descwl-shear-sims

Simulations for testing weak lensing shear measurement

Example Usage

In the following examples, we use galaxy and star classes provided by descwl-shear-sims but note you can make your own

A simple sim

import numpy as np

# Galaxies with fixed size and flux
from descwl_shear_sims.galaxies import FixedGalaxyCatalog

from descwl_shear_sims.sim import make_sim

# convenience function to make a PSF
from descwl_shear_sims.psfs import make_fixed_psf

seed = 8312
rng = np.random.RandomState(seed)

ntrial = 2
coadd_dim = 351
buff = 50

for trial in range(ntrial):
    print('trial: %d/%d' % (trial+1, ntrial))

    # galaxy catalog; you can make your own
    galaxy_catalog = FixedGalaxyCatalog(
        rng=rng,
        coadd_dim=coadd_dim,
        buff=buff,
        layout='random',
        mag=25,
        hlr=1.0,
    )

    # make a constant gaussian psf
    psf = make_fixed_psf(psf_type='gauss')

    # generate some simulation data, with a particular shear

    sim_data = make_sim(
        rng=rng,
        galaxy_catalog=galaxy_catalog,
        coadd_dim=coadd_dim,
        g1=0.02,
        g2=0.00,
        psf=psf,
    )

    # get the first i band exposure
    sim_data['band_data']['i'][0]

    # full list of outputs in the dictionary
    # sim_data: dict
    #     band_data: a dict keyed by band name, holding a list of Exposure
    #        objects
    #     coadd_wcs: lsst.afw.geom.makeSkyWcs
    #     psf_dims: (int, int)
    #     coadd_dims: (int, int)
    #     coadd_bbox: lsst.geom.Box2I
    #     bright_info: structured array
    #         fields are
    #         ra, dec: sky position of bright stars
    #         radius_pixels: radius of mask in pixels
    #         has_bleed: bool, True if there is a bleed trail
    #     se_wcs: list of WCS

A sim with lots of features turned on

import numpy as np

# use galaxy models from WeakLensingDeblending.  Note you need
# to get the data for this, see below for downloading instructions
from descwl_shear_sims.galaxies import WLDeblendGalaxyCatalog

# The star catalog class
from descwl_shear_sims.stars import StarCatalog
from descwl_shear_sims.sim import make_sim

# for making a power spectrum PSF
from descwl_shear_sims.psfs import make_ps_psf

# convert coadd dims to SE dims, need for this PSF
from descwl_shear_sims.sim import get_se_dim

seed = 8312
rng = np.random.RandomState(seed)

ntrial = 2
coadd_dim = 351
buff = 50
rotate = True
dither = True

# this is the single epoch image sized used by the sim, we need
# it for the power spectrum psf
se_dim = get_se_dim(coadd_dim=coadd_dim, rotate=rotate, dither=dither)

for trial in range(ntrial):
    print('trial: %d/%d' % (trial+1, ntrial))

    # galaxy catalog; you can make your own
    galaxy_catalog = WLDeblendGalaxyCatalog(
        rng=rng,
        coadd_dim=coadd_dim,
        buff=buff,
    )
    # star catalog; you can make one of these too
    star_catalog = StarCatalog(
        rng=rng,
        coadd_dim=coadd_dim,
        buff=buff,
    )
    # make a power-spectrum PSF, again you can make your own PSF
    psf = make_ps_psf(rng=rng, dim=se_dim)

    # generate some simulation data, with a particular shear,
    # and dithering, rotation, cosmic rays, bad columns, star bleeds
    # turned on.  By sending the star catalog we generate stars and
    # some can be saturated and bleed

    sim_data = make_sim(
        rng=rng,
        galaxy_catalog=galaxy_catalog,
        star_catalog=star_catalog,
        coadd_dim=coadd_dim,
        g1=0.02,
        g2=0.00,
        psf=psf,
        dither=dither,
        rotate=rotate,
        bands=['r', 'i', 'z'],
        noise_factor=0.58,
        cosmic_rays=True,
        bad_columns=True,
        star_bleeds=True,
    )

Installation

See the requirements.txt for a list of dependencies. Note hexalattice is optional for the hex grid layout

This code uses data structures from the LSST science pipelines. If you need to install that code, it is probably easiest to use the stackvana package in conda forge, as listed in the requirements.txt. If you already have that code installed, you can remove it from the requirements.

# create a conda environment with stackvana in it
conda create -n sims stackvana
source activate sims  # or conda activate sims

# install dependencies.  Make sure that conda-forge is first in your channels
conda install --file requirements.txt

# install descwl-shear-sims
pip install .

Getting the Simulation Input Data

We have packaged some optional but useful data that can be used with descwl-shear-sims.

Galaxy models from WeakLensingDeblending
Realistic star fluxes
Realistic galctic star spatial density distribution
Realistic star bleed trail masks

Do the following to make that data available to descwl-shear-sims

wget https://www.cosmo.bnl.gov/www/esheldon/data/catsim.tar.gz
tar xvfz catsim.tar.gz
export CATSIM_DIR=/path/to/catsim

# or for tcsh
# setenv CATSIM_DIR /path/to/catsim

more examples

More examples are given in the examples/ sub directory

Further Documentation

The doc strings for the main public APIs are complete. See them for more details.

LSSTDESC / descwl-shear-sims

readme