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 mosfire_geoxytrans(x_kfp,
y_kfp,
transform="final.pix2mm.4.972.120k",
database="ale/10March2011.4.972.db",
direction="forward"):
'''Conveninece wrapper around IRAF geoxytran'''
iraf.images()
path = os.path.join(os.environ["MOSPATH"], "platescale",
"10March2011.4.972.db")
database = path
pars = iraf.geoxytran.getParList()
iraf.geoxytran.unlearn()
ins = []
for i in range(len(x_kfp)):
ins.append("%f %f" % (x_kfp[i], y_kfp[i]))
results = iraf.geoxytran("STDIN",
"STDOUT",
Stdin=ins,
Stdout=1,
database=database,
transform=transform,
direction=direction)
iraf.geoxytran.setParList(pars)
poss = []
for result in results:
poss.append(np.array(result.split(), dtype=np.float64))
return np.array(poss)
def imarith(operand1, op, operand2, result, doAirmass=False):
from pyraf import iraf
iraf.images()
pars = iraf.imarith.getParList()
iraf.imcombine.unlearn()
try:
os.remove(result)
except:
pass
print "%s %s %s -> %s" % (operand1, op, operand2, result)
iraf.imarith(operand1=operand1, op=op, operand2=operand2, result=result)
iraf.imarith.setParList(pars)
if doAirmass:
# Adjust FITS header
with pf.open(operand1) as f:
am1 = f[0].header['airmass']
with pf.open(operand2) as f:
am2 = f[0].header['airmass']
of = pf.open(result)
of[0].header['airmass1'] = am1
of[0].header['airmass2'] = am2
of.writeto(result, clobber=True)
def mosfire_geoxytran(x_kfp,
y_kfp,
transform="final.pix2mm.4.972.120k",
database="/platescale/10March2011.4.972.db",
direction="forward"):
'''Conveninece wrapper around IRAF geoxytran'''
iraf.images()
path = os.path.join(os.environ["MOSPATH"], "platescale",
"10March2011.4.972.db")
database = path
pars = iraf.geoxytran.getParList()
iraf.geoxytran.unlearn()
t = iraf.geoxytran("STDIN",
"STDOUT",
Stdin=["%f %f" % (x_kfp, y_kfp)],
Stdout=1,
database=database,
transform=transform,
direction=direction)
iraf.geoxytran.setParList(pars)
(x, y) = np.array(t[0].split(), dtype=np.float64)
return (x, y)
def reduce_images(progargs):
"""Top level function to perform the reduction of data images.
The tasks are performed sequentially: generate all masterbias,
generate all masterflats and finally reduce data images.
Args:
progargs: Program arguments.
"""
logging.info("Starting the reduction of images ...")
# Load the images package and does not show any output.
iraf.images(_doprint=0)
# Generate all the average bias.
generate_all_masterbias(progargs.target_dir, progargs.bias_directory)
# Generate all the average dark.
generate_all_masterdark(progargs.target_dir, progargs.dark_directory,
progargs.bias_directory)
# Generate all the average flat.
generate_all_masterflats(progargs.target_dir, progargs.flat_directory,
progargs.dark_directory, progargs.bias_directory)
# Reduce all the data images applying the average bias and flats.
reduce_data_images(progargs.target_dir, progargs.light_directory,
progargs.dark_directory, progargs.bias_directory,
progargs.flat_directory)
logging.info("Finished the reduction of images.")
def test_whereis(tmpdir):
iraf.plot(_doprint=0)
iraf.images(_doprint=0)
outfile = str(tmpdir.join('output.txt'))
cases = ("pw", "lang", "st", "std", "stdg", "stdpl", "star", "star man",
"vi", "noao", "impl", "ls", "surf", "surf man", "smart man",
"img", "prot", "pro", "prow", "prows", "prowss", "dis", "no")
# Test the whereis function
for arg in cases:
args = arg.split(" ") # convert to a list
iraf.printf("--> whereis " + arg + '\n', StdoutAppend=outfile)
kw = {'StderrAppend': outfile}
iraf.whereis(*args, **kw) # catches stdout+err
with open(outfile) as f:
for line in f:
if 'task not found' in line:
continue
row = line.split()
if line.startswith('-->'):
cmd = row[2]
if len(row) > 3:
cmd2 = row[3]
elif row[0] == 'user.man' and cmd2 == 'man':
pass
else:
assert all([s.split('.')[1].startswith(cmd) for s in row]), \
'{}'.format(row)
def test_whereis(tmpdir):
iraf.plot(_doprint=0)
iraf.images(_doprint=0)
outfile = str(tmpdir.join('output.txt'))
cases = ("pw", "lang", "st", "std", "stdg", "stdpl", "star", "star man",
"vi", "noao", "impl", "ls", "surf", "surf man", "smart man",
"img", "prot", "pro", "prow", "prows", "prowss", "dis", "no")
# Test the whereis function
for arg in cases:
args = arg.split(" ") # convert to a list
iraf.printf("--> whereis " + arg + '\n', StdoutAppend=outfile)
kw = {'StderrAppend': outfile}
iraf.whereis(*args, **kw) # catches stdout+err
with open(outfile) as f:
for line in f:
if 'task not found' in line:
continue
row = line.split()
if line.startswith('-->'):
cmd = row[2]
if len(row) > 3:
cmd2 = row[3]
elif row[0] == 'user.man' and cmd2 == 'man':
pass
else:
assert all([s.split('.')[1].startswith(cmd) for s in row]), \
'{}'.format(row)
def __init__(self, datatype=None):
self.datatype=datatype
self.fn_pattern=re.compile(".*")
iraf.images()
iraf.proto()
self.statarea=StatArea
return
def imcombine(filelist,
out,
options,
bpmask=None,
reject="none",
nlow=None,
nhigh=None):
'''Convenience wrapper around IRAF task imcombine
Args:
filelist: The list of files to imcombine
out: The full path to the output file
options: Options dictionary
bpmask: The full path to the bad pixel mask
reject: none, minmax, sigclip, avsigclip, pclip
nlow,nhigh: Parameters for minmax rejection, see iraf docs
Returns:
None
Side effects:
Creates the imcombined file at location `out'
'''
#TODO: REMOVE Iraf and use python instead. STSCI Python has
# A builtin routine.
from pyraf import iraf
iraf.images()
filelist = [("%s[0]" % f) for f in filelist]
pars = iraf.imcombine.getParList()
iraf.imcombine.unlearn()
path = "flatcombine.lst"
f = open(path, "w")
for file in filelist:
f.write(file + "\n")
f.close()
s = ("%s," * len(filelist))[0:-1]
s = s % tuple(filelist)
f = open("flatcombinelog.txt", "w")
if reject == 'minmax':
t = iraf.imcombine("@%s" % path,
out,
Stdout=f,
reject=reject,
nlow=nlow,
nhigh=nhigh)
else:
t = iraf.imcombine(s, out, Stdin=filelist, Stdout=f, reject=reject)
f.close()
f = open("flatcombinelog.txt")
for line in f:
info(line.rstrip("\n"))
f.close()
iraf.imcombine.setParList(pars)
def display_image(img,frame,_z1,_z2,scale,_xcen=0.5,_ycen=0.5,_xsize=1,_ysize=1,_erase='yes'):
goon='True'
import glob, subprocess, os, time
ds9 = subprocess.Popen("ps -U {:d} u | grep -v grep | grep ds9".format(os.getuid()),shell=True,stdout=subprocess.PIPE).stdout.readlines()
if len(ds9)== 0 :
subproc = subprocess.Popen('ds9',shell=True)
time.sleep(3)
if glob.glob(img):
from pyraf import iraf
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
import string,os
if _z2:
try:
sss=iraf.display(img + '[0]', frame, xcen=_xcen, ycen=_ycen, xsize=_xsize, ysize=_ysize, erase=_erase,\
fill='yes', zscale='no', zrange='no', z1=_z1, z2=_z2,Stdout=1)
except:
print ''
print '### ERROR: PROBLEM OPENING DS9'
print ''
goon='False'
else:
try:
sss=iraf.display(img + '[0]', frame, xcen=_xcen, ycen=_ycen, xsize=_xsize, ysize=_ysize, erase=_erase, fill='yes', Stdout=1)
except:
print ''
print '### ERROR: PROBLEM OPENING DS9'
print ''
goon=False
if scale and goon:
answ0 = raw_input('>>> Cuts OK ? [y/n] ? [y] ')
if not answ0: answ0='y'
elif answ0=='no' or answ0=='NO': answ0='n'
while answ0=='n':
_z11=float(string.split(string.split(sss[0])[0],'=')[1])
_z22=float(string.split(string.split(sss[0])[1],'=')[1])
z11 = raw_input('>>> z1 = ? ['+str(_z11)+'] ? ')
z22 = raw_input('>>> z2 = ? ['+str(_z22)+'] ? ')
if not z11: z11=_z11
else: z11=float(z11)
if not z22: z22=_z22
else: z22=float(z22)
print z11,z22
sss=iraf.display(img + '[0]',frame,fill='yes', xcen=_xcen, ycen=_ycen, xsize=_xsize, ysize=_ysize, erase=_erase,\
zrange='no', zscale='no', z1=z11, z2=z22, Stdout=1)
answ0 = raw_input('>>> Cuts OK ? [y/n] ? [y] ')
if not answ0: answ0='y'
elif answ0=='no' or answ0=='NO': answ0='n'
if goon:
_z1,_z2=string.split(string.split(sss[0])[0],'=')[1],string.split(string.split(sss[0])[1],'=')[1]
else:
print 'Warning: image '+str(img)+' not found in the directory '
return _z1,_z2,goon
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 saveset_peakcoords(basename, threshold=100, hwhm=3):
"""
Attempts to find the peak of each saveset in
an already tprepared image.
Parameters:
-----------
basename : string
Everything before the .fits
threshold : number
Detection threshold for images.imcoords.starfind
in counts
Returns:
--------
c : numpy.array
Row stacked coordinates for each of the savesets
"""
#deleting files from previous runs
iraf.delete('*fits*obj*')
iraf.delete('images_ext.lst')
iraf.images()
iraf.imcoords()
exts = len(pf.open(basename + '.fits')) - 1
f = open('images_ext.lst', 'w')
for i in range(exts):
f.write(basename + '.fits[' + str(i + 1) + '][*,*,3]\n')
f.close()
iraf.starfind(r'@images_ext.lst', 'default', hwhm, threshold)
a = [basename + '.fits' + str(i + 1) + '.obj.1' for i in range(exts)]
b = [loadtxt(a[i]) for i in range(exts) if len(loadtxt(a[i])) != 0]
c = row_stack(b)
f = open(a[0], 'r')
d = f.readlines()
f.close()
header = [i for i in d if i[0] == '#']
f = open(basename + '_coords.dat', 'w')
for i in header:
f.write(i)
f.write('\n')
savetxt(f, c, fmt='%.3f')
f.close()
iraf.delete('*fits*obj*')
iraf.delete('images_ext.lst')
return c
def imarith(operand1, op, operand2, result):
from pyraf import iraf
iraf.images()
pars = iraf.imarith.getParList()
iraf.imcombine.unlearn()
print "%s %s %s -> %s" % (operand1, op, operand2, result)
iraf.imarith(operand1=operand1, op=op, operand2=operand2, result=result)
iraf.imarith.setParList(pars)
def imcombine(filelist, out, options, bpmask=None, reject="none", nlow=None,
nhigh=None):
'''Convenience wrapper around IRAF task imcombine
Args:
filelist: The list of files to imcombine
out: The full path to the output file
options: Options dictionary
bpmask: The full path to the bad pixel mask
reject: none, minmax, sigclip, avsigclip, pclip
nlow,nhigh: Parameters for minmax rejection, see iraf docs
Returns:
None
Side effects:
Creates the imcombined file at location `out'
'''
#TODO: REMOVE Iraf and use python instead. STSCI Python has
# A builtin routine.
from pyraf import iraf
iraf.images()
filelist = [("%s[0]" % f) for f in filelist]
pars = iraf.imcombine.getParList()
iraf.imcombine.unlearn()
path = "flatcombine.lst"
f = open(path, "w")
for file in filelist:
f.write(file + "\n")
f.close()
s = ("%s," * len(filelist))[0:-1]
s = s % tuple(filelist)
f = open("flatcombinelog.txt", "w")
if reject == 'minmax':
t = iraf.imcombine("@%s" % path, out, Stdout=f,
reject=reject, nlow=nlow, nhigh=nhigh)
else:
t = iraf.imcombine(s, out, Stdin=filelist, Stdout=f,
reject=reject)
f.close()
f=open("flatcombinelog.txt")
for line in f:
info(line.rstrip("\n"))
f.close()
iraf.imcombine.setParList(pars)
def get_ao_performance_data():
# Retrieve information from image headers
outfile = workdir + 'data/ao_perform.txt'
ir.images()
ir.imutil()
ir.hselect('*_img.fits', 'DATAFILE,DATE-OBS,UTC,AOLBFWHM,LGRMSWF', 'yes', Stdout=outfile)
# Retrieve MASS/DIMM data files
# get_mass_dimm.go('20081021') # this one doesn't exist
get_mass_dimm.go('20100815')
get_mass_dimm.go('20100828')
get_mass_dimm.go('20100829')
def imcombine(filelist, out, listfile=None, bpmask=None, reject="none",
nlow=None, nhigh=None):
"""Convenience wrapper around IRAF task imcombine
Args:
filelist (list of str): The list of files to imcombine
out (str): The full path to the output file
bpmask (str): The full path to the bad pixel mask
reject (str): none, minmax, sigclip, avsigclip, pclip
nlow,nhigh (int,int): Parameters for minmax rejection, see iraf docs
Returns:
None
Side effects:
Creates the imcombined file at location `out`
"""
#TODO: REMOVE Iraf and use python instead. STSCI Python has
# A builtin routine.
from pyraf import iraf
iraf.images()
filelist = [("%s[0]" % f) for f in filelist]
pars = iraf.imcombine.getParList()
iraf.imcombine.unlearn()
if listfile is None:
path = "flatcombine.lst"
else: path = listfile
f = open(path, "w")
for file in filelist:
f.write(file + "\n")
f.close()
s = ("%s," * len(filelist))[0:-1]
s = s % tuple(filelist)
if reject == 'minmax':
t = iraf.imcombine("@%s" % path, out, combine="average",
reject=reject, nlow=nlow, nhigh=nhigh)
elif reject == 'sigclip':
t = iraf.imcombine("@%s" % path, out, combine="average",
reject=reject, lsigma=nlow, hsigma=nhigh)
else:
t = iraf.imcombine(s, out, Stdin=filelist, Stdout=1, combine="average",
reject=reject)
iraf.imcombine.setParList(pars)
def sub_bias(image, combined_bias, image_b):
# Import IRAF modules:
iraf.images(_doprint=0)
iraf.imutil(_doprint=0)
parList = "bias_subtraction_imarith.par"
# Check input file and combined_bias frame exists before proceeding:
if os.path.isfile(image) == True:
if os.path.isfile(combined_bias) == True:
if os.path.isfile(parList) == True:
# Subtract combined bias frame from input frame (object or flat)
# using IRAF task imarithmetic:
iraf.imarith.setParList(ParList="bias_subtraction_imarith.par")
iraf.imarith(operand1=image, operand2=combined_bias, result=image_b)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Bias frame ' + str(combined_bias)
print 'subtracted from input ' + str(image)
print 'to create ' + str(image_b)
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Bias frame subtraction IRAF .par file '
print str(parList)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Combined bias frame '
print str(combined_bias)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Input frame '
print str(image)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
def image_shift(targetdir):
"""
Shift all B-A images by 48 pixels in y-direction
"""
print 'Target directory is ' + targetdir
print 'Shifting images...'
innames, outnames = [], []
for n in os.listdir(targetdir):
if (n.endswith('.fit')) & (n.startswith('dfcr')):
outnames.append(os.path.join(targetdir,'s' + n ) )
innames.append(os.path.join(targetdir,n) )
if os.path.exists( os.path.join(targetdir,'s' + n) ):
os.remove(os.path.join(targetdir,'s' + n))
print 'Removing file ' + os.path.join(targetdir,'s' + n)
with open(os.path.join(targetdir,'input.list'), 'w') as f:
for name in innames:
print name.replace(targetdir + '/','')
f.write( name + '\n' )
with open(os.path.join(targetdir,'output.list'), 'w') as f:
for name in outnames:
f.write( name + '\n' )
names = np.array([name.replace('dfcr','r') + '[1]' for name in innames])
decoff = np.asarray([iraf.hselect(f , 'DECOFF', 'yes', Stdout=1)[0] for f in names],dtype='float')
with open(os.path.join(targetdir,'shifts.lst'),'w') as f:
for d in decoff:
if d >= -1.0:
yshift = '0.0'
else:
yshift = '-48.0'
f.write('0.0 ' + yshift + '\n')
iraf.images(_doprint=0)
iraf.imgeom(_doprint=0)
iraf.imshift.setParam('input', '@' + os.path.join(targetdir, 'input.list'))
iraf.imshift.setParam('output', '@' + os.path.join(targetdir, 'output.list'))
iraf.imshift.setParam('shifts_file', os.path.join(targetdir,'shifts.lst') )
iraf.imshift()
return None
def marksn2(img,fitstab,frame=1,fitstab2='',verbose=False):
from pyraf import iraf
from numpy import array #,log10
import lsc
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.images(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.proto(_doprint=0)
iraf.set(stdimage='imt1024')
hdr=lsc.util.readhdr(fitstab)
_filter=lsc.util.readkey3(hdr,'filter')
column=lsc.lscabsphotdef.makecatalogue([fitstab])[_filter][fitstab]
rasex=array(column['ra0'],float)
decsex=array(column['dec0'],float)
if fitstab2:
hdr=lsc.util.readhdr(fitstab2)
_filter=lsc.util.readkey3(hdr,'filter')
_exptime=lsc.util.readkey3(hdr,'exptime')
column=lsc.lscabsphotdef.makecatalogue([fitstab2])[_filter][fitstab2]
rasex2=array(column['ra0'],float)
decsex2=array(column['dec0'],float)
iraf.set(stdimage='imt1024')
iraf.display(img + '[0]',frame,fill=True,Stdout=1)
vector=[]
for i in range(0,len(rasex)):
vector.append(str(rasex[i])+' '+str(decsex[i]))
xy = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,'STDIN',Stdin=list(xy),mark="circle",number='yes',label='no',radii=10,nxoffse=5,nyoffse=5,color=207,txsize=2)
if verbose:
# print 2.5*log10(_exptime)
for i in range(0,len(column['ra0'])):
print xy[i],column['ra0'][i],column['dec0'][i],column['magp3'][i],column['magp4'][i],column['smagf'][i],column['magp2'][i]
if fitstab2:
vector2=[]
for i in range(0,len(rasex2)):
vector2.append(str(rasex2[i])+' '+str(decsex2[i]))
xy1 = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector2,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,'STDIN',Stdin=list(xy1),mark="cross",number='yes',label='no',radii=10,nxoffse=5,nyoffse=5,color=205,txsize=2)
def Geoxy(path):
print "Starting Geoxy..."
filename = path
os.chdir(filename)
corrected = glob.glob("*.xy")
#Removing existing files
filelist = glob.glob("*.done")
for f in filelist:
os.remove(f)
#Transforming coordinates
print "Saving files with the new coordinates."
for i in corrected:
iraf.images(_doprint=0)
iraf.immatch(_doprint=0)
iraf.geoxy(input=i, output=i+'.done', database='data3', transforms='trans3')
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 test_which(tmpdir):
iraf.plot(_doprint=0)
iraf.images(_doprint=0)
outfile = str(tmpdir.join('output.txt'))
# To Test: normal case, disambiguation, ambiguous, not found, multiple
# inputs for a single command
cases = ("pw", "lang", "stdg", "stdp", "star", "star man", "vi", "noao",
"impl", "ls", "surf", "surface", "img", "pro", "prot", "prow",
"prows", "prowss", "dis")
# Test the which function
for arg in cases:
args = arg.split(" ") # convert to a list
iraf.printf("--> which " + arg + '\n', StdoutAppend=outfile)
kw = {"StderrAppend": outfile}
iraf.which(*args, **kw) # catches stdout+err
diff_outputs(outfile, 'data/cli_which_output.ref')
def init_iraf():
"""Initializes the pyraf environment. """
# The display of graphics is not used, so skips Pyraf graphics
# initialization and run in terminal-only mode to avoid warning messages.
os.environ['PYRAF_NO_DISPLAY'] = '1'
# Set PyRAF process caching off to avoid errors if spawning multiple
# processes.
iraf.prcacheOff()
# Load iraf packages and does not show any output of the tasks.
iraf.digiphot(_doprint=0)
iraf.apphot(_doprint=0)
iraf.images(_doprint=0)
# Set the iraf.phot routine to scripting mode.
iraf.phot.interactive = "no"
iraf.phot.verify = "no"
def init_iraf():
"""Initializes the pyraf environment. """
# The display of graphics is not used, so skips Pyraf graphics
# initialization and run in terminal-only mode to avoid warning messages.
os.environ['PYRAF_NO_DISPLAY'] = '1'
# Set PyRAF process caching off to avoid errors if spawning multiple
# processes.
iraf.prcacheOff()
# Load iraf packages and does not show any output of the tasks.
iraf.digiphot(_doprint = 0)
iraf.apphot(_doprint = 0)
iraf.images(_doprint = 0)
# Set the iraf.phot routine to scripting mode.
iraf.phot.interactive = "no"
iraf.phot.verify = "no"
def test_which(tmpdir):
iraf.plot(_doprint=0)
iraf.images(_doprint=0)
outfile = str(tmpdir.join('output.txt'))
# To Test: normal case, disambiguation, ambiguous, not found, multiple
# inputs for a single command
cases = ("pw", "lang", "stdg", "stdp", "star", "star man", "vi", "noao",
"impl", "ls", "surf", "surface", "img", "pro", "prot", "prow",
"prows", "prowss", "dis")
# Test the which function
for arg in cases:
args = arg.split(" ") # convert to a list
iraf.printf("--> which " + arg + '\n', StdoutAppend=outfile)
kw = {"StderrAppend": outfile}
iraf.which(*args, **kw) # catches stdout+err
diff_outputs(outfile, 'data/cli_which_output.ref')
def rvsao(bin_sci, task, template_spectra, rvsao_file, rvsao_bin_list, interactive="no", linesig=1.5,
czguess=0., st_lambda="INDEF", end_lambda="INDEF", badlines=None):
"""
Use the rvsao task emsao or xcsao to measure relative velocity from emission or absoption spectra
Note: make sure you select the parameters for your desired use. Refer to the IRAF rvsao package help for description
of what each parameter is/does
"""
assert task == 'xcsao' or task == 'emsao', "task is not either 'xcsao' or 'emsao'"
if os.path.exists(rvsao_file):
print('File {} already exists'.format(rvsao_file))
return
fixbad = "no"
if badlines:
fixbad = "yes"
bin_list = []
for i in range(len(glob.glob(bin_sci.format('*')))): # to ensure order is 0-61 (not 0, 1, 10, 11, etc)
bin_list.append(bin_sci.format(i))
assert len(bin_list) > 0, 'Absorption/emission(?) bin spectra do not exist: {}'.format(em_bin_sci.format('*'))
np.array(bin_list).tofile(rvsao_bin_list, sep='\n')
from pyraf import iraf
iraf.images()
iraf.rvsao()
if task == 'xcsao':
iraf.xcsao('@{}'.format(rvsao_bin_list), templates=bin_sci.format(template_spectra), report_mode=2,
logfiles=rvsao_file, displot=interactive, low_bin=10, top_low=20, top_nrun=80, nrun=211,
zeropad="yes", nzpass=1, curmode="no", pkmode=2, s_emchop="no", vel_init="guess", czguess=czguess,
st_lambda=st_lambda, end_lambda=end_lambda, fixbad=fixbad, badlines=badlines)
elif task == 'emsao':
# Run this interactively as the fit can fail often. Depending on S/N you may have to decrease the linesig
# to detect the lines, or if lines are matched inconsistently play with the st_lambda, end_lambda parameters
iraf.emsao('@{}'.format(rvsao_bin_list), logfiles=rvsao_file, displot=interactive, report_mode=2,
contsub_plot="no", st_lambda=st_lambda, end_lambda=end_lambda, # vel_init="guess", czguess=czguess,
linesig=linesig, fixbad=fixbad, badlines=badlines)
def mosfire_geoxytran(x_kfp, y_kfp, transform="final.pix2mm.4.972.120k",
database="/platescale/10March2011.4.972.db", direction="forward"):
'''Conveninece wrapper around IRAF geoxytran'''
iraf.images()
path = os.path.join(os.environ["MOSPATH"], "platescale",
"10March2011.4.972.db")
database = path
pars = iraf.geoxytran.getParList()
iraf.geoxytran.unlearn()
t = iraf.geoxytran("STDIN", "STDOUT", Stdin=["%f %f" % (x_kfp, y_kfp)], Stdout=1,
database=database,
transform=transform,
direction=direction)
iraf.geoxytran.setParList(pars)
(x,y) = np.array(t[0].split(), dtype=np.float64)
return (x,y)
def rotate(imlist_name, rotation):
"""
rotate and shift a list of images
Rotate input output rotation
Angle of rotation of the image in degrees. Positive angles will rotate the image counter-clockwise from the x axis.
In the case of SAO 1-m longslit data, 180 degree rotation is required for easy wavelength calibration.
"""
import glob
import os, sys
from pyraf import iraf
iraf.images()
iraf.imgeom()
imlist = glob.glob(imlist_name)
imlist.sort()
for i in range(len(imlist)):
inim = imlist[i]
print('Rotate ' + inim + ' with angle of ' + str(round(rotation, 3)))
iraf.rotate(input=inim, output='r' + inim, rotation=rotation)
print('r' + inim + ' is created.')
def cmd_imageCube(self, the_command):
'''This function can be used to pull a series of images from the camera and coadd them in a simple way.
This is slightly better process for measuring the position of a star for the purposes of guiding.
In essence, this will take 10 images, average them and create a master image for analysis to be perfomed on.'''
comands = str.split(the_command)
if len(comands) != 3:
return 'Please specify the name of the final image and the number of images to median through. Alternatively, specify "high" instead of the number of images to acquire a high enough number of average over scintilation.'
if comands[2] == 'high': nims = 30 #3E4/self.set_values[2]
else:
try:
nims = int(comands[2])
except Exception:
return 'Unable to convert number of images to integer'
#make upperlimit images and average combine them.
upperlimit = int(nims)
base_filename = comands[1]
if base_filename in commands.getoutput('ls program_images/'):
os.system('rm program_images/' + base_filename + '*')
print 'Starting to capture images'
capture = self.capture_images('program_images/' + base_filename,
upperlimit,
show=False)
if not capture: return 'ERROR capturing images'
print 'Finished capturing images'
self.check_if_file_exists('program_images/' + base_filename + '.fits')
self.check_if_file_exists('program_images/inlist')
iraf.images(_doprint=0)
os.system('ls program_images/' + base_filename + '_*.fits > inlist')
try:
iraf.imcombine(input='@inlist',
output='program_images/' + base_filename + '.fits',
combine='average',
reject='none',
outtype='integer',
scale='none',
zero='none',
weight='none')
except Exception:
return 'Could not combine images'
return 'Final image created. It is image program_images/' + base_filename + '.fits'
def flat_field(object_b, combined_flat_b_n, object_b_fn):
# Import IRAF modules:
iraf.images(_doprint=0)
iraf.imutil(_doprint=0)
# Check input file and combined_flat frame exist before proceeding:
if os.path.isfile(object_b) == True:
if os.path.isfile(combined_flat_b_n) == True:
# Divide bias-subtracted object frame by normalized, bias
# subtracted combined flat frame using IRAF task imarithmetic:
iraf.imarith(operand1=object_b, op="/", operand2=combined_flat_b_n, result=object_b_fn)
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Bias-subtracted object frame '
print str(object_b)
print 'successfully flat-fielded using division by '
print str(combined_flat_b_n)
print 'to create bias-subtracted, normalized flat-fielded '
print str(object_b_fn) + ' frame.'
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Combined flat frame '
print str(combined_flat_b_n)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
else:
print ' '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Input frame '
print str(object_b)
print 'does not exist. Exiting script. '
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print ' '
print ' '
sys.exit()
def reduce_images(progargs):
"""Top level function to perform the reduction of data images.
The tasks are performed sequentially: generate all masterbias,
generate all masterflats and finally reduce data images.
Args:
progargs: Program arguments.
"""
logging.info("Starting the reduction of images ...")
# Load the images package and does not show any output.
iraf.images(_doprint=0)
# Generate all the average bias.
generate_all_masterbias(progargs.target_dir,
progargs.bias_directory)
# Generate all the average dark.
generate_all_masterdark(progargs.target_dir,
progargs.dark_directory,
progargs.bias_directory)
# Generate all the average flat.
generate_all_masterflats(progargs.target_dir,
progargs.flat_directory,
progargs.dark_directory,
progargs.bias_directory)
# Reduce all the data images applying the average bias and flats.
reduce_data_images(progargs.target_dir,
progargs.light_directory,
progargs.dark_directory,
progargs.bias_directory,
progargs.flat_directory)
logging.info("Finished the reduction of images.")
def mosfire_geoxytrans(
x_kfp, y_kfp, transform="final.pix2mm.4.972.120k", database="ale/10March2011.4.972.db", direction="forward"
):
"""Conveninece wrapper around IRAF geoxytran"""
iraf.images()
path = os.path.join(os.environ["MOSPATH"], "platescale", "10March2011.4.972.db")
database = path
pars = iraf.geoxytran.getParList()
iraf.geoxytran.unlearn()
ins = []
for i in range(len(x_kfp)):
ins.append("%f %f" % (x_kfp[i], y_kfp[i]))
results = iraf.geoxytran(
"STDIN", "STDOUT", Stdin=ins, Stdout=1, database=database, transform=transform, direction=direction
)
iraf.geoxytran.setParList(pars)
poss = []
for result in results:
poss.append(np.array(result.split(), dtype=np.float64))
return np.array(poss)
def imarith(operand1, op, operand2, result, doAirmass=False):
from pyraf import iraf
iraf.images()
pars = iraf.imarith.getParList()
iraf.imcombine.unlearn()
try: os.remove(result)
except: pass
print "%s %s %s -> %s" % (operand1, op, operand2, result)
iraf.imarith(operand1=operand1, op=op, operand2=operand2, result=result)
iraf.imarith.setParList(pars)
if doAirmass:
# Adjust FITS header
with pf.open(operand1) as f:
am1 = f[0].header['airmass']
with pf.open(operand2) as f:
am2 = f[0].header['airmass']
of = pf.open(result)
of[0].header['airmass1'] = am1
of[0].header['airmass2'] = am2
of.writeto(result, clobber=True)
def shift_master(ref, master):
"""
Find the shift between two images.
returns the shift in y direction (along columns)
"""
#make a narow copy of the reference flat and masterflat
iraf.imcopy(input="../"+ref+"[1990:2010,*]", output="tmp/masterflat_ref.fitscut",Stdout="/dev/null")
iraf.imcopy(input="tmp/masterflat.fits[1990:2010,*]", output="tmp/masterflat.fitscut",Stdout="/dev/null")
#find the shift with correlation
iraf.images(_doprint=0,Stdout="/dev/null")
iraf.immatch(_doprint=0,Stdout="/dev/null")
shift=iraf.xregister(input="tmp/masterflat_ref.fitscut, tmp/masterflat.fitscut", reference="tmp/masterflat_ref.fitscut", regions='[*,*]', shifts="tmp/mastershift", output="", databasefmt="no", correlation="fourier", xwindow=3, ywindow=51, xcbox=21, ycbox=21, Stdout=1)
shift=float(shift[-2].split()[-2])
#Shift the list of apertures
f=open(master, "r")
o=open("database/aptmp_masterflat_s", "w")
for line in f:
l=line.split()
if len(l)>0 and l[0]=='begin':
l[-1]=str(float(l[-1])-shift)
o.write(l[0]+"\t"+l[1]+"\t"+"tmp/masterflat_s"+"\t"+l[3]+"\t"+l[4]+"\t"+l[5]+"\n")
elif len(l)>0 and l[0]=='center':
l[-1]=str(float(l[-1])-shift)
o.write("\t"+l[0]+"\t"+l[1]+"\t"+l[2]+"\n")
elif len(l)>0 and l[0]=='image':
o.write("\t"+"image"+"\t"+"tmp/masterflat_s"+"\n")
else:
o.write(line)
f.close()
o.close()
def makeflat(lista):
# print "LOGX:: Entering `makeflat` method/function in %(__file__)s" %
# globals()
flat = ''
import datetime
import glob
import os
import ntt
from ntt.util import readhdr, readkey3, delete, name_duplicate, updateheader, correctcard
from pyraf import iraf
iraf.images(_doprint=0)
iraf.imutil(_doprint=0)
iraf.imgeom(_doprint=0)
# iraf.blkavg(_doprint=0)
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.generic(_doprint=0)
toforget = ['imgeom.blkavg', 'imutil.imarith',
'immatch.imcombine', 'noao.imred']
for t in toforget:
iraf.unlearn(t)
import datetime
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
_date = readkey3(readhdr(lista[0]), 'date-night')
_filter = readkey3(readhdr(lista[0]), 'filter')
output = name_duplicate(
lista[3], 'flat_' + str(_date) + '_' + str(_filter) + '_' + str(MJDtoday), '')
if os.path.isfile(output):
answ = raw_input('file already prooduced, do again [y/[n]] ? ')
if not answ:
answ = 'n'
else:
answ = 'y'
if answ in ['yes', 'y', 'YES', 'Y', 'Yes']:
delete("temp_off.fits,temp_off_mask.fits,temp_on_mask.fits,temp_on.fits")
iraf.image.immatch.imcombine(
lista[0] + ',' + lista[7], output="temp_off.fits")
iraf.image.immatch.imcombine(
lista[1] + ',' + lista[6], output="temp_off_mask.fits")
iraf.image.immatch.imcombine(
lista[2] + ',' + lista[5], output="temp_on_mask.fits")
iraf.image.immatch.imcombine(
lista[3] + ',' + lista[4], output="temp_on.fits")
# create the bias correction for the flat-on according to the
# Lidman technique0
delete("temp_onA.fits,temp_onC.fits,temp_onB.fits,temp_onAC.fits,temp_onACB.fits,temp_onACB_2D.fits")
delete("temp_on_bias.fits")
iraf.imgeom.blkavg(
input="temp_on.fits[500:600,*]", output="temp_onA.fits", option="average", b1=101, b2=1)
iraf.imgeom.blkavg(
input="temp_on_mask.fits[500:600,*]", output="temp_onC.fits", option="average", b1=101, b2=1)
iraf.imgeom.blkavg(
input="temp_on_mask.fits[50:150,*]", output="temp_onB.fits", option="average", b1=101, b2=1)
iraf.imutil.imarith("temp_onA.fits", "-",
"temp_onC.fits", "temp_onAC.fits")
iraf.imutil.imarith("temp_onAC.fits", "+",
"temp_onB.fits", "temp_onACB.fits")
iraf.imgeom.blkrep(input="temp_onACB.fits",
output="temp_onACB_2D.fits", b1=1024, b2=1)
iraf.imutil.imarith("temp_on.fits", "-",
"temp_onACB_2D.fits", "temp_on_bias.fits")
# same as above for the flat-off
delete("temp_offA.fits,temp_offC.fits,temp_offB.fits,temp_offAC.fits,temp_offACB.fits,temp_offACB_2D.fits")
delete("temp_off_bias.fits")
iraf.imgeom.blkavg(
input="temp_off.fits[500:600,*]", output="temp_offA.fits", option="average", b1=101, b2=1)
iraf.imgeom.blkavg(
input="temp_off_mask.fits[500:600,*]", output="temp_offC.fits", option="average", b1=101, b2=1)
iraf.imgeom.blkavg(
input="temp_off_mask.fits[50:150,*]", output="temp_offB.fits", option="average", b1=101, b2=1)
iraf.imutil.imarith("temp_offA.fits", "-",
"temp_offC.fits", "temp_offAC.fits")
iraf.imutil.imarith("temp_offAC.fits", "+",
"temp_offB.fits", "temp_offACB.fits")
iraf.imgeom.blkrep(input="temp_offACB.fits",
output="temp_offACB_2D.fits", b1=1024, b2=1)
iraf.imutil.imarith("temp_off.fits", "-",
"temp_offACB_2D.fits", "temp_off_bias.fits")
# create the corrected flat-field
# output=name_duplicate("temp_on_bias.fits",'flat_'+str(_date)+'_'+str(_filter)+'_'+str(MJDtoday),'')
output = name_duplicate(
lista[3], 'flat_' + str(_date) + '_' + str(_filter) + '_' + str(MJDtoday), '')
# print lista[0],'flat_'+str(_date)+'_'+str(_filter)+'_'+str(MJDtoday)
delete(output)
iraf.imutil.imarith("temp_on_bias.fits", "-",
"temp_off_bias.fits", output)
iraf.noao.imred.generic.normalize(output) # normalize the flat-field
correctcard(output)
delete("temp_on*.fits") # delete the temporary images
delete("temp_off*.fits")
print 'flat -> ' + str(output)
else:
print 'skip redoing the flat'
return output
import shutil import numpy as np import matplotlib.pyplot as plt from datetime import date import operator import time import traceback from lmfit import minimize, Parameters, report_fit #import cosmics iraf.noao(_doprint=0,Stdout="/dev/null") iraf.rv(_doprint=0,Stdout="/dev/null") iraf.imred(_doprint=0,Stdout="/dev/null") iraf.ccdred(_doprint=0,Stdout="/dev/null") iraf.images(_doprint=0,Stdout="/dev/null") iraf.immatch(_doprint=0,Stdout="/dev/null") iraf.onedspec(_doprint=0,Stdout="/dev/null") iraf.twodspec(_doprint=0,Stdout="/dev/null") iraf.apextract(_doprint=0,Stdout="/dev/null") iraf.imutil(_doprint=0,Stdout="/dev/null") iraf.echelle(_doprint=0,Stdout="/dev/null") iraf.astutil(_doprint=0,Stdout="/dev/null") iraf.apextract.dispaxi=1 iraf.echelle.dispaxi=1 #fixes a bug with latest versions of iraf iraf.ccdred.instrum='blank.txt' os.environ['PYRAF_BETA_STATUS'] = '1'
def run_imstat(input):
iraf.images()
for image in input:
iraf.imstat(image)
def telluric_atmo(imgstd):
# print "LOGX:: Entering `telluric_atmo` method/function in %(__file__)s"
# % globals()
import numpy as np
import ntt
from pyraf import iraf
try:
import pyfits
except:
from astropy.io import fits as pyfits
iraf.images(_doprint=0)
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
toforget = [
'imfilter.gauss', 'specred.apall', 'longslit.identify',
'longslit.reidentify', 'specred.standard', 'onedspec.wspectext'
]
for t in toforget:
iraf.unlearn(t)
_grism = ntt.util.readkey3(ntt.util.readhdr(imgstd), 'grism')
imgout = 'invers_atmo_' + imgstd
ntt.util.delete(imgout)
iraf.set(direc=ntt.__path__[0] + '/')
_cursor = 'direc$standard/ident/cursor_sky_0'
iraf.noao.onedspec.bplot(imgstd,
cursor=_cursor,
spec2=imgstd,
new_ima=imgout,
overwri='yes')
xxstd, ffstd = ntt.util.readspectrum(imgout)
if _grism in ['Gr13', 'Gr16']:
llo2 = np.compress(
(np.array(xxstd) >= 7550) & (np.array(xxstd) <= 7750),
np.array(xxstd))
llh2o = np.compress(
(np.array(xxstd) >= 7100) & (np.array(xxstd) <= 7500),
np.array(xxstd))
ffo2 = np.compress(
(np.array(xxstd) >= 7550) & (np.array(xxstd) <= 7750),
np.array(ffstd))
ffh2o = np.compress(
(np.array(xxstd) >= 7100) & (np.array(xxstd) <= 7500),
np.array(ffstd))
elif _grism in ['Gr11']:
llo2 = np.compress(
(np.array(xxstd) >= 6830) & (np.array(xxstd) <= 7100),
np.array(xxstd))
llh2o = np.compress(
(np.array(xxstd) >= 7100) & (np.array(xxstd) <= 7500),
np.array(xxstd))
ffo2 = np.compress(
(np.array(xxstd) >= 6830) & (np.array(xxstd) <= 7100),
np.array(ffstd))
ffh2o = np.compress(
(np.array(xxstd) >= 7100) & (np.array(xxstd) <= 7500),
np.array(ffstd))
if _grism in ['Gr13', 'Gr16', 'Gr11']:
_skyfileh2o = 'direc$standard/ident/ATLAS_H2O.fits'
_skyfileo2 = 'direc$standard/ident/ATLAS_O2.fits'
atlas_smooto2 = '_atlas_smoot_o2.fits'
atlas_smooth2o = '_atlas_smoot_h2o.fits'
_sigma = 200
ntt.util.delete(atlas_smooto2)
ntt.util.delete(atlas_smooth2o)
iraf.imfilter.gauss(_skyfileh2o, output=atlas_smooth2o, sigma=_sigma)
iraf.imfilter.gauss(_skyfileo2, output=atlas_smooto2, sigma=_sigma)
llskyh2o, ffskyh2o = ntt.util.readspectrum(atlas_smooth2o)
llskyo2, ffskyo2 = ntt.util.readspectrum(atlas_smooto2)
ffskyo2cut = np.interp(llo2, llskyo2, ffskyo2)
ffskyh2ocut = np.interp(llh2o, llskyh2o, ffskyh2o)
_scaleh2o = []
integral_h2o = []
for i in range(1, 21):
j = 0.6 + i * 0.04
_ffskyh2ocut = list((np.array(ffskyh2ocut) * j) + 1 - j)
diff_h2o = abs(_ffskyh2ocut - ffh2o)
integraleh2o = np.trapz(diff_h2o, llh2o)
integral_h2o.append(integraleh2o)
_scaleh2o.append(j)
_scaleo2 = []
integral_o2 = []
for i in range(1, 21):
j = 0.6 + i * 0.04
_ffskyo2cut = list((np.array(ffskyo2cut) * j) + 1 - j)
diff_o2 = abs(_ffskyo2cut - ffo2)
integraleo2 = np.trapz(diff_o2, llo2)
integral_o2.append(integraleo2)
_scaleo2.append(j)
sh2o = _scaleh2o[np.argmin(integral_h2o)]
so2 = _scaleo2[np.argmin(integral_o2)]
telluric_features = ((np.array(ffskyh2o) * sh2o) + 1 - sh2o) + (
(np.array(ffskyo2) * so2) + 1 - so2) - 1
telluric_features = np.array([1] + list(telluric_features) + [1])
llskyo2 = np.array([1000] + list(llskyo2) + [15000])
telluric_features_cut = np.interp(xxstd, llskyo2, telluric_features)
_imgout = 'atmo_' + imgstd
data1, hdr = pyfits.getdata(imgstd, 0, header=True)
data1[0] = np.array(telluric_features_cut)
data1[1] = data1[1] / data1[1]
data1[2] = data1[2] / data1[2]
data1[3] = data1[3] / data1[3]
ntt.util.delete(_imgout)
pyfits.writeto(_imgout, np.float32(data1), hdr)
ntt.util.delete(atlas_smooto2)
ntt.util.delete(atlas_smooth2o)
ntt.util.delete(imgout)
else:
_imgout = ''
print '### telluric correction with model not possible '
return _imgout
import pyraf from pyraf import iraf import copy, os, shutil, glob, sys, string, re, math, operator, time import pyfits from types import * from mx.DateTime import * from iqpkg import * import ephem # Necessary packages iraf.images() iraf.immatch() iraf.imfilter() iraf.noao() iraf.imred() iraf.ccdred() iraf.digiphot() iraf.apphot() yes=iraf.yes no=iraf.no INDEF=iraf.INDEF hedit=iraf.hedit imgets=iraf.imgets imcombine=iraf.imcombine pyrafdir="python/pyraf/" pyrafdir_key='PYRAFPARS' if os.environ.has_key(pyrafdir_key):
# ------------------------------------------------------------------------------------------------------------------- #
# Paths Of Files & Directories To Be Used
# ------------------------------------------------------------------------------------------------------------------- #
DIR_CURNT = os.getcwd()
DIR_HOME = "/home/Avinash/Dropbox/PyCharm/Reduction_Pipeline/"
DIR_PHOT = "/home/Avinash/Supernovae_Data/Photometry/"
DIR_SPECS = "/home/Avinash/Supernovae_Data/Final_Spectra/"
FILE_BANDPASS = os.path.join(DIR_HOME, "Filter_BPF.asc")
list_paths = [DIR_HOME, DIR_PHOT, DIR_SPECS, FILE_BANDPASS]
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Load IRAF Packages
# ------------------------------------------------------------------------------------------------------------------- #
iraf.noao(_doprint=0)
iraf.images(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.onedspec(_doprint=0)
# ------------------------------------------------------------------------------------------------------------------- #
# ------------------------------------------------------------------------------------------------------------------- #
# Functions For File Handling
# ------------------------------------------------------------------------------------------------------------------- #
def remove_file(file_name):
"""
Removes the file "file_name" in the constituent directory.
Args:
file_name : Name of the file to be removed from the current directory
Returns:
def run_imstat(input):
iraf.images()
for image in input:
iraf.imstat(image)
#! /usr/bin/env python
from pyraf import iraf
iraf.images()
iraf.imstat("dev$pix")
def plant_kbos(filename, psf, kbos, shifts, prefix):
"""
Add KBOs to an image
:param filename: name of the image to add KBOs to
:param psf: Point Spread Function in IRAF/DAOPHOT format, used by ADDSTAR
:param kbos: list of KBOs to add, has format as returned by KBOGenerator
:param shifts: dictionary to transform coordinates to reference frame.
:param prefix: an estimate FWHM of the image, used to determine trailing.
:return: None
"""
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.images()
if shifts['nmag'] < 4:
logging.warning("Mag shift based on fewer than 4 common stars.")
fd = open("plant.WARNING", 'a')
fd.write("Mag shift based on fewer than 4 common stars.")
fd.close()
if shifts['emag'] > 0.05:
logging.warning("Mag shift has large uncertainty.")
fd = open("plant.WARNING", 'a')
fd.write("Mag shift hsa large uncertainty.")
fd.close()
addstar = tempfile.NamedTemporaryFile(suffix=".add", mode='w')
# transform KBO locations to this frame using the shifts provided.
w = get_wcs(shifts)
header = fits.open(filename)[0].header
# set the rate of motion in units of pixels/hour instead of ''/hour
scale = header['PIXSCAL1']
rate = kbos['sky_rate'] / scale
# compute the location of the KBOs in the current frame.
# offset magnitudes from the reference frame to the current one.
mag = kbos['mag'] - shifts['dmag']
angle = radians(kbos['angle'])
# Move the x/y locations to account for the sky motion of the source.
x = kbos['x'] - rate * 24.0 * shifts['dmjd'] * cos(angle)
y = kbos['y'] - rate * 24.0 * shifts['dmjd'] * sin(angle)
x, y = w.wcs_world2pix(x, y, 1)
# Each source will be added as a series of PSFs so that a new PSF is
# added for each pixel the source moves.
itime = float(header['EXPTIME']) / 3600.0
npsf = fabs(rint(rate * itime)) + 1
mag += 2.5 * log10(npsf)
dt_per_psf = itime / npsf
# Build an addstar file to be used in the planting of source.
idx = 0
for record in transpose([x, y, mag, npsf, dt_per_psf, rate, angle]):
x = record[0]
y = record[1]
mag = record[2]
npsf = record[3]
dt = record[4]
rate = record[5]
angle = record[6]
for i in range(int(npsf)):
idx += 1
x += dt * rate * math.cos(angle)
y += dt * rate * math.sin(angle)
addstar.write("{} {} {} {}\n".format(x, y, mag, idx))
addstar.flush()
fk_image = prefix + filename
try:
os.unlink(fk_image)
except OSError as err:
if err.errno == errno.ENOENT:
pass
else:
raise
# add the sources to the image.
if os.access(f'{fk_image}.art', os.R_OK):
os.unlink(f'{fk_image}.art')
iraf.daophot.addstar(filename,
addstar.name,
psf,
fk_image,
simple=True,
verify=False,
verbose=False)
# convert the image to short integers.
iraf.images.chpix(fk_image, fk_image, 'ushort')
def image_combine(targetdir):
"""
Combine all images. Need to run image_shift before.
"""
print 'Target directory is ' + targetdir
print 'Combining images...'
innames = []
for n in os.listdir(targetdir):
if (n.endswith('.fit')) & (n.startswith('sdfcr')):
innames.append(os.path.join(targetdir,n) )
with open(os.path.join(targetdir,'input.list'), 'w') as f:
for name in innames:
print name.replace(targetdir + '/','')
f.write( name + '\n' )
if os.path.exists( os.path.join(targetdir,'imcomb.fit')):
os.remove(os.path.join(targetdir,'imcomb.fit'))
print 'Removing file ' + os.path.join(targetdir,'imcomb.fit')
iraf.images(_doprint=0)
iraf.immatch(_doprint=0)
iraf.imcombine.setParam('input','@' + os.path.join(targetdir, 'input.list'))
iraf.imcombine.setParam('output',os.path.join(targetdir,'imcomb.fit') )
iraf.imcombine.setParam('combine','average')
iraf.imcombine.setParam('reject','sigclip')
iraf.imcombine.setParam('lsigma',3.0)
iraf.imcombine.setParam('hsigma',3.0)
iraf.imcombine()
# Or, combine without background subtraction
if os.path.exists( os.path.join(targetdir,'imcomb+bkgd.fit')):
os.remove( os.path.join(targetdir, 'imcomb+bkgd.fit') )
print 'Removing file ' + os.path.join(targetdir, 'imcomb+bkgd.fit')
hdulist = fits.open( os.path.join( targetdir, 'imcomb.fit'))
data = hdulist[0].data
hdulist.close()
# import matplotlib.pyplot as plt
# from spectra.range_from_zscale import range_from_zscale
# fig, ax = plt.subplots()
# z1, z2, iteration = range_from_zscale(data)
# ax.imshow(data,cmap='gray',vmin=z1,vmax=z2)
# plt.gca().invert_yaxis()
data = np.concatenate( ( data[32:345,:], data[520:650,:], data[740:940,:]))
sigmaspec = np.std( data, axis = 0)
# fig, ax = plt.subplots()
# ax.plot( sigmaspec, lw=1 )
# Convert sky from adu to electron
skyspec = (4 * sigmaspec)**2
sky_adu = skyspec / 4.0
# add to each pixel
hdulist = fits.open( os.path.join( targetdir, 'imcomb.fit'))
data = hdulist[0].data
hdulist.close()
sky_adu = np.repeat(sky_adu, np.shape(data)[0] ).reshape( np.shape(data)[::-1] ).T
data = data + sky_adu
hdu = fits.PrimaryHDU(data)
hdu.writeto( os.path.join( targetdir, 'imcomb+bkgd.fit') )
return None
def execute():
################NGC1023####################################################
## use GALFIT to fit and to create a model disk vs bulge##
##galfit.feedme##
iraf.images()
# path to fits files:
total = config.iraf_input_dir+config.totalfits
bulge = config.iraf_input_dir+config.bulgefits
fraction = config.iraf_tmp_dir+config.fractionfits
input_dir = config.iraf_input_dir
tmp_dir = config.iraf_tmp_dir
if not os.path.exists(tmp_dir):
os.mkdir(tmp_dir)
if os.path.exists(fraction):
iraf.imdelete(fraction)
print fraction
print bulge
# Makes the fraction image by dividing the bulge by the total:
iraf.imarith(bulge, "/", total, fraction)
""" LLT: I don't think we need this anymore:
if os.path.exists(tmp_dir+"snb.fits"):
iraf.module.imdelete(tmp_dir+"snb.fits")
if os.path.exists(tmp_dir+"tnb.fits"):
iraf.module.imdelete(tmp_dir+"tnb.fits")
iraf.module.imcopy(bulge, tmp_dir+"snb.fits")
iraf.module.imcopy(total, tmp_dir+"tnb.fits")
"""
##########################################################################
##Note: the coordinate in pixel as obtained from RA and Dec differ from the
##pixels one because of distorsion corrections as done with astrometry==>
##RA and Dec coordinate in arcsec are the correct one and don't need do be
##transfer in pixels
############################################################################
##Consistency check
#displ(image="../fits_inputs/galaxy.fits",frame=1)
#tvmark(frame=1,coords="Kall.dat",color=205,lab-,num+)
#displ(image="f.fits",zrange=no,zscale=no,ztrans="linear",z1=0,z2=1,frame=3)
#tvmark(frame=3,coords="Kall.dat",color=201,mark="rectangle",lengths="6",lab-,num-)
###########################################################################
##Use program overplot.sm to create both Kall.dat & compagna.dat
##in the RA & Dec of PNS there is a rotation of 90deg respect to the pixel
## x-->-x
##after long reflexion I decide the right PA is 90-47.11=42.89 (just turning the PNe as observed on the R image)
##########################################################################
############################################################################
## to measure the relative flux use phot (daophot) ##with centerpars=none!!!###
##I used sigma=stdv(skys)=0 ===> no bg
##fwhmpsf=2.27 as in galfit psf2.fits (imexam A enclosed)
##skyvalue=0 ===> no bg
##no centerpars
##apertures=3.0 pixels (=1.8 arcsec)
##zmag=25
##snb.fits & tnb.fits without bg (when I made the model)
# filenames
silentremove(config.bulgemag)
silentremove(config.bulgetxt)
silentremove(config.totalmag)
silentremove(config.totaltxt)
silentremove(config.fractionmag)
silentremove(config.fractiontxt)
iraf.noao(_doprint=False)
iraf.digiphot(_doprint=False)
iraf.daophot(_doprint=False)
iraf.phot(bulge,config.position_file, config.bulgemag, skyfile="", datapars="",scale=1.,fwhmpsf=2.27,emissio="yes",sigma=0.0,datamin=-100, datamax=1e5,noise="poisson", centerpars="",calgorithm="none",cbox=0,cthreshold=0.,minsnratio=1., cmaxiter=0,maxshift=0,clean="no",rclean=0.,rclip=0.,kclean=0., mkcenter="no", fitskypars="",salgorithm="constant",annulus=10,dannulus=10,skyvalue=0., smaxiter=10,sloclip=0.,shiclip=0.,snreject=50,sloreject=3., shireject=3.,khist=3.,binsize=0.1,smooth="no",rgrow=0., mksky="no", photpars="",weighting="constant",apertures=3,zmag=0.,mkapert="no", interactive="no",radplots="no",verify="no",update="no",verbose="no", graphics="stdgraph",display="stdimage",icommands="",gcommands="")
# LLT: Corrected to pyraf, use stdout = instead of >>
iraf.txdump(config.bulgemag, "XCENTER,YCENTER,STDEV,FLUX,SUM,AREA,ID", "yes", headers="no",paramet="no", Stdout=config.bulgetxt)
iraf.phot(total, config.position_file, config.totalmag, skyfile="", datapars="",scale=1.,fwhmpsf=2.27,emissio="yes",sigma=0.0,datamin=-100, datamax=1e5,noise="poisson", centerpars="",calgorithm="none",cbox=0,cthreshold=0.,minsnratio=1., cmaxiter=0,maxshift=0.,clean="no",rclean=0.,rclip=0.,kclean=0., mkcenter="no", fitskypars="",salgorithm="constant",annulus=10,dannulus=10,skyvalue=0., smaxiter=10,sloclip=0.,shiclip=0.,snreject=50,sloreject=3., shireject=3.,khist=3.,binsize=0.1,smooth="no",rgrow=0., mksky="no", photpars="",weighting="constant",apertures=3,zmag=25.,mkapert="no", interactive="no",radplots="no",verify="no",update="no",verbose="no", graphics="stdgraph",display="stdimage",icommands="",gcommands="")
iraf.txdump(config.totalmag, "XCENTER,YCENTER,STDEV,FLUX,SUM,AREA,ID", "yes", headers="no",paramet="no", Stdout=config.totaltxt)
iraf.phot(fraction,config.position_file, config.fractionmag, skyfile="", datapars="", scale=1., fwhmpsf=2.27, emissio="no", sigma=0.0, datamin=-100, datamax=1e5, noise="poisson", centerpars="", calgorithm="none", cbox=0, cthreshold=0., minsnratio=1., cmaxiter=0, maxshift=0., clean="no", rclean=0., rclip=0., kclean=0., mkcenter="no", fitskypars="", salgorithm="constant", annulus=10, dannulus=10, skyvalue=0., smaxiter=10, sloclip=0., shiclip=0., snreject=50, sloreject=3., shireject=3., khist=3., binsize=0.1, smooth="no", rgrow=0., mksky="no", photpars="", weighting="constant", apertures=3, zmag=25., mkapert="no", interactive="no", radplots="no", verify="no", update="no", verbose="no", graphics="stdgraph", display="stdimage", icommands="", gcommands="")
iraf.txdump(config.fractionmag, "XCENTER,YCENTER,STDEV,FLUX,SUM,AREA,ID", "yes", headers="no",paramet="no", Stdout=config.fractiontxt)
def telluric_atmo(imgstd):
# print "LOGX:: Entering `telluric_atmo` method/function in %(__file__)s"
# % globals()
import numpy as np
import ntt
from pyraf import iraf
try: import pyfits
except: from astropy.io import fits as pyfits
iraf.images(_doprint=0)
iraf.noao(_doprint=0)
iraf.twodspec(_doprint=0)
iraf.longslit(_doprint=0)
iraf.onedspec(_doprint=0)
toforget = ['imfilter.gauss', 'specred.apall', 'longslit.identify', 'longslit.reidentify', 'specred.standard',
'onedspec.wspectext']
for t in toforget:
iraf.unlearn(t)
_grism = ntt.util.readkey3(ntt.util.readhdr(imgstd), 'grism')
imgout = 'invers_atmo_' + imgstd
ntt.util.delete(imgout)
iraf.set(direc=ntt.__path__[0] + '/')
_cursor = 'direc$standard/ident/cursor_sky_0'
iraf.noao.onedspec.bplot(imgstd, cursor=_cursor,
spec2=imgstd, new_ima=imgout, overwri='yes')
xxstd, ffstd = ntt.util.readspectrum(imgout)
if _grism in ['Gr13', 'Gr16']:
llo2 = np.compress((np.array(xxstd) >= 7550) & (
np.array(xxstd) <= 7750), np.array(xxstd))
llh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(xxstd))
ffo2 = np.compress((np.array(xxstd) >= 7550) & (
np.array(xxstd) <= 7750), np.array(ffstd))
ffh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(ffstd))
elif _grism in ['Gr11']:
llo2 = np.compress((np.array(xxstd) >= 6830) & (
np.array(xxstd) <= 7100), np.array(xxstd))
llh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(xxstd))
ffo2 = np.compress((np.array(xxstd) >= 6830) & (
np.array(xxstd) <= 7100), np.array(ffstd))
ffh2o = np.compress((np.array(xxstd) >= 7100) & (
np.array(xxstd) <= 7500), np.array(ffstd))
if _grism in ['Gr13', 'Gr16', 'Gr11']:
_skyfileh2o = 'direc$standard/ident/ATLAS_H2O.fits'
_skyfileo2 = 'direc$standard/ident/ATLAS_O2.fits'
atlas_smooto2 = '_atlas_smoot_o2.fits'
atlas_smooth2o = '_atlas_smoot_h2o.fits'
_sigma = 200
ntt.util.delete(atlas_smooto2)
ntt.util.delete(atlas_smooth2o)
iraf.imfilter.gauss(_skyfileh2o, output=atlas_smooth2o, sigma=_sigma)
iraf.imfilter.gauss(_skyfileo2, output=atlas_smooto2, sigma=_sigma)
llskyh2o, ffskyh2o = ntt.util.readspectrum(atlas_smooth2o)
llskyo2, ffskyo2 = ntt.util.readspectrum(atlas_smooto2)
ffskyo2cut = np.interp(llo2, llskyo2, ffskyo2)
ffskyh2ocut = np.interp(llh2o, llskyh2o, ffskyh2o)
_scaleh2o = []
integral_h2o = []
for i in range(1, 21):
j = 0.6 + i * 0.04
_ffskyh2ocut = list((np.array(ffskyh2ocut) * j) + 1 - j)
diff_h2o = abs(_ffskyh2ocut - ffh2o)
integraleh2o = np.trapz(diff_h2o, llh2o)
integral_h2o.append(integraleh2o)
_scaleh2o.append(j)
_scaleo2 = []
integral_o2 = []
for i in range(1, 21):
j = 0.6 + i * 0.04
_ffskyo2cut = list((np.array(ffskyo2cut) * j) + 1 - j)
diff_o2 = abs(_ffskyo2cut - ffo2)
integraleo2 = np.trapz(diff_o2, llo2)
integral_o2.append(integraleo2)
_scaleo2.append(j)
sh2o = _scaleh2o[np.argmin(integral_h2o)]
so2 = _scaleo2[np.argmin(integral_o2)]
telluric_features = ((np.array(ffskyh2o) * sh2o) +
1 - sh2o) + ((np.array(ffskyo2) * so2) + 1 - so2) - 1
telluric_features = np.array([1] + list(telluric_features) + [1])
llskyo2 = np.array([1000] + list(llskyo2) + [15000])
telluric_features_cut = np.interp(xxstd, llskyo2, telluric_features)
_imgout = 'atmo_' + imgstd
data1, hdr = pyfits.getdata(imgstd, 0, header=True)
data1[0] = np.array(telluric_features_cut)
data1[1] = data1[1] / data1[1]
data1[2] = data1[2] / data1[2]
data1[3] = data1[3] / data1[3]
ntt.util.delete(_imgout)
pyfits.writeto(_imgout, np.float32(data1), hdr)
ntt.util.delete(atlas_smooto2)
ntt.util.delete(atlas_smooth2o)
ntt.util.delete(imgout)
else:
_imgout = ''
print '### telluric correction with model not possible '
return _imgout
def makeillumination(lista,flatfield):#,outputfile,illum_frame):
import os,glob,string,re
from astropy.io import fits as pyfits
import ntt
from ntt.util import readhdr, readkey3, delete, display_image, defsex, name_duplicate, correctcard
from numpy import compress, array, argmax, argmin, min, argsort, float32
import datetime
MJDtoday = 55927 + (datetime.date.today() - datetime.date(2012, 01, 01)).days
_date = readkey3(readhdr(lista[0]), 'date-night')
_filter = readkey3(readhdr(lista[0]), 'filter')
illum_frame = name_duplicate(
lista[0], 'illum_' + _date + '_' + _filter + '_' + str(MJDtoday), '')
from pyraf import iraf
iraf.images(_doprint=0)
iraf.imutil(_doprint=0)
iraf.utilities(_doprint=0)
iraf.noao(_doprint=0)
iraf.imred(_doprint=0)
iraf.ccdred(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.generic(_doprint=0)
toforget = ['digiphot.daophot', 'imutil.imarith',
'image', 'utilities.surfit']
for t in toforget:
iraf.unlearn(t)
n = len(lista)
# start loop to read image names from the input file
lista1 = []
iraf.ccdred.verbose = 'no'
ff = open('templist.lst', 'w')
for i in range(0, len(lista)):
ff.write('C' + lista[i] + '\n')
delete('C' + lista[i])
delete('C' + re.sub('.fits', '_sub.fits', lista[i]))
ntt.sofiphotredudef.crosstalk(lista[i], 'C' + lista[i])
iraf.noao.imred.ccdred.ccdproc('C' + lista[i], output='', overscan="no", trim="yes", ccdtype='', darkcor='no', fixpix='no', zerocor="no", flatcor='yes',
illumco='no', trimsec='[1:1024,1:1007]', biassec='', flat=flatfield, illum='')
correctcard('C' + lista[i])
lista1.append('C' + lista[i])
ff.close()
print '\n### prereducing STD frames to compute illumination correction ........'
lista2, skyfile = ntt.sofiphotredudef.skysub(lista1, readkey3(
readhdr(lista1[0]), 'ron'), readkey3(readhdr(lista1[0]), 'gain'), True)
lista2 = ntt.sofiphotredudef.sortbyJD(lista2)
print '\n### use x on the star and q to continue....'
display_image(lista2[0], 2, '', '', False)
delete('tmpone.coo')
iraf.image.tv.imexamine(lista2[0], 2, logfile='tmpone.coo',
keeplog='yes', xformat='', yformat='', wcs='logical')
iraf.tvmark(2, 'tmpone.coo', mark="circle", number='yes',
label='no', radii=8, nxoffse=5, nyoffse=5, color=204, txsize=2)
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x0, y0 = string.split(xycoo[0])
x0 = float(x0)
y0 = float(y0)
xcum0 = readkey3(readhdr(lista2[0]), 'xcum')
ycum0 = readkey3(readhdr(lista2[0]), 'ycum')
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.noao.digiphot.daophot.datapars.datamin = -1000
iraf.noao.digiphot.daophot.datapars.datamax = 60000
iraf.noao.digiphot.daophot.daopars.function = 'gauss'
iraf.noao.digiphot.daophot.photpars.zmag = 0
namesex = defsex('default.sex')
for i in range(0, len(lista2)):
j = i + 1
xcum = readkey3(readhdr(lista2[i]), 'xcum')
ycum = readkey3(readhdr(lista2[i]), 'ycum')
xx = x0 - xcum0 + xcum
yy = y0 - ycum0 + ycum
# sex objects
os.system('sex ' + lista2[i] + ' -c ' + namesex + '> _logsex')
delete('_logsex')
xpix = iraf.proto.fields('detections.cat', fields='2', Stdout=1)
ypix = iraf.proto.fields('detections.cat', fields='3', Stdout=1)
cm = iraf.proto.fields('detections.cat', fields='4', Stdout=1)
cm = compress((array(xpix) != ''), array(cm, float))
ypix = compress((array(xpix) != ''), array(ypix, float))
xpix = compress((array(xpix) != ''), array(xpix, float))
if len(xpix) > 300:
num = 300
else:
num = len(xpix) - 1
xpix = xpix[argsort(cm)][0:num]
ypix = ypix[argsort(cm)][0:num]
distance = (ypix - yy)**2 + (xpix - xx)**2
xx1, yy1 = xpix[argmin(distance)], ypix[argmin(distance)]
f = open('tmpone.coo', 'w')
f.write(str(xx1) + ' ' + str(yy1) + '\n')
f.close()
display_image(lista2[i], 1, '', '', False)
iraf.tvmark(1, 'tmpone.coo', mark="circle", number='yes',
label='no', radii=8, nxoffse=5, nyoffse=5, color=204, txsize=2)
answ = 'n'
while answ != 'y':
answ = raw_input('selected the right one [[y]/n] ?')
if not answ:
answ = 'y'
if answ in ['y', 'YES', 'yes', 'Y']:
print lista2[i]
delete('pippo.' + str(j) + '.mag')
gggg = iraf.digiphot.daophot.phot(
lista2[i], "tmpone.coo", output="pippo." + str(j) + ".mag", verify='no', interac='no', Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
else:
print '\n### select the std star'
display_image(lista2[i], 1, '', '', False)
iraf.image.tv.imexamine(lista2[
i], 1, logfile='tmpone.coo', keeplog='yes', xformat='', yformat='', wcs='logical')
xycoo = iraf.proto.fields('tmpone.coo', '1,2', Stdout=1)
x2, y2 = string.split(xycoo[0])
f = open('tmpone.coo', 'w')
f.write(str(x2) + ' ' + str(y2) + '\n')
f.close()
delete('pippo.' + str(j) + '.mag')
print '###### new selection ' + str(x2), str(y2)
gggg = iraf.digiphot.daophot.phot(
lista2[i], "tmpone.coo", output='pippo.' + str(j) + '.mag', verify='no', interac='no', Stdout=1)
try:
float(string.split(gggg[0])[3])
answ = 'y'
except:
print '\n### warning'
answ = 'n'
os.system('ls pippo.*.mag > tempmag.lst')
tmptbl0 = iraf.txdump(textfile="@tempmag.lst",
fields="XCENTER,YCENTER,FLUX", expr='yes', Stdout=1)
ff = open('magnitudini', 'w')
for i in tmptbl0:
ff.write(i + '\n')
ff.close()
# delete the temporary images and files
delete("temp*.fits")
delete('temp*.lst')
delete(illum_frame)
print '\n### fitting the illumination surface...'
aaa = iraf.utilities.surfit('magnitudini', image=illum_frame, function="polynomial",
xorder=2, yorder=2, xterms="full", ncols=1024, nlines=1024, Stdout=1)
iraf.noao.imred.generic.normalize(illum_frame)
correctcard(lista[0])
data, hdr = pyfits.getdata(illum_frame, 0, header=True)
data0, hdr0 = pyfits.getdata(lista[0], 0, header=True)
delete(illum_frame)
pyfits.writeto(illum_frame, float32(data), hdr0)
flatfield0 = string.split(flatfield, '/')[-1]
ntt.util.updateheader(
illum_frame, 0, {'MKILLUM': [flatfield0, 'flat field']})
display_image(illum_frame, 1, '', '', False)
for i in range(0, len(lista)): # in lista:
img = lista[i]
delete('pippo.' + str(i) + '.mag')
delete('C' + img)
delete('C' + re.sub('.fits', '_sky.fits', img))
# delete('C*.fits.mag.1')
# iraf.hedit(illum_frame,'MKILLUM','Illum. corr. created '+flatfield,add='yes',update='yes',verify='no')
return illum_frame
def rectify_image(image,
reference,
output,
refout=None,
fitgeometry='general',
function='polynomial',
xxorder=3,
yyorder=3,
xyorder=3,
yxorder=3,
clobber=1,
objects=None,
interactive=1,
maxiter=5,
reject=3,
xrange=None,
yrange=None,
thresh=2.0,
scale=0.125,
fwhm=4,
image_sigma=None,
reference_sigma=None,
debug=0,
aoffset=0):
'''This task will take an image and a reference image and apply geomap/
geotran to aligned them. image is registered and output to output.
You can specify the fit geometry (shift, xyscale, rotate, rscale,
rxyscale, general). You can also choose the geenral function
(legendre, chebyshev, polynomial). You can specfy the order of all
the terms (xxorder, yyorder, xyorder, yxorder). If you choose
clobber=1 (the default), new images overwrite older ones. Lastly, if
you want to use a pre-existing list of objects, specify them as the
objects keyword.'''
# First, if we already have objects to use, save time by not finding them
# again!
if objects is None:
sex(image,
output='image.cat',
thresh=thresh,
scale=scale,
fwhm=fwhm,
sigma_map=image_sigma)
sex(reference,
output='reference.cat',
thresh=thresh,
scale=scale,
fwhm=fwhm,
sigma_map=reference_sigma)
im_masks1 = get_mask(image)
im_masks2 = get_mask(reference)
if aoffset == 0:
# Check the ROTANG header field to see if we need to give a rotation
# offset to the object matcher.
rotang1 = get_header(reference, "ROTANG", float)
rotang2 = get_header(image, "ROTANG", float)
if rotang1 is not None and rotang2 is not None:
if abs(rotang1 - rotang2) > 0.1:
aoffset = rotang1 - rotang2
# Run the matchum routine to get corresponding objects:
(x2, y2, x1, y1, o1, o2) = matchum('image.cat',
'reference.cat',
im_masks1=im_masks1,
im_masks2=im_masks2,
xrange=xrange,
yrange=yrange,
debug=debug,
aoffset=aoffset)
f = open('geomap.objects', 'w')
for i in range(len(x1)):
f.write('%.2f %.2f %.2f %.2f\n' % (x1[i], y1[i], x2[i], y2[i]))
f.close()
objects = "geomap.objects"
else:
data = pygplot.columns(objects)
x1 = data[0]
y1 = data[1]
x2 = data[2]
y2 = data[3]
xshift = int(stats.bwt(x2 - x1)[0])
yshift = int(stats.bwt(y2 - y1)[0])
# We now have some selected objects to use with geomap. Let's figure out
# the area of validity for the data using ths shifts computed above.
(r_xmax, r_ymax) = get_image_size(reference)
if xshift < 0:
xmin = -xshift
xmax = r_xmax
else:
xmin = 1
xmax = r_xmax - xshift
if yshift < 0:
ymin = -yshift
ymax = r_ymax
else:
ymin = 1
ymax = r_ymax - yshift
#xmin = min(x1); xmax = max(x1)
#ymin = min(y1); ymax = max(y1)
print xshift, yshift, xmin, xmax, ymin, ymax
iraf.images()
if os.path.isfile('geomap.db'): os.unlink('geomap.db')
# We may not have enough points to do a general fit, so we do them in order of
# complexity. Hopefully, we have at least ONE point (for the shift)
fitgeometries = [fitgeometry, 'rxyscale', 'rotate', 'shift']
success = 0
for fitgeo in fitgeometries:
print "using fitgeometry = ", fitgeo
res = iraf.geomap(input=objects,
database='geomap.db',
xmin=1,
ymin=1,
xmax=r_xmax,
ymax=r_ymax,
fitgeometry=fitgeo,
function=function,
xxorder=xxorder,
yyorder=yyorder,
xyorder=xyorder,
yxorder=yxorder,
results='',
transforms=objects,
interactive=interactive,
maxiter=maxiter,
reject=reject,
Stdout=1)
# assume all went well
success = 1
for line in res:
print line
if re.search('Too few points', line):
success = 0
if success:
break
if clobber and os.path.isfile(output): iraf.imdel(output)
iraf.geotran(input=image,
output='temp.rectify',
database='geomap.db',
transforms=objects,
geometry='geometric',
interpolant="linear")
section = "[%d:%d,%d:%d]" % (xmin, xmax, ymin, ymax)
print 'Valid section of data:', section
iraf.imcopy(input='temp.rectify', output=output)
iraf.imdel('temp.rectify')
return (xmin, xmax, ymin, ymax)
def display_image(img, frame, _z1, _z2, scale, _xcen=0.5, _ycen=0.5, _xsize=1, _ysize=1, _erase='yes'):
# print "LOGX:: Entering `display_image` method/function in %(__file__)s"
# % globals()
goon = 'True'
import glob
import subprocess
import time
import os
ds9 = subprocess.Popen("ps -U" + str(os.getuid()) + "|grep -v grep | grep ds9", shell=True,
stdout=subprocess.PIPE).stdout.readlines()
if len(ds9) == 0:
subproc = subprocess.Popen('ds9', shell=True)
time.sleep(3)
if glob.glob(img):
from pyraf import iraf
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
import string
import os
if _z2:
try:
sss = iraf.display(img, frame, xcen=_xcen, ycen=_ycen, xsize=_xsize, ysize=_ysize, erase=_erase,
fill='yes', zscale='no', zrange='no', z1=_z1, z2=_z2, Stdout=1)
except:
print ''
print '### ERROR: PROBLEM OPENING DS9'
print ''
goon = 'False'
else:
try:
sss = iraf.display(img, frame, xcen=_xcen, ycen=_ycen, xsize=_xsize, ysize=_ysize, erase=_erase,
fill='yes', Stdout=1)
except:
print ''
print '### ERROR: PROBLEM OPENING DS9'
print ''
goon = False
if scale and goon:
answ0 = raw_input('>>> Cuts OK ? [y/n] ? [y] ')
if not answ0:
answ0 = 'y'
elif answ0 == 'no' or answ0 == 'NO':
answ0 = 'n'
while answ0 == 'n':
_z11 = float(string.split(string.split(sss[0])[0], '=')[1])
_z22 = float(string.split(string.split(sss[0])[1], '=')[1])
z11 = raw_input('>>> z1 = ? [' + str(_z11) + '] ? ')
z22 = raw_input('>>> z2 = ? [' + str(_z22) + '] ? ')
if not z11:
z11 = _z11
else:
z11 = float(z11)
if not z22:
z22 = _z22
else:
z22 = float(z22)
print z11, z22
sss = iraf.display(img, frame, fill='yes', xcen=_xcen, ycen=_ycen, xsize=_xsize, ysize=_ysize,
erase=_erase, zrange='no', zscale='no', z1=z11, z2=z22, Stdout=1)
answ0 = raw_input('>>> Cuts OK ? [y/n] ? [y] ')
if not answ0:
answ0 = 'y'
elif answ0 == 'no' or answ0 == 'NO':
answ0 = 'n'
if goon:
_z1, _z2 = string.split(string.split(sss[0])[0], '=')[
1], string.split(string.split(sss[0])[1], '=')[1]
else:
print 'Warning: image ' + str(img) + ' not found in the directory '
return _z1, _z2, goon
def marksn2(img, fitstab, frame=1, fitstab2='', verbose=False):
from pyraf import iraf
from numpy import array #,log10
import lsc
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.images(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.proto(_doprint=0)
iraf.set(stdimage='imt1024')
hdr = lsc.util.readhdr(fitstab)
_filter = lsc.util.readkey3(hdr, 'filter')
column = lsc.lscabsphotdef.makecatalogue([fitstab])[_filter][fitstab]
rasex = array(column['ra0'], float)
decsex = array(column['dec0'], float)
if fitstab2:
hdr = lsc.util.readhdr(fitstab2)
_filter = lsc.util.readkey3(hdr, 'filter')
_exptime = lsc.util.readkey3(hdr, 'exptime')
column = lsc.lscabsphotdef.makecatalogue([fitstab2])[_filter][fitstab2]
rasex2 = array(column['ra0'], float)
decsex2 = array(column['dec0'], float)
iraf.set(stdimage='imt1024')
iraf.display(img + '[0]', frame, fill=True, Stdout=1)
vector = []
for i in range(0, len(rasex)):
vector.append(str(rasex[i]) + ' ' + str(decsex[i]))
xy = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,
'STDIN',
Stdin=list(xy),
mark="circle",
number='yes',
label='no',
radii=10,
nxoffse=5,
nyoffse=5,
color=207,
txsize=2)
if verbose:
# print 2.5*log10(_exptime)
for i in range(0, len(column['ra0'])):
print xy[i], column['ra0'][i], column['dec0'][i], column['magp3'][
i], column['magp4'][i], column['smagf'][i], column['magp2'][i]
if fitstab2:
vector2 = []
for i in range(0, len(rasex2)):
vector2.append(str(rasex2[i]) + ' ' + str(decsex2[i]))
xy1 = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector2,Stdout=1,image=img+'[0]',inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(frame,
'STDIN',
Stdin=list(xy1),
mark="cross",
number='yes',
label='no',
radii=10,
nxoffse=5,
nyoffse=5,
color=205,
txsize=2)
def escut(image, pos_file, fwhm, peak):
# input image file name, file name with matched source positions, **np.array of fwhm measurements for each source
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from pyraf import iraf
# import sewpy
import os
from matplotlib.path import Path
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
iraf.unlearn(iraf.phot, iraf.datapars, iraf.photpars, iraf.centerpars,
iraf.fitskypars)
iraf.apphot.phot.setParam('interactive', "no")
iraf.apphot.phot.setParam('verify', "no")
iraf.datapars.setParam('datamax', 50000.)
iraf.datapars.setParam('gain', "gain")
iraf.datapars.setParam('ccdread', "rdnoise")
iraf.datapars.setParam('exposure', "exptime")
iraf.datapars.setParam('airmass', "airmass")
iraf.datapars.setParam('filter', "filter")
iraf.datapars.setParam('obstime', "time-obs")
# iraf.datapars.setParam('obstime',"date-obs")
iraf.datapars.setParam('sigma', "INDEF")
iraf.photpars.setParam('zmag', 0.)
iraf.centerpars.setParam('cbox', 9.)
iraf.centerpars.setParam('maxshift', 3.)
iraf.fitskypars.setParam('salgorithm', "median")
iraf.fitskypars.setParam('dannulus', 10.)
# clean up the indefs so we can actually do stats, but reassign them to 99999 so we don't lose track of things
# keep a separate list without them to do the median (we need floats)
indefs = np.where(fwhm == 'INDEF')
good = np.where(fwhm != 'INDEF')
fwhm[indefs] = 99.999
fwhm = fwhm.astype(float)
fwhm_good = fwhm[good].astype(float)
indefs = np.where(peak == 'INDEF')
peak[indefs] = -999.999
peak = peak.astype(float)
peak_good = peak[good].astype(float)
if not os.path.isfile(image[0:-5] + '.txdump'):
# findavgfwhm = sewpy.SEW(
# params = ["X_IMAGE", "Y_IMAGE", "FWHM_IMAGE", "FLAGS"],
# config = {"DETECT_THRESH":200.0},
# sexpath = "sex"
# )
#
# out = findavgfwhm(image)["table"]
#
# fwhms = out['FWHM_IMAGE'] # This is an astropy table.
# flags = out['FLAGS']
# get a really rough estimate of the stellar FWHM in the image to set apertures
# use the input fwhm measurement
# ap1x = fwhm_est
# xpos = datatable['X_IMAGE']
# ypos = datatable['Y_IMAGE']
# fwhm = datatable['FWHM_IMAGE']
# flags = datatable['FLAGS']
# idno = datatable['NUMBER']
ap1x = np.median(
fwhm_good
) # only use isolated detections of stars, this is the 1x aperture
# print ap1x
ap2x = 2.0 * ap1x
# these = [ i for i,id in enumerate(idno) if (flags[i] == 0)]
# with open(image[0:-5]+'.escut.pos','w+') as f:
# for j in range(len(xpos)):
# print >> f, xpos[j], ypos[j], fwhm[j], idno[j]
iraf.datapars.setParam('fwhmpsf', ap1x)
iraf.photpars.setParam('apertures', repr(ap1x) + ', ' + repr(ap2x))
iraf.fitskypars.setParam('annulus', 4. * ap1x)
iraf.apphot.phot(image=image,
coords=pos_file,
output=image[0:-5] + '.phot')
with open(image[0:-5] + '.txdump', 'w+') as txdump_out:
iraf.ptools.txdump(
textfiles=image[0:-5] + '.phot',
fields=
"id,mag,merr,msky,stdev,rapert,xcen,ycen,ifilter,xairmass,image",
expr=
'MAG[1] != INDEF && MERR[1] != INDEF && MAG[2] != INDEF && MERR[2] != INDEF',
headers='no',
Stdout=txdump_out)
mag1x, mag2x = np.loadtxt(image[0:-5] + '.txdump',
usecols=(1, 2),
unpack=True)
iraf_id = np.loadtxt(image[0:-5] + '.txdump',
usecols=(0, ),
dtype=int,
unpack=True)
# idno = np.loadtxt(image[0:-5]+'.escut.pos', usecols=(3,), dtype=int, unpack=True)
xpos, ypos = np.loadtxt(pos_file, usecols=(0, 1), unpack=True)
keepIndex = iraf_id - 1
xpos, ypos, fwhm, peak = xpos[keepIndex], ypos[keepIndex], fwhm[
keepIndex], peak[keepIndex]
# print idno.size, iraf_id.size, xpos.size
diff = mag2x - mag1x
diffCut = diff
magCut = mag2x
xCut = xpos #[good]
yCut = ypos #[good]
idCut = iraf_id
fwhmCut = fwhm #_good
peakCut = peak
print(peakCut.size, magCut.size, diffCut.size)
print(diffCut.size, 0, np.median(diffCut), diffCut.std())
nRemoved = 1
# plt.clf()
# plt.scatter(peakCut, magCut, edgecolor='none')
# plt.savefig('peaktest.pdf')
plt.clf()
# plt.hlines(bin_edges, -2, 1, colors='red', linestyle='dashed')
plt.scatter(diff, mag2x, edgecolor='none', facecolor='black', s=4)
# plt.scatter(diffCut, magCut, edgecolor='none', facecolor='blue', s=4)
magdiff = list(
zip(magCut.tolist(), diffCut.tolist(), peakCut.tolist(),
idCut.tolist()))
dtype = [('mag', float), ('diff', float), ('peak', float), ('id', int)]
magdiff = np.array(magdiff, dtype=dtype)
magSort = np.sort(magdiff, order='peak')
peakRange = (magSort['peak'] > 20000.0) & (magSort['peak'] < 40000.0)
peakVal = np.median((magSort['diff'])[np.where(peakRange)])
# peakVal = np.median(diffCut)
print(peakVal)
plt.scatter((magSort['diff'])[np.where(peakRange)],
(magSort['mag'])[np.where(peakRange)],
edgecolor='none',
facecolor='blue',
s=4)
while nRemoved != 0:
nBefore = diffCut.size
diffCheck = np.where(
abs(peakVal - diffCut) < 2.0 *
diffCut.std()) #[i for i,d in enumerate(diff) if (-0.5 < d < 0.0)]
#
diffCut = diffCut[diffCheck]
nRemoved = nBefore - diffCut.size
magCut = magCut[diffCheck]
xCut = xCut[diffCheck]
yCut = yCut[diffCheck]
idCut = idCut[diffCheck]
fwhmCut = fwhmCut[diffCheck]
print(diffCut.size, nRemoved, np.median(diffCut), diffCut.std())
if 0.05 < diffCut.std() < 0.06:
nRemoved = 0
# plt.fill_betweenx(bin_centers, bin_meds+3.0*bin_stds, bin_meds-3.0*bin_stds, facecolor='red', edgecolor='none', alpha=0.4, label='2x RMS sigma clipping region')
# with open('escutSTD_i.pos','w+') as f:
# for i,blah in enumerate(xCut):
# print >> f, xCut[i], yCut[i], diffCut[i]
bin_meds, bin_edges, binnumber = stats.binned_statistic(magCut,
diffCut,
statistic='median',
bins=24,
range=(-12, 0))
bin_stds, bin_edges, binnumber = stats.binned_statistic(magCut,
diffCut,
statistic=np.std,
bins=24,
range=(-12, 0))
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width / 2
# print bin_meds, bin_stds
bin_hw = np.zeros_like(bin_stds)
for i, bin_std in enumerate(bin_stds):
if bin_std > 0.025:
bin_hw[i] = 3.0 * bin_std
else:
bin_hw[i] = 0.075
# print len(binnumber)
# for i,bin_hwi in enumerate(bin_hw):
left_edge = np.array(list(zip(peakVal - bin_hw, bin_centers)))
right_edge = np.flipud(np.array(list(zip(peakVal + bin_hw, bin_centers))))
# print left_edge, right_edge
verts = np.vstack((left_edge, right_edge))
# print verts
# verts = np.delete(verts, np.array([0,1,2,22,23,24,25,45,46,47]), axis=0)
# DON'T USE A PATH BECAUSE APPARENTLY IT CAN SELECT THE INVERSE SET!! WTF
# print verts
esRegion = Path(verts)
sources = esRegion.contains_points(list(zip(diff, mag2x)))
# print sources
with open('escutREG_i.pos', 'w+') as f:
for i, blah in enumerate(xpos[sources]):
print((xpos[sources])[i], (ypos[sources])[i], (diff[sources])[i],
file=f)
magCut2 = mag2x[sources]
magCut1 = mag1x[sources]
fwhmCut = fwhm[sources]
xCut = xpos[sources]
yCut = ypos[sources]
diffCut = diff[sources]
# find the sources that are in the std method but not the region method
# print idCut, idno[sources]
# extrasSTD = np.setdiff1d(idno[sources], idCut)
# print extrasSTD.size
# print extrasSTD
# with open('escutUNIQUE.pos','w+') as f:
# for i,blah in enumerate(extrasSTD):
# print >> f, xpos[blah-1], ypos[blah-1]
# fwhmcheck = np.loadtxt('testfwhmREG.log', usecols=(10,), unpack=True)
fwhmchk2 = np.where((magCut2 < -4) & (fwhmCut < 90.0))
print(np.median(fwhmCut[fwhmchk2]), np.std(fwhmCut[fwhmchk2]))
fwchk = np.where(
np.abs(fwhmCut - np.median(fwhmCut[fwhmchk2])) > 10.0 *
np.std(fwhmCut[fwhmchk2]))
drop = np.abs(fwhmCut - np.median(fwhmCut[fwhmchk2])) > 10.0 * np.std(
fwhmCut[fwhmchk2])
keep = np.abs(fwhmCut - np.median(fwhmCut[fwhmchk2])) <= 10.0 * np.std(
fwhmCut[fwhmchk2])
with open('escutVBAD_i.pos', 'w+') as f:
for i, blah in enumerate(xCut[fwchk]):
print((xCut[fwchk])[i], (yCut[fwchk])[i], file=f)
with open('escut_i.pos', 'w+') as f:
for i, blah in enumerate(xCut):
if not drop[i]:
print(xCut[i],
yCut[i],
magCut2[i],
fwhmCut[i],
magCut1[i],
file=f)
with open('escut_g.pos', 'w+') as f:
for i, blah in enumerate(xCut):
if not drop[i]:
print(xCut[i],
yCut[i],
magCut2[i],
fwhmCut[i],
magCut1[i],
file=f)
plt.fill_betweenx(bin_centers,
peakVal + bin_hw,
peakVal - bin_hw,
facecolor='red',
edgecolor='none',
alpha=0.4,
label='2x RMS sigma clipping region')
plt.scatter(diffCut[fwchk],
magCut2[fwchk],
edgecolor='none',
facecolor='red',
s=4)
plt.ylim(0, -12)
plt.xlabel('$m_{2x} - m_{1x}$')
plt.ylabel('$m_{2x}$')
plt.xlim(-2, 1)
plt.savefig('testmagiraf.pdf')
plt.clf()
plt.scatter(magCut2, fwhmCut, edgecolor='none', facecolor='black')
plt.scatter(magCut2[fwchk],
fwhmCut[fwchk],
edgecolor='none',
facecolor='red')
plt.hlines([np.median(fwhmCut)], -12, 0, colors='red', linestyle='dashed')
plt.hlines([
np.median(fwhmCut) + fwhmCut.std(),
np.median(fwhmCut) - fwhmCut.std()
],
-12,
0,
colors='red',
linestyle='dotted')
plt.ylim(0, 20)
plt.xlim(-12, 0)
plt.ylabel('fwhm')
plt.xlabel('$m_{2x}$')
plt.savefig('fwhmcheck.pdf')
return fwhmCut[keep]
def ASYM(cutimage, maskimage, ini_xcntr, ini_ycntr, pa, one_minus_eg_sq, r50, background, extraction_radius, angle, flag_image, ABS_ZSUM):
iraf.images(_doprint=0)
iraf.images.imgeom(_doprint=0)
iraf.images.imutil(_doprint=0)
co = n.cos(pa * n.pi / 180.0)
si = n.sin(pa * n.pi / 180.0)
Aabs = n.zeros([9])
Aabs = Aabs.astype(n.float32)
rot_sum = n.zeros([9])
rot_sum = rot_sum.astype(n.float32)
abs_zsum = n.zeros([9])
abs_zsum = abs_zsum.astype(n.float32)
sh_sum = n.zeros([9])
sh_sum = sh_sum.astype(n.float32)
absres_sum = n.zeros([9])
absres_sum = absres_sum.astype(n.float32)
f=pyfits.open(cutimage)
z = f[0].data
z = z - background
f.close()
z = n.swapaxes(z, 0, 1)
iraf.rotate("".join(maskimage), output="MRotated.fits",\
rotation=180, xin = ini_xcntr + 1.0,\
yin = ini_ycntr + 1.0, \
xout = ini_xcntr + 1.0, yout = ini_ycntr + 1.0, \
ncols= "INDEF",\
nlines = "INDEF", interpo= "linear", \
boundar="nearest", verbose="no")
# iraf.imarith("".join(maskimage), op="*", operand2="MRotated.fits", \
# result="AMask.fits")
# f=pyfits.open("AMask.fits")
# mask = f[0].data
# f.close()
f=pyfits.open("MRotated.fits")
mask1 = f[0].data
f.close()
mask1 = n.swapaxes(mask1, 0, 1)
f=pyfits.open(maskimage)
mask2 = f[0].data
f.close()
mask2 = n.swapaxes(mask2, 0, 1)
mask = mask1 + mask2
maskedgalaxy = ma.masked_array(z, mask)
z = ma.filled(maskedgalaxy, value = background)
hdu = pyfits.PrimaryHDU(n.swapaxes(z, 0, 1).astype(n.float32))
hdu.writeto('MaskedGalaxy.fits')
NXPTS = z.shape[0]
NYPTS = z.shape[1]
#r50=c.r50
center_rad = 0.01 * r50
# print 'center_rad', center_rad
# center_rad = 0.5#0.01*r50 The centering correction has to be done by calculating asymmetric parameters at different points 0.1% of r50 away around the center
flag_center = 0 #flag_center=1 if the program finds the exact center else center=0
#extraction_radius=total_rad ie. 1.5 times the radius at eta=0.2
x = n.reshape(n.arange(NXPTS * NYPTS), (NXPTS, NYPTS)) / NYPTS
x = x.astype(n.float32)
y = n.reshape(n.arange(NXPTS * NYPTS), (NXPTS, NYPTS)) % NYPTS
y = y.astype(n.float32)
nn = 0 #This nn is given here and the following loop run either it finds the minimum asymmetry or n=20
while flag_center == 0:
flag_out = 0
#x and y coordinates of different center points in 1-d array
xcntr = n.reshape(n.array([[ini_xcntr] * 3, [ini_xcntr - center_rad] *\
3, [ini_xcntr + center_rad] * 3]), (9, ))
xcntr = xcntr.astype(n.float32)
ycntr = n.array([ini_ycntr, ini_ycntr - center_rad, ini_ycntr + \
center_rad]*3)
ycntr = ycntr.astype(n.float32)
for iter in range(9):#The below is done in order to keep the extraction radius inside the image. If the extraction radius goes outside the image then flag_out=1
if(flag_image):#flag_image will tell whether the input is image (flag_image=1) or background (flag_image=0)
if(xcntr[iter] - 2 - extraction_radius < 0):
extraction_radius = xcntr[iter] - 2
flag_out = 1
if(ycntr[iter] - 2 - extraction_radius < 0):
extraction_radius = ycntr[iter] - 2
flag_out = 1
if(xcntr[iter] + 2 + extraction_radius > NXPTS):
extraction_radius = NXPTS - xcntr[iter] - 2
flag_out = 1
if(ycntr[iter] + 2 + extraction_radius > NYPTS):
extraction_radius = NYPTS - ycntr[iter] - 2
flag_out = 1
tx = (x - xcntr[iter]) * co + (y - ycntr[iter]) * si
ty = (xcntr[iter] - x) * si + (y - ycntr[iter]) * co
R = n.sqrt(tx**2.0 + ty**2.0 / one_minus_eg_sq)
iraf.rotate("".join("MaskedGalaxy.fits"), output="Rotated.fits",\
rotation = 180, xin = xcntr[iter] + 1.0, \
yin = ycntr[iter] + 1.0, \
xout = xcntr[iter] + 1.0, yout = ycntr[iter] + 1.0, \
ncols = "INDEF",\
nlines = "INDEF", interpo = "linear", \
boundar = "nearest", verbose = "no")
f = pyfits.open("Rotated.fits")
rz = f[0].data
f.close()
rz = n.swapaxes(rz, 0, 1)
res = z - rz
for myfile in ['Rotated.fits', 'Residual.fits', 'AResidual.fits']:
if os.access(myfile, os.F_OK):
os.remove(myfile)
sh_sum[iter] = z[n.where(R <= extraction_radius)].sum()
rot_sum[iter] = rz[n.where(R <= extraction_radius)].sum()
abs_zsum[iter] = abs(z[n.where(R <= extraction_radius)]).sum()
absres_sum[iter] = abs(res[n.where(R <= extraction_radius)]).sum()
if(flag_image):
Aabs[iter] = absres_sum[iter] / (abs_zsum[iter])
else:
Aabs[iter] = absres_sum[iter] / (ABS_ZSUM)
Aabso = Aabs.min()
index = Aabs.argmin()
if(nn > 20):
xcntr[index] = ini_xcntr
ycntr[index] = ini_ycntr
if(xcntr[index] == ini_xcntr and ycntr[index] == ini_ycntr):
iraf.rotate("".join("MaskedGalaxy.fits"), output = "Rotated.fits",\
rotation = 180, xin = xcntr[iter] + 1.0, \
yin = ycntr[iter] + 1.0, \
xout = xcntr[iter] + 1.0, yout = ycntr[iter] + 1.0, \
ncols = "INDEF",\
nlines = "INDEF", interpo = "linear", \
boundar = "nearest", verbose = "no")
f = pyfits.open("Rotated.fits")
rz = f[0].data
f.close()
rz = n.swapaxes(rz, 0, 1)
res = z - rz
hdu = pyfits.PrimaryHDU(n.swapaxes(res, 0, 1).astype(n.float32))
hdu.writeto('AResidual.fits')
flag_center = 1
rot_sum = rot_sum[index]
abs_zsum = abs_zsum[index]
if(flag_image == 0):
abs_zsum = ABS_ZSUM
sh_sum = sh_sum[index]
absres_sum = absres_sum[index]
if(flag_image):
error_asym = n.sqrt( Aabso**2*( ((sh_sum + rot_sum + 4 * \
background * \
R[n.where(R <= n.floor(extraction_radius))].size) \
/ absres_sum**2.0) + ((sh_sum + 2.0 * background *\
R[n.where(R <= n.floor(extraction_radius))].size) \
/ abs_zsum**2) ) )
else:
error_asym = n.sqrt( Aabso**2 * (((sh_sum+rot_sum + 2 * \
background * \
R[n.where(R <= n.floor(extraction_radius))].size) \
/ absres_sum**2.0) + ((sh_sum + 2.0 * background \
* R[n.where(R <= n.floor(extraction_radius))].size) \
/ abs_zsum**2) ) )
else:
ini_xcntr = xcntr[index]
ini_ycntr = ycntr[index]
nn += 1
for myfile in ['MaskedGalaxy.fits', 'MRotated.fits']:
if os.access(myfile, os.F_OK):
os.remove(myfile)
return Aabso, error_asym, ini_xcntr, ini_ycntr, nn, flag_out,\
abs_zsum, sh_sum, extraction_radius
def perform_subtraction(input, template_dict, path, bump):
log = open(path + "log.txt", "a+")
iraf.images() # load iraf images package for imstat
for inim in input:
if os.path.exists("%sdiff_%s" % (path, inim.split("/")[-1])):
print "[*] %sdiff_%s already exists." % (path, inim.split("/")[-1])
continue
print inim
hdulist = fits.open(inim)
filter = hdulist[0].header["HIERARCH FPA.FILTER"][0]
tmplim = template_dict[filter]
try:
dao_params = extract_dao_params(inim.strip(".fits") + "_dao_params.txt")
except IOError:
print "[-] params file does not exist!"
continue
sky = dao_params[0]
print get_imstats(inim), get_imstats(tmplim)
iu, il = get_imstats(inim)
offset = round_up(np.abs(il), 500)
if bump:
try:
iraf.imarith(
operand1=inim, op="+", operand2=offset, result=inim.strip(".fits") + "_p%s.fits" % (str(offset))
)
except:
pass
inim = inim.strip(".fits") + "_p%s.fits" % (str(offset))
hdulist = fits.open(tmplim)
tu, tl = get_imstats(tmplim)
offset = round_up(np.abs(tl), 2000)
if bump:
try:
iraf.imarith(
operand1=tmplim, op="+", operand2=offset, result=tmplim.strip(".fits") + "_p%s.fits" % (str(offset))
)
except:
pass
tmplim = tmplim.strip(".fits") + "_p%s.fits" % (str(offset))
print get_imstats(inim), get_imstats(tmplim)
tu, tl = get_imstats(tmplim)
iu, il = get_imstats(inim)
# tl = round_down(tl,100)
# il = round_down(tl,10)
# tu = round_up(tu,1000)
# iu = round_up(iu,100)
# cmd = "hotpants -n i -c t -inim %s -tmplim %s -outim %sdiff_%s -tu %d -iu %d" % (inim, tmplim, path, inim.split("/")[-1], tu, iu)
# cmd = "hotpants -n i -c t -inim %s -tmplim %s -outim %sdiff_%s -tu %f -tl %f -iu %f" % (inim, tmplim, path, inim.split("/")[-1], tu, tl, iu)
# cmd = "hotpants -n i -c t -inim %s -tmplim %s -outim %sdiff_%s -tu %d -tl %d -iu %d -il %.3f" % (inim, tmplim, path, inim.split("/")[-1], tu, tl, iu, il)
cmd = "hotpants -n i -c t -inim %s -tmplim %s -outim %sdiff_%s -tl %d -il %.3f" % (
inim,
tmplim,
path,
inim.split("/")[-1],
tl,
il,
)
log.write(cmd + "\n\n")
print cmd
subprocess.call(cmd, shell=True)
print
log.close()
def main(argv):
home_root = os.environ['HOME']
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
for file_ in os.listdir("./"):
if file_.endswith("_i.fits"):
fits_file_i = file_
title_string = file_[0:9]
if file_.endswith("_g.fits"):
fits_file_g = file_
fits_h_i = fits.open(fits_file_i)
fits_h_g = fits.open(fits_file_g)
fwhm_i = fits_h_i[0].header['FWHMPSF']
fwhm_g = fits_h_g[0].header['FWHMPSF']
print 'Image FWHM :: g = {0:5.3f} : i = {1:5.3f}'.format(fwhm_g,fwhm_i)
# if not os.path.isdir('psf'): # make a psf subfolder if it doesn't exist
# os.mkdir('psf')
# # and then copy in the data files if they don't already exist as files
# if not os.path.isfile('psf/'+fits_file_i):
# iraf.images.imcopy(fits_file_i,'psf/'+fits_file_i,verbose="yes")
#
# if not os.path.isfile('psf/'+fits_file_g) :
# iraf.images.imcopy(fits_file_g,'psf/'+fits_file_g,verbose="yes")
#
# # also copy in the apcor star lists
# if not (os.path.isfile('psf/apcor_stars_i.txt') or os.path.islink('psf/apcor_stars_i.txt')) :
# shutil.copyfile('apcor_stars_i.txt','psf/apcor_stars_i.txt')
#
# if not (os.path.isfile('psf/apcor_stars_g.txt') or os.path.islink('psf/apcor_stars_g.txt')) :
# shutil.copyfile('apcor_stars_g.txt','psf/apcor_stars_g.txt')
#
# # and change to the psf folder
# os.chdir('psf')
iraf.unlearn(iraf.phot,iraf.datapars,iraf.photpars,iraf.centerpars,iraf.fitskypars)
iraf.apphot.phot.setParam('interactive',"no")
iraf.apphot.phot.setParam('verify',"no")
iraf.datapars.setParam('datamax',"INDEF")
iraf.datapars.setParam('gain',"gain")
iraf.datapars.setParam('ccdread',"rdnoise")
iraf.datapars.setParam('exposure',"exptime")
iraf.datapars.setParam('airmass',"airmass")
iraf.datapars.setParam('filter',"filter")
iraf.datapars.setParam('obstime',"time-obs")
iraf.datapars.setParam('sigma',"INDEF")
iraf.photpars.setParam('zmag',0.)
iraf.centerpars.setParam('cbox',9.)
iraf.centerpars.setParam('maxshift',3.)
iraf.fitskypars.setParam('salgorithm',"median")
iraf.fitskypars.setParam('dannulus',10.)
iraf.datapars.setParam('fwhmpsf',fwhm_g)
iraf.photpars.setParam('apertures',iraf.nint(4.*fwhm_g))
iraf.fitskypars.setParam('annulus',(6.*fwhm_g))
iraf.apphot.phot(image=fits_file_g, coords='apcor_stars_g.txt', output="g_psfstars.mag.1")
iraf.datapars.setParam('fwhmpsf',fwhm_i)
iraf.photpars.setParam('apertures',iraf.nint(4.*fwhm_i))
iraf.fitskypars.setParam('annulus',(6.*fwhm_i))
iraf.apphot.phot(image=fits_file_i, coords='apcor_stars_i.txt', output="i_psfstars.mag.1")
with open("apcor_stars_i.txt") as foo:
lines = len(foo.readlines())
iraf.daophot(_doprint=0)
iraf.pstselect.setParam('image',fits_file_i)
iraf.pstselect.setParam('photfile',"i_psfstars.mag.1")
iraf.pstselect.setParam('pstfile',"default")
iraf.pstselect.setParam('maxnpsf',lines)
iraf.pstselect.setParam('plottype',"mesh")
iraf.daophot.pstselect(verify='no', mode='h')
with open("apcor_stars_g.txt") as foo:
lines = len(foo.readlines())
iraf.pstselect.setParam('image',fits_file_g)
iraf.pstselect.setParam('photfile',"g_psfstars.mag.1")
iraf.pstselect.setParam('pstfile',"default")
iraf.pstselect.setParam('maxnpsf',lines)
iraf.pstselect.setParam('plottype',"mesh")
iraf.daophot.pstselect()
iraf.datapars.setParam('datamax',"INDEF")
iraf.datapars.setParam('gain',"gain")
iraf.datapars.setParam('ccdread',"rdnoise")
iraf.datapars.setParam('exposure',"exptime")
iraf.datapars.setParam('airmass',"airmass")
iraf.datapars.setParam('filter',"filter")
iraf.datapars.setParam('obstime',"time-obs")
iraf.datapars.setParam('sigma',"INDEF")
iraf.daopars.setParam('psfrad',iraf.nint(3.*fwhm_i))
iraf.daopars.setParam('fitrad',fwhm_i)
iraf.psf.setParam('image',fits_file_i)
iraf.psf.setParam('photfile',"i_psfstars.mag.1")
iraf.psf.setParam('pstfile',fits_file_i+".pst.1")
iraf.psf.setParam('interactive', 'no')
iraf.daophot.psf()
iraf.psf.setParam('image',fits_file_g)
iraf.psf.setParam('photfile',"g_psfstars.mag.1")
iraf.psf.setParam('pstfile',fits_file_g+".pst.1")
iraf.psf.setParam('interactive', 'no')
iraf.daophot.psf()
def absphot(img,_field,_catalogue,_fix,_color,rejection,_interactive,_type='fit',redo=False,show=False,cutmag=-1,database='dataredulco',_calib='sloan'):
from astropy.io import fits
import lsc
import math
import sys,re,string,os
from lsc.util import readkey3, readhdr
from numpy import array, compress, zeros, median, std, asarray, isfinite,mean
from pyraf import iraf
if show:
from pylab import ion,plot,draw,clf
import time
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.daophot(_doprint=0)
iraf.images(_doprint=0)
iraf.imcoords(_doprint=0)
iraf.proto(_doprint=0)
t = fits.open(img)
tbdata = t[1].data
hdr2=t[1].header
hdr=lsc.util.readhdr(img)
_cat=readkey3(hdr,'catalog')
_telescope=lsc.util.readkey3(hdr,'telescop')
_instrume=lsc.util.readkey3(hdr,'instrume')
_filter=lsc.util.readkey3(hdr,'filter')
_airmass=lsc.util.readkey3(hdr,'airmass')
_exptime=lsc.util.readkey3(hdr,'exptime')
_date=lsc.util.readkey3(hdr,'date-obs')
_object=lsc.util.readkey3(hdr,'object')
_ra=lsc.util.readkey3(hdr,'RA')
_dec=lsc.util.readkey3(hdr,'DEC')
print _filter
if _telescope in ['lsc','1m0-04','1m0-05','1m0-09']: kk=lsc.sites.extintion('ctio')
elif _telescope in ['elp','1m0-08']: kk=lsc.sites.extintion('mcdonald')
elif _telescope in ['cpt','1m0-12','1m0-10','1m0-13']: kk=lsc.sites.extintion('southafrica')
elif _telescope in ['ftn','Faulkes Telescope North']: kk=lsc.sites.extintion('mauna')
elif _telescope in ['1m0-03','1m0-11','coj','fts','Faulkes Telescope South']: kk=lsc.sites.extintion('siding')
if _calib not in ['sloan','sloanprime','natural','apass','']: colorefisso=lsc.sites.colfix(_instrume)
else: colorefisso=lsc.sites.colfix(_instrume,_calib)
print redo
print _cat
if _cat and not redo:
print 'already calibrated'
else:
try:
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
except: print 'module mysqldef not found'
column=makecatalogue([img])[_filter][img]
rasex=array(column['ra0'],float)
decsex=array(column['dec0'],float)
if _type=='fit':
magsex=array(column['smagf'],float)
magerrsex=array(column['smagerrf'],float)
elif _type=='ph':
magsex=array(column['magp3'],float)
magerrsex=array(column['merrp3'],float)
else: sys.exit(_type+' not valid (ph or fit)')
print len(rasex)
if not cutmag: cutmag=99
#else: cutmag= cutmag-2.5*math.log10(float(_exptime))
if len(compress( array(magsex) < float(cutmag) , magsex)) < 5 : cutmag=99 # not cut if only few object
rasex = compress(array(magsex,float)<=cutmag,rasex)
decsex = compress(array(magsex,float)<=cutmag,decsex)
magerrsex = compress(array(magsex,float)<=cutmag,magerrsex)
magsex = compress(array(magsex,float)<=cutmag,array(magsex))
print len(rasex)
if _interactive:
iraf.set(stdimage='imt1024')
iraf.display(re.sub('.sn2','',img),1,fill=True,Stdout=1)
vector=[]
for i in range(0,len(rasex)):
vector.append(str(rasex[i])+' '+str(decsex[i]))
xy = iraf.wcsctran('STDIN',output="STDOUT",Stdin=vector,Stdout=1,image=img,inwcs='world',units='degrees degrees',outwcs='logical',\
formats='%10.1f %10.1f',verbose='yes')[3:]
iraf.tvmark(1,'STDIN',Stdin=list(xy),mark="circle",number='yes',label='no',radii=10,nxoffse=5,nyoffse=5,color=207,txsize=2)
# raw_input('here')
if _catalogue:
######## use external catalogue
if _catalogue[0]=='/': stdcooC=lsc.lscastrodef.readtxt(_catalogue)
else: stdcooC=lsc.lscastrodef.readtxt(lsc.__path__[0]+'/standard/cat/'+_catalogue)
rastdC,decstdL=array(stdcooC['ra'],float),array(stdcooC['dec'],float)
lsc.util.delete('tmp.stdL.pix')
colonne=str(stdcooC['rapos'])+' '+str(stdcooC['decpos'])
if _catalogue[0]=='/':
iraf.wcsctran(_catalogue,'tmp.stdL.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',\
columns=colonne,formats='%10.1f %10.1f',verbose='no')
else:
iraf.wcsctran(lsc.__path__[0]+'/standard/cat/'+_catalogue,'tmp.stdL.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',\
columns=colonne,formats='%10.1f %10.1f',verbose='no')
standardpixC=lsc.lscastrodef.readtxt('tmp.stdL.pix')
if _interactive:
iraf.tvmark(1,'tmp.stdL.pix',mark="circle",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=204,txsize=2)
xstdC=standardpixC['ra']
ystdC=standardpixC['dec']
idstdC=standardpixC['id']
xstdC=compress((array(xstdC,float)<readkey3(hdr,'XDIM'))&(array(xstdC,float)>0)&(array(ystdC,float)>0)&(array(ystdC,float)<readkey3(hdr,'YDIM')),xstdC)
xstdL=xstdLL=xstdS=xstdC
standardpixL=standardpixLL=standardpixS=standardpixC
stdcooL=stdcooLL=stdcooS=stdcooC
else:
######## check if it is landolt field
stdcooL=lsc.lscastrodef.readtxt(lsc.__path__[0]+'/standard/cat/landolt.cat')
rastdL,decstdL=array(stdcooL['ra'],float),array(stdcooL['dec'],float)
lsc.util.delete('tmp.stdL.pix')
iraf.wcsctran(lsc.__path__[0]+'/standard/cat/landolt.cat','tmp.stdL.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',\
columns='1 2',formats='%10.1f %10.1f',verbose='no')
standardpixL=lsc.lscastrodef.readtxt('tmp.stdL.pix')
if _interactive:
iraf.tvmark(1,'tmp.stdL.pix',mark="circle",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=204,txsize=2)
print 'yelow circles sextractor'
xstdL=standardpixL['ra']
ystdL=standardpixL['dec']
idstdL=standardpixL['id']
xstdL=compress((array(xstdL,float)<readkey3(hdr,'XDIM'))&(array(xstdL,float)>0)&(array(ystdL,float)>0)&(array(ystdL,float)<readkey3(hdr,'YDIM')),xstdL)
######## check if it is Stetson field
stdcooLL=lsc.lscastrodef.readtxt(lsc.__path__[0]+'/standard/cat/StetsonCat.dat')
ww=asarray([i for i in range(len(stdcooLL['ra'])) if ( abs(float(stdcooLL['ra'][i])-float(_ra))<.2 and abs(float(stdcooLL['dec'][i])-_dec)<.2 )])
if len(ww)>0:
for hh in stdcooLL.keys():
if type(stdcooLL[hh])!=int:
if hh not in ['id','ra','dec']:
stdcooLL[hh]=array(array(stdcooLL[hh])[ww],float)
else:
stdcooLL[hh]=array(stdcooLL[hh])[ww]
lll=[]
for i in range(0,len(stdcooLL['ra'])): lll.append(stdcooLL['ra'][i]+' '+stdcooLL['dec'][i])
rastdLL,decstdLL=array(stdcooLL['ra'],float),array(stdcooLL['dec'],float)
lsc.util.delete('tmp.stdLL.pix')
iraf.wcsctran('STDIN','tmp.stdLL.pix',img,inwcs='world',Stdin=lll,units='degrees degrees',outwcs='logical',\
columns='1 2',formats='%10.1f %10.1f',verbose='no')
if _interactive:
iraf.tvmark(1,'tmp.stdLL.pix',mark="cross",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=204,txsize=2)
print 'red crosses Stetson'
standardpixLL={}
for ii in stdcooLL.keys(): standardpixLL[ii]=stdcooLL[ii]
standardpixLL['ra']=array(iraf.proto.fields('tmp.stdLL.pix',fields='1',Stdout=1),float) #standardpixLL['ra']
standardpixLL['dec']=array(iraf.proto.fields('tmp.stdLL.pix',fields='2',Stdout=1),float) #standardpixLL['dec']
xstdLL=array(iraf.proto.fields('tmp.stdLL.pix',fields='1',Stdout=1),float) #standardpixLL['ra']
ystdLL=array(iraf.proto.fields('tmp.stdLL.pix',fields='2',Stdout=1),float) #standardpixLL['dec']
idstdLL=standardpixLL['id']
xstdLL=compress((array(xstdLL,float)<readkey3(hdr,'XDIM'))&(array(xstdLL,float)>0)&(array(ystdLL,float)>0)&(array(ystdLL,float)<readkey3(hdr,'YDIM')),xstdLL)
######## check if it is sloan field
magsel0,magsel1=12,18
_ids=lsc.lscastrodef.sloan2file(_ra,_dec,20,float(magsel0),float(magsel1),'_tmpsloan.cat')
ascifile='_tmpsloan.cat'
stdcooS=lsc.lscastrodef.readtxt(ascifile)
rastdS,decstdS=array(stdcooS['ra'],float),array(stdcooS['dec'],float)
lsc.util.delete('tmp.stdS.pix')
iraf.wcsctran(ascifile,'tmp.stdS.pix',img,inwcs='world',units='degrees degrees',outwcs='logical',columns='1 2',formats='%10.1f %10.1f',verbose='no')
standardpixS=lsc.lscastrodef.readtxt('tmp.stdS.pix')
if _interactive:
iraf.tvmark(1,'tmp.stdS.pix',mark="cross",number='yes',label='no',radii=8,nxoffse=5,nyoffse=5,color=205,txsize=2)
print 'green cross sloan'
xstdS=standardpixS['ra']
ystdS=standardpixS['dec']
idstdS=standardpixS['id']
xstdS=compress((array(xstdS,float)<readkey3(hdr,'XDIM'))&(array(xstdS,float)>0)&(array(ystdS,float)>0)&(array(ystdS,float)<readkey3(hdr,'YDIM')),xstdS)
##############################################3
if not _catalogue and len(xstdLL)>0:
xstdL=xstdLL
standardpixL=standardpixLL
stdcooL=stdcooLL
if _filter in ['U', 'B', 'V', 'R','I','Bessell-B','Bessell-V','Bessell-R','Bessell-I']:
filters={'U':'U', 'B':'B', 'V':'V', 'R':'R', 'I':'I','Bessell-B':'B','Bessell-V':'V','Bessell-R':'R','Bessell-I':'I'}
if _color:
colors=lsc.myloopdef.chosecolor(_color,False)
if not colors: colors={'U':['UB'],'B':['UB','BV'],'V':['BV','VR'],'R':['VR','RI'],'I':['RI']}
else:
colors={'U':['UB'],'B':['UB','BV'],'V':['BV','VR'],'R':['VR','RI'],'I':['RI']}
if _field=='sloan':
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
print 'filters and field selected do not match'
else:
_field='landolt'
if len(xstdL)>=1:
standardpix=standardpixL
stdcoo=stdcooL
if not _catalogue:
if len(xstdLL)>0: _catalogue='StetsonCat.dat'
else: _catalogue='landolt.dat'
elif len(xstdS)>=1:
if not _catalogue: _catalogue='sdss8'
standardpix=standardpixS
stdcoo=stdcooS
stdcoo=lsc.lscastrodef.transformsloanlandolt(stdcoo)
if not _catalogue: _catalogue='sdss8'
print '\n### transform sloan in landolt'
else:
print 'landolt, but catalogue not found'
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
elif _filter in ['up','gp','rp','ip','zs','SDSS-G','SDSS-R','SDSS-I','Pan-Starrs-Z']:
filters={'up':'u','ip':'i','gp':'g','rp':'r','zs':'z','SDSS-G':'g','SDSS-R':'r','SDSS-I':'i','Pan-Starrs-Z':'z'}
if _color:
colors=lsc.myloopdef.chosecolor(_color,False)
if not colors: colors={'i':['ri','iz'],'r':['gr','ri'],'g':['ug','gr'],'z':['iz'],'u':['ug']}
else:
colors={'i':['ri','iz'],'r':['gr','ri'],'g':['ug','gr'],'z':['iz'],'u':['ug']}
if _field=='landolt':
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
print 'filters and field selected do not match'
else:
_field='sloan'
if len(xstdS)>=1:
if not _catalogue: _catalogue='sdss8'
standardpix=standardpixS
stdcoo=stdcooS
elif len(xstdL)>=1:
standardpix=standardpixL
stdcoo=stdcooL
stdcoo=lsc.lscastrodef.transformlandoltsloan(stdcoo)
if not _catalogue: _catalogue='landolt.dat'
print '\n### transform landolt to sloan'
else:
print 'sloan, but not in the sdss footprint'
standardpix,stdcoo={'ra':[9999],'dec':[9999],'id':[1]},{'ra':[9999],'dec':[9999]}
xstd=standardpix['ra']
ystd=standardpix['dec']
idstd=standardpix['id']
rastd,decstd=array(stdcoo['ra'],float),array(stdcoo['dec'],float)
xstd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)&(array(ystd,float)<readkey3(hdr,'YDIM')),xstd)
if len(xstd0)>1: ######## go only if standard stars are in the field ##########
magstd0={}
airmass0={}
result={}
fileph={}
print '\n### standard field: '+str(_field)
ystd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),ystd)
rastd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),rastd)
decstd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),decstd)
idstd0=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),idstd)
stdcoo0={}
for key in stdcoo.keys():
if key in 'ugrizUBVRI':
stdcoo0[key]=compress((array(xstd,float)<readkey3(hdr,'XDIM'))&(array(xstd,float)>0)&(array(ystd,float)>0)\
&(array(ystd,float)<readkey3(hdr,'YDIM')),stdcoo[key])
###############################################################
# pos0 = standard pos1 = sextractor
distvec,pos0,pos1=lsc.lscastrodef.crossmatch(array(rastd0),array(decstd0),array(rasex),array(decsex),10)
for key in stdcoo0.keys(): stdcoo0[key]=stdcoo0[key][pos0]
rastd0=rastd0[pos0]
decstd0=decstd0[pos0]
idstd0=idstd0[pos0]
rasex=rasex[pos1]
decsex=decsex[pos1]
# after change in may 2013 mag in sn2.fits file are already at 1s
magsex=magsex[pos1]-kk[filters[_filter]]*float(_airmass) # - K x airmass
# magsex=magsex[pos1]+2.5*math.log10(float(_exptime))-kk[filters[_filter]]*float(_airmass) # mag exptime - K x airmass
#################################################################################
if _field=='landolt':
print '\n### landolt system'
for _filtlandolt in 'UBVRI':
if _filtlandolt==filters[_filter]: airmass0[_filtlandolt]= 0 #_airmass
else: airmass0[_filtlandolt]= 0
magstd0[_filtlandolt]=stdcoo0[_filtlandolt]
fileph['mU']=zeros(len(rastd0))+999
fileph['mB']=zeros(len(rastd0))+999
fileph['mV']=zeros(len(rastd0))+999
fileph['mR']=zeros(len(rastd0))+999
fileph['mI']=zeros(len(rastd0))+999
fileph['V']=magstd0['V']
fileph['BV']=array(array(magstd0['B'],float)-array(magstd0['V'],float),str)
fileph['UB']=array(array(magstd0['U'],float)-array(magstd0['B'],float),str)
fileph['VR']=array(array(magstd0['V'],float)-array(magstd0['R'],float),str)
fileph['RI']=array(array(magstd0['R'],float)-array(magstd0['I'],float),str)
elif _field=='sloan':
for _filtsloan in 'ugriz':
if _filtsloan==filters[_filter]: airmass0[_filtsloan]= 0 # _airmass
else: airmass0[_filtsloan]=0
magstd0[_filtsloan]=stdcoo0[_filtsloan]
fileph['mu']=zeros(len(rastd0))+999
fileph['mg']=zeros(len(rastd0))+999
fileph['mr']=zeros(len(rastd0))+999
fileph['mi']=zeros(len(rastd0))+999
fileph['mz']=zeros(len(rastd0))+999
fileph['r']=magstd0['r']
fileph['gr']=array(array(magstd0['g'],float)-array(magstd0['r'],float),str)
fileph['ri']=array(array(magstd0['r'],float)-array(magstd0['i'],float),str)
fileph['ug']=array(array(magstd0['u'],float)-array(magstd0['g'],float),str)
fileph['iz']=array(array(magstd0['i'],float)-array(magstd0['z'],float),str)
########################################################################################
zero=[]
magcor=[]
fil = open(re.sub('.fits','.ph',img),'w')
fil.write(str(_instrume)+' '+str(_date)+'\n')
fil.write('*** '+_object+' '+str(len(magsex))+'\n')
if _field=='landolt':
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(1),str(1),str(1),str(1),str(1))) # exptime
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(airmass0['U']),str(airmass0['B']),str(airmass0['V']),str(airmass0['R']),str(airmass0['I'])))
elif _field=='sloan':
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(1),str(1),str(1),str(1),str(1))) # exptime
fil.write('%6.6s\t%6.6s\t%6.6s\t%6.6s\t%6.6s\n' % (str(airmass0['u']),str(airmass0['g']),str(airmass0['r']),str(airmass0['i']),str(airmass0['z'])))
for i in range(0,len(magsex)):
fileph['m'+filters[_filter]][i]=magsex[i] # instrumental mangitude of std in pos0[i]
if _field=='landolt':
stringastandard='%12.12s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s' % (idstd0[i],fileph['V'][i],fileph['BV'][i],fileph['UB'][i],\
fileph['VR'][i],fileph['RI'][i])
fil.write('%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%60.60s\n' \
% (str(fileph['mU'][i]),str(fileph['mB'][i]),str(fileph['mV'][i]),str(fileph['mR'][i]),str(fileph['mI'][i]),str(stringastandard)))
elif _field=='sloan':
stringastandard='%12.12s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s' % (idstd0[i],fileph['r'][i],fileph['gr'][i],fileph['ug'][i],\
fileph['ri'][i],fileph['iz'][i])
fil.write('%7.7s\t%7.7s\t%7.7s\t%7.7s\t%7.7s\t%60.60s\n' \
% (str(fileph['mu'][i]),str(fileph['mg'][i]),str(fileph['mr'][i]),str(fileph['mi'][i]),str(fileph['mz'][i]),str(stringastandard)))
zero.append(float(float(magstd0[filters[_filter]][i]))-float(magsex[i]))
magcor.append(magsex[i])
fil.close()
if show:
import time
from pylab import ion,plot,draw
ion()
aa=mean(compress(abs(array(zero))<99,zero))
xxx=compress((abs(array(magcor))<99)&(abs(array(zero))<99),magcor)
yyy=compress((abs(array(zero))<99)&(abs(array(magcor))<99),zero)
plot(xxx,yyy,'or')
plot([min(compress(abs(array(magcor))<99,magcor)),max(compress(abs(array(magcor))<99,magcor))],[aa,aa],'-b')
draw()
print std(compress(abs(array(zero))<99,zero))
time.sleep(5)
colorvec=colors[filters[_filter]]
for col in colorvec:
col0=magstd0[col[0]]
col1=magstd0[col[1]]
colstd0=array(col0,float)-array(col1,float)
################## sex ######################
colore=[]
for i in range(0,len(pos1)): colore.append(colstd0[i])
colore1=compress(abs(array(zero))<50,array(colore))
zero1=compress(abs(array(zero))<50,array(zero))
zero2=compress(abs(array(colore1))<2,array(zero1))
colore2=compress(abs(array(colore1))<2,array(colore1))
if _fix: fisso=colorefisso[filters[_filter]+col]
else: fisso=''
if len(colore2)==0:
print 'no calibration, '+_filter+' '+_field
b,a,sa,sb=9999,9999,0,0
else:
if _interactive: a,sa,b,sb=fitcol(colore2,zero2,_filter,col,fisso)
else: a,sa,b,sb=fitcol2(colore2,zero2,_filter,col,fisso,show,rejection)
print a,sa,b,sb
result[filters[_filter]+col]=[a,sa,b,sb]
if result:
print '\n### zeropoint ..... done at airmass 0'
if _catalogue:
lsc.util.updateheader(img,0,{'CATALOG':[str(string.split(_catalogue,'/')[-1]),'catalogue source']})
stringa=''
for ll in result:
for kk in range(0,len(result[ll])):
if not isfinite(result[ll][kk]): result[ll][kk]=0.0
valore='%3.3s %6.6s %6.6s %6.6s %6.6s' % (str(ll),str(result[ll][0]),str(result[ll][2]),str(result[ll][1]),str(result[ll][3]))
print '### ',valore
lsc.util.updateheader(img,0,{'zp'+ll:[str(valore),'a b sa sb in y=a+bx']})
if ll[0]==ll[2]: num=2
elif ll[0]==ll[1]: num=1
else: sys.exit('somthing wrong with color '+ll)
print ll,num
try:
print 'zcol'+str(num),ll[1:],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1]
lsc.mysqldef.updatevalue(database,'zcol'+str(num),ll[1:],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'z'+str(num),result[ll][0],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'c'+str(num),result[ll][2],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dz'+str(num),result[ll][1],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dc'+str(num),result[ll][3],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcol'+str(num),ll[1:],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'z'+str(num),result[ll][0],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'c'+str(num),result[ll][2],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dz'+str(num),result[ll][1],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
lsc.mysqldef.updatevalue(database,'dc'+str(num),result[ll][3],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
if result[ll][0]!=9999:
print _catalogue
lsc.mysqldef.updatevalue(database,'zcat',string.split(_catalogue,'/')[-1],string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcat',string.split(_catalogue,'/')[-1],string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
else:
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.sn2.fits','.fits',img),'/')[-1])
if os.path.isfile(string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1]):
lsc.mysqldef.updatevalue(database,'zcat','X',string.split(re.sub('.diff.sn2.fits','.fits',img),'/')[-1])
except: print 'module mysqldef not found'
#! /usr/local/bin/python
import os, sys
import numpy as np
from pyraf import iraf
from odi_calibrate import download_sdss, js_calibrate
from sdss_fit import getVabs
from collections import OrderedDict
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
def getHImass(object, dm):
# print object, mpc
uchvcdb = os.path.dirname(os.path.abspath(__file__))+'/predblist.sort.csv'
name, mass = np.loadtxt(uchvcdb, usecols=(1,6), dtype=str, delimiter=',', unpack=True)
# find the right row
coord = [i for i,this in enumerate(mass) if object.upper() in name[i]][0]
# print 'the HI mass of', name[coord], 'is', mass[coord], 'at 1 Mpc'
# mpc = mpc/1000.
mpc = pow(10,((dm + 5.)/5.))/1000000.
logm = float(mass[coord])
mass = mpc*mpc*10**logm # make sure to scale by the distance in Mpc^2
print '{:3.1f}'.format(np.log10(mass))
def display_image(img,
frame,
_z1,
_z2,
scale,
_xcen=0.5,
_ycen=0.5,
_xsize=1,
_ysize=1,
_erase='yes'):
goon = 'True'
import glob, subprocess, os, time
# removing u : this option does not work on mac
ds9 = subprocess.Popen("ps -U {:d} | grep -v grep | grep ds9".format(
os.getuid()),
shell=True,
stdout=subprocess.PIPE).stdout.readlines()
if len(ds9) == 0:
subproc = subprocess.Popen('ds9', shell=True)
time.sleep(3)
if glob.glob(img):
from pyraf import iraf
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
import string, os
if _z2:
try:
sss=iraf.display(img + '[0]', frame, xcen=_xcen, ycen=_ycen, xsize=_xsize, ysize=_ysize, erase=_erase,\
fill='yes', zscale='no', zrange='no', z1=_z1, z2=_z2,Stdout=1)
except:
print ''
print '### ERROR: PROBLEM OPENING DS9'
print ''
goon = 'False'
else:
try:
sss = iraf.display(img + '[0]',
frame,
xcen=_xcen,
ycen=_ycen,
xsize=_xsize,
ysize=_ysize,
erase=_erase,
fill='yes',
Stdout=1)
except:
print ''
print '### ERROR: PROBLEM OPENING DS9'
print ''
goon = False
if scale and goon:
answ0 = raw_input('>>> Cuts OK ? [y/n] ? [y] ')
if not answ0: answ0 = 'y'
elif answ0 == 'no' or answ0 == 'NO': answ0 = 'n'
while answ0 == 'n':
_z11 = float(string.split(string.split(sss[0])[0], '=')[1])
_z22 = float(string.split(string.split(sss[0])[1], '=')[1])
z11 = raw_input('>>> z1 = ? [' + str(_z11) + '] ? ')
z22 = raw_input('>>> z2 = ? [' + str(_z22) + '] ? ')
if not z11: z11 = _z11
else: z11 = float(z11)
if not z22: z22 = _z22
else: z22 = float(z22)
print z11, z22
sss=iraf.display(img + '[0]',frame,fill='yes', xcen=_xcen, ycen=_ycen, xsize=_xsize, ysize=_ysize, erase=_erase,\
zrange='no', zscale='no', z1=z11, z2=z22, Stdout=1)
answ0 = raw_input('>>> Cuts OK ? [y/n] ? [y] ')
if not answ0: answ0 = 'y'
elif answ0 == 'no' or answ0 == 'NO': answ0 = 'n'
if goon:
_z1, _z2 = string.split(string.split(sss[0])[0],
'=')[1], string.split(
string.split(sss[0])[1], '=')[1]
else:
print 'Warning: image ' + str(img) + ' not found in the directory '
return _z1, _z2, goon
def escut(image, pos_file, fwhm, peak):
# input image file name, file name with matched source positions, **np.array of fwhm measurements for each source
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from pyraf import iraf
# import sewpy
import os
from matplotlib.path import Path
iraf.images(_doprint=0)
iraf.tv(_doprint=0)
iraf.ptools(_doprint=0)
iraf.noao(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.photcal(_doprint=0)
iraf.apphot(_doprint=0)
iraf.imutil(_doprint=0)
iraf.unlearn(iraf.phot,iraf.datapars,iraf.photpars,iraf.centerpars,iraf.fitskypars)
iraf.apphot.phot.setParam('interactive',"no")
iraf.apphot.phot.setParam('verify',"no")
iraf.datapars.setParam('datamax',50000.)
iraf.datapars.setParam('gain',"gain")
iraf.datapars.setParam('ccdread',"rdnoise")
iraf.datapars.setParam('exposure',"exptime")
iraf.datapars.setParam('airmass',"airmass")
iraf.datapars.setParam('filter',"filter")
iraf.datapars.setParam('obstime',"time-obs")
# iraf.datapars.setParam('obstime',"date-obs")
iraf.datapars.setParam('sigma',"INDEF")
iraf.photpars.setParam('zmag',0.)
iraf.centerpars.setParam('cbox',9.)
iraf.centerpars.setParam('maxshift',3.)
iraf.fitskypars.setParam('salgorithm',"median")
iraf.fitskypars.setParam('dannulus',10.)
# clean up the indefs so we can actually do stats, but reassign them to 99999 so we don't lose track of things
# keep a separate list without them to do the median (we need floats)
indefs = np.where(fwhm=='INDEF')
good = np.where(fwhm!='INDEF')
fwhm[indefs] = 99.999
fwhm = fwhm.astype(float)
fwhm_good = fwhm[good].astype(float)
indefs = np.where(peak=='INDEF')
peak[indefs] = -999.999
peak = peak.astype(float)
peak_good = peak[good].astype(float)
if not os.path.isfile(image[0:-5]+'.txdump'):
# findavgfwhm = sewpy.SEW(
# params = ["X_IMAGE", "Y_IMAGE", "FWHM_IMAGE", "FLAGS"],
# config = {"DETECT_THRESH":200.0},
# sexpath = "sex"
# )
#
# out = findavgfwhm(image)["table"]
#
# fwhms = out['FWHM_IMAGE'] # This is an astropy table.
# flags = out['FLAGS']
# get a really rough estimate of the stellar FWHM in the image to set apertures
# use the input fwhm measurement
# ap1x = fwhm_est
# xpos = datatable['X_IMAGE']
# ypos = datatable['Y_IMAGE']
# fwhm = datatable['FWHM_IMAGE']
# flags = datatable['FLAGS']
# idno = datatable['NUMBER']
ap1x = np.median(fwhm_good) # only use isolated detections of stars, this is the 1x aperture
# print ap1x
ap2x = 2.0*ap1x
# these = [ i for i,id in enumerate(idno) if (flags[i] == 0)]
# with open(image[0:-5]+'.escut.pos','w+') as f:
# for j in range(len(xpos)):
# print >> f, xpos[j], ypos[j], fwhm[j], idno[j]
iraf.datapars.setParam('fwhmpsf',ap1x)
iraf.photpars.setParam('apertures',repr(ap1x)+', '+repr(ap2x))
iraf.fitskypars.setParam('annulus',4.*ap1x)
iraf.apphot.phot(image=image, coords=pos_file, output=image[0:-5]+'.phot')
with open(image[0:-5]+'.txdump','w+') as txdump_out :
iraf.ptools.txdump(textfiles=image[0:-5]+'.phot', fields="id,mag,merr,msky,stdev,rapert,xcen,ycen,ifilter,xairmass,image", expr='MAG[1] != INDEF && MERR[1] != INDEF && MAG[2] != INDEF && MERR[2] != INDEF', headers='no', Stdout=txdump_out)
mag1x, mag2x = np.loadtxt(image[0:-5]+'.txdump', usecols=(1,2), unpack=True)
iraf_id = np.loadtxt(image[0:-5]+'.txdump', usecols=(0,), dtype=int, unpack=True)
# idno = np.loadtxt(image[0:-5]+'.escut.pos', usecols=(3,), dtype=int, unpack=True)
xpos, ypos = np.loadtxt(pos_file, usecols=(0,1), unpack=True)
keepIndex = iraf_id - 1
xpos, ypos, fwhm, peak = xpos[keepIndex], ypos[keepIndex], fwhm[keepIndex], peak[keepIndex]
# print idno.size, iraf_id.size, xpos.size
diff = mag2x - mag1x
diffCut = diff
magCut = mag2x
xCut = xpos#[good]
yCut = ypos#[good]
idCut = iraf_id
fwhmCut = fwhm#_good
peakCut = peak
print peakCut.size, magCut.size, diffCut.size
print diffCut.size, 0, np.median(diffCut), diffCut.std()
nRemoved = 1
# plt.clf()
# plt.scatter(peakCut, magCut, edgecolor='none')
# plt.savefig('peaktest.pdf')
plt.clf()
# plt.hlines(bin_edges, -2, 1, colors='red', linestyle='dashed')
plt.scatter(diff, mag2x, edgecolor='none', facecolor='black', s=4)
# plt.scatter(diffCut, magCut, edgecolor='none', facecolor='blue', s=4)
magdiff = zip(magCut.tolist(), diffCut.tolist(), peakCut.tolist(), idCut.tolist())
dtype = [('mag',float), ('diff', float), ('peak', float), ('id', int)]
magdiff = np.array(magdiff, dtype=dtype)
magSort = np.sort(magdiff, order='peak')
peakRange = (magSort['peak'] > 20000.0) & (magSort['peak'] < 40000.0)
peakVal = np.median((magSort['diff'])[np.where(peakRange)])
# peakVal = np.median(diffCut)
print peakVal
plt.scatter((magSort['diff'])[np.where(peakRange)], (magSort['mag'])[np.where(peakRange)], edgecolor='none', facecolor='blue', s=4)
while nRemoved != 0:
nBefore = diffCut.size
diffCheck = np.where(abs(peakVal-diffCut) < 2.5*diffCut.std())#[i for i,d in enumerate(diff) if (-0.5 < d < 0.0)]
#
diffCut = diffCut[diffCheck]
nRemoved = nBefore - diffCut.size
magCut = magCut[diffCheck]
xCut = xCut[diffCheck]
yCut = yCut[diffCheck]
idCut = idCut[diffCheck]
fwhmCut = fwhmCut[diffCheck]
print diffCut.size, nRemoved, np.median(diffCut), diffCut.std()
if 0.05 < diffCut.std() <0.06:
nRemoved=0
# plt.fill_betweenx(bin_centers, bin_meds+3.0*bin_stds, bin_meds-3.0*bin_stds, facecolor='red', edgecolor='none', alpha=0.4, label='2x RMS sigma clipping region')
# with open('escutSTD_i.pos','w+') as f:
# for i,blah in enumerate(xCut):
# print >> f, xCut[i], yCut[i], diffCut[i]
bin_meds, bin_edges, binnumber = stats.binned_statistic(magCut, diffCut, statistic='median', bins=24, range=(magCut.min(),magCut.max()))
bin_stds, bin_edges, binnumber = stats.binned_statistic(magCut, diffCut, statistic=np.std, bins=24, range=(magCut.min(),magCut.max()))
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width/2
# print bin_meds, bin_stds
left_edge = np.array(zip(peakVal-2.5*bin_stds, bin_centers))
right_edge = np.flipud(np.array(zip(peakVal+2.5*bin_stds, bin_centers)))
# print left_edge, right_edge
verts = np.vstack((left_edge, right_edge))
# print verts
# verts = np.delete(verts, np.array([0,1,2,22,23,24,25,45,46,47]), axis=0)
# print verts
esRegion = Path(verts)
sources = esRegion.contains_points(zip(diff,mag2x))
# print sources
with open('escutREG_i.pos','w+') as f:
for i,blah in enumerate(xpos[sources]):
print >> f, (xpos[sources])[i], (ypos[sources])[i], (diff[sources])[i]
magCut = mag2x[sources]
fwhmCut = fwhm[sources]
xCut = xpos[sources]
yCut = ypos[sources]
diffCut = diff[sources]
# find the sources that are in the std method but not the region method
# print idCut, idno[sources]
# extrasSTD = np.setdiff1d(idno[sources], idCut)
# print extrasSTD.size
# print extrasSTD
# with open('escutUNIQUE.pos','w+') as f:
# for i,blah in enumerate(extrasSTD):
# print >> f, xpos[blah-1], ypos[blah-1]
# fwhmcheck = np.loadtxt('testfwhmREG.log', usecols=(10,), unpack=True)
fwhmchk2 = np.where((magCut<-4) & (fwhmCut<90.0))
print np.median(fwhmCut[fwhmchk2]), np.std(fwhmCut[fwhmchk2])
fwchk = np.where(np.abs(fwhmCut-np.median(fwhmCut[fwhmchk2])) > 10.0*np.std(fwhmCut[fwhmchk2]))
drop = np.abs(fwhmCut-np.median(fwhmCut[fwhmchk2])) > 10.0*np.std(fwhmCut[fwhmchk2])
# fwmag = mag2x[sources]
with open('escutVBAD_i.pos','w+') as f:
for i,blah in enumerate(xCut[fwchk]):
print >> f, (xCut[fwchk])[i], (yCut[fwchk])[i]
with open('escut_r.pos','w+') as f:
for i,blah in enumerate(xCut):
if not drop[i]:
print >> f, xCut[i], yCut[i]
with open('escut_g.pos','w+') as f:
for i,blah in enumerate(xCut):
if not drop[i]:
print >> f, xCut[i], yCut[i]
plt.fill_betweenx(bin_centers, peakVal+2.5*bin_stds, peakVal-2.5*bin_stds, facecolor='red', edgecolor='none', alpha=0.4, label='2x RMS sigma clipping region')
plt.scatter(diffCut[fwchk], magCut[fwchk], edgecolor='none', facecolor='red', s=4)
plt.ylim(0,-12)
plt.xlabel('$m_{2x} - m_{1x}$')
plt.ylabel('$m_{2x}$')
plt.xlim(-2,1)
plt.savefig('testmagiraf.pdf')
plt.clf()
plt.scatter(magCut, fwhmCut, edgecolor='none', facecolor='black')
plt.scatter(magCut[fwchk], fwhmCut[fwchk], edgecolor='none', facecolor='red')
plt.hlines([np.median(fwhmCut)], -12, 0, colors='red', linestyle='dashed')
plt.hlines([np.median(fwhmCut)+fwhmCut.std(), np.median(fwhmCut)-fwhmCut.std()], -12, 0, colors='red', linestyle='dotted')
plt.ylim(0,20)
plt.xlim(-12,0)
plt.ylabel('fwhm')
plt.xlabel('$m_{2x}$')
plt.savefig('fwhmcheck.pdf')
return True