# If the file exists, then read it into a dictionary
if os.path.isfile(fileName):
polAngs.append(polAng)
polAngImgs.append(AstroImage(fileName))
else:
fileCheck = False
# Check that all the necessarry files are present
if not fileCheck:
print("\tSome required Polaroid Angle files are missing.")
continue
# Use the "align_stack" method to align the newly created image list
print('\nAligning images\n')
polAngImgs = image_tools.align_images(polAngImgs,
mode='cross_correlate', subPixel=True, padding=np.nan)
# Convert the list into a dictionary
polAngImgs = dict(zip(polAngs, polAngImgs)) #aligned
#**********************************************************************
# Stokes I
#**********************************************************************
# Average the images to get stokes I
stokesI = image_tools.combine_images([polAngImgs[0],
polAngImgs[200],
polAngImgs[400],
polAngImgs[600]])
stokesI = 2 * stokesI
# Perform astrometry to apply to the headers of all the other images...
# Catch saturated negative values
badInds = np.where(Aimg.arr < -3*read_noise/effective_gain)
Aimg.arr[badInds] = np.NaN
# Perform the actual background subtraction
Aimg.arr = Aimg.arr - B1bkg.background
# Append the background subtracted image to the list
sciImgList.append(Aimg.copy())
# Now that all the backgrounds have been subtracted,
# let's align the images and compute an average image
# TODO Should I use "cross-correlation" alignment?
sciImgList = image_tools.align_images(sciImgList, padding=np.NaN)
avgImg = image_tools.combine_images(sciImgList, output = 'MEAN',
effective_gain = effective_gain,
read_noise = read_noise)
else:
# Handle HEX-DITHERS images
# (mostly for calibration images as of now)
continue
##### The Indexing Program should parse hex pointing positions, too.
# Test if numImgs matches the ABBA pattern
if (numImgs % 6) != 0:
print('The HEX dither pattern is not there...')
pdb.set_trace()
# Now align and combine these images into a single average image
# # Loop through each image in the image list and compute its uncertainty
# imgList1 = imgList.copy()
# for imgNum, img in enumerate(imgList):
# imgList1[imgNum].sigma = np.sqrt((backgroundLevels[imgNum] + img.arr)/effective_gain)
#
# imgList = imgList1.copy()
# del imgList1
# Now apply the scale factors to the images in imgList
imgList = [CF*img for CF, img in zip(scaleFactors, imgList)]
# Now that the background free images are stored, combine them
# Align the images
print('\tAligning the background removed images for IPPA {0}'.format(ippa))
imgList = image_tools.align_images(imgList, mode='wcs', padding=np.nan)
# Perform identical alignments for the xPos and yPos images
xPosList = image_tools.align_images(xPosList, mode='wcs', padding=-1e6)
yPosList = image_tools.align_images(yPosList, mode='wcs', padding=-1e6)
# Loop through the position lists and cull any stupid numbers
for imgNum in range(len(xPosList)):
# Create the temporary position arrays with masks
tmpXarr = np.ma.array(xPosList[imgNum].arr)
tmpYarr = np.ma.array(yPosList[imgNum].arr)
tmpXarr.mask = np.zeros_like(tmpXarr, dtype=bool)
tmpYarr.mask = tmpXarr.mask.copy()
# Find the positions of the bad values
badInds = np.where(np.logical_or(tmpXarr < 0, tmpYarr < 0))
# If the file exists, then read it into a dictionary
if os.path.isfile(fileName):
polAngs.append(polAng)
polAngImgs.append(AstroImage(fileName))
else:
fileCheck = False
# Check that all the necessarry files are present
if not fileCheck:
print("\tSome required Polaroid Angle files are missing.")
continue
# Use the "align_stack" method to align the newly created image list
print('\nAligning images\n')
polAngImgs = image_tools.align_images(polAngImgs,
mode='cross_correlate',
subPixel=True,
padding=np.nan)
# Convert the list into a dictionary
polAngImgs = dict(zip(polAngs, polAngImgs)) #aligned
#**********************************************************************
# Stokes I
#**********************************************************************
# Average the images to get stokes I
stokesI = image_tools.combine_images(
[polAngImgs[0], polAngImgs[200], polAngImgs[400], polAngImgs[600]])
stokesI = 2 * stokesI
# Perform astrometry to apply to the headers of all the other images...
stokesI, success = image_tools.astrometry(stokesI)
# Catch saturated negative values
badInds = np.where(Atmp.arr < -3 * read_noise / effective_gain)
Atmp.arr[badInds] = np.NaN
# Perform the actual background subtraction by scaling the average
# *NORMALIZED* background image to the interpolated background level
Atmp.arr = Atmp.arr - bkgModel(Atime) * bkgImg
# Append the background subtracted image to the list
sciImgList.append(Atmp.copy())
# Now that all the backgrounds have been subtracted,
# let's align the images and compute an average image
# TODO Should I use "cross-correlation" alignment?
sciImgList = image_tools.align_images(sciImgList, padding=np.NaN, mode="cross_correlate", subPixel=True)
avgImg = image_tools.combine_images(
sciImgList, output="MEAN", mean_bkg=mean_bkg, effective_gain=effective_gain, read_noise=read_noise
)
else:
# Handle HEX-DITHERS images
# (mostly for calibration images as of now)
continue
##### The Indexing Program should parse hex pointing positions, too.
# Test if numImgs matches the ABBA pattern
if (numImgs % 6) != 0:
print("The HEX dither pattern is not there...")
pdb.set_trace()
# Now align and combine these images into a single average image
