def filter_func(field, *args):
# these next steps are a kind of hack we have to turn keyword arugments into regular arguments
# the reason for doing this is that Xarray's apply_ufunc machinery works a lot better
# with regular arguments
assert len(args) == len(Laplacian.required_grid_args())
grid_vars = {
k: v
for k, v in zip(Laplacian.required_grid_args(), args)
}
laplacian = Laplacian(**grid_vars)
np = get_array_module(field)
field_bar = field.copy() # Initalize the filtering process
# prepare field for filtering (this multiplies by area for simple fixed factor
# filters, and does nothing for all other filters)
field_bar = laplacian.prepare(field_bar)
T_minus_2 = field_bar.copy()
T_minus_1 = shifted_laplacian(field_bar, filter_spec.s_max, laplacian,
filter_spec.dx_min_sq)
field_bar = filter_spec.p[0] * T_minus_2 + filter_spec.p[1] * T_minus_1
for i in range(2, filter_spec.n_steps + 1):
T_minus_0 = (
2 * shifted_laplacian(T_minus_1, filter_spec.s_max, laplacian,
filter_spec.dx_min_sq) - T_minus_2)
field_bar += filter_spec.p[i] * T_minus_0
T_minus_2 = T_minus_1.copy()
T_minus_1 = T_minus_0.copy()
# finalize filtering (this divides by area for simple fixed factor filters,
# and does nothing for all other filters)
field_bar = laplacian.finalize(field_bar)
return field_bar
def filter_func(field, *args):
# these next steps are a kind of hack we have to turn keyword arugments into regular arguments
# the reason for doing this is that Xarray's apply_ufunc machinery works a lot better
# with regular arguments
assert len(args) == len(Laplacian.required_grid_args())
grid_vars = {
k: v
for k, v in zip(Laplacian.required_grid_args(), args)
}
laplacian = Laplacian(**grid_vars)
np = get_array_module(field)
field_bar = field.copy() # Initalize the filtering process
# prepare field for filtering (this multiplies by area for simple fixed factor
# filters, and does nothing for all other filters)
field_bar = laplacian.prepare(field_bar)
for i in range(filter_spec.n_steps_total):
if filter_spec.is_laplacian[i]:
s_l = np.real(filter_spec.s[i])
tendency = laplacian(field_bar) # Compute Laplacian
field_bar += (1 / s_l) * tendency # Update filtered field
else:
s_b = filter_spec.s[i]
temp_l = laplacian(field_bar) # Compute Laplacian
temp_b = laplacian(
temp_l) # Compute Biharmonic (apply Laplacian twice)
field_bar += (temp_l * 2 * np.real(s_b) / np.abs(s_b)**2 +
temp_b * 1 / np.abs(s_b)**2)
# finalize filtering (this divides by area for simple fixed factor filters,
# and does nothing for all other filters)
field_bar = laplacian.finalize(field_bar)
return field_bar
def filter_func(field, *args):
# these next steps are a kind of hack we have to turn keyword arugments into regular arguments
# the reason for doing this is that Xarray's apply_ufunc machinery works a lot better
# with regular arguments
assert len(args) == len(Laplacian.required_grid_args())
grid_vars = {
k: v
for k, v in zip(Laplacian.required_grid_args(), args)
}
laplacian = Laplacian(**grid_vars)
np = get_array_module(field)
field_bar = field.copy() # Initalize the filtering process
for i in range(filter_spec.n_lap_steps):
s_l = filter_spec.s_l[i]
tendency = laplacian(field_bar) # Compute Laplacian
field_bar += (1 / s_l) * tendency # Update filtered field
for i in range(filter_spec.n_bih_steps):
s_b = filter_spec.s_b[i]
temp_l = laplacian(field_bar) # Compute Laplacian
temp_b = laplacian(
temp_l) # Compute Biharmonic (apply Laplacian twice)
field_bar += (temp_l * 2 * np.real(s_b) / np.abs(s_b)**2 +
temp_b * 1 / np.abs(s_b)**2)
return field_bar