def hat_avg_scalar(self, data, numfile=0):
rho = self.IO.load_from('data', 'RHO', numfile)
print(gmp.switch)
varshape = np.shape(rho)
rho = self.nfo_merge(rho)
rho_bar = self.IO.load_from('newdata', 'RHO_BAR', numfile)
X = self.IO.load_from('data', data, numfile)
X = self.nfo_merge(X)
X_hat = self.create_array(varshape)
math.hat_scalar(X, rho, rho_bar, self.c_area, self.c_mem_idx, X_hat)
return X_hat
def compute_T(self, filenum=0):
rho = self.IO.load_from('data', 'RHO', filenum)
varshape = list(np.shape(rho))
rho = self.nfo_merge(rho)
rho_bar = self.IO.load_from('newdata', 'RHO_BAR')
theta = self.IO.load_from('data', 'THETA_V', filenum)
exner = self.IO.load_from('data', 'EXNER', filenum)
T = self.create_array(varshape)
print('computing the true Temperature values ...')
phys.thet_ex_to_T(theta, exner, T)
self.IO.write_to('data', T, name='T',
attrs = {
'long_name': 'Temperature',
'coordinates': 'vlat vlon',
'_FillValue': float('nan'),
'grid_type' : 'unstructured'
}, filenum=filenum
)
del theta, exner
T = self.nfo_merge(T)
T_hat = self.create_array(varshape)
print('computing the density weighted coarse Temperature values ...')
math.hat_scalar(
T, rho, rho_bar,
self.c_area, self.c_mem_idx,
T_hat
)
self.IO.write_to('data', T_hat, name='T_HAT',
attrs={
'long_name': 'density weighted Temperature',
'coordinates': 'vlat vlon',
'_FillValue' : float('nan'),
'grid_type' : 'unstructured'
}, filenum=filenum
)
def test_hat_scalar_par(self):
gmp.set_parallel_proc(True)
return_array[:] = np.zeros((1, 3, 3))
math.hat_scalar(test_array, rho, [rho_bar], [total_area], c_mem_idx,
return_array)
self.assertTrue(([np.ones((3, 3))] == return_array).all())
def prepare_pres(self, filenum=0):
rho = self.IO.load_from('data', 'RHO', filenum)
varshape = list(np.shape(rho))
rho = self.nfo_merge(rho)
rho_bar = self.IO.load_from('newdata', 'RHO_BAR')
theta = self.IO.load_from('data', 'THETA_V', filenum)
theta = self.nfo_merge(theta)
theta_hat = self.create_array(varshape)
print('computing the density weighted coarse theta values ...')
math.hat_scalar(
theta, rho, rho_bar,
self.c_area, self.c_mem_idx,
theta_hat
)
self.IO.write_to('data', theta_hat, name='THETA_HAT',
attrs={
'long_name': 'density weighted theta_v',
'coordinates': 'vlat vlon',
'_FillValue' : float('nan'),
'grid_type' : 'unstructured'
}, filenum=filenum
)
exner = self.IO.load_from('data', 'EXNER', filenum)
exner = self.nfo_merge(exner)
exner_hat = self.create_array(varshape)
print('computing the density weighted coarse exner values ...')
math.hat_scalar(
exner, rho, rho_bar,
self.c_area, self.c_mem_idx, exner_hat
)
del exner
self.IO.write_to('data', exner_hat, name='EXNER_HAT',
attrs={
'long_name': 'density weighted exner pressure',
'coordinates': 'vlat vlon',
'_FillValue' : float('nan'),
'grid_type' : 'unstructured'
}, filenum=filenum
)
print('computing the coarse exner gradients')
exner_gradient = self.x_hat_gradients(exner_hat)
del exner_hat
self.IO.write_to('data', exner_gradient[:,0,:, ], name='DEXX',
attrs={
'long_name': 'x grad of density weighted exner pressure',
'coordinates': 'vlat vlon',
'_FillValue' : float('nan'),
'grid_type' : 'unstructured'
}, filenum=filenum
)
self.IO.write_to('data', exner_gradient[:,1,:, ], name='DEXY',
attrs={
'long_name': 'y grad of density weighted exner pressure',
'coordinates': 'vlat vlon',
'_FillValue' : float('nan'),
'grid_type' : 'unstructured'
}, filenum=filenum
)
del exner_gradient