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 crmedian(arr, thresh, mbox, bbox):
"""Identify cosmic rays through median technique. Similar implimentation to
crmedian in iraf.imred.crutil.crmedian
"""
#Check to make sure the background box is an appropriate size
#If it is too small, then insufficient statistics are generated
if bbox < 10: bbox=10
cbox=int(0.5*bbox)
#make the background image
#barr=generic_filter(arr,sigma_func, size=(bbox,bbox))
barr=arr*0.0+arr.std()
xlen=len(arr[0])
ylen=len(arr)
for i in range(mbox,xlen,bbox):
for j in range(mbox,ylen,bbox):
x1,x2,y1,y2=setbox(i,j,cbox, xlen, ylen)
barr[y1:y2,x1:x2]=sigma_func(arr[y1:y2,x1:x2])
#Median smooth the image
marr=saltstat.median_image(arr, mbox)
#Find the residual image
rarr=(arr-marr)/barr
#identify all sources
crarr=marr*(rarr > thresh)
return crarr
def crmedian(arr, thresh, mbox, bbox):
"""Identify cosmic rays through median technique. Similar implimentation to
crmedian in iraf.imred.crutil.crmedian
"""
#Check to make sure the background box is an appropriate size
#If it is too small, then insufficient statistics are generated
if bbox < 10: bbox = 10
cbox = int(0.5 * bbox)
#make the background image
#barr=generic_filter(arr,sigma_func, size=(bbox,bbox))
barr = arr * 0.0 + arr.std()
xlen = len(arr[0])
ylen = len(arr)
for i in range(mbox, xlen, bbox):
for j in range(mbox, ylen, bbox):
x1, x2, y1, y2 = setbox(i, j, cbox, xlen, ylen)
barr[y1:y2, x1:x2] = sigma_func(arr[y1:y2, x1:x2])
#Median smooth the image
marr = saltstat.median_image(arr, mbox)
#Find the residual image
rarr = (arr - marr) / barr
#identify all sources
crarr = marr * (rarr > thresh)
return crarr
def subbackground(image, sig, nbin, order, iter, type):
"""Calculate and subtract a global background from an image
using a number of different methods. This is assuming that
slotmode data is being used.
return data array
"""
# return the same image if no background subtraction is required
if type=='none': return image
# calculate the image statistics
if np.size(image)>0:
mean, median, stddev=saltstat.iterstat(image,sig,iter)
if stddev==0:
raise SaltError('Standard deviation = 0')
# Create a row median image. This will be useful for a number of purposes
image_mrow=med_row_image(image)
# Replace bright objects in the frame with the median value of that row
mask=((image-mean) < sig*stddev)*(image-mean > -sig*stddev)
image_sig=image*mask+(1-mask)*image_mrow
if type == 'median-row':
image_back=image*0.0+image_mrow
# Median smooth the image
if type == 'median':
image_back=median_image(image_sig,nbin)
# Make a fit to the surface
if type=='surf':
image_back=image.copy()
if type=='both':
image_surf=image.copy()
for x in range(len(image[0])):
y=np.arange(len(image))
my=((image[:,x]-mean) < sig*stddev)*(image[:,x] -mean > -sig*stddev)
cy=np.compress(my,y)
cim=np.compress(my,image_med[:,x])
coef=np.polyfit(cy,cim,order)
image_surf[:,x]=np.polyval(coef,y)
# subtract the fit
image=image-image_back
# return the image
return image
