def do_astrodrizzle(filters, imdrizzlepath):
"""
Runs astrodrizzle in order to stack all images. Writes output as <filter>_drz.fits
Note that we haven't dithered; the stack is just a higher S/N image of our FoV
INPUTS:
--------
filters : dictionary {filter : filelist}
imdrizzle path : string
path where full frame images have ben stored for stacking
"""
teal.unlearn(
'astrodrizzle'
) #teal mimics IRAF functionality, unlearn sets all parameters to default
for filter in filters:
if filter[0].lower() == 'f': #Check that we only have images
if not os.path.isfile(
os.path.join(imdrizzlepath, filter + '_drz.fits')):
old_dir = os.getcwd()
os.chdir(
imdrizzlepath) #do the stacking in the imdrizzle directory
astrodrizzle.AstroDrizzle('@%s.lis' % (filter),
output=filter,
build='yes',
skysub=False)
os.chdir(old_dir) #switch back to original working directory
def mytweakreg_quad_main(path, refim):
os.chdir(path)
os.system('pwd')
updatewcs.updatewcs('*flc.fits')
teal.unlearn('tweakreg')
findpars = {
'computesig': True,
'threshold': 100.,
'conv_width': 3.5,
'peakmin': 100.,
'peakmax': 90000.,
'nsigma': 1.5,
'ratio': 1.0,
'theta': 0.0,
'dqbits': None,
'use_sharp_round': False,
'ylimit': 0.1
}
tweakreg.TweakReg(glob.glob('*flc.fits'),
expand_refcat=False,
enforce_user_order=False,
updatehdr=False,
shiftfile=True,
writecat=True,
clean=False,
interactive=True,
verbose=False,
headerlet=False,
minobj=15,
searchrad=250.0,
searchunits='pixels',
use2dhist=True,
see2dplot=True,
separation=0.5,
fitgeometry='rscale',
residplot='both',
labelsize=8,
nclip=5,
sigma=3.0,
refimage=refim,
imagefindcfg=findpars,
refimagefindcfg=findpars)
def do_drizzle(asns, outname, working_dir='sameasoutname', cte=True, **options):
"""
Note that this requires Ureka to be activated
A good baseline of options for 2-image ACS/WFC is:
``final_pixfrac=0.6,final_scale=.03, final_wcs=True``
`working_dir` can have the special value "sameasoutname", or anything ending
with an "_" will have `outname` added at the end
"""
from stsci.tools import teal
from drizzlepac import astrodrizzle
if working_dir == 'sameasoutname':
working_dir = outname
if working_dir.endswith('_'):
working_dir = working_dir + outname
#toprocess_fns are *relative* to the working_dir, not absolute paths
toprocess_fns = copy_files(asns, working_dir, cte=cte, incldrz=False,
allowexistingdata=True)
#reset to defaults
teal.unlearn('astrodrizzle')
options.setdefault('preserve', False)
options.setdefault('restore', False)
if len(set([asn.filter for asn in asns])) > 1:
if 'final_wht_type' not in options:
print("You are using multiple filters at the same time but didn't "
"set a wheight type. You probably don't want EXP, so we'll "
"try IVM")
options['final_wht_type'] = 'IVM'
olddir = os.path.abspath(os.curdir)
try:
os.chdir(working_dir)
astrodrizzle.AstroDrizzle(toprocess_fns, output=outname, **options)
finally:
os.chdir(olddir)
return working_dir
input_images = sorted(glob.glob('i*flc.fits'))
threshold = 100.
elif detector == 'ir':
input_images = sorted(glob.glob('i*flt.fits'))
threshold = 20.
cw = 2.5
# Parallelized option
p = Pool(8)
derp = p.map(updatewcs.updatewcs, input_images)
p.close()
p.join()
cat = 'gaia.cat'
wcsname ='GAIA'
teal.unlearn('tweakreg')
teal.unlearn('imagefindpars')
tweakreg.TweakReg(input_images, # Pass input images
updatehdr=True, # update header with new WCS solution
imagefindcfg={'threshold':threshold,'conv_width':cw},# Detection parameters, threshold varies for different data
separation=0., # Allow for very small shifts
refcat=cat, # Use user supplied catalog (Gaia)
clean=True, # Get rid of intermediate files
interactive=False,
see2dplot=True,
shiftfile=True, # Save out shift file (so we can look at shifts later)
wcsname=wcsname, # Give our WCS a new name
reusename=True,
fitgeometry='general') # Use the 6 parameter fit
os.rename('shifts.txt','shifts_{}.txt'.format(detector))
'''code written by Matt Wilde to efficiently drizzle already tweaked images suitable for
photometry and blinking. Must run in the /Users/mattwilde/47Tuc/BLINK/ ... with
appropriate detectrfilter
'''
from glob import glob
#from astropy.io import fits
from drizzlepac import *
from stsci.tools import teal
teal.unlearn('updatenpol')
teal.unlearn('astrodrizzle')
'''
need to implement this to be extensible to other directories:
updatenpol.update(list_of_images,refdir='/path/to/reference/files/',\
local=True)
'''
###seams to only work in python, not pyraf...
def drizzler(filter,detector):
'''function to auto generate drizzle functions based on a given filter and detector.
Takes in a filter and detector as arguments in quotations seperated by a comma.
Ex: drizzler('F435W','WFPC2')
'''
epochs1 = glob('9*')
epochs2 = glob('0*')
USE:
python run_tweakback.py -drz drz.fits --> apply WCS solution from drz.fits to constituent flts (must be located in cwd).
'''
__author__='D.M. HAMMER'
__version__= 0.1
import os, glob, argparse, pyfits, pdb
from pyraf import iraf
import numpy as np
from drizzlepac import tweakback
from stsci.tools import teal
if __name__=='__main__':
# -- Parse input parameters
parser = argparse.ArgumentParser(description='Run tweakback to apply WCS from drz to fl? images.')
parser.add_argument('-drz', '--drzim',default='NONE', type=str, help='Input drizzled image (no wildcards). \
Default is NONE - requires input.')
options = parser.parse_args()
# -- initialize names of drizzled/flt(c) images
if options.drzim == 'NONE': raise Exception('Must input a drizzled image.')
drzim = options.drzim
# -- run tweakback
iraf.unlearn('tweakback')
teal.unlearn('tweakback')
tweakback.tweakback(drzim,verbose=True)
f = open('imlist.dat', 'w')
for ff in imlist: f.write(ff+'\n')
f.close()
# -- get instrument and filter name
instrum = pyfits.getheader(imlist[0])['INSTRUME']
if instrum == 'WFC3':
filtname = pyfits.getheader(imlist[0])['FILTER']
elif instrum == 'ACS':
filtname = pyfits.getheader(imlist[0])['FILTER1']
if filtname[0] == 'C': filtname = pyfits.getheader(imlist[0])['FILTER2']
# -- run AstroDrizzle
teal.unlearn('astrodrizzle')
iraf.unlearn('astrodrizzle')
#astrodrizzle.AstroDrizzle('@imlist.dat',configobj='drizzle_'+instrum+'.cfg')
if instrum == 'WFC3':
astrodrizzle.AstroDrizzle('@imlist.dat',output=filtname.lower(),num_cores=4,final_bits='64', in_memory=True,clean=True, combine_type='imedian',preseve=False, \
final_wcs=True,final_rot=0.0,final_scale=0.05,final_pixfrac=0.4)
elif instrum == 'ACS':
astrodrizzle.AstroDrizzle('@imlist.dat',output=filtname.lower(),num_cores=4,final_bits='64,32',in_memory=True,clean=True, combine_type=imedian,preserve=False,\
final_wcs=True,final_rot=0.0,final_scale=0.05,final_pixfrac=0.7, final_wht_type='IVM') # weigh type dependent on avail. of rms maps
else: raise Exception('Instrument '+instrum+' not covered in our case list.')
# -- remove unwanted astrodrizzle files
tmp=np.concatenate((glob.glob('*single*fits'),glob.glob('*mask.fits'),glob.glob('tmp*.fits'), glob.glob(*blt.fits')))
for ff in tmp: os.remove(ff)
import drizzlepac
from drizzlepac import astrodrizzle
from drizzlepac import tweakreg
from drizzlepac import tweakback
import pylab as p
import numpy
from numpy import ma, finfo, float32
from stsci.tools import teal
teal.unlearn('astrodrizzle')
teal.unlearn('tweakreg')
adriz = astrodrizzle.AstroDrizzle
twreg = tweakreg.TweakReg
twback = tweakback.tweakback
from utils import basic_params
import numpy as np
from astropy.io import fits
import shutil
import glob
import os
filt = np.array(['f125', 'f140', 'f150', 'f165'])
filt_num = np.array(['125', '140', '150', '165'])
def drizzle_SBC(params, params_gal, i):
"""Entry point for combining different extensions of FLT images
Args:
params: (dict) Dictionary of 'basic' section of config file (common for all ULIRGs)
params_gal: (dict) Dictionary of galaxy parameters from config file
i : (int) ULIRG number (It goes from 0 to 4)
# -- initialize "conv_width" based on filter name (IR = 2.5; optical=3.5)
fheader = pyfits.getheader(im[0])
instr = fheader['INSTRUME']
if instr == 'WFC3':
filtname = fheader['FILTER']
elif instr == 'ACS':
filtname = fheader['FILTER1']
if filtname[0] == 'C': filtname = fheader['FILTER2']
else: raise Exception('Instrument '+instr+' not covered in case list.')
if filtname[0:2] == 'F1': conv_wid = 2.5
else: conv_wid = 3.5
# -- run tweakreg
teal.unlearn('tweakreg')
if USE_REF:
tweakreg.TweakReg('@imlist.dat',runfile=logfile,shiftfile=True,outshifts='shift.dat',updatehdr=True, \
fitgeometry=fittype,catfile=catfilename,xcol=xcol,ycol=ycol, \
refimage=irefim,refcat=irefcat,refxyunits='pixels',refxcol=rxcol,refycol=rycol, \
conv_width=conv_wid,searchrad=3.0,nclip=7, \
see2dplot=False,residplot='No plot')
else:
tweakreg.TweakReg('@imlist.dat',runfile=logfile,shiftfile=True,outshifts='shift.dat',updatehdr=True, \
fitgeometry=fittype,catfile=catfilename,xcol=xcol,ycol=ycol, \
conv_width=conv_wid,searchrad=3.0,nclip=7, \
see2dplot=False,residplot='No plot')
# -- remove unwanted tweakreg files
for ff in imlist: f.write(ff+'\n')
f.close()
# -- get instrument and filter name
instrum = pyfits.getheader(imlist[0])['INSTRUME']
if instrum == 'WFC3':
filtname = pyfits.getheader(imlist[0])['FILTER']
if pixscale < 0: pixscale = 0.1283
elif instrum == 'ACS':
filtname = pyfits.getheader(imlist[0])['FILTER1']
if filtname[0] == 'C': filtname = pyfits.getheader(imlist[0])['FILTER2']
if pixscale < 0: pixscale = 0.0496
# -- run AstroDrizzle
teal.unlearn('astrodrizzle')
iraf.unlearn('astrodrizzle')
if instrum == 'WFC3':
astrodrizzle.AstroDrizzle('@imlist.dat',output=filtname.lower(),num_cores=NCORES,final_bits='64', in_memory=True,clean=True, combine_type=imedian,preseve=False, \
final_wcs=True,final_rot=0.0,final_scale=pixscale,final_pixfrac=pixfrac,final_kernel='gaussian')
elif instrum == 'ACS':
astrodrizzle.AstroDrizzle('@imlist.dat',output=filtname.lower(),num_cores=NCORES,final_bits='64,32',in_memory=True,clean=True, combine_type=imedian,preserve=False,\
final_wcs=True,final_rot=0.0,final_scale=pixscale,final_pixfrac=pixfrac,final_kernel='gaussian')
else: raise Exception('Instrument '+instrum+' not covered in our case list.')
# -- remove unwanted astrodrizzle files
tmp=np.concatenate((glob.glob('*single*fits'),glob.glob('*mask.fits'),glob.glob('tmp*.fits'), glob.glob(*blt.fits')))
for ff in tmp: os.remove(ff)
numflts = len(imlist)
if numflts < 4:
cnhigh = 0
cnlow = 0
combtype = "minmed"
elif numflts < 6:
cnhigh = 1
cnlow = 0
combtype = "median"
else:
cnhigh = 2
cnlow = 1
combtype = "median"
# -- run AstroDrizzle
teal.unlearn("astrodrizzle")
if outname == "NA":
outname = filtname.lower() + "_" + finalwht.lower()
else:
outname = outname + "_" + finalwht.lower()
astrodrizzle.AstroDrizzle(
"@imlist.dat",
output=outname,
num_cores=NCORES,
clean=False,
preserve=False,
combine_type=combtype,
combine_nhigh=cnhigh,
combine_nlow=cnlow,
skywidth=0.1,
skystat=skystat,
def align(self,
subtract_background=True,
align_method=None,
ref_catalog=None,
create_diff_source_lists=True,
updatehdr=True,
updatewcs=True,
wcsname='DASH',
threshold=50.,
cw=3.5,
searchrad=20.,
astrodriz=True,
cat_file='catalogs/diff_catfile.cat',
drz_output=None,
move_files=False):
'''
Aligns new FLT's to reference catalog.
Parameters
----------
self : object
DashData object created from an individual IMA file.
subtract_background : bool, optional
If True, runs subtract_background_reads functions.
align_method : string, optional
Defines alignment method to be used. Default is None (input files
will align to each other).
ref_catalog : cat file, optional
Defines reference image that will be referenced for CATALOG
alignment method.
create_diff_source_lists : bool, optional
Specifies whether or not to create a segmentation image and source
list from the difference files
updatehdr : bool, optional
Specifies whether to update the headers after aligning during
TweakReg. Default is True.
updatewcs : bool, optional
Specifies whether to update the WCS after aligning during TweakReg.
Default is True.
wcsname : str, optional (as long as name you choose doesn't already exist)
Specifies name of WCS. Default is 'DASH'
threshold : float, optional
cw : float, optional
searchrad : float, optional
Radius (in pixels) that TweakReg will search around sources to find
matches. Default is 20 pixels.
astrodriz : bool, optional
Specifies whether to drizzle images together using Astrodrizzle.
Default is True.
cat_file : str, optional
Name of catfile to be used to align sources in TweakReg. Default is
the catfile created by setting create_diff_source_lists to True,
catalogs/diff_catfile.cat
drz_output : str, optional
Name of output file after drizzling using AstroDrizzle. Default is
the root name of the original IMA.
Outputs
-------
Shifts file : txt file
File containing shifts during TweakReg
WCS Shifts file : fits file
File containing WCS shifts during TweakReg
Residual plot : png
Plot showing residuals from TweakReg alignment
Vector plot : png
Plot showing vector poitnings from TweakReg alignment
Aligned FLT's : fits file
Same diff files created in split_ima that have been aligned using TweakReg
Drizzled Image : fits file
Setting astrodriz to True will output a drizzled image form a single
exposure (produced only if astrodriz is set to True)
'''
#Set name for output drizzled image to the rootname of the original IMA if it is not specified
if drz_output is None:
drz_output = self.root
input_images = sorted(glob('diff/{}_*_diff.fits'.format(self.root)))
if not os.path.exists('shifts'):
os.mkdir('shifts')
outshifts = 'shifts/shifts_{}.txt'.format(self.root)
outwcs = 'shifts/shifts_{}_wcs.fits'.format(self.root)
if subtract_background:
self.subtract_background_reads()
#Align images to a catalog
if align_method == 'CATALOG':
if (ref_catalog is not None):
##Create source list and segmentation maps based on difference files
if create_diff_source_lists is True:
diffpath = os.path.dirname(
os.path.abspath('diff/{}_*_diff.fits'.format(
self.root)))
cat_images = sorted([
os.path.basename(x)
for x in glob('diff/{}_*_diff.fits'.format(self.root))
])
sc_diff_files = [diffpath + '/' + s for s in cat_images]
self.diff_seg_map(cat_images=sc_diff_files)
teal.unlearn('tweakreg')
teal.unlearn('imagefindpars')
tweakreg.TweakReg(input_images,
refcat=ref_catalog,
catfile=cat_file,
xcol=2,
ycol=3,
updatehdr=updatehdr,
updatewcs=updatewcs,
wcsname=wcsname,
verbose=True,
imagefindcfg={
'threshold': threshold,
'conv_width': cw
},
searchrad=searchrad,
searchunits='pixels',
shiftfile=True,
outshifts=outshifts,
outwcs=outwcs,
interactive=False,
fitgeometry='rscale',
minobj=5)
else:
raise Exception(
'Need to specify reference catalog and reference image.')
#Align images to the first image
else:
teal.unlearn('tweakreg')
teal.unlearn('imagefindpars')
tweakreg.TweakReg(input_images,
catfile=cat_file,
xcol=2,
ycol=3,
updatehdr=updatehdr,
updatewcs=updatewcs,
wcsname=wcsname,
verbose=True,
imagefindcfg={
'threshold': threshold,
'conv_width': cw
},
searchrad=searchrad,
searchunits='pixels',
shiftfile=True,
outshifts=outshifts,
outwcs=outwcs,
interactive=False,
fitgeometry='rscale',
minobj=5)
pass
#Drizzle the images together
if astrodriz is True:
#Do not have drizzle take 256 flags into account
no_tfs = 2, 4, 8, 16, 32, 64, 128, 512, 2048, 4096, 8192, 16384
astrodrizzle.AstroDrizzle(input_images,
output=drz_output,
clean=False,
final_pixfrac=1.0,
context=False,
resetbits=0,
preserve=False,
driz_cr_snr='8.0 5.0',
driz_cr_scale='2.5 0.7',
driz_sep_bits=no_tfs,
final_bits=no_tfs)
if move_files is True:
self.move_files()
