def make_subset_data(data, random_subset=None, pixels=None, return_selection=False):
if random_subset is None and pixels is None:
return data
if random_subset is not None and pixels is not None:
raise ValueError("You can only specify one of pixels or random_subset")
tot_pix = len(data.x)*len(data.y)
if pixels is not None:
n_sel = pixels
else:
n_sel = int(np.ceil(tot_pix*random_subset))
selection = np.random.choice(tot_pix, n_sel, replace=False)
subset = flat(data).isel(flat=selection)
subset = copy_metadata(data, subset, do_coords=False)
shape = (len(data.x), len(data.y))
spacing = (get_spacing(data))
start = (np.asscalar(data.x[0]), np.asscalar(data.y[0]))
coords = {key:val.values for key, val in dict_without(dict(data.coords), ['x','y','z']).items()}
subset.attrs['original_dims'] = yaml.dump((shape, spacing, start, coords))
if return_selection:
return subset, selection
else:
return subset
def _find_new_ties(self):
reference_parameters = self.raw_parameters
for fullkey, par in reference_parameters.items():
if fullkey not in self._all_ties:
# not already in the list of ties, so check if it should be
for ref_key, ref_par in dict_without(
reference_parameters, fullkey).items():
# can't simply check par in parameters because then two
# priors defined separately, but identically will match
# whereas this way they are counted as separate objects.
if par is ref_par and not isinstance(par, Number):
self.add_tie(ref_key, fullkey)
break
def forward(self, pars):
if hasattr(self.data, 'original_dims'):
# dealing with subset data
original_dims = self.data.original_dims
# can't currently handle non-0 values of z, as in detector_grid
x = original_dims['x']
y = original_dims['y']
shape = (len(x), len(y))
spacing = (np.diff(x)[0], np.diff(y)[0])
extra_dims = dict_without(original_dims, ['x', 'y', 'z'])
schema = detector_grid(shape, spacing, extra_dims=extra_dims)
schema = copy_metadata(self.data, schema, do_coords=False)
schema['x'] = x
schema['y'] = y
else:
schema = self.data
return self.model.forward(pars, schema)
def unpack_attrs(a):
if len(a) == 0:
return a
new_attrs = {}
attr_ref = yaml.load(a[attr_coords], Loader=FullLoader)
attrs_to_ignore = ['spacing', 'name', '_dummy_channel', '_image_scaling']
for attr in dict_without(attr_ref, attrs_to_ignore):
if attr_ref[attr]:
new_attrs[attr] = xr.DataArray(a[attr],
coords=attr_ref[attr],
dims=list(attr_ref[attr].keys()))
elif attr in a:
try:
new_attrs[attr] = yaml.safe_load(a[attr])
except AttributeError:
from holopy.inference.result import warn_text
warnings.warn(warn_text)
new_attrs[attr] = a[attr]
else:
new_attrs[attr] = None
return new_attrs
def test_dict_without(self):
input_dict = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
output_dict = dict_without(input_dict, ['a', 'd', 'e'])
self.assertEqual(input_dict, {'a': 1, 'b': 2, 'c': 3, 'd': 4})
self.assertEqual(output_dict, {'b': 2, 'c': 3})