def display(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", "display", "starting"))
# Get parameters from RC
threshold = rc["threshold"]
remove_bias = rc["remove_bias"]
# Get inputs
adinput = rc.get_inputs_as_astrodata()
orig_input = adinput
deepcopied = False
# Threshold and bias parameters only make sense for SCI extension;
# turn it off for others
extname = rc["extname"]
if extname!="SCI":
threshold = None
remove_bias = False
elif threshold=="None":
threshold = None
elif threshold=="auto":
dqext = np.array([ad["DQ"] for ad in adinput])
mosaic = np.array([((ad.phu_get_key_value(
self.timestamp_keys["mosaicDetectors"])
is not None)
or
(ad.phu_get_key_value(
self.timestamp_keys["tileArrays"])
is not None))
for ad in adinput])
if not np.all(dqext):
if not np.any(mosaic):
# This is the first possible modification to the data;
# always deepcopy before proceeding
adinput = [deepcopy(ad) for ad in orig_input]
deepcopied = True
adinput = sdz.add_dq(adinput, bpm=None,
copy_input=False, index=rc["index"])
if not isinstance(adinput,list):
adinput = [adinput]
else:
log.warning("Cannot add DQ plane to mosaicked data; " \
"no threshold mask will be displayed")
threshold=None
# Check whether approximate bias level should be removed
if remove_bias:
# Copy the original input if necessary, before
# modifying it
if not deepcopied:
adinput = [deepcopy(ad) for ad in orig_input]
deepcopied = True
new_adinput = []
for ad in adinput:
# Check whether data has been bias- or dark-subtracted
biasim = ad.phu_get_key_value("BIASIM")
darkim = ad.phu_get_key_value("DARKIM")
# Check whether data has been overscan-subtracted
overscan = np.array([ext.get_key_value("OVERSCAN")
for ext in ad["SCI"]])
if np.any(overscan) or biasim or darkim:
log.fullinfo("Bias level has already been removed "
"from data; no approximate correction "
"will be performed")
else:
# Get the bias level
bias_level = gdc.get_bias_level(adinput=ad)
if bias_level is not None:
# Subtract the bias level from each science extension
log.stdinfo("Subtracting approximate bias level "
"from %s for display" % ad.filename)
log.fullinfo("Bias levels used: %s" % str(bias_level))
ad = ad.sub(bias_level)
else:
log.warning("Bias level not found for %s; "
"approximate bias will not be removed" %
ad.filename)
new_adinput.append(ad)
adinput = new_adinput
# Check whether data needs to be tiled before displaying
# Otherwise, flatten all desired extensions into a single list
tile = rc["tile"]
if tile:
next = np.array([ad.count_exts(extname) for ad in adinput])
if np.any(next>1):
log.fullinfo("Tiling extensions together before displaying")
if not deepcopied:
adinput = [deepcopy(ad) for ad in orig_input]
deepcopied = True
adinput = gm.tile_arrays(adinput, tile_all=True,
copy_input=False, index=rc["index"])
if not isinstance(adinput,list):
adinput = [adinput]
else:
extinput = []
for ad in adinput:
exts = ad[extname]
if exts is None:
continue
for ext in exts:
if extname=="SCI" and threshold=="auto":
dqext = ad["DQ",ext.extver()]
if dqext is not None:
ext.append(dqext)
extinput.append(ext)
adinput = extinput
# Get overlays from RC if available (eg. IQ overlays made by measureIQ)
# then clear them out so they don't persist to the next display call
overlay_dict = gt.make_dict(key_list=adinput, value_list=rc["overlay"])
rc["overlay"] = None
# Set the starting frame
frame = rc["frame"]
if frame is None:
frame = 1
# Initialize the local version of numdisplay
# (overrides the display function to allow for quick overlays)
lnd = _localNumDisplay()
# Loop over each input AstroData object in the input list
if len(adinput)<1:
log.warning("No extensions to display with extname %s" % extname)
for ad in adinput:
if frame>16:
log.warning("Too many images; only the first 16 are displayed.")
break
# Check for more than one extension
ndispext = ad.count_exts(extname)
if ndispext==0:
log.warning("No extensions to display in "\
"%s with extname %s" %
(ad.filename,extname))
continue
elif ndispext>1:
raise Errors.InputError("Found %i extensions for "\
"%s[%s]; exactly 1 is required" %
(ndispext,ad.filename,extname))
dispext = ad[extname]
# Squeeze the data to get rid of any empty dimensions
# (eg. in raw F2 data)
data = np.squeeze(dispext.data)
# Check for 1-D data (ie. extracted spectra)
if len(data.shape)==1:
# Use splot to display instead of numdisplay
log.fullinfo("Calling IRAF task splot to display data")
splot_task = eti.sploteti.SplotETI(rc,ad)
splot_task.run()
continue
# Make threshold mask if desired
masks = []
mask_colors = []
if threshold is not None:
if threshold!="auto":
# Make threshold mask from user supplied value;
# Assume units match units of data
threshold = float(threshold)
satmask = np.where(data>threshold)
else:
# Make the mask from the nonlinear and
# saturation bits in the DQ plane
dqext = ad["DQ",dispext.extver()]
if dqext is None:
log.warning("No DQ plane found; cannot make threshold "\
"mask")
satmask = None
else:
dqdata = np.squeeze(dqext.data)
satmask = np.where(np.logical_or(dqdata & 2,
dqdata & 4))
if satmask is not None:
masks.append(satmask)
mask_colors.append(204)
overlay = overlay_dict[ad]
if overlay is not None:
masks.append(overlay)
mask_colors.append(206)
# ds9 color codes: should make this into a dictionary
# and allow user to specify
#
# red = 204
# green = 205
# blue = 206
# yellow = 207
# light blue = 208
# magenta = 209
# orange = 210
# dark pink = 211
# bright orange = 212
# light yellow = 213
# pink = 214
# blue-green = 215
# pink = 216
# peach = 217
# Define the display name
if tile and extname=="SCI":
name = ad.filename
elif tile:
# numdisplay/ds9 doesn't seem to like square brackets
# or spaces in the name, so use parentheses for extension
name = "%s(%s)" % (ad.filename,extname)
else:
name = "%s(%s,%d)" % (ad.filename,extname,dispext.extver())
# Display the data
try:
lnd.display(data,name=name,
frame=frame,zscale=rc["zscale"],quiet=True,
masks=masks, mask_colors=mask_colors)
except IOError:
log.warning("DS9 not found; cannot display input")
frame+=1
# Print some statistics for flats
if "GMOS_IMAGE_FLAT" in ad.types and extname=="SCI":
scidata = ad["SCI"].data
dqext = ad["DQ"]
if dqext is not None:
dqdata = dqext.data
good_data = scidata[dqdata==0]
else:
good_data = scidata
log.stdinfo("Twilight flat counts for %s:" % ad.filename)
log.stdinfo(" Mean value: %.0f" % np.mean(good_data, dtype=np.float64))
log.stdinfo(" Median value: %.0f" % np.median(good_data))
rc.report_output(orig_input)
yield rc
def attachWavelengthSolution(self,rc):
# Instantiate the log
log = logutils.get_logger(__name__)
# Define the keyword to be used for the time stamp
timestamp_key = self.timestamp_keys["attachWavelengthSolution"]
# Log the standard "starting primitive" debug message
log.debug(gt.log_message("primitive", "attachWavelengthSolution",
"starting"))
# Initialize the list of output AstroData objects
adoutput_list = []
# Check for a user-supplied arc
adinput = rc.get_inputs_as_astrodata()
arc_param = rc["arc"]
arc_dict = None
if arc_param is not None:
# The user supplied an input to the arc parameter
if not isinstance(arc_param, list):
arc_list = [arc_param]
else:
arc_list = arc_param
# Convert filenames to AD instances if necessary
tmp_list = []
for arc in arc_list:
if type(arc) is not AstroData:
arc = AstroData(arc)
tmp_list.append(arc)
arc_list = tmp_list
arc_dict = gt.make_dict(key_list=adinput, value_list=arc_list)
for ad in adinput:
if arc_dict is not None:
arc = arc_dict[ad]
else:
arc = rc.get_cal(ad, "processed_arc")
# Take care of the case where there was no arc
if arc is None:
log.warning("Could not find an appropriate arc for %s" \
% (ad.filename))
adoutput_list.append(ad)
continue
else:
arc = AstroData(arc)
wavecal = arc["WAVECAL"]
if wavecal is not None:
# Remove old versions
if ad["WAVECAL"] is not None:
for wc in ad["WAVECAL"]:
ad.remove((wc.extname(),wc.extver()))
# Append new solution
ad.append(wavecal)
# 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)
adoutput_list.append(ad)
else:
log.warning("No wavelength solution found for %s" % ad.filename)
adoutput_list.append(ad)
# Report the list of output AstroData objects to the reduction
# context
rc.report_output(adoutput_list)
yield rc
def subtractDark(self, rc):
"""
This primitive will subtract each SCI extension of the inputs by those
of the corresponding dark. If the inputs contain VAR or DQ frames,
those will also be updated accordingly due to the subtraction on the
data.
"""
# Instantiate the log
log = logutils.get_logger(__name__)
# Log the standard "starting primitive" debug message
log.debug(gt.log_message("primitive", "subtractDark", "starting"))
# Define the keyword to be used for the time stamp for this primitive
timestamp_key = self.timestamp_keys["subtractDark"]
# Initialize the list of output AstroData objects
adoutput_list = []
# Check for a user-supplied dark
adinput = rc.get_inputs_as_astrodata()
dark_param = rc["dark"]
dark_dict = None
if dark_param is not None:
# The user supplied an input to the dark parameter
if not isinstance(dark_param, list):
dark_list = [dark_param]
else:
dark_list = dark_param
# Convert filenames to AD instances if necessary
tmp_list = []
for dark in dark_list:
if type(dark) is not AstroData:
dark = AstroData(dark)
tmp_list.append(dark)
dark_list = tmp_list
dark_dict = gt.make_dict(key_list=adinput, value_list=dark_list)
# Loop over each input AstroData object in the input list
for ad in adinput:
# Check whether the subtractDark 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 subtractDark" \
% (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 dark
if dark_dict is not None:
dark = dark_dict[ad]
else:
dark = rc.get_cal(ad, "processed_dark")
# If there is no appropriate dark, there is no need to
# subtract the dark
if dark is None:
log.warning("No changes will be made to %s, since no " \
"appropriate dark 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:
dark = AstroData(dark)
# Subtract the dark from the input AstroData object
log.fullinfo("Subtracting the dark (%s) from the input " \
"AstroData object %s" \
% (dark.filename, ad.filename))
ad = ad.sub(dark)
# Record the dark file used
ad.phu_set_key_value("DARKIM",
os.path.basename(dark.filename),
comment=self.keyword_comments["DARKIM"])
# 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 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 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 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
def attachWavelengthSolution(self, rc):
# Instantiate the log
log = logutils.get_logger(__name__)
# Define the keyword to be used for the time stamp
timestamp_key = self.timestamp_keys["attachWavelengthSolution"]
# Log the standard "starting primitive" debug message
log.debug(
gt.log_message("primitive", "attachWavelengthSolution",
"starting"))
# Initialize the list of output AstroData objects
adoutput_list = []
# Check for a user-supplied arc
adinput = rc.get_inputs_as_astrodata()
arc_param = rc["arc"]
arc_dict = None
if arc_param is not None:
# The user supplied an input to the arc parameter
if not isinstance(arc_param, list):
arc_list = [arc_param]
else:
arc_list = arc_param
# Convert filenames to AD instances if necessary
tmp_list = []
for arc in arc_list:
if type(arc) is not AstroData:
arc = AstroData(arc)
tmp_list.append(arc)
arc_list = tmp_list
arc_dict = gt.make_dict(key_list=adinput, value_list=arc_list)
for ad in adinput:
if arc_dict is not None:
arc = arc_dict[ad]
else:
arc = rc.get_cal(ad, "processed_arc")
# Take care of the case where there was no arc
if arc is None:
log.warning("Could not find an appropriate arc for %s" \
% (ad.filename))
adoutput_list.append(ad)
continue
else:
arc = AstroData(arc)
wavecal = arc["WAVECAL"]
if wavecal is not None:
# Remove old versions
if ad["WAVECAL"] is not None:
for wc in ad["WAVECAL"]:
ad.remove((wc.extname(), wc.extver()))
# Append new solution
ad.append(wavecal)
# Add the appropriate time stamps to the PHU
gt.mark_history(adinput=ad,
primname=self.myself(),
keyword=timestamp_key)
# Change the filename
ad.filename = gt.filename_updater(adinput=ad,
suffix=rc["suffix"],
strip=True)
adoutput_list.append(ad)
else:
log.warning("No wavelength solution found for %s" %
ad.filename)
adoutput_list.append(ad)
# Report the list of output AstroData objects to the reduction
# context
rc.report_output(adoutput_list)
yield rc
