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 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 __init__(
self,
imageList, # list of input data to be combined.
weightImageList, # 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=None, # list of imput data weight masks to use for pixel rejection.
combine_grow=1, # Radius (pixels) for neighbor rejection
combine_nsigma1=4, # Significance for accepting minimum instead of median
combine_nsigma2=3, # Significance for accepting minimum instead of median
fillval=False # Turn on use of imedian/imean
):
warnings.warn(
"The 'minmed' class is deprecated and may be removed"
" in a future version. Use 'min_med()' instead.",
DeprecationWarning)
# Define input variables
self._imageList = imageList
self._weightImageList = weightImageList
self._weightMaskList = weightMaskList
self._exposureTimeList = exposureTimeList
self._readnoiseList = readnoiseList
self._backgroundValueList = backgroundValueList
self._numberOfImages = len(self._imageList)
self._combine_grow = combine_grow
self._combine_nsigma1 = combine_nsigma1
self._combine_nsigma2 = combine_nsigma2
if fillval:
combtype_mean = 'imean'
combtype_median = 'imedian'
else:
combtype_mean = 'mean'
combtype_median = 'median'
# Create a different median image based upon the number of images in the input list.
median_file = np.zeros(self._imageList[0].shape,
dtype=self._imageList[0].dtype)
if (self._numberOfImages == 2):
tmp = numCombine(self._imageList,
numarrayMaskList=self._weightMaskList,
combinationType=combtype_mean,
nlow=0,
nhigh=0,
nkeep=1,
upper=None,
lower=None)
median_file = tmp.combArrObj
else:
# The value of NHIGH=1 will cause problems when there is only 1 valid
# unmasked input image for that pixel due to a difference in behavior
# between 'numcombine' and 'iraf.imcombine'.
# This value may need to be adjusted on the fly based on the number of
# inputs and the number of masked values/pixel.
#
tmp = numCombine(self._imageList,
numarrayMaskList=self._weightMaskList,
combinationType=combtype_median,
nlow=0,
nhigh=1,
nkeep=1,
upper=None,
lower=None)
median_file = tmp.combArrObj
if self._weightMaskList in [None, []]:
self._weightMaskList = [
np.zeros(self._imageList[0].shape,
dtype=self._imageList[0].dtype)
] * len(self._imageList)
# The following section of code will address the problem caused by having
# a value of nhigh = 1. This will behave in a way similar to the way the
# IRAF task IMCOMBINE behaves. In order to accomplish this, the following
# procedure will be followed:
# 1) The input masks will be summed.
# 2) The science data will be summed.
# 3) In the locations of the summed mask where the sum is 1 less than the
# the total number of images, the value of that location in the summed
# sceince image will be used to replace the existing value in the
# existing median_file.
#
# This procuedure is being used to prevent too much data from being thrown
# out of the image. Take for example the case of 3 input images. In two
# of the images the pixel locations have been masked out. Now, if nhigh
# is applied there will be no value to use for that position. However,
# if this new procedure is used that value in the resulting images will
# be the value that was rejected by the nhigh rejection step.
#
# We need to make certain that "bad" pixels in the sci data are set to 0. That way,
# when the sci images are summed, the value of the sum will only come from the "good"
# pixels.
tmpList = []
for image in range(len(self._imageList)):
tmp = np.where(self._weightMaskList[image] == 1, 0,
self._imageList[image])
tmpList.append(tmp)
# Sum the mask files
maskSum = self._sumImages(self._weightMaskList)
# Sum the science images
sciSum = self._sumImages(tmpList)
del (tmpList)
# Use the summed sci image values in locations where the maskSum indicates
# that there is only 1 good pixel to use. The value will be used in the
# median_file image
median_file = np.where(maskSum == self._numberOfImages - 1, sciSum,
median_file)
if self._weightMaskList in [None, []]:
self._weightMaskList = [
np.zeros(self._imageList[0].shape,
dtype=self._imageList[0].dtype)
] * len(self._imageList)
# Sum the weightMaskList elements
maskSum = self._sumImages(self._weightMaskList)
# Create the minimum image from the stack of input images.
# Find the maximum pixel value for the image stack.
maxValue = -1e+9
for image in self._imageList:
newMax = image.max()
if (newMax > maxValue):
maxValue = newMax
# For each image, set pixels masked as "bad" to the "super-maximum" value.
for image in range(len(self._imageList)):
self._imageList[image] = np.where(self._weightMaskList[image] == 1,
maxValue + 1,
self._imageList[image])
# Call numcombine throwing out the highest N - 1 pixels.
tmp = numCombine(self._imageList,
numarrayMaskList=None,
combinationType=combtype_median,
nlow=0,
nhigh=self._numberOfImages - 1,
nkeep=1,
upper=None,
lower=None)
minimum_file = tmp.combArrObj
# Reset any pixl at maxValue + 1 to 0.
minimum_file = np.where(maskSum == self._numberOfImages, 0,
minimum_file)
# Scale the weight images by the background values and add them to the bk
backgroundFileList = []
for image in range(len(self._weightImageList)):
tmp = self._weightImageList[image] * (
self._backgroundValueList[image] /
(self._exposureTimeList[image]))
backgroundFileList.append(tmp)
# Create an image of the total effective background (in DN) per pixel:
# (which is the sum of all the background-scaled weight files)
#
bkgd_file = self._sumImages(backgroundFileList)
del (backgroundFileList)
#
# Scale the weight mask images by the square of the readnoise values
#
readnoiseFileList = []
for image in range(len(self._weightMaskList)):
tmp = (np.logical_not(self._weightMaskList[image]) *
(self._readnoiseList[image] * self._readnoiseList[image]))
readnoiseFileList.append(tmp)
# Create an image of the total readnoise**2 per pixel:
# (which is the sum of all the input readnoise values)
#
readnoise_file = self._sumImages(readnoiseFileList)
del (readnoiseFileList)
# Create an image of the total effective exposure time per pixel:
# (which is simply the sum of all the drizzle output weight files)
#
weight_file = self._sumImages(self._weightImageList)
# Scale up both the median and minimum arrays by the total effective exposure time
# per pixel.
#
minimum_file_weighted = minimum_file * weight_file
median_file_weighted = median_file * weight_file
del (weight_file)
# Calculate the 1-sigma r.m.s.:
# variance = median_electrons + bkgd_electrons + readnoise**2
# rms = sqrt(variance)
# This image has units of electrons.
#
# make this the abs value so that negative numbers dont throw an exception?
rms_file2 = np.fmax(median_file_weighted + bkgd_file + readnoise_file,
np.zeros_like(median_file_weighted))
rms_file = np.sqrt(rms_file2)
del bkgd_file
del readnoise_file
# For the median array, calculate the n-sigma lower threshold to the array
# and incorporate that into the pixel values.
#
median_rms_file = median_file_weighted - (rms_file *
self._combine_nsigma1)
if self._combine_grow != 0:
#
# Do a more sophisticated rejection: For all cases where the minimum pixel will
# be accepted instead of the median, set a lower threshold for that pixel and the
# ones around it (ie become less conservative in rejecting the median). This is
# because in cases of triple-incidence cosmic rays, quite often the low-lying
# outliers of the CRs can influence the median for the initial relatively high
# value of sigma, so a lower threshold must be used to mnake sure that the minimum
# is selected.
#
# This is done as follows:
# 1) make an image which is zero everywhere except where the minimum will be accepted
# 2) box-car smooth this image, to make these regions grow.
# 3) In the file "median_rms_file_electrons", replace these pixels
# by median - combine_nsigma2 * rms
#
# Then use this image in the final replacement, in the same way as for the
# case where this option is not selected.
minimum_flag_file = np.where(
np.less(minimum_file_weighted, median_rms_file), 1, 0)
# The box size value must be an integer. This is not a problem since __combine_grow should always
# be an integer type. The combine_grow column in the MDRIZTAB should also be an integer type.
boxsize = int(2 * self._combine_grow + 1)
boxshape = (boxsize, boxsize)
minimum_grow_file = np.zeros(self._imageList[0].shape,
dtype=self._imageList[0].dtype)
# If the boxcar convolution has failed it is potentially for two reasons:
# 1) The kernel size for the boxcar is bigger than the actual image.
# 2) The grow parameter was specified with a value < 0. This would result
# in an illegal boxshape kernel. The dimensions of the kernel box *MUST*
# be integer and greater than zero.
#
# If the boxcar convolution has failed, try to give a meaningfull explanation
# as to why based upon the conditionals described above.
if (boxsize <= 0):
errormsg1 = "############################################################\n"
errormsg1 += "# The boxcar convolution in minmed has failed. The 'grow' #\n"
errormsg1 += "# parameter must be greater than or equal to zero. You #\n"
errormsg1 += "# specified an input value for the 'grow' parameter of: #\n"
errormsg1 += " combine_grow: " + str(
self._combine_grow) + '\n'
errormsg1 += "############################################################\n"
raise ValueError(errormsg1)
if (boxsize > self._imageList[0].shape[0]):
errormsg2 = "############################################################\n"
errormsg2 += "# The boxcar convolution in minmed has failed. The 'grow' #\n"
errormsg2 += "# parameter specified has resulted in a boxcar kernel that #\n"
errormsg2 += "# has dimensions larger than the actual image. You #\n"
errormsg2 += "# specified an input value for the 'grow' parameter of: #\n"
errormsg2 += " combine_grow: " + str(
self._combine_grow) + '\n'
errormsg2 += "############################################################\n"
print(self._imageList[0].shape)
raise ValueError(errormsg2)
# Attempt the boxcar convolution using the boxshape based upon the user input value of "grow"
boxcar(minimum_flag_file,
boxshape,
output=minimum_grow_file,
mode='constant',
cval=0)
del (minimum_flag_file)
temp1 = (median_file_weighted - (rms_file * self._combine_nsigma1))
temp2 = (median_file_weighted - (rms_file * self._combine_nsigma2))
median_rms2_file = np.where(np.equal(minimum_grow_file, 0), temp1,
temp2)
del (temp1)
del (temp2)
del (rms_file)
del (minimum_grow_file)
# Finally decide whether to use the minimim or the median (in counts/s),
# based on whether the median is more than 3 sigma above the minimum.
#
self.combArrObj = np.where(
np.less(minimum_file_weighted, median_rms2_file), minimum_file,
median_file)
else:
# Finally decide whether to use the minimim or the median (in counts/s),
# based on whether the median is more than 3 sigma above the minimum.
#
self.combArrObj = np.where(
np.less(minimum_file_weighted, median_rms_file), minimum_file,
median_file)
# Set fill regions to a pixel value of 0.
self.combArrObj = np.where(maskSum == self._numberOfImages, 0,
self.combArrObj)
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 __init__(self,
imageList, # list of input data to be combined.
weightImageList, # 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= None, # list of imput data weight masks to use for pixel rejection.
combine_grow = 1, # Radius (pixels) for neighbor rejection
combine_nsigma1 = 4, # Significance for accepting minimum instead of median
combine_nsigma2 = 3, # Significance for accepting minimum instead of median
fillval = False # Turn on use of imedian/imean
):
warnings.warn("The 'minmed' class is deprecated and may be removed"
" in a future version. Use 'min_med()' instead.",
DeprecationWarning)
# Define input variables
self._imageList = imageList
self._weightImageList = weightImageList
self._weightMaskList = weightMaskList
self._exposureTimeList = exposureTimeList
self._readnoiseList = readnoiseList
self._backgroundValueList = backgroundValueList
self._numberOfImages = len( self._imageList)
self._combine_grow = combine_grow
self._combine_nsigma1 = combine_nsigma1
self._combine_nsigma2 = combine_nsigma2
if fillval:
combtype_mean = 'imean'
combtype_median = 'imedian'
else:
combtype_mean = 'mean'
combtype_median = 'median'
# Create a different median image based upon the number of images in the input list.
median_file = np.zeros(self._imageList[0].shape,dtype=self._imageList[0].dtype)
if (self._numberOfImages == 2):
tmp = numCombine(self._imageList,numarrayMaskList=self._weightMaskList,
combinationType=combtype_mean,nlow=0,nhigh=0,
nkeep=1,upper=None,lower=None)
median_file = tmp.combArrObj
else:
# The value of NHIGH=1 will cause problems when there is only 1 valid
# unmasked input image for that pixel due to a difference in behavior
# between 'numcombine' and 'iraf.imcombine'.
# This value may need to be adjusted on the fly based on the number of
# inputs and the number of masked values/pixel.
#
tmp = numCombine(self._imageList,numarrayMaskList=self._weightMaskList,
combinationType=combtype_median,nlow=0,nhigh=1,
nkeep=1,upper=None,lower=None)
median_file = tmp.combArrObj
if self._weightMaskList in [None,[]]:
self._weightMaskList = [np.zeros(self._imageList[0].shape,dtype=self._imageList[0].dtype)]*len(self._imageList)
# The following section of code will address the problem caused by having
# a value of nhigh = 1. This will behave in a way similar to the way the
# IRAF task IMCOMBINE behaves. In order to accomplish this, the following
# procedure will be followed:
# 1) The input masks will be summed.
# 2) The science data will be summed.
# 3) In the locations of the summed mask where the sum is 1 less than the
# the total number of images, the value of that location in the summed
# sceince image will be used to replace the existing value in the
# existing median_file.
#
# This procuedure is being used to prevent too much data from being thrown
# out of the image. Take for example the case of 3 input images. In two
# of the images the pixel locations have been masked out. Now, if nhigh
# is applied there will be no value to use for that position. However,
# if this new procedure is used that value in the resulting images will
# be the value that was rejected by the nhigh rejection step.
#
# We need to make certain that "bad" pixels in the sci data are set to 0. That way,
# when the sci images are summed, the value of the sum will only come from the "good"
# pixels.
tmpList = []
for image in range(len(self._imageList)):
tmp =np.where(self._weightMaskList[image] == 1, 0, self._imageList[image])
tmpList.append(tmp)
# Sum the mask files
maskSum = self._sumImages(self._weightMaskList)
# Sum the science images
sciSum = self._sumImages(tmpList)
del(tmpList)
# Use the summed sci image values in locations where the maskSum indicates
# that there is only 1 good pixel to use. The value will be used in the
# median_file image
median_file = np.where(maskSum == self._numberOfImages-1,sciSum,median_file)
if self._weightMaskList in [None,[]]:
self._weightMaskList = [np.zeros(self._imageList[0].shape,dtype=self._imageList[0].dtype)]*len(self._imageList)
# Sum the weightMaskList elements
maskSum = self._sumImages(self._weightMaskList)
# Create the minimum image from the stack of input images.
# Find the maximum pixel value for the image stack.
maxValue = -1e+9
for image in self._imageList:
newMax = image.max()
if (newMax > maxValue):
maxValue = newMax
# For each image, set pixels masked as "bad" to the "super-maximum" value.
for image in range(len(self._imageList)):
self._imageList[image] = np.where(self._weightMaskList[image] == 1,maxValue+1,self._imageList[image])
# Call numcombine throwing out the highest N - 1 pixels.
tmp = numCombine(self._imageList,numarrayMaskList=None,
combinationType=combtype_median,nlow=0,nhigh=self._numberOfImages-1,
nkeep=1,upper=None,lower=None)
minimum_file = tmp.combArrObj
# Reset any pixl at maxValue + 1 to 0.
minimum_file = np.where(maskSum == self._numberOfImages, 0, minimum_file)
# Scale the weight images by the background values and add them to the bk
backgroundFileList = []
for image in range(len(self._weightImageList)):
tmp = self._weightImageList[image] * (self._backgroundValueList[image]/(self._exposureTimeList[image]))
backgroundFileList.append(tmp)
# Create an image of the total effective background (in DN) per pixel:
# (which is the sum of all the background-scaled weight files)
#
bkgd_file = self._sumImages(backgroundFileList)
del(backgroundFileList)
#
# Scale the weight mask images by the square of the readnoise values
#
readnoiseFileList = []
for image in range(len(self._weightMaskList)):
tmp = (np.logical_not(self._weightMaskList[image]) *
(self._readnoiseList[image] * self._readnoiseList[image]))
readnoiseFileList.append(tmp)
# Create an image of the total readnoise**2 per pixel:
# (which is the sum of all the input readnoise values)
#
readnoise_file = self._sumImages(readnoiseFileList)
del(readnoiseFileList)
# Create an image of the total effective exposure time per pixel:
# (which is simply the sum of all the drizzle output weight files)
#
weight_file = self._sumImages(self._weightImageList)
# Scale up both the median and minimum arrays by the total effective exposure time
# per pixel.
#
minimum_file_weighted = minimum_file * weight_file
median_file_weighted = median_file * weight_file
del(weight_file)
# Calculate the 1-sigma r.m.s.:
# variance = median_electrons + bkgd_electrons + readnoise**2
# rms = sqrt(variance)
# This image has units of electrons.
#
# make this the abs value so that negative numbers dont throw an exception?
rms_file2 = np.fmax(
median_file_weighted + bkgd_file + readnoise_file,
np.zeros_like(median_file_weighted)
)
rms_file = np.sqrt(rms_file2)
del bkgd_file
del readnoise_file
# For the median array, calculate the n-sigma lower threshold to the array
# and incorporate that into the pixel values.
#
median_rms_file = median_file_weighted - (rms_file * self._combine_nsigma1)
if self._combine_grow != 0:
#
# Do a more sophisticated rejection: For all cases where the minimum pixel will
# be accepted instead of the median, set a lower threshold for that pixel and the
# ones around it (ie become less conservative in rejecting the median). This is
# because in cases of triple-incidence cosmic rays, quite often the low-lying
# outliers of the CRs can influence the median for the initial relatively high
# value of sigma, so a lower threshold must be used to mnake sure that the minimum
# is selected.
#
# This is done as follows:
# 1) make an image which is zero everywhere except where the minimum will be accepted
# 2) box-car smooth this image, to make these regions grow.
# 3) In the file "median_rms_file_electrons", replace these pixels
# by median - combine_nsigma2 * rms
#
# Then use this image in the final replacement, in the same way as for the
# case where this option is not selected.
minimum_flag_file = np.where(np.less(minimum_file_weighted,median_rms_file), 1, 0)
# The box size value must be an integer. This is not a problem since __combine_grow should always
# be an integer type. The combine_grow column in the MDRIZTAB should also be an integer type.
boxsize = int(2 * self._combine_grow + 1)
boxshape = (boxsize, boxsize)
minimum_grow_file = np.zeros(self._imageList[0].shape,dtype=self._imageList[0].dtype)
# If the boxcar convolution has failed it is potentially for two reasons:
# 1) The kernel size for the boxcar is bigger than the actual image.
# 2) The grow parameter was specified with a value < 0. This would result
# in an illegal boxshape kernel. The dimensions of the kernel box *MUST*
# be integer and greater than zero.
#
# If the boxcar convolution has failed, try to give a meaningfull explanation
# as to why based upon the conditionals described above.
if (boxsize <= 0):
errormsg1 = "############################################################\n"
errormsg1 += "# The boxcar convolution in minmed has failed. The 'grow' #\n"
errormsg1 += "# parameter must be greater than or equal to zero. You #\n"
errormsg1 += "# specified an input value for the 'grow' parameter of: #\n"
errormsg1 += " combine_grow: " + str(self._combine_grow)+'\n'
errormsg1 += "############################################################\n"
raise ValueError(errormsg1)
if (boxsize > self._imageList[0].shape[0]):
errormsg2 = "############################################################\n"
errormsg2 += "# The boxcar convolution in minmed has failed. The 'grow' #\n"
errormsg2 += "# parameter specified has resulted in a boxcar kernel that #\n"
errormsg2 += "# has dimensions larger than the actual image. You #\n"
errormsg2 += "# specified an input value for the 'grow' parameter of: #\n"
errormsg2 += " combine_grow: " +str(self._combine_grow)+'\n'
errormsg2 += "############################################################\n"
print(self._imageList[0].shape)
raise ValueError(errormsg2)
# Attempt the boxcar convolution using the boxshape based upon the user input value of "grow"
boxcar(minimum_flag_file, boxshape, output=minimum_grow_file,
mode='constant', cval=0)
del(minimum_flag_file)
temp1 = (median_file_weighted - (rms_file * self._combine_nsigma1))
temp2 = (median_file_weighted - (rms_file * self._combine_nsigma2))
median_rms2_file = np.where(np.equal(minimum_grow_file, 0), temp1, temp2)
del(temp1)
del(temp2)
del(rms_file)
del(minimum_grow_file)
# Finally decide whether to use the minimim or the median (in counts/s),
# based on whether the median is more than 3 sigma above the minimum.
#
self.combArrObj = np.where(
np.less(minimum_file_weighted, median_rms2_file),
minimum_file,
median_file
)
else:
# Finally decide whether to use the minimim or the median (in counts/s),
# based on whether the median is more than 3 sigma above the minimum.
#
self.combArrObj = np.where(
np.less(minimum_file_weighted, median_rms_file),
minimum_file,
median_file
)
# Set fill regions to a pixel value of 0.
self.combArrObj = np.where(maskSum == self._numberOfImages, 0, self.combArrObj)
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 _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