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
