pywiggle 0.1.26

Installing

Make sure your pip tool is up-to-date. To install wiggle, run:

$ pip install pywiggle --user

This will install a pre-compiled binary suitable for your system (only Linux and Mac OS X with Python>=3.9 are supported). After installation, make sure to run a test with:

$ pytest --pyargs pywiggle.tests

If you require more control over your installation, e.g. using Intel compilers, please see the section below on compiling from source.

$ pip install --upgrade pip meson ninja meson-python cython numpy pybind11
$ pip install  --no-build-isolation --editable .

$ pytest

Quick Usage

Accurate power spectrum estimation requires you to first convert a pixelated and masked map to its spherical harmonic coefficients. wiggle does not provide tools for SHTs and expects you to have the alm coefficients both for the masked fields and the mask itself already in hand. These can be obtained using a code like healpy in the case of HEALPix maps or a code like pixell in the case of rectangular pixelization maps.

If you are interested in accurate power spectra out to some maximum multipole lmax, we recommend you evaluate SHTs out to lmax for the masked fields, but out to 2 lmax for the mask itself. With these in hand, you can obtain unbiased power spectra as follows, in the case of a spin-0 field for example:

> import pywiggle
> import numpy as np
> import healpy as hp # or use pixell for rectangular pixel maps

> lmax = 4000 # Max multipole out to which you want reliable answers
> bin_edges = np.arange(40,lmax,40) # Choose bin edges
> alms1 = hp.map2alm(map1 * mask, lmax=lmax) # or get these from your favorite way of getting an SHT of masked maps
> alms2 = hp.map2alm(map2 * mask, lmax=lmax) # same as above
> mask_alm = hp.map2alm(mask, lmax=2*lmax)  # Notice that mask alms are needed out to 2*lmax
> ret = pywiggle.get_powers(alms1,alms2, mask_alm1, return_theory_filter=True,lmax=lmax,bin_edges=bin_edges)
> bcls = ret['TT']['Cls'] # The debiased power spectrum for spin-0
> th_filt = ret['TT']['Th'] # Optional binning matrix for precision theory comparisons

The interface to get_powers is flexible enough to allow all auto- and cross- spectra of spin-0 and spin-2 fields. If the input spherical harmonics are (1,nalm) or (nalm,) dimensional, where nalm is the number of spherical harmonic a_lm elements, the field is assumed to be spin-0 and only the TT-like spectrum is returned in the dictionary. If the input spherical harmonics are (2,nalm) dimensional, then the inputs are assumed to be E/B decompositions of a spin-2 field, and EE, EB, BE and BB are returned. If the input spherical harmonics are (3,nalm) dimensional, then the inputs are assumed to be a scalar field along with E/B decompositions of a spin-2 field, and TT, TE, ET, EE, EB, BE and BB are returned.

Here bcls is the mode-decoupled unbiased power spectrum and th_filt is a matrix that can be dotted with a theory spectrum to obtain the binned theory to compare the power spectrum to (e.g. for inference):

> chisquare = get_chisquare(bcls,th_filt @ theory_cls,cinv)

Contributing

If you have write access to this repository, please:

1. create a new branch

2. push your changes to that branch

3. merge or rebase to get in sync with master

4. submit a pull request on github

If you do not have write access, create a fork of this repository and proceed as described above.