Commit 4f930cb3 authored by syz's avatar syz
Browse files

Extended get unit values to positions as well

parent f0a79209
......@@ -1598,18 +1598,20 @@ def create_spec_inds_from_vals(ds_spec_val_mat):
return ds_spec_inds_mat
def get_unit_values(h5_spec_ind, h5_spec_val, dim_names=None):
def get_unit_values(h5_inds, h5_vals, is_spec=True, dim_names=None):
Gets the unit arrays of values that describe the spectroscopic dimensions
h5_spec_ind : h5py.Dataset
Spectroscopic Indices dataset
h5_spec_val : h5py.Dataset
Spectroscopic Values dataset
h5_inds : h5py.Dataset
Spectroscopic or Position Indices dataset
h5_vals : h5py.Dataset
Spectroscopic or Position Values dataset
is_spec : bool, recommended
Are the provided datasets spectral. Default = True
dim_names : str, or list of str, Optional
Names of the dimensions of interest
Names of the dimensions of interest. Default = all
Note - this function can be extended / modified for ancillary position dimensions as well
......@@ -1619,33 +1621,41 @@ def get_unit_values(h5_spec_ind, h5_spec_val, dim_names=None):
Dictionary containing the unit array for each dimension. The name of the dimensions are the keys.
# First load to memory
inds_mat = h5_inds[()]
vals_mat = h5_vals[()]
if not is_spec:
# Convert to spectral shape
inds_mat = np.transpose(inds_mat)
vals_mat = np.transpose(vals_mat)
# For all dimensions, find where the index = 0
# basically, we are indexing all dimensions to 0
first_indices = []
for dim_ind in range(h5_spec_ind.shape[0]):
first_indices.append(h5_spec_ind[dim_ind] == 0)
for dim_ind in range(inds_mat.shape[0]):
first_indices.append(inds_mat[dim_ind] == 0)
first_indices = np.vstack(first_indices)
spec_dim_names = get_attr(h5_spec_ind, 'labels')
full_dim_names = get_attr(h5_inds, 'labels')
if dim_names is None:
dim_names = spec_dim_names
dim_names = full_dim_names
elif not isinstance(dim_names, list):
dim_names = [dim_names]
unit_values = dict()
for dim_name in dim_names:
# Find the row in the spectroscopic indices that corresponds to the dimensions we want to slice:
desired_row_ind = np.where(spec_dim_names == dim_name)[0][0]
desired_row_ind = np.where(full_dim_names == dim_name)[0][0]
# Find indices of all other dimensions
remaining_dims = list(range(h5_spec_ind.shape[0]))
remaining_dims = list(range(inds_mat.shape[0]))
# The intersection of all these indices should give the desired index for the desired row
intersections = np.all(first_indices[remaining_dims, :], axis=0)
# apply this slicing to the values dataset:
unit_values[dim_name] = h5_spec_val[desired_row_ind, intersections]
unit_values[dim_name] = vals_mat[desired_row_ind, intersections]
return unit_values
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