pyrecon is a package to perform reconstruction within Python, using different algorithms, so far:
- MultiGridReconstruction, based on Martin J. White's code https://github.com/martinjameswhite/recon_code
- IterativeFFTParticleReconstruction, based on Julian E. Bautista's code https://github.com/julianbautista/eboss_clustering/blob/master/python/recon.py
- IterativeFFTReconstruction, iterative algorithm of Burden et al. 2015 (https://arxiv.org/abs/1504.02591) at the field-level (as opposed to IterativeFFTParticleReconstruction)
- PlaneParallelFFTReconstruction, base algorithm of Eisenstein et al. 2007 (https://arxiv.org/pdf/astro-ph/0604362.pdf), in the plane-parallel approximation.
With Python, a typical reconstruction run is (e.g. for MultiGridReconstruction; the same works for other algorithms):
from pyrecon import MultiGridReconstruction
# line-of-sight "los" can be local (None, default) or an axis, 'x', 'y', 'z', or a 3-vector
# Instead of boxsize and boxcenter, one can provide a (N, 3) array of Cartesian positions: positions=
recon = MultiGridReconstruction(f=0.8, bias=2.0, los=None, nmesh=512, boxsize=1000., boxcenter=2000.)
recon.assign_data(data_positions, data_weights) # data_positions is a (N, 3) array of Cartesian positions, data_weights a (N,) array
# You can skip the following line if you assume uniform selection function (randoms)
recon.assign_randoms(randoms_positions, randoms_weights)
recon.set_density_contrast(smoothing_radius=15.)
recon.run()
# A shortcut of the above is:
# recon = MultiGridReconstruction(f=0.8, bias=2.0, data_positions=data_positions, data_weights=data_weights, randoms_positions=randoms_positions, randoms_weights=randoms_weights, los=None, nmesh=512, boxsize=1000., boxcenter=2000.)
# If you are using IterativeFFTParticleReconstruction, displacements are to be taken at the reconstructed data real-space positions;
# in this case, do: data_positions_rec = recon.read_shifted_positions('data')
data_positions_rec = recon.read_shifted_positions(data_positions)
# RecSym = remove large scale RSD from randoms
randoms_positions_rec = recon.read_shifted_positions(randoms_positions)
# or RecIso
# randoms_positions_rec = recon.read_shifted_positions(randoms_positions, field='disp')
Also provided a script to run reconstruction as a standalone:
pyrecon [-h] config-fn [--data-fn [<fits, hdf5 file>]] [--randoms-fn [<fits, hdf5 file>]] [--output-data-fn [<fits, hdf5 file>]] [--output-randoms-fn [<fits, hdf5file>]]
An example of configuration file is provided in config. data-fn, randoms-fn are input data and random file names to override those in configuration file. The same holds for output files output-data-fn, output-randoms-fn.
Check algorithm details (see notes in docstrings).
Documentation is hosted on Read the Docs, pyrecon docs.
Only strict requirements are:
- numpy
- scipy
- pmesh
Extra requirements are:
- mpytools, fitsio, h5py to run pyrecon as a standalone
- pypower to evaluate reconstruction metrics (correlation, transfer function and propagator)
See pyrecon docs.
pyrecon is free software distributed under a BSD3 license. For details see the LICENSE.
- Martin J. White for https://github.com/martinjameswhite/recon_code
- Julian E. Bautista for https://github.com/julianbautista/eboss_clustering/blob/master/python/recon.py
- Pedro Rangel Caetano for inspiration for the script bin/recon
- Sesh Nadathur for careful checks against Revolver https://github.com/seshnadathur/Revolver/blob/main/python_tools/recon.py
- Enrique Paillas for bug reports
- Grant Merz for propagator https://github.com/grantmerz/DESI_Recon
