def cal_image(filepath,plot = True,path=None,band='gp',source='BD710031',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)
image0, header0 = qi.readimage(filepath)
refh = h.pyfits.getheader(filepath)
im = h.pyfits.open(filepath)
newim = h.hcongrid((im[0].data-dark-bias)/flat, im[0].header,refh)
if plot:
qi.display_image(newim)
return newim,header0
def display_raws(path=None,band='gp',source='BD710031'):
if not path:
path = set_path()
files,sz = tp.get_files(dir=path, tag=source+'.'+band)
for i in range(len(files)):
image = fits.getdata(files[i],0)
qi.display_image(image,fignum=i)
def find_stars(image, plot = False, fwhm = 20.0, threshold=3.):
from astropy.stats import sigma_clipped_stats
mean, median, std = sigma_clipped_stats(image, sigma=3.0)
from photutils import daofind
sources = daofind(image - median, fwhm=fwhm, threshold=threshold*std)
# stars already found accurately, vet_sources will be implemented when working properly
# vet_sources(10.0,10.0)
if plot == True:
# from astropy.visualization import SqrtStretch
# from astropy.visualization.mpl_normalize import ImageNormalize
positions = (sources['xcentroid'], sources['ycentroid'])
apertures = CircularAperture(positions, r=4.)
#norm = ImageNormalize(stretch=SqrtStretch())
#plt.imshow(image, cmap='Greys', origin='lower', norm=norm)
qi.display_image(image)
apertures.plot(color='blue', lw=1.5, alpha=0.5)
return sources
def find_flux(image):
model = fit(*indices(image.shape))
qi.display_image(model)
resid = model - image
qi.display_image(resid)
#plt.figure(2)
#plt.hist(resid, bins=50)
plt.figure(3)
qi.display_image(resid)
A = params[0]
Xsig = params[3]
Ysig = params[4]
#Check if sigmas x and y = xcen and ycen
flux = (A * 2 * pi * Xsig * Ysig)/10.
num = (np.sum(image))/10.
return num, flux
plt.ylim(0,2048)
darks,dct = tp.get_files(tag='Dark')
biases,bct = tp.get_files(tag='Bias')
flats,fct = tp.get_files(tag='SkyFlat')
targetfiles,tct = tp.get_files(tag='KIC10935310')
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]))
