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Correcting for a missing gain factor in the noise image creation #133

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Correcting for a missing gain factor in the noise image creation #133

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0e810f9
Adding a missing gain factor in the noise image creation
gterreran Mar 13, 2024
4c99fb0
bug fix related to the noise image
gterreran Mar 22, 2024
fd15085
bug fix related to the noise image
gterreran Mar 22, 2024
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26 changes: 18 additions & 8 deletions trunk/bin/lscdiff.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,21 @@
from reproject import reproject_interp
import shutil

def create_variance_image(_data, _gain, _ron):
median = np.median(_data)
MAD = np.median(np.abs(_data - median)) #median absolute deviation

#assuming the distribution is gaussian (normal), which is a good approximation
#of the poissonian distribution for large number of events, then we can convert
#the MAD to a pseudo pseudo standard deviation usinge 1.4826 as the scale factor
std = 1.4826 * MAD

#pssl = previously subtracted sky level
pssl = _gain*std**2 - _ron**2/_gain - median

#return variance in ADUs^2
return _data/_gain + pssl/_gain + _ron**2/_gain**2, pssl


def crossmatchtwofiles(img1, img2, radius=3):
''' This module is crossmatch two images:
Expand Down Expand Up @@ -182,10 +197,8 @@ def crossmatchtwofiles(img1, img2, radius=3):
targnoise = 'targnoise.fits'

# create the noise images
median = np.median(data_targ)
noise = 1.4826*np.median(np.abs(data_targ - median))
pssl_targ = gain_targ*noise**2 - rn_targ**2/gain_targ - median
noiseimg = data_targ + pssl_targ + rn_targ**2
noiseimg, pssl_targ = create_variance_image(data_targ, gain_targ, rn_targ)

targmask_data = fits.getdata(_dir + targmask0)
if noiseimg.size == targmask_data.size:
noiseimg[targmask_data > 0] = sat_targ
Expand All @@ -197,10 +210,7 @@ def crossmatchtwofiles(img1, img2, radius=3):
fits.writeto(targnoise, noiseimg, output_verify='fix', overwrite=True)

if not os.path.isfile(_dirtemp + tempnoise0):
median = np.median(data_temp)
noise = 1.4826*np.median(np.abs(data_temp - median))
pssl_temp = gain_temp*noise**2 - rn_temp**2/gain_temp - median
noiseimg = data_temp + pssl_temp + rn_temp**2
noiseimg = create_variance_image(data_temp, gain_temp, rn_temp)
tempmask_data = fits.getdata(_dirtemp + tempmask0)
if noiseimg.size == tempmask_data.size:
noiseimg[tempmask_data > 0] = sat_temp
Expand Down