def test_stack(self):
ccd = MaskedCCD(self.image_file)
overscan = makeAmplifierGeometry(self.image_file)
for method in ['mean', 'row', 'func']:
corrected = []
for image in ccd.values():
corrected.append(
imutils.unbias_and_trim(image,
overscan.serial_overscan,
bias_method=method,
**self.kwargs).getImage())
stacked = imutils.stack(corrected)
imarr = stacked.getArray()
if method == 'mean':
self.assertTrue(max(np.abs(imarr.flat)) < 2)
else:
self.assertTrue(max(np.abs(imarr.flat)) < 1e-6)
def calibrated_stack(infiles,
outfile,
bias_frame=None,
dark_frame=None,
linearity_correction=None,
bitpix=32):
ccds = [
MaskedCCD(infile,
bias_frame=bias_frame,
dark_frame=dark_frame,
linearity_correction=linearity_correction)
for infile in infiles
]
all_amps = imutils.allAmps(infiles[0])
amp_images = {}
for amp in all_amps:
amp_ims = [ccd.bias_subtracted_image(amp) for ccd in ccds]
amp_images[amp] = imutils.stack(amp_ims).getImage()
imutils.writeFits(amp_images, outfile, infiles[0], bitpix=bitpix)
def run(self,
sensor_name,
infiles,
bias_frame=None,
dark_frame=None,
linearity_correction=None):
if not isinstance(infiles, list):
infiles = [infiles]
## Get sensor information from header
all_amps = imutils.allAmps(infiles[0])
with fits.open(infiles[0]) as hdulist:
lsst_num = hdulist[0].header['LSST_NUM']
teststand = hdulist[0].header['TSTAND']
manufacturer = lsst_num[:3]
## Interface with SQL database
database = self.config.database
logging.info(
"{0} Running CrosstalkColumnTask using database {1}".format(
datetime.now(), database))
with db_session(database) as session:
## Get sensor from database
try:
sensor = Sensor.from_db(session, sensor_name=sensor_name)
except NoResultFound:
sensor = Sensor(sensor_name=sensor_name,
lsst_num=lsst_num,
manufacturer=manufacturer,
namps=len(all_amps))
sensor.segments = {
i: Segment(segment_name=AMP2SEG[i], amplifier_number=i)
for i in all_amps
}
sensor.add_to_db(session)
session.commit()
logging.info("{0} New sensor {1} added to database".format(
datetime.now(), sensor_name))
## Set configuration and analysis settings
ly = self.config.length_y
lx = self.config.length_x
threshold = self.config.threshold
if len(infiles) > 1:
is_coadd = True
else:
is_coadd = False
ccds = [
MaskedCCD(infile,
bias_frame=bias_frame,
dark_frame=dark_frame,
linearity_correction=linearity_correction)
for infile in infiles
]
logging.info("{0} ".format(datetime.now()) + \
"Processing files: {}".format(' '.join(map(str, infiles))))
## Aggressor amplifiers
for i in all_amps:
## Find aggressor regions
exptime = 1
gain = 1
bp = BrightPixels(ccds[0], i, exptime, gain, ethresh=threshold)
pixels, columns = bp.find()
if len(columns) == 0:
continue
col = columns[0]
read_noise = calculate_read_noise(ccds[0], i) * np.sqrt(
2. / len(ccds))
aggressor_images = [
ccd.unbiased_and_trimmed_image(i).getImage()
for ccd in ccds
]
aggressor_imarr = imutils.stack(aggressor_images).getArray()
signal = np.mean(aggressor_imarr[:, col])
mask = rectangular_mask(aggressor_imarr,
1000,
col,
ly=ly,
lx=lx)
## Victim amplifiers
for j in all_amps:
victim_images = [
ccd.unbiased_and_trimmed_image(j).getImage()
for ccd in ccds
]
victim_imarr = imutils.stack(victim_images).getArray()
## Add crosstalk result to database
res = crosstalk_fit(aggressor_imarr,
victim_imarr,
mask,
noise=read_noise)
result = Result(aggressor_id=sensor.segments[i].id,
victim_id=sensor.segments[j].id,
aggressor_signal=signal,
coefficient=res[0],
error=res[4],
methodology='MODEL_LSQ',
image_type='brightcolumn',
teststand=teststand,
analysis='CrosstalkColumnTask',
is_coadd=is_coadd,
z_offset=res[1],
y_tilt=res[2],
x_tilt=res[3])
result.add_to_db(session)
logging.info(
"{0} Injested C({1},{2}) for signal {3:.1f}".format(
datetime.now(), i, j, signal))
logging.info("{0} Task completed successfully.".format(
datetime.now()))
def run(self,
sensor_name,
infiles,
bias_frame=None,
dark_frame=None,
linearity_correction=None):
if not isinstance(infiles, list):
infiles = [infiles]
## Get sensor information from header
all_amps = imutils.allAmps(infiles[0])
with fits.open(infiles[0]) as hdulist:
lsst_num = hdulist[0].header['LSST_NUM']
teststand = hdulist[0].header['TSTAND']
manufacturer = lsst_num[:3]
## Interface with SQL database
database = self.config.database
logging.info(
"{0} Running CrosstalkSatelliteTask using database {1}".format(
datetime.now(), database))
with db_session(database) as session:
## Get sensor from database
try:
sensor = Sensor.from_db(session, sensor_name=sensor_name)
except NoResultFound:
sensor = Sensor(sensor_name=sensor_name,
lsst_num=lsst_num,
manufacturer=manufacturer,
namps=len(all_amps))
sensor.segments = {
i: Segment(segment_name=AMP2SEG[i], amplifier_number=i)
for i in all_amps
}
sensor.add_to_db(session)
session.commit()
logging.info("{0} New sensor {1} added to database".format(
datetime.now(), sensor_name))
## Set configuration and analysis settings
width = self.config.width
restrict_to_side = self.config.restrict_to_side
canny_sigma = self.config.canny_sigma
low_threshold = self.config.low_threshold
high_threshold = self.config.high_threshold
if len(infiles) > 1:
is_coadd = True
else:
is_coadd = False
ccds = [
MaskedCCD(infile,
bias_frame=bias_frame,
dark_frame=dark_frame,
linearity_correction=linearity_correction)
for infile in infiles
]
logging.info("{0} ".format(datetime.now()) + \
"Processing files: {}".format(' '.join(map(str, infiles))))
## Aggressor amplifiers
for i in all_amps:
aggressor_images = [
ccd.unbiased_and_trimmed_image(i).getImage()
for ccd in ccds
]
aggressor_imarr = imutils.stack(aggressor_images).getArray()
## Find aggressor regions
tested_angles = np.linspace(-np.pi / 2, np.pi / 2, 1000)
edges = feature.canny(aggressor_imarr,
sigma=canny_sigma,
low_threshold=low_threshold,
high_threshold=high_threshold)
h, theta, d = hough_line(edges, theta=tested_angles)
_, angle, dist = hough_line_peaks(h, theta, d)
if len(angle) != 2:
continue
read_noise = calculate_read_noise(ccds[0], i) * np.sqrt(
2. / len(ccds))
mean_angle = np.mean(angle)
mean_dist = np.mean(dist)
mask = satellite_mask(aggressor_imarr,
mean_angle,
mean_dist,
width=width)
signal = np.max(aggressor_imarr[~mask])
## Victim amplifiers
if restrict_to_side:
if i < 9:
vic_amps = range(1, 9)
else:
vic_amps = range(9, 17)
else:
vic_amps = all_amps
for j in vic_amps:
victim_images = [
ccd.unbiased_and_trimmed_image(j).getImage()
for ccd in ccds
]
victim_imarr = imutils.stack(victim_images).getArray()
res = crosstalk_fit(aggressor_imarr,
victim_imarr,
mask,
noise=read_noise)
## Add result to database
result = Result(aggressor_id=sensor.segments[i].id,
victim_id=sensor.segments[j].id,
aggressor_signal=signal,
coefficient=res[0],
error=res[4],
methodology='MODEL_LSQ',
teststand=teststand,
image_type='satellite',
analysis='CrosstalkSatelliteTask',
is_coadd=is_coadd,
z_offset=res[1],
y_tilt=res[2],
x_tilt=res[3])
result.add_to_db(session)
logging.info(
"{0} Injested C({1},{2}) for signal {3:.1f}".format(
datetime.now(), i, j, signal))
logging.info("{0} Task completed successfully.".format(
datetime.now()))
def stack_images(butler, in_files, statistic=afwMath.MEDIAN, **kwargs):
"""Stack a set of images
Parameters
----------
butler : `Butler` or `None`
Data Butler (or none)
in_files : `list`
Data to stack, either data_ids or filenames
statistic : `int`
Statisitic used to make superbias image
Keywords
--------
bias_type : `str`
Unbiasing method to use
superbias_frame : `MaskedCCD` or `None`
Bias image to subtract
log : `log`
Logging stream
Returns
-------
out_dict : `dict`
Mapping amplifier index to stacked image
"""
bias_type = kwargs.get('bias_type', 'spline')
bias_type_col = kwargs.get('bias_type_col', None)
superbias_frame = kwargs.get('superbias_frame', None)
log = kwargs.get('log', None)
gains = kwargs.get('gains', None)
nlc = kwargs.get('nlc', None)
stat_ctrl = kwargs.get('stat_ctrl', None)
if stat_ctrl is None:
stat_ctrl = afwMath.StatisticsControl()
amp_stack_dict = {}
out_dict = {}
exp_time = 0.0
used_files = 0
for ifile, in_file in enumerate(in_files):
if ifile % 10 == 0:
if log is not None:
log.info(" %i" % ifile)
try:
ccd = get_ccd_from_id(butler, in_file, mask_files=[])
except Exception:
if log is not None:
log.warn(" Failed to read %s, skipping" % (str(in_file)))
continue
used_files += 1
exp_time += get_exposure_time(ccd)
amps = get_amp_list(ccd)
offset = get_amp_offset(ccd, superbias_frame)
for iamp, amp in enumerate(amps):
regions = get_geom_regions(ccd, amp)
serial_oscan = regions['serial_overscan']
parallel_oscan = regions['parallel_overscan']
img = get_raw_image(ccd, amp)
superbias_im = raw_amp_image(superbias_frame, amp + offset)
unbiased = unbias_amp(img,
serial_oscan,
bias_type=bias_type,
superbias_im=superbias_im,
bias_type_col=bias_type_col,
parallel_oscan=parallel_oscan)
if gains is not None:
unbiased.image.array *= gains[iamp]
if nlc is not None:
unbiased.image.array = nlc(unbiased.image.array)
if ifile == 0:
amp_stack_dict[amp] = [unbiased]
else:
amp_stack_dict[amp].append(unbiased)
if used_files:
exp_time /= float(used_files)
else:
return None
out_dict['METADATA'] = dict(EXPTIME=exp_time)
for key, val in amp_stack_dict.items():
if butler is None:
outkey = key
else:
outkey = key + 1
stackimage = imutil.stack(val, statistic, stat_ctrl=stat_ctrl)
out_dict[outkey] = stackimage.image
if log is not None:
log.info("Done!")
return out_dict