def main(raft_id, database, main_dir, calib_dir, output_dir='./'):
logfile = database.replace('.db', '.log')
logging.basicConfig(filename=logfile, level=logging.INFO)
sensor_list = ['S00', 'S01', 'S02',
'S10', 'S11', 'S12',
'S20', 'S21', 'S22']
sensor_ids = ['{0}_{1}'.format(raft_id, sensor_id) for sensor_id in sensor_list]
## Get bias frame
bias_frames = {sensor_id : os.path.join(calib_dir,
'{0}_superbias.fits'.format(sensor_id)) for sensor_id in sensor_ids}
## Get projector positions and exptimes
camera = camMapper._makeCamera()
lct = LsstCameraTransforms(camera)
subdir_list = [x.path for x in os.scandir(main_dir) if isdir(x.path)]
for subdir in sorted(subdir_list):
base = basename(subdir)
if "xtalk" not in base: continue
xpos, ypos, exptime = base.split('_')[-4:-1]
central_sensor, ccdX, ccdY = lct.focalMmToCcdPixel(float(ypos), float(xpos))
infiles = glob.glob(join(subdir, '*{0}.fits'.format(central_sensor)))
## Run crosstalk task
for infile in infiles:
crosstalk_task = CrosstalkSpotTask()
crosstalk_task.config.database = database
crosstalk_task.run(central_sensor, infile, bias_frame=bias_frames[central_sensor])
def __init__(self, ccdid, led, path_to_data, gainfile, biasfile=None, is_PTC=False):
self.ccdid = ccdid
self.gains = u.gains(gainfile, is_PTC=is_PTC)
self.biasfile= biasfile
self.led = led
self.path_to_data = path_to_data
self.data = {}
camera = camMapper._makeCamera()
self.lct = LsstCameraTransforms(camera)
def main(raft_id, main_dir, calib_dir, output_dir='./'):
sensor_list = [
'S00', 'S01', 'S02', 'S10', 'S11', 'S12', 'S20', 'S21', 'S22'
]
sensor_ids = [
'{0}_{1}'.format(raft_id, sensor_id) for sensor_id in sensor_list
]
for sensor_id in sensor_ids:
print("Sensor: {0}".format(sensor_id))
## Get calibration products
bias_frame = join(calib_dir, '{0}_superbias.fits'.format(sensor_id))
dark_frame = join(calib_dir, '{0}_superdark.fits'.format(sensor_id))
## Get projector positions and exptimes
position_set = set()
exptime_set = set()
camera = camMapper._makeCamera()
lct = LsstCameraTransforms(camera)
subdir_list = [x.path for x in os.scandir(main_dir) if isdir(x.path)]
for subdir in subdir_list:
base = basename(subdir)
if "xtalk" not in base: continue
xpos, ypos, exptime = base.split('_')[-4:-1]
central_ccd, ccdX, ccdY = lct.focalMmToCcdPixel(
float(ypos), float(xpos))
if central_ccd == sensor_id:
position_set.add((xpos, ypos))
exptime_set.add(exptime)
## For each exptime construct calibrated crosstalk image
for exptime in exptime_set:
for i, pos in enumerate(position_set):
xpos, ypos = pos
infiles = glob.glob(
join(main_dir, '*{0}_{1}_{2}*'.format(xpos, ypos, exptime),
'*{0}.fits'.format(sensor_id)))
pos_output_dir = join(
output_dir,
'xtalk_{0}_{1}_{2}_calibrated'.format(xpos, ypos, exptime))
os.mkdir(pos_output_dir) # add check if already existing
base = basename(infiles[0])
outfile = join(
pos_output_dir,
base.replace(sensor_id,
'{0}_calibrated'.format(sensor_id)))
calibrated_stack(infiles,
outfile,
bias_frame=bias_frame,
dark_frame=dark_frame)
def main(sensor_id, main_dir, calib_dir, output_dir='./'):
gain_results = pickle.load(open(join(calib_dir, 'et_results.pkl'), 'rb'))
gains = gain_results.get_amp_gains(sensor_id)
## Get projector positions and exptimes
position_set = set()
exptime_set = set()
camera = camMapper._makeCamera()
lct = LsstCameraTransforms(camera)
subdir_list = [x.path for x in os.scandir(main_dir) if isdir(x.path)]
for subdir in subdir_list:
base = basename(subdir)
if "xtalk" not in base: continue
xpos, ypos, exptime = base.split('_')[-4:-1]
central_sensor, ccdX, ccdY = lct.focalMmToCcdPixel(
float(ypos), float(xpos))
if central_sensor == sensor_id:
position_set.add((xpos, ypos))
exptime_set.add(exptime)
## For each exptime calculate crosstalk
for exptime in exptime_set:
outfile = join(
output_dir,
'{0}_{0}_{1}_crosstalk_matrix.fits'.format(sensor_id, exptime))
## Get calibrated crosstalk infiles
infiles = []
for i, pos in enumerate(position_set):
xpos, ypos = pos
infile = glob.glob(
join(main_dir, '*{0}_{1}_{2}*'.format(xpos, ypos, exptime),
'*{0}_calibrated.fits'.format(sensor_id)))[0]
infiles.append(infile)
## Run crosstalk task
crosstalk_task = CrosstalkTask()
crosstalk_task.config.outfile = outfile
crosstalk_task.run(sensor_id, infiles, gains)
# Offsets
xoffset = 5.0 # mm
yoffset = -7.0 # mm
# LED setup: current and exposure times
led = ['uv', 'blue', 'red', 'nm750', 'nm850', 'nm960']
current = [0.1, 0.006, 0.009, 0.01, 0.01, 0.01]
exp_time_orig = [0.02, 0.05, 0.05, 0.09, 0.06, 0.12]
exp_time_corr_factor = [1.33, 1.69, 1.46, 1.4, 1.5, 2.7]
exp_time = np.array(exp_time_orig) * np.array(exp_time_corr_factor)
led_config = list(zip(led, current, np.around(exp_time, 2)))
print(led_config)
# Full focal plane pointings.
# 5 pointings/CCD. One in the center and one in the center of each quadrant
camera = camMapper._makeCamera()
lct = LsstCameraTransforms(camera)
raft_list = [
'R01', 'R02', 'R03', 'R10', 'R11', 'R12', 'R13', 'R14', 'R20', 'R21',
'R22', 'R23', 'R24', 'R30', 'R31', 'R32', 'R33', 'R34', 'R41', 'R42', 'R43'
]
sensor_list = ['S00', 'S01', 'S02', 'S10', 'S11', 'S12', 'S20', 'S21', 'S22']
fp_pos_e2v = [(1024, 3003), (3072, 3003), (2048, 2002), (1024, 1001),
(3072, 1001)]
fp_pos_itl = [(1018, 3000), (3054, 3000), (2036, 2000), (1018, 1000),
(3054, 1000)]
xall = np.zeros((len(raft_list), len(sensor_list) * len(fp_pos_e2v)))
yall = np.zeros((len(raft_list), len(sensor_list) * len(fp_pos_e2v)))
for i, raft in enumerate(raft_list):
def __init__(self):
"""Initialize the LSST full-array mode (FAM) camera class."""
super(LsstFamCam, self).__init__(LsstCamMapper().camera)
self._initDetectors(DetectorType.SCIENCE)
def __init__(self):
"""Initialize the LSST camera class."""
super(LsstCam, self).__init__(LsstCamMapper().camera)
self._initDetectors(DetectorType.WAVEFRONT)
def _setupLSSTCamera(self):
"""If we want to include the camera chip gaps, etc"""
mapper = LsstCamMapper()
self.camera = mapper.camera
self.epoch = 2000.0
def main(configfile):
config = yaml.load(open(configfile, 'rb'), Loader=yaml.FullLoader)
print(config)
plt.ioff() # turn off interactive mode to avoid problem with X forwarding
path_to_beam = config['path_to_beam_model']
model_ccdid = config['model_ccdid']
model_ref_amp = config['model_ref_amp']
path_to_bias = config['path_to_bias']
led_beam = config['led_beam_model']
led = config['led_analysis']
raft_list = config['raft_list']
sensor_list = config['sensor_list']
outdir_figs = config['outdir_figs']
outdir_fits = config['outdir_fits']
basedir1 = '/gpfs/slac/lsst/fs3/g/data/jobHarness/jh_stage-test/LCA-10134_Cryostat/LCA-10134_Cryostat-0001/'
basedir2 = '/gpfs/slac/lsst/fs3/g/data/jobHarness/jh_stage/LCA-10134_Cryostat/LCA-10134_Cryostat-0001/'
path_to_data = {
'R01': basedir1 + '6848D/BOT_acq/v0/48087/',
'R02': basedir1 + '6849D/BOT_acq/v0/48093/',
'R10': None,
'R11': basedir1 + '6851D/BOT_acq/v0/48108/',
'R12': basedir1 + '6852D/BOT_acq/v0/48113/',
'R20': basedir1 + '6853D/BOT_acq/v0/48118/',
'R21': None,
'R22': basedir2 + '11974/BOT_acq/v0/93868/',
'R30': basedir1 + '6843D/BOT_acq/v0/48047/'
}
beam_model = load_beam_model(path_to_beam,
led_name=led_beam,
ref_amp=model_ref_amp,
ccdid=model_ccdid)
camera = camMapper._makeCamera()
lct = LsstCameraTransforms(camera)
delta_x, delta_y = compute_offsets(beam_model,
lct,
ccdid=model_ccdid,
ref_pix_x=2304,
ref_pix_y=3003)
ccdid_list = sorted(
[raft + '_' + sensor for sensor in sensor_list for raft in raft_list])
for data_ccdid in ccdid_list:
print(data_ccdid)
gainfile = '/gpfs/slac/lsst/fs3/g/data/jobHarness/jh_stage-test/LCA-10134_Cryostat/LCA-10134_Cryostat-0001/6801D/fe55_analysis_BOT/v0/47706/' + data_ccdid + '_6801D_eotest_results.fits'
biasfile = os.path.join(path_to_bias, f'{data_ccdid}_sbias.fits')
raft = data_ccdid.split('_')[0]
data = qe_data.CcobQeData(data_ccdid,
led,
path_to_data[raft],
gainfile,
biasfile=biasfile)
data.find_dir()
for pos in data.pos_list:
data.make_avg_mosaic_at_pos(pos, '/home/combet/tmp_9rafts/')
mosaic = data.data[pos]['mosaic']
bbox = define_model_bbox(beam_model, mosaic, data.lct, pos,
delta_x, delta_y)
xarr = np.linspace(bbox[0], bbox[1], mosaic.shape[0])
yarr = np.linspace(bbox[2], bbox[3], mosaic.shape[1])
tmp = beam_model.beam_image['f_interp'](
xarr, yarr) # interp works in cam coordinates
tmp = np.flip(np.flip(tmp, axis=0),
axis=1) # invert the model in x and in y
model_eotestDef = np.flip(
tmp.T, axis=1) # beam model followinf EOTest convention
model_normalised = model_eotestDef / np.max(
model_eotestDef.flatten())
qe = mosaic / model_normalised
qe_norm = qe / np.median(qe.flatten())
outfile = os.path.join(outdir_fits,
f'QE_{data_ccdid}_{led}_{pos}.fits')
amp_coord = data.data[pos]['amp_coord']
make_fits(qe, amp_coord, outfile, data.template_file)
figfile = os.path.join(outdir_figs,
f'QE_{data_ccdid}_{led}_{pos}.png')
plot_results(qe, model_eotestDef, mosaic, data_ccdid, data.lct,
pos, delta_x, delta_y, figfile)
figfile = os.path.join(outdir_figs,
f'QE_{data_ccdid}_{led}_{pos}_norm.png')
plot_results(qe_norm, model_normalised, mosaic, data_ccdid,
data.lct, pos, delta_x, delta_y, figfile)