def beam_recons(led_names,
ref_slot='11',
ref_amp=4,
ref_pix_x=1000,
ref_pix_y=256,
npix_for_avg=30,
npix_beam_image=300):
mean_bias_file = ref_slot + '_mean_bias_image_RTM-006.fits'
recons = {}
for led in led_names:
config = u.load_ccob_config(
'/home/combet/ccob-wb/ccob_config_RTM-006.yaml')
config['led_name'] = led
config['path'] = os.path.join(config['path'], config['led_name'])
flist = sorted(u.find_files(config, slot=ref_slot))
ccd_dict = sensorTest.MaskedCCD(flist[0],
bias_frame=os.path.join(
config['tmp_dir'], mean_bias_file))
eotest_results_file = os.path.join(
config['eo_data_path'],
'{}_eotest_results.fits'.format(ccd_dict.md('LSST_NUM')))
gains_dict = u.gains(eotest_results_file)
nodes = {}
nodes['xarr'] = []
nodes['yarr'] = []
nodes['val'] = []
for i, f in enumerate(sorted(flist)):
nodes['xarr'].append(
float(os.path.basename(f).split('_')[3].split('x')[1]))
nodes['yarr'].append(
float(os.path.basename(f).split('_')[4].split('y')[1]))
ccd_dict = sensorTest.MaskedCCD(f,
bias_frame=os.path.join(
config['tmp_dir'],
mean_bias_file))
image = ccd_dict.unbiased_and_trimmed_image(ref_amp)
image *= gains_dict[ref_amp]
arr = image.getArrays()
nodes['val'].append(
np.mean(
(arr[0][ref_pix_x - npix_for_avg / 2:ref_pix_x +
npix_for_avg / 2, ref_pix_y -
npix_for_avg / 2:ref_pix_y + npix_for_avg / 2])))
f_interp = interpolate.interp2d(np.unique(nodes['xarr']),
np.unique(nodes['yarr']),
nodes['val'],
kind='cubic')
xarr2 = np.linspace(min(nodes['xarr']), max(nodes['xarr']),
npix_beam_image)
yarr2 = np.linspace(min(nodes['yarr']), max(nodes['yarr']),
npix_beam_image)
tmp = f_inerp(xarr2, yarr2)
recons[led] = tmp / max(tmp.flatten())
return recons
def main():
# print command line arguments
config = sys.argv[0]
print(config)
config = u.load_ccob_config("beam_config.yaml")
dirlist = []
for d in config['rundir']:
dirlist += glob.glob(config['rootdir'] + d + 'ccob_' +
config['led_name'] + '*')
print(len(dirlist))
assert (len(dirlist) /
config['scan_size']).is_integer, 'Wrong number of scan locations'
print(f'Scan over {len(dirlist)} locations')
b = beam.CcobBeam(config)
for i in np.arange(config['scan_size']):
# that's just to allow saving the data at every line of the scan in case
# the job is interrupted.
start = i * config['scan_size']
end = (i + 1) * config['scan_size']
b.read_multibunch(config, dirlist=dirlist[start:end])
b.save(
os.path.join(
config['tmpdir'], 'beam_object_' + config['ref_raft'] + '_' +
config['ref_slot'] + '_' + config['led_name'] + '.pkl'))
def main():
"""
First, finds the list of files relevant to a CCOB scan, given the paths available in the configuration file.
Then,generates a CcobBeam object containing the raw data, the interpolated model of the beam, the 2D beam array,
and the location of the beam maximum.
"""
configfile = sys.argv[1]
config = u.load_ccob_config(configfile)
dirlist = []
for d in config['rundir']:
dirlist += glob.glob(config['rootdir'] + d + 'ccob_' +
config['led_name'] + '*')
outfile = os.path.join(
config['tmpdir'], 'beam_object_' + config['ref_raft'] + '_' +
config['ref_slot'] + '_' + config['led_name'] + '.pkl')
assert (len(dirlist) / config['scan_size']
).is_integer, f'{len(dirlsit)} = Wrong number of scan locations'
print(f'Scan over {len(dirlist)} locations')
b = CcobBeam(config)
print(outfile)
if os.path.isfile(outfile):
b = pickle.load(open(outfile, 'rb'))
if b.config == config:
print(
"Configuration matches - continuing from existing partial file"
)
else:
sys.exit(
"Configuration does not match that of existing file - Exit")
for i in np.arange(config['scan_size']):
# that's just to allow saving the data at every line of the scan in case
# the job is interrupted.
print(f'Loading row {i}')
start = i * config['scan_size']
end = (i + 1) * config['scan_size']
b.read_multibunch(config, dirlist=dirlist[start:end], silent=True)
b.save(outfile)
b.interp_beam_BOT(amp=config['ref_amps'][0], pd_corr=True)
im = b.make_image_BOT()
b.find_max_from_avg()
b.save(outfile)
def main():
config_file = 'ccob_config.yaml'
config = ccob_utils.load_ccob_config(config_file)
slot_names = ['00', '01', '02', '10', '11', '12', '20', '21', '22']
led_names = ['uv', 'blue', 'nm750', 'nm850', 'red', 'nm960']
currents = ['0.012A', '0.012A', '0.012A', '0.012A', '0.012A', '0.012A']
exp_times = ['0.6s', '0.03s', '0.06s', '0.12s', '0.06s', '0.18s']
led_names = ['red']
currents = ['0.012A']
exp_times = ['0.06s']
xpos = ['258', '300', '342']
ypos = ['234', '192', '150']
# for slot in slot_names:
# ccob_utils.build_mean_bias_frame(config, slot)
for i, led in enumerate(led_names):
config['led_name'] = led
config['current'] = currents[i]
config['exp_time'] = exp_times[i]
for x in xpos:
for y in ypos:
print "Working on led = " + led + ", x = " + x + ", y = " + y
config['xpos'] = x
config['ypos'] = y
im_raw, im_corr, im_corr_wbias = ccob_utils.make_image(
config, slot_names)
fig_raw = im_raw.plot(nsig=2, title=config['led_name'])
fig_corr = im_corr.plot(nsig=2, title=config['led_name'])
fig_corr_wbias = im_corr_wbias.plot(nsig=2,
title=config['led_name'])
filename = 'raft_image_raw_ccob_X' + config[
'xpos'] + '_Y' + config['ypos'] + '_' + config[
'led_name'] + '.png'
fig_raw.savefig(os.path.join(config['tmp_dir'], filename))
filename = 'raft_image_corr_ccob_X' + config[
'xpos'] + '_Y' + config['ypos'] + '_' + config[
'led_name'] + '.png'
fig_corr.savefig(os.path.join(config['tmp_dir'], filename))
filename = 'raft_image_corr_wbias_ccob_X' + config[
'xpos'] + '_Y' + config['ypos'] + '_' + config[
'led_name'] + '.png'
fig_corr_wbias.savefig(
os.path.join(config['tmp_dir'], filename))
def __init__(self, config_file_beam, config_file_data):
# self.config_file_beam = config_file_beam
# self.config_file_data = config_file_data
self.config_beam = u.load_ccob_config(config_file_beam)
self.config_data = u.load_ccob_config(config_file_data)