def mask_cube(cube_in, mask):
''' Mask cube where mask is larger than zero
Input:
cube_in: iris.cube.Cube
unmasked data cube
mask: numpy.ndarray or dask.array
mask to use for masking, >0 where cube is supposed to be masked
Output:
cube_out: iris.cube.Cube
Masked cube
'''
from dask.array import ma
from copy import deepcopy
cube_out = deepcopy(cube_in)
cube_out.data = ma.masked_where(mask != 0, cube_in.core_data())
return cube_out
def mask_cube_untracked(variable_cube, mask):
''' Mask cube for untracked volume
Input:
variable_cube: iris.cube.Cube
unmasked data cube
mask: iris.cube.Cube
cube containing mask (int id for tacked volumes 0 everywhere else)
Output:
variable_cube_out: iris.cube.Cube
Masked cube for untracked volume
'''
from dask.array import ma
from copy import deepcopy
variable_cube_out = deepcopy(variable_cube)
variable_cube_out.data = ma.masked_where(mask.core_data() > 0,
variable_cube_out.core_data())
return variable_cube_out
def mask_cube_features(variable_cube, mask, feature_ids):
''' Mask cube for tracked volume of an individual cell
Input:
variable_cube: iris.cube.Cube
unmasked data cube
mask: iris.cube.Cube
cube containing mask (int id for tacked volumes 0 everywhere else)
cell: int
interger id of cell to create masked cube for
Output:
variable_cube_out: iris.cube.Cube
Masked cube with data for respective cell
'''
from dask.array import ma, isin
from copy import deepcopy
variable_cube_out = deepcopy(variable_cube)
variable_cube_out.data = ma.masked_where(
~isin(mask.core_data(), feature_ids), variable_cube_out.core_data())
return variable_cube_out
