"""Launches a worker with the appropriate queue and database based on an index into a config file."""
from pathlib import Path
from iris.data import PersistentQueue, Database
from iris.rings import W2
from iris.worker import Worker
# shield for multiprocessing
if __name__ == '__main__':
root = Path(
__file__).parent / 'data' / 'arbitrary-spherical-coma-astigmatism'
q = PersistentQueue(root / 'queue.pkl')
db = Database(root,
fields=[
'truth_rmswfe', 'cost_first', 'cost_final',
'rrmswfe_final', 'time', 'nit'
])
w = Worker(q,
db,
optmode='global',
simopts={
'guess': [0] * 18,
'decoder_ring': W2,
},
optopts={
'parallel': True,
'nthreads': 7,
'ftol': 1e-7,
},
work_time=60 * 17)
w.start()
low_value, mid_value, high_value, x_factor, y_factor = make_mag_xy_random_values(
)
xmag_low = low_value * x_factor
ymag_low = low_value * y_factor
xmag_mid = mid_value * x_factor
ymag_mid = mid_value * y_factor
xmag_high = high_value * x_factor
ymag_high = high_value * y_factor
z7 = xmag_low
z8 = ymag_low
z14 = xmag_mid
z15 = ymag_mid
z23 = xmag_high
z24 = ymag_high
# stack all truths together and split them into 5 chunks for 5 workers per datatype
alltruth = np.stack(
(z4, z5, z6, z7, z8, z9, z12, z13, z14, z15, z16, z21, z22, z23, z24, z25),
axis=1)
truths = list(alltruth)
p = Path(__file__).parent #/ '..' / '..' /
p = p / 'data' / 'arbitrary-spherical-coma-astigmatism'
q = PersistentQueue(p / 'queue.pkl', overwrite=True)
q.put_many(truths)
xcoef, ycoef = np.cos(angles), np.sin(angles) # flipped -- optics convention xcoef = (xcoef * coma_amounts_tiled.reshape((s, 1))).ravel() ycoef = (ycoef * coma_amounts_tiled.reshape((s, 1))).ravel() out_coma = np.zeros((sphcoef.shape[0], 16)) out_coma[:, 5] = sphcoef out_coma[:, 10] = sphcoef / -2 out_coma[:, 15] = sphcoef / 4 out_coma[:, 3] = xcoef out_coma[:, 4] = ycoef out_astig = np.zeros((sphcoef.shape[0], 16)) out_astig[:, 5] = sphcoef out_astig[:, 10] = sphcoef / -2 out_astig[:, 15] = sphcoef / 4 out_astig[:, 1] = xcoef out_astig[:, 2] = ycoef root = Path(__file__).parent / 'data' / 'coma-vs-angle' p = root / 'queue.pkl' q = PersistentQueue(p, overwrite=True) items = list(out_coma) q.put_many(items) root = Path(__file__).parent / 'data' / 'astigmatism-vs-angle' p = root / 'queue.pkl' q = PersistentQueue(p, overwrite=True) items = list(out_astig) q.put_many(items)
from iris.data import PersistentQueue
# folders for each queue
root = Path(__file__).parent / 'data' / 'cost-function-design'
stem1 = root / 'diffraction_sumsquarediff'
stem2 = root / '____________sumsquarediff'
stem3 = root / 'diffraction_manhattan'
stem4 = root / '____________manhattan'
stems = (stem1, stem2, stem3, stem4)
qs = []
for stem in stems:
counter, local = 1, []
for i in range(5):
p = stem / str(counter)
p.mkdir(parents=True, exist_ok=True)
local.append(PersistentQueue(p / 'queue.pkl', overwrite=True))
counter += 1
qs.append(local)
# set the seed and generate 'fudge factor' arrays centered on 1 with stdev 1/2 and 500 samples e/a
np.random.seed(1234)
fudge1 = np.random.normal(loc=1, scale=0.5, size=500)
fudge2 = np.random.normal(loc=1, scale=0.5, size=500)
z16_factor, z25_factor = -0.5, 0.5**2
# no defocus
# uniform normal distributions of size 500 and range [0, 0.25]
truths_z4 = np.zeros(500)
truths_z9 = np.random.random(truths_z4.shape) * .25
# Z16 and Z25 are derived from Z9 and have value -1/2 and +1/4 respectively
opt = _mtf_cost_core_manhattan
ftol = 5e-4
else:
opt = _mtf_cost_core_sumsquarediff
ftol = 1e-7
if diff:
optargs = ((_mtf_cost_core_diffractiondiv, opt), None)
else:
optargs = ((opt, ), None)
# shield for multiprocessing
if __name__ == '__main__':
root = Path(cfg['root']).expanduser()
base = root / target[0] / target[1]
q = PersistentQueue(base / 'queue.pkl')
db = Database(base,
fields=[
'truth_rmswfe', 'cost_first', 'cost_final',
'rrmswfe_final', 'time', 'nit'
])
w = Worker(q,
db,
optmode='global',
optopts={
'parallel': True,
'nthreads': 23,
'ftol': ftol,
},
optcoreopts=optargs,
work_time=60 * 7)