def psfmeasure(image,
coords,
radius,
output='STDOUT',
size='FWHM',
pxscale=None):
iraf.noao(_doprint=0)
iraf.obsutil(_doprint=0)
if size == 'all':
sname = ['FWHM', 'GFWHM', 'MFWHM']
tbls = []
for s in sname:
stbl = psfmeasure(image, coords, radius, output, s, pxscale)
tbls.append(stbl)
tbl = join(tbls[0], tbls[1])
tbl = join(tbl, tbls[2])
return tbl
results = iraf.obsutil.psfmeasure(image,
coords='mark1',
size=size,
imagecur=coords,
display='no',
radius=radius,
logfile='',
Stdout=1,
StdoutG="dev$null",
scale=1)
#print results
results = results[2:4]
results[0] = results[0].strip()
results = {k: v for k, v in zip(results[0].split(), results[1].split())}
for k in results.keys():
try:
results[k] = np.float(results[k])
except ValueError:
continue
tbl = Table([results])
tbl = tbl[[k for k in P_COLMAP.keys() if k in tbl.colnames]]
for k, v in P_COLMAP.iteritems():
if k in tbl.colnames:
tbl.rename_column(k, v)
fwhmcol = tbl.colnames[-1]
tbl[fwhmcol] /= 2.0 # make hwhm
if pxscale:
tbl.add_column(
Column(tbl[fwhmcol] * u.pix * pxscale,
name='%s_s' % fwhmcol,
unit=u.arcsec,
format='%.3f'))
tbl[fwhmcol].unit = u.pix
return tbl
def fwhm(directory,folders):
iraf.noao(_doprint=0)
iraf.obsutil(_doprint=0)
iraf.unlearn("psfmeasure")
rootdir = par.rootdir[0]
dir_contents = os.listdir(directory+'/data')
ds9s = [fn for fn in dir_contents if fn.startswith('ds9') and fn.endswith('.reg')]
ds9 = directory+'/data/'+ds9s[0]
print "\nMeasuring the fwhm of selected reference stars..."
iraf.noao.obsutil.psfmeasure.coords = "mark1"
iraf.noao.obsutil.psfmeasure.display = "no"
iraf.noao.obsutil.psfmeasure.size = "FWHM"
iraf.noao.obsutil.psfmeasure.scale = "1"
iraf.noao.obsutil.psfmeasure.radius = "11"
iraf.noao.obsutil.psfmeasure.swidth = "15"
iraf.noao.obsutil.psfmeasure.iterati = "1"
iraf.noao.obsutil.psfmeasure.imagecu = ds9
iraf.noao.obsutil.psfmeasure.graphcur = rootdir+"/src/q.reg"
iraf.noao.obsutil.psfmeasure.logfile = directory+"/data/fwhm.log"
if os.path.isfile(directory+"/data/fwhm.log"):
os.system("rm "+directory+"/data/fwhm.log")
imgs = [fn for fn in dir_contents if fn.startswith(par.image_name[0]) and fn.endswith(par.image_name[1])]
imgs = sorted(imgs)
for i in range(len(imgs)):
iraf.noao.obsutil.psfmeasure.imagecu = ds9
iraf.noao.obsutil.psfmeasure(directory+'/data/'+imgs[i])
N_stars = len(ds9)
fwhm = [[] for _ in range(len(imgs))]
values = [ line for line in open(directory+"/data/fwhm.log") if '.' in line and 'NOAO' not in line]
j = 0
for i in range(len(values)):
if values[i][2:9] == 'Average':
j += 1
if values[i][2:9] != 'Average':
fwhm[j].append(float(values[i][41:47]))
f = open(directory+'/data/fwhm.txt', 'w+')
for i in range(len(imgs)):
fwhm[i] = np.array(fwhm[i])
print >> f, np.median(fwhm[i]), imgs[i]
f.close()
def init(iraf_dir=DEFAULT_IRAF_DIR):
'''Initialize IRAF for use in photometry
@param iraf_dir The home directory for iraf, i.e. where login.cl
and uparm are located, slightly annoying that we need this, but not
much we can do about it'''
global _initialized
if _initialized:
#already initialized no need to run again
return
global iraf
global yes
global no
with workingDirectory(iraf_dir):
from pyraf import iraf
yes = iraf.yes
no = iraf.no
#now load the packages we need
iraf.noao()
iraf.digiphot()
iraf.daophot()
iraf.obsutil()
_initialized = True
return
def calculate_seeing(args):
""" Program to estimate the seeing from an image and a list of estimates
for the positions of stars. After calculating the seeing, some of the
stars might get recalculated centers. The list will be updated with the
"""
for im, im_cat in zip(args.input, args.cat):
output = "output.txt"
ignore = "ignore.txt"
# Victor Terron, esto es horroroso, sugerencias?
utilities.if_exists_remove("q.txt", output, ignore)
q = open("q.txt", "w")
q.write("q")
q.close()
iraf.noao()
iraf.obsutil()
iraf.module.psfmeasure(im, coords = "mark1", size = "MFWHM",
sbuffer = 10, swidth=10,radius=10,
satura = 55000, ignore = "yes",
imagecur = im_cat, display = "no",
graphcur = "q.txt", Stdout=ignore,
wcs = args.wcs, logfile=output)
# Now we read the input im_cat and the output output.txt and compare
# the location of stars. Those stars that have moved will not be
# trusted.
xout = np.array([])
yout = np.array([])
FWHM = np.array([])
for line in open(output, 'r'):
# First line is a description of the file, second is a newline \n
# and third the description of the columns.
if line != "\n" and line.split()[0] in im:
xout = np.append(xout, float(line.split()[1]))
yout = np.append(yout, float(line.split()[2]))
FWHM = np.append(FWHM, float(line.split()[4]))
else:
try:
xout = np.append(xout, float(line.split()[0]))
yout = np.append(yout, float(line.split()[1]))
FWHM = np.append(FWHM, float(line.split()[3]))
except:
pass
xin, yin = np.genfromtxt(im_cat, dtype="float", unpack=True)
xin, yin = np.array(xin), np.array(yin) # case it is only one value
# If args.wcs is "world" it means the input is in (RA, DEC), while
# the output is in pixels (X,Y). We need to convert one to the other
# and we choose to follow the (RA,DEC) which, after all, is meaningful
if args.wcs == "world":
coords_xy = np.array(zip(xout, yout))
hdr = fits.open(im)[0].header
remove_keys = ["PC001001", "PC001002", "PC002001", "PC002002"]
for keys in remove_keys:
hdr.pop(keys,None)
w = wcs.WCS(hdr)
coords_RADEC = w.all_pix2world(coords_xy,1)
xout = coords_RADEC[:,0]
yout = coords_RADEC[:,1]
# find common stars using KDtrees
if xin.size > 1 :
tree_in = spatial.KDTree(zip(xin, yin))
elif xin.size == 1:
tree_in = spatial.KDTree([(float(xin), float(yin))])
if xout.size > 1:
tree_out = spatial.KDTree(zip(xout, yout))
elif xout.size == 1:
tree_out = spatial.KDTree([(float(xout), float(yout))])
# If WCS use 0.001 degree (~3.6 arcsec) as limit. If not, assume
# pixels and say 4 pixels
if args.wcs == "world":
limit = 0.001
else:
limit = 4
matches = tree_out.query_ball_tree(tree_in, limit)
# Two close stars in the original .cat could resolve into one when
# the FWHM is calculated. This will appear as several hits in the
# matches with exactly the same numbers: [[0], [1,2], [1,2], [3]]
# One solution is to erase one of them
for index, value in enumerate(matches):
if matches.count(value) > 1:
matches[index] = []
# Now restrict to the common objects
remove_indices = [ii for ii,jj in enumerate(matches) if jj == []]
xout = np.delete(xout, remove_indices)
yout = np.delete(yout, remove_indices)
FWHM = np.delete(FWHM, remove_indices)
# Finally, calculate the median FWHM of the image and rewrite valid
# stars to the im_cat file.
median_fwhm = np.median(FWHM)
utilities.header_update_keyword(im, "seeing", median_fwhm, "FWHM of image")
f = open(im_cat, 'w') # write the "good" stars in the catalogue
for ii in range(len(xout)):
f.write(str(xout[ii]) + " " + str(yout[ii]) + "\n")
f.close()
# And clean after yourself!
utilities.if_exists_remove("q.txt", output, ignore)
# modified by SV for lsc
################################################################
import os, sys, shutil, subprocess
import time
from optparse import OptionParser
from pyraf import iraf
from astropy.io import fits
from astropy.io import fits
import lsc
import re
import string
import traceback
import numpy as np
iraf.noao(_doprint=0)
iraf.obsutil(_doprint=0)
def runsex(img, fwhm, thresh, pix_scale): ## run_sextractor fwhm in pixel
import lsc
mina = 5.
seeing = fwhm * pix_scale
cdef = open(lsc.__path__[0] + '/standard/sex/default2.param')
riga = cdef.readlines()
cparam = []
for r in riga:
if r[0] != '#' and len(r.strip()) > 0: \
cparam.append(r.split()[0])
import numpy as np
from scipy import interpolate
from scipy.optimize import leastsq
from astropy import wcs
from astropy.coordinates import ICRS, Galactic
from astropy import units
from astropy.io import fits,ascii
from astropy.modeling import models, fitting
from astropy.table import Table, Column
from astLib import astCoords
import matplotlib.pyplot as plt
import pyfits
import psycopg2
from pyraf import iraf
iraf.obsutil(_doprint=0)
iraf.digiphot(_doprint=0)
iraf.digiphot.daophot(_doprint=0)
import traceback
if ndmode in ['d','D','database','Database']:
conn_string = "host='10.36.2.113' dbname='postgres' user='******' password='******'"
print "Connecting to database\n ->%s" % (conn_string)
conn = psycopg2.connect(conn_string)
cursor = conn.cursor()
errlogname = 'ErrorPhot_'+time.strftime("%Y-%m-%dT%H:%M:%S", time.localtime())+'.log'
logerror = open(errlogname,'wa')
import os
from pyraf import iraf
iraf.noao()
iraf.noao.onedspec()
iraf.obsutil()
iraf.imred()
iraf.ccdr()
iraf.ech()
def normalise_mike_spectra(filename):
os.system(
"rm -f red.fits red_multic.fits red_multicim.fits red_multicom.fits red_multicimcom.fits endred.fits a.fits b.fits c.fits d.fits e.fits f.fits *.par"
)
iraf.noao.onedspec.scopy(input=filename, output="red.fits", aperture="", bands=2, beams="1", format="multispec")
iraf.noao.onedspec.cont(input="red.fits", output="red_multic.fits", order=6, overrid="yes", markrej="no")
iraf.images.imutil.imarith(operand1="red.fits", op="/", operand2="red_multic.fits", result="red_multicim.fits")
iraf.noao.onedspec.scombine("red.fits", "red_multicom.fits", combine="sum", group="images")
iraf.noao.onedspec.scombine("red_multicim.fits", "red_multicimcom.fits", combine="sum", group="images")
iraf.images.imutil.imarith(
operand1="red_multicom.fits", op="/", operand2="red_multicimcom.fits", result="endred.fits"
)
os.system("cp endred.fits %s_norm.fits" % (filename.replace(".fits", ""),))
iraf.noao.onedspec.wspectext(
input="%s_norm.fits" % (filename.replace(".fits", ""),),
output="%s_norm.txt" % (filename.replace(".fits", ""),),
header="no",
)
os.system(
