def test_sigclip(capsys):
# Confirm rejection of high pixel and correct output DQ
data = np.array([1., 1., 1., 2., 2., 2., 2., 100.]).reshape(8, 1)
ndd = NDAstroData(data)
stackit = NDStacker(combine="mean",
reject="sigclip",
lsigma=3,
hsigma=3,
debug_pixel=0)
result = stackit(ndd, save_rejection_map=True)
assert_allclose(result.data, 1.5714285714285714) # 100 is rejected
assert result.meta['other']['REJMAP'].data[0] == 1
out = capsys.readouterr().out
expected = """\
Rejection: sigclip {'lsigma': 3, 'hsigma': 3}
img data mask variance immediately after rejection
0 1.0000 0 -
1 1.0000 0 -
2 1.0000 0 -
3 2.0000 0 -
4 2.0000 0 -
5 2.0000 0 -
6 2.0000 0 -
7 100.0000 32768 -
"""
assert expected.splitlines() == out.splitlines()[13:23]
stackit = NDStacker(combine="mean", reject="sigclip", lsigma=5, hsigma=5)
result = stackit(ndd)
assert_allclose(result.data, 13.875) # 100 is not rejected
def test_ndstacker(capsys):
stacker = NDStacker(combine="foo")
assert capsys.readouterr().out == \
'No such combiner as foo. Using mean instead.\n'
assert stacker._combiner is NDStacker.mean
stacker = NDStacker(reject="foo")
assert capsys.readouterr().out == \
'No such rejector as foo. Using none instead.\n'
assert stacker._rejector is NDStacker.none
def test_varclip():
# Confirm rejection of high pixel and correct output DQ
data = np.array([1., 1., 2., 2., 2., 100.]).reshape(6, 1)
ndd = NDAstroData(data)
ndd.mask = np.zeros_like(data, dtype=DQ.datatype)
ndd.mask[5, 0] = DQ.saturated
ndd.variance = np.ones_like(data)
stackit = NDStacker(combine="mean", reject="varclip")
result = stackit(ndd)
np.testing.assert_array_almost_equal(result.data, [1.6])
np.testing.assert_array_equal(result.mask, [0])
def test_varclip():
# Confirm rejection of high pixel and correct output DQ
data = np.array([1., 1., 2., 2., 2., 100.]).reshape(6, 1)
ndd = NDAstroData(data,
mask=np.zeros_like(data, dtype=DQ.datatype),
variance=np.ones_like(data))
ndd.mask[5, 0] = DQ.saturated
stackit = NDStacker(combine="mean", reject="varclip")
result = stackit(ndd)
assert_allclose(result.data, 1.6) # 100 is rejected
assert_allclose(result.mask, 0)
data = np.array([1., 1., 2., 2., 2., 100.]).reshape(6, 1)
ndd = NDAstroData(data, variance=np.ones_like(data))
ndd.variance[5] = 400
stackit = NDStacker(combine="mean", reject="varclip", lsigma=3, hsigma=3)
result = stackit(ndd)
assert_allclose(result.data, 1.6) # 100 is rejected
stackit = NDStacker(combine="mean", reject="varclip", lsigma=5, hsigma=5)
result = stackit(ndd)
assert_allclose(result.data, 18) # 100 is not rejected
def stackFrames(self, adinputs=None, **params):
"""
This primitive will stack each science extension in the input dataset.
New variance extensions are created from the stacked science extensions
and the data quality extensions are propagated through to the final
file.
Parameters
----------
adinputs : list of :class:`~astrodata.AstroData`
Any set of 2D.
suffix : str
Suffix to be added to output files.
apply_dq : bool
Apply DQ mask to data before combining?
nlow, nhigh : int
Number of low and high pixels to reject, for the 'minmax' method.
The way it works is inherited from IRAF: the fraction is specified
as the number of high and low pixels, the nhigh and nlow
parameters, when data from all the input images are used. If
pixels have been rejected by offseting, masking, or
thresholding then a matching fraction of the remaining pixels,
truncated to an integer, are used. Thus::
nl = n * nlow/nimages + 0.001
nh = n * nhigh/nimages + 0.001
where n is the number of pixels surviving offseting, masking,
and thresholding, nimages is the number of input images, nlow
and nhigh are task parameters and nl and nh are the final
number of low and high pixels rejected by the algorithm. The
factor of 0.001 is to adjust for rounding of the ratio.
operation : str
Combine method.
reject_method : str
Pixel rejection method (none, minmax, sigclip, varclip).
zero : bool
Apply zero-level offset to match background levels?
scale : bool
Scale images to the same intensity?
memory : float or None
Available memory (in GB) for stacking calculations.
statsec : str
Section for statistics.
separate_ext : bool
Handle extensions separately?
Returns
-------
list of :class:`~astrodata.AstroData`
Sky stacked image. This list contains only one element. The list
format is maintained so this primitive is consistent with all the
others.
Raises
------
IOError
If the number of extensions in any of the `AstroData` objects is
different.
IOError
If the shape of any extension in any `AstroData` object is different.
AssertError
If any of the `.gain()` descriptors is None.
AssertError
If any of the `.read_noise()` descriptors is None.
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys["stackFrames"]
sfx = params["suffix"]
memory = params["memory"]
if memory is not None:
memory = int(memory * 1000000000)
zero = params["zero"]
scale = params["scale"]
apply_dq = params["apply_dq"]
separate_ext = params["separate_ext"]
statsec = params["statsec"]
reject_method = params["reject_method"]
save_rejection_map = params["save_rejection_map"]
if statsec:
statsec = tuple([
slice(int(start) - 1, int(end))
for x in reversed(statsec.strip('[]').split(','))
for start, end in [x.split(':')]
])
if len(adinputs) <= 1:
log.stdinfo("No stacking will be performed, since at least two "
"input AstroData objects are required for stackFrames")
return adinputs
if (reject_method == "minmax" and self.mode == "qa"
and params["nlow"] + params["nhigh"] >= len(adinputs)):
log.warning(
"Trying to reject too many images. Setting nlow=nhigh=0.")
params["nlow"] = 0
params["nhigh"] = 0
if len({len(ad) for ad in adinputs}) > 1:
raise OSError("Not all inputs have the same number of extensions")
if len({ext.nddata.shape for ad in adinputs for ext in ad}) > 1:
raise OSError("Not all inputs images have the same shape")
# We will determine the average gain from the input AstroData
# objects and add in quadrature the read noise
gains = [ad.gain() for ad in adinputs]
read_noises = [ad.read_noise() for ad in adinputs]
# Determine whether we can construct these averages
process_gain = all(g is not None for gain in gains for g in gain)
process_rn = all(rn is not None for read_noise in read_noises
for rn in read_noise)
# Compute gain and read noise of final stacked images
num_img = len(adinputs)
num_ext = len(adinputs[0])
if process_gain:
gain_list = [
np.mean([gain[i] for gain in gains]) for i in range(num_ext)
]
if process_rn:
read_noise_list = [
np.sqrt(np.sum([rn[i] * rn[i]
for rn in read_noises])) / num_img
for i in range(num_ext)
]
zero_offsets = np.zeros((num_ext, num_img), dtype=np.float32)
scale_factors = np.ones_like(zero_offsets)
# Try to determine how much memory we're going to need to stack and
# whether it's necessary to flush pixel data to disk first
# Also determine kernel size from offered memory and bytes per pixel
bytes_per_ext = []
for ext in adinputs[0]:
bytes = 0
# Count _data twice to handle temporary arrays
bytes += 2 * ext.data.dtype.itemsize
if ext.variance is not None:
bytes += ext.variance.dtype.itemsize
bytes += 2 # mask always created
bytes_per_ext.append(bytes * np.product(ext.shape))
if memory is not None and (num_img * max(bytes_per_ext) > memory):
adinputs = self.flushPixels(adinputs)
# Compute the scale and offset values by accessing the memmapped data
# so we can pass those to the stacking function
# TODO: Should probably be done better to consider only the overlap
# regions between frames
if scale or zero:
levels = np.empty((num_img, num_ext), dtype=np.float32)
for i, ad in enumerate(adinputs):
for index in range(num_ext):
nddata = (ad[index].nddata.window[:] if statsec is None
else ad[i][index].nddata.window[statsec])
#levels[i, index] = np.median(nddata.data)
levels[i,
index] = gt.measure_bg_from_image(nddata,
value_only=True)
if scale and zero:
log.warning(
"Both scale and zero are set. Setting scale=False.")
scale = False
if separate_ext:
# Target value is corresponding extension of first image
if scale:
scale_factors = (levels[0] / levels).T
else: # zero=True
zero_offsets = (levels[0] - levels).T
else:
# Target value is mean of all extensions of first image
target = np.mean(levels[0])
if scale:
scale_factors = np.tile(target / np.mean(levels, axis=1),
num_ext).reshape(num_ext, num_img)
else: # zero=True
zero_offsets = np.tile(target - np.mean(levels, axis=1),
num_ext).reshape(num_ext, num_img)
if scale and np.min(scale_factors) < 0:
log.warning("Some scale factors are negative. Not scaling.")
scale_factors = np.ones_like(scale_factors)
scale = False
if scale and np.any(np.isinf(scale_factors)):
log.warning("Some scale factors are infinite. Not scaling.")
scale_factors = np.ones_like(scale_factors)
scale = False
if scale and np.any(np.isnan(scale_factors)):
log.warning("Some scale factors are undefined. Not scaling.")
scale_factors = np.ones_like(scale_factors)
scale = False
if reject_method == "varclip" and any(ext.variance is None
for ad in adinputs
for ext in ad):
log.warning("Rejection method 'varclip' has been chosen but some"
" extensions have no variance. 'sigclip' will be used"
" instead.")
reject_method = "sigclip"
log.stdinfo("Combining {} inputs with {} and {} rejection".format(
num_img, params["operation"], reject_method))
stack_function = NDStacker(combine=params["operation"],
reject=reject_method,
log=self.log,
**params)
# NDStacker uses DQ if it exists; if we don't want that, delete the DQs!
if not apply_dq:
[setattr(ext, 'mask', None) for ad in adinputs for ext in ad]
ad_out = astrodata.create(adinputs[0].phu)
for index, (ext, sfactors, zfactors) in enumerate(
zip(adinputs[0], scale_factors, zero_offsets)):
status = (f"Combining extension {ext.id}."
if num_ext > 1 else "Combining images.")
if scale:
status += " Applying scale factors."
numbers = sfactors
elif zero:
status += " Applying offsets."
numbers = zfactors
log.stdinfo(status)
if ((scale or zero) and (index == 0 or separate_ext)):
for ad, value in zip(adinputs, numbers):
log.stdinfo("{:40s}{:10.3f}".format(ad.filename, value))
shape = adinputs[0][index].nddata.shape
if memory is None:
kernel = shape
else:
# Chop the image horizontally into equal-sized chunks to process
# This uses the minimum number of steps and uses minimum memory
# per step.
oversubscription = (bytes_per_ext[index] *
num_img) // memory + 1
kernel = ((shape[0] + oversubscription - 1) //
oversubscription, ) + shape[1:]
with_uncertainty = True # Since all stacking methods return variance
with_mask = apply_dq and not any(
ad[index].nddata.window[:].mask is None for ad in adinputs)
result = windowedOp(stack_function,
[ad[index].nddata for ad in adinputs],
scale=sfactors,
zero=zfactors,
kernel=kernel,
dtype=np.float32,
with_uncertainty=with_uncertainty,
with_mask=with_mask,
save_rejection_map=save_rejection_map)
ad_out.append(result)
log.stdinfo("")
# Propagate REFCAT as the union of all input REFCATs
refcats = [ad.REFCAT for ad in adinputs if hasattr(ad, 'REFCAT')]
if refcats:
try:
out_refcat = table.unique(table.vstack(
refcats, metadata_conflicts='silent'),
keys=('RAJ2000', 'DEJ2000'))
except KeyError:
pass
else:
out_refcat['Id'] = list(range(1, len(out_refcat) + 1))
ad_out.REFCAT = out_refcat
# Set AIRMASS to be the mean of the input values
try:
airmass_kw = ad_out._keyword_for('airmass')
mean_airmass = np.mean([ad.airmass() for ad in adinputs])
except Exception: # generic implementation failure (probably non-Gemini)
pass
else:
ad_out.phu.set(airmass_kw, mean_airmass,
"Mean airmass for the exposure")
# Set GAIN to the average of input gains. Set the RDNOISE to the
# sum in quadrature of the input read noises.
if process_gain:
for ext, gain in zip(ad_out, gain_list):
ext.hdr.set('GAIN', gain, self.keyword_comments['GAIN'])
ad_out.phu.set('GAIN', gain_list[0], self.keyword_comments['GAIN'])
if process_rn:
for ext, rn in zip(ad_out, read_noise_list):
ext.hdr.set('RDNOISE', rn, self.keyword_comments['RDNOISE'])
ad_out.phu.set('RDNOISE', read_noise_list[0],
self.keyword_comments['RDNOISE'])
# Add suffix to datalabel to distinguish from the reference frame
if sfx[0] == '_':
extension = sfx.replace('_', '-', 1).upper()
else:
extension = '-' + sfx.upper()
ad_out.phu.set('DATALAB', "{}{}".format(ad_out.data_label(),
extension),
self.keyword_comments['DATALAB'])
# Add other keywords to the PHU about the stacking inputs
ad_out.orig_filename = ad_out.phu.get('ORIGNAME')
ad_out.phu.set('NCOMBINE', len(adinputs),
self.keyword_comments['NCOMBINE'])
for i, ad in enumerate(adinputs, start=1):
ad_out.phu.set('IMCMB{:03d}'.format(i),
ad.phu.get('ORIGNAME', ad.filename))
# Timestamp and update filename and prepare to return single output
gt.mark_history(ad_out, primname=self.myself(), keyword=timestamp_key)
ad_out.update_filename(suffix=sfx, strip=True)
return [ad_out]
def removePatternNoise(self, adinputs=None, **params):
"""
This attempts to remove the pattern noise in NIRI/GNIRS data. In each
quadrant, boxes of a specified size are extracted and, for each pixel
location in the box, the median across all the boxes is determined.
The resultant median is then tiled to the size of the quadrant and
subtracted. Optionally, the median of each box can be subtracted
before performing the operation.
Based on Andy Stephens's "cleanir"
Parameters
----------
suffix: str
suffix to be added to output files
force: bool
perform operation even if standard deviation in quadrant increases?
hsigma/lsigma: float
sigma-clipping limits
pattern_x_size: int
size of pattern "box" in x direction
pattern_y_size: int
size of pattern "box" in y direction
subtract_background: bool
remove median of each "box" before calculating pattern noise?
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
hsigma, lsigma = params["hsigma"], params["lsigma"]
pxsize, pysize = params["pattern_x_size"], params["pattern_y_size"]
bgsub = params["subtract_background"]
force = params["force"]
stack_function = NDStacker(combine='median', reject='sigclip',
hsigma=hsigma, lsigma=lsigma)
sigclip = partial(sigma_clip, sigma_lower=lsigma, sigma_upper=hsigma)
zeros = None # will remain unchanged if not subtract_background
for ad in adinputs:
if ad.phu.get(timestamp_key):
log.warning("No changes will be made to {}, since it has "
"already been processed by removePatternNoise".
format(ad.filename))
continue
for ext in ad:
qysize, qxsize = [size // 2 for size in ext.data.shape]
yticks = [(y, y + pysize) for y in range(0, qysize, pysize)]
xticks = [(x, x + pxsize) for x in range(0, qxsize, pxsize)]
for ystart in (0, qysize):
for xstart in (0, qxsize):
quad = ext.nddata[ystart:ystart + qysize, xstart:xstart + qxsize]
sigma_in = sigclip(np.ma.masked_array(quad.data, quad.mask)).std()
# print sigma_in
blocks = [quad[tuple(slice(start, end)
for (start, end) in coords)]
for coords in cart_product(yticks, xticks)]
if bgsub:
# If all pixels are masked in a box, we'll get no
# result from the mean. Suppress warning.
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
zeros = np.nan_to_num([-np.ma.masked_array(block.data, block.mask).mean()
for block in blocks])
out = stack_function(blocks, zero=zeros).data
out_quad = (quad.data + np.mean(out) -
np.tile(out, (len(yticks), len(xticks))))
sigma_out = sigclip(np.ma.masked_array(out_quad, quad.mask)).std()
if sigma_out > sigma_in:
qstr = (f"{ad.filename} extension {ext.id} "
f"quadrant ({xstart},{ystart})")
if force:
log.stdinfo("Forcing cleaning on " + qstr)
else:
log.stdinfo("No improvement for "+qstr)
continue
ext.data[ystart:ystart + qysize, xstart:xstart + qxsize] = out_quad
# Timestamp and update filename
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=params["suffix"], strip=True)
return adinputs
def stackFrames(self, adinputs=None, **params):
"""
This primitive will stack each science extension in the input dataset.
New variance extensions are created from the stacked science extensions
and the data quality extensions are propagated through to the final
file.
Parameters
----------
suffix: str
suffix to be added to output files
apply_dq: bool
apply DQ mask to data before combining?
nhigh: int
number of high pixels to reject
nlow: int
number of low pixels to reject
operation: str
combine method
reject_method: str
type of pixel rejection (passed to gemcombine)
zero: bool
apply zero-level offset to match background levels?
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys["stackFrames"]
sfx = params["suffix"]
memory = params["memory"]
if memory is not None:
memory = int(memory * 1000000000)
zero = params["zero"]
scale = params["scale"]
apply_dq = params["apply_dq"]
separate_ext = params["separate_ext"]
statsec = params["statsec"]
reject_method = params["reject_method"]
if statsec:
statsec = tuple([
slice(int(start) - 1, int(end))
for x in reversed(statsec.strip('[]').split(','))
for start, end in [x.split(':')]
])
if len(adinputs) <= 1:
log.stdinfo("No stacking will be performed, since at least two "
"input AstroData objects are required for stackFrames")
return adinputs
if (reject_method == "minmax" and self.mode == "qa"
and params["nlow"] + params["nhigh"] >= len(adinputs)):
log.warning(
"Trying to reject too many images. Setting nlow=nhigh=0.")
params["nlow"] = 0
params["nhigh"] = 0
# Perform various checks on inputs
for ad in adinputs:
if not "PREPARED" in ad.tags:
raise IOError("{} must be prepared".format(ad.filename))
if len(set(len(ad) for ad in adinputs)) > 1:
raise IOError("Not all inputs have the same number of extensions")
if len(set([ext.nddata.shape for ad in adinputs for ext in ad])) > 1:
raise IOError("Not all inputs images have the same shape")
# Determine the average gain from the input AstroData objects and
# add in quadrature the read noise
gains = [ad.gain() for ad in adinputs]
read_noises = [ad.read_noise() for ad in adinputs]
assert all(gain is not None for gain in gains), "Gain problem"
assert all(rn is not None for rn in read_noises), "RN problem"
# Compute gain and read noise of final stacked images
nexts = len(gains[0])
gain_list = [
np.mean([gain[i] for gain in gains]) for i in range(nexts)
]
read_noise_list = [
np.sqrt(np.sum([rn[i] * rn[i] for rn in read_noises]))
for i in range(nexts)
]
num_img = len(adinputs)
num_ext = len(adinputs[0])
zero_offsets = np.zeros((num_ext, num_img), dtype=np.float32)
scale_factors = np.ones_like(zero_offsets)
# Try to determine how much memory we're going to need to stack and
# whether it's necessary to flush pixel data to disk first
# Also determine kernel size from offered memory and bytes per pixel
bytes_per_ext = []
for ext in adinputs[0]:
bytes = 0
# Count _data twice to handle temporary arrays
for attr in ('_data', '_data', '_uncertainty'):
item = getattr(ext.nddata, attr)
if item is not None:
# A bit of numpy weirdness in the difference between normal
# python types ("float32") and numpy types ("np.uint16")
try:
bytes += item.dtype.itemsize
except TypeError:
bytes += item.dtype().itemsize
except AttributeError: # For non-lazy VAR
bytes += item._array.dtype.itemsize
bytes += 2 # mask always created
bytes_per_ext.append(bytes * np.multiply.reduce(ext.nddata.shape))
if memory is not None and (num_img * max(bytes_per_ext) > memory):
adinputs = self.flushPixels(adinputs)
# Compute the scale and offset values by accessing the memmapped data
# so we can pass those to the stacking function
# TODO: Should probably be done better to consider only the overlap
# regions between frames
if scale or zero:
levels = np.empty((num_img, num_ext), dtype=np.float32)
for i, ad in enumerate(adinputs):
for index in range(num_ext):
nddata = (ad[index].nddata.window[:] if statsec is None
else ad[i][index].nddata.window[statsec])
#levels[i, index] = np.median(nddata.data)
levels[i,
index] = gt.measure_bg_from_image(nddata,
value_only=True)
if scale and zero:
log.warning(
"Both scale and zero are set. Setting scale=False.")
scale = False
if separate_ext:
# Target value is corresponding extension of first image
if scale:
scale_factors = (levels[0] / levels).T
else: # zero=True
zero_offsets = (levels[0] - levels).T
else:
# Target value is mean of all extensions of first image
target = np.mean(levels[0])
if scale:
scale_factors = np.tile(target / np.mean(levels, axis=1),
num_ext).reshape(num_ext, num_img)
else: # zero=True
zero_offsets = np.tile(target - np.mean(levels, axis=1),
num_ext).reshape(num_ext, num_img)
if scale and np.min(scale_factors) < 0:
log.warning("Some scale factors are negative. Not scaling.")
scale_factors = np.ones_like(scale_factors)
scale = False
if scale and np.any(np.isinf(scale_factors)):
log.warning("Some scale factors are infinite. Not scaling.")
scale_factors = np.ones_like(scale_factors)
scale = False
if scale and np.any(np.isnan(scale_factors)):
log.warning("Some scale factors are undefined. Not scaling.")
scale_factors = np.ones_like(scale_factors)
scale = False
if reject_method == "varclip" and any(ext.variance is None
for ad in adinputs
for ext in ad):
log.warning("Rejection method 'varclip' has been chosen but some"
"extensions have no variance. 'sigclip' will be used"
"instead.")
reject_method = "sigclip"
stack_function = NDStacker(combine=params["operation"],
reject=reject_method,
log=self.log,
**params)
# NDStacker uses DQ if it exists; if we don't want that, delete the DQs!
if not apply_dq:
[setattr(ext, 'mask', None) for ad in adinputs for ext in ad]
ad_out = astrodata.create(adinputs[0].phu)
for index, (extver, sfactors, zfactors) in enumerate(
zip(adinputs[0].hdr.get('EXTVER'), scale_factors,
zero_offsets)):
status = ("Combining EXTVER {}.".format(extver)
if num_ext > 1 else "Combining images.")
if scale:
status += " Applying scale factors."
numbers = sfactors
elif zero:
status += " Applying offsets."
numbers = zfactors
log.stdinfo(status)
if ((scale or zero) and (index == 0 or separate_ext)):
for ad, value in zip(adinputs, numbers):
log.stdinfo("{:40s}{:10.3f}".format(ad.filename, value))
shape = adinputs[0][index].nddata.shape
if memory is None:
kernel = shape
else:
# Chop the image horizontally into equal-sized chunks to process
# This uses the minimum number of steps and uses minimum memory
# per step.
oversubscription = (bytes_per_ext[index] *
num_img) // memory + 1
kernel = ((shape[0] + oversubscription - 1) //
oversubscription, ) + shape[1:]
with_uncertainty = True # Since all stacking methods return variance
with_mask = apply_dq and not any(
ad[index].nddata.window[:].mask is None for ad in adinputs)
result = windowedOp(partial(stack_function,
scale=sfactors,
zero=zfactors),
[ad[index].nddata for ad in adinputs],
kernel=kernel,
dtype=np.float32,
with_uncertainty=with_uncertainty,
with_mask=with_mask)
ad_out.append(result)
log.stdinfo("")
# Propagate REFCAT as the union of all input REFCATs
refcats = [ad.REFCAT for ad in adinputs if hasattr(ad, 'REFCAT')]
if refcats:
out_refcat = table.unique(table.vstack(
refcats, metadata_conflicts='silent'),
keys='Cat_Id')
out_refcat['Cat_Id'] = list(range(1, len(out_refcat) + 1))
ad_out.REFCAT = out_refcat
# Set AIRMASS to be the mean of the input values
try:
airmass_kw = ad_out._keyword_for('airmass')
mean_airmass = np.mean([ad.airmass() for ad in adinputs])
except: # generic implementation failure (probably non-Gemini)
pass
else:
ad_out.phu.set(airmass_kw, mean_airmass,
"Mean airmass for the exposure")
# Set GAIN to the average of input gains. Set the RDNOISE to the
# sum in quadrature of the input read noises.
for ext, gain, rn in zip(ad_out, gain_list, read_noise_list):
ext.hdr.set('GAIN', gain, self.keyword_comments['GAIN'])
ext.hdr.set('RDNOISE', rn, self.keyword_comments['RDNOISE'])
# Stick the first extension's values in the PHU
ad_out.phu.set('GAIN', gain_list[0], self.keyword_comments['GAIN'])
ad_out.phu.set('RDNOISE', read_noise_list[0],
self.keyword_comments['RDNOISE'])
# Add suffix to datalabel to distinguish from the reference frame
ad_out.phu.set('DATALAB', "{}{}".format(ad_out.data_label(), sfx),
self.keyword_comments['DATALAB'])
# Add other keywords to the PHU about the stacking inputs
ad_out.orig_filename = ad_out.phu.get('ORIGNAME')
ad_out.phu.set('NCOMBINE', len(adinputs),
self.keyword_comments['NCOMBINE'])
for i, ad in enumerate(adinputs, start=1):
ad_out.phu.set('IMCMB{:03d}'.format(i),
ad.phu.get('ORIGNAME', ad.filename))
# Timestamp and update filename and prepare to return single output
gt.mark_history(ad_out, primname=self.myself(), keyword=timestamp_key)
ad_out.update_filename(suffix=sfx, strip=True)
return [ad_out]