def make_analysis(self, dataset, xy_mask):
masked_dataset = MaskedDataset(dataset=dataset, xy_mask=xy_mask)
transformers = []
for i in range(masked_dataset.uv_wavelengths.shape[0]):
transformers.append(
self.transformer_class(
uv_wavelengths=masked_dataset.uv_wavelengths[i],
grid=masked_dataset.grid_3d.grid_2d.in_radians))
"""
def get_continuum(masked_dataset, regions):
pass
continuum = np.zeros(
shape=(dataset.visibilities.shape[0], ),
dtype=bool
)
for region in self.regions:
continuum += region
def func(masked_dataset, continuum):
argspec = inspect.getargspec(MaskedDatasetLite.__init__)
args = {}
for argname in argspec.args:
if argname not in ["self"]:
if hasattr(masked_dataset, argname):
array = getattr(masked_dataset, argname)
args[argname] = reshape_array(
array=array[~continuum]
)
return MaskedDatasetLite(**args)
masked_dataset_continuum = func(masked_dataset, continuum)
#print(masked_dataset_continuum.uv_wavelengths.shape)
transformer_continuum = self.transformer_class(
uv_wavelengths=masked_dataset_continuum.uv_wavelengths,
grid=masked_dataset.grid_3d.grid_2d.in_radians
)
# dirty_image = transformer_continuum.image_from_visibilities(
# visibilities=masked_dataset_continuum.visibilities
# )
# plt.figure()
# plt.imshow(dirty_image[::-1], cmap="jet")
# plt.xticks([])
# plt.yticks([])
# plt.show()
"""
return Analysis(masked_dataset=masked_dataset,
transformers=transformers,
transformer_continuum=None,
lens_redshift=self.lens_redshift,
source_redshift=self.source_redshift,
image_path=self.optimizer.paths.image_path)
def make_analysis(self, dataset, xy_mask):
# NOTE: Multiple lines can be present in a cube, in which
# case region will be a list (renamed to regions)
masked_dataset = MaskedDataset(
dataset=dataset,
xy_mask=xy_mask,
region=self.region
)
masked_dataset_continuum = RegionMaskedDataset(
dataset=masked_dataset.dataset_outside_region,
uv_mask=masked_dataset.uv_mask_outside_region,
continuum=True
)
masked_dataset_line = RegionMaskedDataset(
dataset=masked_dataset.dataset_inside_region,
uv_mask=masked_dataset.uv_mask_inside_region,
continuum=False
)
transformers = []
for i in range(masked_dataset.uv_wavelengths.shape[0]):
transformers.append(
self.transformer_class(
uv_wavelengths=masked_dataset.uv_wavelengths[i],
grid=masked_dataset.grid_3d.grid_2d.in_radians
)
)
transformer_continuum = self.transformer_class(
uv_wavelengths=masked_dataset_continuum.uv_wavelengths,
grid=masked_dataset.grid_3d.grid_2d.in_radians
)
# # NOTE: EXPERIMENTAL
# holder = RegionMaskedDatasetsHolder(
# region_masked_datasets=[
# masked_dataset_continuum,
# masked_dataset_line
# ]
# )
# exit()
# TODO: region_masked_datasets can be a class that holds individual
# masked datasets and it's only function will be to differentiate between
# a masked_dataset corresponding to the continuum and the rest.
return Analysis(
masked_dataset=masked_dataset,
region_masked_datasets=[
masked_dataset_continuum,
masked_dataset_line
],
transformers=transformers,
transformer_continuum=transformer_continuum,
image_path=self.optimizer.paths.image_path
)
def make_analysis(self, dataset, xy_mask):
masked_dataset = MaskedDataset(dataset=dataset, xy_mask=xy_mask)
transformer = self.transformer_class(
uv_wavelengths=masked_dataset.uv_wavelengths,
grid=masked_dataset.grid.in_radians)
return Analysis(masked_dataset=masked_dataset,
transformer=transformer,
self_calibration=self.self_calibration,
image_path=self.optimizer.paths.image_path)
def make_analysis(self, dataset, xy_mask):
masked_dataset = MaskedDataset(dataset=dataset, xy_mask=xy_mask)
transformers = []
for i in range(masked_dataset.uv_wavelengths.shape[0]):
transformers.append(
self.transformer_class(
uv_wavelengths=masked_dataset.uv_wavelengths[i],
grid=masked_dataset.grid_3d.grid_2d.in_radians))
return Analysis(masked_dataset=masked_dataset,
transformers=transformers,
image_path=self.optimizer.paths.image_path)
def make_analysis(self, dataset, xy_mask):
# NOTE: The masked_dataset is no longer being used, instead each compoent
# (i.e. continuum + emission line regions) each have their own masked_dataset
# which is initialized here.
def masked_datasets_from_regions(masked_dataset, regions):
args = {
"continuum":{}
}
idx = np.zeros(
shape=(masked_dataset.visibilities.shape[0], ),
dtype=bool
)
for i, region in enumerate(regions):
idx += region.idx
args["region_{}".format(i)] = {}
if all(idx):
continuum = False
else:
continuum = True
argspec = inspect.getargspec(MaskedDatasetLite.__init__)
#args = {}
for argname in argspec.args:
if argname not in ["self"]:
if hasattr(masked_dataset, argname):
array = getattr(masked_dataset, argname)
if continuum:
args["continuum"][argname] = reshape_array(
array=array[~idx]
)
for i, region in enumerate(regions):
args["region_{}".format(i)][argname] = reshape_array(
array=array[region.idx]
)
#args["region_{}".format(i)][argname] = np.average(a=array[region.idx], axis=0)
masked_datasets = {
"continuum":MaskedDatasetLite(**args["continuum"]) if continuum else None
}
for i, region in enumerate(regions):
masked_datasets["region_{}".format(i)] = MaskedDatasetLite(**args["region_{}".format(i)])
return masked_datasets
# NOTE: Multiple lines can be present in a cube, in which
# case region will be a list (renamed to regions) - DONE
# NOTE: Can we skip the initialization of the masked dataset?
masked_dataset = MaskedDataset(
dataset=dataset,
xy_mask=xy_mask,
)
masked_datasets = masked_datasets_from_regions(
masked_dataset=masked_dataset,
regions=self.regions
)
transformers = {}
for key in masked_datasets.keys():
if masked_datasets[key] is not None:
transformers[key] = self.transformer_class(
uv_wavelengths=masked_datasets[key].uv_wavelengths,
grid=masked_dataset.grid_3d.grid_2d.in_radians
)
else:
transformers[key] = None
return Analysis(
masked_datasets=masked_datasets,
transformers=transformers,
grid=masked_dataset.grid_3d.grid_2d,
image_path=self.optimizer.paths.image_path
)
def make_analysis(self, dataset, mask):
masked_dataset = MaskedDataset(dataset=dataset, mask=mask)
return Analysis(masked_dataset=masked_dataset,
image_path=self.optimizer.paths.image_path)