def reduc(files, dir='/Users/nickedwards/python/independent/'):
cal = divide_flat(files, dir=dir)
keys = [cal['header'][i]['OBJECT'] for i in range(len(cal['header']))]
data = {}
for key in keys:
data[key] = {
'datetime': [],
'optimal_aperture': [],
'xcen': [],
'ycen': [],
'fwhm': [],
'aspect': [],
'snrmax': [],
'totflux': [],
'totflux_aperture': [],
'chisq': [],
'curve_of_growth': [],
'secz': [],
'totfluxerr': []
}
dkeys = np.array(data.keys())
dkeys = dkeys[dkeys != 'datetime']
for i in range(len(files)):
print files[i]
ra = angcor(cal['header'][i]['RA']).d
print ra
dec = angcor(cal['header'][i]['DEC']).d
print dec
op = tp.optimal_aperture(cal['image'][i],
cal['header'][i], [20, 30],
ra=ra,
dec=dec)
key = cal['header'][i]['OBJECT']
print key
data[key]['optimal_aperture'] = np.append(
data[key]['optimal_aperture'], op['optimal_aperture'])
data[key]['xcen'] = np.append(data[key]['xcen'], op['xcen'])
data[key]['ycen'] = np.append(data[key]['ycen'], op['ycen'])
data[key]['fwhm'] = np.append(data[key]['fwhm'], op['fwhm'])
data[key]['aspect'] = np.append(data[key]['aspect'], op['aspect'])
data[key]['snrmax'] = np.append(data[key]['snrmax'], op['snrmax'])
data[key]['totflux'] = np.append(data[key]['totflux'], op['totflux'])
data[key]['totflux_aperture'] = np.append(
data[key]['totflux_aperture'], op['totflux_aperture'])
data[key]['chisq'] = np.append(data[key]['chisq'], op['chisq'])
data[key]['curve_of_growth'] = np.append(data[key]['curve_of_growth'],
op['curve_of_growth'])
data[key]['secz'] = np.append(data[key]['secz'], op['secz'])
data[key]['totfluxerr'] = np.append(data[key]['totfluxerr'],
op['totfluxerr'])
datetime = cal['header'][i]['DATE'] + " " + cal['header'][i]['UT']
datetime = dt.strptime(datetime, '%d/%m/%y %H:%M:%S')
data[key]['datetime'] = np.append(data[key]['datetime'], datetime)
return data
def flux_all(path=None,band='gp',extraprecision=True,extraprecisionrange=100,source='BD710031',badimindices = [],bias=None,dark=None,flat=None):
if not path:
path = set_path()
if length(bias) == 1:
bias = make_bias(path=path)
if length(dark) == 1:
dark = make_dark(path=path)
if length(flat) == 1:
flat = make_flat(path=path,band=band,bias=bias,dark=dark)
files,osz = tp.get_files(dir=path, tag=source+'.'+band)
sz = osz
#remove faulty images
if len(badimindices) != 0:
for g in range(osz):
if g in badimindices:
del files[g]
sz=sz-1
flux = []
for file in files:
image,header = cal_image(file,plot=False,path=path,band=band,source=source,bias=bias,dark=dark,flat=flat)
w = wcs.WCS(header)
x,y = w.all_world2pix(float(header['TARGRA']), float(header['TARGDEC']), 1)
position = (float(x),float(y))
if extraprecision == True:
for k in range(extraprecisionrange):
if check_radii(image,header,position,rin=k+10,rout=k+15) == True:
op_ap,xval,yval,fwhm,aspect,snrmax,totflux,totap,chisq = \
tp.optimal_aperture(float(x),float(y),image,[k+10,k+15],use_old=True)
break
else:
op_ap,xval,yval,fwhm,aspect,snrmax,totflux,totap,chisq = \
tp.optimal_aperture(float(x),float(y),image,[10,15],use_old=True)
#print "Final aperture: " + str(op_ap)
phot = do_phot(image,position,radius=op_ap)
flux = np.append(flux,phot['aperture_sum_bkgsub'])
return flux
def get_fiber_flux(file,fwhm=20,threshold=100):
image,header = qi.readimage(file,plot=False)
mean, median, std = sigma_clipped_stats(image, sigma=3.0)
source = daofind(image - median, fwhm=fwhm, threshold=threshold*std)
xcen = np.int(np.round(source['xcentroid'][0]))
ycen = np.int(np.round(source['ycentroid'][0]))
plt.plot([xcen],[ycen],'rx',markersize=20)
params = fitgaussian(image)
y,x = params[1],params[2]
apdict = tp.optimal_aperture(x,y,image,[150,160])
return apdict
bias = tp.master_bias(biases,outdir='./Photometry/',readnoise=False)
dark = tp.master_dark(darks,bias=bias,outdir='./Photometry/')
flat = tp.master_flat(flats,bias=bias,dark=dark,outdir='./Photometry/')
cal = (image - dark - bias)/flat
qi.display_image(cal,siglo=2,sighi=2)
plt.plot([x0],[y0],'y+',markersize=30,linewidth=1e6)
plt.xlim(0,2048)
plt.ylim(0,2048)
# define the aperture
radius,ynew,xnew,fwhm,aspect,snrmax,totflux,totap,chisq = \
tp.optimal_aperture(x0,y0,image,skyrad=[15,20])
pref = np.array([[x0,y0],[836,1154],[645,1152], [1234,1002], [1131,1338], [377,426]])
coords0 = w.wcs_pix2world(pref,1)
ras = coords0[:,0]
decs = coords0[:,1]
info = tp.batch_phot(targetfiles[:-1],ras,decs,bias=bias,dark=dark,flat=flat,
outdir='./Photometry/',skyrad=np.array([15,20]))
plt.figure(56)
plt.plot(info['jd']-np.median(info['jd']),info['flux'][:,0],'o-')
sys.exit()
