def printratirhtml():
#Reads in final magnitudes
plotra, plotdec, plotrmag, plotrmagerr, plotimag, plotimagerr, plotzmag, plotzmagerr, plotymag, plotymagerr, plotJmag, plotJmagerr, plotHmag, plotHmagerr = np.loadtxt('./finalmags.txt', unpack=True)
pl.figure()
pl.xlim([15,22])
pl.ylim([-0.01,0.2])
pl.plot(plotrmag, plotrmagerr, marker='o',linestyle='None', label='r', color='purple')
pl.plot(plotimag, plotimagerr, marker='o',linestyle='None', label='i', color='blue')
pl.plot(plotzmag, plotzmagerr, marker='o',linestyle='None', label='z', color='aqua')
pl.plot(plotymag, plotymagerr, marker='o',linestyle='None', label='y', color='green')
pl.plot(plotJmag, plotJmagerr, marker='o',linestyle='None', label='J', color='orange')
pl.plot(plotHmag, plotHmagerr, marker='o',linestyle='None', label='H', color='red')
pl.xlabel('AB Magnitude')
pl.ylabel(r"$\Delta$ Mag")
pl.legend(loc='lower right')
pl.savefig( 'photcomp.png', bbox_inches='tight')
pl.clf()
f = open( './ratir.html', 'w' )
f.write( '<!DOCTYPE HTML>\n' )
f.write( '<HTML>\n' )
f.write( '<HEAD>\n' )
f.write( '<TITLE>RATIR DATA</TITLE>\n' )
f.write( '</HEAD>\n' )
f.write( '<BODY BGCOLOR="#FFFFFF" TEXT="#003300">\n' )
#Finds filter image files with green circles over sources and writes to HTML
prefchar = 'coadd'
zffiles = pplib.choosefiles( prefchar + '*_?.png' )
im_wid = 400
f.write( '<IMG SRC="./color.png" width="' + `im_wid` + '"><BR>\n' )
for i in range(len(zffiles)):
f.write( '<IMG SRC="./' + zffiles[i] + '" width="' + `im_wid` + '">\n' )
if (i+1)%3 == 0:
f.write( '<BR>\n' )
f.write( '<BR><HR><FONT SIZE="+2" COLOR="#006600">AB System Photometry (sources within 1 arcmin):</FONT><BR>\n' )
f.write( 'Notes: Non-zero magnitudes with uncertainty of zero are 3-sigma upper limits. Sources with magnitudes of 0.0000 are unobserved.<BR>\n' )
f.write( ' Circles in images above match aperture size used in sextractor.<BR>\n' )
f.write( '<br />\n' )
#Writes table with all magnitudes
f.write( '<table border="1" width="100%">\n' )
f.write( '<tr><th>#</th><th>RA</th><th>DEC</th><th>r MAG</th><th>r MAG ERR</th><th>i MAG</th><th>i MAG ERR</th><th>z MAG</th><th>z MAG ERR</th><th>y MAG</th><th>y MAG ERR</th><th>J MAG</th><th>J MAG ERR</th><th>H MAG</th><th>H MAG ERR</th><tr>\n' )
for i in range(len(plotra)):
f.write( '<tr><td>{:.0f}</td><td>{:.6f}</td><td>{:.6f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td><td>{:.2f}</td></tr>\n'.format(i,plotra[i],plotdec[i],plotrmag[i],plotrmagerr[i],plotimag[i],plotimagerr[i],plotzmag[i],plotzmagerr[i],plotymag[i],plotymagerr[i],plotJmag[i],plotJmagerr[i],plotHmag[i],plotHmagerr[i]) )
f.write( '</table>\n' )
f.write( '</PRE><BR><HR>\n' )
f.write( '<IMG SRC="photcomp.png">\n' )
f.write( '</PRE><BR><HR>\n' )
f.write( '</BODY>\n' )
f.write( '</HTML>\n' )
f.close()
def completephot(wildcardsearch):
fitsfiles = pplib.choosefiles(wildcardsearch)
f = open('photometrymultiple.txt','w')
for file in fitsfiles:
[fitsfile, filter, mag, magerr, correrr] = quickphot(file)
f.write(fitsfile + '\t' + filter + '\t' + ut + '\t' + str(mag) + '\t' + str(magerr) + '\t' + str(correrr) + '\n')
f.close()
def printhtml(filters, colnames):
#Reads in final magnitudes
colgrab = np.loadtxt('./finalmags.txt', unpack=True)
#Store each column into dictionary based on colnames and create header for HTML header from colnames
plotdict = {}
t = '<tr><th>#</th>'
for i in np.arange(len(colnames)):
plotdict[colnames[i]] = colgrab[i,:]
t = t + '<th>' + colnames[i]+'</th>'
t = t + '<tr>\n'
colors = ['black', 'purple','blue','aqua','green','orange','red','yellow', 'magenta']
pl.figure()
pl.xlim([15,22])
pl.ylim([-0.01,0.2])
#For each filter, plot mag vs. error for photocomp.png
counter = 0
for filter in filters:
pl.plot(plotdict[filter+'mag'], plotdict[filter+'magerr'], marker='o',linestyle='None', label=filter, color=colors[counter])
counter = counter + 1
pl.xlabel('AB Magnitude')
pl.ylabel(r"$\Delta$ Mag")
pl.legend(loc='lower right')
pl.savefig( 'photcomp.png', bbox_inches='tight')
pl.clf()
#Create html page that displays images made in plotphotom.py and values from finalmags.txt
f = open( './photom.html', 'w' )
f.write( '<!DOCTYPE HTML>\n' )
f.write( '<HTML>\n' )
f.write( '<HEAD>\n' )
f.write( '<TITLE>PHOTOMETRY DATA</TITLE>\n' )
f.write( '</HEAD>\n' )
f.write( '<BODY BGCOLOR="#FFFFFF" TEXT="#003300">\n' )
#Finds filter image files with green circles over sources and writes to HTML
prefchar = 'coadd'
zffiles = pplib.choosefiles( prefchar + '*_?.png' )
im_wid = 400
f.write( '<IMG SRC="./color.png" width="' + `im_wid` + '"><BR>\n' )
for i in range(len(zffiles)):
f.write( '<IMG SRC="./' + zffiles[i] + '" width="' + `im_wid` + '">\n' )
if (i+1)%3 == 0:
f.write( '<BR>\n' )
f.write( '<BR><HR><FONT SIZE="+2" COLOR="#006600">AB System Photometry (sources within 1 arcmin):</FONT><BR>\n' )
f.write( 'Notes: Non-zero magnitudes with uncertainty of zero are 3-sigma upper limits. Sources with magnitudes of 0.0000 are unobserved.<BR>\n' )
f.write( ' Circles in images above match aperture size used in sextractor.<BR>\n' )
f.write( '<br />\n' )
#Writes table with all magnitudes
f.write( '<table border="1" width="100%">\n' )
f.write( t )
for j in np.arange(len(plotdict[colnames[0]])):
f.write('<tr><td>{:.0f}</td>'.format(j))
for col in colnames:
f.write('<td>{:.3f}</td>'.format(plotdict[col][j]))
f.write('<tr>\n')
f.write( '</table>\n' )
f.write( '</PRE><BR><HR>\n' )
f.write( '<IMG SRC="photcomp.png">\n' )
f.write( '</PRE><BR><HR>\n' )
f.write( '</BODY>\n' )
f.write( '</HTML>\n' )
f.close()
def plotphotom(prefchar='coadd'):
#Initialize arrays
filters = ['r','i','z','y','J','H']
arr_size = 10000
#Creates a list of names: ['ra', 'dec', '(FILTER)mag', '(FILTER)magerr'] including
#all filters in filters list
#Also initializes filter index dictionary for use later
names = ['ra', 'dec']
ifiltdict = {}
for filter in filters:
names.extend([filter+'mag', filter+'magerr'])
ifiltdict[filter] = -1
#Create dictionary with keys from names list and all set to np.zeros(arr_size)
#easily changed for other filters
plotdict = {}
for name in names:
plotdict[name] = np.zeros(arr_size)
# retrieve detection files
zffiles = pplib.choosefiles( prefchar+'*_?.ref.multi.fits' )
#Save filter and data from each file into arrays and find overlapping stars
#between filter files using final photometry file (comparing distances from RA and DEC)
cfilterarr = []
imgarr = []
harr = []
for i in range(len(zffiles)):
#Find filter of each file and make sure that has the right capitalization
cfilter = zffiles[i].split('_')[1].split('.')[0]
if cfilter == 'Z' or cfilter == 'Y':
cfilter = cfilter.lower()
cfilterarr.append(cfilter)
#Save data scaled by scale factor to imgarr
ifile = zffiles[i]
hdulist = pf.open(ifile)
h = hdulist[0].header
img = hdulist[0].data
im_size = np.shape(img)
scalefactor = 1.
img = zoom( img, 1./scalefactor, order=0 )
imgarr.append(img)
harr.append(h)
#Read in finalphot[FILTER].am which has the instrument corrected photometry
pfile = 'finalphot' + cfilter + '.am'
x, y, ra, dec, mag, magerr = np.loadtxt(pfile, unpack=True)
clen = len(mag)
#For first file initialize variables for following files, use temporary variables
#for comparison
if i == 0:
plotdict['ra'][0:clen] = ra
plotdict['dec'][0:clen] = dec
plotdict[cfilter+'mag'][0:clen] = mag
plotdict[cfilter+'magerr'][0:clen] = magerr
nstars = clen
else:
compra = ra
compdec = dec
compmag = mag
compmagerr = magerr
#For each source in file find any sources that are within 1 arcsecond
#if these exist then store information in same index but different filter's magnitude
#array. If these don't exist, put on the end of filter's (and position) arrays
#to signify a new source
for j in range(len(compra)):
smatch = pplib.nearest( compra[j]*np.cos(compdec[j]*np.pi/180.), compdec[j],
plotdict['ra']*np.cos(plotdict['dec']*np.pi/180.), plotdict['dec'], maxdist=1./3600. )
if any(smatch):
plotdict[cfilter+'mag'][smatch] = compmag[j]
plotdict[cfilter+'magerr'][smatch] = compmagerr[j]
else:
plotdict['ra'][nstars] = compra[j]
plotdict['dec'][nstars] = compdec[j]
plotdict[cfilter+'mag'][nstars] = compmag[j]
plotdict[cfilter+'magerr'][nstars] = compmagerr[j]
nstars += 1
imgarr = np.array(imgarr)
#Save stars to finalmags.txt with correct format and removes zeros
store = np.zeros(nstars)
for name in names:
store = np.vstack( (store,plotdict[name][:nstars]) )
store = store[1:, :] #Removes 0's from initialization
#Finds sources that are cut off on at least one filter using weight maps
#and checking if 25% of the number of pixels in a 10 pixel radius circle
#are 0 in the weight map of each filter (i.e. cut off)
sra = store[0]
sdec = store[1]
removesource = []
for s in np.arange(len(sra)):
for file in zffiles:
nzero = 0
wmultifile = file[:-4]+'weight.fits'
hlist = pf.open(wmultifile)
wdata = hlist[0].data
wh = hlist[0].header
w = wcs.WCS(wh)
spix = w.wcs_world2pix(sra[s],sdec[s], 1)
wcir = pplib.circle(spix[0],spix[1],10)
ncir = len(wcir)
for coord in wcir:
if (wh['NAXIS1'] > coord[0] > 0) & (wh['NAXIS2'] > coord[1] > 0):
if wdata[int(coord[1])][int(coord[0])] == 0: nzero += 1
else:
ncir -= 1
if nzero >= 0.25*ncir:
removesource.append(s)
break
store = np.delete(store, removesource, axis=1)
np.savetxt('finalmags.txt', store.T, fmt='%12.6f')
#Find the index of the file that corresponds to each filter and save
#to ifiltdict (initialized to -1)
for item in ifiltdict:
try:
ifiltdict[item] = cfilterarr.index(item)
except ValueError:
pass
#Determines colors based on which filters are present.
#Red = J/H, green = z/y, blue = r/i
#If neither filter present, set to 0, if one present, use imgarr of data from that filter
#if both present use half from imgarr of data from each filter
def fcolor(filt1, filt2, ifiltdict, imgarr):
if filt1 and filt2 in ifiltdict:
if ifiltdict[filt1] >= 0 and ifiltdict[filt2] >= 0:
x = imgarr[ifiltdict[filt1],:,:] * 0.5 + imgarr[ifiltdict[filt2],:,:] * 0.5
if ifiltdict[filt1] >= 0 and ifiltdict[filt2] < 0:
x = imgarr[ifiltdict[filt1],:,:]
if ifiltdict[filt2] >= 0 and ifiltdict[filt1] < 0:
x = imgarr[ifiltdict[filt2],:,:]
if ifiltdict[filt2] < 0 and ifiltdict[filt1] < 0:
x = 0
else:
print 'Valid filters were not supplied, set color to 0'
x = 0
return x
red = fcolor('J', 'H', ifiltdict, imgarr)
green = fcolor('z', 'y', ifiltdict, imgarr)
blue = fcolor('r', 'i', ifiltdict, imgarr)
#Determine image size base on if color filter exists (priority: red, green, blue in that order)
if np.size(red) > 1:
im_size = np.shape(red)
elif np.size(green) > 1:
im_size = np.shape(green)
elif np.size(blue) > 1:
im_size = np.shape(blue)
def bytearr( x, y, z ):
return np.zeros((x,y,z)).astype(np.uint8)
#Create color of image and save to color.png
color = bytearr( im_size[0], im_size[1], 3 )
#Changes color into bytescale range and saves to color array
if np.size(blue) > 1:
blue = bytescale(blue, 0, 8, 250)
color[:,:,2] = blue * 0.5
if np.size(green) > 1:
green = bytescale(green, 0, 8, 250)
color[:,:,1] = green * 0.5
if np.size(red) > 1:
red = bytescale(red, 0, 8, 250)
color[:,:,0] = red * 0.5
color = color[::-1,:]
fig = pl.figure('color image')
pl.axis('off')
pl.imshow( color, interpolation='None', origin='lower' )
sp.misc.imsave( 'color.png', color )
#Find aperture size to make circles around sources that match sextractor aperture
pline=''
sfile = open('ratir_weighted.sex', 'r')
for line in sfile:
if 'PHOT_APERTURES' in line: pline=line
sfile.close()
bpline = pline.split()
if len(bpline) != 0:
aper = int(bpline[1])/2.0 #radius
else:
aper = 10
objra = store[0]
objdec = store[1]
#Plot each image with circles on star identification
for i in range(len(zffiles)):
ifile = zffiles[i]
img = imgarr[i]
h = harr[i]
cfilter = cfilterarr[i]
scale = bytescale(img, 0, 10, 255)
dpi = 72. # px per inch
figsize = (np.array(img.shape)/dpi)[::-1]
fig = pl.figure(i)
pl.imshow( scale, interpolation='None', cmap=pl.cm.gray, origin='lower' )
xlims = pl.xlim()
ylims = pl.ylim()
# Parse the WCS keywords in the primary HDU
w = wcs.WCS(h)
world = np.transpose([objra, objdec])
pixcrd = w.wcs_world2pix(world, 1)
fs = 20
fw = 'normal'
lw = 2
#For each star create a circle and plot in green
#If pixel coordinates of star (from WCS conversion of RA and DEC) and within the
#x and y limits, then put text on right side, otherwise put on left
for j in range(len(objra)):
ctemp = pplib.circle( pixcrd[j][0]/scalefactor, pixcrd[j][1]/scalefactor, aper ).T
pl.plot( ctemp[0], ctemp[1], c='#00ff00', lw=lw )
if pixcrd[j][0]/scalefactor+40 < xlims[1] and pixcrd[j][1]/scalefactor+20 < ylims[1]:
pl.text( pixcrd[j][0]/scalefactor+15, pixcrd[j][1]/scalefactor, `j`, color='#00ff00', fontsize=fs, fontweight=fw )
else:
pl.text( pixcrd[j][0]/scalefactor-45, pixcrd[j][1]/scalefactor-20, `j`, color='#00ff00', fontsize=fs, fontweight=fw )
#Label plot and remove axes, save to filename+.png
pl.text( 0.2*xlims[1], 0.9*ylims[1], cfilter+'-Band', color='r', fontsize=fs, fontweight=fw )
a = pl.gca()
a.set_frame_on(False)
a.set_xticks([]); a.set_yticks([])
pl.axis('off')
pl.xlim(xlims)
pl.ylim(ylims)
fig.set_size_inches(figsize[0], figsize[1])
ofile = ifile.split('.')[0] + '.png'
pl.savefig( ofile, bbox_inches='tight', pad_inches=0, transparent=True, dpi=dpi )
pl.close()
#Create HTML to do quick look at data
printhtml.printhtml(filters, names)
def plotphotom(prefchar='coadd'):
#Initialize arrays
filters = ['r', 'i', 'z', 'y', 'J', 'H']
arr_size = 10000
#Creates a list of names: ['ra', 'dec', '(FILTER)mag', '(FILTER)magerr'] including
#all filters in filters list
#Also initializes filter index dictionary for use later
names = ['ra', 'dec']
ifiltdict = {}
for filter in filters:
names.extend([filter + 'mag', filter + 'magerr'])
ifiltdict[filter] = -1
#Create dictionary with keys from names list and all set to np.zeros(arr_size)
#easily changed for other filters
plotdict = {}
for name in names:
plotdict[name] = np.zeros(arr_size)
# retrieve detection files
zffiles = pplib.choosefiles(prefchar + '*_?.ref.multi.fits')
#Save filter and data from each file into arrays and find overlapping stars
#between filter files using final photometry file (comparing distances from RA and DEC)
cfilterarr = []
imgarr = []
harr = []
for i in range(len(zffiles)):
#Find filter of each file and make sure that has the right capitalization
cfilter = zffiles[i].split('_')[1].split('.')[0]
if cfilter == 'Z' or cfilter == 'Y':
cfilter = cfilter.lower()
cfilterarr.append(cfilter)
#Save data scaled by scale factor to imgarr
ifile = zffiles[i]
hdulist = pf.open(ifile)
h = hdulist[0].header
img = hdulist[0].data
im_size = np.shape(img)
scalefactor = 1.
img = zoom(img, 1. / scalefactor, order=0)
imgarr.append(img)
harr.append(h)
#Read in finalphot[FILTER].am which has the instrument corrected photometry
pfile = 'finalphot' + cfilter + '.am'
x, y, ra, dec, mag, magerr = np.loadtxt(pfile, unpack=True)
clen = len(mag)
#For first file initialize variables for following files, use temporary variables
#for comparison
if i == 0:
plotdict['ra'][0:clen] = ra
plotdict['dec'][0:clen] = dec
plotdict[cfilter + 'mag'][0:clen] = mag
plotdict[cfilter + 'magerr'][0:clen] = magerr
nstars = clen
else:
compra = ra
compdec = dec
compmag = mag
compmagerr = magerr
#For each source in file find any sources that are within 1 arcsecond
#if these exist then store information in same index but different filter's magnitude
#array. If these don't exist, put on the end of filter's (and position) arrays
#to signify a new source
for j in range(len(compra)):
smatch = pplib.nearest(
compra[j] * np.cos(compdec[j] * np.pi / 180.),
compdec[j],
plotdict['ra'] * np.cos(plotdict['dec'] * np.pi / 180.),
plotdict['dec'],
maxdist=1. / 3600.)
if any(smatch):
plotdict[cfilter + 'mag'][smatch] = compmag[j]
plotdict[cfilter + 'magerr'][smatch] = compmagerr[j]
else:
plotdict['ra'][nstars] = compra[j]
plotdict['dec'][nstars] = compdec[j]
plotdict[cfilter + 'mag'][nstars] = compmag[j]
plotdict[cfilter + 'magerr'][nstars] = compmagerr[j]
nstars += 1
imgarr = np.array(imgarr)
#Save stars to finalmags.txt with correct format and removes zeros
store = np.zeros(nstars)
for name in names:
store = np.vstack((store, plotdict[name][:nstars]))
store = store[1:, :] #Removes 0's from initialization
#Finds sources that are cut off on at least one filter using weight maps
#and checking if 25% of the number of pixels in a 10 pixel radius circle
#are 0 in the weight map of each filter (i.e. cut off)
sra = store[0]
sdec = store[1]
removesource = []
for s in np.arange(len(sra)):
for file in zffiles:
nzero = 0
wmultifile = file[:-4] + 'weight.fits'
hlist = pf.open(wmultifile)
wdata = hlist[0].data
wh = hlist[0].header
w = wcs.WCS(wh)
spix = w.wcs_world2pix(sra[s], sdec[s], 1)
wcir = pplib.circle(spix[0], spix[1], 10)
ncir = len(wcir)
for coord in wcir:
if (wh['NAXIS1'] > coord[0] > 0) & (wh['NAXIS2'] > coord[1] >
0):
if wdata[int(coord[1])][int(coord[0])] == 0: nzero += 1
else:
ncir -= 1
if nzero >= 0.25 * ncir:
removesource.append(s)
break
store = np.delete(store, removesource, axis=1)
np.savetxt('finalmags.txt', store.T, fmt='%12.6f')
#Find the index of the file that corresponds to each filter and save
#to ifiltdict (initialized to -1)
for item in ifiltdict:
try:
ifiltdict[item] = cfilterarr.index(item)
except ValueError:
pass
#Determines colors based on which filters are present.
#Red = J/H, green = z/y, blue = r/i
#If neither filter present, set to 0, if one present, use imgarr of data from that filter
#if both present use half from imgarr of data from each filter
def fcolor(filt1, filt2, ifiltdict, imgarr):
if filt1 and filt2 in ifiltdict:
if ifiltdict[filt1] >= 0 and ifiltdict[filt2] >= 0:
x = imgarr[ifiltdict[filt1], :, :] * 0.5 + imgarr[
ifiltdict[filt2], :, :] * 0.5
if ifiltdict[filt1] >= 0 and ifiltdict[filt2] < 0:
x = imgarr[ifiltdict[filt1], :, :]
if ifiltdict[filt2] >= 0 and ifiltdict[filt1] < 0:
x = imgarr[ifiltdict[filt2], :, :]
if ifiltdict[filt2] < 0 and ifiltdict[filt1] < 0:
x = 0
else:
print 'Valid filters were not supplied, set color to 0'
x = 0
return x
red = fcolor('J', 'H', ifiltdict, imgarr)
green = fcolor('z', 'y', ifiltdict, imgarr)
blue = fcolor('r', 'i', ifiltdict, imgarr)
#Determine image size base on if color filter exists (priority: red, green, blue in that order)
if np.size(red) > 1:
im_size = np.shape(red)
elif np.size(green) > 1:
im_size = np.shape(green)
elif np.size(blue) > 1:
im_size = np.shape(blue)
def bytearr(x, y, z):
return np.zeros((x, y, z)).astype(np.uint8)
#Create color of image and save to color.png
color = bytearr(im_size[0], im_size[1], 3)
#Changes color into bytescale range and saves to color array
if np.size(blue) > 1:
blue = bytescale(blue, 0, 8, 250)
color[:, :, 2] = blue * 0.5
if np.size(green) > 1:
green = bytescale(green, 0, 8, 250)
color[:, :, 1] = green * 0.5
if np.size(red) > 1:
red = bytescale(red, 0, 8, 250)
color[:, :, 0] = red * 0.5
color = color[::-1, :]
fig = pl.figure('color image')
pl.axis('off')
pl.imshow(color, interpolation='None', origin='lower')
sp.misc.imsave('color.png', color)
#Find aperture size to make circles around sources that match sextractor aperture
pline = ''
sfile = open('ratir_weighted.sex', 'r')
for line in sfile:
if 'PHOT_APERTURES' in line: pline = line
sfile.close()
bpline = pline.split()
if len(bpline) != 0:
aper = int(bpline[1]) / 2.0 #radius
else:
aper = 10
objra = store[0]
objdec = store[1]
#Plot each image with circles on star identification
for i in range(len(zffiles)):
ifile = zffiles[i]
img = imgarr[i]
h = harr[i]
cfilter = cfilterarr[i]
scale = bytescale(img, 0, 10, 255)
dpi = 72. # px per inch
figsize = (np.array(img.shape) / dpi)[::-1]
fig = pl.figure(i)
pl.imshow(scale, interpolation='None', cmap=pl.cm.gray, origin='lower')
xlims = pl.xlim()
ylims = pl.ylim()
# Parse the WCS keywords in the primary HDU
w = wcs.WCS(h)
world = np.transpose([objra, objdec])
pixcrd = w.wcs_world2pix(world, 1)
fs = 20
fw = 'normal'
lw = 2
#For each star create a circle and plot in green
#If pixel coordinates of star (from WCS conversion of RA and DEC) and within the
#x and y limits, then put text on right side, otherwise put on left
for j in range(len(objra)):
ctemp = pplib.circle(pixcrd[j][0] / scalefactor,
pixcrd[j][1] / scalefactor, aper).T
pl.plot(ctemp[0], ctemp[1], c='#00ff00', lw=lw)
if pixcrd[j][0] / scalefactor + 40 < xlims[
1] and pixcrd[j][1] / scalefactor + 20 < ylims[1]:
pl.text(pixcrd[j][0] / scalefactor + 15,
pixcrd[j][1] / scalefactor, ` j `,
color='#00ff00',
fontsize=fs,
fontweight=fw)
else:
pl.text(pixcrd[j][0] / scalefactor - 45,
pixcrd[j][1] / scalefactor - 20, ` j `,
color='#00ff00',
fontsize=fs,
fontweight=fw)
#Label plot and remove axes, save to filename+.png
pl.text(0.2 * xlims[1],
0.9 * ylims[1],
cfilter + '-Band',
color='r',
fontsize=fs,
fontweight=fw)
a = pl.gca()
a.set_frame_on(False)
a.set_xticks([])
a.set_yticks([])
pl.axis('off')
pl.xlim(xlims)
pl.ylim(ylims)
fig.set_size_inches(figsize[0], figsize[1])
ofile = ifile.split('.')[0] + '.png'
pl.savefig(ofile,
bbox_inches='tight',
pad_inches=0,
transparent=True,
dpi=dpi)
pl.close()
#Create HTML to do quick look at data
printhtml.printhtml(filters, names)
def printhtml(filters, colnames, omitted_colnames, headers, headers_names):
#Reads in final magnitudes
colgrab = np.loadtxt('./finalmags.txt', unpack=True)
#Store each column into dictionary based on colnames and create header for HTML header from colnames
plotdict = {}
t = '<tr><th>#</th>'
for i in np.arange(len(colnames)):
plotdict[colnames[i]] = colgrab[i, :]
if not (colnames[i] in omitted_colnames):
t = t + '<th>' + colnames[i] + '</th>'
t = t + '</tr>\n'
colors = [
'black', 'purple', 'blue', 'aqua', 'green', 'orange', 'red', 'yellow',
'magenta'
]
print 'Plotting AB Magnitude comparison'
plot_mag(filters, plotdict, colors)
print 'Plotting SEDs'
plot_seds(filters)
#Create html page that displays images made in plotphotom.py and values from finalmags.txt
f = open('./photom.html', 'w')
f.write('<!DOCTYPE HTML>\n')
f.write('<HTML>\n')
f.write('<HEAD>\n')
f.write('<TITLE>PHOTOMETRY DATA</TITLE>\n')
f.write('</HEAD>\n')
f.write('<BODY BGCOLOR="#FFFFFF" TEXT="#003300">\n')
#Finds filter image files with green circles over sources and writes to HTML
prefchar = 'coadd'
zffiles = pplib.choosefiles(prefchar + '*_?.png')
im_wid = 400
f.write('<IMG SRC="./color.png" width="' + ` im_wid ` + '"><BR>\n')
for i in range(len(zffiles)):
f.write('<IMG SRC="./' + zffiles[i] + '" width="' + ` im_wid ` +
'">\n')
if (i + 1) % 3 == 0:
f.write('<BR>\n')
#Write table with header info from each image
f.write('<BR>\n')
f.write(generate_headers_table(headers, headers_names))
f.write('<BR>\n')
f.write(
'<BR><HR><FONT SIZE="+2" COLOR="#006600">AB System Photometry (sources within 1 arcmin):</FONT><BR>\n'
)
f.write(
'Notes: Non-zero magnitudes with uncertainty of zero are 3-sigma upper limits. Sources with magnitudes of 0.0000 are unobserved.<BR>\n'
)
f.write(
' Circles in images above match aperture size used in sextractor.<BR>\n'
)
f.write('<BR>\n')
#Writes table with all magnitudes
f.write('<table border="1" width="100%">\n')
f.write(t)
for j in np.arange(len(plotdict[colnames[0]])):
f.write('<tr><td>{:.0f}</td>'.format(j))
for col in colnames:
if not (col in omitted_colnames):
f.write('<td>{:.3f}</td>'.format(plotdict[col][j]))
f.write('<tr>\n')
f.write('</table>\n')
f.write('<BR><HR>\n')
f.write(
'<table><tbody><tr><td><IMG SRC="photcomp.plot.png"/></td></tr></tbody></table>\n'
)
f.write('<BR><HR>\n')
f.write('</BODY>\n')
f.write('</HTML>\n')
f.close()
def photom():
#Identify files (must have same number of images files as weight files)
prefchar = 'coadd'
zffiles = pplib.choosefiles(prefchar + '*_?.fits')
weightfiles = pplib.choosefiles(prefchar + '*_?.weight.fits')
if len(zffiles) > len(weightfiles):
print 'Must have matching weight file to each image file to run automatic crop.'
print 'To use manual crop user manualcrop keyword and change crop values by hand'
return -1
numfiles = len(zffiles)
#Resample all images using SWarp to a reference image called multicolor using weight files
swarpstr = ''
for i in range(numfiles):
swarpstr = swarpstr + zffiles[i] + ' '
stackcmd = 'swarp ' + swarpstr + '-DELETE_TMPFILES N -WRITE_XML N -SUBTRACT_BACK N -WEIGHT_TYPE MAP_WEIGHT -IMAGEOUT_NAME multicolor.fits -WEIGHTOUT_NAME multicolor.weight.fits'
stackcmd = stackcmd + ' -COPY_KEYWORDS OBJECT,TARGNAME,TELESCOP,FILTER,INSTRUME,OBSERVAT,PIXSCALE,ORIGIN,CCD_TYPE,JD,DATE-OBS,AIRMASS,FLATFLD,FLATTYPE,SEEPIX,ABSZPT,ABSZPTSC,ABSZPRMS'
print stackcmd
os.system( stackcmd )
#Rename all the resampled files to crop files
for i in range(numfiles):
tmp = zffiles[i].split('.')[0]
ifile = tmp + '.resamp.fits'
ofile = tmp + '.ref.fits'
mvcmd = 'mv -f ' + ifile + ' ' + ofile
os.system(mvcmd)
ifile = tmp + '.resamp.weight.fits'
ofile = tmp + '.ref.weight.fits'
mvcmd = 'mv -f ' + ifile + ' ' + ofile
os.system(mvcmd)
#CHANGE FOR RIMAS
coaddfiles = pplib.choosefiles('coadd*_?.ref.fits')
ra1arr = []
dec1arr = []
ra2arr = []
dec2arr = []
#Finds the RA and DEC of the first and the last pixel of each cropped coadded file
for files in coaddfiles:
fitsfile = pf.open(files)
fitsheader = fitsfile[0].header
data = fitsfile[0].data
imSize = shape(data)
#Converts pixel value to RA and DEC using header information (AstroPy function)
w = wcs.WCS(fitsheader)
pixcrd = [[0.,0.], [imSize[1]-1.0, imSize[0]-1.0]]
[[ra1,dec1],[ra2,dec2]] = w.wcs_pix2world(pixcrd, 0)
#Stores information into arrays
ra1arr.append(ra1)
dec1arr.append(dec1)
ra2arr.append(ra2)
dec2arr.append(dec2)
#Finds the coordinates that fit all of the data
raleft = min(ra1arr)
raright = max(ra2arr)
decbot = max(dec1arr)
dectop = min(dec2arr)
#Crops data so the size of every filter image matches and saves to file 'coadd*.multi.fits'
#Same for weight file
for files in coaddfiles:
newfile = files[:-4]+'multi.fits'
fitsfile = pf.open(files)
fitsheader = fitsfile[0].header
data = fitsfile[0].data
w = wcs.WCS(fitsheader)
[[x1,y1],[x2,y2]] = w.wcs_world2pix([[raleft, decbot], [raright, dectop]], 0)
pplib.hextractlite(newfile, data, fitsheader, x1+1, x2, y1+1, y2)
wnewfile = files[:-4]+'multi.weight.fits'
wfitsfile = pf.open(files[:-4]+'weight.fits')
wfitsheader = wfitsfile[0].header
wdata = wfitsfile[0].data
w = wcs.WCS(wfitsheader)
[[x1,y1],[x2,y2]] = w.wcs_world2pix([[raleft, decbot], [raright, dectop]], 0)
pplib.hextractlite(wnewfile, wdata, wfitsheader, x1+1, x2, y1+1, y2)
#Crops the multicolor (data file and weight) fits files to match the same coordinates
mixfile = 'multicolor.fits'
mixfitsfile = pf.open(mixfile)
mixfitsheader = mixfitsfile[0].header
mixdata = mixfitsfile[0].data
mixw = wcs.WCS(mixfitsheader)
[[mx1,my1],[mx2,my2]] = mixw.wcs_world2pix([[raleft, decbot], [raright, dectop]], 0)
pplib.hextractlite(mixfile, mixdata, mixfitsheader, mx1+1, mx2, my1+1, my2)
wmixfile = mixfile[:-4]+'weight.fits'
wmixfitsfile = pf.open(wmixfile)
wmixfitsheader = wmixfitsfile[0].header
wmixdata = wmixfitsfile[0].data
wmixw = wcs.WCS(wmixfitsheader)
[[wmx1,wmy1],[wmx2,wmy2]] = mixw.wcs_world2pix([[raleft, decbot], [raright, dectop]], 0)
pplib.hextractlite(wmixfile, wmixdata, wmixfitsheader, wmx1+1, wmx2, wmy1+1, wmy2)
#Find directory where this python code is located
propath = os.path.dirname(os.path.realpath(__file__))
#Make sure configuration file is in current working directory, if not copy it from
#location where this code is stored
if not os.path.exists('ratir_weighted.sex'):
os.system('cp '+propath+'/defaults/ratir_weighted.sex .')
if not os.path.exists('weightedtemp.param'):
os.system('cp '+propath+'/defaults/weightedtemp.param .')
if not os.path.exists('ratir.conv'):
os.system('cp '+propath+'/defaults/ratir.conv .')
if not os.path.exists('ratir_nir.nnw'):
os.system('cp '+propath+'/defaults/ratir_nir.nnw .')
#Uses sextractor to find the magnitude and location of sources for each file
#Saves this information into 'fluxes_*.txt' files
for files in coaddfiles:
hdr = pf.getheader(files)
filter = hdr['FILTER']
abszpt = hdr['ABSZPT']
abszprms = hdr['ABSZPRMS']
pixscale = hdr['PIXSCALE']
#Finds filter name and makes sure it is capitalized correctly
filter = files.split('_')[1].split('.')[0]
if filter.lower() in ('j','h','k'):
filter = filter.upper()
else:
filter = filter.lower()
compfile = files[:-4]+'multi.fits'
#Call to sextractor in double image mode (image1 used for detection of sources, image2 only for measurements - must be same size)
#"sex image1,image2 -c configuration file" uses multicolor file for source detection and filter file for magnitude measurements
os.system('sex ' + mixfile + ',' + compfile + ' -WEIGHT_IMAGE '+ wmixfile+','+compfile[:-4]+'weight.fits' + \
' -c ratir_weighted.sex -SEEING_FWHM 1.5 -PIXEL_SCALE '+str(pixscale)+' -DETECT_THRESH 3.0 -ANALYSIS_THRESH 3.0 -PHOT_APERTURES ' + \
str(hdr['SEEPIX']*1.38)+ ' -MAG_ZEROPOINT ' + str(hdr['ABSZPT']))
os.system('mv -f temp.cat fluxes_'+filter+'.txt')
#Columns unpacked for fluxes*.txt are: (x,y,ra,dec,mag,magerr,e,fwhm,flags)
sexout = np.loadtxt('fluxes_'+filter+'.txt', unpack=True)
magcol = 4
magerrcol = 5
tout = np.transpose(sexout[0:6,:]) #Only include through magerr
for i in np.arange(len(tout[:,magerrcol])):
tout[i,magerrcol] = max(tout[i,magerrcol], 0.01)
tout[:,magerrcol] = np.sqrt(tout[:,magerrcol]**2 + abszprms**2)
tsorted = tout[np.argsort(tout[:,magcol])]
#Creates Absolute Magnitude file with coordinates
amfile = 'finalphot'+filter+'.am'
np.savetxt(amfile, tsorted, fmt='%15.6f',
header='X\t Y\t RA\t DEC\t CAL_MAG\t CAL_MAG_ERR\t')
def photom():
#Identify files (must have same number of images files as weight files)
prefchar = 'coadd'
zffiles = pplib.choosefiles(prefchar + '*_?.fits')
weightfiles = pplib.choosefiles(prefchar + '*_?.weight.fits')
if len(zffiles) > len(weightfiles):
print 'Must have matching weight file to each image file to run automatic crop.'
print 'To use manual crop user manualcrop keyword and change crop values by hand'
return -1
numfiles = len(zffiles)
#Resample all images using SWarp to a reference image called multicolor using weight files
swarpstr = ''
for i in range(numfiles):
swarpstr = swarpstr + zffiles[i] + ' '
stackcmd = 'swarp ' + swarpstr + '-DELETE_TMPFILES N -WRITE_XML N -SUBTRACT_BACK N -WEIGHT_TYPE MAP_WEIGHT -IMAGEOUT_NAME multicolor.fits -WEIGHTOUT_NAME multicolor.weight.fits'
stackcmd = stackcmd + ' -COPY_KEYWORDS OBJECT,TARGNAME,TELESCOP,FILTER,INSTRUME,OBSERVAT,PIXSCALE,ORIGIN,CCD_TYPE,JD,DATE-OBS,AIRMASS,FLATFLD,FLATTYPE,SEEPIX,ABSZPT,ABSZPTSC,ABSZPRMS'
print stackcmd
os.system(stackcmd)
#Rename all the resampled files to crop files
for i in range(numfiles):
tmp = zffiles[i].split('.')[0]
ifile = tmp + '.resamp.fits'
ofile = tmp + '.ref.fits'
mvcmd = 'mv -f ' + ifile + ' ' + ofile
os.system(mvcmd)
ifile = tmp + '.resamp.weight.fits'
ofile = tmp + '.ref.weight.fits'
mvcmd = 'mv -f ' + ifile + ' ' + ofile
os.system(mvcmd)
#CHANGE FOR RIMAS
coaddfiles = pplib.choosefiles('coadd*_?.ref.fits')
ra1arr = []
dec1arr = []
ra2arr = []
dec2arr = []
#Finds the RA and DEC of the first and the last pixel of each cropped coadded file
for files in coaddfiles:
fitsfile = pf.open(files)
fitsheader = fitsfile[0].header
data = fitsfile[0].data
imSize = shape(data)
#Converts pixel value to RA and DEC using header information (AstroPy function)
w = wcs.WCS(fitsheader)
pixcrd = [[0., 0.], [imSize[1] - 1.0, imSize[0] - 1.0]]
[[ra1, dec1], [ra2, dec2]] = w.wcs_pix2world(pixcrd, 0)
#Stores information into arrays
ra1arr.append(ra1)
dec1arr.append(dec1)
ra2arr.append(ra2)
dec2arr.append(dec2)
#Finds the coordinates that fit all of the data
raleft = min(ra1arr)
raright = max(ra2arr)
decbot = max(dec1arr)
dectop = min(dec2arr)
#Crops data so the size of every filter image matches and saves to file 'coadd*.multi.fits'
#Same for weight file
for files in coaddfiles:
newfile = files[:-4] + 'multi.fits'
fitsfile = pf.open(files)
fitsheader = fitsfile[0].header
data = fitsfile[0].data
w = wcs.WCS(fitsheader)
[[x1, y1],
[x2, y2]] = w.wcs_world2pix([[raleft, decbot], [raright, dectop]], 0)
pplib.hextractlite(newfile, data, fitsheader, x1 + 1, x2, y1 + 1, y2)
wnewfile = files[:-4] + 'multi.weight.fits'
wfitsfile = pf.open(files[:-4] + 'weight.fits')
wfitsheader = wfitsfile[0].header
wdata = wfitsfile[0].data
w = wcs.WCS(wfitsheader)
[[x1, y1],
[x2, y2]] = w.wcs_world2pix([[raleft, decbot], [raright, dectop]], 0)
pplib.hextractlite(wnewfile, wdata, wfitsheader, x1 + 1, x2, y1 + 1,
y2)
#Crops the multicolor (data file and weight) fits files to match the same coordinates
mixfile = 'multicolor.fits'
mixfitsfile = pf.open(mixfile)
mixfitsheader = mixfitsfile[0].header
mixdata = mixfitsfile[0].data
mixw = wcs.WCS(mixfitsheader)
[[mx1, my1],
[mx2, my2]] = mixw.wcs_world2pix([[raleft, decbot], [raright, dectop]], 0)
pplib.hextractlite(mixfile, mixdata, mixfitsheader, mx1 + 1, mx2, my1 + 1,
my2)
wmixfile = mixfile[:-4] + 'weight.fits'
wmixfitsfile = pf.open(wmixfile)
wmixfitsheader = wmixfitsfile[0].header
wmixdata = wmixfitsfile[0].data
wmixw = wcs.WCS(wmixfitsheader)
[[wmx1, wmy1],
[wmx2, wmy2]] = mixw.wcs_world2pix([[raleft, decbot], [raright, dectop]],
0)
pplib.hextractlite(wmixfile, wmixdata, wmixfitsheader, wmx1 + 1, wmx2,
wmy1 + 1, wmy2)
#Find directory where this python code is located
propath = os.path.dirname(os.path.realpath(__file__))
#Make sure configuration file is in current working directory, if not copy it from
#location where this code is stored
if not os.path.exists('ratir_weighted.sex'):
os.system('cp ' + propath + '/defaults/ratir_weighted.sex .')
if not os.path.exists('weightedtemp.param'):
os.system('cp ' + propath + '/defaults/weightedtemp.param .')
if not os.path.exists('ratir.conv'):
os.system('cp ' + propath + '/defaults/ratir.conv .')
if not os.path.exists('ratir_nir.nnw'):
os.system('cp ' + propath + '/defaults/ratir_nir.nnw .')
#Uses sextractor to find the magnitude and location of sources for each file
#Saves this information into 'fluxes_*.txt' files
for files in coaddfiles:
hdr = pf.getheader(files)
filter = hdr['FILTER']
abszpt = hdr['ABSZPT']
abszprms = hdr['ABSZPRMS']
pixscale = hdr['PIXSCALE']
#Finds filter name and makes sure it is capitalized correctly
filter = files.split('_')[1].split('.')[0]
if filter.lower() in ('j', 'h', 'k'):
filter = filter.upper()
else:
filter = filter.lower()
compfile = files[:-4] + 'multi.fits'
#Call to sextractor in double image mode (image1 used for detection of sources, image2 only for measurements - must be same size)
#"sex image1,image2 -c configuration file" uses multicolor file for source detection and filter file for magnitude measurements
os.system('sex ' + mixfile + ',' + compfile + ' -WEIGHT_IMAGE '+ wmixfile+','+compfile[:-4]+'weight.fits' + \
' -c ratir_weighted.sex -SEEING_FWHM 1.5 -PIXEL_SCALE '+str(pixscale)+' -DETECT_THRESH 3.0 -ANALYSIS_THRESH 3.0 -PHOT_APERTURES ' + \
str(hdr['SEEPIX']*1.38)+ ' -MAG_ZEROPOINT ' + str(hdr['ABSZPT']))
os.system('mv -f temp.cat fluxes_' + filter + '.txt')
#Columns unpacked for fluxes*.txt are: (x,y,ra,dec,mag,magerr,e,fwhm,flags)
sexout = np.loadtxt('fluxes_' + filter + '.txt', unpack=True)
magcol = 4
magerrcol = 5
tout = np.transpose(sexout[0:6, :]) #Only include through magerr
for i in np.arange(len(tout[:, magerrcol])):
tout[i, magerrcol] = max(tout[i, magerrcol], 0.01)
tout[:, magerrcol] = np.sqrt(tout[:, magerrcol]**2 + abszprms**2)
tsorted = tout[np.argsort(tout[:, magcol])]
#Creates Absolute Magnitude file with coordinates
amfile = 'finalphot' + filter + '.am'
np.savetxt(amfile,
tsorted,
fmt='%15.6f',
header='X\t Y\t RA\t DEC\t CAL_MAG\t CAL_MAG_ERR\t')
def printhtml(filters, colnames):
#Reads in final magnitudes
colgrab = np.loadtxt('./finalmags.txt', unpack=True)
#Store each column into dictionary based on colnames and create header for HTML header from colnames
plotdict = {}
t = '<tr><th>#</th>'
for i in np.arange(len(colnames)):
plotdict[colnames[i]] = colgrab[i, :]
t = t + '<th>' + colnames[i] + '</th>'
t = t + '<tr>\n'
colors = [
'black', 'purple', 'blue', 'aqua', 'green', 'orange', 'red', 'yellow',
'magenta'
]
pl.figure()
pl.xlim([15, 22])
pl.ylim([-0.01, 0.2])
#For each filter, plot mag vs. error for photocomp.png
counter = 0
for filter in filters:
pl.plot(plotdict[filter + 'mag'],
plotdict[filter + 'magerr'],
marker='o',
linestyle='None',
label=filter,
color=colors[counter])
counter = counter + 1
pl.xlabel('AB Magnitude')
pl.ylabel(r"$\Delta$ Mag")
pl.legend(loc='lower right')
pl.savefig('photcomp.png', bbox_inches='tight')
pl.clf()
#Create html page that displays images made in plotphotom.py and values from finalmags.txt
f = open('./photom.html', 'w')
f.write('<!DOCTYPE HTML>\n')
f.write('<HTML>\n')
f.write('<HEAD>\n')
f.write('<TITLE>PHOTOMETRY DATA</TITLE>\n')
f.write('</HEAD>\n')
f.write('<BODY BGCOLOR="#FFFFFF" TEXT="#003300">\n')
#Finds filter image files with green circles over sources and writes to HTML
prefchar = 'coadd'
zffiles = pplib.choosefiles(prefchar + '*_?.png')
im_wid = 400
f.write('<IMG SRC="./color.png" width="' + ` im_wid ` + '"><BR>\n')
for i in range(len(zffiles)):
f.write('<IMG SRC="./' + zffiles[i] + '" width="' + ` im_wid ` +
'">\n')
if (i + 1) % 3 == 0:
f.write('<BR>\n')
f.write(
'<BR><HR><FONT SIZE="+2" COLOR="#006600">AB System Photometry (sources within 1 arcmin):</FONT><BR>\n'
)
f.write(
'Notes: Non-zero magnitudes with uncertainty of zero are 3-sigma upper limits. Sources with magnitudes of 0.0000 are unobserved.<BR>\n'
)
f.write(
' Circles in images above match aperture size used in sextractor.<BR>\n'
)
f.write('<br />\n')
#Writes table with all magnitudes
f.write('<table border="1" width="100%">\n')
f.write(t)
for j in np.arange(len(plotdict[colnames[0]])):
f.write('<tr><td>{:.0f}</td>'.format(j))
for col in colnames:
f.write('<td>{:.3f}</td>'.format(plotdict[col][j]))
f.write('<tr>\n')
f.write('</table>\n')
f.write('</PRE><BR><HR>\n')
f.write('<IMG SRC="photcomp.png">\n')
f.write('</PRE><BR><HR>\n')
f.write('</BODY>\n')
f.write('</HTML>\n')
f.close()
