrum). Primary motivations for creating the code were the simple creation of log-normal mock galaxy
distributions, but the methodology can be used for other applications.
- Works in any number of dimensions.
- Really simple.
- Arbitrary isotropic power-spectra.
- Create Gaussian or Log-Normal fields
- Create discrete samples following the field, assuming it describes an over-density.
- Measure power spectra of output fields to ensure consistency.
- Seamlessly uses pyFFTW if available for ~double the speed.
powerbox only depends on
numpy >= 1.6.2, which will be installed automatically if
powerbox is installed
pip (see below). Furthermore, it has the optional dependency of
pyfftw, which if installed will offer
~2x performance increase in large fourier transforms. This will be seamlessly used if installed.
pyfftw, simply do:
pip install pyfftw
pip install powerbox
Alternatively, the bleeding-edge version from git can be installed with:
pip install git+git://github.com/steven-murray/powerbox.git
Finally, for a development installation, download the source code and then run (in the top-level directory):
pip install -e .
If you find
powerbox useful in your research, please cite the Journal of Open Source Software paper at
- v0.5.7 [24 Oct 2018]
- v0.5.6 [23 Oct 2018]
- v0.5.5 [19 July 2018]
- v0.5.4 [30 May 2018]
- v0.5.3 [22 May 2018]
- v0.5.2 [17 May 2018]
- v0.5.1 [4 May 2018]
- v0.4.3 [29 March 2017]
- v0.4.2 [28 March 2017]
- v0.2.3 [11 Jan 2017]
- v0.2.2 [11 Jan 2017]
- v0.2.1 [10 Jan 2017]
- v0.2.0 [10 Jan 2017]
- v0.1.0 [27 Oct 2016]
- API Summary