def ImageIter(imagelist, **kargs):
"""
This is an iterator:
giving a list containing the image names, it will
return images one by one.
Useful to process all the images, one at a time without
needing to load them all in memory at the same time.
EX:
ilist = ['image1.fits', 'image2.fits', ... 'imageN.fits']
images = ImageIter(ilist)
for image in images:
some processing
"""
#default extension is 0
ext = kargs.get('ext', 0)
#Make sure image list are strings
if all([isinstance(i, str) for i in imagelist]):
for i in imagelist:
yield Image(i, ext)
#Or Make sure image list are Path
elif all([isinstance(i, Path) for i in imagelist]):
for i in imagelist:
yield Image(i, ext)
def extractchannel(filelist, channel):
"""
extractchannel is a utility to read an extension from a multi
extension fits file and save it as a fits file.
It's usefull if we want to extract only one channel from,
for example, a list of MUSE files.
Usage:
1)single case example
extractchannel('MUSE_WFM_FLAT303_0031.fits','CHAN04')
extract CHAN04 extension and create MUSE_WFM_FLAT303_0031_CHAN04.fits
2)multi file example
extractchannel('listing.txt','CHAN04')
listing.txt is a text file with the listing of all files we want to
extract the channel, it can be generated with the 'ls' command, like
ls MUSE*WFM*fits > listing.txt
3)filelist is python list with files names
ex: flist=['OMEGACAM_100.fits', 'OMEGACAM_102.fits', 'OMEGACAM_103.fits']
extractchannel(filelist, 'CCD_78')
TODO: add OUTPATH which points to the directory where the extracted channels will be saved
"""
#check if filelist is a fits file name
if isinstance(filelist,str) and filelist.upper().endswith('.FITS'):
try:
im = Image(filelist, channel)
im.save(filelist[:-5]+'_'+channel+'.fits')
except:
print('Bad channel designator')
#check if filelist is a file
elif os.path.isfile(filelist):
files = open(filelist, 'r').readlines()
for lines in files:
try:
#need to remove /n and split() generate a list, so
#take first element
im = Image(lines.split()[0], channel)
im.save(lines[:-5]+'_'+channel+'.fits')
except:
print('Bad channel designator')
#check if filelist is a python list of strings finished in fits
elif isinstance(filelist,list) and all([isinstance(x, int) for x in filelist]):
for files in filelist:
try:
im = Image(files, channel)
im.save(lines[:-5]+'_'+channel+'.fits')
except:
print('Bad channel designator')
else:
print("Not valid name or file name list")
def medianstack(filelist, ext=0, **options):
"""
Compute the median for a list of images.
Usefull to elliminate cosmic rays from dark images
Syntax:
medianstack(filelist)
return an image which is the median stack form the files in the current directory
example:
med = medianstack([dk1, dk2, dk3], 0)
compute median for images dk1, dk2 and dk3 using extension 0
TODO:
Check if filelist is a list of images o list of strings and then perform
the computation accordingly
"""
#check if filelist is a list, if not raise error
try:
if (isinstance(filelist, list) and len(filelist) > 1):
pass
else:
raise Exception
except:
print("Not a list or len < 2")
return None
#check if elements in filelist is Image or string
if all([isinstance(i, Image) for i in filelist]):
imagesdata = [i.get_data() for i in filelist]
im = filelist[0].copy()
im.filename = 'medianstack'
im.data = np.median(imagesdata, axis=0)
return im
if all([isinstance(i, str) for i in filelist]):
#copy first image on list to get same dim
imagesdata = [Image(i, ext).get_data() for i in filelist]
im = Image(filelist[0], ext)
im.filename = 'medianstack'
im.data = np.median(imagesdata, axis=0)
return im
elif all([isinstance(i, Path) for i in filelist]):
#copy first image on list to get same dim
imagesdata = [Image(i, ext).get_data() for i in filelist]
im = Image(filelist[0], ext)
im.filename = 'medianstack'
im.data = np.median(imagesdata, axis=0)
return im
return None
def stdstack(filelist, ext=0, **options):
"""
Compute the standard deviation in z direction for a list of images.
Syntax:
stdstack(filelist)
return an image which is the std pixel by pixel for the files in the list
example:
stdmap = stdstack([dk1, dk2, dk3,..,dkn], 0)
compute median for images dk1, dk2, dk3 to dkn using extension 0
"""
#check if filelist is a list, if not raise error
try:
if (isinstance(filelist, list) and len(filelist) > 1):
pass
else:
raise Exception
except:
print("Not a list or len < 2")
return None
#check if elements in filelist are Images or string
if all([isinstance(i, Image) for i in filelist]):
imagesdata = [i.get_data() for i in filelist]
im = filelist[0].copy()
im.filename = 'stdstack'
im.data = np.std(imagesdata, axis=0)
return im
#check if filelist are files names
if all([isinstance(i, str) for i in filelist]):
#copy first image on list to get same dim
imagesdata = [Image(i, ext).get_data() for i in filelist]
im = Image(filelist[0], ext)
im.filename = 'stdstack'
im.data = np.std(imagesdata, axis=0)
return im
elif all([isinstance(i, Path) for i in filelist]):
#copy first image on list to get same dim
imagesdata = [Image(i, ext).get_data() for i in filelist]
im = Image(filelist[0], ext)
im.filename = 'stdstack'
im.data = np.std(imagesdata, axis=0)
return im
return None
def meanstack(filelist, ext=0, **options):
"""
Compute the mean for a list of images.
Syntax:
medianstack(filelist)
return an image which is the median stack form the files in the current directory
example:
med = meanstack([dk1, dk2, dk3], 0)
compute median for images dk1, dk2 and dk3 using extension 0
TODO:
Check if filelist is a list of images o list of strings and then perform
the computation accordingly
"""
#check if filelist is a list, if not raise error
try:
if (isinstance(filelist,list) and len(filelist)>1):
pass
else:
raise Exception
except:
print("Not a list or len < 2")
return None
#check if elements in filelist is Image or string
allimages = [isinstance(i,Image) for i in filelist]
if all(allimages):
imagesdata = [i.get_data() for i in filelist]
im = filelist[0].copy()
im.filename = 'meanstack'
im.data=np.mean(imagesdata, axis = 0)
return im
#check if filelist are files names
allfiles = [isinstance(i,str) for i in filelist]
if all(allfiles):
#copy first image on list to get same dim
imagesdata = [Image(i,ext).get_data() for i in filelist]
im = Image(filelist[0],ext)
im.filename = 'meanstack'
im.data=np.mean(imagesdata, axis = 0)
return im
return None
def stdstack(filelist, ext=0, **options):
"""
Compute the standard deviation in z direction for a list of images.
Syntax:
stdstack(filelist)
return an image which is the std pixel by pixel for the files in the list
example:
stdmap = stdstack([dk1, dk2, dk3,..,dkn], 0)
compute median for images dk1, dk2, dk3 to dkn using extension 0
"""
#check if filelist is a list, if not raise error
try:
if (isinstance(filelist,list) and len(filelist)>1):
pass
else:
raise Exception
except:
print("Not a list or len < 2")
return None
#check if elements in filelist are Images or string
allimages = [isinstance(i,Image) for i in filelist]
if all(allimages):
imagesdata = [i.get_data() for i in filelist]
im = filelist[0].copy()
im.filename = 'stdstack'
im.data=np.std(imagesdata, axis = 0)
return im
#check if filelist are files names
allfiles = [isinstance(i,str) for i in filelist]
if all(allfiles):
#copy first image on list to get same dim
imagesdata = [Image(i,ext).get_data() for i in filelist]
im = Image(filelist[0],ext)
im.filename = 'stdstack'
im.data=np.std(imagesdata, axis = 0)
return im
return None
def linearity_residual(imagelist, *coor, **kargs):
"""
Compute linearity residual using an image list starting
with 2 bias and then pairs of FF at diferent levels
LR = 100*(1 -(Sm/Tm)/(S/t))
TODO: need to complete!!
"""
MAXSIGNAL = kargs.get('MAXSIGNAL', 65535.0)
VERBOSE = kargs.get('VERBOSE', False)
# read coordinates of first image
x1, x2, y1, y2 = Image(imagelist[0], ext).get_windowcoor(*coor)
def ptc_2ff2bias(bias1, bias2, ff1, ff2, *coor, **kargs):
"""
Perform ptc plot with two bias and 2 ff images which have many .
ex:
ptc_2tdi(b1,tdi1,tdi2,50, 2000, 100, 170)
compute CF using 2 bias and 2 FF in an area defined by [50:2000,100:170] plot the ptc curve and compute the CF using
a first order polynomia
ptc_2tdi(b1,tdi1,tdi2,50, 2000, 100, 170, RETURN=True)
compute CF using 2 bias and 2 FF in an area defined by [50:2000,100:170] return the vectors and the CF, using
a first order polynomia
ptc_2tdi(b1,tdi1,tdi2,50, 2000, 100, 170, ORDER=2)
compute CF using 2 bias and 2 FF in an area defined by [50:2000,100:170] plot the ptc curve, and use a
polynomia of order 2
The 2 tdi images should have a slope in flux to compute the ptc.
To eliminate the FPN, the 'shotnoise' image is computed as the subtraction
of two debiased flat field images
optional kargs arguments:
NSTD (default = 3) Default number of std deviation to elliminate outlayers
VERBOSE (default=False)
This method can be used on the TestBench
"""
nstd = kargs.get('SIGMA', 3) # factor to elliminate outlayers
order = kargs.get('ORDER', 2)
axis = kargs.get('AXIS', 1) # make computation along columns
ext = kargs.get('ext', 0)
x1 = coor[0]
x2 = coor[1]
y1 = coor[2]
y2 = coor[3]
ff1w = Image(ff1, ext=ext).crop(x1, x2, y1, y2).astype(float)
ff2w = Image(ff2, ext=ext).crop(x1, x2, y1, y2).astype(float)
bias1w = Image(bias1, ext=ext).crop(x1, x2, y1, y2).astype(float)
bias2w = Image(bias2, ext=ext).crop(x1, x2, y1, y2).astype(float)
bias_mean = sigma_clip(bias1w + bias2w, axis=axis).mean(axis=axis) / 2.0
bias_var = 0.5 * sigma_clip(bias1w - bias2w, axis=axis).std(axis=axis)**2
signal_mean = sigma_clip(ff1w + ff2w,
axis=axis).mean(axis=axis) / 2.0 - bias_mean
ratioffs = sigma_clip(ff1w, axis=axis).mean(axis=axis) / sigma_clip(
ff2w, axis=axis).mean(axis=axis)
total_var = sigma_clip(ff1w - ratioffs * ff2w, axis=axis).std(axis=axis)**2
signal_var = (total_var - 2 * bias_var) / 2.0
coefts = np.polyfit(signal_mean, signal_var, order)
polycoef = np.poly1d(coefts)
var_fitted = np.polyval(polycoef, signal_mean[:])
# if RETURN equal True, return signal_masked, variance masked, fitted variance and CF
if kargs.get('RETURN', False):
if order == 1:
return signal_mean, signal_var, var_fitted, coefts
else:
return signal_mean, signal_var, var_fitted, coefts
else:
fig = plt.figure() # create a figure object
ax = fig.add_subplot(1, 1, 1) # create an axes object in the figure
ax.plot(signal_mean, signal_var, '.b', signal_mean, var_fitted, 'r')
ax.grid()
title = 'PTC CF=%f'
if order == 2:
title = title % (1 / coefts[1])
else:
title = title % (1 / coefts[0])
ax.set_title(title)
ax.set_ylabel('Variance [ADU]**2')
ax.set_xlabel('Signal [ADU]')
def ptc_ffpairs_mw(imagelist, *coor, **kargs):
"""
Perform ptc plot for pairs of ff at same level in multiple windows.
The pairs of ff should have the same light level.
The first 2 images in the list must be bias
To eliminate the FPN, the 'shotnoise' image is computed as the subtraction
of two debiased flat field images
optional kargs arguments:
FACTOR (default = 2.0)
MAXSIGNAL (default 65535) => compute PTC only for signal values less than MAXSIGNAL
VERBOSE (default=False) ==> print out table with signal and variance
CLIP (default=True) ==> Use clipped statistic to on images before computing CF
"""
order = kargs.get('ORDER', 2) # order of polynomial regression
if order > 2:
order = 2
MAXSIGNAL = kargs.get('MAXSIGNAL', 65535.0)
sigma = kargs.get('SIGMA', 3)
ext = kargs.get('ext', 0)
x1 = coor[0]
x2 = coor[1]
y1 = coor[2]
y2 = coor[3]
oddimageindex = list(range(3, len(imagelist), 2))
evenimageindex = list(range(2, len(imagelist), 2))
# Read in bias1 and bias2
b1 = Image(imagelist[0], ext).crop(x1, x2, y1, y2).get_data()
b2 = Image(imagelist[1], ext).crop(x1, x2, y1, y2).get_data()
biasRON = sigma_clip(b1 - b2).std() / np.sqrt(2.0)
# Separate images in even and odd (crop the images..)
ff1list = [
Image(imagelist[i], ext).crop(x1, x2, y1, y2).get_data()
for i in oddimageindex
]
ff2list = [
Image(imagelist[i], ext).crop(x1, x2, y1, y2).get_data()
for i in evenimageindex
]
# NHA new implementation with multi windows START
# generate auxiliary arrays of nwx * nwy elements and initialize to zero
meanff = []
signalvar = []
for ff1, ff2 in zip(ff1list, ff2list):
# windows is a generator of subwindows
windows = subwindowcoor(0, ff1.shape[0], 0, ff2.shape[0], **kargs)
for i, j, xi, xf, yi, yf in windows:
win = slice(xi, xf), slice(yi, yf)
# compute mean value on each window for normalized ff
meanff.append(
sigma_clip((ff1[win] + ff2[win]) / 2.0, sigma=sigma).mean() -
sigma_clip((b1[win] + b2[win]) / 2.0, sigma=sigma).mean())
# compute standard deviation on each window for normalized ff
if kargs.get('NORMFF2', True):
ff_diff = ff1[win] - ff2[win] * (
sigma_clip(ff1[win], sigma=sigma).mean() /
sigma_clip(ff2[win]).mean())
else:
ff_diff = ff1[win] - ff2[win]
var_ff_diff = sigma_clip(ff_diff, sigma=sigma).std()**2
# Measure RON from difference of two bias
var_ron = (sigma_clip(b1[win] - b2[win], sigma=sigma).std()**
2) / 2.0
signalvar.append(0.5 * (var_ff_diff - 2 * var_ron))
# compute polynomial coeficients
meanff = np.array(meanff)
signalvar = np.array(signalvar)
coefts = np.polyfit(meanff, signalvar, order)
polyts = np.poly1d(coefts)
variance_fitted = np.polyval(polyts, meanff)
fig = plt.figure() # create a figure object
ax = fig.add_subplot(1, 1, 1) # create an axes object in the figure
ax.set_ylabel('Variance')
ax.set_xlabel('Signal')
ax.grid(True)
ax.set_title('Photon Transfer Curve')
# plot variance v/s signal
# figure()
ax.plot(meanff, signalvar, 'b.')
ax.plot(meanff, variance_fitted, 'r-')
plt.show()
if order == 1:
cf = 1 / coefts[0]
print(
f'Extension: {ext} CF = {cf:2.3f} -e/ADU RON = {cf * biasRON:2.3f}'
)
elif order == 2:
cf = 1 / coefts[1]
print(
f'Extension: {ext} CF = {cf:2.3f} -e/ADU RON = {cf * biasRON:2.3f} -e'
)
if kargs.get('RETURN', True):
return meanff, signalvar
def gain(imagelist, *coor, **kargs):
"""
Compute the gain of the system using 2 Bias and 2 FF. The procedure divides the window
in NWX*NWY subwindows and computes the Gain for each one of them and then computes the mean
value and display an histogram. If the windows coordinates are not given, it will use the full
CCD.
Syntax:
gain(imagelist[,xi,xf,yi,yf][,NWX=10][,NWY=10][,VERBOSE=True/False][,SAVE=True][,TITLE='Graph Title'][,RETURN=True/False][, MEDIAN=True/False])
Note: the image list must contain 2 bias and 2 ff in that order!
imagelist can be a list of names, a list of Paths or list of images
b1,b2= bias images
f1,f2= ff images
*coor=[xi,xf,yi,yf] = coordinates of the window to analize (should be a flat region)
kargs
-------
VERBOSE=True => print intermediate values
TITLE='Graph Title' => put a title in the graph
SAVE=True/False => if True, it saves the graph in pnp format
RETURN=True/False => if True, return only ConFAc without plots
MEDIAN=True/False => default True, computes median instead of mean
NORMFF2=True/False => default False, normalize FF2 level to set its mean level equal to FF1
RATIO=True/FALSE => defaul True. just change the way the FPN is elliminated. Both
methods give almost the same rusults
NWX= number of windows in X direction (default 10)
NWY= number of windows in Y direction (default 10)
EXT=image extension to load, default 0
SIGMA = std deviation used by sigma_clip, default=3
"""
ext = kargs.get('ext', 0)
sigma = kargs.get('SIGMA', 3)
if len(imagelist) != 4:
print('imagelist len different from 4')
return None
# if imagelist contains only image names, load them
if all([isinstance(i, str) for i in imagelist]):
images = [Image(i, ext) for i in imagelist]
print(f'Extension={ext}')
b1 = images[0]
b2 = images[1]
ff1 = images[2]
ff2 = images[3]
# elif all are images, just assign them
elif all([isinstance(i, Path) for i in imagelist]):
images = [Image(i, ext) for i in imagelist]
b1 = images[0]
b2 = images[1]
ff1 = images[2]
ff2 = images[3]
# elif all are images, just assign them
elif all([isinstance(i, Image) for i in imagelist]):
b1 = imagelist[0]
b2 = imagelist[1]
ff1 = imagelist[2]
ff2 = imagelist[3]
else:
print("Not all objects in image list are Images or filenames")
return None
x1 = coor[0]
x2 = coor[1]
y1 = coor[2]
y2 = coor[3]
b1 = b1.get_data()
b2 = b2.get_data()
ff1 = ff1.get_data()
ff2 = ff2.get_data()
nwx = kargs.get('NWX', 10) # set number of windows in x direction
nwy = kargs.get('NWY', 10) # set number of windows in y direction
if kargs.get('VERBOSE', False):
print(f'format images X={b1.shape[0]} pix Y={b1.shape[1]} pix')
print(
f'Nx:{nwx} Ny:{nwy} X1:{x1} X2:{x2} Y1:{y1} Y2:{y2} WX:{(x2-x1)//nwx} WY:{(y2-y1)//nwy}'
)
print('')
# generate auxiliary arrays of nwx * nwy elements and initialize to zero
meansig = np.zeros((nwx, nwy))
stdbias = np.zeros((nwx, nwy))
cf = np.zeros((nwx, nwy))
stdsig = np.zeros((nwx, nwy))
# windows is a generator of subwindows
windows = subwindowcoor(x1, x2, y1, y2, **kargs)
for i, j, xi, xf, yi, yf in windows:
win = slice(xi, xf), slice(yi, yf)
# compute mean value on each window for normalized ff
meansig[i, j] = sigma_clip(
(ff1[win] + ff2[win]) / 2.0).mean() - sigma_clip(
(b1[win] + b2[win]) / 2.0).mean()
# compute standard deviation on each window for normalized ff
if kargs.get('NORMFF2', False):
ff_diff = ff1[win] - ff2[win] * (sigma_clip(ff1[win]).mean() /
sigma_clip(ff2[win]).mean())
else:
ff_diff = ff1[win] - ff2[win]
var_ff_diff = sigma_clip(ff_diff).std()**2
# Measure RON from difference of two bias
var_ron = (sigma_clip(b1[win] - b2[win]).std()**2) / 2.0
stdsig[i, j] = (var_ff_diff - 2 * var_ron)
cf[i,
j] = 2 * meansig[i, j] / stdsig[i, j] # compute CF for each window
# compute standard deviation for each window of bias difference
stdbias[i, j] = np.sqrt(var_ron)
if kargs.get('VERBOSE', False):
print(
f"X({xi+x1},{xf+x1}) Y({yi+y1},{yf+y2}) Mean:{meansig[i, j]:.2f} stdff:{stdsig[i, j]:.2f} CF:{cf[i, j]:.2f}"
)
if kargs.get('MEDIAN', False):
ConFac = np.median(cf, axis=None)
RON = np.median(stdbias, axis=None)
else:
ConFac = sigma_clip(cf, sigma=sigma).mean()
RON = sigma_clip(stdbias, sigma=sigma).mean()
# RON = RMS / sqrt(2) #RON in ADUs
RONe = RON * ConFac # RON in electrons
# Error in CF estimation is the std/sqrt(number of windows)
#CFstd = np.std(cf, axis=None)/np.sqrt(nwx*nwy)
CFstd = sigma_clip(cf).std() #np.std(cf, axis=None)/np.sqrt(nwx*nwy)
# Check if run as ROUTINE, in that case return only the Conversion Factor and don't continue with plotting
if kargs.get('RETURN', False):
return ConFac, RONe, np.mean(meansig, axis=None),np.median(meansig, axis=None), \
np.mean(stdsig, axis=None)**2,np.median(stdsig, axis=None)**2,x1, x2, y1, y2
else:
plt.figure()
print("*******************************************")
print(f"*CF ={ConFac:.2f} +/-{CFstd:.2f} e/ADU")
print(f"*RON ={RONe:.3f} -e")
print(f"*RON ={RON:.3f} ADUs")
print("*******************************************")
# change shape of cf array to later compute the standard deviation and also make the histogram
cf.shape = (nwx * nwy, )
cfstd = np.std(cf, axis=None)
plt.clf()
plt.hist(cf, range=(ConFac - 3 * cfstd, ConFac + 3 * cfstd), bins=20)
plt.figtext(0.15,
0.8, ("CF mean=%5.3f +/-%5.3f e/ADU") % (ConFac, CFstd),
fontsize=11,
bbox=dict(facecolor='yellow', alpha=0.5))
plt.figtext(0.15,
0.75, ("RON =%6.3f -e") % (RONe),
fontsize=11,
bbox=dict(facecolor='yellow', alpha=0.5))
plt.figtext(0.15,
0.70, ("Computed @ %6.3f ADUs") % (np.mean(meansig)),
fontsize=11,
bbox=dict(facecolor='yellow', alpha=0.5))
Title = kargs.get('TITLE', '')
plt.title(Title)
filetitle = Title.replace(' ', '_')
plt.show()
if kargs.get('SAVE', False):
plt.savefig('ConFac_' + filetitle + '.png')
def ptc_ffpairs(imagelist, *coor, **kargs):
"""
TODO: Need to be finished !!
NHA
Perform ptc plot for pairs of ff at same level.
The pairs of ff should have the same light level.
The first 2 images in the list must be bias
To eliminate the FPN, the 'shotnoise' image is computed as the subtraction
of two debiased flat field images
optional kargs arguments:
FACTOR (default = 2.0)
MAXSIGNAL (default 65535) => compute PTC only for signal values less than MAXSIGNAL
VERBOSE (default=False) ==> print out table with signal and variance
CLIP (default=True) ==> Use clipped statistic to on images before computing CF
"""
order = kargs.get('ORDER', 1) # order of polynomial regression
if order > 2:
order = 2
MAXSIGNAL = kargs.get('MAXSIGNAL', 65535.0)
sigma = kargs.get('SIGMA', 3)
ext = kargs.get('ext', 0)
x1 = coor[0]
x2 = coor[1]
y1 = coor[2]
y2 = coor[3]
oddimageindex = list(range(3, len(imagelist), 2))
evenimageindex = list(range(2, len(imagelist), 2))
# Read in bias1 and bias2
bias1 = Image(imagelist[0], ext).crop(x1, x2, y1, y2).get_data()
bias2 = Image(imagelist[1], ext).crop(x1, x2, y1, y2).get_data()
biasRON = (sigma_clip(bias2 - bias1).std()) / np.sqrt(2)
if kargs.get('DEBUG', False):
print(f'biasRON {biasRON}')
bias_mean = sigma_clip((bias1 + bias2) / 2.0).mean()
if kargs.get('DEBUG', False):
print(f'Bias mean: {bias_mean}')
# Separate images in even and odd (crop the images..)
ff1 = [
Image(imagelist[i], ext).crop(x1, x2, y1, y2).get_data()
for i in oddimageindex
]
ff2 = [
Image(imagelist[i], ext).crop(x1, x2, y1, y2).get_data()
for i in evenimageindex
]
factor = [
sigma_clip(image1 / image2).mean() for image1, image2 in zip(ff1, ff2)
]
if kargs.get('DEBUG', False):
print(f'Factors: {factor}')
ff2 = [image2 * factor for factor, image2 in zip(factor, ff2)]
signal_mean = [
sigma_clip((image1 + image2) / 2.0).mean() - bias_mean
for image1, image2 in zip(ff1, ff2)
]
variance = [
sigma_clip(image1 - image2, sigma=sigma).std()**2
for image1, image2 in zip(ff1, ff2)
]
Truevariance = [0.5 * (var - 2 * biasRON**2) for var in variance]
# Need to sort both signal and variance according to list containing mean signal
zipped = zip(signal_mean, Truevariance)
zipped_sorted = sorted(zipped)
# remove signal,variance pairs where signal is above MAXSIGNAL
zipped_sorted = [x for x in zipped_sorted if x[0] <= MAXSIGNAL]
# Now we unpack to get back signal and variance sorted
signal, variance = zip(*zipped_sorted)
if kargs.get('VERBOSE', False):
print('Mean signal\tVariance\tCF')
for s, v in zip(signal, variance):
print(f'{s:6.1f}\t\t{v:6.1f}\t\t{s/v:2.3f}')
# compute polynomial coeficients
coefts = np.polyfit(signal, variance, order)
polyts = np.poly1d(coefts)
# compute the fitted values for variance
variance_fitted = np.polyval(polyts, signal)
# print('Intercept = {}'.format(polyts(0)))
fig = plt.figure() # create a figure object
ax = fig.add_subplot(1, 1, 1) # create an axes object in the figure
ax.set_ylabel('Variance')
ax.set_xlabel('Signal')
ax.grid(True)
ax.set_title('Photon Transfer Curve')
# plot variance v/s signal
# figure()
ax.plot(signal, variance, 'b.')
ax.plot(signal, variance_fitted, 'r-')
plt.show()
cf = 1 / coefts[0]
if order == 1:
cf = 1 / coefts[0]
print(
f'Extension: {ext} CF = {cf:2.3f} -e/ADU RON = {cf * biasRON:2.3f}'
)
elif order == 2:
cf = 1 / coefts[1]
print(
f'Extension: {ext} CF = {cf:2.3f} -e/ADU RON = {cf * biasRON:2.3f} -e'
)
if kargs.get('RETURN', True):
return cf, cf * biasRON, coefts
def gain2(imagelist, *coor, **kargs):
"""
Variation of CCD gain computation
Compute the gain of the system using 2 Bias and 2 FF. The procedure devides the window
in NWX*NWY subwindows and computes the Gain for each one of them and then computes the mean
value and display an histogram. If the windows coordinates are not given, it will use the full
CCD.
Syntax:
gain(imagelist[,xi,xf,yi,yf][,NWX=10][,NWY=10][,VERBOSE=True/False][,SAVE=True][,TITLE='Graph Title'][,RETURN=True/False][, MEDIAN=True/False])
Note: the image list must contain 2 bias and 2 ff in that order!
imagelist can be a list of names, a list of Path or list of images
b1,b2= bias images
f1,f2= ff images
*coor=[xi,xf,yi,yf] = coordinates of the window to analize (should be a flat region)
kargs
-------
VERBOSE=True => print intermediate values
TITLE='Graph Title' => put a title in the graph
SAVE=True/False => if True, it saves the graph in pnp format
RETURN=True/False => if True, return only ConFAc without plots
MEDIAN=True/False => default True, computes median instead of mean
RATIO=True/FALSE => defaul True. just change the way the FPN is elliminated. Both
methods give almost the same rusults
NWX= number of windows in X direction (default 10)
NWY= number of windows in Y direction (default 10)
EXT=image extension to load, default 0
"""
ext = kargs.get('ext', 0)
if len(imagelist) != 4:
print('imagelist len different from 4')
return None
# if imagelist contains only image names, load them
if all([isinstance(i, str) for i in imagelist]):
images = [Image(i, ext) for i in imagelist]
print(f'Extension={ext}')
b1 = images[0]
b2 = images[1]
ff1 = images[2]
ff2 = images[3]
elif all([isinstance(i, Path) for i in imagelist]):
images = [Image(i, ext) for i in imagelist]
print(f'Extension={ext}')
b1 = images[0]
b2 = images[1]
ff1 = images[2]
ff2 = images[3]
# elif all are images, just assign them
elif all([isinstance(i, Image) for i in imagelist]):
b1 = imagelist[0]
b2 = imagelist[1]
ff1 = imagelist[2]
ff2 = imagelist[3]
else:
print("Not all objects in image list are Images or filenames")
return None
#Check if bias have EXPTIME = 0.0 and FF EXPTIME > 0
testlist = []
testlist.append(b1.header['EXPTIME'] == 0.0)
testlist.append(b2.header['EXPTIME'] == 0.0)
testlist.append(ff1.header['EXPTIME'] > 0.0)
testlist.append(ff2.header['EXPTIME'] > 0.0)
print(testlist)
if not all(testlist):
print('Exposure times for at least one file are not correct')
return None
nwx = kargs.get('NWX', 10) # set number of windows in x direction
nwy = kargs.get('NWY', 10) # set number of windows in y direction
x1, x2, y1, y2 = b1.get_windowcoor(*coor)
# print(x1,x2,y1,y2)
# now work with cropped images, where the signal is more or less flat....
b1 = b1.crop(x1, x2, y1, y2)
b2 = b2.crop(x1, x2, y1, y2)
ff1 = ff1.crop(x1, x2, y1, y2)
ff2 = ff2.crop(x1, x2, y1, y2)
dbiasff1 = ff1 - b1 # debiased FF1
dbiasff2 = ff2 - b2 # debiased FF2
meanff2 = dbiasff2.mean() # mean signal on FF2 debiased
meanff1 = dbiasff1.mean() # mean signal on FF1 debiased
#ratio = meanff1/meanff2
# print(ratio)
if kargs.get('VERBOSE', False):
print(f'format images X={b1.shape[0]} pix Y={b1.shape[1]} pix')
print(
f'Nx:{nwx} Ny:{nwy} X1:{x1} X2:{x2} Y1:{y1} Y2:{y2} WX:{(x2-x1)//nwx} WY:{(y2-y1)//nwy}'
)
print('')
print(f'meanff2 ={meanff2}')
#dbiasff2 = dbiasff2*ratio
#dbias_ff_diff = dbiasff1 - dbiasff2
#dbias_ff_sig = (dbiasff1 + dbiasff2)/2.0
# from Fabrice Chisthen
dbias_ff_sig = (dbiasff1 / dbiasff2) * meanff2
# compute difference of 2 bias to get the RON
dbias = b1 - b2
# generate auxiliary arrays of nwx * nwy elements and initialize to zero
meansig = np.zeros((nwx, nwy))
stdff = np.zeros((nwx, nwy))
stdbias = np.zeros((nwx, nwy))
cf = np.zeros((nwx, nwy))
signal = (dbiasff1 / dbiasff2)
stdsig = np.zeros((nwx, nwy))
# windows is a generator of subwindows
windows = subwindowcoor(0, b1.shape[0], 0, b1.shape[1], **kargs)
for i, j, xi, xf, yi, yf in windows:
# compute mean value on each window for normalized ff
meansig[i, j] = np.mean(dbias_ff_sig[xi:xf, yi:yf])
# compute standard deviation on each window for normalized ff
stdsig[i, j] = np.std(dbias_ff_sig[xi:xf, yi:yf]) / np.sqrt(2.0)
cf[i,
j] = meansig[i, j] / (stdsig[i, j]**2) # compute CF for each window
# compute standard deviation for each window of bias difference
stdbias[i, j] = np.std(dbias[xi:xf, yi:yf]) / np.sqrt(2.0)
if kargs.get('VERBOSE', False):
print(
f"X({xi+x1},{xf+x1}) Y({yi+y1},{yf+y2}) Mean:{meansig[i, j]:.2f} stdff:{stdsig[i, j]:.2f} CF:{cf[i, j]:.2f}"
)
if kargs.get('MEDIAN', True):
ConFac = np.median(cf, axis=None)
RON = np.median(stdbias, axis=None)
else:
ConFac = np.mean(cf, axis=None)
RON = np.mean(stdbias, axis=None) # RON in ADUs
# RON = RMS / sqrt(2) #RON in ADUs
RONe = RON * ConFac # RON in electrons
# Error in CF estimation is the std/sqrt(number of windows)
CFstd = np.std(cf, axis=None) / np.sqrt(nwx * nwy)
# Check if run as ROUTINE, in that case return only the Conversion Factor and don't continue with plotting
if kargs.get('RETURN', False):
return x1, x2, y1, y2, ConFac, RONe, meanff2
else:
plt.figure()
print("*******************************************")
print(f"*CF ={ConFac:.2f} +/-{CFstd:.2f} e/ADU")
print(f"*RON ={RONe:.3f} -e")
print(f"*RON ={RON:.3f} ADUs")
print("*******************************************")
# change shape of cf array to later compute the standard deviation and also make the histogram
cf.shape = (nwx * nwy, )
cfstd = np.std(cf, axis=None)
plt.clf()
plt.hist(cf, range=(ConFac - 3 * cfstd, ConFac + 3 * cfstd), bins=20)
plt.figtext(0.15,
0.8, ("CF mean=%5.3f +/-%5.3f e/ADU") % (ConFac, CFstd),
fontsize=11,
bbox=dict(facecolor='yellow', alpha=0.5))
plt.figtext(0.15,
0.75, ("RON =%6.3f -e") % (RONe),
fontsize=11,
bbox=dict(facecolor='yellow', alpha=0.5))
plt.figtext(0.15,
0.70, ("Computed @ %6.3f ADUs") % (np.mean(meansig)),
fontsize=11,
bbox=dict(facecolor='yellow', alpha=0.5))
Title = kargs.get('TITLE', '')
plt.title(Title)
filetitle = Title.replace(' ', '_')
plt.show()
if kargs.get('SAVE', False):
plt.savefig('ConFac_' + filetitle + '.png')
def ptc_pixels(biaslist, fflist, ext=0, *coor, **kargs):
"""
Perform ptc computation to get gain and RON from a list of bias names and a
list of ff images names.
The ff images should be the same scene with all possible light levels.
An example would be a grism ff on FORS or MUSE
To eliminate the FPN, the analysis is done pixel by pixel.
optional kargs:
LOW: low level in ADUs to compute ptc
HIGH: high level in ADU to compute ptc
STEP: step in ADUs to compute variance (small steps will slow down the computation)
ORDER (default = 1) polynomial order to fit the ptc
RETURN (default=FALSE) returns CF and RON
Ex:
ptc_pixels(biaslist, fflst, 0,2000,300,600, LOW=100, HIGH=50000, STEP=10)
signal, var, var_fitted, cf = ptc_pixels(
b1, ff1, ff2, 100,200,10, 2000, LOW=100, HIGH=50000, STEP=100, OLAYERS=0.4, RETURN=True)
Compute the PTC in the window [100:200,10:2000] from 100ADUs up to 50000 ADUs each 100 ADUs,
"""
low = kargs.get('LOW', 0) # minimum signal level to explore
high = kargs.get('HIGH', 60000) # maximum signal level to explore
step = kargs.get('STEP', 100) # step size, minimum is 1
nwx = kargs.get('NWX', 10) # size of windows in X to compute RON
nwy = kargs.get('NWY', 10) # size of windows in Y to compute RON
# print("Low = {}".format(low))
# print("High = {}".format(high))
# print("Step = {}".format(step))
order = kargs.get('ORDER', 1) # order of polynomial regression
if order > 2:
order = 2
# print("Order = {}".format(order))
# read biaslist
biasimages = [Image(i, ext) for i in biaslist]
# read fflist
ffimages = [Image(i, ext) for i in fflist]
x1, x2, y1, y2 = biasimages[0].get_windowcoor(*coor)
# print("{},{},{},{}".format(x1,x2,y1,y2))
# crop bias images
biascroped = [i.crop(*coor) for i in biasimages]
# compute bias mean
biasmean = meanstack(biascroped)
# print(biasmean.shape)
# compute
stdsig = np.zeros((nwx, nwy))
windows = subwindowcoor((x2 - x1) // 2 - 5 * nwx, (x2 - x1) // 2 + 5 * nwx,
(y2 - y1) // 2 - 5 * nwy, (y2 - y1) // 2 + 5 * nwy,
**kargs)
for i, j, xi, xf, yi, yf in windows:
stdsig[i, j] = biasmean[xi:xf, yi:yf].std() * np.sqrt(len(biascroped))
# crop ff images
ffcroped = [i.crop(*coor) for i in ffimages]
# print(ffcroped[0].shape)
# debiased ff
ffcropdb = [(i - biasmean) for i in ffcroped]
ffcropdb_data = [i.get_data() for i in ffcropdb]
# compute signal
ffsignal = meanstack(ffcropdb)
# use only data from images
ffsignal_data = ffsignal.get_data()
# compute variance of all ffcropdb images along axis 0
ffcropdb_stacked = np.stack(ffcropdb_data)
ffvar = ffcropdb_stacked.var(ddof=1, axis=0)
# flatten resulting arrays
ffsignal_flatten = ffsignal_data.flatten()
ffvar_flatten = ffvar.flatten()
# print("ffsignal_flatten, ffvar_flatten : {}, {}".format(len(ffsignal_flatten), len(ffvar_flatten)))
# convert ffsignal_flatten in integer
ffsignal_flatten = ffsignal_flatten.astype(
int) # [int(i) for i in ffsignal_flatten]
ffvar_flatten = ffvar_flatten.astype(int)
# sort ffsignal_flatten and ffvar_flatten
# indx = np.argsort(ffsignal_flatten)
# ffsignal_flatten = ffsignal_flatten[indx]
# ffvar_flatten =ffvar_flatten[indx]
# get unique values in ff
ffsignal_unique = np.unique(ffsignal_flatten)
# print("ffsignal_unique : {}".format(len(ffsignal_unique)))
# filter out values lower than LOW and higher than HIGH
ffsig_unique = [i for i in ffsignal_unique if i >= low and i <= high]
# print("ffsig_unique: {}".format(len(ffsig_unique)))
# generate sampling values
sampling = list(range(low, len(ffsig_unique), step))
# print("sampling ={}".format(len(sampling)))
# create subset of unique values using sampling
ffsampled = [ffsig_unique[i] for i in sampling]
# print("ffsampled : {}".format(len(ffsampled)))
# by default use mean computation for variance
if kargs.get('MEDIAN', False):
# Compute variance mean for values in ffsignalflatten that are in ffsampled
variance = [
np.median(ffvar_flatten[ffsignal_flatten == i]) for i in ffsampled
]
else:
variance = [
np.mean(ffvar_flatten[ffsignal_flatten == i]) for i in ffsampled
]
# filter out the pixels with variance==0 or greater than 200000
variance = np.array(variance)
ffsampled = np.array(ffsampled)
vfiltered_index = np.where((variance > 0) & (variance < 200000))
ffsampled = ffsampled[vfiltered_index]
variance = variance[vfiltered_index]
# print(len(ffsignal_unique), len(variance))
plt.scatter(ffsampled[::], variance[::])
plt.grid()
plt.show()
# compute polynomial without filtering outlayers
coefts_nf = np.polyfit(ffsampled, variance, order)
polyts_nf = np.poly1d(coefts_nf)
# var_fitted = polyts_nf(ffsampled)
if order == 2:
gain = 1 / coefts_nf[1]
print(
f"GAIN = {1/coefts_nf[1]} -e/ADU RON = {gain*np.median(stdsig)} -e"
)
else:
gain = 1 / coefts_nf[0]
print(
f"GAIN = {1/coefts_nf[0]} -e/ADU RON = {gain*np.median(stdsig)} -e"
)
def extractchannel(filelist, channel):
"""
extractchannel is a utility to read an extension from a multi
extension fits file and save it as a fits file.
It's usefull if we want to extract only one channel from,
for example, a list of MUSE files.
Usage:
1)single case example
extractchannel('MUSE_WFM_FLAT303_0031.fits','CHAN04')
extract CHAN04 extension and create MUSE_WFM_FLAT303_0031_CHAN04.fits
2)multi file example
extractchannel('listing.txt','CHAN04')
listing.txt is a text file with the listing of all files we want to
extract the channel, it can be generated with the 'ls' command, like
ls MUSE*WFM*fits > listing.txt
3)filelist is python list with files names
ex: flist=['OMEGACAM_100.fits', 'OMEGACAM_102.fits', 'OMEGACAM_103.fits']
extractchannel(filelist, 'CCD_78')
TODO: add OUTPATH which points to the directory where the extracted channels will be saved
"""
#check if filelist is a fits file name
if isinstance(filelist, str) and filelist.upper().endswith('.FITS'):
try:
im = Image(filelist, channel)
im.save(filelist[:-5] + '_' + channel + '.fits')
except:
print('Bad channel designator')
#check if filelist is a file
elif os.path.isfile(filelist):
files = open(filelist, 'r').readlines()
for lines in files:
try:
#need to remove /n and split() generate a list, so
#take first element
im = Image(lines.split()[0], channel)
im.save(lines[:-5] + '_' + channel + '.fits')
except:
print('Bad channel designator')
#check if filelist is a python list of strings finished in fits
elif isinstance(filelist, list) and all(
[isinstance(x, int) for x in filelist]):
for files in filelist:
try:
im = Image(files, channel)
im.save(lines[:-5] + '_' + channel + '.fits')
except:
print('Bad channel designator')
else:
print("Not valid name or file name list")
def ptc_ffpairs(imagelist, *coor, **kargs):
"""
TODO: Need to be finished !!
NHA
Perform ptc plot for pairs of ff at same level.
The pairs of ff should have the same light level.
The first 2 images in the list must be bias
To eliminate the FPN, the 'shotnoise' image is computed as the subtraction
of two debiased flat field images
optional kargs arguments:
FACTOR (default = 2.0)
MAXSIGNAL (default 65535) => compute PTC only for signal values less than MAXSIGNAL
VERBOSE (default=False) ==> print out table with signal and variance
"""
order = kargs.get('ORDER', 1) # order of polynomial regression
if order > 2:
order = 2
MAXSIGNAL = kargs.get('MAXSIGNAL', 65535.0)
VERBOSE = kargs.get('VERBOSE', False)
ext = kargs.get('EXT', 0)
# read coordinates of first image
x1, x2, y1, y2 = Image(imagelist[0], ext).get_windowcoor(*coor)
oddimageindex = list(range(3, len(imagelist), 2))
evenimageindex = list(range(2, len(imagelist), 2))
# Read in bias1 and bias2
bias1 = Image(imagelist[0], ext).crop(x1, x2, y1, y2)
bias2 = Image(imagelist[1], ext).crop(x1, x2, y1, y2)
bias_dif = bias2 - bias1
# mask out all pixels with value greater or lower than 3*std
bias_dif.mask()
# Separate images in even and odd (crop the images..)
ff1 = [Image(imagelist[i], ext).crop(x1, x2, y1, y2) for i in oddimageindex]
ff2 = [Image(imagelist[i], ext).crop(x1, x2, y1, y2) for i in evenimageindex]
# remove bias from both ff images
ff1d = [(image - bias1) for image in ff1]
ff2d = [(image - bias2) for image in ff2]
if kargs.get('USE_FFMEAN', False):
ffmean = [(image1/image2)*image2.mean() for image1, image2 in zip(ff1d, ff2d)]
else:
ffmean = [(image1+image2)/2.0 for image1, image2 in zip(ff1d, ff2d)]
shotnoise = [(image1 - image2) for image1, image2 in zip(ff1d, ff2d)]
signal = [image.mean() for image in ff1d] # ffmean]
variance = [image.var()/2.0 for image in shotnoise]
# Need to sort both signal and variance according to list containing mean signal
zipped = zip(signal, variance)
zipped_sorted = sorted(zipped)
# remove signal,variance pairs where signal is above MAXSIGNAL
zipped_sorted = [x for x in zipped_sorted if x[0] <= MAXSIGNAL]
# Now we unpack to get back signal and variance sorted
signal, variance = zip(*zipped_sorted)
if kargs.get('VERBOSE', False):
print('Mean signal Variance')
for s, v in zip(signal, variance):
print(' {:6.1f} {:6.1f}'.format(s, v))
# compute polynomial coeficients
coefts = np.polyfit(signal, variance, order)
polyts = np.poly1d(coefts)
# compute the fitted values for variance
variance_fitted = np.polyval(polyts, signal)
# print('Intercept = {}'.format(polyts(0)))
fig = plt.figure() # create a figure object
ax = fig.add_subplot(1, 1, 1) # create an axes object in the figure
ax.set_ylabel('Variance')
ax.set_xlabel('Signal')
ax.grid(True)
ax.set_title('Photon Transfer Curve')
# plot variance v/s signal
# figure()
ax.plot(signal, variance, 'b.')
ax.plot(signal, variance_fitted, 'r-')
cf = 1/coefts[0]
if order == 1:
cf = 1/coefts[0]
print(
'Extension: {} CF = {:2.3f} -e/ADU RON = {:2.3f}'.format(ext, cf, cf * bias_dif.std()/np.sqrt(2.0)))
elif order == 2:
cf = 1/coefts[1]
print(
'Extension: {} CF = {:2.3f} -e/ADU RON = {:2.3f} -e'.format(ext, cf, cf * bias_dif.std()/np.sqrt(2.0)))
