def do_median(drizzle_groups_sci, drizzle_groups_wht, **pars):
# start by interpreting input parameters
nlow = pars.get('nlow', 0)
nhigh = pars.get('nhigh', 0)
high_threshold = pars.get('hthresh', None)
low_threshold = pars.get('lthresh', None)
nsigma = pars.get('nsigma', '4 3')
maskpt = pars.get('maskpt', 0.7)
# Perform additional interpretation of some parameters
sigmaSplit = nsigma.split()
nsigma1 = float(sigmaSplit[0])
nsigma2 = float(sigmaSplit[1])
if high_threshold is not None and (high_threshold.strip() == ""
or high_threshold < 0):
high_threshold = None
if low_threshold is not None and (low_threshold.strip() == ""
or low_threshold < 0):
low_threshold = None
if high_threshold is not None: high_threshold = float(high_threshold)
if low_threshold is not None: low_threshold = float(low_threshold)
_weight_mask_list = []
for weight_arr in drizzle_groups_wht:
# Initialize an output mask array to ones
# This array will be reused for every output weight image
_weight_mask = np.zeros(weight_arr.shape, dtype=np.uint8)
try:
tmp_mean_value = ImageStats(weight_arr,
lower=1e-8,
fields="mean",
nclip=0).mean
except ValueError:
tmp_mean_value = 0.0
_wht_mean = tmp_mean_value * maskpt
# 0 means good, 1 means bad here...
np.putmask(_weight_mask, np.less(weight_arr, _wht_mean), 1)
#_weight_mask.info()
_weight_mask_list.append(_weight_mask)
# Create the combined array object using the numcombine task
result = numcombine.numCombine(drizzle_groups_sci,
numarrayMaskList=_weight_mask_list,
combinationType="median",
nlow=nlow,
nhigh=nhigh,
upper=high_threshold,
lower=low_threshold)
median_array = result.combArrObj
del _weight_mask_list
return median_array
def getcorr():
imstat = ImageStats(image.science[i][BMask],
'midpt',
lower=lower,
upper=upper,
nclip=0)
if imstat.npix != NPix:
raise ValueError(f'imstate.npix ({imstat.npix}) != '
f'NPix ({NPix})')
return (imstat.midpt)
def calc_sky(self, data):
""" Computes statistics on data.
Parameters
-----------
data : numpy.ndarray
A numpy array of values for which the statistics needs to be computed.
Returns
--------
statistics : tuple
A tuple of two values: (`skyvalue`, `npix`), where `skyvalue` is the statistics
specified by the `skystat` parameter during the initialization
of the `SkyStats` object and `npix` is the number of pixels used
in computing the statistics reported in `skyvalue`.
"""
imstat = ImageStats(image=data, fields=self._fields, **(self._kwargs))
self.skyval = self._skystat(imstat)
self.npix = imstat.npix
return (self.skyval, self.npix)
def _median(imageObjectList, paramDict):
"""Create a median image from the list of image Objects
that has been given.
"""
newmasks = paramDict['median_newmasks']
comb_type = paramDict['combine_type'].lower()
nlow = paramDict['combine_nlow']
nhigh = paramDict['combine_nhigh']
grow = paramDict['combine_grow'] if 'minmed' in comb_type else 0
maskpt = paramDict['combine_maskpt']
proc_units = paramDict['proc_unit']
compress = paramDict['compress']
bufsizeMB = paramDict['combine_bufsize']
sigma = paramDict["combine_nsigma"]
sigmaSplit = sigma.split()
nsigma1 = float(sigmaSplit[0])
nsigma2 = float(sigmaSplit[1])
if paramDict['combine_lthresh'] is None:
lthresh = None
else:
lthresh = float(paramDict['combine_lthresh'])
if paramDict['combine_hthresh'] is None:
hthresh = None
else:
hthresh = float(paramDict['combine_hthresh'])
# the name of the output median file isdefined in the output wcs object and
# stuck in the image.outputValues["outMedian"] dict of every imageObject
medianfile = imageObjectList[0].outputNames["outMedian"]
# Build combined array from single drizzled images.
# Start by removing any previous products...
if os.access(medianfile, os.F_OK):
os.remove(medianfile)
# Define lists for instrument specific parameters, these should be in
# the image objects need to be passed to the minmed routine
readnoiseList = []
exposureTimeList = []
backgroundValueList = [] # list of MDRIZSKY *platescale values
singleDrizList = [] # these are the input images
singleWeightList = [] # pointers to the data arrays
wht_mean = [] # Compute the mean value of each wht image
single_hdr = None
virtual = None
# for each image object
for image in imageObjectList:
if virtual is None:
virtual = image.inmemory
det_gain = image.getGain(1)
img_exptime = image._image['sci', 1]._exptime
native_units = image.native_units
native_units_lc = native_units.lower()
if proc_units.lower() == 'native':
if native_units_lc not in [
'counts', 'electrons', 'counts/s', 'electrons/s'
]:
raise ValueError(
"Unexpected native units: '{}'".format(native_units))
if lthresh is not None:
if native_units_lc.startswith('counts'):
lthresh *= det_gain
if native_units_lc.endswith('/s'):
lthresh *= img_exptime
if hthresh is not None:
if native_units_lc.startswith('counts'):
hthresh *= det_gain
if native_units_lc.endswith('/s'):
hthresh *= img_exptime
singleDriz = image.getOutputName("outSingle")
singleDriz_name = image.outputNames['outSingle']
singleWeight = image.getOutputName("outSWeight")
singleWeight_name = image.outputNames['outSWeight']
# If compression was used, reference ext=1 as CompImageHDU only writes
# out MEF files, not simple FITS.
if compress:
wcs_ext = '[1]'
wcs_extnum = 1
else:
wcs_ext = '[0]'
wcs_extnum = 0
if not virtual:
if isinstance(singleDriz, str):
iter_singleDriz = singleDriz + wcs_ext
iter_singleWeight = singleWeight + wcs_ext
else:
iter_singleDriz = singleDriz[wcs_extnum]
iter_singleWeight = singleWeight[wcs_extnum]
else:
iter_singleDriz = singleDriz_name + wcs_ext
iter_singleWeight = singleWeight_name + wcs_ext
# read in WCS from first single drizzle image to use as WCS for
# median image
if single_hdr is None:
if virtual:
single_hdr = singleDriz[wcs_extnum].header
else:
single_hdr = fits.getheader(singleDriz_name,
ext=wcs_extnum,
memmap=False)
single_image = iterfile.IterFitsFile(iter_singleDriz)
if virtual:
single_image.handle = singleDriz
single_image.inmemory = True
singleDrizList.append(single_image) # add to an array for bookkeeping
# If it exists, extract the corresponding weight images
if (not virtual
and os.access(singleWeight, os.F_OK)) or (virtual
and singleWeight):
weight_file = iterfile.IterFitsFile(iter_singleWeight)
if virtual:
weight_file.handle = singleWeight
weight_file.inmemory = True
singleWeightList.append(weight_file)
try:
tmp_mean_value = ImageStats(weight_file.data,
lower=1e-8,
fields="mean",
nclip=0).mean
except ValueError:
tmp_mean_value = 0.0
wht_mean.append(tmp_mean_value * maskpt)
# Extract instrument specific parameters and place in lists
# If an image has zero exposure time we will
# redefine that value as '1'. Although this will cause inaccurate
# scaling of the data to occur in the 'minmed' combination
# algorith, this is a necessary evil since it avoids divide by
# zero exceptions. It is more important that the divide by zero
# exceptions not cause AstroDrizzle to crash in the pipeline than
# it is to raise an exception for this obviously bad data even
# though this is not the type of data you would wish to process
# with AstroDrizzle.
#
# Get the exposure time from the InputImage object
#
# MRD 19-May-2011
# Changed exposureTimeList to take exposure time from img_exptime
# variable instead of hte image._exptime attribute, since
# image._exptime was just giving 1.
#
exposureTimeList.append(img_exptime)
# Use only "commanded" chips to extract subtractedSky and rdnoise:
rdnoise = 0.0
nchips = 0
bsky = None # minimum sky across **used** chips
for chip in image.returnAllChips(extname=image.scienceExt):
# compute sky value as sky/pixel using the single_drz
# pixel scale:
if bsky is None or bsky > chip.subtractedSky:
bsky = chip.subtractedSky * chip._conversionFactor
# Extract the readnoise value for the chip
rdnoise += chip._rdnoise**2
nchips += 1
if bsky is None:
bsky = 0.0
if nchips > 0:
rdnoise = math.sqrt(rdnoise / nchips)
backgroundValueList.append(bsky)
readnoiseList.append(rdnoise)
print("reference sky value for image '{}' is {}".format(
image._filename, backgroundValueList[-1]))
#
# END Loop over input image list
#
# create an array for the median output image, use the size of the first
# image in the list. Store other useful image characteristics:
single_driz_data = singleDrizList[0].data
data_item_size = single_driz_data.itemsize
single_data_dtype = single_driz_data.dtype
imrows, imcols = single_driz_data.shape
medianImageArray = np.zeros_like(single_driz_data)
del single_driz_data
if comb_type == "minmed" and not newmasks:
# Issue a warning if minmed is being run with newmasks turned off.
print('\nWARNING: Creating median image without the application of '
'bad pixel masks!\n')
# The overlap value needs to be set to 2*grow in order to
# avoid edge effects when scrolling down the image, and to
# insure that the last section returned from the iterator
# has enough rows to span the kernel used in the boxcar method
# within minmed.
overlap = 2 * grow
buffsize = BUFSIZE if bufsizeMB is None else (BUFSIZE * bufsizeMB)
section_nrows = min(imrows, int(buffsize / (imcols * data_item_size)))
if section_nrows == 0:
buffsize = imcols * data_item_size
print("WARNING: Buffer size is too small to hold a single row.\n"
" Buffer size size will be increased to minimal "
"required: {}MB".format(float(buffsize) / 1048576.0))
section_nrows = 1
if section_nrows < overlap + 1:
new_grow = int((section_nrows - 1) / 2)
if section_nrows == imrows:
print("'grow' parameter is too large for actual image size. "
"Reducing 'grow' to {}".format(new_grow))
else:
print("'grow' parameter is too large for requested buffer size. "
"Reducing 'grow' to {}".format(new_grow))
grow = new_grow
overlap = 2 * grow
nbr = section_nrows - overlap
nsec = (imrows - overlap) // nbr
if (imrows - overlap) % nbr > 0:
nsec += 1
for k in range(nsec):
e1 = k * nbr
e2 = e1 + section_nrows
u1 = grow
u2 = u1 + nbr
if k == 0: # first section
u1 = 0
if k == nsec - 1: # last section
e2 = min(e2, imrows)
e1 = min(e1, e2 - overlap - 1)
u2 = e2 - e1
imdrizSectionsList = np.empty((len(singleDrizList), e2 - e1, imcols),
dtype=single_data_dtype)
for i, w in enumerate(singleDrizList):
imdrizSectionsList[i, :, :] = w[e1:e2]
if singleWeightList:
weightSectionsList = np.empty(
(len(singleWeightList), e2 - e1, imcols),
dtype=single_data_dtype)
for i, w in enumerate(singleWeightList):
weightSectionsList[i, :, :] = w[e1:e2]
else:
weightSectionsList = None
weight_mask_list = None
if newmasks and weightSectionsList is not None:
# Build new masks from single drizzled images.
# Generate new pixel mask file for median step.
# This mask will be created from the single-drizzled
# weight image for this image.
# The mean of the weight array will be computed and all
# pixels with values less than 0.7 of the mean will be flagged
# as bad in this mask. This mask will then be used when
# creating the median image.
# 0 means good, 1 means bad here...
weight_mask_list = np.less(
weightSectionsList,
np.asarray(wht_mean)[:, None, None]).astype(np.uint8)
if 'minmed' in comb_type: # Do MINMED
# set up use of 'imedian'/'imean' in minmed algorithm
fillval = comb_type.startswith('i')
# Create the combined array object using the minmed algorithm
result = min_med(imdrizSectionsList,
weightSectionsList,
readnoiseList,
exposureTimeList,
backgroundValueList,
weight_masks=weight_mask_list,
combine_grow=grow,
combine_nsigma1=nsigma1,
combine_nsigma2=nsigma2,
fillval=fillval)
else: # DO NUMCOMBINE
# Create the combined array object using the numcombine task
result = numcombine.num_combine(imdrizSectionsList,
masks=weight_mask_list,
combination_type=comb_type,
nlow=nlow,
nhigh=nhigh,
upper=hthresh,
lower=lthresh)
# Write out the processed image sections to the final output array:
medianImageArray[e1 + u1:e1 + u2, :] = result[u1:u2, :]
# Write out the combined image
# use the header from the first single drizzled image in the list
pf = _writeImage(medianImageArray, inputHeader=single_hdr)
if virtual:
mediandict = {}
mediandict[medianfile] = pf
for img in imageObjectList:
img.saveVirtualOutputs(mediandict)
else:
try:
print("Saving output median image to: '{}'".format(medianfile))
pf.writeto(medianfile)
except IOError:
msg = "Problem writing file '{}'".format(medianfile)
print(msg)
raise IOError(msg)
# Always close any files opened to produce median image; namely,
# single drizzle images and singly-drizzled weight images
#
for img in singleDrizList:
if not virtual:
img.close()
# Close all singly drizzled weight images used to create median image.
for img in singleWeightList:
if not virtual:
img.close()
def addMember(self, imagePtr=None):
"""
Combines the input image with the static mask that
has the same signature.
Parameters
----------
imagePtr : object
An imageObject reference
Notes
-----
The signature parameter consists of the tuple::
(instrument/detector, (nx,ny), chip_id)
The signature is defined in the image object for each chip
"""
numchips=imagePtr._numchips
log.info("Computing static mask:\n")
chips = imagePtr.group
if chips is None:
chips = imagePtr.getExtensions()
#for chip in range(1,numchips+1,1):
for chip in chips:
chipid=imagePtr.scienceExt + ','+ str(chip)
chipimage=imagePtr.getData(chipid)
signature=imagePtr[chipid].signature
# If this is a new signature, create a new Static Mask file which is empty
# only create a new mask if one doesn't already exist
if ((signature not in self.masklist) or (len(self.masklist) == 0)):
self.masklist[signature] = self._buildMaskArray(signature)
maskname = constructFilename(signature)
self.masknames[signature] = maskname
else:
chip_sig = buildSignatureKey(signature)
for s in self.masknames:
if chip_sig in self.masknames[s]:
maskname = self.masknames[s]
break
imagePtr[chipid].outputNames['staticMask'] = maskname
stats = ImageStats(chipimage,nclip=3,fields='mode')
mode = stats.mode
rms = stats.stddev
nbins = len(stats.histogram)
del stats
log.info(' mode = %9f; rms = %7f; static_sig = %0.2f' %
(mode, rms, self.static_sig))
if nbins >= 2: # only combine data from new image if enough data to mask
sky_rms_diff = mode - (self.static_sig*rms)
np.bitwise_and(self.masklist[signature],
np.logical_not(np.less(chipimage, sky_rms_diff)),
self.masklist[signature])
del chipimage
def run(self):
"""
Run the median combine step
The code was either directly stolen from the corresponding
pydrizzle version or done after this version. Necessary
adjustments to the slitless data were applied.
"""
sci_data = []
for one_image in self.input_data['sci_imgs']:
if os.access(one_image, os.F_OK):
in_fits = fits.open(one_image, 'readonly')
sci_data.append(in_fits[0].data)
in_fits.close()
wht_data = []
for one_image in self.input_data['wht_imgs']:
if os.access(one_image, os.F_OK):
in_fits = fits.open(one_image, 'readonly')
wht_data.append(in_fits[0].data)
in_fits.close()
else:
_log.info("{0:s} not found/created by drizzle"
"...skipping it.".format(one_image))
if len(sci_data) != len(wht_data):
_log.info("The number of single_sci images created by "
"drizzle does not match the number of single_wht"
" files created!")
raise aXeError("drizzle error")
weight_mask_list = []
# added the except so that if the image area contains only
# zeros then the zero value is returned which is better for later
# processing
# we dont understand why the original lower=1e-8 value was
# supplied unless it was for the case of spectral in the normal
# field of view see #1110
for wht_arr in wht_data:
try:
tmp_mean_value = self.combine_maskpt * ImageStats(wht_arr,lower=1e-8,lsig=None,usig=None,fields="mean",nclip=0).mean
except (ValueError, AttributeError):
tmp_mean_value = 0.
_log.info("tmp_mean_value set to 0 because no good "
"pixels found; {0:s}".format(self.ext_names["MEF"]))
except:
tmp_mean_value = 0.
_log.info("tmp_mean_value set to 0; possible uncaught "
"exception in dither.py; {0:s}"
.format(self.ext_names["MEF"]))
weight_mask = np.zeros(wht_arr.shape, dtype=np.uint8)
np.putmask(weight_mask, np.less(wht_arr, tmp_mean_value), 1)
weight_mask_list.append(weight_mask)
if len(sci_data) < 2:
_log.info('\nNumber of images to flatten: %i!' % len(sci_data))
_log.info('Set combine type to "minimum"!')
self.combine_type = 'minimum'
if (self.combine_type == "minmed"):
# Create the combined array object using the minmed algorithm
result = minmed(sci_data, # list of input data to be combined.
wht_data,# list of input data weight images to be combined.
self.input_data['rdn_vals'], # list of readnoise values to use for the input images.
self.input_data['exp_vals'], # list of exposure times to use for the input images.
self.input_data['sky_vals'], # list of image background values to use for the input images
weightMaskList = weight_mask_list, # list of imput data weight masks to use for pixel rejection.
combine_grow = self.combine_grow, # Radius (pixels) for neighbor rejection
combine_nsigma1 = self.combine_nsigma1, # Significance for accepting minimum instead of median
combine_nsigma2 = self.combine_nsigma2 # Significance for accepting minimum instead of median
)
else:
# _log.info 'going to other', combine_type
# Create the combined array object using the numcombine task
result = numCombine(sci_data,
numarrayMaskList=weight_mask_list,
combinationType=self.combine_type,
nlow=self.combine_nlow,
nhigh=self.combine_nhigh,
upper=self.combine_hthresh,
lower=self.combine_lthresh
)
# _log.info result.combArrObj
hdu = fits.PrimaryHDU(result.combArrObj)
hdulist = fits.HDUList([hdu])
hdulist[0].header['EXPTIME'] = (self.input_data['exp_tot'],
'total exposure time')
hdulist.writeto(self.median_image)
# delete the various arrays
for one_item in sci_data:
del one_item
del sci_data
for one_item in wht_data:
del one_item
del wht_data
for one_item in weight_mask_list:
del one_item
del weight_mask_list
def _median(imageObjectList, paramDict):
"""Create a median image from the list of image Objects
that has been given.
"""
newmasks = paramDict['median_newmasks']
comb_type = paramDict['combine_type']
nlow = paramDict['combine_nlow']
nhigh = paramDict['combine_nhigh']
grow = paramDict['combine_grow']
maskpt = paramDict['combine_maskpt']
proc_units = paramDict['proc_unit']
compress = paramDict['compress']
bufsizeMb = paramDict['combine_bufsize']
sigma = paramDict["combine_nsigma"]
sigmaSplit = sigma.split()
nsigma1 = float(sigmaSplit[0])
nsigma2 = float(sigmaSplit[1])
#print "Checking parameters:"
#print comb_type,nlow,nhigh,grow,maskpt,nsigma1,nsigma2
if (paramDict['combine_lthresh'] == None):
lthresh = None
else:
lthresh = float(paramDict['combine_lthresh'])
if (paramDict['combine_hthresh'] == None):
hthresh = None
else:
hthresh = float(paramDict['combine_hthresh'])
#the name of the output median file isdefined in the output wcs object
#and stuck in the image.outputValues["outMedian"] dict of every imageObject
medianfile = imageObjectList[0].outputNames["outMedian"]
""" Builds combined array from single drizzled images."""
# Start by removing any previous products...
if (os.access(medianfile, os.F_OK)):
os.remove(medianfile)
# Define lists for instrument specific parameters, these should be in the image objects
# need to be passed to the minmed routine
readnoiseList = []
exposureTimeList = []
backgroundValueList = [] #list of MDRIZSKY *platescale values
singleDrizList = [] #these are the input images
singleWeightList = [] #pointers to the data arrays
#skylist=[] #the list of platescale values for the images
_wht_mean = [] # Compute the mean value of each wht image
_single_hdr = None
virtual = None
#for each image object
for image in imageObjectList:
if virtual is None:
virtual = image.inmemory
det_gain = image.getGain(1)
img_exptime = image._image['sci', 1]._exptime
native_units = image.native_units
if lthresh is not None:
if proc_units.lower() == 'native':
if native_units.lower() == "counts":
lthresh = lthresh * det_gain
if native_units.lower() == "counts/s":
lthresh = lthresh * img_exptime
if hthresh is not None:
if proc_units.lower() == 'native':
if native_units.lower() == "counts":
hthresh = hthresh * det_gain
if native_units.lower() == "counts/s":
hthresh = hthresh * img_exptime
singleDriz = image.getOutputName("outSingle")
singleDriz_name = image.outputNames['outSingle']
singleWeight = image.getOutputName("outSWeight")
singleWeight_name = image.outputNames['outSWeight']
#singleDriz=image.outputNames["outSingle"] #all chips are drizzled to a single output image
#singleWeight=image.outputNames["outSWeight"]
# If compression was used, reference ext=1 as CompImageHDU only writes
# out MEF files, not simple FITS.
if compress:
wcs_ext = '[1]'
wcs_extnum = 1
else:
wcs_ext = '[0]'
wcs_extnum = 0
if not virtual:
if isinstance(singleDriz, str):
iter_singleDriz = singleDriz + wcs_ext
iter_singleWeight = singleWeight + wcs_ext
else:
iter_singleDriz = singleDriz[wcs_extnum]
iter_singleWeight = singleWeight[wcs_extnum]
else:
iter_singleDriz = singleDriz_name + wcs_ext
iter_singleWeight = singleWeight_name + wcs_ext
# read in WCS from first single drizzle image to use as WCS for median image
if _single_hdr is None:
if virtual:
_single_hdr = singleDriz[wcs_extnum].header
else:
_single_hdr = fits.getheader(singleDriz_name, ext=wcs_extnum)
_singleImage = iterfile.IterFitsFile(iter_singleDriz)
if virtual:
_singleImage.handle = singleDriz
_singleImage.inmemory = True
singleDrizList.append(_singleImage) #add to an array for bookkeeping
# If it exists, extract the corresponding weight images
if (not virtual
and os.access(singleWeight, os.F_OK)) or (virtual
and singleWeight):
_weight_file = iterfile.IterFitsFile(iter_singleWeight)
if virtual:
_weight_file.handle = singleWeight
_weight_file.inmemory = True
singleWeightList.append(_weight_file)
try:
tmp_mean_value = ImageStats(_weight_file.data,
lower=1e-8,
fields="mean",
nclip=0).mean
except ValueError:
tmp_mean_value = 0.0
_wht_mean.append(tmp_mean_value * maskpt)
# Extract instrument specific parameters and place in lists
# If an image has zero exposure time we will
# redefine that value as '1'. Although this will cause inaccurate scaling
# of the data to occur in the 'minmed' combination algorith, this is a
# necessary evil since it avoids divide by zero exceptions. It is more
# important that the divide by zero exceptions not cause Multidrizzle to
# crash in the pipeline than it is to raise an exception for this obviously
# bad data even though this is not the type of data you would wish to process
# with Multidrizzle.
#
# Get the exposure time from the InputImage object
#
# MRD 19-May-2011
# Changed exposureTimeList to take exposure time from img_exptime
# variable instead of hte image._exptime attribute, since
# image._exptime was just giving 1.
#
exposureTimeList.append(img_exptime)
# Use only "commanded" chips to extract subtractedSky and rdnoise:
rdnoise = 0.0
nchips = 0
bsky = None # minimum sky across **used** chips
for chip in image.returnAllChips(extname=image.scienceExt):
# compute sky value as sky/pixel using the single_drz pixel scale
if bsky is None or bsky > chip.subtractedSky:
bsky = chip.subtractedSky
# Extract the readnoise value for the chip
rdnoise += (chip._rdnoise)**2
nchips += 1
if bsky is None:
bsky = 0.0
if nchips > 0:
rdnoise = math.sqrt(rdnoise / nchips)
backgroundValueList.append(bsky)
readnoiseList.append(rdnoise)
## compute sky value as sky/pixel using the single_drz pixel scale
#bsky = image._image[image.scienceExt,1].subtractedSky# * (image.outputValues['scale']**2)
#backgroundValueList.append(bsky)
## Extract the readnoise value for the chip
#sci_chip = image._image[image.scienceExt,1]
#readnoiseList.append(sci_chip._rdnoise) #verify this is calculated correctly in the image object
print("reference sky value for image ", image._filename, " is ",
backgroundValueList[-1])
#
# END Loop over input image list
#
# create an array for the median output image, use the size of the first image in the list
medianImageArray = np.zeros(singleDrizList[0].shape,
dtype=singleDrizList[0].type())
if (comb_type.lower() == "minmed") and not newmasks:
# Issue a warning if minmed is being run with newmasks turned off.
print(
'\nWARNING: Creating median image without the application of bad pixel masks!\n'
)
# create the master list to be used by the image iterator
masterList = []
masterList.extend(singleDrizList)
masterList.extend(singleWeightList)
print('\n')
# Specify the location of the drz image sections
startDrz = 0
endDrz = len(singleDrizList) + startDrz
# Specify the location of the wht image sections
startWht = len(singleDrizList) + startDrz
endWht = startWht + len(singleWeightList)
_weight_mask_list = None
# Fire up the image iterator
#
# The overlap value needs to be set to 2*grow in order to
# avoid edge effects when scrolling down the image, and to
# insure that the last section returned from the iterator
# has enough row to span the kernel used in the boxcar method
# within minmed.
_overlap = 2 * int(grow)
#Start by computing the buffer size for the iterator
_imgarr = masterList[0].data
_bufsize = nimageiter.BUFSIZE
if bufsizeMb is not None:
_bufsize *= bufsizeMb
_imgrows = _imgarr.shape[0]
_nrows = nimageiter.computeBuffRows(_imgarr)
# _overlaprows = _nrows - (_overlap+1)
# _niter = int(_imgrows/_nrows)
# _niter = 1 + int( (_imgrows - _overlaprows)/_nrows)
niter = nimageiter.computeNumberBuff(_imgrows, _nrows, _overlap)
#computeNumberBuff actually returns (niter,buffrows)
_niter = niter[0]
_nrows = niter[1]
_lastrows = _imgrows - (_niter * (_nrows - _overlap))
# check to see if this buffer size will leave enough rows for
# the section returned on the last iteration
if _lastrows < _overlap + 1:
_delta_rows = (_overlap + 1 - _lastrows) // _niter
if _delta_rows < 1 and _delta_rows >= 0: _delta_rows = 1
_bufsize += (_imgarr.shape[1] * _imgarr.itemsize) * _delta_rows
if not virtual:
masterList[0].close()
del _imgarr
for imageSectionsList, prange in nimageiter.FileIter(masterList,
overlap=_overlap,
bufsize=_bufsize):
if newmasks:
""" Build new masks from single drizzled images. """
_weight_mask_list = []
listIndex = 0
for _weight_arr in imageSectionsList[startWht:endWht]:
# Initialize an output mask array to ones
# This array will be reused for every output weight image
_weight_mask = np.zeros(_weight_arr.shape, dtype=np.uint8)
""" Generate new pixel mask file for median step.
This mask will be created from the single-drizzled
weight image for this image.
The mean of the weight array will be computed and all
pixels with values less than 0.7 of the mean will be flagged
as bad in this mask. This mask will then be used when
creating the median image.
"""
# Compute image statistics
_mean = _wht_mean[listIndex]
# 0 means good, 1 means bad here...
np.putmask(_weight_mask, np.less(_weight_arr, _mean), 1)
#_weight_mask.info()
_weight_mask_list.append(_weight_mask)
listIndex += 1
# Do MINMED
if ("minmed" in comb_type.lower()):
if comb_type.lower()[0] == 'i':
# set up use of 'imedian'/'imean' in minmed algorithm
fillval = True
else:
fillval = False
if (_weight_mask_list in [None, []]):
_weight_mask_list = None
# Create the combined array object using the minmed algorithm
result = minmed(
imageSectionsList[
startDrz:endDrz], # list of input data to be combined.
imageSectionsList[
startWht:
endWht], # list of input data weight images to be combined.
readnoiseList, # list of readnoise values to use for the input images.
exposureTimeList, # list of exposure times to use for the input images.
backgroundValueList, # list of image background values to use for the input images
weightMaskList=
_weight_mask_list, # list of imput data weight masks to use for pixel rejection.
combine_grow=grow, # Radius (pixels) for neighbor rejection
combine_nsigma1=
nsigma1, # Significance for accepting minimum instead of median
combine_nsigma2=
nsigma2, # Significance for accepting minimum instead of median
fillval=fillval # turn on use of imedian/imean
)
# medianOutput[prange[0]:prange[1],:] = result.out_file1
# minOutput[prange[0]:prange[1],:] = result.out_file2
# DO NUMCOMBINE
else:
# Create the combined array object using the numcombine task
result = numcombine.numCombine(imageSectionsList[startDrz:endDrz],
numarrayMaskList=_weight_mask_list,
combinationType=comb_type.lower(),
nlow=nlow,
nhigh=nhigh,
upper=hthresh,
lower=lthresh)
# We need to account for any specified overlap when writing out
# the processed image sections to the final output array.
if prange[1] != _imgrows:
medianImageArray[prange[0]:prange[1] -
_overlap, :] = result.combArrObj[:-_overlap, :]
else:
medianImageArray[prange[0]:prange[1], :] = result.combArrObj
del result
del _weight_mask_list
_weight_mask_list = None
# Write out the combined image
# use the header from the first single drizzled image in the list
#header=fits.getheader(imageObjectList[0].outputNames["outSingle"])
_pf = _writeImage(medianImageArray, inputHeader=_single_hdr)
if virtual:
mediandict = {}
mediandict[medianfile] = _pf
for img in imageObjectList:
img.saveVirtualOutputs(mediandict)
else:
try:
print("Saving output median image to: ", medianfile)
_pf.writeto(medianfile)
except IOError:
msg = "Problem writing file: " + medianfile
print(msg)
raise IOError(msg)
del _pf
# Always close any files opened to produce median image; namely,
# single drizzle images and singly-drizzled weight images
#
for img in singleDrizList:
if not virtual:
img.close()
singeDrizList = []
# Close all singly drizzled weight images used to create median image.
for img in singleWeightList:
if not virtual:
img.close()
singleWeightList = []
# If new median masks was turned on, close those files
if _weight_mask_list:
for arr in _weight_mask_list:
del arr
_weight_mask_list = None
del masterList
del medianImageArray
def getcorr():
imstat = ImageStats(image.science[i][BMask], 'midpt',
lower=lower, upper=upper, nclip=0)
assert(imstat.npix == NPix)
return (imstat.midpt)
