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# Copyright (c) 2012-2018 by the GalSim developers team on GitHub
# https://github.com/GalSim-developers
#
# This file is part of GalSim: The modular galaxy image simulation toolkit.
# https://github.com/GalSim-developers/GalSim
#
# 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 an 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, wave_type, flux_type)
- SED2 = SED.atRedshift(redshift)
- bandpass = galsim.Bandpass(filename, wave_type)
- bandpass2 = bandpass.truncate(relative_throughput)
- bandpass3 = bandpass2.thin(rel_err)
- gal = GSObject * SED
- obj = galsim.Add([list of ChromaticObjects])
- ChromaticObject.drawImage(bandpass)
- PSF = galsim.ChromaticAtmosphere(GSObject, base_wavelength, zenith_angle)
"""
import sys
import os
import logging
import galsim
def main(argv):
# Where to find and output data
path, filename = os.path.split(__file__)
outpath = os.path.abspath(os.path.join(path, "output/"))
if not os.path.isdir(outpath):
os.mkdir(outpath)
# Make output directory if not already present.
if not os.path.isdir(outpath):
os.mkdir(outpath)
datapath = galsim.meta_data.share_dir
# 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, 'SEDs/{0}.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. We use a set of files that are
# distributed with GalSim in the share/ directory.
SED = galsim.SED(SED_filename, wave_type='Ang', flux_type='flambda')
# 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, 'bandpasses/LSST_{0}.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 throughput. The example filter files
# units of nanometers for the wavelength type, so we specify that using the required
# `wave_type` argument.
filters[filter_name] = galsim.Bandpass(filter_filename, wave_type='nm')
# 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 A: chromatic de Vaucouleurs galaxy
# Here we create a chromatic version of a de Vaucouleurs profile by multipying a GSObject by an
# SED. This is how one generally constructs _separable_ ChromaticObjects in GalSim, that is,
# those objects whose spatial dependence and wavelength dependence factor.
logger.info('')
logger.info('Starting part A: chromatic De Vaucouleurs galaxy')
redshift = 0.8
mono_gal = galsim.DeVaucouleurs(half_light_radius=0.5)
SED = SEDs['CWW_E_ext'].atRedshift(redshift)
gal = mono_gal * SED
# You can still shear, shift, and dilate the resulting chromatic object.
gal = gal.shear(g1=0.5, g2=0.3).dilate(1.05).shift((0.0, 0.1))
logger.debug('Created separable ChromaticObject')
# convolve with PSF to make final profile
PSF = galsim.Moffat(fwhm=0.6, beta=2.5)
final = galsim.Convolve([gal, PSF])
logger.debug('Created final profile')
# draw profile through LSST filters
gaussian_noise = galsim.GaussianNoise(rng, sigma=0.1)
for filter_name, filter_ in filters.items():
img = galsim.ImageF(64, 64, scale=pixel_scale)
final.drawImage(filter_, image=img)
img.addNoise(gaussian_noise)
logger.debug('Created {0}-band image'.format(filter_name))
out_filename = os.path.join(outpath, 'demo12a_{0}.fits'.format(filter_name))
galsim.fits.write(img, out_filename)
logger.debug('Wrote {0}-band image to disk'.format(filter_name))
logger.info('Added flux for {0}-band image: {1}'.format(filter_name, img.added_flux))
logger.info('You can display the output in ds9 with a command line that looks something like:')
logger.info('ds9 output/demo12a_*.fits -match scale -zoom 2 -match frame image &')
#-----------------------------------------------------------------------------------------------
# Part B: chromatic bulge+disk galaxy
logger.info('')
logger.info('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)
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
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.items():
img = galsim.ImageF(64, 64, scale=pixel_scale)
bdfinal.drawImage(filter_, image=img)
img.addNoise(gaussian_noise)
logger.debug('Created {0}-band image'.format(filter_name))
out_filename = os.path.join(outpath, 'demo12b_{0}.fits'.format(filter_name))
galsim.fits.write(img, out_filename)
logger.debug('Wrote {0}-band image to disk'.format(filter_name))
logger.info('Added flux for {0}-band image: {1}'.format(filter_name, img.added_flux))
logger.info('You can display the output in ds9 with a command line that looks something like:')
logger.info('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 &')
#-----------------------------------------------------------------------------------------------
# Part C: chromatic PSF
logger.info('')
logger.info('Starting part C: chromatic PSF')
redshift = 0.0
mono_gal = galsim.Exponential(half_light_radius=0.5)
SED = SEDs['CWW_Im_ext'].atRedshift(redshift)
# Here's another way to set the normalization of the SED. If we want 50 counts to be drawn
# when observing an object with this SED through the LSST g-band filter, for instance, then we
# can do:
SED = SED.withFlux(50.0, filters['g'])
# The flux drawn through other bands, which sample different parts of the SED and have different
# throughputs, will, of course, be different.
gal = mono_gal * SED
gal = gal.shear(g1=0.5, g2=0.3)
logger.debug('Created chromatic galaxy')
# For a ground-based PSF, two chromatic effects are introduced by the atmosphere:
# (i) differential chromatic refraction (DCR), and (ii) wavelength-dependent seeing.
#
# DCR shifts the position of the PSF as a function of wavelength. Blue light is shifted
# toward the zenith slightly more than red light.
#
# Kolmogorov turbulence in the atmosphere leads to a seeing size (e.g., FWHM) that scales with
# wavelength to the (-0.2) power.
#
# The ChromaticAtmosphere function will attach both of these effects to a fiducial PSF at
# some fiducial wavelength.
# First we define a monochromatic PSF to be the fiducial PSF.
PSF_500 = galsim.Moffat(beta=2.5, fwhm=0.5)
# Then we use ChromaticAtmosphere to manipulate this fiducial PSF as a function of wavelength.
# ChromaticAtmosphere also needs to know the wavelength of the fiducial PSF, and the location
# and orientation of the object with respect to the zenith. This final piece of information
# can be specified in several ways (see the ChromaticAtmosphere docstring for all of them).
# Here are a couple ways: let's pretend our object is located near M101 on the sky, we observe
# it 1 hour before it transits and we're observing from Mauna Kea.
ra = galsim.Angle.from_hms("14:03:13") # hours : minutes : seconds
dec = galsim.Angle.from_dms("54:20:57") # degrees : minutes : seconds
m101 = galsim.CelestialCoord(ra, dec)
latitude = 19.8207 * galsim.degrees # latitude of Mauna Kea
HA = -1.0 * galsim.hours # Hour angle = one hour before transit
# Then we can compute the zenith angle and parallactic angle (which is is the position angle
# of the zenith measured from North through East) of this object:
za, pa = galsim.dcr.zenith_parallactic_angles(m101, HA=HA, latitude=latitude)
# And then finally, create the chromatic PSF
PSF = galsim.ChromaticAtmosphere(PSF_500, 500.0, zenith_angle=za, parallactic_angle=pa)
# We could have also just passed `m101`, `latitude` and `HA` to ChromaticAtmosphere directly:
PSF = galsim.ChromaticAtmosphere(PSF_500, 500.0, obj_coord=m101, latitude=latitude, HA=HA)
# and proceed like normal.
# convolve with galaxy to create final profile
final = galsim.Convolve([gal, PSF])
logger.debug('Created chromatic PSF final profile')
# Draw profile through LSST filters
gaussian_noise = galsim.GaussianNoise(rng, sigma=0.03)
for filter_name, filter_ in filters.items():
img = galsim.ImageF(64, 64, scale=pixel_scale)
final.drawImage(filter_, image=img)
img.addNoise(gaussian_noise)
logger.debug('Created {0}-band image'.format(filter_name))
out_filename = os.path.join(outpath, 'demo12c_{0}.fits'.format(filter_name))
galsim.fits.write(img, out_filename)
logger.debug('Wrote {0}-band image to disk'.format(filter_name))
logger.info('Added flux for {0}-band image: {1}'.format(filter_name, img.added_flux))
logger.info('You can display the output in ds9 with a command line that looks something like:')
logger.info('ds9 output/demo12c_*.fits -match scale -zoom 2 -match frame image -blink &')
if __name__ == "__main__":
main(sys.argv)