def run_imstat(input):
"""
Call imstatistics for a list of images and show in terminal
"""
#iraf.images()
for image in input:
iraf.imstat(image)
def badimage(img, _type):
from pyraf import iraf
import string
import floyds
hdr = floyds.util.readhdr(img)
_tel = floyds.util.readkey3(hdr, 'telescop')
rr1 = float(
string.split(iraf.imstat(img + '[1250:1270,140:200]', Stdout=1)[1])[2])
rr2 = float(
string.split(iraf.imstat(img + '[1570:1620,170:210]', Stdout=1)[1])[2])
bb1 = float(
string.split(iraf.imstat(img + '[1090:1110,370:400]', Stdout=1)[1])[2])
bb2 = float(
string.split(iraf.imstat(img + '[1560:1700,340:360]', Stdout=1)[1])[2])
bg1 = float(
string.split(iraf.imstat(img + '[1500:1540,290:310]', Stdout=1)[1])[2])
#print rr1,rr2,bb1,bb2,bg1
good = 1
if _type in ['flat', 'arc', 'lamp']:
if rr1 / rr2 <= 1.25 and bb1 / bb2 <= 1.25:
good = 0
if good == 1:
if _type in ['flat']:
if rr1 / bg1 < 20 and bb1 / bg1 > 2.5 and 'South' in _tel:
good = 0
if bb1 / bg1 < 1.0:
good = 0
if rr1 < 1000:
good = 0
if good == 0: print '\n### warning: image ' + img + ' rejected'
return good
def badimage(img,_type):
from pyraf import iraf
import string
import floyds
hdr=floyds.util.readhdr(img)
_tel=floyds.util.readkey3(hdr,'telescop')
rr1=float(string.split(iraf.imstat(img+'[1250:1270,140:200]',Stdout=1)[1])[2])
rr2=float(string.split(iraf.imstat(img+'[1570:1620,170:210]',Stdout=1)[1])[2])
bb1=float(string.split(iraf.imstat(img+'[1090:1110,370:400]',Stdout=1)[1])[2])
bb2=float(string.split(iraf.imstat(img+'[1560:1700,340:360]',Stdout=1)[1])[2])
bg1=float(string.split(iraf.imstat(img+'[1500:1540,290:310]',Stdout=1)[1])[2])
#print rr1,rr2,bb1,bb2,bg1
good=1
if _type in ['flat','arc','lamp']:
if rr1/rr2<=1.25 and bb1/bb2<=1.25:
good=0
if good==1:
if _type in ['flat']:
if rr1/bg1<20 and bb1/bg1>2.5 and 'South' in _tel:
good=0
if bb1/bg1<1.0:
good=0
if rr1<1000:
good=0
if good==0: print '\n### warning: image '+img+' rejected'
return good
def get_imstats(image):
output = []
with Capturing(output) as output:
iraf.imstat(image)
output = output[1].split(" ")
print output
max = float(output[-1])
min = float(output[-2])
return max, min
def get_imstats(image):
output = []
with Capturing(output) as output:
iraf.imstat(image)
output = output[1].split(" ")
print output
max = float(output[-1])
min = float(output[-2])
return max, min
def rejectbias(lista, _interactive, nn=10):
# print "LOGX:: Entering `rejectbias` method/function in %(__file__)s" %
# globals()
import numpy as np
import ntt
from pyraf import iraf
iraf.images(_doprint=0)
listgood = []
f = open('_listgoodbias', 'w')
for img in lista:
f.write(img + '\n')
f.close()
biasstd = np.array(iraf.imstat(
'@_listgoodbias', fields='stddev', format='no', Stdout=1), float)
# biasmedian=np.array(iraf.imstat('@_listgoodbias', fields='mean',format='no',Stdout=1),float)
# print lista
median = np.median(biasstd)
sigma = (np.percentile(biasstd, 75) - np.percentile(biasstd, 25)) * 1.349
lista1 = np.compress((biasstd < (median + nn * sigma))
& (biasstd > (median - nn * sigma)), np.array(lista))
# lista1=np.compress((np.mean(biasstd)-2*np.std(biasstd)<=biasstd)&(np.mean(biasstd)+2*np.std(biasstd)>=biasstd)&
# (np.mean(biasmedian)-2*np.std(biasmedian)<=biasmedian)&
# (np.mean(biasmedian)+2*np.std(biasmedian)>=biasmedian), np.array(lista))
ntt.util.delete('_listgoodbias')
for i in range(0, len(lista)):
if lista[i] not in lista1:
print lista[i] + ' ' + str(biasstd[i]) + ' rejected'
else:
print lista[i], biasstd[i]
for img in lista1:
if _interactive:
aa, bb, cc = ntt.util.display_image(img, 1, '', '', False)
titolo, result = iraf.imstat(img, Stdout=1)
print titolo[1:]
print result
answ = 'nn'
while answ not in ['g', 'G', 'b', 's']:
answ = raw_input('good/bad [[g]/b/G(all good)/s(stop) ] ? ')
if not answ:
answ = 'g'
if answ not in ['g', 'G', 'b', 's']:
print 'warning: value not valid, try again'
if answ == 'g':
listgood.append(img)
elif answ == 'G':
listgood = lista1
break
elif answ == 's':
break
else:
listgood.append(img)
return listgood
def writecountingstatstoafile(images, ditgroupcounter, bandpass, target):
imagesinirafformat = str(images).replace("', '", ",").replace("['",
"").replace(
"']", "")
date = images[0][5:11]
outputfilename = '%s.%s.%s.%s.txt' % (target, date, bandpass,
ditgroupcounter)
iraf.imstat.unlearn()
iraf.imstat(imagesinirafformat,
field='image, mean, midpt, mode, stddev, npix',
Stdout=outputfilename)
def darkcom(input_dark):
import glob
import os, sys
import numpy as np
from pyraf import iraf
from astropy.io import fits
from astropy.io import ascii
curdir = os.getcwd()
curdate = curdir.split('/')[-1]
#dark = ", ".join(input_dark)
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.ccdred.setinst(instrume='camera', directo='/iraf/iraf/noao/imred/ccdred/ccddb/', query='q', review='no')
#iraf.chdir('./dark')
#dark = glob.glob('cal*dk*.fit')
#dark = ascii.read('dark.list', guess=True, data_start=0)
'''
allexptime = []
for i in range(len(dark)) :
hdr = fits.getheader(dark[i])
allexptime.append(hdr['exptime'])
expset = set(allexptime)
exptime = list(sorted(expset))
i=0
for i in range(len(exptime)) :
print('Find images with exptime of '+str(exptime[i]))
imlist = []
for j in range(len(dark)) :
hdr = fits.getheader(dark[j])
if hdr['exptime'] == exptime[i] :
imlist.append(dark[j])
else :
pass
print(imlist)
input_name = 'dark'+str(int(exptime[i]))+'.list'
output_name = curdate+'_dark'+str(int(exptime[i]))+'.fits'
#input_name = output_name[:-5]+'.list'
f=open(input_name,'w+')
for k in range(len(imlist)) :
f.write(imlist[k]+'\n')
f.close()
'''
#output_name = curdate+'_dark'+str(int(exptime[i]))+'.fits'
output_name = curdate+'_'+input_dark[:-5]+'.fits'
print('Darkcombine is running...')
iraf.imstat(images='z@'+input_dark)
iraf.darkcombine(input='z@'+input_dark, output=output_name, combine='median', reject='minmax', process='no', scale='none', ccdtype='' )
#os.system('/usr/bin/cp '+output_name+' ../')
os.system('/usr/bin/cp '+output_name+' /data1/KHAO/MDFTS/red/masterdark/')
#os.system('/usr/bin/rm d*.list')
#iraf.chdir('../')
iraf.dir('.')
print('Output master '+output_name+' is created.')
def flatcom(input_flat='zFLAT*.fit'):
import glob
import os, sys
import itertools
import numpy as np
from pyraf import iraf
from astropy.io import fits
from astropy.io import ascii
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.ccdred.setinst(instrume='camera', directo='/iraf/iraf/noao/imred/ccdred/ccddb/', query='q', review='no')
# Filter classification
calflat = glob.glob(input_flat)
allfilter = []
i=0
for i in range(len(calflat)) :
hdr = fits.getheader(calflat[i])
allfilter.append(hdr['filter'])
filterset = set(allfilter)
infilter = list(sorted(filterset))
i=0
for i in range(len(infilter)) :
print('Find images with filter of '+str(infilter[i]))
imlist = []
for j in range(len(calflat)) :
hdr = fits.getheader(calflat[j])
if hdr['filter'] == infilter[i] :
imlist.append(calflat[j])
else :
pass
print(imlist)
imlist.sort()
input_name = str(infilter[i])+'flat.list'
k=0
f=open(input_name,'w+')
for k in range(len(imlist)) :
f.write(imlist[k]+'\n')
f.close()
output_name = input_name[:-5]+'.fits'
#iraf.flatcombine(input='@'+input_name, output=output_name, combine='average', reject='crreject', process='no', scale='mode', ccdtype='', lsigma='3.', hsigma='3.' )
iraf.flatcombine(input='@'+input_name, output=output_name, combine='median', reject='minmax', process='no', scale='mode', ccdtype='')
print(output_name+' is created. Normalizing...')
data, newhdr = fits.getdata(output_name, header=True)
x = np.mean(data)
nimage = data/x
newflat_name = curdate+'_n'+str(infilter[i])+'flat.fits'
fits.writeto(newflat_name, nimage, header=newhdr, overwrite=True)
#os.system('/usr/bin/cp '+newflat_name+' ../')
os.system('/usr/bin/cp '+newflat_name+' /data1/KHAO/MDFTS/red/masterflat_'+infilter[i]+'/')
print('Normalised master flats are created.')
iraf.imstat(images='*n?flat.fits')
def cull_bias(biaslist, listname):
iraf.imstat("@{}".format(listname))
bads = raw_input("Select any biasi you want to remove, q to continue\n")
while bads.lower() != "q":
badlist = bads.split(",")
print "badlist is {}".format(badlist)
newlist = [i for i in biaslist if not any([j.strip() in i for j in badlist])]
write_list(newlist, listname)
iraf.imstat("@{}".format(listname))
bads = raw_input("Select any biasi you want to remove, q to continue\n")
return
def main(fits, x0, x1, y0, y1):
x0 = int(round(x0, 0))
x1 = int(round(x1, 0))
y0 = int(round(y0, 0))
y1 = int(round(y1, 0))
reg = "[%d:%d,%d:%d]" % (x0, x1 - 1, y0, y1 - 1)
key_ls = ["IMAGE", "NPIX", "MEAN", "STDDEV", "MIDPT", "MODE", "MIN", "MAX"]
fields = ",".join(key_ls).lower()
result = iraf.imstat(fits + reg,
Stdout=1,
nclip=5,
lsigma=3.,
usigma=3.,
fields=fields,
format="no")
#result = iraf.imstat(fits+reg,Stdout=1,fields=fields,format="no")
val_ls = [
float(val) if str(val).replace(".", "").isdigit() else str(val)
for val in result[0].split()
]
d = dict([(key, val) for key, val in zip(key_ls, val_ls)])
print d
return d
def getObjectStatistics(self, photObject, multiple=2.5):
#photObject.printInfo()
# Will get statistics around a box of width = w + multiple/2., height = h + multiple/2.
_boxX1 = int(photObject._pixelx - multiple*(self._MEDFWHM/2.))
_boxX2 = int(photObject._pixelx + multiple*(self._MEDFWHM/2.))
_boxY1 = int(photObject._pixely - multiple*(self._MEDFWHM/2.))
_boxY2 = int(photObject._pixely + multiple*(self._MEDFWHM/2.))
irafinput = "%s[%d:%d,%d:%d]" % (self._Name, _boxX1, _boxX2, _boxY1, _boxY2)
tmp = iraf.imstat(irafinput,
fields='npix,mean,stddev,min,max,midpt',
format=0,
Stdout=1
)
tmp = [i for i in tmp[0].split(" ") if i != ""]
photObject.setBox([_boxX1,_boxX2,_boxY1,_boxY2])
photObject.setNPIX(float(tmp[0]))
photObject.setMEAN(float(tmp[1]))
photObject.setSTDDEV(float(tmp[2]))
photObject.setMIN(float(tmp[3]))
photObject.setMAX(float(tmp[4]))
photObject.setMEDIAN(float(tmp[5]))
def show_bias_files_statistics(list_of_files):
""" Show the statistics for the bias files received.
This function applies imstat to the files received and print the results.
Args:
list_of_files: List of files to get their statistics.
"""
# Control pyraf exception.
try:
# Get statistics for the list of bias files using imstat.
means = iraf.imstat(list_of_files, fields='mean', Stdout=1)
means = means[IMSTAT_FIRST_VALUE:]
mean_strings = [str(m).translate(None, ",\ ") for m in means]
mean_values = [float(m) for m in mean_strings]
# Print the stats results.
logging.debug("Bias images stats: Max. mean: %d Min. mean: %d" %
(max(mean_values), min(mean_values)))
except iraf.IrafError as exc:
logging.error("Error executing imstat: Stats for bias images: %s" %
(list_of_files))
logging.error("Iraf error is: %s" % (exc))
except ValueError as ve:
logging.error("Error calculating mean values: %s" % (mean_strings))
logging.error("Error is: %s" % (ve))
def get_stat_from_files(input, stat):
iraf.images()
result = []
for image in input:
result.append(
float(iraf.imstat(image, fields=stat, format=0, Stdout=1)[0]))
return result
def show_bias_files_statistics(list_of_files):
""" Show the statistics for the bias files received.
This function applies imstat to the files received and print the results.
Args:
list_of_files: List of files to get their statistics.
"""
# Control pyraf exception.
try:
# Get statistics for the list of bias files using imstat.
means = iraf.imstat(list_of_files, fields='mean', Stdout=1)
means = means[IMSTAT_FIRST_VALUE:]
mean_strings = [str(m).translate(None, ",\ ") for m in means]
mean_values = [float(m) for m in mean_strings]
# Print the stats results.
logging.debug("Bias images stats: Max. mean: %d Min. mean: %d" %
(max(mean_values), min(mean_values)))
except iraf.IrafError as exc:
logging.error("Error executing imstat: Stats for bias images: %s" %
(list_of_files))
logging.error("Iraf error is: %s" % (exc))
except ValueError as ve:
logging.error("Error calculating mean values: %s" % (mean_strings))
logging.error("Error is: %s" % (ve))
def createFlatFiles():
import re
print "CreateFlatFiles start"
for f in FILTERS:
if(os.listdir(os.path.join(OUTPUTDIR, "flat", f))):
iraf.imcombine.setParam("input", os.path.join(OUTPUTDIR, "flat", f) + "/*.fits")
flatFile = os.path.join(OUTPUTDIR, "flat", f , "Flat.fits")
if os.path.exists(flatFile):
print("flatFile %s alreday exists deleting" % flatFile)
os.remove(flatFile)
iraf.imcombine.setParam("output", flatFile)
#from doc:
#http://www.iac.es/sieinvens/siepedia/pmwiki.php?n=HOWTOs.PythonianIRAF
#--> iraf.listpix(mode='ql') # confirms parameter
#--> iraf.listpix(mode='h') # doesn't ask for parameter...@@
iraf.imcombine(mode="h")
#NORMALIZE
#imstat
res = iraf.imstat(flatFile, Stdout=1)
print(res[0].strip())
print(res[1].strip())
resArray = re.split("\s+", res[1].strip())
#max value
#toDivValue = float(resArray[5])
#meanValue
toDivValue = float(resArray[2])
flatNormFile = os.path.join(OUTPUTDIR, "flat", f , "FlatNorm.fits")
if os.path.exists(flatNormFile):
print("flatNormFile %s alreday exists deleting" % flatNormFile)
os.remove(flatNormFile)
#divide by max value
iraf.imarith(flatFile, '/', toDivValue, flatNormFile)
else:
print("NO FLAT FILES for filter %s PRESENT" %f)
print "CreateFlatFiles end"
def getBackgroundSTDEV(self):
# load the header
self.loadHeader()
# Use iraf to get the sky background STDEV
skySTDEV = iraf.imstat(images=self._Name,
fields='stddev',
nclip = 25,
format=0,
Stdout=1
)
self._skySTDEV = float(skySTDEV[0])
# Skynoise in JHK is interpolation smoothed due to blowing up the images
# In the Drizzle algorithm the ratio of true to output noise is R = 1/(1-r/3) where r is pixfrac/scale
if self._Band in 'JHK':
self._skySTDEV = self._skySTDEV * self.getHeader("INTERPSM", 1.662)
else:
self._skySTDEV = self._skySTDEV * self.getHeader("INTERPSM", 1.07)
print "SKY STDDEV: %f" % (self._skySTDEV)
return self._skySTDEV
def doDarks(dark, readnoise, images):
'''This function will take the provided name of the dark frame (dark),
determine the dark signal (median of the frame) and compute
the dark current expected for each file name in images. This is then
compared to the readnoise. A list of all images in which the dark
current is expected to be greater than the readnoise is printed,
separated by commas, so you can cut-n-paste into ccdproc's epar.'''
if not iraf.imaccess(dark):
print "Error: can't open dark frame %s" % (dark)
return
# First off, get the dark current and exposure time
dc = float(iraf.imstat(images=dark, fields='mean', Stdout=1)[1])
dt = get_header(dark, 'darktime', float)[dark]
print 'dark: count=%f exptime=%f dark current rate=%f' % (dc, dt,
dc / dt)
# Next, get the exposure times for each image
res = get_header(images=images, field='exptime', type=float)
# a list to be printed out at the end (good for cut-n-paste)
names = []
print "expected dark currents:"
for name in res.keys():
print name, "%.2f" % (res[name] * dc / dt)
if res[name] * dc / dt > readnoise:
names.append(name)
print "expected dark current greater than read noise:"
print string.join(names, ',')
def getPeakPixel(): x1=int(eval(xySkyDict[1][0]))-smallBox x2=int(eval(xySkyDict[1][0]))+smallBox y1=int(eval(xySkyDict[1][1]))-smallBox y2=int(eval(xySkyDict[1][1]))+smallBox foo = iraf.imstat(fitsDir+fitsFile+"["+str(x1)+":"+str(x2)+","+str(y1)+":"+str(y2)+"]",Stdout=1) # assumes max is last thing in imstat bar = foo[-1].split()[-1] return eval(bar)
def biascom(input_bias='bias.list'):
"""
1. Description
: This function makes a master bias image of SAO 1-m using Pyraf. Put bias images to 201XXXXX/bias/ directory. Run this code on 201XXXXX directory, then pyraf chdir task enter bias directory and makes process. Output image is zero.fits and it will be copied on upper directory. Due to iraf.chdir task, you should reset python when this code is finished in order to make confusion of current directory between iraf and python!
2. Usage
: Start on 2018XXXX directory. Make bias directory which contains each bias frame. Naming of each bias images should be cal*bias.fit. Then just use SAO_biascom()
>>> SAO_biascom()
3. History
2018.03 Created by G.Lim.
2018.12.17 Edited by G.Lim. Define SAO_biascom function.
2019.02.07 Assign archive of masterbias in each date by G. Lim
"""
import glob
import os, sys
import numpy as np
from pyraf import iraf
from astropy.io import fits
curdir = os.getcwd()
curdate = curdir.split('/')[-1]
iraf.noao()
iraf.imred()
iraf.ccdred()
iraf.ccdred.setinst(instrume='camera', directo='/iraf/iraf/noao/imred/ccdred/ccddb/', query='q', review='no')
#input_name = 'bias.list'
output_name = curdate+'_zero.fits'
#calibrations = input_bias
#f = open(input_name,'w+')
#for i in range(len(calibrations)):
# f.write(calibrations[i]+'\n')
#f.close()
print('Zerocombine is running...')
iraf.imstat(images='@'+input_bias)
iraf.zerocombine(input='@'+input_bias, output=output_name, combine='median', reject='minmax', process='no', scale='none', ccdtype='' )
print('Output master '+output_name+' is created.')
os.system('/usr/bin/cp '+output_name+' /data1/KHAO/MDFTS/red/masterbias/')
iraf.dir('.')
def photometry(filenames):
"""
Do photometry of the files
"""
for item in range(len(filenames)):
print('Photometry on file', filenames[item][0])
iraf.imstat(filenames[item][0])
iraf.display(filenames[item][0], 1)
print(
'Press , (comma) on your target to get the coord and then q, but DONT press r \n'
)
imx = iraf.imexam(Stdout=1)
# If there is an error like "# ^SyntaxError: unexpected EOF while parsing" then make imx[2] instead imx[3] in all places. It is mainly a file reading problem
Xref = eval(
imx[1].split()
[0]) #Using the first star as ref star for cose shift calculation
Yref = eval(imx[1].split()[1])
if (imx[1].split()[10] == 'INDEF'):
fwhm = 4.0
print('fwhm:', 'INDEF', 'but taken as 4.0')
else:
fwhm = eval(imx[1].split()[10])
print('fwhm:', fwhm)
print('Reference cord:', Xref, Yref)
file1 = open('coord_list', 'w')
file1.write('{0:0.3f}\t{1:0.3f}\n'.format(Xref, Yref))
file1.close()
Xref1, Yref1 = np.loadtxt('coord_list')
print('Xref1, Yref1', Xref1, Yref1)
do_phot(filenames[item][0],
'coord_list',
fwhm=fwhm,
sky_annulus=10.0,
width_sky=10.,
zeropoint=25.)
print('All photometry is done. Enjoy! Check .phot file')
def scaleimages(images):
ims = str(images).replace("', '", ", ").replace("']", "").replace("['", "")
iraf.imstat.unlearn()
imstat = iraf.imstat(ims, field='image, mean, stddev', Stdout=1)
means = [float(x.split()[1]) for x in imstat[1:]]
stds = [float(x.split()[2]) for x in imstat[1:]]
meanofmeans = np.mean(means)
upperlimits = np.array(meanofmeans) + 2. * np.array(stds)
lowerlimits = np.array(meanofmeans) - 2. * np.array(stds)
means2 = np.array([])
stds2 = np.array([])
for index, im in enumerate(images):
iraf.imstat.unlearn()
lower = lowerlimits[index]
upper = upperlimits[index]
imstat = iraf.imstat(im,
field='image, mean, stddev',
Stdout=1,
lower=lower,
upper=upper)
means2 = np.append(means2, float(imstat[1].split()[1]))
stds2 = np.append(stds2, float(imstat[1].split()[2]))
meanofmeans = np.mean(means2)
for index, im in enumerate(images):
scale = meanofmeans / means[index]
scaledimage = 'sc' + im
iraf.imarith.unlearn()
iraf.imarith(im, '*', scale, result=scaledimage)
oim = 'o' + im
expr = 'cp %s %s' % (im, oim)
os.system(expr)
expr = 'mv %s %s' % (scaledimage, im)
os.system(expr)
def subtractskyrescaling(images, sky):
for im in images:
iraf.imstat.unlearn()
imstat = iraf.imstat(im,
field='image, mean, midpt, mode, stddev',
Stdout=1)
#meanim = np.mean([float(x.split()[1]) for x in imstat[1:]])
iraf.imstat.unlearn()
skystat = iraf.imstat(sky,
field='image, mean, midpt, mode, stddev',
Stdout=1)
#meansky = np.mean([float(x.split()[1]) for x in skystat[1:]])
#addresfact = meanim - meansky
#addresfact=0.
iraf.imarith.unlearn()
#iraf.imarith(sky, '-' , addresfact, 'res'+sky)
#iraf.imarith(im, '-', 'res'+sky, 's'+im)
iraf.imarith(im, '-', sky, 's' + im)
#removetempsky = 'rm %s' % ('res'+sky)
#os.system(removetempsky)
skysubtim = ['s' + x for x in images]
return skysubtim
def get_sky(image):
'''return estimate for sky value and standard deviation of sky
implicit assumption is that the sky is constant throughout the image
and that most of the image actually shows the sky (no very crowded fields)!
calls iraf.imstat with ncli=10
this calculates mean and standard dev of mean, clips all values significantly above
the standard and then recalculates mean and std dev. This loop runs 10 times to
clip of values of the stellar counts.
stat=iraf.imstat(image,fields='midpt,stddev',ncli=10,Stdout=1)
return float(stat[1].split()[0]), float(stat[1].split()[1])
Usage: sky,skydev=get_sky(filename)'''
stat=iraf.imstat(image,fields='midpt,stddev',ncli=10,Stdout=1)
return float(stat[1].split()[0]), float(stat[1].split()[1])
def trim_remove_bias(WhatToTrim):
imstat = WhatToTrim + "[2098:2147,*]"
mean_overscan = iraf.imstat(imstat, Stdout=1, fields="mean", format="no")
single_file_new = WhatToTrim.replace(".fits",
".-overscan_will_be_removed.fits")
iraf.imarith(WhatToTrim, "-", mean_overscan[0], single_file_new)
old = single_file_new + "[51:2097,3:2063]"
new = WhatToTrim.replace(".fits", ".trim_will_be_removed.fits")
iraf.imcopy(old, new)
os.remove(single_file_new)
return new
def _imstat(filename):
"""
Call imstat on a file and convert the result to a dictionary.
"""
s = iraf.imstat(filename, Stdout=1)
if s[1].startswith("Error"):
return {}
stat_dict = dict(zip(s[0].split()[1:], s[1].split()))
for key, val in stat_dict.items():
try:
val = float(val)
except ValueError:
pass
stat_dict[key] = val
return stat_dict
def normalize_flats(files):
""" Normalize a set of flat files.
This function receives a list of flat files and returns a list of
files of the flat files after normalize them.
The normalization is performed dividing the flat image by the mean
value of the flat image. This mean is the result of applying imstat
to each image.
Args:
files: The names of the files corresponding to the flat images.
"""
for fl in files:
# Get the 'work' and 'normalized' names for the flat files to process.
work_file = fl.replace("." + FIT_FILE_EXT, WORK_FILE_SUFFIX)
norm_file = fl.replace("." + FIT_FILE_EXT, NORM_FILE_SUFFIX)
# Getting statistics for flat file.
try:
flat_stats = iraf.imstat(work_file, fields=IMSTAT_MEAN, Stdout=1)
flat_stats = flat_stats[IMSTAT_FIRST_VALUE]
try:
mean_value = float(flat_stats)
# Normalize flat dividing flat by its mean value.
iraf.imarith(work_file, '/', mean_value, norm_file)
except iraf.IrafError as exc:
logging.error(
"Error executing imarith: normalizing flat image: %s" %
(fl))
logging.error("Iraf error is: %s" % (exc))
except ValueError as ve:
logging.error("Error calculating mean value for: %s" %
(flat_stats))
logging.error("Error is: %s" % (ve))
except iraf.IrafError as exc:
logging.error(
"Error executing imstat: getting stats for flat image: %s" %
(fl))
logging.error("Iraf error is: %s" % (exc))
def normalize_flats(files):
""" Normalize a set of flat files.
This function receives a list of flat files and returns a list of
files of the flat files after normalize them.
The normalization is performed dividing the flat image by the mean
value of the flat image. This mean is the result of applying imstat
to each image.
Args:
files: The names of the files corresponding to the flat images.
"""
for fl in files:
# Get the 'work' and 'normalized' names for the flat files to process.
work_file = fl.replace("." + FIT_FILE_EXT, WORK_FILE_SUFFIX)
norm_file = fl.replace("." + FIT_FILE_EXT, NORM_FILE_SUFFIX)
# Getting statistics for flat file.
try:
flat_stats = iraf.imstat(work_file, fields=IMSTAT_MEAN, Stdout=1)
flat_stats = flat_stats[IMSTAT_FIRST_VALUE]
try:
mean_value = float(flat_stats)
# Normalize flat dividing flat by its mean value.
iraf.imarith(work_file, '/', mean_value, norm_file)
except iraf.IrafError as exc:
logging.error("Error executing imarith: normalizing flat image: %s" %
(fl))
logging.error("Iraf error is: %s" % (exc))
except ValueError as ve:
logging.error("Error calculating mean value for: %s" %
(flat_stats))
logging.error("Error is: %s" % (ve))
except iraf.IrafError as exc:
logging.error("Error executing imstat: getting stats for flat image: %s" %
(fl))
logging.error("Iraf error is: %s" % (exc))
def rejectflat(lista, _interactive):
from numpy import where, size
from ntt.util import display_image
from pyraf import iraf
iraf.images(_doprint=0)
import os
import string
listone = []
for img in lista:
dataimg = pyfits.open(img)[0].data
numberpixels = size(dataimg)
indices = where(dataimg > 60000)
numbersaturated = size(dataimg[indices])
indices2 = where(dataimg < 1000)
numberlow = size(dataimg[indices2])
if 100 * float(numbersaturated) / float(numberpixels) <= 5 and \
100 * float(numberlow) / float(numberpixels) <= 10:
listone.append(img)
listgood = []
if _interactive:
for img in listone:
aa, bb, cc = display_image(img, 1, '', '', False)
titolo, result = iraf.imstat(img, Stdout=1)
print titolo[1:]
print result
answ = 'nn'
while answ not in ['g', 'G', 'b', 's']:
answ = raw_input('good/bad [[g]/b/G(all good)/s(stop)]? ')
if not answ:
answ = 'g'
if answ not in ['g', 'G', 'b', 's']:
print 'warning: value not valid, try again'
if answ == 'g':
listgood.append(img)
elif answ == 'G':
listgood = listone
break
elif answ == 's':
break
else:
listgood = listone
return listgood
def trimMyself(self, outname="remcut.fits", region="[400:500,400:500]", verbose=False):
self._logger["trimMyself"] = []
if self._Name == "None":
self._logger["trimMyself"].append("No filename set, please check.")
imCopy = imFits()
imCopy._Name = outname
iraf.imcopy("%s%s" % (self._Name,region), imCopy._Name)
if verbose:
self._logger["trimMyself"].append(iraf.imstat(self._Name))
for log in self._logger["trimMyself"]:
print log
return imCopy
def calculate_datamin(image_file_name, phot_params):
""" Calculate a datamin value for the image received using imstat.
Args:
image_file_name: Name of the file with the image.
phot_params: Parameters for phot.
"""
# Set a default value for datamin.
datamin = phot_params.datamin
try:
imstat_output = iraf.imstat(image_file_name, fields='mean,stddev', \
Stdout=1)
imstat_values = imstat_output[IMSTAT_FIRST_VALUE]
values = imstat_values.split()
# Set a calculated value for datamin.
datamin = float(
values[0]) - phot_params.datamin_mult * float(values[1])
except iraf.IrafError as exc:
logging.error("Error executing imstat: Stats for data image: %s" %
(image_file_name))
logging.error("Iraf error is: %s" % (exc))
except ValueError as ve:
logging.error("Value Error calculating datamin for image: %s" %
(image_file_name))
logging.error("mean is: %s stddev is: %s" % (values[0], values[1]))
logging.error("Value Error is: %s" % (ve))
if datamin < phot_params.datamin:
datamin = phot_params.datamin
return datamin
def createFlatFiles():
import re
print "CreateFlatFiles start"
for f in FILTERS:
if (os.listdir(os.path.join(OUTPUTDIR, "flat", f))):
iraf.imcombine.setParam(
"input",
os.path.join(OUTPUTDIR, "flat", f) + "/*.fits")
flatFile = os.path.join(OUTPUTDIR, "flat", f, "Flat.fits")
if os.path.exists(flatFile):
print("flatFile %s alreday exists deleting" % flatFile)
os.remove(flatFile)
iraf.imcombine.setParam("output", flatFile)
#from doc:
#http://www.iac.es/sieinvens/siepedia/pmwiki.php?n=HOWTOs.PythonianIRAF
#--> iraf.listpix(mode='ql') # confirms parameter
#--> iraf.listpix(mode='h') # doesn't ask for parameter...@@
iraf.imcombine(mode="h")
#NORMALIZE
#imstat
res = iraf.imstat(flatFile, Stdout=1)
print(res[0].strip())
print(res[1].strip())
resArray = re.split("\s+", res[1].strip())
#max value
#toDivValue = float(resArray[5])
#meanValue
toDivValue = float(resArray[2])
flatNormFile = os.path.join(OUTPUTDIR, "flat", f, "FlatNorm.fits")
if os.path.exists(flatNormFile):
print("flatNormFile %s alreday exists deleting" % flatNormFile)
os.remove(flatNormFile)
#divide by max value
iraf.imarith(flatFile, '/', toDivValue, flatNormFile)
else:
print("NO FLAT FILES for filter %s PRESENT" % f)
print "CreateFlatFiles end"
def skyflat(date, low=15000, high=23000, numimages=5):
'''
make a combined b skyflat.
Requires: a bias image in same directory to do the bias subtraction
skyflats must be offset and have appropriate count number
input: the date the skyflats were observed YYMMDD
output: flat.B, a text file that lists the names of the skyflat fits files
ccdYYMMDD.skyflatB.fits, the combined skyflat
'''
#check if biases are in this directory
if len(glob.glob('*.bias.*')) < 1:
print "no combined bias found, exiting"
return
#get image name and mean pixel value for all skyflat images
stats=iraf.imstat('*sky*',format=False,fields='image,mean',Stdout=1)
pairs=[i.split() for i in stats]
#write the names of the skyflats w right ammount of counts to file
#keep track of how many good ones there are
goodCount=0
with open("flat.B",'w') as FB:
for i in pairs:
if float(i[1]) > low and float(i[1]) < high:
FB.write(i[0]+'\n')
goodCount+=1
if goodCount < numimages:
print "only "+str(goodCount)+" skyflats have counts between "+str(low)+" and "+str(high)
print "no combined skyflat made"
return
else:
iraf.ccdproc(images="@flat.B",output=" ",fixpix="no",overscan="yes",trim="no",zerocor="yes",darkcor="no",flatcor="no",illumcor="no",fringecor="no",readcor="no",scancor="no",readaxis="line",biassec="[3:14,1:1024]",zero="*.bias.fits",interactive="no",functio="spline3",order=11)
iraf.flatcombine("@flat.B",output="FLAT",combine="median",reject="minmax",process="no",scale="mode",ccdtype="")
os.system("mv FLAT.fits ccd"+str(date)+".skyflatB.fits")
print ("made combined skyflat ccd"+str(date)+".skyflatB.fits")
return
def calculate_datamin(image_file_name, phot_params):
""" Calculate a datamin value for the image received using imstat.
Args:
image_file_name: Name of the file with the image.
phot_params: Parameters for phot.
"""
# Set a default value for datamin.
datamin = phot_params.datamin
try:
imstat_output = iraf.imstat(image_file_name, fields='mean,stddev', \
Stdout=1)
imstat_values = imstat_output[IMSTAT_FIRST_VALUE]
values = imstat_values.split()
# Set a calculated value for datamin.
datamin = float(values[0]) - phot_params.datamin_mult * float(values[1])
except iraf.IrafError as exc:
logging.error("Error executing imstat: Stats for data image: %s" %
(image_file_name))
logging.error("Iraf error is: %s" % (exc))
except ValueError as ve:
logging.error("Value Error calculating datamin for image: %s" %
(image_file_name))
logging.error("mean is: %s stddev is: %s" % (values[0], values[1]))
logging.error("Value Error is: %s" % (ve))
if datamin < phot_params.datamin:
datamin = phot_params.datamin
return datamin
if not os.path.exists(save_path):
os.makedirs(save_path)
#copy bias images to save_path
os.system('cp bias*.fits '+save_path)
#change to sabe_path
os.chdir(save_path)
#verify if previous superbias exist
if os.path.isfile('superbias.fits') == True:
os.system('rm superbias.fits')
#create the list of bias images
bias_list = string.join(bias,',')
#combine the bias image and create the superbias
iraf.imcombine(bias_list,'superbias.fits')
iraf.imstat('superbias.fits')
#clean previos bias files
print '\n Cleaning bias*.fits images ....\n'
os.system('rm bias*.fits')
print '\n.... done.'
#Return to original directory
os.chdir(original_path)
#END of the masterbias reduction messsage
print '\nsuperbias.fits created!\n'
print '\nEND of superbias reduction!\n'
def run_imstat(input):
iraf.images()
for image in input:
iraf.imstat(image)
def imshiftcomb(inlist,
outimg,
fitgeom='shift',
inpref='',
objmask='none',
combine='average',
reject='none',
fscale=False,
fbase=100,
fhead='F1',
second=False,
first_pref='sdfr',
second_pref='sdf2r',
indep=False,
sigmap='none',
expmap='none',
whtmap='none',
gain=5.6,
ffpref=''):
# check output image
if os.access(outimg, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % outimg
return 1
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check input image list
if sigmap != 'none':
ffimg_arr = check_input2(inlist, ffpref)
if isinstance(ffimg_arr, int):
return 1
# check optional output image
if sigmap != 'none':
if os.access(sigmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % sigmap
return 1
if expmap != 'none':
if os.access(expmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % expmap
return 1
if whtmap != 'none':
if os.access(whtmap, os.R_OK):
print >> sys.stderr, 'operation would overwrite existing image (%s)' % whtmap
return 1
# get array size
im = pyfits.open(inimg_arr[0])
nx = im[0].header['NAXIS1']
ny = im[0].header['NAXIS2']
im.close()
# check geomap data
dx = []
dy = []
gmp_arr = []
gmp2_arr = []
dbs_arr = []
for i in range(len(inimg_arr)):
fname, ext = os.path.splitext(inimg_arr[i])
gmp = fname + '.gmp'
gmp2 = fname + '.gmp2'
dbs = fname + '.dbs'
gmp_arr.append(gmp)
gmp2_arr.append(gmp2)
dbs_arr.append(dbs)
if not os.access(gmp, os.R_OK):
print >> sys.stderr, 'geomap file (%s) does not exist' % (gmp)
return 1
if os.access(dbs, os.R_OK):
print >> sys.stderr, 'database file (%s) is already exist' % (dbs)
return 1
if os.access(gmp2, os.R_OK):
print >> sys.stderr, 'modified geomap file (%s) is already exist' % (
gmp2)
return 1
fgmp = open(gmp)
nl = 1
dx_ave = 0.0
dy_ave = 0.0
for line in fgmp:
if not line.startswith('#'):
param = line[:-1].split()
if len(param) != 4:
print >> sys.stderr, 'Invalid format in line %d of %s: %s' % (
nl, gmp, line[:-1])
fgmp.close()
return 1
else:
if isfloat(param[0]) == False or isfloat(
param[1]) == False or isfloat(
param[2]) == False or isfloat(
param[3]) == False:
print >> sys.stderr, 'failed to decode line %d of %s: %s' % (
nl, gmp, line[:-1])
fgmp.close()
return 1
else:
dx_ave += float(param[0]) - float(param[2])
dy_ave += float(param[1]) - float(param[3])
nl += 1
#print inimg_arr[i],nl
dx.append(dx_ave / (nl - 1))
dy.append(dy_ave / (nl - 1))
if len(inimg_arr) != len(dx):
print >> sys.stderr, 'number of input images does not match with that of offsets'
return 1
#print 'debug'
#print dx, max(dx), min(dx)
#print dy, max(dy), min(dy)
# check object mask
if objmask.lower() == 'none':
objmask = ''
else:
objmask_arr = check_inpref(objmask, inimg_arr)
if isinstance(objmask_arr, int):
return 1
# independent run flag
if indep:
second = True
# prepare for temporary file
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# calculate image median for zero shift
iraf.unlearn('imstat')
iraf.unlearn('mimstat')
bgmed = []
for i in range(len(inimg_arr)):
if objmask == '':
ret = iraf.imstat(inimg_arr[i],
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
else:
if not second:
ret = iraf.mimstat(inimg_arr[i],
imasks=objmask_arr[i] + '[pl]',
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
else:
ret = iraf.mimstat(inimg_arr[i],
imasks=objmask_arr[i],
format='no',
fields='midpt',
nclip=50,
lsigma=3.,
usigma=3.,
Stdout=1)
if len(ret) == 1:
bgmed.append(-1.0 * float(ret[0]))
else:
fout.close()
remove_temp_all(tmp_prefix)
print >> sys.stderr, 'failed to calculate median of the background in %s' % inimg_arr[
i]
return 1
# get large array size and combined image size
ret = get_large_region(nx, ny, dx, dy)
if len(ret) != 6:
print >> sys.stderr, 'failed to get large array size'
return 1
x_size = ret[0]
y_size = ret[1]
xcmin = ret[2]
xcmax = ret[3]
ycmin = ret[4]
ycmax = ret[5]
# calculate image region in the large format
xmin = int((x_size - nx) / 2) + 1
xmax = nx + int((x_size - nx) / 2)
ymin = int((y_size - ny) / 2) + 1
ymax = ny + int((y_size - ny) / 2)
#print 'debug'
#print x_size, y_size, xcmin, xcmax, ycmin, ycmax
#print xmin, xmax, ymin, ymax
# copy image to larger format and shift image #
iraf.unlearn('geomap')
iraf.unlearn('geotran')
obj_list = tmp_prefix + '_obj.lst'
if os.access(obj_list, os.R_OK):
os.remove(obj_list)
fobj = open(obj_list, 'w')
# for exposure time weight
expweight = tmp_prefix + '_exp.lst'
if os.access(expweight, os.R_OK):
os.remove(expweight)
fexp = open(expweight, 'w')
# for zero offset
zeroshift = tmp_prefix + '_zeroshift.dat'
if os.access(zeroshift, os.R_OK):
os.remove(zeroshift)
fzero = open(zeroshift, 'w')
# save the original fit geometry
fitgeom_org = fitgeom
# preparing for the sigma list and mask
if sigmap == 'none':
tmp_rejmask = ''
else:
tmp_rejmask = tmp_prefix + 'rejmask.fits'
if os.access(tmp_rejmask, os.R_OK):
os.remove(tmp_rejmask)
inverse_var_list = tmp_prefix + '_var.lst'
if os.access(inverse_var_list, os.R_OK):
os.remove(inverse_var_list)
finverse_var = open(inverse_var_list, 'w')
for i in range(len(inimg_arr)):
# restore the original fit geometry
fitgeom = fitgeom_org
# geometry transformation
fgmp = open(gmp_arr[i])
fgmp2 = open(gmp2_arr[i], 'w')
nobj = 0
for line in fgmp:
if not line.startswith('#'):
param = line[:-1].split()
xref = float(param[0]) + xmin - 1
yref = float(param[1]) + ymin - 1
xin = float(param[2]) + xmin - 1
yin = float(param[3]) + ymin - 1
fgmp2.write('%.3f %.3f %.3f %.3f\n' % (xref, yref, xin, yin))
nobj += 1
fgmp.close()
fgmp2.close()
# check number of objects
if i == 0 and nobj == 1 and fitgeom == 'rotate':
print 'Warning: Number of reference objects is not enought to measure the rotation'
print 'Warning: Only shift applied for all images'
fitgeom = 'shift'
fitgeom_org = 'shift'
if nobj == 1 and fitgeom == 'rotate':
print 'Warning: Number of objects in %s is not enought to measure the rotation' % (
inimg_arr[i])
print 'Warning: Only shift applied for %s' % (inimg_arr[i])
fitgeom = 'shift'
# mapping geometry
iraf.geomap(gmp2_arr[i],
dbs_arr[i],
1,
x_size,
1,
y_size,
fitgeom=fitgeom,
interac='no')
# mask frame
msk = np.ones((y_size, x_size))
msk[ymin - 1:ymax, xmin - 1:xmax] = 0
hdu = pyfits.PrimaryHDU(msk)
msk_img = pyfits.HDUList([hdu])
msk_fits = tmp_prefix + 'mask' + os.path.basename(inimg_arr[i])
msktr_fits = tmp_prefix + 'masktr' + os.path.basename(inimg_arr[i])
if os.access(msk_fits, os.R_OK):
os.remove(msk_fits)
if os.access(msktr_fits, os.R_OK):
os.remove(msktr_fits)
msk_img.writeto(msk_fits)
msk_img.close()
# transform mask geometry
iraf.geotran(msk_fits,
msktr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=1)
os.remove(msk_fits)
convert_maskfits_int(msktr_fits, msktr_fits)
# load original frame
img = pyfits.open(inimg_arr[i])
if sigmap != 'none':
ffimg = pyfits.open(ffimg_arr[i])
# for exposure time weight
try:
t = float(img[0].header['EXP1TIME'])
coadd = float(img[0].header['COADDS'])
expt = t * coadd
except KeyError:
print >> sys.stderr, 'can not read exposure time from the header of %s' % inimg_arr[
i]
img.close()
return 1
# for flux scaling and weight
if fscale:
try:
flux = float(img[0].header[fhead])
except KeyError:
print >> sys.stderr, 'can not read flux keyword (%s) from the header of %s' % (
fhead, inimg_arr[i])
img.close()
return 1
flux_scale = fbase / flux
weight = expt * (1.0 / flux_scale)**2
else:
flux_scale = 1.0
weight = expt
fzero.write('%f\n' % (bgmed[i] * flux_scale))
fexp.write('%f\n' % weight)
# object frame
obj = np.zeros((y_size, x_size))
obj[ymin - 1:ymax, xmin - 1:xmax] = img[0].data * flux_scale
hdu = pyfits.PrimaryHDU(obj)
obj_img = pyfits.HDUList([hdu])
obj_img[0].header = img[0].header
obj_img[0].header['bpm'] = msktr_fits
obj_img[0].header['expmap'] = expt
obj_fits = tmp_prefix + 'obj' + os.path.basename(inimg_arr[i])
objtr_fits = tmp_prefix + 'objtr' + os.path.basename(inimg_arr[i])
if os.access(obj_fits, os.R_OK):
os.remove(obj_fits)
obj_img.writeto(obj_fits)
obj_img.close()
iraf.geotran(obj_fits,
objtr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=0)
fobj.write('%s\n' % objtr_fits)
img.close()
if sigmap != 'none':
inverse_var = np.zeros((y_size, x_size))
inverse_var[ymin - 1:ymax, xmin -
1:xmax] = (np.sqrt(ffimg[0].data / (gain * expt)) *
flux_scale)**-2
hdu_var = pyfits.PrimaryHDU(inverse_var)
inverse_var_img = pyfits.HDUList([hdu_var])
inverse_var_img[0].header = img[0].header
inverse_var_img[0].header['bpm2'] = '%s[*,*,%d]' % (tmp_rejmask,
i + 1)
inverse_var_fits = tmp_prefix + 'var' + os.path.basename(
inimg_arr[i])
inverse_vartr_fits = tmp_prefix + 'vartr' + os.path.basename(
inimg_arr[i])
if os.access(inverse_var_fits, os.R_OK):
os.remove(inverse_var_fits)
if os.access(inverse_vartr_fits, os.R_OK):
os.remove(inverse_vartr_fits)
inverse_var_img.writeto(inverse_var_fits)
inverse_var_img.close()
iraf.geotran(inverse_var_fits,
inverse_vartr_fits,
dbs_arr[i],
gmp2_arr[i],
geometr='linear',
boundar='constant',
constant=0)
finverse_var.write('%s\n' % inverse_vartr_fits)
ffimg.close()
# close file handlers
fobj.close()
fexp.close()
fzero.close()
if sigmap != 'none':
finverse_var.close()
# combine image
comb_img = tmp_prefix + '_comb.fits'
if os.access(comb_img, os.R_OK):
os.remove(comb_img)
if expmap == 'none':
tmp_expmap = ''
else:
tmp_expmap = tmp_prefix + 'expmap.fits'
if os.access(tmp_expmap, os.R_OK):
os.remove(tmp_expmap)
iraf.unlearn('imcombine')
try:
iraf.imcombine('@' + obj_list,
comb_img,
sigma='',
rejmask=tmp_rejmask,
expmasks=tmp_expmap,
combine=combine,
reject=reject,
masktype='!BPM',
maskvalue=0.0,
zero='@' + zeroshift,
weight='@' + expweight,
expname='EXPMAP')
except:
if os.access(comb_img, os.R_OK):
os.remove(comb_img)
if expmap != 'none':
if os.access(tmp_expmap, os.R_OK):
os.remove(tmp_expmap)
iraf.imcombine('@' + obj_list,
comb_img,
sigma='',
rejmask='',
expmasks=tmp_expmap,
combine=combine,
reject=reject,
masktype='!BPM',
maskvalue=0.0,
zero='@' + zeroshift,
weight='@' + expweight,
expname='EXPMAP')
if sigmap != 'none':
tmp_inverse_var_sum = tmp_prefix + 'inverse_var.fits'
if os.access(tmp_inverse_var_sum, os.R_OK):
os.remove(tmp_inverse_var_sum)
iraf.imcombine('@' + inverse_var_list,
tmp_inverse_var_sum,
combine='sum',
reject='none',
masktype='!BPM',
maskvalue=0.0)
iraf.stsdas()
tmp_sigma = tmp_prefix + 'sigma.fits'
if os.access(tmp_sigma, os.R_OK):
os.remove(tmp_sigma)
iraf.imcalc(tmp_inverse_var_sum,
tmp_sigma,
'sqrt(1.0/im1)',
pixtype='double')
# cut image
iraf.unlearn('imcopy')
cut_img = '%s[%d:%d,%d:%d]' % (comb_img, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_img, outimg)
if expmap != 'none':
cut_exp = '%s[%d:%d,%d:%d]' % (tmp_expmap, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_exp, expmap)
if sigmap != 'none':
cut_sigma = '%s[%d:%d,%d:%d]' % (tmp_sigma, xcmin, xcmax, ycmin, ycmax)
iraf.imcopy(cut_sigma, sigmap)
if whtmap != 'none':
cut_wht = '%s[%d:%d,%d:%d]' % (tmp_inverse_var_sum, xcmin, xcmax,
ycmin, ycmax)
iraf.imcopy(cut_wht, whtmap)
# delete temporary object files
remove_temp_all(tmp_prefix + 'obj')
os.remove(obj_list)
os.remove(comb_img)
# record relative offset between input images and combined image and rotation
for i in range(len(inimg_arr)):
im = pyfits.open(inimg_arr[i], mode='update')
if second:
if indep:
# calculate offset
dxc = xcmin - xmin - dx[i]
dyc = ycmin - ymin - dy[i]
# retrieve rotation
rot = 0.0
fdbs = open(dbs_arr[i])
for line in fdbs:
param = line[:-1].split()
if param[0] == 'xrotation':
rot = float(param[1])
if rot > 180.0:
rot = rot - 360.0
im[0].header['dx'] = dx[i]
im[0].header['dy'] = dy[i]
im[0].header['dxc'] = dxc
im[0].header['dyc'] = dyc
im[0].header['rotation'] = rot
else:
# check number of objects in the geomap file
nobj = 0
fgmp = open(gmp_arr[0])
for line in fgmp:
nobj += 1
im1 = pyfits.open(inimg_arr[i].replace(second_pref,
first_pref),
mode='update')
for j in range(nobj):
key = 'XC%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'YC%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'PEAK%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'FWHM%d' % (j + 1)
im[0].header[key] = float(im1[0].header[key])
key = 'DX'
im[0].header[key] = float(im1[0].header[key])
key = 'DY'
im[0].header[key] = float(im1[0].header[key])
key = 'DXC'
im[0].header[key] = float(im1[0].header[key])
key = 'DYC'
im[0].header[key] = float(im1[0].header[key])
key = 'ROTATION'
im[0].header[key] = float(im1[0].header[key])
im1.close()
else:
# calculate offset
dxc = xcmin - xmin - dx[i]
dyc = ycmin - ymin - dy[i]
# retrieve rotation
rot = 0.0
fdbs = open(dbs_arr[i])
for line in fdbs:
param = line[:-1].split()
if param[0] == 'xrotation':
rot = float(param[1])
if rot > 180.0:
rot = rot - 360.0
im[0].header['dx'] = dx[i]
im[0].header['dy'] = dy[i]
im[0].header['dxc'] = dxc
im[0].header['dyc'] = dyc
im[0].header['rotation'] = rot
im.close()
# remove all temporary files
remove_temp_all(tmp_prefix)
return 0
def lacos_im(_input,
_output='clean.fits',
outmask='mask.fits',
gain=1.3,
readn=9,
xorder=9,
yorder=9,
sigclip=4.5,
sigfrac=0.5,
objlim=1,
skyval=0,
niter=2,
verbose=True,
interactive=False):
import floyds
from floyds.util import delete
import sys, re, os, string
from pyraf import iraf
import numpy as np
iraf.convolve.bilinear = 'no'
iraf.convolve.radsym = 'no'
# make temporary files
oldoutput, galaxy, skymod, med5 = 'oldoutput.fits', 'galaxy.fits', 'skymod.fits', 'med5.fits'
blk, lapla, med3, med7, sub5, sigima, finalsel = 'blk.fits', 'lapla.fits', 'med3.fits', 'med7.fits', 'sub5.fits',\
'sigima.fits', 'finalsel.fits'
deriv2, noise, sigmap, firstsel, starreject = 'deriv2.fits', 'noise.fits', 'sigmap.fits', 'firstsel.fits',\
'starreject.fits'
inputmask, gfirstsel = 'inputmask.fits', 'gfirstsel.fits'
f = open('_kernel', 'w')
f.write('0 -1 0;\n-1 4 -1;\n0 -1 0')
f.close()
# create growth kernel
f = open('_gkernel', 'w')
f.write('1 1 1;\n1 1 1;\n1 1 1')
f.close()
gkernel = np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
# initialize loop
usegain = gain
i = 1
stop = 'no'
previous = 0
if not _output: _output = _input
arrayinput, headerinput = floyds.cosmics.fromfits(_input, verbose=False)
floyds.cosmics.tofits(outmask,
np.float32(arrayinput - arrayinput),
headerinput,
verbose=False)
delete(oldoutput)
if skyval > 0:
arrayoldoutput = arrayinput + skyval
else:
arrayoldoutput = arrayinput
floyds.cosmics.tofits(oldoutput,
np.float32(arrayoldoutput),
headerinput,
verbose=False)
# start iterations
while stop == 'no':
# take second-order derivative (Laplacian) of input image
# kernel is convolved with subsampled image, in order to remove negative
# pattern around high pixels
delete(blk)
delete(lapla)
delete(deriv2)
iraf.blkrep(oldoutput, blk, 2, 2)
iraf.convolve(blk, lapla, '_kernel')
iraf.imreplace(lapla, 0, upper=0, lower='INDEF', radius=0)
iraf.blkavg(lapla, deriv2, 2, 2, option="average")
delete(med5)
# create model of background flux - 5x5 box should exclude all CRs
iraf.median(oldoutput,
med5,
5,
5,
zlo='INDEF',
zhi='INDEF',
verbose='no')
iraf.imreplace(med5, 0.0001, upper=0, lower='INDEF', radius=0)
# create noise model
delete(noise)
iraf.imutil.imexpr(expr='sqrt(a*' + str(usegain) + '+' + str(readn) +
'**2)/' + str(usegain),
a=med5,
output=noise,
verbose='no')
# divide Laplacian by noise model
delete(sigmap)
iraf.imarith(deriv2, "/", noise, sigmap)
# Laplacian of blkreplicated image counts edges twice:
iraf.imarith(sigmap, "/", 2., sigmap)
# removal of large structure (bright, extended objects)
delete(med5)
iraf.median(sigmap, med5, 5, 5, zlo='INDEF', zhi='INDEF', verbose='no')
arraysigmap, headersigmap = floyds.cosmics.fromfits(sigmap,
verbose=False)
arraymed5, headermed5 = floyds.cosmics.fromfits(med5, verbose=False)
arraysigmap = arraysigmap - arraymed5
iraf.imarith(sigmap, "-", med5, sigmap)
# find all candidate cosmic rays
# this selection includes sharp features such as stars and HII regions
delete(firstsel)
iraf.imcopy(sigmap, firstsel, verbose='no')
iraf.imreplace(firstsel, 0, upper=sigclip, lower='INDEF', radius=0)
iraf.imreplace(firstsel, 1, lower=0.1, upper='INDEF', radius=0)
# arraygfirst=arraysigmap
# arraygfirst = np.where(arraygfirst<sigclip,0,arraygfirst)
# arraygfirst = np.where(arraygfirst>0.1,1,arraygfirst)
# compare candidate CRs to median filtered image
# this step rejects bright, compact sources from the initial CR list
# subtract background and smooth component of objects
delete(med3)
delete(med7)
iraf.median(oldoutput,
med3,
3,
3,
zlo='INDEF',
zhi='INDEF',
verbose='no')
iraf.median(med3, med7, 7, 7, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imarith(med3, "-", med7, med3)
iraf.imarith(med3, "/", noise, med3)
iraf.imreplace(med3, 0.01, upper=0.01, lower='INDEF', radius=0)
# compare CR flux to object flux
delete(starreject)
iraf.imutil.imexpr(expr="(a*b)/c",
a=firstsel,
b=sigmap,
c=med3,
output=starreject,
verbose='no')
# discard if CR flux <= objlim * object flux
iraf.imreplace(starreject, 0, upper=objlim, lower='INDEF', radius=0)
iraf.imreplace(starreject, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(firstsel, "*", starreject, firstsel)
# grow CRs by one pixel and check in original sigma map
delete(gfirstsel)
iraf.convolve(firstsel, gfirstsel, '_gkernel')
iraf.imreplace(gfirstsel, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(gfirstsel, "*", sigmap, gfirstsel)
iraf.imreplace(gfirstsel, 0, upper=sigclip, lower='INDEF', radius=0)
iraf.imreplace(gfirstsel, 1, lower=0.1, upper='INDEF', radius=0)
# grow CRs by one pixel and lower detection limit
sigcliplow = sigfrac * sigclip
delete(finalsel)
iraf.convolve(gfirstsel, finalsel, '_gkernel')
iraf.imreplace(finalsel, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(finalsel, "*", sigmap, finalsel)
iraf.imreplace(finalsel, 0, upper=sigcliplow, lower='INDEF', radius=0)
iraf.imreplace(finalsel, 1, lower=0.1, upper='INDEF', radius=0)
# determine number of CRs found in this iteration
delete(gfirstsel)
iraf.imutil.imexpr(expr="(1-b)*a",
a=outmask,
b=finalsel,
output=gfirstsel,
verbose='no')
npix = iraf.imstat(gfirstsel,
fields="npix",
lower=0.5,
upper='INDEF',
Stdout=1)
# create cleaned output image; use 3x3 median with CRs excluded
delete(med5)
iraf.imarith(outmask, "+", finalsel, outmask)
iraf.imreplace(outmask, 1, lower=1, upper='INDEF', radius=0)
delete(inputmask)
iraf.imutil.imexpr(expr="(1-10000*a)",
a=outmask,
output=inputmask,
verbose='no')
iraf.imarith(oldoutput, "*", inputmask, inputmask)
delete(med5)
iraf.median(inputmask,
med5,
5,
5,
zloreject=-9999,
zhi='INDEF',
verbose='no')
iraf.imarith(outmask, "*", med5, med5)
if i > 1: delete(_output)
delete(_output)
iraf.imutil.imexpr(expr="(1.-b)*a+c",
a=oldoutput,
b=outmask,
c=med5,
output=_output,
verbose='no')
# cleanup and get ready for next iteration
delete(oldoutput)
iraf.imcopy(_output, oldoutput, verbose='no')
if npix == 0: stop = 'yes'
i = i + 1
if i > niter: stop = 'yes'
# delete temp files
delete(blk + "," + lapla + "," + deriv2 + "," + med5)
delete(med3 + "," + med7 + "," + noise + "," + sigmap)
delete(firstsel + "," + starreject + "," + gfirstsel)
delete(finalsel + "," + inputmask)
if skyval > 0: iraf.imarith(_output, "-", skyval, _output)
delete('_kernel' + "," + '_gkernel')
delete(oldoutput)
def get_photometry(filelist, fwhmpsf, thresh, mkiraf=True):
"""
Performs sextracting by daofind and photometry by daophot
filename is WITHOUT suffix .fits
fwhmpsf is rough estimation of typical frame FWHM
THRESH is signal-to-noise ratio, typically 4 or so
Outputs are two files *.coo.1 and *.mag.1 with x-y coordinates
"""
# Create login.cl at execution of the script if flag set to true
if mkiraf:
proc = subprocess.Popen(['mkiraf'],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
outs, errs = proc.communicate('y\nxterm')
# Now we make the Pyraf imports
from pyraf import iraf
from pyraf.iraf import daophot
# Parameter to get rid of the faint stars with high magnitude error
magnitude_error_threshold = 0.5
iraf.noao
iraf.digiphot
iraf.daophot
iraf.unlearn('phot')
iraf.unlearn('datapars')
iraf.unlearn('photpars')
iraf.unlearn('daopars')
iraf.datapars.epadu = 1
iraf.datapars.readnoi = 6
iraf.datapars.datamin = "INDEF"
iraf.datapars.datamax = "50000"
# fwhm tarot 1.5 oaj 3.5
iraf.datapars.fwhm = fwhmpsf
for filename in filelist:
IMstatres = iraf.imstat(filename, Stdout=1)
IMmean = IMstatres[1].split()[2]
iraf.datapars.sigma = math.sqrt(
float(IMmean) * float(iraf.datapars.epadu) +
float(iraf.datapars.readnoi)**2) / float(iraf.datapars.epadu)
print("--- performing daophot sextracting ---")
iraf.daofind(filename,
output="default",
verify="no",
verbose="no",
threshold=thresh)
iraf.datapars.datamax = "INDEF"
print("--- performing daophot photometry ---")
iraf.daophot.phot(image=filename,
coords="default",
output="default",
interactive="no",
sigma="INDEF",
airmass="AIRMASS",
exposure="EXPOSURE",
filter="FILTER",
obstime="JD",
calgorithm="gauss",
verify="no",
verbose="no")
def errore(img,imgpsf,coordlist,size,truemag,fwhm0,leng0,_show,_interactive,_numiter,z11,z22,midpt,nax,nay,xbgord0,ybgord0,_recenter,apco0,dmax=51000, dmin=-100):
import agnkey
import os,sys,re,string
from numpy import array,mean,std,compress,average
from pyraf import iraf
if not _numiter: _numiter=3
dartf=100
while dartf>=size-1:
if _interactive:
artfac0 =raw_input('>>> Dispersion of artificial star positions (in units of FWHM) [1] ')
if not artfac0: artfac0=1
else:
artfac0=1
try:
artfac0=float(artfac0)
if float(artfac0)>=size-1:
print '!!! WARNING: '+str(artfac0)+' too large (max '+str(size)+'- 1)'
print 'try again....'
else:
dartf = artfac0
except:
print '#### WARNING: '+str(artfac0)+' should be a number !!!!'
print 'try again....'
agnkey.util.delete("tmpar?")
agnkey.util.delete('artskyfit.fits')
os.system('cp skyfit.fits artskyfit.fits')
i=0
tmpart=[]
while i<=8:
agnkey.util.delete("reserr.fit?,artbg.fit?,artstar.fit?,artres.fit?,artfit.fit?")
artrad = fwhm0/2.
#artseed = artseed+1234
artx = int(i/3.)-1
if i<=2: arty = artx+i
if 3<=i<=5: arty = artx-1+i-3
if i>=6: arty = artx-2+i-6
ff=open(img+".sn.coo",'r')
ss=ff.readline()
ff.close()
xbb=float(string.split(ss)[0])
ybb=float(string.split(ss)[1])
xbb=xbb+artx*fwhm0*artfac0
ybb=ybb+arty*fwhm0*artfac0
agnkey.util.delete(coordlist)
ff=open(coordlist,'w')
ff.write(str(xbb)+' '+str(ybb)+' '+str(truemag[0])+" 1")
ff.close()
xb1 = int(float(xbb)-fwhm0*float(leng0)/2)
xb2 = int(float(xbb)+fwhm0*float(leng0)/2)
yb1 = int(float(ybb)-fwhm0*float(leng0)/2)
yb2 = int(float(ybb)+fwhm0*float(leng0)/2)
sec="1 "+str(xb1)+" 1 "+str(nay)+'\n'
sec=sec+str(xb2)+' '+str(nax)+" 1 "+str(nay)+'\n'
sesc=sec+str(xb1)+' '+str(xb2)+" 1 "+str(yb1)+'\n'
sec=sec+str(xb1)+' '+str(xb2)+' '+str(yb2)+' '+str(nay)+'\n'
ff = open('sec','w')
ff.write(sec)
ff.close()
agnkey.util.delete("reserr.ar?")
agnkey.util.delete("artlist.ma?")
agnkey.util.delete("artsky.fit?")
agnkey.util.delete("artbg.fit?")
agnkey.util.delete("artbgs.fit?")
agnkey.util.delete("artsn.fit?")
agnkey.util.delete("artres.fit?")
agnkey.util.delete("artlist.al?")
iraf.addstar("artskyfit",coordlist,imgpsf,"reserr",nstar=1,veri='no',simple='yes',verb='no') # reserr = skyfit + artificial star
########
inp = "artbg.fits["+str(xb1)+":"+str(xb2)+","+str(yb1)+":"+str(yb2)+"]"
out = "artsky.fits["+str(xb1)+":"+str(xb2)+","+str(yb1)+":"+str(yb2)+"]"
iraf.imsurfit("reserr","artbg",xorder=xbgord0,yorder=ybgord0,regions="section",section="sec")
midpt=float(iraf.imstat("artbg",field="mean", Stdout=1)[1])
iraf.imcopy('reserr.fits','artsky.fits')
iraf.imcopy(inp,'artbgs.fits')
iraf.imcopy("artbgs.fits",out)
iraf.imarith("reserr","-","artsky","artsn",calctype="r",pixtype="r",verb='no')
iraf.imarith("artsn","+",midpt,"artsn",verb='no')
artap1,artap2,artap3,artmag1,artmag2,artmag3,artfitmag,arttruemag,artmagerr,artcentx,artcenty = \
fitsn(img,imgpsf,coordlist,_recenter,fwhm0,'reserr','artsn','artres',_show,_interactive,z11,z22,midpt,size,apco0,dmax)
for ii in range(0,_numiter):
agnkey.util.delete("reserr.ar?")
agnkey.util.delete("artlist.ma?")
agnkey.util.delete("artsky.fit?")
agnkey.util.delete("artbg.fit?")
agnkey.util.delete("artbgs.fit?")
agnkey.util.delete("artsn.fit?")
agnkey.util.delete("artres.fit?")
agnkey.util.delete("artlist.al?")
iraf.imsurfit("skyfit","artbg",xorder=xbgord0,yorder=ybgord0,regions="section",section="sec")
midpt=float(iraf.imstat("artbg",field="mean", Stdout=1)[1])
iraf.imcopy("reserr.fits","artsky.fits")
iraf.imcopy(inp,"artbgs.fits")
iraf.imcopy("artbgs.fits",out)
iraf.imarith("reserr","-","artsky","artsn",calctype="r",pixtype="r",verb='no')
iraf.imarith("artsn.fits","+",midpt,"artsn.fits",verb='no')
artap1,artap2,artap3,artmag1,artmag2,artmag3,artfitmag,arttruemag,artmagerr,artcentx,artcenty=fitsn(img,imgpsf,coordlist,_recenter,fwhm0,'reserr','artsn','artres',_show,_interactive,z11,z22,midpt,size,0, dmax, dmin)
#######
if i==0: era='yes'
else: era='no'
artx = .5+.25*artx
arty = .5+.25*arty
if _show:
_tmp1,_tmp2,goon=agnkey.util.display_image('skyfit.fits',1,'', '', False, _xcen=artx, _ycen=arty, _xsize=.25, _ysize=.25, _erase=era)
try:
tmpart.append(float(arttruemag[0]))
except: pass
i=i+1
for i in tmpart: print i
print " ########## "
try:
media=mean(array(tmpart))
arterr=std(array(tmpart))
arterr2=std(compress((average(tmpart)-std(tmpart)<array(tmpart))&(array(tmpart)<average(tmpart)+std(tmpart)),array(tmpart)))
except:
media=0
arterr=0
arterr2=0
print '### average = %6.6s \t arterr= %6.6s ' % (str(media),str(arterr))
print '### %6.6s \t (error at 1 sigma rejection) ' % (str(arterr2))
agnkey.util.delete("reserr.fit?,artbg.fit?,artstar.fit?,artres.fit?,artfit.fit?,artskyfit.fit?")
agnkey.util.delete("reserr.ar?")
agnkey.util.delete("artlist.co?")
return arterr2,arterr
def ecpsf(img, ofwhm, threshold, psfstars, distance, interactive, ds9, psffun='gauss', fixaperture=False,_catalog='',_datamax=''):
try:
import agnkey
hdr = agnkey.util.readhdr(img + '.fits')
instrument = agnkey.util.readkey3(hdr, 'instrume')
print 'INSTRUMENT:', instrument
if 'PIXSCALE' in hdr:
pixelscale = agnkey.util.readkey3(hdr, 'PIXSCALE')
elif 'CCDSCALE' in hdr:
if 'CCDXBIN' in hdr:
pixelscale = agnkey.util.readkey3(hdr, 'CCDSCALE') * agnkey.util.readkey3(hdr, 'CCDXBIN')
elif 'CCDSUM' in hdr:
pixelscale = agnkey.util.readkey3(hdr, 'CCDSCALE') * int(
string.split(agnkey.util.readkey3(hdr, 'CCDSUM'))[0])
if 'kb' in instrument:
scale = pixelscale
if not _datamax:
_datamax = 45000
elif 'fl' in instrument:
scale = pixelscale
if not _datamax:
_datamax = 60000
elif 'fs' in instrument:
scale = pixelscale
if not _datamax:
_datamax = 65000
try:
#if 1==1:
if 'WCSERR' in hdr:
_wcserr = hdr['WCSERR']
elif 'WCS_ERR' in hdr:
_wcserr = hdr['WCS_ERR']
print _wcserr
if float(_wcserr) == 0:
if 'kb' in instrument:
seeing = float(agnkey.util.readkey3(hdr, 'L1FWHM')) * .75
elif 'fl' in instrument:
seeing = float(agnkey.util.readkey3(hdr, 'L1FWHM')) * .75
elif 'fs' in instrument:
if 'L1FWHM' in hdr:
seeing = float(agnkey.util.readkey3(hdr, 'L1FWHM')) * .75
elif 'L1SEEING' in hdr:
seeing = float(agnkey.util.readkey3(hdr, 'L1SEEING')) * scale
else:
seeing = 3
else:
seeing = 3
else:
seeing = float(agnkey.util.readkey3(hdr, 'PSF_FWHM'))
sys.exit('astrometry not good')
except ValueError:
# except:
sys.exit('astrometry not good')
fwhm = seeing / scale
print 'FWHM[header] ', fwhm, ' in pixel'
if ofwhm: fwhm = float(ofwhm)
print ' FWHM[input] ', fwhm, ' in pixel'
xdim, ydim = iraf.hselect(img+'.fits[0]', 'i_naxis1,i_naxis2', 'yes', Stdout=1)[0].split()
print img, fwhm, threshold, scale
#################################################################################
################### write file to compute psf _psf.coo ############
#################################################################################
if interactive:
iraf.set(stdimage='imt1024')
iraf.display(img+'.fits[0]', 1, fill=True)
iraf.delete('tmp.lo?', verify=False)
print '_' * 80
print '>>> Mark reference stars with "a". Then "q"'
print '-' * 80
iraf.imexamine(img+'[0]', 1, wcs='logical', logfile='tmp.log', keeplog=True)
xyrefer = iraf.fields('tmp.log', '1,2,6,15', Stdout=1)
xns, yns, _fws = [], [], []
############# write file for PSF #########################
ff = open('_psf.coo', 'w')
for i in range(len(xyrefer)):
xns.append(float(xyrefer[i].split()[0]))
yns.append(float(xyrefer[i].split()[1]))
_fws.append(float(xyrefer[i].split()[3]))
ff.write('%10.3f %10.3f %7.2f \n' % (xns[i], yns[i], float(_fws[i])))
ff.close()
fwhm = np.median(_fws)
else:
############ run sextractor #####################################
xs, ys, ran, decn, magbest, classstar, fluxrad, bkg = runsex(img, fwhm, threshold, scale)
_ra1,_dec1,xx11,yy11,_mag,_dist = agnkey.agnastrodef.starsfields(img+'.fits',20,19)
if len(_ra1):
dist,pos0,pos1 = agnkey.agnastrodef.crossmatchxy(xs,ys,xx11,yy11,10)
if len(pos0):
xs = xs[pos0]
ys = ys[pos0]
ran = ran[pos0]
decn = decn[pos0]
magbest = magbest[pos0]
classstar = classstar[pos0]
fluxrad = fluxrad[pos0]
bkg = bkg[pos0]
ff = open('tmp.cursor', 'w')
for i in range(len(xs)):
x1, x2 = int(xs[i] - fwhm * 3), int(xs[i] + fwhm * 3)
y1, y2 = int(ys[i] - fwhm * 3), int(ys[i] + fwhm * 3)
sect = '[' + str(x1) + ':' + str(x2) + ',' + str(y1) + ':' + str(y2) + ']'
try:
fmax = iraf.imstat(img+'.fits[0]' + sect, fields='max', Stdout=1)[1]
########## cut saturated object ########################
if float(fmax) < _datamax: # not saturated
ff.write('%10.3f %10.3f 1 m \n' % (xs[i], ys[i]))
except:
print sect
# print 'problem here'
# pass
ff.close()
iraf.delete('tmp.lo?,tmp.sta?,tmp.gk?', verify=False)
iraf.psfmeasure(img+'[0]', imagecur='tmp.cursor', logfile='tmp.log', radius=int(fwhm), iter=3,
display=False, StdoutG='tmp.gki')
ff = open('tmp.log')
righe = ff.readlines()
xn = [float(righe[3].split()[1])]
yn = [float(righe[3].split()[2])]
_fw = [float(righe[3].split()[4])]
for r in righe[4:-2]:
if len(r) > 0:
xn.append(float(r.split()[0]))
yn.append(float(r.split()[1]))
_fw.append(float(r.split()[3]))
print 'FWHM: ', righe[-1].split()[-1]
print 80 * "#"
######
############## eliminate double object identification ###########################
xns, yns, _fws = [xn[0]], [yn[0]], [_fw[0]]
for i in range(1, len(xn)):
if abs(xn[i] - xn[i - 1]) > .2 and abs(yn[i] - yn[i - 1]) > .2:
xns.append(xn[i])
yns.append(yn[i])
_fws.append(_fw[i])
######### write clean file for PSF #########################
fw = []
ff = open('_psf.coo', 'w')
for i in range(len(xns)):
ff.write('%10.3f %10.3f %7.2f \n' % (xns[i], yns[i], float(_fws[i])))
fw.append(_fws[i])
ff.close() ## End automatic selection
######################################################################################
################### write file of object to store in fits table #############
######################################################################################
if interactive:
xs, ys, ran, decn, magbest, classstar, fluxrad, bkg = runsex(img, fwhm, threshold, scale)
ff = open('_psf2.coo', 'w')
for i in range(len(xs)):
ff.write('%10s %10s %10s \n' % (xs[i], ys[i], fluxrad[i]))
ff.close()
elif _catalog:
print '\n#### use catalog '
ddd=iraf.wcsctran(input=_catalog,output='STDOUT',Stdout=1,image=img,inwcs='world',outwcs='logical',
units='degrees degrees',columns='1 2',formats='%10.1f %10.1f',verbose='no')
ddd=[i for i in ddd if i[0]!='#']
ddd=[' '.join(i.split()[0:3]) for i in ddd]
ff = open('_psf2.coo', 'w')
for i in ddd:
a,b,c = string.split(i)
ff.write('%10s %10s %10s \n' % (a, b, c))
ff.close()
print 'use catalog'
else:
os.system('cp _psf.coo _psf2.coo')
# print '\n### use sextractor'
# xs, ys, ran, decn, magbest, classstar, fluxrad, bkg = runsex(img, fwhm, threshold, scale)
# ff = open('_psf2.coo', 'w')
# for i in range(len(xs)):
# ff.write('%10s %10s %10s \n' % (xs[i], ys[i], fluxrad[i]))
# ff.close()
###################################################################################
print 80 * "#"
photmag, pst, fitmag = psffit(img, fwhm, psfstars, hdr, interactive, _datamax, psffun, fixaperture)
photmag2, fitmag2 = psffit2(img, fwhm, psfstars, hdr, _datamax, psffun, fixaperture)
radec = iraf.wcsctran(input='STDIN', output='STDOUT', Stdin=photmag, \
Stdout=1, image=img+'.fits[0]', inwcs='logical', outwcs='world', columns="1 2", \
format='%13.3H %12.2h', min_sig=9, mode='h')[3:]
radec2 = iraf.wcsctran(input='STDIN', output='STDOUT', Stdin=photmag2, \
Stdout=1, image=img+'.fits[0]', inwcs='logical', outwcs='world', columns="1 2", \
format='%13.3H %12.2h', min_sig=9, mode='h')[3:]
if ds9 == 0 and interactive:
iraf.set(stdimage='imt1024')
iraf.display(img, 1, fill=True, Stdout=1)
iraf.tvmark(1, coords='STDIN', mark='circle', radii=15, label=False, Stdin=photmag)
iraf.tvmark(1, coords='STDIN', mark='rectangle', length=35, label=False, Stdin=pst)
iraf.tvmark(1, coords='STDIN', mark='cross', length=35, label=False, Stdin=fitmag2, color=204)
idpsf = []
for i in range(len(pst)):
idpsf.append(pst[i].split()[2])
dmag = []
for i in range(len(radec)):
ra, dec, idph, magp2, magp3, magp4, merrp2, merrp3, merrp4 = radec[i].split()
dmag.append(9.99)
for j in range(len(fitmag)):
raf, decf, idf, magf, magerrf = fitmag[j].split()
if idph == idf and idph in idpsf and \
magp3 != 'INDEF' and magf != 'INDEF':
dmag[i] = float(magp3) - float(magf)
break
_dmag = np.compress(np.array(dmag) < 9.99, np.array(dmag))
print '>>> Aperture correction (phot) %6.3f +/- %5.3f %3d ' % \
(np.mean(_dmag), np.std(_dmag), len(_dmag))
if len(_dmag) > 3:
_dmag = np.compress(abs(_dmag - np.median(_dmag)) < 2 * np.std(_dmag), _dmag)
print '>>> 2 sigma rejection) %6.3f +/- %5.3f %3d [default]' \
% (np.mean(_dmag), np.std(_dmag), len(_dmag))
print '>>> fwhm %s ' % (str(fwhm))
for i in range(len(dmag)):
if dmag[i] == 9.99:
dmag[i] = ''
else:
dmag[i] = '%6.3f' % (dmag[i])
exptime = agnkey.util.readkey3(hdr, 'exptime')
object = agnkey.util.readkey3(hdr, 'object').replace(' ', '')
filtro = agnkey.util.readkey3(hdr, 'filter')
#######################################
rap, decp, magp2, magp3, magp4, smagf = [], [], [], [], [], []
merrp2, merrp3, merrp4, smagerrf = [], [], [], []
rap0, decp0 = [], []
for i in range(len(radec2)):
aa = radec2[i].split()
rap.append(aa[0])
decp.append(aa[1])
rap0.append(agnkey.agnabsphotdef.deg2HMS(ra=aa[0]))
decp0.append(agnkey.agnabsphotdef.deg2HMS(dec=aa[1]))
idp = aa[2]
magp2.append(aa[3])
magp3.append(aa[4])
magp4.append(aa[5])
merrp2.append(aa[6])
merrp3.append(aa[7])
merrp4.append(aa[8])
_smagf, _smagerrf = 9999, 9999
for j in range(len(fitmag2)):
raf, decf, idf, magf, magerrf = fitmag2[j].split()
if idf == idp:
_smagf = magf
_smagerrf = magerrf
break
smagf.append(_smagf)
smagerrf.append(_smagerrf)
new_cols = pyfits.ColDefs([
pyfits.Column(name='ra', format='20A', array=np.array(rap)),
pyfits.Column(name='dec', format='20A', array=np.array(decp)),
pyfits.Column(name='ra0', format='E', array=np.array(rap0)),
pyfits.Column(name='dec0', format='E', array=np.array(decp0)),
pyfits.Column(name='magp2', format='E', array=np.array(np.where((np.array(magp2) != 'INDEF'),
np.array(magp2), 9999), float)),
pyfits.Column(name='magp3', format='E', array=np.array(np.where((np.array(magp3) != 'INDEF'),
np.array(magp3), 9999), float)),
pyfits.Column(name='magp4', format='E', array=np.array(np.where((np.array(magp4) != 'INDEF'),
np.array(magp4), 9999), float)),
pyfits.Column(name='merrp2', format='E', array=np.array(np.where((np.array(merrp2) != 'INDEF'),
np.array(merrp2), 9999), float)),
pyfits.Column(name='merrp3', format='E', array=np.array(np.where((np.array(merrp3) != 'INDEF'),
np.array(merrp3), 9999), float)),
pyfits.Column(name='merrp4', format='E', array=np.array(np.where((np.array(merrp4) != 'INDEF'),
np.array(merrp4), 9999), float)),
pyfits.Column(name='smagf', format='E', array=np.array(np.where((np.array(smagf) != 'INDEF'),
np.array(smagf), 9999), float)),
pyfits.Column(name='smagerrf', format='E', array=np.array(np.where((np.array(smagerrf) != 'INDEF'),
np.array(smagerrf), 9999), float)),
])
tbhdu = pyfits.BinTableHDU.from_columns(new_cols)
hdu = pyfits.PrimaryHDU(header=hdr)
thdulist = pyfits.HDUList([hdu, tbhdu])
agnkey.util.delete(img + '.sn2.fits')
thdulist.writeto(img + '.sn2.fits')
agnkey.util.updateheader(img + '.sn2.fits', 0, {'APCO': [np.mean(_dmag), 'Aperture correction']})
agnkey.util.updateheader(img + '.sn2.fits', 0, {'APCOERR': [np.std(_dmag), 'Aperture correction error']})
agnkey.util.updateheader(img + '.sn2.fits', 0,
{'XDIM': [agnkey.util.readkey3(hdr, 'naxis1'), 'x number of pixels']})
agnkey.util.updateheader(img + '.sn2.fits', 0,
{'YDIM': [agnkey.util.readkey3(hdr, 'naxis2'), 'y number of pixels']})
agnkey.util.updateheader(img + '.sn2.fits', 0,
{'PSF_FWHM': [fwhm * scale, 'FWHM (arcsec) - computed with daophot']})
os.chmod(img + '.sn2.fits', 0664)
os.chmod(img + '.psf.fits', 0664)
result = 1
except IOError as e:
print e
result = 0
fwhm = 0.0
traceback.print_exc()
return result, fwhm * scale
# pixtype = "",\
# calctype = "",\
# verbose = 1,\
# noact = 0,\
# mode = "ql")
# os.system("rm " + "master_smooth.fits")
# os.system("mv " + "temp_master_smooth.fits master_smooth.fits")
smooth_max = iraf.imstat(
images = "master_smooth.fits[*,1,1]",\
fields = "max",\
lower = "INDEF",\
upper = "INDEF",\
nclip = 1,\
lsigma = 5.0,\
usigma = 5.0,\
binwidth = 0.1,\
format = 1,\
cache = 1,\
mode = "al",\
Stdout = 1)
iraf.imarith(
operand1 = "master_smooth.fits",\
op = "/",\
operand2 = smooth_max[1],\
result = "temp_master_smooth.fits",\
title = "",\
divzero = 0.,\
hparams = "",\
def performPhotometry(task, logger):
#iraf.prcacheOff()
[iraf.unlearn(t) for t in ('phot','pstselect','psf','allstar')]
iraf.set(imtype="fits,noinherit") # set image output format
iraf.set(clobber="yes")
hdu=pyfits.open(task['images'])[0]
hdr = hdu.header
imdata = hdu.data
for key,value in task['fits'].iteritems():
task[key] = hdr.get(value,1)
#Sextractor to find stars; add an object for the force detect
logger.info('Running SExtractor on [%s]' % os.path.basename(task['images']))
sex = sextractor.SExtractor()
makeSexConfig(sex,task)
sex.run(task['images'])
catalog = sex.catalog()
#Set up image parameters
MIN_X = max(1,int(task['numpixx']*task['filtfactor']))
MIN_Y = max(1,int(task['numpixy']*task['filtfactor']))
MAX_X = int(task['numpixx']*(1-task['filtfactor']))
MAX_Y = int(task['numpixy']*(1-task['filtfactor']))
AREAXY = '[%s:%s,%s:%s]' % (MIN_X, MAX_X, MIN_Y, MAX_Y)
AREANO = '[%s:%s,%s:%s]' % (MIN_X, MAX_X-2*MIN_X, MIN_Y, MAX_Y-2*MIN_Y)
try:
task['pixscale'] = abs(hdr.get('CD1_1'))*3600.
except TypeError:
task['pixscale'] = abs(hdr.get('CDELT1'))*3600.
task['seeing'] = np.median( sorted([i['FWHM_IMAGE'] for i in catalog])[:int(-len(catalog)*0.5)] ) #Take the median of the "bottom" 50% of objects
logger.info('--> %s SExtractor detected bright objects in the field' % (len(catalog),) )
logger.info('--> %0.2f median FWHM of bright objects in the field, in arcsec' % (task['seeing']*task['pixscale'],))
task['objects'] = [(i['ALPHA_J2000'],i['DELTA_J2000']) for i in catalog]
task['objects'].append(task['objwcs'])
task['objectlist'] = open(os.path.join(task['output_directory'],'objectlist'),'w')
task['objectlist'].write('\n'.join([' %s %s' % (i[0],i[1]) for i in task['objects']]))
task['objectlist'].close()
logger.info('Running iraf.imstat')
irafoutput = iraf.imstat(images=task['images']+AREANO,fields='midpt,min,max,stddev', format=0, Stdout=1)
task['nimgs'] = hdr.get('NIMGS',1)
task['gain'] *= task['nimgs']*2/3.
task['ron'] *= np.sqrt(task['nimgs'])/task['nimgs']*constants.INTERPSM[task['band']]
task['datamean'], task['datamin'], task['datamax'], task['datastdev'] = map(float, irafoutput[0].split())
irafoutput = iraf.imstat(images=task['images'],fields='stddev,midpt',nclip=25,format=0,cache='yes',Stdout=1)
task['skynoise'], task['datamean'] = map(float, irafoutput[0].split() )
task['skynoise'] *= constants.INTERPSM[task['band']]
task['airmass'] = hdr.get('AIRMASS',1)
task['zmag'] -= (float(task['airmass'])-1.0)*constants.extinction_coefficients[task['band']]
task['match_proximity'] = 2.5 * task['seeing']
logger.info('--> %5.2f counts: Sky noise, corrected for drizzle imcombine' % task['skynoise'])
logger.info('--> %5.2f Median count value, after background subtraction' % task['datamean'])
logger.info('--> %5.2f Airmass' % task['airmass'])
#prepare temp files that iraf will use
for filename in ('photfile','pstfile','psfimg','opstfile','groupfile','allstarfile','rejfile','subimage'):
task[filename] = open(os.path.join(task['output_directory'],filename),'w')
task[filename].close()
#iraf.phot to get APP magnitudes
logger.info('Running iraf.apphot.phot')
#apsizes = [i*task['faperture']*task['seeing'] for i in (0.4,0.5,0.6,0.8,1.0,1.2,1.5,2.0,2.5,3.0)]
#irafapsizes = ','.join(['%.2f' % i for i in apsizes])
irafapsizes = '%0.2f' % (task['faperture']*task['seeing'])
kwargs = dict(image=task['images'],coords=task['objectlist'].name,
output=task['photfile'].name,
interac='no',scale=1,
fwhmpsf=task['seeing'],
wcsin='world', wcsout='physical',
sigma=task['skynoise'],
datamin=task['datamin'],
datamax=task['datamax'],
readnoi=task['ron'],
epadu=task['gain'],
itime=task['exposure'],
xairmass=task['airmass'],
ifilter=task['band'],
otime=task['dateobs'],
aperture= irafapsizes,
zmag=task['zmag'],
annulus=task['fannulus']*task['seeing'],
dannulus=task['fdannulus']*task['seeing'],
calgorithm='gauss',
cbox = 1.5*task['seeing'],
maxshift=2.0*task['seeing'],
mode="h",Stdout=1,verify=0)
iraf.phot(**kwargs)
if task['band'] not in constants.infrared:
#iraf.pstselect to choose objects for PSF modelling
logger.info('Running iraf.daophot.pstselect')
kwargs = dict(image=task['images'],
photfile=task['photfile'].name,pstfile=task['pstfile'].name,
maxnpsf=task['pstnumber'],
wcsin='physical',
wcsout='physical',
interac="no",verify='no',scale=1,
fwhmpsf=task['seeing'],
datamin=0,
datamax=task['datamax'],
psfrad=3.0*task['seeing'],
fitrad=1.0*task['seeing'],
recente='yes',
nclean=task['nclean'],
mode="h",Stdout=1)
iraf.pstselect(**kwargs)
#iraf.psf to model PSF
logger.info('Running iraf.daophot.psf')
kwargs = dict( image=task['images'],
photfile=task['photfile'].name,
pstfile=task['pstfile'].name,
psfimage=task['psfimg'].name,
opstfile=task['opstfile'].name,
groupfile=task['groupfile'].name,
wcsin='physical',wcsout='physical',
interac="no",verify="no",scale=1,
fwhmpsf=task['seeing'],
sigma=task['skynoise'],
datamin=task['datamin'],
datamax=task['datamax'],
readnoi=task['ron'],
epadu=task['gain'],
itime=task['exposure'],
xairmass=task['airmass'],
ifilter=task['band'],
otime=task['dateobs'],
function=task['func'],
varorder=task['varorder'],
saturat='no',
psfrad=3.0*task['seeing'],
fitrad=1.*task['faperture']*task['seeing'],
nclean=task['nclean'],
mergerad=1.5*task['seeing'],
mode='h',Stdout=1)
iraf.psf(**kwargs)
logger.info('Running iraf.daophot.allstar')
#iraf.allstars to compute PSF photometry; recenter with recenter='yes', mergerad=<value> to avoid duplicate detection
kwargs = dict(image=task['images'],
photfile=task['photfile'].name,
wcsin='physical',
wcsout='physical',
psfimage=task['psfimg'].name,
allstarf=task['allstarfile'].name,
rejfile=task['rejfile'].name,
subimage=task['subimage'].name,
verbose=1,verify='no',scale=1,
fwhmpsf=task['seeing'],
sigma=task['skynoise'],
datamin=task['datamin'],
datamax=task['datamax'],
readnoi=task['ron'],
epadu=task['gain'],
itime=task['exposure'],
xairmass=task['airmass'],
ifilter=task['band'],
otime=task['dateobs'],
function=task['func'],
varorder=task['varorder'],
psfrad=3.*task['seeing'],
fitrad=1.*task['faperture']*task['seeing'],
recenter='yes',
mergerad=1.5*task['seeing'],
mode='h',Stdout=1)
iraf.allstar(**kwargs)
#Parse both photometry, convert to RA,DEC,MAG,MAGERR
logger.info('iraf tasks complete. Parsing results and calibrating')
photometry = {}
photometry['APP'] = iraf.txdump(textfiles=task['photfile'].name,
fields='XCENTER,YCENTER,MAG,MERR',expr='yes',
headers='no',Stdout=1)
if task['band'] not in constants.infrared:
photometry['PSF'] = iraf.txdump(textfiles=task['allstarfile'].name,
fields='XCENTER,YCENTER,MAG,MERR',expr='yes',
headers='no',Stdout=1)
for phototype in photometry:
kwargs = dict(input='STDIN',
output='STDOUT',
insystem='%s physical' % task['images'],
outsystem='%s world' % task['images'],
ilatuni='physical',
ilnguni='physical',
olnguni='degrees',
olatuni='degrees',
ilngfor='%10.7f',
ilatfor='%10.7f',
olngfor='%10.5f',
olatfor='%10.5f',
Stdin=photometry[phototype],Stdout=1)
photometry[phototype] = [i.split() for i in iraf.skyctran(**kwargs) if i and not i.startswith('#') and 'INDEF' not in i]
photometry[phototype] = [map(float,(i[4],i[5],i[2],i[3])) for i in photometry[phototype] ] #Now we have [(ra,dec,'mag','mageerr'),...]
results = calibrate((task['objwcs'][0],task['objwcs'][1]),task,photometry,logger)
# if 'PSF' not in results:
return results
_ybgord0 = ybgord0
try:
float(xbgord0)
float(ybgord0)
checkorder = 'no'
except:
print 'WARNING: value not valid !!'
checkorder = 'yes'
iraf.imsurfit("original",
"bg",
xorder=xbgord0,
yorder=ybgord0,
regions="sections",
sections="sec")
midpt = float(iraf.imstat("bg", field="mean", Stdout=1)[1])
iraf.imcopy("original.fits", "sky.fits")
iraf.imcopy(inp, "bgs.fits")
iraf.imcopy("bgs.fits", out)
iraf.imarith("original.fits", "-", "sky.fits", "sn.fits")
iraf.imarith("sn.fits", "+", midpt, "sn.fits")
answ0 = 'y'
print answ0
if _show or _interactive:
_tmp1, _tmp2, goon = agnkey.util.display_image(
'original.fits',
1,
z11,
z22,
False,
_xcen=.25,
def create_masterflat(flatdir=None, biasdir=None, channel='rc'):
'''
Creates a masterflat from both dome flats and sky flats if the number of counts in the given filter
is not saturated and not too low (between 1500 and 40000).
'''
if (flatdir == None or flatdir==""): flatdir = "."
if (biasdir == None or biasdir==""): biasdir = "."
os.chdir(flatdir)
if (len(glob.glob("Flat_%s*norm.fits"%channel)) == 4):
print "Master Flat exists!"
return
else:
print "Starting the Master Flat creation!"
bias_slow = "Bias_%s_fast.fits"%channel
bias_fast = "Bias_%s_fast.fits"%channel
if (not os.path.isfile(bias_slow) and not os.path.isfile(bias_fast) ):
create_masterbias(biasdir)
lsflat = []
lfflat = []
#Select all filts that are Flats with same instrument
for f in glob.glob("*fits"):
#try:
if fitsutils.has_par(f, "OBJECT"):
obj = str.upper(fitsutils.get_par(f, "OBJECT"))
else:
continue
if ( ("DOME" in obj or "FLAT" in obj) and (channel == fitsutils.get_par(f, "CHANNEL"))):
if (fitsutils.get_par(f, "ADCSPEED")==2):
lfflat.append(f)
else:
lsflat.append(f)
#except:
# print "Error with retrieving parameters for file", f
# pass
print "Files for slow flat", lsflat
print "Files for fast flat", lfflat
fsfile ="lflat_slow_"+channel
np.savetxt(fsfile, np.array(lsflat), fmt="%s")
fffile ="lflat_fast_"+channel
np.savetxt(fffile, np.array(lfflat), fmt="%s")
# Running IRAF
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
#Remove bias from the flat
if len(lsflat) >0:
iraf.imarith("@"+fsfile, "-", bias_slow, "b_@"+fsfile)
if len(lfflat) >0:
iraf.imarith("@"+fffile, "-", bias_fast, "b_@"+fffile)
#Slices the flats.
debiased_flats = glob.glob("b_*.fits")
for f in debiased_flats:
print "Slicing file", f
slice_rc(f)
#Remove the un-sliced file
os.remove(f)
#Selects the ones that are suitable given the number of counts and combines them.
bands = ['u', 'g', 'r', 'i']
for b in bands:
out = "Flat_%s_%s.fits"%(channel, b)
out_norm = out.replace(".fits","_norm.fits")
if (os.path.isfile(out_norm)):
print "Master Flat for filter %s exists. Skipping..."%b
continue
lfiles = []
for f in glob.glob('b_*_%s.fits'%b):
d = pf.open(f)[0].data
if np.percentile(d, 90)>1500 and np.percentile(d, 90)<40000:
lfiles.append(f)
if len(lfiles) == 0:
print "WARNING!!! Could not find suitable flats for band %s"%b
continue
ffile ="lflat_"+b
np.savetxt(ffile, np.array(lfiles), fmt="%s")
#Cleaning of old files
if(os.path.isfile(out)): os.remove(out)
if(os.path.isfile(out_norm)): os.remove(out_norm)
if(os.path.isfile("Flat_stats")): os.remove("Flat_stats")
#Combine flats
iraf.imcombine(input = "@"+ffile, \
output = out, \
combine = "median",\
scale = "mode",
weight = "exposure")
iraf.imstat(out, fields="image,npix,mean,stddev,min,max,mode", Stdout="Flat_stats")
st = np.genfromtxt("Flat_stats", names=True, dtype=None)
#Normalize flats
iraf.imarith(out, "/", st["MODE"], out_norm)
#Do some cleaning
print 'Removing from lfiles'
for f in glob.glob('b_*_%s.fits'%b):
os.remove(f)
os.remove(ffile)
if os.path.isfile(fsfile):
os.remove(fsfile)
if os.path.isfile(fffile):
os.remove(fffile)
print bexoplanet
#exoplanet = string.join(exoplanet,',') #create the list string of exoplanet science images
#bexoplanet = string.join(bexoplanet,',')#create the list string of bexoplanet science images
print '\nSubtracting superbias.fits from all '+planet+'*.fits images ....\n'
for i in range(len(exoplanet)):
iraf.imarith(exoplanet[i],'-','superbias.fits',bexoplanet[i])
update_progress((i+1.)/len(bexoplanet))
print '\n.... cleaning '+planet+'*.fits images\n'
os.system('rm '+planet+'*.fits')
print '\n Statistics of B'+planet+'*.fits images: \n'
for i in range(len(bexoplanet)):
iraf.imstat(bexoplanet[i])
#%%
print '\nFlatfielding the B'+planet+'*.fits ....\n'
#create the names for exoplanet science images with bias subtracted and flatfielding
abexoplanet = []
for i in bexoplanet:
abexoplanet.append('A'+i)
#verify if previous superbias exist
if os.path.isfile('A'+i) == True:
os.system('rm A'+i)
print '\n Will be create this images: \n'
print abexoplanet
#flatifielding images
for i in range(len(abexoplanet)):
iraf.imarith(bexoplanet[i],'/','superflat.fits',abexoplanet[i])
def run_imstat(input):
iraf.images()
for image in input:
iraf.imstat(image)
def fringe_sub(inlist, inpref='', outpref='s', mskpref='none', iternum=50, nsigma=3, bpm='none', second=False, fr_x0=0.0, fr_y0=0.0, fr_step=5, surfit=False, xsorder=4, ysorder=4):
# load IRAF package
iraf.proto()
# check input image list
inimg_arr = check_input(inlist, inpref)
if isinstance(inimg_arr, int):
return 1
# check output
outimg_arr = check_outpref(outpref, inimg_arr)
if isinstance(outimg_arr, int):
return 1
# check mask images
if mskpref.lower() == 'none' or mskpref == '':
mask = 0
else:
mask = 1
mskimg_arr = check_inpref(mskpref, inimg_arr)
if isinstance(mskimg_arr, int):
return 1
# check bad pixel mask
if bpm.lower() != 'none':
bpmex = 1
if os.access(bpm, os.R_OK) == False:
print >> sys.stderr, 'cannot read bad pixel mask (%s)' % bpm
return 1
else:
bpmex = 0
# prefix for temoprary images
tmp = tempfile.NamedTemporaryFile(suffix='', prefix='', dir='/tmp')
tmp_prefix = tmp.name
tmp.close()
# subtract sky
for i in range(len(inimg_arr)):
if mask == 1:
iraf.unlearn('mimstat')
if not second:
ret = iraf.mimstat(inimg_arr[i], imasks=mskimg_arr[i]+'[pl]', format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
else:
if bpmex == 1:
tmp_mask = tmp_prefix + os.path.basename(mask_img_arr[i])
iraf.imarith(maskimg_arr[i], '+', bpm, tmp_mask, verbose='no')
convert_maskfits_int(tmp_mask, tmp_mask)
ret = iraf.mimstat(inimg_arr[i], imasks=tmp_mask, format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
else:
ret = iraf.mimstat(inimg_arr[i], imasks=mskimg_arr[i], format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
else:
iraf.unlearn('imstat')
ret = iraf.imstat(inimg_arr[i], format='no', fields='midpt', nclip=iternum, lsigma=nsigma, usigma=nsigma, Stdout=1)
if len(ret) == 1:
objmed = float(ret[0])
else :
print >> sys.stderr, 'failed to calculate median of the object background'
return 1
# record input fits data into memory
im = pyfits.open(inimg_arr[i])
im_hdr = im[0].header
im_data = im[0].data
out_data = im_data - objmed
if surfit:
out_data_org = np.copy(out_data)
im.close()
# fringe subtraction for NB imaging
if mask == 1:
if bpmex == 1:
tmp_mask = tmp_prefix + os.path.basename(mask_img_arr[i])
mask_im = pyfits.open(tmp_mask)
else:
mask_im = pyfits.open(mskimg_arr[i])
if not second:
mask_data = mask_im[1].data
else:
mask_data = mask_im[0].data
mask_im.close()
x = np.ones(out_data.shape[0]*out_data.shape[1]).reshape(out_data.shape[0],out_data.shape[1])* np.arange(out_data.shape[1]) + 1
y = (np.ones(out_data.shape[1]*out_data.shape[0]).reshape(out_data.shape[1],out_data.shape[0]) * np.arange(out_data.shape[0]) + 1).T
r = np.sqrt((x-fr_x0)*(x-fr_x0) + (y-fr_y0)*(y-fr_y0))
rmax = np.max(r)
rmin = np.min(r)
r1 = rmin
while r1 <= rmax:
r2 = r1 + fr_step
idx = np.logical_and(r>=r1, r<r2)
out_subset = out_data[idx]
if mask == 1:
mask_subset = mask_data[idx]
idx_sky = np.where(mask_subset == 0)
#if len(out_subset[idx_sky]) != len(out_subset):
# print "masked:",inimg_arr[i],r1,r2,fr_x0,fr_y0,len(out_subset[idx_sky]),"/",len(out_subset),meanclip(out_subset[idx_sky],median=1)[0],meanclip(out_subset, median=1)[0]
med, sigma = meanclip(out_subset[idx_sky],median=1)
else:
med, sigma = meanclip(out_data[idx],median=1)
out_data[idx] = out_data[idx] - med
r1 += fr_step
# save output fits file
hdu = pyfits.PrimaryHDU(out_data)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
if surfit:
tmp_fsub = tmp_prefix + "fsub" + os.path.basename(inimg_arr[i])
tmp_fsub_fit = tmp_prefix + "fsub_fit" + os.path.basename(inimg_arr[i])
imgout.writeto(tmp_fsub)
imgout.close()
iraf.imsurfit(tmp_fsub, tmp_fsub_fit, xsorder, ysorder)
imfit = pyfits.open(tmp_fsub_fit)
imfit_data = imfit[0].data
out_data_fs = np.copy(out_data_org - imfit_data)
r1 = rmin
while r1 <= rmax:
r2 = r1 + fr_step
idx = np.logical_and(r>=r1, r<r2)
out_subset = out_data_fs[idx]
if mask == 1:
mask_subset = mask_data[idx]
idx_sky = np.where(mask_subset == 0)
med, sigma = meanclip(out_subset[idx_sky],median=1)
else:
med, sigma = meanclip(out_data_fs[idx],median=1)
out_data_org[idx] = out_data_org[idx] - med
r1 += fr_step
hdu = pyfits.PrimaryHDU(out_data_org)
imgout = pyfits.HDUList([hdu])
imgout[0].header = im_hdr
imgout.writeto(outimg_arr[i])
imgout.close()
remove_temp_all(tmp_prefix)
else:
imgout.writeto(outimg_arr[i])
imgout.close()
if bpmex == 1:
remove_temp_all(tmp_prefix)
return 0
def doflatsofi(flats, _doflat, illum, _output):
# print "LOGX:: Entering `doflatsofi` method/function in %(__file__)s" %
# globals()
import ntt
from ntt.util import display_image, delete, searchflat, name_duplicate
from pyraf import iraf
import glob
import string
onofflimit = {'J': 1000, 'H': 1000, 'Ks': 5000}
masklimit = {'J': {'ON': 1000, 'OFF': 30}, 'H': {
'ON': 1000, 'OFF': 40}, 'Ks': {'ON': 1000, 'OFF': 1000}}
if flats and _doflat:
listaflat = []
for _filter in flats:
for ID in flats[_filter]:
images = flats[_filter][ID]
if len(images) == 8:
mflat = makeflat(images)
listaflat.append(mflat)
display_image(mflat, 1, '', '', False)
raw_input('go on ')
elif len(images) != 8: # % 8 == 0:
print '\n### to compute a flat field you need a sequence of 8 calibration files in the following orders:'
print 'OFF OFFMASK ONMASK ON ON ONMASK OFFMASK OFF\n'
print len(images), _filter, ID
tipo = ['OFF', 'OFFMASK', 'ONMASK', 'ON',
'ON', 'ONMASK', 'OFFMASK', 'OFF']
listtmp = []
ii = 0
nn = 0
for img in images:
onoffvalue = float(string.split(iraf.imstat(
img + '[500:600,900:1000]', Stdout=1)[1])[2])
maskvalue = float(string.split(iraf.imstat(
img + '[100:200,900:1000]', Stdout=1)[1])[2])
if onoffvalue >= onofflimit[_filter]:
onoff = 'ON'
else:
onoff = 'OFF'
if maskvalue >= masklimit[_filter][onoff]:
mask = 'none'
else:
mask = 'MASK'
# display_image(img,1,'','',False)
print onoff, mask, onoffvalue, maskvalue, img, tipo[nn]
for img in images:
onoffvalue = float(string.split(iraf.imstat(
img + '[500:600,900:1000]', Stdout=1)[1])[2])
maskvalue = float(string.split(iraf.imstat(
img + '[100:200,900:1000]', Stdout=1)[1])[2])
if onoffvalue >= onofflimit[_filter]:
onoff = 'ON'
else:
onoff = 'OFF'
if maskvalue >= masklimit[_filter][onoff]:
mask = 'none'
else:
mask = 'MASK'
display_image(img, 1, '', '', False)
print onoff, mask, onoffvalue, maskvalue, img, tipo[nn]
answ = raw_input('ok [[y]/n/r/s] ? ')
if not answ:
answ = 'y'
if answ == 'y':
listtmp.append(img)
ii = ii + 1
nn = nn + 1
if len(listtmp) == 8:
print '### number images selected: ', str(len(listtmp))
mflat = ntt.soficalibdef.makeflat(listtmp)
listaflat.append(mflat)
display_image(mflat, 1, '', '', False)
nn = 0
listtmp = []
elif answ == 'r':
listtmp = []
ii = 0
nn = 0
elif answ == 's':
break
else:
print len(images), _filter, ID
print '### number images selected: ', str(len(listtmp))
else:
listaflat = flats
listaillum = []
if illum:
for _filter in illum:
for ID in illum[_filter]:
images = illum[_filter][ID]
flatfield = searchflat(images[0], listaflat)[0]
if flatfield:
illum_frame = ntt.soficalibdef.makeillumination(
images, flatfield)
listaillum.append(illum_frame)
else:
print 'flat field not found'
for img in listaflat:
try:
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.updateheader(
img, 0, {'BUNIT': ['ADU', 'pixel units(ADU,electrons)']})
ntt.util.updateheader(
img, 0, {'FILETYPE': [31202, 'flat field']})
except:
print '\n### problems with phase 3 definitions'
for img in listaillum:
try:
ntt.util.phase3header(img) # phase 3 definitions
ntt.util.updateheader(
img, 0, {'BUNIT': ['ADU', 'pixel units(ADU,electrons)']})
ntt.util.updateheader(
img, 0, {'FILETYPE': [31213, 'illum corr frames']})
except:
print '\n### problems with phase 3 definitions'
return listaflat, listaillum
def create_masterbias(biasdir=None, channel='rc'):
'''
Combines slow and fast readout mode biases for the specified channel.
'''
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
if (biasdir is None) or biasdir=="": biasdir = "."
outs = "Bias_%s_slow.fits"%channel
outf = "Bias_%s_fast.fits"%channel
doslow = True
dofast = True
if (os.path.isfile(os.path.join(biasdir,outs))):
logger.warn( "%s master Bias exists!"%outs)
doslow = False
if ( os.path.isfile(os.path.join(biasdir,outf))):
logger.warn("%s master Bias exists!"%outs)
dofast = False
if(doslow or dofast):
logger.info("Starting the Master Bias creation!")
else:
return
os.chdir(biasdir)
lfastbias = []
lslowbias = []
#Select all filts that are Bias with same instrument
for f in glob.glob("rc*fits"):
try:
if ( "BIAS" in str.upper(fitsutils.get_par(f, "IMGTYPE")) ):
if (fitsutils.get_par(f, "ADCSPEED")==2):
lfastbias.append(f)
else:
lslowbias.append(f)
except:
pass
logger.info("Files for bias SLOW mode: %s"% lslowbias)
logger.info( "Files for bias FAST mode: %s"% lfastbias)
if len(lfastbias) > 0 and dofast:
bfile_fast ="lbias_fast_"+channel
np.savetxt(bfile_fast, np.array(lfastbias), fmt="%s")
if (os.path.isfile("Bias_stats_fast")): os.remove("Bias_stats_fast")
iraf.imstat("@"+bfile_fast, Stdout="Bias_stats_fast")
st = np.genfromtxt("Bias_stats_fast", names=True, dtype=None)
logger.info("%s"%st)
iraf.imcombine(input = "@"+bfile_fast, \
output = outf, \
combine = "median",\
scale = "mode")
os.remove(bfile_fast)
#copy into the reference folder with current date
newdir = os.path.join("../../refphot/", os.path.basename(os.path.abspath(biasdir)))
if (not os.path.isdir(newdir)):
os.makedirs(newdir)
shutil.copy(outf, os.path.join(newdir, os.path.basename(outf)) )
else:
copy_ref_calib(biasdir, outf)
if len(lslowbias) > 0 and doslow:
bfile_slow ="lbias_slow_"+channel
np.savetxt(bfile_slow, np.array(lslowbias), fmt="%s")
if (os.path.isfile("Bias_stats_slow")): os.remove("Bias_stats_slow")
iraf.imstat("@"+bfile_slow, Stdout="Bias_stats_slow")
st = np.genfromtxt("Bias_stats_slow", names=True, dtype=None)
logger.info("%s"%st)
iraf.imcombine(input = "@"+bfile_slow, \
output = outs, \
combine = "median",\
scale = "mode")
os.remove(bfile_slow)
#copy into the reference folder with current date
newdir = os.path.join("../../refphot/", os.path.basename(os.path.abspath(biasdir)))
if (not os.path.isdir(newdir)):
os.makedirs(newdir)
shutil.copy(outs, os.path.join(newdir, os.path.basename(outs)) )
else:
copy_ref_calib(biasdir, outs)
def irafflatten():
os.system('cp /Users/rfinn/clusters/spitzer/flatsexfiles/* .')
infile=open('InputImageList.txt','r')
outfile=open('FlatImageList.txt','w')
sky=[]
for line in infile:
im=line[0:(len(line)-1)]
mask='mask'+im
skyim='s'+im
outline='f'+line
iraf.imgets(im,'DRIBKGND')
t=iraf.imgets.value
sky.append(float(t))
outfile.write(outline)
#get object positions using sextractor
iraf.imarith(im,'-',t,skyim)#subtract sky before running sextractor (otherwise it doesn't detect any objects - don't know why...)
s='sex '+skyim
os.system(s)
x=[]
y=[]
catfile=open('test.cat','r')
for line in catfile:
if line.find('#') > -1:
continue
t=line.split()
x.append(float(t[10]))
y.append(float(t[11]))
catfile.close()
x=N.array(x,'f')
y=N.array(y,'f')
try:#create image of ones same size as image
iraf.imarith(im,'/',im,'mask')
except:
iraf.imdelete('mask')
iraf.imarith(im,'/',im,'mask')
print "masking objects"
for j in range(len(x)): #mask objects and radius around each position using imreplace, radius=10 pix
for k in range(11):
y1=int(y[j]+5-k)
if y1 < 1:
continue
if y1 > 128:
continue
xmin=int(x[j]-5)
if xmin < 1:
xmin=1
xmax=int(x[j]+5)
if xmax > 128:
xmax=128
s='mask['+str(xmin)+':'+str(xmax)+","+str(y1)+":"+str(y1)+"]"
iraf.imreplace(s,0.)
iraf.imrename('mask',mask)
print "updating BPM field in header"
iraf.hedit(im,fields='BPM',value=mask,add='yes',verify='no',update='yes')
outfile.close()
infile.close()
avesky=N.average(N.array(sky,'f'))
lthresh=avesky-1.
hthresh=avesky+.6
iraf.imcombine('@InputImageList.txt','flat',combine='average',reject='ccdclip',scale='none',zero='mode',lthreshold=lthresh,hthreshold=hthresh,lsigma=2.,hsigma=2.,rdnoise=5.,gain=5.,blank=1.,grow=12.,masktype='badvalue',maskvalue='0')
t=iraf.imstat('flat',fields='mean',format='no',Stdout=1)
ave=float(t[0])
iraf.imarith('flat','/',ave,'nflat')
iraf.imarith('@InputImageList.txt','/','nflat','@FlatImageList.txt')
def masterflat(input_file):
"""
Obtain the masterflat image for calibration.
___
INPUT:
For obtain this parameters, use the input_info function.
data_path: string, path where are the images data.
save_path: string, path where will save all reduced images.
input_file: dict, with information describe in the YAML file.
OUTPUT:
It is possible that the function return some of these values:
0. Create the masterflat image on the save_path.
1. It do not create the masterflat image, because of some erros.
"""
#set original directory
original_path = os.getcwd()
data_path = input_file['data_path']
save_path = input_file['save_path']
#Change your directory to data diretory
os.chdir(data_path)
#list all flat images
flat = glob.glob('flat*.fits')
print 'Loading flat images \nTotal of flat files = ',len(flat),'\nFiles = \n'
print flat
#if save_path exist, continue; if not, create.
if not os.path.exists(save_path):
os.makedirs(save_path)
#create a list of bias images and copy images to save_path
os.system('cp flat*.fits '+save_path)
#creating the names of flat with bias subctracted
bflat = []
for i in flat:
bflat.append('B'+i)
print '\n Names os flat images with bias subtracted: \n \n',bflat
#change for save_path directory
os.chdir(save_path)
#verify if previous superbias exist
if os.path.isfile('superflat.fits') == True:
os.system('rm superflat.fits')
#verify if exits previous bflat*.fits files and remove then.
for i in bflat:
if os.path.isfile(i) == True:
os.system('rm -f '+i)
print '\nCreating superflat .... \n'
#create the list of flat images and bflat images
#flat = string.join(flat,',')
#bflat = string.join(bflat,',')
print '\n Subtracting bias from flat images and creating bflat images.... \n'
#iraf.imarith()
for i in range(len(flat)):
iraf.imarith(flat[i],'-','superbias.fits',bflat[i])
#print statistics from bflat*.fits images
iraf.imstat(bflat[i])
print '\n .... done \n'
#clean previos flat*.fits files
print '\n Clean flat*.fits images .... \n'
os.system('rm flat*.fits')
print '\n .... done. \n'
#normalizing each flat
print '\nNormalizing each flat ....\n'
#checking if mean from numpy is the same from your bflat images using imstat
#take the mean of each bflat image
bflat_mean = np.zeros(len(bflat))
for i in range(len(bflat)):
image = fits.getdata(bflat[i])
image = np.array(image,dtype='Float64')
bflat_mean[i] = round(np.mean(image))
image = 0 #clean image allocate to this variable
print 'The mean of each bflat image, respectivaly ...'
print bflat_mean
#creating the names of bflat images after the normalization:
abflat = []
for i in bflat:
abflat.append('A'+i)
print '\n Names os bflat images with bias subtracted and normalizad: \n \n',abflat
#verify if exist previous ABflat*.fits images and remove then.
for i in abflat:
if os.path.isfile(i) == True:
os.system('rm -f '+i)
for i in range(len(abflat)):
iraf.imarith(bflat[i],'/',bflat_mean[i],abflat[i])
print '\n.... done!\n'
print '\n Cleaning bflat*.fits images ....\n'
os.system('rm Bflat*.fits')
print '\n.... done.\n'
print 'Statistics of the abflat*.fits images .... \n'
for i in range(len(abflat)):
iraf.imstat(abflat[i])
print '\n Combining abflat images ....\n'
ablist = string.join(abflat,',')
iraf.imcombine(ablist,'superflat.fits')
iraf.imstat('superflat.fits')
print '\n .... done. \n'
print '\nCleaning ABflat*.fits images ....\n'
os.system('rm ABflat*.fits')
print '\n.... done!'
#Verify if the image was created:
output = glob.glob('superflat*.fits')
if len(output) != 0:
output = 0
else:
output = 1
#Return to original directory
os.chdir(original_path)
#last mensage
print '\n MASTERFLAT.FITS created! \n'
print '\n END of Data Reduction for create a masterflat.fits file. \n'
#obtain the value of return
if output == 1:
print '!!! ERROR/WARNING !!!'
print 'Check if the superbias was created or if there is more than one superbias image.'
return output
def lacos_im(_input, _output='clean.fits', outmask='mask.fits', gain=1.3, readn=9, xorder=9, yorder=9, sigclip=4.5, sigfrac=0.5, objlim=1, skyval=0, niter=2, verbose=True, interactive=False):
# print "LOGX:: Entering `lacos_im` method/function in %(__file__)s" %
# globals()
import ntt
from ntt.util import delete
import sys
import re
import os
import string
from pyraf import iraf
import numpy as np
iraf.convolve.bilinear = 'no'
iraf.convolve.radsym = 'no'
# make temporary files
oldoutput, galaxy, skymod, med5 = 'oldoutput.fits', 'galaxy.fits', 'skymod.fits', 'med5.fits'
blk, lapla, med3, med7, sub5, sigima, finalsel = 'blk.fits', 'lapla.fits', 'med3.fits', 'med7.fits', 'sub5.fits', 'sigima.fits', 'finalsel.fits'
deriv2, noise, sigmap, firstsel, starreject = 'deriv2.fits', 'noise.fits', 'sigmap.fits', 'firstsel.fits', 'starreject.fits'
inputmask, gfirstsel = 'inputmask.fits', 'gfirstsel.fits'
f = open('_kernel', 'w')
f.write('0 -1 0;\n-1 4 -1;\n0 -1 0')
f.close()
# create growth kernel
f = open('_gkernel', 'w')
f.write('1 1 1;\n1 1 1;\n1 1 1')
f.close()
gkernel = np.array([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
# initialize loop
usegain = gain
i = 1
stop = 'no'
previous = 0
if not _output:
_output = _input
arrayinput, headerinput = ntt.cosmics.fromfits(_input, verbose=False)
ntt.cosmics.tofits(outmask, np.float32(
arrayinput - arrayinput), headerinput, verbose=False)
delete(oldoutput)
if skyval > 0:
arrayoldoutput = arrayinput + skyval
else:
arrayoldoutput = arrayinput
ntt.cosmics.tofits(oldoutput, np.float32(
arrayoldoutput), headerinput, verbose=False)
# start iterations
while stop == 'no':
# take second-order derivative (Laplacian) of input image
# kernel is convolved with subsampled image, in order to remove negative
# pattern around high pixels
delete(blk)
delete(lapla)
delete(deriv2)
iraf.blkrep(oldoutput, blk, 2, 2)
iraf.convolve(blk, lapla, '_kernel')
iraf.imreplace(lapla, 0, upper=0, lower='INDEF', radius=0)
iraf.blkavg(lapla, deriv2, 2, 2, option="average")
delete(med5)
# create model of background flux - 5x5 box should exclude all CRs
iraf.median(oldoutput, med5, 5, 5, zlo='INDEF',
zhi='INDEF', verbose='no')
iraf.imreplace(med5, 0.0001, upper=0, lower='INDEF', radius=0)
# create noise model
delete(noise)
iraf.imutil.imexpr(expr='sqrt(a*' + str(usegain) + '+' + str(readn) +
'**2)/' + str(usegain), a=med5, output=noise, verbose='no')
# divide Laplacian by noise model
delete(sigmap)
iraf.imarith(deriv2, "/", noise, sigmap)
# Laplacian of blkreplicated image counts edges twice:
iraf.imarith(sigmap, "/", 2., sigmap)
# removal of large structure (bright, extended objects)
delete(med5)
iraf.median(sigmap, med5, 5, 5, zlo='INDEF', zhi='INDEF', verbose='no')
arraysigmap, headersigmap = ntt.cosmics.fromfits(sigmap, verbose=False)
arraymed5, headermed5 = ntt.cosmics.fromfits(med5, verbose=False)
arraysigmap = arraysigmap - arraymed5
iraf.imarith(sigmap, "-", med5, sigmap)
# find all candidate cosmic rays
# this selection includes sharp features such as stars and HII regions
delete(firstsel)
iraf.imcopy(sigmap, firstsel, verbose='no')
iraf.imreplace(firstsel, 0, upper=sigclip, lower='INDEF', radius=0)
iraf.imreplace(firstsel, 1, lower=0.1, upper='INDEF', radius=0)
# arraygfirst=arraysigmap
# arraygfirst = np.where(arraygfirst<sigclip,0,arraygfirst)
# arraygfirst = np.where(arraygfirst>0.1,1,arraygfirst)
# compare candidate CRs to median filtered image
# this step rejects bright, compact sources from the initial CR list
# subtract background and smooth component of objects
delete(med3)
delete(med7)
iraf.median(oldoutput, med3, 3, 3, zlo='INDEF',
zhi='INDEF', verbose='no')
iraf.median(med3, med7, 7, 7, zlo='INDEF', zhi='INDEF', verbose='no')
iraf.imarith(med3, "-", med7, med3)
iraf.imarith(med3, "/", noise, med3)
iraf.imreplace(med3, 0.01, upper=0.01, lower='INDEF', radius=0)
# compare CR flux to object flux
delete(starreject)
iraf.imutil.imexpr(expr="(a*b)/c", a=firstsel, b=sigmap,
c=med3, output=starreject, verbose='no')
# discard if CR flux <= objlim * object flux
iraf.imreplace(starreject, 0, upper=objlim, lower='INDEF', radius=0)
iraf.imreplace(starreject, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(firstsel, "*", starreject, firstsel)
# grow CRs by one pixel and check in original sigma map
delete(gfirstsel)
iraf.convolve(firstsel, gfirstsel, '_gkernel')
iraf.imreplace(gfirstsel, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(gfirstsel, "*", sigmap, gfirstsel)
iraf.imreplace(gfirstsel, 0, upper=sigclip, lower='INDEF', radius=0)
iraf.imreplace(gfirstsel, 1, lower=0.1, upper='INDEF', radius=0)
# grow CRs by one pixel and lower detection limit
sigcliplow = sigfrac * sigclip
delete(finalsel)
iraf.convolve(gfirstsel, finalsel, '_gkernel')
iraf.imreplace(finalsel, 1, lower=0.5, upper='INDEF', radius=0)
iraf.imarith(finalsel, "*", sigmap, finalsel)
iraf.imreplace(finalsel, 0, upper=sigcliplow, lower='INDEF', radius=0)
iraf.imreplace(finalsel, 1, lower=0.1, upper='INDEF', radius=0)
# determine number of CRs found in this iteration
delete(gfirstsel)
iraf.imutil.imexpr(expr="(1-b)*a", a=outmask,
b=finalsel, output=gfirstsel, verbose='no')
npix = iraf.imstat(gfirstsel, fields="npix",
lower=0.5, upper='INDEF', Stdout=1)
# create cleaned output image; use 3x3 median with CRs excluded
delete(med5)
iraf.imarith(outmask, "+", finalsel, outmask)
iraf.imreplace(outmask, 1, lower=1, upper='INDEF', radius=0)
delete(inputmask)
iraf.imutil.imexpr(expr="(1-10000*a)", a=outmask,
output=inputmask, verbose='no')
iraf.imarith(oldoutput, "*", inputmask, inputmask)
delete(med5)
iraf.median(inputmask, med5, 5, 5, zloreject=-
9999, zhi='INDEF', verbose='no')
iraf.imarith(outmask, "*", med5, med5)
if i > 1:
delete(_output)
delete(_output)
iraf.imutil.imexpr(expr="(1.-b)*a+c", a=oldoutput,
b=outmask, c=med5, output=_output, verbose='no')
# cleanup and get ready for next iteration
delete(oldoutput)
iraf.imcopy(_output, oldoutput, verbose='no')
if npix == 0:
stop = 'yes'
i = i + 1
if i > niter:
stop = 'yes'
# delete temp files
delete(blk + "," + lapla + "," + deriv2 + "," + med5)
delete(med3 + "," + med7 + "," + noise + "," + sigmap)
delete(firstsel + "," + starreject + "," + gfirstsel)
delete(finalsel + "," + inputmask)
if skyval > 0:
iraf.imarith(_output, "-", skyval, _output)
delete('_kernel' + "," + '_gkernel')
delete(oldoutput)
def create_masterbias(biasdir=None, channel='rc'):
'''
Combines slow and fast readout mode biases for the specified channel.
'''
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
if (biasdir == None) or biasdir=="": biasdir = "."
outs = "Bias_%s_slow.fits"%channel
outf = "Bias_%s_fast.fits"%channel
if (os.path.isfile(os.path.join(biasdir,outs)) and os.path.isfile(os.path.join(biasdir,outf))):
print "Master Bias exists!"
return
else:
print "Starting the Master Bias creation!"
os.chdir(biasdir)
lfastbias = []
lslowbias = []
#Select all filts that are Bias with same instrument
for f in glob.glob("*fits"):
try:
if (channel == fitsutils.get_par(f, "CHANNEL") and "BIAS" in str.upper(fitsutils.get_par(f, "OBJECT")) ):
if (fitsutils.get_par(f, "ADCSPEED")==2):
lfastbias.append(f)
else:
lslowbias.append(f)
except:
pass
print "Files for bias SLOW mode: ", lslowbias
print "Files for bias FAST mode: ", lfastbias
if len(lfastbias) > 0:
bfile_fast ="lbias_fast_"+channel
np.savetxt(bfile_fast, np.array(lfastbias), fmt="%s")
if (os.path.isfile("Bias_stats_fast")): os.remove("Bias_stats_fast")
iraf.imstat("@"+bfile_fast, Stdout="Bias_stats_fast")
st = np.genfromtxt("Bias_stats_fast", names=True, dtype=None)
print st
iraf.imcombine(input = "@"+bfile_fast, \
output = outf, \
combine = "median",\
scale = "mode")
os.remove(bfile_fast)
if len(lslowbias) > 0:
bfile_slow ="lbias_slow_"+channel
np.savetxt(bfile_slow, np.array(lslowbias), fmt="%s")
if (os.path.isfile("Bias_stats_slow")): os.remove("Bias_stats_slow")
iraf.imstat("@"+bfile_slow, Stdout="Bias_stats_slow")
st = np.genfromtxt("Bias_stats_slow", names=True, dtype=None)
print st
iraf.imcombine(input = "@"+bfile_slow, \
output = outs, \
combine = "median",\
scale = "mode")
os.remove(bfile_slow)
def science_reduction(input_file):
"""
Calibrate science images with masterflat (or superflat) and masterbias (or superbias) images.
___
INPUT:
For obtain this parameters, use the input_info function.
data_path: string, path where are the images data.
save_path: string, path where will save all reduced images.
input_file: dict, with information describe in the YAML file.
OUTPUT:
It is possible that the function return some of these values:
0. Create the masterflat image on the save_path.
1. It do not create the masterflat image, because of some erros.
"""
#name of the planet
planet = input_file['exoplanet']
#set original directory
original_path = os.getcwd()
save_path = input_file['save_path']
data_path = input_file['data_path']
#Change your directory to data diretory
os.chdir(data_path)
#list all flat images
exoplanet = glob.glob(planet+'*.fits')
print '\nLoading exoplanet images \nTotal of '+planet+'*.fits files = ',len(exoplanet),'\nFiles = \n'
print exoplanet
#if save_path exist, continue; if not, create.
if not os.path.exists(save_path):
os.makedirs(save_path)
#create a list of bias images and copy images to save_path
print '\nCopy science images to save_path directory to main reduction: ....'
os.system('cp '+planet+'*.fits '+save_path)
print '\n .... done. \n'
#change to save_path
os.chdir(save_path)
#create the names for exoplanet science mages with bias subtracted
bexoplanet = []
for i in exoplanet:
bexoplanet.append('B'+i)
#verify if previous superbias exist
if os.path.isfile('B'+i) == True:
os.system('rm B'+i)
print '\n Will be create this images: \n'
print bexoplanet
#exoplanet = string.join(exoplanet,',') #create the list string of exoplanet science images
#bexoplanet = string.join(bexoplanet,',')#create the list string of bexoplanet science images
print '\nSubtracting superbias.fits from all '+planet+'*.fits images ....\n'
for i in range(len(exoplanet)):
iraf.imarith(exoplanet[i],'-','superbias.fits',bexoplanet[i])
use.update_progress((i+1.)/len(bexoplanet))
print '\n.... cleaning '+planet+'*.fits images\n'
os.system('rm '+planet+'*.fits')
print '\n Statistics of B'+planet+'*.fits images: \n'
for i in range(len(bexoplanet)):
iraf.imstat(bexoplanet[i])
print '\nFlatfielding the B'+planet+'*.fits ....\n'
#create the names for exoplanet science images with bias subtracted and flatfielding
abexoplanet = []
for i in bexoplanet:
abexoplanet.append('A'+i)
#verify if previous superbias exist
if os.path.isfile('A'+i) == True:
os.system('rm A'+i)
print '\n Will be create this images: \n'
print abexoplanet
#flatifielding images
for i in range(len(abexoplanet)):
iraf.imarith(bexoplanet[i],'/','superflat.fits',abexoplanet[i])
use.update_progress((i+1.)/len(abexoplanet))
print '\n.... cleaning B'+planet+'*.fits images\n'
os.system('rm B'+planet+'*.fits')
print '\n Statistics of AB'+planet+'*.fits images: \n'
for i in range(len(abexoplanet)):
iraf.imstat(abexoplanet[i])
os.chdir(original_path) #change to save_path
return
#! /usr/bin/env python
from pyraf import iraf
iraf.images()
iraf.imstat("dev$pix")
def masterbias(input_file):
"""
Obtain the masterbias.fits image.
___
Input:
For obtain this parameters, use the input_info function.
data_path: string, path where are the images data.
save_path: string, path where will save all reduced images.
input_file: dict, with information describe in the YAML file.
Output:
It is possible that the function return some of these values:
0. Create the masterbias image on the save_path.
1. It do not create the masterbias image, because of some error
___
"""
#Set original directory
original_path = os.getcwd()
save_path = input_file['save_path']
data_path = input_file['data_path']
#Change your directory to data diretory
os.chdir(data_path)
#list all bias images
bias = glob.glob('bias*.fits')
print 'Loading bias images \nTotal of bias files = ',len(bias),'\nFiles = \n'
print bias
print '\nCreating superbias \n'
#if save_path exist, continue; if not, create.
if not os.path.exists(save_path):
os.makedirs(save_path)
#copy bias images to save_path
os.system('cp bias*.fits '+save_path)
#change to sabe_path
os.chdir(save_path)
#verify if previous superbias exist
if os.path.isfile('superbias.fits') == True:
os.system('rm superbias.fits')
#create the list of bias images
bias_list = string.join(bias,',')
#combine the bias image and create the superbias
iraf.imcombine(bias_list,'superbias.fits')
iraf.imstat('superbias.fits')
#clean previos bias files
print '\n Cleaning bias*.fits images ....\n'
os.system('rm bias*.fits')
print '\n.... done.'
#print output
#test of outpu value
#os.remove('superbias.fits')
#Verify if the image was created:
output = glob.glob('superbias*.fits')
if len(output) != 0:
output = 0
else:
output = 1
#Return to original directory
os.chdir(original_path)
#END of the masterbias reduction messsage
print '\nsuperbias.fits created!\n'
print '\nEND of superbias reduction!\n'
#obtain the value of return
if output == 1:
print '!!! ERROR/WARNING !!!'
print 'Check if the superbias was created or if there is more than one superbias image.'
return output