def main():
args = get_args()
pid = os.getpid()
hostname = socket.gethostname()
src['id'] = '{}/{}'.format(args.case_id, args.subcase_id)
if not os.path.exists(files['src_dir'].abs):
os.makedirs(files['src_dir'].abs)
logfile = os.path.join(files['src_dir'].abs,
'{}-{}.log'.format(hostname, pid))
logging.basicConfig(filename=logfile, level=logging.INFO)
logging.info(str(args))
aoc = AdjOpticsCase(case_id=args.case_id, subcase_id=args.subcase_id,
clip=args.clip, n_ss=args.ss,
piston_tilt=bool(args.piston_tilt),
displ_axes=['X'], corr_axes=['X'],
n_proc=None, n_strips=args.n_strips)
outfile = os.path.join(files['src_dir'].abs,
'act_fail-{}-{}.dat'.format(hostname, pid))
# Make an ordered list 0 .. n_acts-1 for shuffling later.
ifuncs_x = aoc.ifuncs['X'].copy()
ifuncs_ry = aoc.ifuncs['RY'].copy()
n_acts = ifuncs_x.shape[0]
i_acts = np.arange(n_acts)
for i in range(args.max_sim):
# Force some actuators to "fail" by taking them out of the optimization
if args.max_fail > 0:
n_fail = np.random.randint(args.max_fail) + 1
np.random.shuffle(i_acts)
ok = np.ones(n_acts, dtype=bool)
i_fail = i_acts[:n_fail]
ok[i_fail] = False
aoc.ifuncs['X'] = ifuncs_x[ok]
aoc.ifuncs['RY'] = ifuncs_ry[ok]
else:
n_fail = 0
i_fail = [-1]
aoc.calc_adj()
aoc.calc_stats()
aoc.calc_scatter(calc_input=False)
hpd = aoc.scatter['corr']['X']['hpd']
rmsd = aoc.scatter['corr']['X']['rmsd']
resid_std = aoc.resid['X']['X']['std']['clip']
displ_std = aoc.displ['X']['std']['clip']
with open(outfile, 'a') as f:
i_fail_str = ','.join(str(x) for x in i_fail)
print >>f, ' '.join(str(x) for x in
(n_fail, hpd, rmsd, displ_std,
resid_std, i_fail_str))
def main():
args = get_args()
pid = os.getpid()
hostname = socket.gethostname()
src['id'] = '{}/{}'.format(args.case_id, args.subcase_id)
if not os.path.exists(files['src_dir'].abs):
os.makedirs(files['src_dir'].abs)
logfile = os.path.join(files['src_dir'].abs,
'{}-{}.log'.format(hostname, pid))
logging.basicConfig(filename=logfile, level=logging.INFO)
logging.info(str(args))
aoc = AdjOpticsCase(case_id=args.case_id,
subcase_id=args.subcase_id,
clip=args.clip,
n_ss=args.ss,
piston_tilt=bool(args.piston_tilt),
displ_axes=['X'],
corr_axes=['X'],
n_proc=None,
n_strips=args.n_strips)
outfile = os.path.join(files['src_dir'].abs,
'act_fail-{}-{}.dat'.format(hostname, pid))
# Make an ordered list 0 .. n_acts-1 for shuffling later.
ifuncs_x = aoc.ifuncs['X'].copy()
ifuncs_ry = aoc.ifuncs['RY'].copy()
n_acts = ifuncs_x.shape[0]
i_acts = np.arange(n_acts)
for i in range(args.max_sim):
# Force some actuators to "fail" by taking them out of the optimization
if args.max_fail > 0:
n_fail = np.random.randint(args.max_fail) + 1
np.random.shuffle(i_acts)
ok = np.ones(n_acts, dtype=bool)
i_fail = i_acts[:n_fail]
ok[i_fail] = False
aoc.ifuncs['X'] = ifuncs_x[ok]
aoc.ifuncs['RY'] = ifuncs_ry[ok]
else:
n_fail = 0
i_fail = [-1]
aoc.calc_adj()
aoc.calc_stats()
aoc.calc_scatter(calc_input=False)
hpd = aoc.scatter['corr']['X']['hpd']
rmsd = aoc.scatter['corr']['X']['rmsd']
resid_std = aoc.resid['X']['X']['std']['clip']
displ_std = aoc.displ['X']['std']['clip']
with open(outfile, 'a') as f:
i_fail_str = ','.join(str(x) for x in i_fail)
print >> f, ' '.join(
str(x)
for x in (n_fail, hpd, rmsd, displ_std, resid_std, i_fail_str))
'axis': axis,
'corr': corr,
'ratios': ratios[axis][corr],
'displ': aoc.displ[axis],
'resid': aoc.resid[axis][corr],
})
out = template.render(subcases=subcases, aoc=aoc)
with open(files['index.html'].abs, 'w') as f:
f.write(out)
# Save version of aoc without ifuncs, which are not interesting and big
delattr(aoc, 'ifuncs')
pickle.dump(aoc, open(files['aoc.pkl'].rel, 'w'), protocol=-1)
if __name__ == '__main__':
args = get_args()
displ_axes = args.displ_axes.split(',')
corr_axes = args.corr_axes.split(',')
aoc = AdjOpticsCase(case_id=args.case_id,
subcase_id=args.subcase_id,
clip=args.clip,
n_ss=args.ss,
bias_mult=args.bias_mult,
piston_tilt=bool(args.piston_tilt),
n_strips=args.n_strips,
displ_axes=displ_axes,
corr_axes=corr_axes)
make_report(aoc)
parser.add_argument("--drive-ref", type=str, default="median", help="Drive reference (median|max)")
args = parser.parse_args()
return args
if __name__ == "__main__":
corr = "X"
args = get_args()
displ_axes = args.displ_axes.split(",")
corr_axes = args.corr_axes.split(",")
aoc = AdjOpticsCase(
case_id=args.case_id,
subcase_id=args.subcase_id,
clip=args.clip,
n_ss=args.ss,
piston_tilt=bool(args.piston_tilt),
n_strips=args.n_strips,
displ_axes=displ_axes,
corr_axes=corr_axes,
n_proc=0,
)
# Compute stats for zero-noise case
aoc.calc_adj()
aoc.calc_stats()
aoc.calc_scatter()
coeffs = aoc.coeffs["X"]
drive_ref = getattr(np, args.drive_ref)(abs(coeffs))
# noises = (0.00001, 0.001, 0.002, 0.005, 0.01, 0.02,
# 0.05, 0.1, 0.15, 0.2)
default="median",
help='Drive reference (median|max)')
args = parser.parse_args()
return args
if __name__ == '__main__':
corr = 'X'
args = get_args()
displ_axes = args.displ_axes.split(',')
corr_axes = args.corr_axes.split(',')
aoc = AdjOpticsCase(case_id=args.case_id,
subcase_id=args.subcase_id,
clip=args.clip,
n_ss=args.ss,
piston_tilt=bool(args.piston_tilt),
n_strips=args.n_strips,
displ_axes=displ_axes,
corr_axes=corr_axes,
n_proc=0)
# Compute stats for zero-noise case
aoc.calc_adj()
aoc.calc_stats()
aoc.calc_scatter()
coeffs = aoc.coeffs['X']
drive_ref = getattr(np, args.drive_ref)(abs(coeffs))
# noises = (0.00001, 0.001, 0.002, 0.005, 0.01, 0.02,
# 0.05, 0.1, 0.15, 0.2)
noises = np.random.uniform(0, 0.2, args.n_sim)
