Ejemplo n.º 1
0
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
Ejemplo n.º 2
0
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	
Ejemplo n.º 3
0
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]
Ejemplo n.º 4
0
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
Ejemplo n.º 5
0
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()
Ejemplo n.º 6
0
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
Ejemplo n.º 7
0
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()
Ejemplo n.º 8
0
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()
Ejemplo n.º 9
0
        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)
Ejemplo n.º 10
0
#! /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))
Ejemplo n.º 11
0
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
Ejemplo n.º 12
0
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
Ejemplo n.º 13
0
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()