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.

Acknowledgments

• 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.
• Davide Bianchi for cross-checks of PIP correction.

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}
}