def setup():
"""General variables needed by multiple benchmark classes."""
global data_1d
global data_2d
global general_cube
data_2d = np.zeros((ARTIFICIAL_DIM_SIZE, ) * 2)
data_1d = data_2d[0]
general_cube = cube.Cube(data_2d)
def setup(self, n_faces):
mesh_kwargs = dict(
n_nodes=n_faces + 2, n_edges=n_faces * 2, n_faces=n_faces
)
self.mesh_coord = sample_meshcoord(sample_mesh_kwargs=mesh_kwargs)
self.data = np.zeros(n_faces)
self.cube_blank = cube.Cube(data=self.data)
self.cube = self.create()
def setup(self):
# Manufacture data from which contours can be derived.
# Should generate 10 distinct contours, regardless of dim size.
dim_size = int(ARTIFICIAL_DIM_SIZE / 5)
repeat_number = int(dim_size / 10)
repeat_range = range(int((dim_size ** 2) / repeat_number))
data = np.repeat(repeat_range, repeat_number)
data = data.reshape((dim_size,) * 2)
# These benchmarks are from a user perspective, so setting up a
# user-level case that will prompt the calling of aux_coords.sort in plot.py.
dim_coord = coords.DimCoord(np.arange(dim_size))
local_cube = cube.Cube(data)
local_cube.add_aux_coord(dim_coord, 0)
self.cube = local_cube
def time_basic(self):
cube.Cube(data_2d)
def create(self):
"""Generic cube creation. cube_kwargs allow dynamic inclusion of
different components; specified in subclasses."""
return cube.Cube(data=data_2d, **self.cube_kwargs)
def create(self):
return cube.Cube(
data=self.data, aux_coords_and_dims=[(self.mesh_coord, 0)]
)
