def load_model_align_primary():
ds1 = mf.Dataset(from_file='20181205_primary_receiver.txt',
name='ds20181205')
ds2 = mf.Dataset(from_file='20181208_primary_receiver.txt',
name='ds20181208')
ds3 = mf.Dataset(
from_file=
'/home/dave/dev/PB/Photogrammetry_20181119/Photogrammetry/PB_point_clouds/COSPOL/20190319/REFLECTORAcheckout.txt'
)
dssim = mf.Dataset(from_file='test_simdata.txt')
#ds3 = mf.Dataset(from_file='../PB_point_clouds/SA_NORTH_FIELD/primary_driver.txt', name='PRIMARY_DRIVER')
with mf.AlignMirror2(ds=dssim,
mirror_definition='./POLARBEAR/SA_Primary_North.json',
use_marker='PRIMARY',
fitmap={
'tx': True,
'ty': True,
'tz': True,
'rx': True,
'ry': True,
'rz': False,
's': False,
'R': True,
'mirror_std': True
}) as model:
return model
def load_multi_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.AlignManyDatasets(reference=ds1,
datasets=[ds2, ds3],
use_marker='TARGET',
error_scale1=1.,
error_scale2=1.) as model:
return model
def load_multi_model_primary():
ds1 = mf.Dataset(from_file='20181205_primary_receiver.txt',
name='ds20181205')
ds2 = mf.Dataset(from_file='20181208_primary_receiver.txt',
name='ds20181208')
ds3 = mf.Dataset(
from_file='../PB_point_clouds/SA_NORTH_FIELD/primary_driver.txt',
name='PRIMARY_DRIVER')
ds4 = mf.Dataset(
from_file=
'../PB_point_clouds/SA_NORTH_FIELD/20171214_2/driveraligned_2.txt',
name='ds20171214')
with mf.AlignManyDatasets(reference=ds1,
datasets=[ds2, ds3, ds4],
use_marker='PRIMARY') as model:
return model
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
def load_model_add_reciver_points():
ds1 = mf.Dataset(from_file='20181208_primary_receiver.txt',
name='ds20181208')
theory_set = mf.Dataset(from_file='')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--dataset",
help="Path to the dataset file you want to rename")
parser.add_argument(
"--template",
help="Path to the template file you want to rename with",
action="append")
parser.add_argument("--outfile",
help="Path to the file to output",
default='renamed_output.txt')
args = parser.parse_args()
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: