def test_clip_auxiliary_data(self):
ad = astrodata.open(os.path.join(TESTDATAPATH, 'NIRI',
'N20160620S0035.fits'))
bpm_ad = astrodata.open('geminidr/niri/lookups/BPM/NIRI_bpm.fits')
ret = gt.clip_auxiliary_data(ad, bpm_ad, 'bpm', np.int16)
assert ret[0].data.shape == ad[0].data.shape
assert np.all(ret[0].data == bpm_ad[0].data[256:768,256:768])
def addLatencyToDQ(self, adinputs=None, **params):
"""
Flags pixels in the DQ plane of an image based on whether the same
pixel has been flagged as saturated in a previous image.
Parameters
----------
suffix: str
suffix to be added to output files
non_linear : bool
flag non-linear pixels (as well as saturated ones)?
time: float
time (in seconds) for which latency is an issue
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
flags = DQ.saturated | (DQ.non_linear if params["non_linear"] else 0)
# Create a timedelta object using the value of the "time" parameter
seconds = datetime.timedelta(seconds=params["time"])
# Avoids n^2 calls to the descriptor
times = [ad.ut_datetime() for ad in adinputs]
for i, ad in enumerate(adinputs):
# Find which frames have their bright pixels propagated
propagated = [
x for x in zip(adinputs, times)
if (x[1] < times[i] and times[i] - x[1] < seconds)
]
if propagated:
log.stdinfo('{} affected by {}'.format(
ad.filename,
','.join([x[0].filename for x in propagated])))
for ad_latent in list(zip(*propagated))[0]:
# AD extensions might not be in the same order
# Set aux_type to 'bpm' which means hot pixels in a subarray
# can still be propagated to a subsequent full-array image
ad_latent = gt.clip_auxiliary_data(ad,
aux=ad_latent,
aux_type='bpm')
for ext, ext_latent in zip(ad, ad_latent):
if ext_latent.mask is not None:
latency = np.where(ext_latent.mask & flags,
DQ.cosmic_ray,
0).astype(DQ.datatype)
ext.mask = latency if ext.mask is None \
else ext.mask | latency
else:
log.stdinfo('{} is not affected by latency'.format(
ad.filename))
ad.update_filename(suffix=params["suffix"], strip=True)
return adinputs
def addIllumMaskToDQ(self, adinputs=None, suffix=None, illum_mask=None):
"""
Adds an illumination mask to each AD object
Parameters
----------
suffix: str
suffix to be added to output files
mask: str/None
name of illumination mask mask (None -> use default)
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
# Getting all the filenames first prevents reopening the same file
# for each science AD
if illum_mask is None:
illum_mask = [self._get_illum_mask_filename(ad) for ad in adinputs]
for ad, illum in zip(
*gt.make_lists(adinputs, illum_mask, force_ad=True)):
if ad.phu.get(timestamp_key):
log.warning(
'No changes will be made to {}, since it has '
'already been processed by addIllumMaskToDQ'.format(
ad.filename))
continue
if illum is None:
# So it can be zipped with the AD
final_illum = [None] * len(ad)
else:
log.fullinfo("Using {} as illumination mask".format(
illum.filename))
final_illum = gt.clip_auxiliary_data(ad,
aux=illum,
aux_type='bpm',
return_dtype=DQ.datatype)
for ext, illum_ext in zip(ad, final_illum):
if illum_ext is not None:
# Ensure we're only adding the unilluminated bit
iext = np.where(illum_ext.data > 0, DQ.unilluminated,
0).astype(DQ.datatype)
ext.mask = iext if ext.mask is None else ext.mask | iext
# Timestamp and update filename
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=suffix, strip=True)
return adinputs
def addIllumMaskToDQ(self, adinputs=None, suffix=None, illum_mask=None):
"""
Adds an illumination mask to each AD object
Parameters
----------
suffix: str
suffix to be added to output files
mask: str/None
name of illumination mask mask (None -> use default)
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
# Getting all the filenames first prevents reopening the same file
# for each science AD
if illum_mask is None:
illum_mask = [self._get_illum_mask_filename(ad) for ad in adinputs]
for ad, illum in zip(*gt.make_lists(adinputs, illum_mask, force_ad=True)):
if ad.phu.get(timestamp_key):
log.warning('No changes will be made to {}, since it has '
'already been processed by addIllumMaskToDQ'.
format(ad.filename))
continue
if illum is None:
# So it can be zipped with the AD
final_illum = [None] * len(ad)
else:
log.fullinfo("Using {} as illumination mask".format(illum.filename))
final_illum = gt.clip_auxiliary_data(ad, aux=illum, aux_type='bpm',
return_dtype=DQ.datatype)
for ext, illum_ext in zip(ad, final_illum):
if illum_ext is not None:
# Ensure we're only adding the unilluminated bit
iext = np.where(illum_ext.data > 0, DQ.unilluminated,
0).astype(DQ.datatype)
ext.mask = iext if ext.mask is None else ext.mask | iext
# Timestamp and update filename
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=suffix, strip=True)
return adinputs
def addLatencyToDQ(self, adinputs=None, **params):
"""
Flags pixels in the DQ plane of an image based on whether the same
pixel has been flagged as saturated in a previous image.
Parameters
----------
suffix: str
suffix to be added to output files
non_linear : bool
flag non-linear pixels (as well as saturated ones)?
time: float
time (in seconds) for which latency is an issue
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
flags = DQ.saturated | (DQ.non_linear if params["non_linear"] else 0)
# Create a timedelta object using the value of the "time" parameter
seconds = datetime.timedelta(seconds=params["time"])
# Avoids n^2 calls to the descriptor
times = [ad.ut_datetime() for ad in adinputs]
for i, ad in enumerate(adinputs):
# Find which frames have their bright pixels propagated
propagated = [x for x in zip(adinputs, times) if (x[1]<times[i] and times[i]-x[1]<seconds)]
if propagated:
log.stdinfo('{} affected by {}'.format(ad.filename,
','.join([x[0].filename for x in propagated])))
for ad_latent in list(zip(*propagated))[0]:
# AD extensions might not be in the same order
# Set aux_type to 'bpm' which means hot pixels in a subarray
# can still be propagated to a subsequent full-array image
ad_latent = gt.clip_auxiliary_data(ad, aux=ad_latent,
aux_type='bpm')
for ext, ext_latent in zip(ad, ad_latent):
if ext_latent.mask is not None:
latency = np.where(ext_latent.mask & flags, DQ.cosmic_ray,
0).astype(DQ.datatype)
ext.mask = latency if ext.mask is None \
else ext.mask | latency
else:
log.stdinfo('{} is not affected by latency'.format(ad.filename))
ad.update_filename(suffix=params["suffix"], strip=True)
return adinputs
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
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
# 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.
if not any(self._needs_fringe_correction(ad) for ad in adinputs):
log.stdinfo("No input images require a fringe correction.")
return adinputs
fringe = params["fringe"]
scale = params["scale"]
if fringe is None:
try:
fringe_list = self.streams['fringe']
assert len(fringe_list) == 1
scale = False
log.stdinfo("Using fringe frame in 'fringe' stream. "
"Setting scale=False")
except (KeyError, AssertionError):
self.getProcessedFringe(adinputs)
fringe_list = self._get_cal(adinputs, "processed_fringe")
else:
fringe_list = fringe
# 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 in zip(*gt.make_lists(adinputs, fringe_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 subtractFringe".
format(ad.filename))
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)
if scale:
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:
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)
return adinputs
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 IOError('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)
return adinputs
def pointing_in_field(pos, refpos, frac_FOV=1.0, frac_slit=1.0):
"""
See gemini_tools.pointing_in_field() for the API. This is an
instrument-specific back end that you shouldn't be calling directly.
No inputs are validated at this level; that's the responsibility of the
calling function, for reasons of efficiency.
The GNIRS FOV is determined by whether the calculated center point
(according to the center of mass of the illumination mask) of the
image falls within the illumination mask of the reference image.
:param pos: AstroData instance to be checked for whether it belongs
in the same sky grouping as refpos
:type pos: AstroData instance
:param refpos: This is the POFFSET and QOFFSET of the reference image
:type refpos: tuple of floats
:param frac_FOV: For use with spectroscopy data
:type frac_FOV: float
:param frac_slit: For use with spectroscopy data
:type frac_slit: float
"""
# Since this function gets looked up and evaluated, we have to do any
# essential imports in-line (but Python caches them)
import math
# Extract pointing info in terms of the x and y offsets
xshift = refpos[1] - pos.phu['QOFFSET']
yshift = refpos[0] - pos.phu['POFFSET']
ad = pos
# Imaging:
if 'IMAGE' in pos.tags:
illum = get_illum_mask_filename(ad)
if illum:
illum_ad = gt.clip_auxiliary_data(adinput=pos,
aux=astrodata.open(illum), aux_type="bpm")
illum_data = illum_ad[0].data
else:
raise OSError("Cannot find illumination mask for {}".
format(ad.filename))
# Finding the center of the illumination mask
center_illum = (illum_ad.phu['CENMASSX'], illum_ad.phu['CENMASSY'])
checkpos = (int(center_illum[0] + xshift),
int(center_illum[1] + yshift))
# If the position to check is going to fall outside the illumination
# mask, return straight away to avoid an error
if ((abs(xshift) >= abs(center_illum[0])) or
(abs(yshift) >= abs(center_illum[1]))):
return False
# Note that numpy data arrays are reversed in x and y
return illum_data[checkpos[1], checkpos[0]] == 0
# Spectroscopy:
elif 'SPECT' in ad.tags:
raise NotImplementedError("FOV lookup not yet supported for GNIRS "
"Spectroscopy")
# Some engineering observation or bad mask value etc.:
else:
raise ValueError("Can't determine FOV for unrecognized GNIRS config "
"({}, {})".format(ad.focal_plane_mask(), ad.disperser()))
def addIllumMaskToDQ(self, adinputs=None, **params):
"""
This primitive combines the illumination mask from the lookup directory
into the DQ plane
Parameters
----------
suffix: str
suffix to be added to output files
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
# Since this primitive needs a reference, it must no-op without any
if not adinputs:
return adinputs
# Get list of input and identify a suitable reference frame.
# In most case it will be the first image, but for lamp-on, lamp-off
# flats, one wants the reference frame to be a lamp-on since there's
# next to no signal in the lamp-off.
#
# BEWARE: See note on the only GCAL_IR_OFF case below this block.
lampons = self.selectFromInputs(adinputs, 'GCAL_IR_ON')
reference = lampons[0] if lampons else adinputs[0]
# When only a GCAL_IR_OFF is available:
# To cover the one-at-a-time mode check for a compatible list
# if list found, try to find a lamp-on in there to use as
# reference for the mask and the shifts.
# The mask's name and the shifts should stored in the headers
# of the reference to simplify this and speed things up.
#
# NOT NEEDED NOW because we calling addIllumMask after the
# lamp-offs have been subtracted. But kept the idea here
# in case we needed.
# Fetching a corrected illumination mask with a keyhole that aligns
# with the science data
illum = self._get_illum_mask_filename(reference)
if illum is None:
log.warning("No illumination mask found for {}, no mask can "
"be added to the DQ planes of the inputs".
format(reference.filename))
return adinputs
illum_ad = astrodata.open(illum)
corr_illum_ad = _position_illum_mask(reference, illum_ad, log)
for ad in adinputs:
final_illum = gt.clip_auxiliary_data(ad, aux=corr_illum_ad,
aux_type="bpm")
# binary_OR the illumination mask or create a DQ plane from it.
if ad[0].mask is None:
ad[0].mask = final_illum[0].data
else:
ad[0].mask |= final_illum[0].data
# Update the filename
ad.update_filename(suffix=params["suffix"], strip=True)
return adinputs
def addDQ(self, adinputs=None, **params):
"""
This primitive is used to add a DQ extension to the input AstroData
object. The value of a pixel in the DQ extension will be the sum of the
following: (0=good, 1=bad pixel (found in bad pixel mask), 2=pixel is
in the non-linear regime, 4=pixel is saturated). This primitive will
trim the BPM to match the input AstroData object(s).
Parameters
----------
suffix: str
suffix to be added to output files
static_bpm: str
Name of bad pixel mask ("default" -> use default from look-up table)
If set to None, no static_bpm will be added.
user_bpm: str
Name of the bad pixel mask created by the user from flats and
darks. It is an optional BPM that can be added to the static one.
illum_mask: bool
add illumination mask?
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys["addDQ"]
sfx = params["suffix"]
# Getting all the filenames first prevents reopening the same file
# for each science AD
static_bpm_list = params['static_bpm']
user_bpm_list = params['user_bpm']
if static_bpm_list == "default":
static_bpm_list = [self._get_bpm_filename(ad) for ad in adinputs]
for ad, static, user in zip(*gt.make_lists(
adinputs, static_bpm_list, user_bpm_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 addDQ'.format(
ad.filename))
continue
if static is None:
# So it can be zipped with the AD
final_static = [None] * len(ad)
else:
log.fullinfo("Using {} as static BPM".format(static.filename))
final_static = gt.clip_auxiliary_data(ad,
aux=static,
aux_type='bpm',
return_dtype=DQ.datatype)
if user is None:
final_user = [None] * len(ad)
else:
log.fullinfo("Using {} as user BPM".format(user.filename))
final_user = gt.clip_auxiliary_data(ad,
aux=user,
aux_type='bpm',
return_dtype=DQ.datatype)
for ext, static_ext, user_ext in zip(ad, final_static, final_user):
extver = ext.hdr['EXTVER']
if ext.mask is not None:
log.warning(
'A mask already exists in extver {}'.format(extver))
continue
non_linear_level = ext.non_linear_level()
saturation_level = ext.saturation_level()
# Need to create the array first for 3D raw F2 data, with 2D BPM
ext.mask = np.zeros_like(ext.data, dtype=DQ.datatype)
if static_ext is not None:
ext.mask |= static_ext.data
if user_ext is not None:
ext.mask |= user_ext.data
if saturation_level:
log.fullinfo('Flagging saturated pixels in {}:{} '
'above level {:.2f}'.format(
ad.filename, extver, saturation_level))
ext.mask |= np.where(ext.data >= saturation_level,
DQ.saturated, 0).astype(DQ.datatype)
if non_linear_level:
if saturation_level:
if saturation_level > non_linear_level:
log.fullinfo('Flagging non-linear pixels in {}:{} '
'above level {:.2f}'.format(
ad.filename, extver,
non_linear_level))
ext.mask |= np.where(
(ext.data >= non_linear_level) &
(ext.data < saturation_level), DQ.non_linear,
0).astype(DQ.datatype)
# Readout modes of IR detectors can result in
# saturated pixels having values below the
# saturation level. Flag those. Assume we have an
# IR detector here because both non-linear and
# saturation levels are defined and nonlin<sat
regions, nregions = measurements.label(
ext.data < non_linear_level)
# In all my tests, region 1 has been the majority
# of the image; however, I cannot guarantee that
# this is always the case and therefore we should
# check the size of each region
region_sizes = measurements.labeled_comprehension(
ext.data, regions, np.arange(1, nregions + 1),
len, int, 0)
# First, assume all regions are saturated, and
# remove any very large ones. This is much faster
# than progressively adding each region to DQ
hidden_saturation_array = np.where(
regions > 0, 4, 0).astype(DQ.datatype)
for region in range(1, nregions + 1):
# Limit of 10000 pixels for a hole is a bit arbitrary
if region_sizes[region - 1] > 10000:
hidden_saturation_array[regions ==
region] = 0
ext.mask |= hidden_saturation_array
elif saturation_level < non_linear_level:
log.warning('{}:{} has saturation level less than '
'non-linear level'.format(
ad.filename, extver))
else:
log.fullinfo('Saturation and non-linear levels '
'are the same for {}:{}. Only '
'flagging saturated pixels'.format(
ad.filename, extver))
else:
log.fullinfo('Flagging non-linear pixels in {}:{} '
'above level {:.2f}'.format(
ad.filename, extver,
non_linear_level))
ext.mask |= np.where(ext.data >= non_linear_level,
DQ.non_linear,
0).astype(DQ.datatype)
# Handle latency if reqested
if params.get("latency", False):
try:
adinputs = self.addLatencyToDQ(adinputs,
time=params["time"],
non_linear=params["non_linear"])
except AttributeError:
log.warning(
"addLatencyToDQ() not defined in primitivesClass " +
self.__class__.__name__)
# Add the illumination mask if requested
if params['add_illum_mask']:
adinputs = self.addIllumMaskToDQ(adinputs,
illum_mask=params["illum_mask"])
# Timestamp and update filenames
for ad in adinputs:
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=sfx, strip=True)
return adinputs
def divideByFlat(self, rc):
"""
This primitive will divide each SCI extension of the inputs by those
of the corresponding flat. If the inputs contain VAR or DQ frames,
those will also be updated accordingly due to the division on the data.
"""
# Instantiate the log
log = logutils.get_logger(__name__)
# Log the standard "starting primitive" debug message
log.debug(gt.log_message("primitive", "divideByFlat", "starting"))
# Define the keyword to be used for the time stamp for this primitive
timestamp_key = self.timestamp_keys["divideByFlat"]
# Initialize the list of output AstroData objects
adoutput_list = []
# Check for a user-supplied flat
adinput = rc.get_inputs_as_astrodata()
flat_param = rc["flat"]
flat_dict = None
if flat_param is not None:
# The user supplied an input to the flat parameter
if not isinstance(flat_param, list):
flat_list = [flat_param]
else:
flat_list = flat_param
# Convert filenames to AD instances if necessary
tmp_list = []
for flat in flat_list:
if type(flat) is not AstroData:
flat = AstroData(flat)
tmp_list.append(flat)
flat_list = tmp_list
flat_dict = gt.make_dict(key_list=adinput, value_list=flat_list)
# Loop over each input AstroData object in the input list
for ad in adinput:
# Check whether the divideByFlat primitive has been run previously
if ad.phu_get_key_value(timestamp_key):
log.warning("No changes will be made to %s, since it has " \
"already been processed by divideByFlat" \
% (ad.filename))
# Append the input AstroData object to the list of output
# AstroData objects without further processing
adoutput_list.append(ad)
continue
# Retrieve the appropriate flat
if flat_dict is not None:
flat = flat_dict[ad]
else:
flat = rc.get_cal(ad, "processed_flat")
# If there is no appropriate flat, there is no need to divide by
# the flat in QA context; in SQ context, raise an error
if flat is None:
if "qa" in rc.context:
log.warning("No changes will be made to %s, since no " \
"appropriate flat could be retrieved" \
% (ad.filename))
# Append the input AstroData object to the list of output
# AstroData objects without further processing
adoutput_list.append(ad)
continue
else:
raise Errors.PrimitiveError("No processed flat found "\
"for %s" % ad.filename)
else:
flat = AstroData(flat)
# Check the inputs have matching filters, binning, and SCI shapes.
try:
gt.check_inputs_match(ad1=ad, ad2=flat)
except Errors.ToolboxError:
# If not, try to clip the flat frame to the size
# of the science data
# For a GMOS example, this allows a full frame flat to
# be used for a CCD2-only science frame.
flat = gt.clip_auxiliary_data(
adinput=ad,aux=flat,aux_type="cal")[0]
# Check again, but allow it to fail if they still don't match
gt.check_inputs_match(ad1=ad, ad2=flat)
# Divide the adinput by the flat
log.fullinfo("Dividing the input AstroData object (%s) " \
"by this flat:\n%s" % (ad.filename,
flat.filename))
ad = ad.div(flat)
# Record the flat file used
ad.phu_set_key_value("FLATIM",
os.path.basename(flat.filename),
comment=self.keyword_comments["FLATIM"])
# Add the appropriate time stamps to the PHU
gt.mark_history(adinput=ad, keyword=timestamp_key)
# Change the filename
ad.filename = gt.filename_updater(adinput=ad, suffix=rc["suffix"],
strip=True)
# Append the output AstroData object to the list
# of output AstroData objects
adoutput_list.append(ad)
# Report the list of output AstroData objects to the reduction
# context
rc.report_output(adoutput_list)
yield rc
def addDQ(self, rc):
"""
This primitive is used to add a DQ extension to the input AstroData
object. The value of a pixel in the DQ extension will be the sum of the
following: (0=good, 1=bad pixel (found in bad pixel mask), 2=pixel is
in the non-linear regime, 4=pixel is saturated). This primitive will
trim the BPM to match the input AstroData object(s).
:param bpm: The file name, including the full path, of the BPM(s) to be
used to flag bad pixels in the DQ extension. If only one
BPM is provided, that BPM will be used to flag bad pixels
in the DQ extension for all input AstroData object(s). If
more than one BPM is provided, the number of BPMs must
match the number of input AstroData objects. If no BPM is
provided, the primitive will attempt to determine an
appropriate BPM.
:type bpm: string or list of strings
"""
# Instantiate the log
log = logutils.get_logger(__name__)
# Log the standard "starting primitive" debug message
log.debug(gt.log_message("primitive", "addDQ", "starting"))
# Define the keyword to be used for the time stamp for this primitive
timestamp_key = self.timestamp_keys["addDQ"]
# Initialize the list of output AstroData objects
adoutput_list = []
# Set the data type of the data quality array
# It can be uint8 for now, it will get converted up as we assign higher bit values
# shouldn't need to force it up to 16bpp yet.
dq_dtype = np.dtype(np.uint8)
#dq_dtype = np.dtype(np.uint16)
# Get the input AstroData objects
adinput = rc.get_inputs_as_astrodata()
# Loop over each input AstroData object in the input list
for ad in adinput:
# Check whether the addDQ primitive has been run previously
if ad.phu_get_key_value(timestamp_key):
log.warning("No changes will be made to %s, since it has "
"already been processed by addDQ" % ad.filename)
# Append the input AstroData object to the list of output
# AstroData objects without further processing
adoutput_list.append(ad)
continue
# Parameters specified on the command line to reduce are converted
# to strings, including None
##M What about if a user doesn't want to add a BPM at all?
##M Are None's not converted to Nonetype from the command line?
if rc["bpm"] and rc["bpm"] != "None":
# The user supplied an input to the bpm parameter
bpm = rc["bpm"]
else:
# The user did not supply an input to the bpm parameter, so try
# to find an appropriate one. Get the dictionary containing the
# list of BPMs for all instruments and modes.
all_bpm_dict = Lookups.get_lookup_table("Gemini/BPMDict",
"bpm_dict")
# Call the _get_bpm_key helper function to get the key for the
# lookup table
key = self._get_bpm_key(ad)
# Get the appropriate BPM from the look up table
if key in all_bpm_dict:
bpm = lookup_path(all_bpm_dict[key])
else:
bpm = None
log.warning("No BPM found for %s, no BPM will be "
"included" % ad.filename)
# Ensure that the BPMs are AstroData objects
bpm_ad = None
if bpm is not None:
log.fullinfo("Using %s as BPM" % str(bpm))
if isinstance(bpm, AstroData):
bpm_ad = bpm
else:
bpm_ad = AstroData(bpm)
##M Do we want to fail here depending on context?
if bpm_ad is None:
log.warning("Cannot convert %s into an AstroData "
"object, no BPM will be added" % bpm)
final_bpm = None
if bpm_ad is not None:
# Clip the BPM data to match the size of the input AstroData
# object science and pad with overscan region, if necessary
final_bpm = gt.clip_auxiliary_data(adinput=ad, aux=bpm_ad,
aux_type="bpm")[0]
# Get the non-linear level and the saturation level using the
# appropriate descriptors - Individual values get checked in the
# next loop
non_linear_level_dv = ad.non_linear_level()
saturation_level_dv = ad.saturation_level()
# Loop over each science extension in each input AstroData object
for ext in ad[SCI]:
# Retrieve the extension number for this extension
extver = ext.extver()
# Check whether an extension with the same name as the DQ
# AstroData object already exists in the input AstroData object
if ad[DQ, extver]:
log.warning("A [%s,%d] extension already exists in %s"
% (DQ, extver, ad.filename))
continue
# Get the non-linear level and the saturation level for this
# extension
non_linear_level = non_linear_level_dv.get_value(extver=extver)
saturation_level = saturation_level_dv.get_value(extver=extver)
# To store individual arrays created for each of the DQ bit
# types
dq_bit_arrays = []
# Create an array that contains pixels that have a value of 2
# when that pixel is in the non-linear regime in the input
# science extension
if non_linear_level is not None:
non_linear_array = None
if saturation_level is not None:
# Test the saturation level against non_linear level
# They can be the same or the saturation level can be
# greater than but not less than the non-linear level.
# If they are the same then only flag saturated pixels
# below. This just means not creating an unneccessary
# intermediate array.
if saturation_level > non_linear_level:
log.fullinfo("Flagging pixels in the DQ extension "
"corresponding to non linear pixels "
"in %s[%s,%d] using non linear "
"level = %.2f" % (ad.filename, SCI,
extver,
non_linear_level))
non_linear_array = np.where(
((ext.data >= non_linear_level) &
(ext.data < saturation_level)), 2, 0)
elif saturation_level < non_linear_level:
log.warning("%s[%s,%d] saturation_level value is"
"less than the non_linear_level not"
"flagging non linear pixels" %
(ad.filname, SCI, extver))
else:
log.fullinfo("Saturation and non-linear values "
"for %s[%s,%d] are the same. Only "
"flagging saturated pixels."
% (ad.filename, SCI, extver))
else:
log.fullinfo("Flagging pixels in the DQ extension "
"corresponding to non linear pixels "
"in %s[%s,%d] using non linear "
"level = %.2f" % (ad.filename, SCI, extver,
non_linear_level))
non_linear_array = np.where(
(ext.data >= non_linear_level), 2, 0)
dq_bit_arrays.append(non_linear_array)
# Create an array that contains pixels that have a value of 4
# when that pixel is saturated in the input science extension
if saturation_level is not None:
saturation_array = None
log.fullinfo("Flagging pixels in the DQ extension "
"corresponding to saturated pixels in "
"%s[%s,%d] using saturation level = %.2f" %
(ad.filename, SCI, extver, saturation_level))
saturation_array = np.where(
ext.data >= saturation_level, 4, 0)
dq_bit_arrays.append(saturation_array)
# BPMs have an EXTNAME equal to DQ
bpmname = None
if final_bpm is not None:
bpm_array = None
bpmname = os.path.basename(final_bpm.filename)
log.fullinfo("Flagging pixels in the DQ extension "
"corresponding to bad pixels in %s[%s,%d] "
"using the BPM %s[%s,%d]" %
(ad.filename, SCI, extver, bpmname, DQ, extver))
bpm_array = final_bpm[DQ, extver].data
dq_bit_arrays.append(bpm_array)
# Create a single DQ extension from the three arrays (BPM,
# non-linear and saturated)
if not dq_bit_arrays:
# The BPM, non-linear and saturated arrays were not
# created. Create a single DQ array with all pixels set
# equal to 0
log.fullinfo("The BPM, non-linear and saturated arrays "
"were not created. Creating a single DQ "
"array with all the pixels set equal to zero")
final_dq_array = np.zeros(ext.data.shape).astype(dq_dtype)
else:
final_dq_array = self._bitwise_OR_list(dq_bit_arrays)
final_dq_array = final_dq_array.astype(dq_dtype)
# Create a data quality AstroData object
dq = AstroData(data=final_dq_array)
dq.rename_ext(DQ, ver=extver)
dq.filename = ad.filename
# Call the _update_dq_header helper function to update the
# header of the data quality extension with some useful
# keywords
dq = self._update_dq_header(sci=ext, dq=dq, bpmname=bpmname)
# Append the DQ AstroData object to the input AstroData object
log.fullinfo("Adding extension [%s,%d] to %s"
% (DQ, extver, ad.filename))
ad.append(moredata=dq)
# Add the appropriate time stamps to the PHU
gt.mark_history(adinput=ad, keyword=timestamp_key)
# Change the filename
ad.filename = gt.filename_updater(adinput=ad, suffix=rc["suffix"],
strip=True)
# Append the output AstroData object to the list of output
# AstroData objects
adoutput_list.append(ad)
# Report the list of output AstroData objects to the reduction context
rc.report_output(adoutput_list)
yield rc
def subtractFringe(self, rc):
# Instantiate the log
log = gemLog.getGeminiLog(logType=rc["logType"],
logLevel=rc["logLevel"])
# Log the standard "starting primitive" debug message
log.debug(gt.log_message("primitive", "subtractFringe",
"starting"))
# Define the keyword to be used for the time stamp for this primitive
timestamp_key = self.timestamp_keys["subtractFringe"]
# Initialize the list of output AstroData objects
adoutput_list = []
# Check for a user-supplied fringe
adinput = rc.get_inputs_as_astrodata()
fringe_param = rc["fringe"]
fringe_dict = None
if fringe_param is not None:
# The user supplied an input to the fringe parameter
if not isinstance(fringe_param, list):
fringe_list = [fringe_param]
else:
fringe_list = fringe_param
# Convert filenames to AD instances if necessary
tmp_list = []
for fringe in fringe_list:
if type(fringe) is not AstroData:
fringe = AstroData(fringe)
tmp_list.append(fringe)
fringe_list = tmp_list
fringe_dict = gt.make_dict(key_list=adinput, value_list=fringe_list)
# Loop over each input AstroData object in the input list
for ad in adinput:
# Check whether the subtractFringe primitive has been run
# previously
if ad.phu_get_key_value(timestamp_key):
log.warning("No changes will be made to %s, since it has " \
"already been processed by subtractFringe" \
% (ad.filename))
# Append the input AstroData object to the list of output
# AstroData objects without further processing
adoutput_list.append(ad)
continue
# Retrieve the appropriate fringe
if fringe_dict is not None:
fringe = fringe_dict[ad]
else:
fringe = rc.get_cal(ad, "processed_fringe")
# Take care of the case where there was no fringe
if fringe is None:
log.warning("Could not find an appropriate fringe for %s" \
% (ad.filename))
# Append the input to the output without further processing
adoutput_list.append(ad)
continue
else:
fringe = AstroData(fringe)
# Check the inputs have matching filters, binning and SCI shapes.
try:
gt.check_inputs_match(ad1=ad, ad2=fringe)
except Errors.ToolboxError:
# If not, try to clip the fringe frame to the size of the
# science data
# For a GMOS example, this allows a full frame fringe to
# be used for a CCD2-only science frame.
fringe = gt.clip_auxiliary_data(
adinput=ad, aux=fringe, aux_type="cal")[0]
# Check again, but allow it to fail if they still don't match
gt.check_inputs_match(ad1=ad, ad2=fringe)
# Subtract the fringe from the science
ad = ad.sub(fringe)
# Record the fringe file used
ad.phu_set_key_value("FRINGEIM",
os.path.basename(fringe.filename),
comment=self.keyword_comments["FRINGEIM"])
# Add the appropriate time stamps to the PHU
gt.mark_history(adinput=ad, keyword=timestamp_key)
# Change the filename
ad.filename = gt.filename_updater(adinput=ad, suffix=rc["suffix"],
strip=True)
# Append the output AstroData object to the list
# of output AstroData objects
adoutput_list.append(ad)
# Report the list of output AstroData objects to the reduction context
rc.report_output(adoutput_list)
yield rc
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 pointing_in_field(ad, refpos, frac_FOV=1.0, frac_slit=1.0):
"""
See gemini_tools.pointing_in_field() for the API. This is an
instrument-specific back end that you shouldn't be calling directly.
No inputs are validated at this level; that's the responsibility of the
calling function, for reasons of efficiency.
The GNIRS FOV is determined by whether the calculated center point
(according to the center of mass of the illumination mask) of the
image falls within the illumination mask of the reference image.
:param pos: AstroData instance to be checked for whether it belongs
in the same sky grouping as refpos
:type pos: AstroData instance
:param refpos: This is the POFFSET and QOFFSET of the reference image
:type refpos: tuple of floats
:param frac_FOV: For use with spectroscopy data
:type frac_FOV: float
:param frac_slit: For use with spectroscopy data
:type frac_slit: float
"""
# Object location in AD = refpos + shift
xshift = ad.detector_x_offset() - refpos[0]
yshift = ad.detector_y_offset() - refpos[1]
# We inverse-scale by frac_FOV
fov_xshift = -int(xshift / frac_FOV)
fov_yshift = -int(yshift / frac_FOV)
# Imaging:
if 'IMAGE' in ad.tags:
if (abs(fov_yshift) >= ad[0].shape[1]
or abs(fov_xshift) >= ad[0].shape[1]):
return False
illum = get_illum_mask_filename(ad)
if illum:
illum_ad = gt.clip_auxiliary_data(adinput=ad,
aux=astrodata.open(illum),
aux_type="bpm")
illum_data = illum_ad[0].data
else:
raise OSError("Cannot find illumination mask for {}".format(
ad.filename))
# Shift the illumination mask and see if the shifted keyhole
# overlaps with the original keyhole
shifted_data = shift(illum_data, (fov_yshift, fov_xshift),
order=0,
cval=DQ.unilluminated)
return (illum_data | shifted_data == 0).any()
# Spectroscopy:
elif 'SPECT' in ad.tags:
raise NotImplementedError("FOV lookup not yet supported for GNIRS "
"Spectroscopy")
# Some engineering observation or bad mask value etc.:
else:
raise ValueError("Can't determine FOV for unrecognized GNIRS config "
"({}, {})".format(ad.focal_plane_mask(),
ad.disperser()))
def processSlits(self, adinputs=None, **params):
"""
Compute and record the mean exposure epoch for a slit viewer image
The 'slit viewer image' for each observation will almost certainly
be a sequence of short exposures of the slit viewer camera,
collected together for convenience. However, it cannot be guaranteed
that slit viewer exposures will be taken throughout an entire
science exposure; therefore, it is necessary to be able to compute
the mean exposure epoch (i.e. the effective time that the combined
slit viewer exposures were taken at). This allows a single science
observation to be calibrated using multiple packets of slit viewer
exposures, with appropriate weighting for the time delay between them.
``processSlits`` effectively computes a weighted average of the
exposure epoch of all constituent slit viewer exposures, taking into
account:
- Length of each exposure;
- Whether there is any overlap between the start/end of the
exposure and the start/end of the overall 'image';
- Time of each exposure, relative to the start of the 'image'.
Parameters
----------
suffix: str
suffix to be added to output files
slitflat: str/None
name of the slitflat to use (if None, use the calibration
system)
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
flat_list = params["flat"]
if flat_list is None:
self.getProcessedSlitFlat(adinputs)
flat_list = [
self._get_cal(ad, 'processed_slitflat') for ad in adinputs
]
for ad, slitflat in zip(
*gt.make_lists(adinputs, flat_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 processSlits".format(
ad.filename))
continue
if slitflat is None:
log.warning("Unable to find slitflat calibration for {}; "
"skipping".format(ad.filename))
continue
else:
sv_flat = slitflat[0].data
# accumulators for computing the mean epoch
sum_of_weights = 0.0
accum_weighted_time = 0.0
# Check the inputs have matching binning and SCI shapes.
try:
gt.check_inputs_match(adinput1=ad,
adinput2=slitflat,
check_filter=False)
except ValueError:
# This is most likely because the science frame has multiple
# extensions and the slitflat needs to be copied
slitflat = gt.clip_auxiliary_data(ad, slitflat, aux_type='cal')
# An Error will be raised if they don't match now
gt.check_inputs_match(ad, slitflat, check_filter=False)
# get science start/end times
sc_start = parse_timestr(ad.phu['UTSTART'])
sc_end = parse_timestr(ad.phu['UTEND'])
res = ad.res_mode()
for ext in ad:
sv_start = parse_timestr(ext.hdr['EXPUTST'])
sv_end = parse_timestr(ext.hdr['EXPUTEND'])
# compute overlap percentage and slit view image duration
latest_start = max(sc_start, sv_start)
earliest_end = min(sc_end, sv_end)
overlap = (earliest_end - latest_start).seconds
overlap = 0.0 if overlap < 0.0 else overlap # no overlap edge case
sv_duration = (sv_end - sv_start).seconds
overlap /= sv_duration # convert into a percentage
# compute the offset (the value to be weighted), in seconds,
# from the start of the science exposure
offset = 42.0 # init value: overridden if overlap, else 0-scaled
if sc_start <= sv_start and sv_end <= sc_end:
offset = (sv_start - sc_start).seconds + sv_duration / 2.0
elif sv_start < sc_start:
offset = overlap * sv_duration / 2.0
elif sv_end > sc_end:
offset = overlap * sv_duration / 2.0
offset += (sv_start - sc_start).seconds
# add flux-weighted offset (plus weight itself) to accumulators
flux = _total_obj_flux(res, ext.data, sv_flat)
weight = flux * overlap
sum_of_weights += weight
accum_weighted_time += weight * offset
# final mean exposure epoch computation
if sum_of_weights > 0.0:
mean_offset = accum_weighted_time / sum_of_weights
mean_offset = timedelta(seconds=mean_offset)
# write the mean exposure epoch into the PHU
sc_start = parse_timestr(ad.phu['UTSTART'])
mean_epoch = sc_start + mean_offset
ad.phu['AVGEPOCH'] = ( # hope this keyword string is ok
mean_epoch.strftime("%H:%M:%S.%f")[:-3],
'Mean Exposure Epoch')
# 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 addIllumMaskToDQ(self,
adinputs=None,
suffix=None,
illum_mask=None,
shift=None,
max_shift=20):
"""
Adds an illumination mask to each AD object. This is only done for
full-frame (not Central Spectrum) GMOS spectra, and is calculated by
making a model illumination patter from the attached MDF and cross-
correlating it with the spatial profile of the data.
Parameters
----------
suffix : str
suffix to be added to output files
illum_mask : str/None
name of illumination mask mask (None -> use default)
shift : int/None
user-defined shift to apply to illumination mask
max_shift : int
maximum shift (in unbinned pixels) allowable for the cross-
correlation
"""
offset_dict = {
("GMOS-N", "Hamamatsu-N"): 1.5,
("GMOS-N", "e2vDD"): -0.2,
("GMOS-N", "EEV"): 0.7,
("GMOS-S", "Hamamatsu-S"): 5.5,
("GMOS-S", "EEV"): 3.8
}
edges = 50 # try to eliminate issues at the very edges
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
# Do this now for memory management reasons. We'll be creating large
# arrays temporarily and don't want the permanent mask arrays to
# fragment the free memory.
for ad in adinputs:
for ext in ad:
if ext.mask is None:
ext.mask = np.zeros_like(ext.data).astype(DQ.datatype)
for ad, illum in zip(
*gt.make_lists(adinputs, illum_mask, force_ad=True)):
if ad.phu.get(timestamp_key):
log.warning(
'No changes will be made to {}, since it has '
'already been processed by addIllumMaskToDQ'.format(
ad.filename))
continue
ybin = ad.detector_y_bin()
ad_detsec = ad.detector_section()
no_bridges = all(detsec.y1 > 1600 and detsec.y2 < 2900
for detsec in ad_detsec)
has_48rows = (all(detsec.y2 == 4224 for detsec in ad_detsec)
and 'Hamamatsu' in ad.detector_name(pretty=True))
if illum:
log.fullinfo("Using {} as illumination mask".format(
illum.filename))
final_illum = gt.clip_auxiliary_data(ad,
aux=illum,
aux_type='bpm',
return_dtype=DQ.datatype)
for ext, illum_ext in zip(ad, final_illum):
if illum_ext is not None:
# Ensure we're only adding the unilluminated bit
iext = np.where(illum_ext.data > 0, DQ.unilluminated,
0).astype(DQ.datatype)
ext.mask |= iext
elif not no_bridges: # i.e. there are bridges.
try:
mdf = ad.MDF
except AttributeError:
log.warning(f"MDF not found for {ad.filename} - cannot "
"add illumination mask.")
continue
# Default operation for GMOS full-frame LS
# Sadly, we cannot do this reliably without concatenating the
# arrays and using a big chunk of memory.
row_medians = np.percentile(np.concatenate(
[ext.data for ext in ad], axis=1),
95,
axis=1)
row_medians -= at.boxcar(row_medians, size=50 // ybin)
# Construct a model of the slit illumination from the MDF
# coefficients are from G-IRAF except c0, approx. from data
model = np.zeros_like(row_medians, dtype=int)
for ypos, ysize in mdf['slitpos_my', 'slitsize_my']:
y = ypos + np.array([-0.5, 0.5]) * ysize
c0 = offset_dict[ad.instrument(),
ad.detector_name(pretty=True)]
if ad.instrument() == "GMOS-S":
c1, c2, c3 = (0.99911, -1.7465e-5, 3.0494e-7)
else:
c1, c2, c3 = (0.99591859227, 5.3042211333437e-8,
1.7447902551997e-7)
yccd = ((c0 + y *
(c1 + y *
(c2 + y * c3))) * 1.611444 / ad.pixel_scale() +
0.5 * model.size).astype(int)
model[yccd[0]:yccd[1] + 1] = 1
log.stdinfo("Expected slit location from pixels "
f"{yccd[0]+1} to {yccd[1]+1}")
if shift is None:
max_shift = 50
mshift = max_shift // ybin + 2
mshift2 = mshift + edges
# model[] indexing avoids reduction in signal as slit
# is shifted off the top of the image
cntr = model.size - edges - mshift2 - 1
xcorr = correlate(row_medians[edges:-edges],
model[mshift2:-mshift2],
mode='full')[cntr - mshift:cntr + mshift]
# This line avoids numerical errors in the spline fit
xcorr -= np.median(xcorr)
# This calculates the offsets of each point from the
# straight line between its neighbours
std = (xcorr[1:-1] - 0.5 *
(xcorr + np.roll(xcorr, 2))[2:]).std()
xspline = fit_1D(xcorr,
function="spline3",
order=None,
weights=np.full(len(xcorr),
1. / std)).evaluate()
yshift = xspline.argmax() - mshift
maxima = xspline[1:-1][np.logical_and(
np.diff(xspline[:-1]) > 0,
np.diff(xspline[1:]) < 0)]
significant_maxima = (maxima >
xspline.max() - 3 * std).sum()
if significant_maxima > 1 or abs(
yshift // ybin) > max_shift:
log.warning(
f"{ad.filename}: cross-correlation peak is"
" untrustworthy so not adding illumination "
"mask. Please re-run with a specified shift.")
yshift = None
else:
yshift = shift
if yshift is not None:
log.stdinfo(
f"{ad.filename}: Shifting mask by {yshift} pixels")
row_mask = np.ones_like(model, dtype=int)
if yshift < 0:
row_mask[:yshift] = 1 - model[-yshift:]
elif yshift > 0:
row_mask[yshift:] = 1 - model[:-yshift]
else:
row_mask[:] = 1 - model
for ext in ad:
ext.mask |= (row_mask * DQ.unilluminated).astype(
DQ.datatype)[:, np.newaxis]
if has_48rows:
actual_rows = 48 // ybin
for ext in ad:
ext.mask[:actual_rows] |= DQ.unilluminated
# Timestamp and update filename
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=suffix, strip=True)
return adinputs
def pointing_in_field(pos, refpos, frac_FOV=1.0, frac_slit=1.0):
"""
See gemini_tools.pointing_in_field() for the API. This is an
instrument-specific back end that you shouldn't be calling directly.
No inputs are validated at this level; that's the responsibility of the
calling function, for reasons of efficiency.
The GNIRS FOV is determined by whether the calculated center point
(according to the center of mass of the illumination mask) of the
image falls within the illumination mask of the reference image.
:param pos: AstroData instance to be checked for whether it belongs
in the same sky grouping as refpos
:type pos: AstroData instance
:param refpos: This is the POFFSET and QOFFSET of the reference image
:type refpos: tuple of floats
:param frac_FOV: For use with spectroscopy data
:type frac_FOV: float
:param frac_slit: For use with spectroscopy data
:type frac_slit: float
"""
# Since this function gets looked up and evaluated, we have to do any
# essential imports in-line (but Python caches them)
import math
# Extract pointing info in terms of the x and y offsets
xshift = refpos[1] - pos.phu['QOFFSET']
yshift = refpos[0] - pos.phu['POFFSET']
ad = pos
# Imaging:
if 'IMAGE' in pos.tags:
illum = get_illum_mask_filename(ad)
if illum:
illum_ad = gt.clip_auxiliary_data(adinput=pos,
aux=astrodata.open(illum), aux_type="bpm")
illum_data = illum_ad[0].data
else:
raise IOError("Cannot find illumination mask for {}".
format(ad.filename))
# Finding the center of the illumination mask
center_illum = (illum_ad.phu['CENMASSX'], illum_ad.phu['CENMASSY'])
checkpos = (int(center_illum[0] + xshift),
int(center_illum[1] + yshift))
# If the position to check is going to fall outside the illumination
# mask, return straight away to avoid an error
if ((abs(xshift) >= abs(center_illum[0])) or
(abs(yshift) >= abs(center_illum[1]))):
return False
# Note that numpy data arrays are reversed in x and y
return illum_data[checkpos[1], checkpos[0]] == 0
# Spectroscopy:
elif 'SPECT' in ad.tags:
raise NotImplementedError("FOV lookup not yet supported for GNIRS "
"Spectroscopy")
# Some engineering observation or bad mask value etc.:
else:
raise ValueError("Can't determine FOV for unrecognized GNIRS config "
"({}, {})".format(ad.focal_plane_mask(), ad.disperser()))
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
def addIllumMaskToDQ(self, adinputs=None, suffix=None, illum_mask=None):
"""
Adds an illumination mask to each AD object
Parameters
----------
suffix: str
suffix to be added to output files
illum_mask: str/None
name of illumination mask mask (None -> use default)
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys[self.myself()]
for ad, illum in zip(
*gt.make_lists(adinputs, illum_mask, force_ad=True)):
if ad.phu.get(timestamp_key):
log.warning(
'No changes will be made to {}, since it has '
'already been processed by addIllumMaskToDQ'.format(
ad.filename))
continue
ad_detsec = ad.detector_section()
no_bridges = all(detsec.y1 > 1600 and detsec.y2 < 2900
for detsec in ad_detsec)
has_48rows = (all(detsec.y2 == 4224 for detsec in ad_detsec)
and 'Hamamatsu' in ad.detector_name(pretty=True))
if illum:
log.fullinfo("Using {} as illumination mask".format(
illum.filename))
final_illum = gt.clip_auxiliary_data(ad,
aux=illum,
aux_type='bpm',
return_dtype=DQ.datatype)
for ext, illum_ext in zip(ad, final_illum):
if illum_ext is not None:
# Ensure we're only adding the unilluminated bit
iext = np.where(illum_ext.data > 0, DQ.unilluminated,
0).astype(DQ.datatype)
ext.mask = iext if ext.mask is None else ext.mask | iext
elif not no_bridges: # i.e. there are bridges.
# Default operation for GMOS full-frame LS
# The 95% cut should ensure that we're sampling something
# bright (even for an arc)
# The max is intended to handle R150 data, where many of
# the extensions are unilluminated
row_medians = np.max(np.array(
[np.percentile(ext.data, 95, axis=1) for ext in ad]),
axis=0)
rows = np.arange(len(row_medians))
m_init = models.Polynomial1D(degree=3)
fit_it = fitting.FittingWithOutlierRemoval(
fitting.LinearLSQFitter(),
outlier_func=sigma_clip,
sigma_upper=1,
sigma_lower=3)
m_final, _ = fit_it(m_init, rows, row_medians)
model_fit = m_final(rows)
# Find points which are significantly below the smooth illumination fit
# First ensure we don't worry about single rows
row_mask = at.boxcar(model_fit - row_medians > 0.1 * model_fit,
operation=np.logical_and,
size=1)
row_mask = at.boxcar(row_mask, operation=np.logical_or, size=3)
for ext in ad:
ext.mask |= (row_mask * DQ.unilluminated).astype(
DQ.datatype)[:, np.newaxis]
if has_48rows:
actual_rows = 48 // ad.detector_y_bin()
for ext in ad:
ext.mask[:actual_rows] |= DQ.unilluminated
# Timestamp and update filename
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=suffix, strip=True)
return adinputs
def addDQ(self, adinputs=None, **params):
"""
This primitive is used to add a DQ extension to the input AstroData
object. The value of a pixel in the DQ extension will be the sum of the
following: (0=good, 1=bad pixel (found in bad pixel mask), 2=pixel is
in the non-linear regime, 4=pixel is saturated). This primitive will
trim the BPM to match the input AstroData object(s).
Parameters
----------
suffix: str
suffix to be added to output files
static_bpm: str
Name of bad pixel mask ("default" -> use default from look-up table)
If set to None, no static_bpm will be added.
user_bpm: str
Name of the bad pixel mask created by the user from flats and
darks. It is an optional BPM that can be added to the static one.
illum_mask: bool
add illumination mask?
"""
log = self.log
log.debug(gt.log_message("primitive", self.myself(), "starting"))
timestamp_key = self.timestamp_keys["addDQ"]
sfx = params["suffix"]
# Getting all the filenames first prevents reopening the same file
# for each science AD
static_bpm_list = params['static_bpm']
user_bpm_list = params['user_bpm']
if static_bpm_list == "default":
static_bpm_list = [self._get_bpm_filename(ad) for ad in adinputs]
for ad, static, user in zip(*gt.make_lists(adinputs, static_bpm_list,
user_bpm_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 addDQ'.format(ad.filename))
continue
if static is None:
# So it can be zipped with the AD
final_static = [None] * len(ad)
else:
log.fullinfo("Using {} as static BPM".format(static.filename))
final_static = gt.clip_auxiliary_data(ad, aux=static,
aux_type='bpm', return_dtype=DQ.datatype)
if user is None:
final_user = [None] * len(ad)
else:
log.fullinfo("Using {} as user BPM".format(user.filename))
final_user = gt.clip_auxiliary_data(ad, aux=user,
aux_type='bpm', return_dtype=DQ.datatype)
for ext, static_ext, user_ext in zip(ad, final_static, final_user):
extver = ext.hdr['EXTVER']
if ext.mask is not None:
log.warning('A mask already exists in extver {}'.
format(extver))
continue
non_linear_level = ext.non_linear_level()
saturation_level = ext.saturation_level()
# Need to create the array first for 3D raw F2 data, with 2D BPM
ext.mask = np.zeros_like(ext.data, dtype=DQ.datatype)
if static_ext is not None:
ext.mask |= static_ext.data
if user_ext is not None:
ext.mask |= user_ext.data
if saturation_level:
log.fullinfo('Flagging saturated pixels in {}:{} '
'above level {:.2f}'.
format(ad.filename, extver, saturation_level))
ext.mask |= np.where(ext.data >= saturation_level,
DQ.saturated, 0).astype(DQ.datatype)
if non_linear_level:
if saturation_level:
if saturation_level > non_linear_level:
log.fullinfo('Flagging non-linear pixels in {}:{} '
'above level {:.2f}'.
format(ad.filename, extver,
non_linear_level))
ext.mask |= np.where((ext.data >= non_linear_level) &
(ext.data < saturation_level),
DQ.non_linear, 0).astype(DQ.datatype)
# Readout modes of IR detectors can result in
# saturated pixels having values below the
# saturation level. Flag those. Assume we have an
# IR detector here because both non-linear and
# saturation levels are defined and nonlin<sat
regions, nregions = measurements.label(
ext.data < non_linear_level)
# In all my tests, region 1 has been the majority
# of the image; however, I cannot guarantee that
# this is always the case and therefore we should
# check the size of each region
region_sizes = measurements.labeled_comprehension(
ext.data, regions, np.arange(1, nregions+1),
len, int, 0)
# First, assume all regions are saturated, and
# remove any very large ones. This is much faster
# than progressively adding each region to DQ
hidden_saturation_array = np.where(regions > 0,
4, 0).astype(DQ.datatype)
for region in range(1, nregions+1):
# Limit of 10000 pixels for a hole is a bit arbitrary
if region_sizes[region-1] > 10000:
hidden_saturation_array[regions==region] = 0
ext.mask |= hidden_saturation_array
elif saturation_level < non_linear_level:
log.warning('{}:{} has saturation level less than '
'non-linear level'.format(ad.filename, extver))
else:
log.fullinfo('Saturation and non-linear levels '
'are the same for {}:{}. Only '
'flagging saturated pixels'.
format(ad.filename, extver))
else:
log.fullinfo('Flagging non-linear pixels in {}:{} '
'above level {:.2f}'.
format(ad.filename, extver,
non_linear_level))
ext.mask |= np.where(ext.data >= non_linear_level,
DQ.non_linear, 0).astype(DQ.datatype)
# Handle latency if reqested
if params.get("latency", False):
try:
adinputs = self.addLatencyToDQ(adinputs, time=params["time"],
non_linear=params["non_linear"])
except AttributeError:
log.warning("addLatencyToDQ() not defined in primitivesClass "
+ self.__class__.__name__)
# Add the illumination mask if requested
if params['add_illum_mask']:
adinputs = self.addIllumMaskToDQ(adinputs, illum_mask=params["illum_mask"])
# Timestamp and update filenames
for ad in adinputs:
gt.mark_history(ad, primname=self.myself(), keyword=timestamp_key)
ad.update_filename(suffix=sfx, strip=True)
return adinputs
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 scaleFringeToScience(self, rc):
"""
This primitive will scale the fringes to their matching science data
The fringes should be in the stream this primitive is called on,
and the reference science frames should be loaded into the RC,
as, eg. rc["science"] = adinput.
There are two ways to find the value to scale fringes by:
1. If stats_scale is set to True, the equation:
(letting science data = b (or B), and fringe = a (or A))
arrayB = where({where[SCIb < (SCIb.median+2.5*SCIb.std)]}
> [SCIb.median-3*SCIb.std])
scale = arrayB.std / SCIa.std
The section of the SCI arrays to use for calculating these statistics
is the CCD2 SCI data excluding the outer 5% pixels on all 4 sides.
Future enhancement: allow user to choose section
2. If stats_scale=False, then scale will be calculated using:
exposure time of science / exposure time of fringe
:param stats_scale: Use statistics to calculate the scale values,
rather than exposure time
:type stats_scale: Python boolean (True/False)
"""
# Instantiate the log
log = gemLog.getGeminiLog(logType=rc["logType"],
logLevel=rc["logLevel"])
# Log the standard "starting primitive" debug message
log.debug(gt.log_message("primitive", "scaleFringeToScience",
"starting"))
# Define the keyword to be used for the time stamp for this primitive
timestamp_key = self.timestamp_keys["scaleFringeToScience"]
# Check for user-supplied science frames
fringe = rc.get_inputs_as_astrodata()
science_param = rc["science"]
fringe_dict = None
if science_param is not None:
# The user supplied an input to the science parameter
if not isinstance(science_param, list):
science_list = [science_param]
else:
science_list = science_param
# If there is one fringe and multiple science frames,
# the fringe must be deepcopied to allow it to be
# scaled separately for each frame
if len(fringe)==1 and len(science_list)>1:
fringe = [deepcopy(fringe[0]) for img in science_list]
# Convert filenames to AD instances if necessary
tmp_list = []
for science in science_list:
if type(science) is not AstroData:
science = AstroData(science)
tmp_list.append(science)
science_list = tmp_list
fringe_dict = gt.make_dict(key_list=science_list,
value_list=fringe)
fringe_output = []
else:
log.warning("No science frames specified; no scaling will be done")
science_list = []
fringe_output = fringe
# Loop over each AstroData object in the science list
for ad in science_list:
# Retrieve the appropriate fringe
fringe = fringe_dict[ad]
# Check the inputs have matching filters, binning and SCI shapes.
try:
gt.check_inputs_match(ad1=ad, ad2=fringe)
except Errors.ToolboxError:
# If not, try to clip the fringe frame to the size of the
# science data
# For a GMOS example, this allows a full frame fringe to
# be used for a CCD2-only science frame.
fringe = gt.clip_auxiliary_data(
adinput=ad, aux=fringe, aux_type="cal")[0]
# Check again, but allow it to fail if they still don't match
gt.check_inputs_match(ad1=ad, ad2=fringe)
# Check whether statistics should be used
stats_scale = rc["stats_scale"]
# Calculate the scale value
scale = 1.0
if not stats_scale:
# Use the exposure times to calculate the scale
log.fullinfo("Using exposure times to calculate the scaling"+
" factor")
try:
scale = ad.exposure_time() / fringe.exposure_time()
except:
raise Errors.InputError("Could not get exposure times " +
"for %s, %s. Try stats_scale=True" %
(ad.filename,fringe.filename))
else:
# Use statistics to calculate the scaling factor
log.fullinfo("Using statistics to calculate the " +
"scaling factor")
# Deepcopy the input so it can be manipulated without
# affecting the original
statsad = deepcopy(ad)
statsfringe = deepcopy(fringe)
# Trim off any overscan region still present
statsad,statsfringe = gt.trim_to_data_section([statsad,
statsfringe])
# Check the number of science extensions; if more than
# one, use CCD2 data only
nsciext = statsad.count_exts("SCI")
if nsciext>1:
# Get the CCD numbers and ordering information
# corresponding to each extension
log.fullinfo("Trimming data to data section to remove "\
"overscan region")
sci_info,frng_info = gt.array_information([statsad,
statsfringe])
# Pull out CCD2 data
scidata = []
frngdata = []
dqdata = []
for i in range(nsciext):
# Get the next extension in physical order
sciext = statsad["SCI",sci_info["amps_order"][i]]
frngext = statsfringe["SCI",frng_info["amps_order"][i]]
# Check to see if it is on CCD2; if so, keep it
if sci_info[
"array_number"][("SCI",sciext.extver())]==2:
scidata.append(sciext.data)
dqext = statsad["DQ",sci_info["amps_order"][i]]
maskext = statsad["OBJMASK",
sci_info["amps_order"][i]]
if dqext is not None and maskext is not None:
dqdata.append(dqext.data | maskext.data)
elif dqext is not None:
dqdata.append(dqext.data)
elif maskext is not None:
dqdata.append(maskext.data)
if frng_info[
"array_number"][("SCI",frngext.extver())]==2:
frngdata.append(frngext.data)
# Stack data if necessary
if len(scidata)>1:
scidata = np.hstack(scidata)
frngdata = np.hstack(frngdata)
else:
scidata = scidata[0]
frngdata = frngdata[0]
if len(dqdata)>0:
if len(dqdata)>1:
dqdata = np.hstack(dqdata)
else:
dqdata = dqdata[0]
else:
dqdata = None
else:
scidata = statsad["SCI"].data
frngdata = statsfringe["SCI"].data
dqext = statsad["DQ"]
maskext = statsad["OBJMASK"]
if dqext is not None and maskext is not None:
dqdata = dqext.data | maskext.data
elif dqext is not None:
dqdata = dqext.data
elif maskext is not None:
dqdata = maskext.data
else:
dqdata = None
if dqdata is not None:
# Replace any DQ-flagged data with the median value
smed = np.median(scidata[dqdata==0])
scidata = np.where(dqdata!=0,smed,scidata)
# Calculate the maximum and minimum in a box centered on
# each data point. The local depth of the fringe is
# max - min. The overall fringe strength is the median
# of the local fringe depths.
# Width of the box is binning and
# filter dependent, determined by experimentation
# Results don't seem to depend heavily on the box size
if ad.filter_name(pretty=True).as_pytype=="i":
size = 20
else:
size = 40
size /= ad.detector_x_bin().as_pytype()
# Use ndimage maximum_filter and minimum_filter to
# get the local maxima and minima
import scipy.ndimage as ndimage
sci_max = ndimage.filters.maximum_filter(scidata,size)
sci_min = ndimage.filters.minimum_filter(scidata,size)
# Take off 5% of the width as a border
xborder = int(0.05 * scidata.shape[1])
yborder = int(0.05 * scidata.shape[0])
if xborder<20:
xborder = 20
if yborder<20:
yborder = 20
sci_max = sci_max[yborder:-yborder,xborder:-xborder]
sci_min = sci_min[yborder:-yborder,xborder:-xborder]
# Take the median difference
sci_df = np.median(sci_max - sci_min)
# Do the same for the fringe
frn_max = ndimage.filters.maximum_filter(frngdata,size)
frn_min = ndimage.filters.minimum_filter(frngdata,size)
frn_max = frn_max[yborder:-yborder,xborder:-xborder]
frn_min = frn_min[yborder:-yborder,xborder:-xborder]
frn_df = np.median(frn_max - frn_min)
# Scale factor
# This tends to overestimate the factor, but it is
# at least in the right ballpark, unlike the estimation
# used in girmfringe (masked_sci.std/fringe.std)
scale = sci_df / frn_df
log.fullinfo("Scale factor found = "+str(scale))
# Use mult from the arith toolbox to perform the scaling of
# the fringe frame
scaled_fringe = fringe.mult(scale)
# Add the appropriate time stamps to the PHU
gt.mark_history(adinput=scaled_fringe, keyword=timestamp_key)
# Change the filename
scaled_fringe.filename = gt.filename_updater(
adinput=ad, suffix=rc["suffix"], strip=True)
fringe_output.append(scaled_fringe)
# Report the list of output AstroData objects to the reduction context
rc.report_output(fringe_output)
yield rc
