pypower

pypower is a package for auto and cross power spectrum and associated window function estimation, for periodic boxes, survey geometry, in the flat-sky or plane-parallel (plus first odd wide-angle corrections) configurations.

A typical auto power spectrum estimation is as simple as:

import numpy as np
from pypower import CatalogFFTPower

kedges = np.linspace(0., 0.2, 11)
# pass mpiroot=0 if input positions and weights are not MPI-scattered
result = CatalogFFTPower(data_positions1=data_positions1, data_weights1=data_weights1, randoms_positions1=randoms_positions1, randoms_weights1=randoms_weights1,
                         edges=kedges, ells=(0, 2, 4), boxsize=1000., nmesh=512, resampler='tsc', interlacing=2, los=None, position_type='pos')
# wavenumber array in result.poles.k
# multipoles in result.poles.power

Example notebooks presenting most use cases are provided in directory nb/.

Documentation

Documentation is hosted on Read the Docs, pypower docs.

Requirements

Only strict requirements are:

numpy
scipy
pmesh (can be ignored if data types only are required, to e.g. load precomputed power spectra / window functions)

To enable faster spherical harmonics computation:

sympy
numexpr

Installation

pip

Simply run:

python -m pip install git+https://github.com/cosmodesi/pypower

git

First:

git clone https://github.com/cosmodesi/pypower.git

To install the code:

python setup.py install --user

Or in development mode (any change to Python code will take place immediately):

python setup.py develop --user

License

pypower is free software distributed under a BSD3 license. For details see the LICENSE.

Credits

nbodykit for recipe and most routines for power spectrum estimation. Patrick McDonald for recipe for window function matrix estimation. Juan Mena Fernandez for issue reports.

Citations

The power spectrum estimator is (except for to the normalization factor) the same as in

@ARTICLE{2017JCAP...07..002H,
       author = {{Hand}, Nick and {Li}, Yin and {Slepian}, Zachary and {Seljak}, Uro{\v{s}}},
        title = "{An optimal FFT-based anisotropic power spectrum estimator}",
      journal = {\jcap},
     keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics},
         year = 2017,
        month = jul,
       volume = {2017},
       number = {7},
          eid = {002},
        pages = {002},
          doi = {10.1088/1475-7516/2017/07/002},
archivePrefix = {arXiv},
       eprint = {1704.02357},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2017JCAP...07..002H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

MinhMPA / pypower