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 getFWHMfromFITScoo(fitsFile,cooFileFull):
cr=['\n']
# Only use the first coordinate
cooFile=cooFileFull+".short"
os.system(" head -n 1 "+cooFileFull+" > "+cooFile)
iraf.tv()
iraf.imexam.setParam("imagecur",cooFile)
iraf.imexam.setParam("use_display","no")
results=iraf.imexam(fitsFile,frame=1,Stdout=1,Stdin=cr)
# Line 0: headers for coords; 1: coords; 2: header for FWHM etc; we want 3
# Gaussian is -3rd element, Moffat -2, direct -1
moffatFWHM = results[3].split()[-2]
return moffatFWHM
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 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 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 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
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
#from matplotlib.pyplot import plot, draw, show
plt.ion()
plt.plot(datos[200])
plt.show()
print(type(datos), len(datos))
import numpy as np
a = np.asarray(datos)
print(a.shape)
a.shape = (512, 512)
print(type(a))
print(a.shape)
from astropy.io import fits
hdu = fits.PrimaryHDU(a)
hdu.writeto('mask4.fits')
from pyraf import iraf
iraf.tv()
iraf.tv.display('mask4.fits', 1)
plt.imshow(a, cmap='gray')
plt.colorbar()
def main():
fwhm = float(sys.argv[1])
filter_string = 'psmap'
fwhm_string = '{:3.1f}'.format(fwhm)
iraf.tv(_doprint=0)
home_root = os.environ['HOME']
imexam_flag = False
# print "Getting fits files..."
# Load the FITS header using astropy.io.fits
for file_ in os.listdir("./"):
if file_.endswith("i.fits"):
fits_file_i = file_
for file_ in os.listdir("./"):
if file_.endswith("g.fits"):
fits_file_g = file_
# downloadSDSSgal(fits_file_g, fits_file_i)
fits_i = fits.open(fits_file_i)
fits_g = fits.open(fits_file_g)
# print "Opened fits files:",fits_file_g,"&",fits_file_i
# objid = fits_i[0].header['OBJECT']
title_string = fits_file_i[0:9] # get the first 9 characters of the filename.
# set up some filenames
out_file = filter_string + '_' + fwhm_string + '_' + title_string + '.pdf'
mark_file = 'f_list_' + filter_string + '_' + fwhm_string + '_' + title_string + '.reg'
circ_file = 'c_list_' + filter_string + '_' + fwhm_string + '_' + title_string + '.reg'
mag_file = "AGC249525_daophot.dat.cut"
idr,rar,decr,ixr,iyr,am_g,g_ir,g_ierrr,am_i,i_ir,i_ierrr,g_magr,i_magr,gmir,chi,sharp,ebv,gfwhmr,fwhm_sr = np.loadtxt(mag_file,usecols=(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18),unpack=True)
gxr, gyr = ixr, iyr
# read in magnitudes, colors, and positions(x,y)
# mag_file = 'calibrated_mags.dat'
# gxr,gyr,g_magr,g_ierrr,ixr,iyr,i_magr,i_ierrr,gmir= np.loadtxt(mag_file,usecols=(0,1,2,3,4,5,6,7,8),unpack=True)
print(len(gxr), "total stars")
# filter out the things with crappy color errors
mag_error_cut = 0.99
color_error_cut = np.sqrt(2.0)*mag_error_cut
gmi_errr = [np.sqrt(g_ierrr[i]**2 + i_ierrr[i]**2) for i in range(len(gxr))]
gx = [gxr[i] for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
gy = [gyr[i] for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
g_mag = [g_magr[i] for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
g_ierr = [g_ierrr[i] for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
ix = [ixr[i] for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
iy = [iyr[i] for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
i_mag = [i_magr[i] for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
i_ierr = [i_ierrr[i] for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
gmi = [gmir[i] for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
gmi_err = [np.sqrt(g_ierrr[i]**2 + i_ierrr[i]**2) for i in range(len(gxr)) if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))]
# nid = np.loadtxt(mag_file,usecols=(0,),dtype=int,unpack=True)
pixcrd = list(zip(ix,iy))
# print "Reading WCS info from image header..."
# Parse the WCS keywords in the primary HDU
warnings.filterwarnings('ignore', category=UserWarning, append=True)
w = wcs.WCS(fits_i[0].header)
# Print out the "name" of the WCS, as defined in the FITS header
# print w.wcs.name
# Print out all of the settings that were parsed from the header
# w.wcs.print_contents()
# Convert pixel coordinates to world coordinates
# The second argument is "origin" -- in this case we're declaring we
# have 1-based (Fortran-like) coordinates.
world = w.all_pix2world(pixcrd, 1)
ra_corner, dec_corner = w.all_pix2world(0,0,1)
ra_c_d,dec_c_d = deg2HMS(ra=ra_corner, dec=dec_corner, round=True)
print('Corner RA:',ra_c_d,':: Corner Dec:',dec_c_d)
# fwhm_i = fits_i[0].header['FWHMPSF']
# fwhm_g = fits_g[0].header['FWHMPSF']
#
# print 'Image FWHM :: g = {0:5.3f} : i = {1:5.3f}'.format(fwhm_g,fwhm_i)
fits_i.close()
fits_g.close()
# split the ra and dec out into individual arrays and transform to arcmin from the corner
i_ra = [abs((world[i,0]-ra_corner)*60) for i in range(len(world[:,0]))]
i_dec = [abs((world[i,1]-dec_corner)*60) for i in range(len(world[:,1]))]
# also preserve the decimal degrees for reference
i_rad = [world[i,0] for i in range(len(world[:,0]))]
i_decd = [world[i,1] for i in range(len(world[:,1]))]
# bin the filtered stars into a grid with pixel size XXX
# print "Binning for m-M =",dm
bins = 165
width = 22
# galaxyMap(fits_file_i, fwhm, -1.0, filter_file)
grid, xedges, yedges = np.histogram2d(i_dec, i_ra, bins=[bins,bins], range=[[0,width],[0,width]])
hist_points = list(zip(xedges,yedges))
sig = ((bins/width)*fwhm)/2.355
pltsig = fwhm/2.0
# convolve the grid with a gaussian
# print "Convolving for m-M =",dm
grid_gaus = ndimage.filters.gaussian_filter(grid, sig, mode='constant', cval=0)
S = np.array(grid_gaus*0)
S_th = 3.0
# print grid_gaus[0:44][0:44]
grid_mean = np.mean(grid_gaus)
grid_sigma = np.std(grid_gaus)
S = (grid_gaus-grid_mean)/grid_sigma
above_th = [(int(i),int(j)) for i in range(len(S)) for j in range(len(S[i])) if (S[i][j] >= S_th)]
# for i in range(143) :
# for j in range(143) :
# ie = i + 22
# je = j + 22
# grid_mean = np.mean(grid_gaus[i:ie,j:je])
# grid_sigma = np.std(grid_gaus[i:ie,j:je])
# S[(11+i),(11+j)] = (grid_gaus[(11+i),(11+j)]-grid_mean)/grid_sigma
# # print i,j,grid_mean,grid_sigma,S[(22+i),(22+j)]
# find the maximum point in the grid and center the circle there
x_cent, y_cent = np.unravel_index(grid_gaus.argmax(),grid_gaus.shape)
print('Max of gaussian convolved grid located at:','('+'{0:6.3f}'.format(yedges[y_cent])+','+'{0:6.3f}'.format(xedges[x_cent])+')')
# print grid_gaus[x_cent][y_cent]
print('Value of S at above:','{0:6.3f}'.format(S[x_cent][y_cent]))
x_cent_S, y_cent_S = np.unravel_index(S.argmax(),S.shape)
print('Max of S located at:','('+'{0:6.3f}'.format(yedges[y_cent_S])+','+'{0:6.3f}'.format(xedges[x_cent_S])+')')
# print grid_gaus[x_cent][y_cent]
print('Value of S at above:','{0:6.3f}'.format(S[x_cent_S][y_cent_S]))
print('Number of bins above S_th: {0:4d}'.format(len(above_th)))
# make a circle to highlight a certain region
cosd = lambda x : np.cos(np.deg2rad(x))
sind = lambda x : np.sin(np.deg2rad(x))
x_circ = [yedges[y_cent] + 3.0*cosd(t) for t in range(0,359,1)]
y_circ = [xedges[x_cent] + 3.0*sind(t) for t in range(0,359,1)]
xy_points = list(zip(i_ra,i_dec))
verts_circ = list(zip(x_circ,y_circ))
circ_filter = Path(verts_circ)
circ_c_x = ra_corner-(yedges[y_cent]/60.)
circ_c_y = (xedges[x_cent]/60.)+dec_corner
circ_pix_x, circ_pix_y = w.wcs_world2pix(circ_c_x,circ_c_y,1)
ra_c, dec_c = w.all_pix2world(circ_pix_x, circ_pix_y,1)
# ds9_file = 'circles_psmap_' + title_string + '.reg'
# with open(ds9_file,'w+') as ds9:
# print >> ds9, "fk5;circle({:f},{:f},2') # color=cyan width=2 label=psmap".format(ra_c, dec_c)
stars_circ = circ_filter.contains_points(xy_points)
i_mag_c = [i_mag[i] for i in range(len(i_mag)) if (stars_circ[i])]
gmi_c = [gmi[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_ra_c = [i_ra[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_dec_c = [i_dec[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_rad_c = [i_rad[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_decd_c = [i_decd[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_x_c = [ix[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_y_c = [iy[i] for i in range(len(i_mag)) if (stars_circ[i])]
# f2 = open(circ_file, 'w+')
# for i in range(len(i_x_c)) :
# print >> f2, '{0:8.2f} {1:8.2f} {2:12.8f} {3:12.8f} {4:8.2f} '.format(i_x_c[i],i_y_c[i],i_rad_c[i],i_decd_c[i],i_mag_c[i],gmi_c[i])
# f2.close()
# print "{0:3d} stars in filter, {1:3d} stars in circle, {2:3d} stars in both.".format(len(i_mag_f),len(i_mag_c),len(i_mag_fc))
# for i in range(len(i_x_fc)) :
# print (i_x_fc[i],i_y_fc[i])
# circ_c_x = (yedges[y_cent]/60.)+ra_c
# circ_c_y = (xedges[x_cent]/60.)+dec_c
# circ_pix_x, circ_pix_y = w.wcs_world2pix(circ_c_x,circ_c_y,1)
#
# hi_c_ra, hi_c_dec = 142.5104167, 16.6355556
# hi_c_x, hi_c_y = abs((hi_c_ra-ra_c)*60), abs((hi_c_dec-dec_c)*60)
# hi_x_circ = [hi_c_x + pltsig*cosd(t) for t in range(0,359,1)]
# hi_y_circ = [hi_c_y + pltsig*sind(t) for t in range(0,359,1)]
#
# hi_pix_x,hi_pix_y = w.wcs_world2pix(hi_c_ra,hi_c_dec,1)
# center_file = 'im_cens_'+fwhm_string+'.dat'
# im_cens = open(center_file,'w+')
# print >> im_cens, -circ_pix_x, circ_pix_y, 'circle_center'
# print >> im_cens, hi_pix_x, hi_pix_y, 'HI_centroid'
# im_cens.close()
#
# mark_radius = int(pltsig*60/0.11)
# mark_radii = str(repr(mark_radius-2)+','+repr(mark_radius-1)+','+repr(mark_radius)+','+repr(mark_radius+1)+','+repr(mark_radius+2))
# if disp_flag :
# iraf.unlearn(iraf.tv.tvmark)
# iraf.tv.tvmark.setParam('label',"no")
# iraf.tv.tvmark.setParam('pointsize',7)
# iraf.tv.tvmark.setParam('mark',"circle")
# iraf.tv.display(image=fits_file_g, frame=1)
# iraf.tv.display(image=fits_file_i, frame=2)
# iraf.tv.tvmark(frame=1, coords=mark_file, radii="14,15,16", color=207)
# iraf.tv.tvmark(frame=1, coords=circ_file, radii="20,21,22", color=209)
# iraf.tv.tvmark(frame=1, coords=center_file, txsize=4, mark="plus", color=208, label="yes")
# iraf.tv.tvmark(frame=1, coords=center_file, radii=mark_radii, color=208)
# iraf.tv.tvmark(frame=2, coords=mark_file, radii="14,15,16", color=207, label="yes")
# iraf.tv.tvmark(frame=2, coords=circ_file, radii="20,21,22", color=209)
# iraf.tv.tvmark(frame=2, coords=center_file, txsize=4, mark="plus", color=208, label="yes")
# iraf.tv.tvmark(frame=2, coords=center_file, radii=mark_radii, color=208)
#
# setup the pdf output
pp = PdfPages('psmap_'+ out_file)
# plot
# print "Plotting for m-M = ",dm
ax0 = plt.subplot(2,2,1)
plt.scatter(i_ra, i_dec, c=gmi, marker='o', s=(27-np.array(i_mag)), edgecolors='none', cmap=cm.rainbow)
plt.plot(x_circ,y_circ,linestyle='-', color='magenta')
plt.clim(-1,4)
# plt.plot(hi_x_circ,hi_y_circ,linestyle='-', color='black')
plt.colorbar()
# plt.scatter(i_ra_c, i_dec_c, color='red', marker='o', s=3, edgecolors='none')
plt.ylabel('Dec (arcmin)')
plt.xlim(0,max(i_ra))
plt.ylim(0,max(i_dec))
plt.title('sky positions')
ax0.set_aspect('equal')
ax1 = plt.subplot(2,2,2)
plt.scatter(gmi, i_mag, c='black', marker='o', s=1, edgecolors='none')
plt.scatter(gmi_c, i_mag_c, c='red', marker='o', s=3, edgecolors='none')
plt.tick_params(axis='y',left='on',right='off',labelleft='on',labelright='off')
ax1.yaxis.set_label_position('left')
plt.ylabel('$i$')
plt.ylim(25,15)
plt.xlim(-1,4)
ax1.set_aspect(0.5)
ax2 = plt.subplot(2,2,3)
extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
plt.imshow(S, extent=extent, interpolation='nearest', cmap=cm.viridis)
cbar_S = plt.colorbar()
# cbar_S.tick_params(labelsize=10)
plt.plot(x_circ,y_circ,linestyle='-', color='magenta')
# plt.plot(hi_x_circ,hi_y_circ,linestyle='-', color='black')
# X, Y = np.meshgrid(xedges,yedges)
# ax3.pcolormesh(X,Y,grid_gaus)
plt.xlabel('RA (arcmin)')
plt.ylabel('Dec (arcmin)')
# plt.ylabel('Dec (arcmin)')
plt.xlim(0,max(i_ra))
plt.ylim(0,max(i_dec))
ax2.set_aspect('equal')
ax3 = plt.subplot(2,2,4)
# plt.imshow(imagedata)
# extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
# plt.imshow(grid, extent=extent, interpolation='nearest')
# cbar_gaus = plt.colorbar()
# # cbar_gaus.tick_params(labelsize=10)
# plt.plot(x_circ,y_circ,linestyle='-', color='magenta')
# plt.plot(hi_x_circ,hi_y_circ,linestyle='-', color='black')
# plt.xlabel('RA (arcmin)')
# plt.ylabel('Dec (arcmin)')
# plt.xlim(0,max(i_ra))
# plt.ylim(0,max(i_dec))
plt.scatter(gmi_c, i_mag_c, color='red', marker='o', s=3, edgecolors='none')
plt.tick_params(axis='y',left='on',right='off',labelleft='on',labelright='off')
ax0.yaxis.set_label_position('left')
plt.xlabel('$(g-i)$')
plt.ylabel('$i$')
plt.ylim(25,15)
plt.xlim(-1,4)
ax3.set_aspect(0.5)
plt.suptitle(title_string+ ' -- ' + fwhm_string + ' arcmin smoothing')
plt.show
pp.savefig()
pp.close()
coordFile = open(objname + '_' + filter_ + '.fits' + '.coo.1')
except:
coordFile = open(objname + '_' + filter_ + '_sh.fits' + '.coo.1')
coord = coordFile.read()
coordLines = coord.splitlines()
fwhm = float(coordLines[9].split()[3])
sigma = float(coordLines[19].split()[3])
threshold = float(coordLines[27].split()[3])
coordFile.close()
print(objname, filter_, fwhm, sigma, threshold)
print('creating psf...')
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.daophot(_doprint=0)
iraf.tables(_doprint=0)
iraf.unlearn(iraf.ptools.pselect, iraf.tables.tcreate, iraf.tables.tmatch,
iraf.tables.tinfo, iraf.tables.tdump)
# first measure the psf for the image
iraf.unlearn(iraf.phot, iraf.datapars, iraf.photpars, iraf.centerpars,
iraf.fitskypars)
#! /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 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
A comparative login.cl to pyraf """ from pyraf import iraf #load usefull packages for exoplanet reduction iraf.stsdas() iraf.toolbox() iraf.noao() iraf.astutil() iraf.proto() iraf.imred() iraf.ccdred() iraf.digiphot() iraf.apphot() iraf.images() iraf.tv() iraf.imutil() iraf.headers() iraf.immatch() iraf.crutil() #****************************************************************************** #******************** BEGIN CONFIGURATION ************************************* #****************************************************************************** #*********************************************************************** IMEXAM #give the default conditions for imexamine iraf.imexamine.unlearn() iraf.imexamine.frame = 1 iraf.imexamine.nloutput = 101 iraf.imexamine.ncoutput = 101
def main():
fwhm = float(sys.argv[1])
filter_string = 'psmap'
fwhm_string = '{:3.1f}'.format(fwhm)
iraf.tv(_doprint=0)
home_root = os.environ['HOME']
imexam_flag = False
# print "Getting fits files..."
# Load the FITS header using astropy.io.fits
for file_ in os.listdir("./"):
if file_.endswith("i.fits"):
fits_file_i = file_
for file_ in os.listdir("./"):
if file_.endswith("g.fits"):
fits_file_g = file_
# downloadSDSSgal(fits_file_g, fits_file_i)
fits_i = fits.open(fits_file_i)
fits_g = fits.open(fits_file_g)
# print "Opened fits files:",fits_file_g,"&",fits_file_i
# objid = fits_i[0].header['OBJECT']
title_string = fits_file_i[
0:9] # get the first 9 characters of the filename.
# set up some filenames
out_file = filter_string + '_' + fwhm_string + '_' + title_string + '.pdf'
mark_file = 'f_list_' + filter_string + '_' + fwhm_string + '_' + title_string + '.reg'
circ_file = 'c_list_' + filter_string + '_' + fwhm_string + '_' + title_string + '.reg'
mag_file = "AGC249525_daophot.dat.cut"
idr, rar, decr, ixr, iyr, am_g, g_ir, g_ierrr, am_i, i_ir, i_ierrr, g_magr, i_magr, gmir, chi, sharp, ebv, gfwhmr, fwhm_sr = np.loadtxt(
mag_file,
usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18),
unpack=True)
gxr, gyr = ixr, iyr
# read in magnitudes, colors, and positions(x,y)
# mag_file = 'calibrated_mags.dat'
# gxr,gyr,g_magr,g_ierrr,ixr,iyr,i_magr,i_ierrr,gmir= np.loadtxt(mag_file,usecols=(0,1,2,3,4,5,6,7,8),unpack=True)
print((len(gxr), "total stars"))
# filter out the things with crappy color errors
mag_error_cut = 0.99
color_error_cut = np.sqrt(2.0) * mag_error_cut
gmi_errr = [
np.sqrt(g_ierrr[i]**2 + i_ierrr[i]**2) for i in range(len(gxr))
]
gx = [
gxr[i] for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
gy = [
gyr[i] for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
g_mag = [
g_magr[i] for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
g_ierr = [
g_ierrr[i] for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
ix = [
ixr[i] for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
iy = [
iyr[i] for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
i_mag = [
i_magr[i] for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
i_ierr = [
i_ierrr[i] for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
gmi = [
gmir[i] for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
gmi_err = [
np.sqrt(g_ierrr[i]**2 + i_ierrr[i]**2) for i in range(len(gxr))
if (abs(gmi_errr[i] < color_error_cut and i_ierrr[i] < mag_error_cut))
]
# nid = np.loadtxt(mag_file,usecols=(0,),dtype=int,unpack=True)
pixcrd = list(zip(ix, iy))
# print "Reading WCS info from image header..."
# Parse the WCS keywords in the primary HDU
warnings.filterwarnings('ignore', category=UserWarning, append=True)
w = wcs.WCS(fits_i[0].header)
# Print out the "name" of the WCS, as defined in the FITS header
# print w.wcs.name
# Print out all of the settings that were parsed from the header
# w.wcs.print_contents()
# Convert pixel coordinates to world coordinates
# The second argument is "origin" -- in this case we're declaring we
# have 1-based (Fortran-like) coordinates.
world = w.all_pix2world(pixcrd, 1)
ra_corner, dec_corner = w.all_pix2world(0, 0, 1)
ra_c_d, dec_c_d = deg2HMS(ra=ra_corner, dec=dec_corner, round=True)
print(('Corner RA:', ra_c_d, ':: Corner Dec:', dec_c_d))
# fwhm_i = fits_i[0].header['FWHMPSF']
# fwhm_g = fits_g[0].header['FWHMPSF']
#
# print 'Image FWHM :: g = {0:5.3f} : i = {1:5.3f}'.format(fwhm_g,fwhm_i)
fits_i.close()
fits_g.close()
# split the ra and dec out into individual arrays and transform to arcmin from the corner
i_ra = [
abs((world[i, 0] - ra_corner) * 60) for i in range(len(world[:, 0]))
]
i_dec = [
abs((world[i, 1] - dec_corner) * 60) for i in range(len(world[:, 1]))
]
# also preserve the decimal degrees for reference
i_rad = [world[i, 0] for i in range(len(world[:, 0]))]
i_decd = [world[i, 1] for i in range(len(world[:, 1]))]
# bin the filtered stars into a grid with pixel size XXX
# print "Binning for m-M =",dm
bins = 165
width = 22
# galaxyMap(fits_file_i, fwhm, -1.0, filter_file)
grid, xedges, yedges = np.histogram2d(i_dec,
i_ra,
bins=[bins, bins],
range=[[0, width], [0, width]])
hist_points = list(zip(xedges, yedges))
sig = ((bins / width) * fwhm) / 2.355
pltsig = fwhm / 2.0
# convolve the grid with a gaussian
# print "Convolving for m-M =",dm
grid_gaus = ndimage.filters.gaussian_filter(grid,
sig,
mode='constant',
cval=0)
S = np.array(grid_gaus * 0)
S_th = 3.0
# print grid_gaus[0:44][0:44]
grid_mean = np.mean(grid_gaus)
grid_sigma = np.std(grid_gaus)
S = (grid_gaus - grid_mean) / grid_sigma
above_th = [(int(i), int(j)) for i in range(len(S))
for j in range(len(S[i])) if (S[i][j] >= S_th)]
# for i in range(143) :
# for j in range(143) :
# ie = i + 22
# je = j + 22
# grid_mean = np.mean(grid_gaus[i:ie,j:je])
# grid_sigma = np.std(grid_gaus[i:ie,j:je])
# S[(11+i),(11+j)] = (grid_gaus[(11+i),(11+j)]-grid_mean)/grid_sigma
# # print i,j,grid_mean,grid_sigma,S[(22+i),(22+j)]
# find the maximum point in the grid and center the circle there
x_cent, y_cent = np.unravel_index(grid_gaus.argmax(), grid_gaus.shape)
print(('Max of gaussian convolved grid located at:',
'(' + '{0:6.3f}'.format(yedges[y_cent]) + ',' +
'{0:6.3f}'.format(xedges[x_cent]) + ')'))
# print grid_gaus[x_cent][y_cent]
print(('Value of S at above:', '{0:6.3f}'.format(S[x_cent][y_cent])))
x_cent_S, y_cent_S = np.unravel_index(S.argmax(), S.shape)
print(('Max of S located at:', '(' + '{0:6.3f}'.format(yedges[y_cent_S]) +
',' + '{0:6.3f}'.format(xedges[x_cent_S]) + ')'))
# print grid_gaus[x_cent][y_cent]
print(('Value of S at above:', '{0:6.3f}'.format(S[x_cent_S][y_cent_S])))
print(('Number of bins above S_th: {0:4d}'.format(len(above_th))))
# make a circle to highlight a certain region
cosd = lambda x: np.cos(np.deg2rad(x))
sind = lambda x: np.sin(np.deg2rad(x))
x_circ = [yedges[y_cent] + 3.0 * cosd(t) for t in range(0, 359, 1)]
y_circ = [xedges[x_cent] + 3.0 * sind(t) for t in range(0, 359, 1)]
xy_points = list(zip(i_ra, i_dec))
verts_circ = list(zip(x_circ, y_circ))
circ_filter = Path(verts_circ)
circ_c_x = ra_corner - (yedges[y_cent] / 60.)
circ_c_y = (xedges[x_cent] / 60.) + dec_corner
circ_pix_x, circ_pix_y = w.wcs_world2pix(circ_c_x, circ_c_y, 1)
ra_c, dec_c = w.all_pix2world(circ_pix_x, circ_pix_y, 1)
# ds9_file = 'circles_psmap_' + title_string + '.reg'
# with open(ds9_file,'w+') as ds9:
# print >> ds9, "fk5;circle({:f},{:f},2') # color=cyan width=2 label=psmap".format(ra_c, dec_c)
stars_circ = circ_filter.contains_points(xy_points)
i_mag_c = [i_mag[i] for i in range(len(i_mag)) if (stars_circ[i])]
gmi_c = [gmi[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_ra_c = [i_ra[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_dec_c = [i_dec[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_rad_c = [i_rad[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_decd_c = [i_decd[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_x_c = [ix[i] for i in range(len(i_mag)) if (stars_circ[i])]
i_y_c = [iy[i] for i in range(len(i_mag)) if (stars_circ[i])]
# f2 = open(circ_file, 'w+')
# for i in range(len(i_x_c)) :
# print >> f2, '{0:8.2f} {1:8.2f} {2:12.8f} {3:12.8f} {4:8.2f} '.format(i_x_c[i],i_y_c[i],i_rad_c[i],i_decd_c[i],i_mag_c[i],gmi_c[i])
# f2.close()
# print "{0:3d} stars in filter, {1:3d} stars in circle, {2:3d} stars in both.".format(len(i_mag_f),len(i_mag_c),len(i_mag_fc))
# for i in range(len(i_x_fc)) :
# print (i_x_fc[i],i_y_fc[i])
# circ_c_x = (yedges[y_cent]/60.)+ra_c
# circ_c_y = (xedges[x_cent]/60.)+dec_c
# circ_pix_x, circ_pix_y = w.wcs_world2pix(circ_c_x,circ_c_y,1)
#
# hi_c_ra, hi_c_dec = 142.5104167, 16.6355556
# hi_c_x, hi_c_y = abs((hi_c_ra-ra_c)*60), abs((hi_c_dec-dec_c)*60)
# hi_x_circ = [hi_c_x + pltsig*cosd(t) for t in range(0,359,1)]
# hi_y_circ = [hi_c_y + pltsig*sind(t) for t in range(0,359,1)]
#
# hi_pix_x,hi_pix_y = w.wcs_world2pix(hi_c_ra,hi_c_dec,1)
# center_file = 'im_cens_'+fwhm_string+'.dat'
# im_cens = open(center_file,'w+')
# print >> im_cens, -circ_pix_x, circ_pix_y, 'circle_center'
# print >> im_cens, hi_pix_x, hi_pix_y, 'HI_centroid'
# im_cens.close()
#
# mark_radius = int(pltsig*60/0.11)
# mark_radii = str(repr(mark_radius-2)+','+repr(mark_radius-1)+','+repr(mark_radius)+','+repr(mark_radius+1)+','+repr(mark_radius+2))
# if disp_flag :
# iraf.unlearn(iraf.tv.tvmark)
# iraf.tv.tvmark.setParam('label',"no")
# iraf.tv.tvmark.setParam('pointsize',7)
# iraf.tv.tvmark.setParam('mark',"circle")
# iraf.tv.display(image=fits_file_g, frame=1)
# iraf.tv.display(image=fits_file_i, frame=2)
# iraf.tv.tvmark(frame=1, coords=mark_file, radii="14,15,16", color=207)
# iraf.tv.tvmark(frame=1, coords=circ_file, radii="20,21,22", color=209)
# iraf.tv.tvmark(frame=1, coords=center_file, txsize=4, mark="plus", color=208, label="yes")
# iraf.tv.tvmark(frame=1, coords=center_file, radii=mark_radii, color=208)
# iraf.tv.tvmark(frame=2, coords=mark_file, radii="14,15,16", color=207, label="yes")
# iraf.tv.tvmark(frame=2, coords=circ_file, radii="20,21,22", color=209)
# iraf.tv.tvmark(frame=2, coords=center_file, txsize=4, mark="plus", color=208, label="yes")
# iraf.tv.tvmark(frame=2, coords=center_file, radii=mark_radii, color=208)
#
# setup the pdf output
pp = PdfPages('psmap_' + out_file)
# plot
# print "Plotting for m-M = ",dm
ax0 = plt.subplot(2, 2, 1)
plt.scatter(i_ra,
i_dec,
c=gmi,
marker='o',
s=(27 - np.array(i_mag)),
edgecolors='none',
cmap=cm.rainbow)
plt.plot(x_circ, y_circ, linestyle='-', color='magenta')
plt.clim(-1, 4)
# plt.plot(hi_x_circ,hi_y_circ,linestyle='-', color='black')
plt.colorbar()
# plt.scatter(i_ra_c, i_dec_c, color='red', marker='o', s=3, edgecolors='none')
plt.ylabel('Dec (arcmin)')
plt.xlim(0, max(i_ra))
plt.ylim(0, max(i_dec))
plt.title('sky positions')
ax0.set_aspect('equal')
ax1 = plt.subplot(2, 2, 2)
plt.scatter(gmi, i_mag, c='black', marker='o', s=1, edgecolors='none')
plt.scatter(gmi_c, i_mag_c, c='red', marker='o', s=3, edgecolors='none')
plt.tick_params(axis='y',
left='on',
right='off',
labelleft='on',
labelright='off')
ax1.yaxis.set_label_position('left')
plt.ylabel('$i$')
plt.ylim(25, 15)
plt.xlim(-1, 4)
ax1.set_aspect(0.5)
ax2 = plt.subplot(2, 2, 3)
extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
plt.imshow(S, extent=extent, interpolation='nearest', cmap=cm.viridis)
cbar_S = plt.colorbar()
# cbar_S.tick_params(labelsize=10)
plt.plot(x_circ, y_circ, linestyle='-', color='magenta')
# plt.plot(hi_x_circ,hi_y_circ,linestyle='-', color='black')
# X, Y = np.meshgrid(xedges,yedges)
# ax3.pcolormesh(X,Y,grid_gaus)
plt.xlabel('RA (arcmin)')
plt.ylabel('Dec (arcmin)')
# plt.ylabel('Dec (arcmin)')
plt.xlim(0, max(i_ra))
plt.ylim(0, max(i_dec))
ax2.set_aspect('equal')
ax3 = plt.subplot(2, 2, 4)
# plt.imshow(imagedata)
# extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
# plt.imshow(grid, extent=extent, interpolation='nearest')
# cbar_gaus = plt.colorbar()
# # cbar_gaus.tick_params(labelsize=10)
# plt.plot(x_circ,y_circ,linestyle='-', color='magenta')
# plt.plot(hi_x_circ,hi_y_circ,linestyle='-', color='black')
# plt.xlabel('RA (arcmin)')
# plt.ylabel('Dec (arcmin)')
# plt.xlim(0,max(i_ra))
# plt.ylim(0,max(i_dec))
plt.scatter(gmi_c,
i_mag_c,
color='red',
marker='o',
s=3,
edgecolors='none')
plt.tick_params(axis='y',
left='on',
right='off',
labelleft='on',
labelright='off')
ax0.yaxis.set_label_position('left')
plt.xlabel('$(g-i)$')
plt.ylabel('$i$')
plt.ylim(25, 15)
plt.xlim(-1, 4)
ax3.set_aspect(0.5)
plt.suptitle(title_string + ' -- ' + fwhm_string + ' arcmin smoothing')
plt.show
pp.savefig()
pp.close()
