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# Copyright (c) 2012-2014 by the GalSim developers team on GitHub
# This file is part of GalSim: The modular galaxy image simulation toolkit.
# GalSim is free software: redistribution and use in source and binary forms,
# with or without modification, are permitted provided that the following
# conditions are met:
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions, and the disclaimer given in the accompanying LICENSE
# file.
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions, and the disclaimer given in the documentation
# and/or other materials provided with the distribution.
Demo #12
The twelfth script in our tutorial about using GalSim in python scripts: examples/demo*.py.
(This file is designed to be viewed in a window 100 characters wide.)
This script currently doesn't have an equivalent demo*.yaml or demo*.json file. The API for catalog
level chromatic objects has not been written yet.
This script introduces the chromatic objects module galsim.chromatic, which handles wavelength-
dependent profiles. Three uses of this module are demonstrated:
1) A chromatic object representing a De Vaucouleurs galaxy with a early-type SED at redshift 0.8 is
created. The galaxy is then drawn using the six LSST filter throughput curves to demonstrate that
the galaxy is a g-band dropout.
2) A two-component bulge+disk galaxy, in which the bulge and disk have different SEDs, is created
and then drawn using LSST filters.
3) A wavelength-dependent PSF is created to represent atmospheric effects of differential chromatic
refraction, and the wavelength dependence of Kolmogorov-turbulence-induced seeing. This PSF is used
to draw a single Sersic galaxy in the LSST filters.
For all cases, suggested parameters for viewing in ds9 are also included.
New features introduced in this demo:
- SED = galsim.SED(wave, flambda)
- SED2 = SED.atRedshift(redshift)
- bandpass = galsim.Bandpass(filename)
- bandpass2 = bandpass.truncate(relative_throughput)
- bandpass3 = bandpass2.thin(rel_err)
- gal = galsim.Chromatic(GSObject, SED)
- gal = GSObject * SED
- obj = galsim.Add([list of ChromaticObjects])
- ChromaticObject.drawImage(bandpass)
- PSF = galsim.ChromaticAtmosphere(GSObject, base_wavelength, zenith_angle)
from __future__ import print_function
import sys
import os
import math
import numpy
import logging
import galsim
def main(argv):
# Where to find and output data
path, filename = os.path.split(__file__)
datapath = os.path.abspath(os.path.join(path, "data/"))
outpath = os.path.abspath(os.path.join(path, "output/"))
if not os.path.exists(outpath):
print('Creating', outpath)
# In non-script code, use getLogger(__name__) at module scope instead.
logging.basicConfig(format="%(message)s", level=logging.INFO, stream=sys.stdout)
logger = logging.getLogger("demo12")
# initialize (pseudo-)random number generator
random_seed = 1234567
rng = galsim.BaseDeviate(random_seed)
# read in SEDs
SED_names = ['CWW_E_ext', 'CWW_Sbc_ext', 'CWW_Scd_ext', 'CWW_Im_ext']
SEDs = {}
for SED_name in SED_names:
SED_filename = os.path.join(datapath, '{}.sed'.format(SED_name))
# Here we create some galsim.SED objects to hold star or galaxy spectra. The most
# convenient way to create realistic spectra is to read them in from a two-column ASCII
# file, where the first column is wavelength and the second column is flux. Wavelengths in
# the example SED files are in Angstroms, flux in flambda. The default wavelength type for
# galsim.SED is nanometers, however, so we need to override by specifying
# `wave_type = 'Ang'`.
SED = galsim.SED(SED_filename, wave_type='Ang')
# The normalization of SEDs affects how many photons are eventually drawn into an image.
# One way to control this normalization is to specify the flux density in photons per nm
# at a particular wavelength. For example, here we normalize such that the photon density
# is 1 photon per nm at 500 nm.
SEDs[SED_name] = SED.withFluxDensity(target_flux_density=1.0, wavelength=500)
logger.debug('Successfully read in SEDs')
# read in the LSST filters
filter_names = 'ugrizy'
filters = {}
for filter_name in filter_names:
filter_filename = os.path.join(datapath, 'LSST_{}.dat'.format(filter_name))
# Here we create some galsim.Bandpass objects to represent the filters we're observing
# through. These include the entire imaging system throughput including the atmosphere,
# reflective and refractive optics, filters, and the CCD quantum efficiency. These are
# also conveniently read in from two-column ASCII files where the first column is
# wavelength and the second column is dimensionless flux. The example filter files have
# units of nanometers and dimensionless throughput, which is exactly what galsim.Bandpass
# expects, so we just specify the filename.
filters[filter_name] = galsim.Bandpass(filter_filename)
# For speed, we can thin out the wavelength sampling of the filter a bit.
# In the following line, `rel_err` specifies the relative error when integrating over just
# the filter (however, this is not necessarily the relative error when integrating over the
# filter times an SED)
filters[filter_name] = filters[filter_name].thin(rel_err=1e-4)
logger.debug('Read in filters')
pixel_scale = 0.2 # arcseconds
# Part B: chromatic bulge+disk galaxy'')'Starting part B: chromatic bulge+disk galaxy')
redshift = 0.8
# make a bulge ...
mono_bulge = galsim.DeVaucouleurs(half_light_radius=0.5)
bulge_SED = SEDs['CWW_E_ext'].atRedshift(redshift)
# The `*` operator can be used as a shortcut for creating a chromatic version of a GSObject:
bulge = mono_bulge * bulge_SED
bulge = bulge.shear(g1=0.12, g2=0.07)
logger.debug('Created bulge component')
# ... and a disk ...
mono_disk = galsim.Exponential(half_light_radius=2.0)
disk_SED = SEDs['CWW_Im_ext'].atRedshift(redshift)
disk = mono_disk * disk_SED
disk = disk.shear(g1=0.4, g2=0.2)
logger.debug('Created disk component')
# ... and then combine them.
bdgal = 1.1 * (0.8*bulge+4*disk) # you can add and multiply ChromaticObjects just like GSObjects
# convolve with PSF to make final profile
#PSF = galsim.Moffat(fwhm=0.6, beta=2.5)
psf_sigma = 1.5 * pixel_scale # in arcsec
PSF = galsim.Gaussian(flux=1., sigma=psf_sigma)
bdfinal = galsim.Convolve([bdgal, PSF])
# Note that at this stage, our galaxy is chromatic but our PSF is still achromatic. Part C)
# below will dive into chromatic PSFs.
logger.debug('Created bulge+disk galaxy final profile')
# draw profile through LSST filters
gaussian_noise = galsim.GaussianNoise(rng, sigma=0.02)
for filter_name, filter_ in filters.iteritems():
img = galsim.ImageF(64, 64, scale=pixel_scale)
bdfinal.drawImage(filter_, image=img)
logger.debug('Created {}-band image'.format(filter_name))
out_filename = os.path.join(outpath, 'demo12b_{}.fits'.format(filter_name))
nepochs = 3
images = []
for i in range(nepochs):
newImg = img.copy()
galsim.fits.writeCube(images, out_filename)
logger.debug('Wrote {}-band images to disk'.format(filter_name))'Added flux for {}-band image: {}'.format(filter_name, img.added_flux))'You can display the output in ds9 with a command line that looks something like:')'ds9 -rgb -blue -scale limits -0.2 0.8 output/demo12b_r.fits -green -scale limits'
+' -0.25 1.0 output/demo12b_i.fits -red -scale limits -0.25 1.0 output/demo12b_z.fits'
+' -zoom 2 &')
if __name__ == "__main__":