def credge(arr, thresh, gain, rdnoise, b_factor, fthresh):
"""Identify cosmic rays following the van Dokkem (2001) prescription
"""
min_limit = 0.01
#set up the convolution kernel
f_conv = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]])
#set up the array
shape = arr.shape
sdata = arr
#rebin the data
newshape = (b_factor * shape[0], b_factor * shape[1])
ldata = salttran.rebin_factor(sdata, newshape)
#convolve with f_conv
#ldata=conv2d(ldata,f_conv,mode='same')
ldata = conv2d(ldata, f_conv)
mask = (ldata >= 0)
ldata = ldata * mask
#return to the original binning
ldata = salttran.blockave(ldata, shape)
#create noise model
meddata = saltstat.median_image(sdata, 5)
noise = abs(meddata)
noise = (gain * noise + rdnoise**2)**0.5 / gain
#create S/N image
odata = ldata / noise / b_factor
#remove extended objects
odata = odata - saltstat.median_image(odata, 5)
#select objects
masks = (odata > thresh)
#remove compact bright sources
mdata = saltstat.median_image(sdata, 5)
fdata = mdata - saltstat.median_image(mdata, 7)
fdata = fdata / noise
# set a minimum value for all pixels so no divide by zero problems
indf = np.where(fdata < min_limit)
fdata[indf] = min_limit
fdata = odata * masks / fdata
maskf = (fdata > fthresh)
#make the list of cosmic rays
mask = masks * maskf
#identify the cosmic rays
crarr = mdata * mask
return crarr
def credge(arr, thresh, gain, rdnoise, b_factor, fthresh):
"""Identify cosmic rays following the van Dokkem (2001) prescription
"""
min_limit=0.01
#set up the convolution kernel
f_conv=np.array([[0,-1,0],[-1,4,-1],[0,-1,0]])
#set up the array
shape=arr.shape
sdata=arr
#rebin the data
newshape=(b_factor*shape[0],b_factor*shape[1])
ldata=salttran.rebin_factor(sdata,newshape)
#convolve with f_conv
#ldata=conv2d(ldata,f_conv,mode='same')
ldata=conv2d(ldata,f_conv)
mask = (ldata >= 0)
ldata = ldata * mask
#return to the original binning
ldata = salttran.blockave(ldata,shape)
#create noise model
meddata =saltstat.median_image(sdata,5)
noise = abs(meddata)
noise = (gain*noise+rdnoise**2)**0.5/gain
#create S/N image
odata = ldata / noise / b_factor
#remove extended objects
odata = odata - saltstat.median_image(odata,5)
#select objects
masks = (odata>thresh)
#remove compact bright sources
mdata = saltstat.median_image(sdata,5)
fdata = mdata - saltstat.median_image(mdata,7)
fdata = fdata / noise
# set a minimum value for all pixels so no divide by zero problems
indf = np.where(fdata < min_limit)
fdata[indf]=min_limit
fdata = odata * masks/fdata
maskf = (fdata > fthresh)
#make the list of cosmic rays
mask = masks*maskf
#identify the cosmic rays
crarr=mdata*mask
return crarr
def rebin(hdu, xbin, ybin):
"""Given in input SALT image (ie, multi-extension), rebin the data
and write out the results
hdu--an input SALT image
xbin--the factor to rebin the x-data by
ybin--the factor to rebing the y data by
"""
for i in range(1,len(hdu)):
shape=hdu[i].data.shape
newshape=(ybin*shape[0],xbin*shape[1])
hdu[i].data=salttran.rebin_factor(hdu[i].data,newshape)
return hdu
