def mk_skyflats(file_list=None,file_path=None, bias_fil=None):
import xastropy.PH136.experiments.hrdiagram as hrd
from astropy.io.fits import getdata
# Frames
B_sky = 32 + np.arange(5)
V_sky = 37 + np.arange(5)
R_sky = 43 + np.arange(5)
all_sky = [B_sky, V_sky, R_sky]
# Outfiles
outfil = ['Sky_B.fits', 'Sky_V.fits', 'Sky_R.fits']
filters = ['B','V','R']
# Bias
if bias_fil == None:
bias_fil = 'Bias.fits'
bias_img,bias_head = getdata(bias_fil,0,header=True)
# Loop on Filters
for ff in filters:
# Index
idx = filters.index(ff)
# Generate file names
files= hrd.mk_file_list(all_sky[idx])
# Stack with scaling
img = hrd.stack_img(files, bias_img=bias_img, norm=True)
# Trim
trim_img = hrd.trimflip_img(img)
# Deal with zeros
zro = np.where( trim_img == 0.)
trim_img[zro] = 1.
# Write
print 'Sky Flats: Writing ', outfil[idx]
fits.writeto(outfil[idx], trim_img, clobber=True)
print 'Sky Flats: All done'
return
def mk_bias(file_list=None,file_path=None,outfil=None):
# There is no overscan region (yes there is!)
# So, we need a bias image
import xastropy.PH136.experiments.hrdiagram as hrd
from astropy.io.fits import getdata
from astropy.io.fits import Column
from astropy.io import fits
from astropy.stats import sigma_clip
# Defaults
if file_path == None:
file_path = 'Raw/'
if outfil == None:
outfil = 'Bias.fits'
# Files
bias_fil = None
if file_list == None:
# Generate them ourself
biasfrm = 2 + np.arange(10)
bias_fil = hrd.mk_file_list(biasfrm, file_path=file_path)
# Read Noise
arr,head = getdata(str(bias_fil[0]),0,header=True)
clip_arr = sigma_clip(arr, 2.5, None)
rnoise = np.std(clip_arr,dtype=np.float64)
print 'Read Noise = ', rnoise, ' counts'
#pdb.set_trace()
#dpt = ds9.ds9()
#dpt.set_np2arr(arr)
# Stack the frames
img = hrd.stack_img(bias_fil)
# Write
head.update('RNOISE', rnoise, 'READ NOISE')
fits.writeto(outfil, img, head, clobber=True)
return
