def test_getters(laue_inputs, mono_inputs):
for inputs in laue_inputs, mono_inputs:
if BaseModel.is_laue(inputs):
BaseModel.get_harmonic_id(inputs)
BaseModel.get_wavelength(inputs)
BaseModel.get_image_id(inputs)
BaseModel.get_intensities(inputs)
BaseModel.get_metadata(inputs)
BaseModel.get_refl_id(inputs)
BaseModel.get_uncertainties(inputs)
def test_mono_formatter(
intensity_key,
sigma_key,
image_id_key,
separate_outputs,
anomalous,
dmin,
isigi_cutoff,
positional_encoding_keys,
encoding_bit_depth,
mono_data_set,
):
ds = mono_data_set.copy()
f = MonoFormatter(
intensity_key,
sigma_key,
image_id_key,
metadata_keys,
separate_outputs,
anomalous,
dmin,
isigi_cutoff,
positional_encoding_keys,
encoding_bit_depth,
)
inputs, rac = f([ds])
length = None
for v in inputs:
assert v.ndim == 2
assert v.dtype in ('float32', 'int64')
if length is None:
length = v.shape[0]
assert v.shape[0] == length
metadata = BaseModel.get_metadata(inputs)
def build_model(self,
parser=None,
surrogate_posterior=None,
prior=None,
likelihood=None,
scaling_model=None,
mc_sample_size=None):
"""
Build the model specified in parser, a careless.parser.parser.parse_args() result. Optionally override any of the
parameters taken by the VariationalMergingModel constructor.
The `parser` parameter is required if self.parser is not set.
"""
from careless.models.merging.surrogate_posteriors import TruncatedNormal
from careless.models.merging.variational import VariationalMergingModel
from careless.models.scaling.image import HybridImageScaler, ImageScaler
from careless.models.scaling.nn import MLPScaler
if parser is None:
parser = self.parser
if parser is None:
raise ValueError("No parser supplied, but self.parser is unset")
if parser.type == 'poly':
from careless.models.likelihoods.laue import NormalLikelihood, StudentTLikelihood
elif parser.type == 'mono':
from careless.models.likelihoods.mono import NormalLikelihood, StudentTLikelihood
if prior is None:
prior = self.get_wilson_prior(parser.wilson_prior_b)
loc, scale = prior.mean(), prior.stddev() / 10.
low = (1e-32 * self.asu_collection.centric).astype('float32')
if surrogate_posterior is None:
surrogate_posterior = TruncatedNormal.from_loc_and_scale(
loc, scale, low)
if likelihood is None:
dof = parser.studentt_likelihood_dof
if dof is None:
likelihood = NormalLikelihood()
else:
likelihood = StudentTLikelihood(dof)
if scaling_model is None:
mlp_width = parser.mlp_width
if mlp_width is None:
mlp_width = BaseModel.get_metadata(self.inputs).shape[-1]
mlp_scaler = MLPScaler(parser.mlp_layers, mlp_width)
if parser.use_image_scales:
n_images = np.max(BaseModel.get_image_id(self.inputs)) + 1
image_scaler = ImageScaler(n_images)
scaling_model = HybridImageScaler(mlp_scaler, image_scaler)
else:
scaling_model = mlp_scaler
model = VariationalMergingModel(surrogate_posterior, prior, likelihood,
scaling_model, parser.mc_samples)
opt = tf.keras.optimizers.Adam(
parser.learning_rate,
parser.beta_1,
parser.beta_2,
)
model.compile(opt)
return model