def test_add_duplicate_provenance(ad):
timestamp = datetime.now().strftime(PROVENANCE_DATE_FORMAT)
filename = "filename"
md5 = "md5"
primitive = "provenance_added_by"
add_provenance(ad, filename, md5, primitive, timestamp=timestamp)
add_provenance(ad, filename, md5, primitive, timestamp=timestamp)
# was a dupe, so should have been skipped
assert len(ad.PROVENANCE) == 1
def test_clone_provenance(ad, ad2):
timestamp = datetime.now().strftime(PROVENANCE_DATE_FORMAT)
filename = "filename"
md5 = "md5"
primitive = "provenance_added_by"
add_provenance(ad, filename, md5, primitive, timestamp=timestamp)
clone_provenance(ad.PROVENANCE, ad2)
assert len(ad2.PROVENANCE) == 1
assert tuple(ad2.PROVENANCE[0]) == (timestamp, filename, md5, primitive)
def prepare(self, adinputs=None, **params):
"""
Validate and standardize the datasets to ensure compatibility
with the subsequent primitives. The outputs, if written to
disk will be given the suffix "_prepared".
Currently, there are no input parameters associated with
this primitive.
Parameters
----------
adinputs : None or list
Input files that will be prepared. If `None`, it runs on the
list of AstroData objects in the main stream.
suffix: str
Suffix to be added to output files (Default: "_prepared").
"""
log = self.log
log.debug(gt.log_message("primitive", "prepare", "starting"))
filenames = [ad.filename for ad in adinputs]
paths = [ad.path for ad in adinputs]
timestamp_key = self.timestamp_keys["prepare"]
sfx = params["suffix"]
for primitive in ('validateData', 'standardizeStructure',
'standardizeHeaders', 'standardizeWCS'):
# No need to call standardizeWCS if all adinputs are single-extension
# images (tags should be the same for all adinputs)
if ('WCS' not in primitive or 'SPECT' in adinputs[0].tags
or any(len(ad) > 1 for ad in adinputs)):
passed_params = self._inherit_params(params, primitive)
adinputs = getattr(self, primitive)(adinputs, **passed_params)
for ad in adinputs:
gt.mark_history(ad, self.myself(), timestamp_key)
ad.update_filename(suffix=sfx, strip=True)
for ad, filename, path in zip(adinputs, filenames, paths):
if path:
add_provenance(ad, filename, md5sum(path) or "", self.myself())
return adinputs
def test_add_get_provenance(ad):
timestamp = datetime.now().strftime(PROVENANCE_DATE_FORMAT)
filename = "filename"
md5 = "md5"
primitive = "provenance_added_by"
# if md5 is None, nothing is added
add_provenance(ad, filename, None, primitive)
assert not hasattr(ad, 'PROVENANCE')
add_provenance(ad, filename, md5, primitive, timestamp=timestamp)
assert len(ad.PROVENANCE) == 1
assert tuple(ad.PROVENANCE[0]) == (timestamp, filename, md5, primitive)
# entry is updated and a default timestamp is created
add_provenance(ad, filename, md5, primitive)
assert len(ad.PROVENANCE) == 1
assert tuple(ad.PROVENANCE[0])[1:] == (filename, md5, primitive)
# add new entry
add_provenance(ad, filename, 'md6', 'other primitive')
assert len(ad.PROVENANCE) == 2
assert tuple(ad.PROVENANCE[0])[1:] == (filename, md5, primitive)
assert tuple(ad.PROVENANCE[1])[1:] == (filename, 'md6', 'other primitive')
def biasCorrect(self, adinputs=None, suffix=None, bias=None, do_bias=True):
"""
The biasCorrect primitive will subtract the science extension of the
input bias frames from the science extension of the input science
frames. The variance and data quality extension will be updated, if
they exist. If no bias is provided, getProcessedBias will be called
to ensure a bias exists for every adinput.
Parameters
----------
suffix: str
suffix to be added to output files
bias: str/list of str
bias(es) to subtract
do_bias: bool
perform bias subtraction?
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
if not do_bias:
log.warning("Bias correction has been turned off.")
return adinputs
if bias is None:
self.getProcessedBias(adinputs, refresh=False)
bias_list = self._get_cal(adinputs, 'processed_bias')
else:
bias_list = bias
# Provide a bias AD object for every science frame
for ad, bias in zip(
*gt.make_lists(adinputs, bias_list, force_ad=True)):
if ad.phu.get(timestamp_key):
log.warning("No changes will be made to {}, since it has "
"already been processed by biasCorrect".format(
ad.filename))
continue
if bias is None:
if 'qa' in self.mode:
log.warning("No changes will be made to {}, since no "
"bias was specified".format(ad.filename))
continue
else:
raise OSError('No processed bias listed for {}'.format(
ad.filename))
try:
gt.check_inputs_match(ad,
bias,
check_filter=False,
check_units=True)
except ValueError:
bias = gt.clip_auxiliary_data(ad, aux=bias, aux_type='cal')
# An Error will be raised if they don't match now
gt.check_inputs_match(ad,
bias,
check_filter=False,
check_units=True)
log.fullinfo('Subtracting this bias from {}:\n{}'.format(
ad.filename, bias.filename))
ad.subtract(bias)
# Record bias used, timestamp, and update filename
ad.phu.set('BIASIM', bias.filename,
self.keyword_comments['BIASIM'])
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=suffix, strip=True)
if bias.path:
add_provenance(ad, bias.filename,
md5sum(bias.path) or "", self.myself())
timestamp = datetime.now()
return adinputs
def slitIllumCorrect(self,
adinputs=None,
slit_illum=None,
do_illum=True,
suffix="_illumCorrected"):
"""
This primitive will divide each SCI extension of the inputs by those
of the corresponding slit illumination image. If the inputs contain
VAR or DQ frames, those will also be updated accordingly due to the
division on the data.
Parameters
----------
adinputs : list of AstroData
Data to be corrected.
slit_illum : str or AstroData
Slit illumination path or AstroData object.
do_illum: bool, optional
Perform slit illumination correction? (Default: True)
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
qecorr_key = self.timestamp_keys['QECorrect']
if not do_illum:
log.warning("Slit Illumination correction has been turned off.")
return adinputs
if slit_illum is None:
raise NotImplementedError
else:
slit_illum_list = slit_illum
# Provide a Slit Illum Ad object for every science frame
ad_outputs = []
for ad, slit_illum_ad in zip(
*gt.make_lists(adinputs, slit_illum_list, force_ad=True)):
if ad.phu.get(timestamp_key):
log.warning("No changes will be made to {}, since it has "
"already been processed by flatCorrect".format(
ad.filename))
continue
if slit_illum_ad is None:
if self.mode in ['sq']:
raise OSError(
"No processed slit illumination listed for {}".format(
ad.filename))
else:
log.warning("No changes will be made to {}, since no slit "
"illumination has been specified".format(
ad.filename))
continue
gt.check_inputs_match(ad, slit_illum_ad, check_shape=False)
if not all(
[e1.shape == e2.shape for (e1, e2) in zip(ad, slit_illum_ad)]):
slit_illum_ad = gt.clip_auxiliary_data(adinput=[ad],
aux=[slit_illum_ad])[0]
log.info("Dividing the input AstroData object {} by this \n"
"slit illumination file: \n{}".format(
ad.filename, slit_illum_ad.filename))
ad_out = deepcopy(ad)
ad_out.divide(slit_illum_ad)
# Update the header and filename, copying QECORR keyword from flat
ad_out.phu.set("SLTILLIM", slit_illum_ad.filename,
self.keyword_comments["SLTILLIM"])
try:
qecorr_value = slit_illum_ad.phu[qecorr_key]
except KeyError:
pass
else:
log.fullinfo(
"Copying {} keyword from slit illumination".format(
qecorr_key))
ad_out.phu.set(qecorr_key, qecorr_value,
slit_illum_ad.phu.comments[qecorr_key])
gt.mark_history(ad_out,
primname=self.myself(),
keyword=timestamp_key)
ad_out.update_filename(suffix=suffix, strip=True)
if slit_illum_ad.path:
add_provenance(ad_out, slit_illum_ad.filename,
md5sum(slit_illum_ad.path) or "", self.myself())
ad_outputs.append(ad_out)
return ad_outputs
def fringeCorrect(self, adinputs=None, **params):
"""
Correct science frames for the effects of fringing, using a fringe
frame. The fringe frame is obtained either from a specified parameter,
or the "fringe" stream, or the calibration database. This is basically
a bookkeeping wrapper for subtractFringe(), which does all the work.
Parameters
----------
suffix: str
suffix to be added to output files
fringe: list/str/AstroData/None
fringe frame(s) to subtract
do_fringe: bool/None
apply fringe correction? (None => use pipeline default for data)
scale: bool/None
scale fringe frame? (None => False if fringe frame has same
group_id() as data
scale_factor: float/sequence/None
factor(s) to scale fringe
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
fringe = params["fringe"]
scale = params["scale"]
do_cal = params["do_cal"]
# Exit now if nothing needs a correction, to avoid an error when the
# calibration search fails. If images with different exposure times
# are used, some frames may not require a correction (but the calibration
# search will succeed), so still need to check individual inputs later.
needs_correction = [self._needs_fringe_correction(ad) for ad in adinputs]
if any(needs_correction):
if do_cal == 'skip':
log.warning("Fringe correction has been turned off but is "
"recommended.")
return adinputs
else:
if do_cal == 'procmode' or do_cal == 'skip':
log.stdinfo("No input images require a fringe correction.")
return adinputs
else: # do_cal == 'force':
log.warning("Fringe correction has been forced on but may not "
"be required.")
if fringe is None:
# This logic is for QAP
try:
fringe_list = self.streams['fringe']
assert len(fringe_list) == 1
scale = False
log.stdinfo("Using fringe frame in 'fringe' stream. "
"Setting scale=False")
fringe_list = (fringe_list[0], "stream")
except (KeyError, AssertionError):
fringe_list = self.caldb.get_processed_fringe(adinputs)
else:
fringe_list = (fringe, None)
# Usual stuff to ensure that we have an iterable of the correct length
# for the scale factors regardless of what the input is
scale_factor = params["scale_factor"]
try:
factors = iter(scale_factor)
except TypeError:
factors = iter([scale_factor] * len(adinputs))
else:
# In case a single-element list was passed
if len(scale_factor) == 1:
factors = iter(scale_factor * len(adinputs))
# Get a fringe AD object for every science frame
for ad, fringe, origin, correct in zip(*gt.make_lists(
adinputs, *fringe_list, needs_correction, force_ad=(1,))):
if ad.phu.get(timestamp_key):
log.warning(f"{ad.filename}: already processed by "
"fringeCorrect. Continuing.")
continue
# Logic to deal with different exposure times where only
# some inputs might require fringe correction
# KL: for now, I'm not allowing the "force" to do anything when
# the correction is not needed.
if (do_cal == 'procmode' or do_cal == 'force') and not correct:
log.stdinfo("{} does not require a fringe correction".
format(ad.filename))
ad.update_filename(suffix=params["suffix"], strip=True)
continue
# At this point, we definitely want to do a fringe correction
# so we'd better have a fringe frame!
if fringe is None:
if 'sq' not in self.mode and do_cal != 'force':
log.warning("No changes will be made to {}, since no "
"fringe frame has been specified".
format(ad.filename))
continue
else:
log.warning(f"{ad.filename}: no fringe was specified. "
"Continuing.")
continue
# Check the inputs have matching filters, binning, and shapes
try:
gt.check_inputs_match(ad, fringe)
except ValueError:
fringe = gt.clip_auxiliary_data(adinput=ad, aux=fringe,
aux_type="cal")
gt.check_inputs_match(ad, fringe)
#
origin_str = f" (obtained from {origin})" if origin else ""
log.stdinfo(f"{ad.filename}: using the fringe frame "
f"{fringe.filename}{origin_str}")
matched_groups = (ad.group_id() == fringe.group_id())
if scale or (scale is None and not matched_groups):
factor = next(factors)
if factor is None:
factor = self._calculate_fringe_scaling(ad, fringe)
log.stdinfo("Scaling fringe frame by factor {:.3f} before "
"subtracting from {}".format(factor, ad.filename))
# Since all elements of fringe_list might be references to the
# same AD, need to make a copy before multiplying
fringe_copy = deepcopy(fringe)
fringe_copy.multiply(factor)
ad.subtract(fringe_copy)
else:
if scale is None:
log.stdinfo("Not scaling fringe frame with same group ID "
"as {}".format(ad.filename))
ad.subtract(fringe)
# Timestamp and update header and filename
ad.phu.set("FRINGEIM", fringe.filename, self.keyword_comments["FRINGEIM"])
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=params["suffix"], strip=True)
if fringe.path:
add_provenance(ad, fringe.filename, md5sum(fringe.path) or "", self.myself())
return adinputs
def biasCorrect(self, adinputs=None, suffix=None, bias=None, do_cal=None):
"""
The biasCorrect primitive will subtract the science extension of the
input bias frames from the science extension of the input science
frames. The variance and data quality extension will be updated, if
they exist. If no bias is provided, the calibration database(s) will
be queried.
Parameters
----------
suffix: str
suffix to be added to output files
bias: str/list of str
bias(es) to subtract
do_cal: str
perform bias subtraction?
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
if do_cal == 'skip':
log.warning("Bias correction has been turned off.")
return adinputs
if bias is None:
bias_list = self.caldb.get_processed_bias(adinputs)
else:
bias_list = (bias, None)
# Provide a bias AD object for every science frame, and an origin
for ad, bias, origin in zip(*gt.make_lists(adinputs, *bias_list,
force_ad=(1,))):
if ad.phu.get(timestamp_key):
log.warning(f"{ad.filename}: already processed by "
"biasCorrect. Continuing.")
continue
if bias is None:
if 'sq' not in self.mode and do_cal != 'force':
log.warning("No changes will be made to {}, since no "
"bias was specified".format(ad.filename))
continue
else:
log.warning(f"{ad.filename}: no bias was specified. "
"Continuing.")
continue
try:
gt.check_inputs_match(ad, bias, check_filter=False,
check_units=True)
except ValueError:
bias = gt.clip_auxiliary_data(ad, aux=bias, aux_type='cal')
# An Error will be raised if they don't match now
gt.check_inputs_match(ad, bias, check_filter=False,
check_units=True)
origin_str = f" (obtained from {origin})" if origin else ""
log.stdinfo(f"{ad.filename}: subtracting the bias "
f"{bias.filename}{origin_str}")
ad.subtract(bias)
# Record bias used, timestamp, and update filename
ad.phu.set('BIASIM', bias.filename, self.keyword_comments['BIASIM'])
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=suffix, strip=True)
if bias.path:
add_provenance(ad, bias.filename, md5sum(bias.path) or "", self.myself())
timestamp = datetime.now()
return adinputs
