mockfactory is a MPI-parallel Python toolkit to generate Gaussian and lognormal mocks and apply cut-sky geometry to box galaxy mocks. Its main purpose is to study geometry effects on the power spectrum.
A typical run, generating lognormal mocks and applying some cutsky geometry is (pseudo-code, for an example with all variables defined see this notebook):
from mockfactory import LagrangianLinearMock, utils, setup_logging
# First generate mock in box
# power is the callable power spectrum as a function of k
mock = LagrangianLinearMock(power, nmesh=nmesh, boxsize=boxsize, boxcenter=boxcenter, unitary_amplitude=False)
# This is Lagrangian bias, Eulerian bias - 1
mock.set_real_delta_field(bias=bias - 1)
mock.set_analytic_selection_function(nbar=nbar)
mock.poisson_sample(seed=43)
data = mock.to_catalog()
# We've got data, now turn to randoms
from mockfactory.make_survey import RandomBoxCatalog
randoms = RandomBoxCatalog(nbar=10. * nbar, boxsize=boxsize)
# Apply cutsky geometry
randoms = randoms.cutsky(drange=drange, rarange=rarange, decrange=decrange)
# For data, we want to apply RSD *before* selection function
isometry, mask_radial, mask_angular = data.isometry_for_cutsky(drange=drange, rarange=rarange, decrange=decrange)
# First move data to its final position
data = data.cutsky_from_isometry(isometry, dradec=None)
# Apply RSD
data['RSDPosition'] = data.rsd_position(f=f)
data['Distance'], data['RA'], data['DEC'] = utils.cartesian_to_sky(data['RSDPosition'])
# Apply selection function
mask = mask_radial(data['Distance']) & mask_angular(data['RA'], data['DEC'])
data = data[mask]
# Distance to redshift relation
from mockfactory.make_survey import DistanceToRedshift
distance_to_redshift = DistanceToRedshift(distance=cosmo.comoving_radial_distance)
for catalog in [data, randoms]:
catalog['Distance'], catalog['RA'], catalog['DEC'] = utils.cartesian_to_sky(catalog.position)
catalog['Z'] = distance_to_redshift(catalog['Distance'])
# Let us apply some redshift cuts
from mockfactory.make_survey import TabulatedRadialMask
mask_radial = TabulatedRadialMask(z=z, nbar=nbar)
data = data[mask_radial(data['Z'], seed=84)]
randoms = randoms[mask_radial(randoms['Z'], seed=85)]
# Save to disk
data.write(data_fn)
randoms.write(randoms_fn)
One can also apply Jordan Carlson and Martin White's remapping algorithm to any periodic mock, e.g. (pseudo-code, for an example with all variables defined see this notebook):
# We start from a random catalog, but can be anything with a periodic box geometry
from mockfactory.make_survey import RandomBoxCatalog
randoms = RandomBoxCatalog(nbar=nbar, boxsize=boxsize)
# Let's choose the 3 lattice vectors in available ones
from mockfactory.remap import Cuboid
lattice = Cuboid.generate_lattice_vectors(maxint=1, maxcomb=1, sort=False, boxsize=catalog.boxsize)
# lattice is a dictionary of cuboidsize: [basis]
# Choose the cuboid (final) size that best suits you and:
remapped_randoms = randoms.remap(*basis)
Example notebooks are provided in directory nb/. Example scripts are provided in directory mockfactory/tests/scripts.
Strict requirements are:
- numpy
- scipy
- mpi4py
- pmesh
- mpytools
Simply run:
python -m pip install git+https://github.com/cosmodesi/mockfactory
First:
git clone https://github.com/cosmodesi/mockfactory.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
mockfactory is free software distributed under a BSD3 license. For details see the LICENSE.
nbodykit for recipe for lognormal mocks, and mpi helper functions. cuboid_remap by Duncan Campbell, based on Jordan Carlson and Martin White's algorithm. Edmond Chaussidon for box-to-cutsky debugging and DESI cutsky script. Antoine Rocher for DESI SV3 cutsky script and debugging. Marie Laplace and Christophe Yèche for implementation of DESI QSO redshift smearing. Jiaxi Yu for implementation of DESI ELG, LRG and BGS redshift smearing.
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