def add_receiver_theory_points(
ds,
output_file=None,
theory_ds='POLARBEAR/pb2a_cryostat_japan_measurements.txt'):
if type(ds) is str:
ds = mf.Dataset(from_file=ds)
elif type(ds) is mf.Dataset:
ds = ds.copy()
else:
raise ValueError('Type of dataset argument not recoginized.')
if type(theory_ds) is str:
theoryds = mf.Dataset(from_file=theory_ds)
elif type(theory_ds) is mf.Dataset:
theoryds = theory_ds.copy()
else:
raise ValueError('Type of theory dataset argument not recoginized.')
dsuse = ds.subset_from_labels(MEASURED_RECEIVER_TARGET_NAMES)
with mf.AlignDatasets(ds1=dsuse, ds2=theoryds, fitmap={'s':
False}) as tamodel:
tatrace = pm.sample(2000,
tune=5500,
init='advi+adapt_diag',
nuts_kwargs={
'target_accept': .90,
'max_treedepth': 25
},
error_scale1=1.,
error_scale2=1.)
pm.save_trace(tatrace)
pm.traceplot(tatrace)
plt.show()
fptheory = theoryds.subset_from_marker('FOCALPLANE')
pos, err = tamodel.use_transform_trace(fptheory.to_tensors(), tatrace)
newtheoryarray = mf.DatasetArrays(pos=np.mean(pos, axis=0),
err=np.std(pos, axis=0),
serr=np.std(pos, axis=0))
newfptheory = fptheory.remake_from_arrays(newtheoryarray)
for p in newfptheory.values():
ds.add_point(p)
print(ds)
def load_model_primary():
ds1 = mf.Dataset(from_file='20181205_primary_receiver.txt',
name='ds20181205')
ds2 = mf.Dataset(from_file='20181208_primary_receiver.txt',
name='ds20181208')
with mf.AlignDatasets(ds1=ds1,
ds2=ds2,
use_marker='PRIMARY',
fitmap={
'tx': True,
'ty': True,
'tz': True,
's': True,
'rx': True,
'ry': True,
'rz': True,
'rescale_errors': True
}) as model:
return model
def load_model_moons():
ds1 = mf.Dataset(from_file='./MOONS/VSTARS/moons_20160802_1_aligned.txt',
name='DS1')
ds2 = mf.Dataset(from_file='./MOONS/VSTARS/moons_20160802_2_aligned.txt',
name='DS2')
ds3 = mf.Dataset(from_file='./MOONS/VSTARS/moons_20160802_3_aligned.txt',
name='DS3')
with mf.AlignDatasets(ds1=ds1,
ds2=ds3,
use_marker='TARGET',
fitmap={
'tx': True,
'ty': True,
'tz': True,
's': True,
'rx': True,
'ry': True,
'rz': True
}) as model:
return model
rename_ds = mf.Dataset(from_file=args.dataset)
rename_vals = []
if has_duplicate_label(rename_ds):
print(
f'Dataset {rename_ds.label} has duplicate labels. If this is true after renaming it will not be saved.'
)
for template in args.template:
this_temp = mf.Dataset(from_file=template)
if has_duplicate_label(this_temp):
print(f'Template {this_temp.name} has duplicate labels. Skipping.')
continue
with mf.AlignDatasets(ds1=this_temp, ds2=rename_ds,
use_marker='CODE') as model:
maptrans = pm.find_MAP(model=model)
vals = ['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 's']
tdict = {}
for val in vals:
for key in maptrans:
if val in key:
tdict[val] = maptrans[key]
trans = mf.TheanoTransform(trans=tdict)
renamet = rename_ds.to_tensors()
renametprime = trans * renamet
rename_pos = renametprime.pos.eval()
for point in this_temp.values():
this_vec_dists = rename_pos.T - point.pos
this_dists = np.linalg.norm(this_vec_dists, axis=1)
minidx = np.argsort(this_dists)