def test_recursively_compute_variable_multi_level(load_variable_data_loader):
one_level = Var(
name='one_level', variables=(condensation_rain, condensation_rain),
func=lambda x, y: x + y)
multi_level = Var(
name='multi_level', variables=(one_level, condensation_rain),
func=lambda x, y: x + y)
result = load_variable_data_loader.recursively_compute_variable(
multi_level, DatetimeNoLeap(5, 1, 1), DatetimeNoLeap(5, 12, 31),
intvl_in='monthly')
filepath = os.path.join(os.path.split(ROOT_PATH)[0], 'netcdf',
'00050101.precip_monthly.nc')
expected = 3. * _open_ds_catch_warnings(filepath)['condensation_rain']
np.testing.assert_array_equal(result.values, expected.values)
def test_recursively_compute_grid_attr_multi_level(load_variable_data_loader):
one_level = Var(name='one_level', variables=(bk, ), func=lambda x: 2 * x)
multi_level = Var(name='multi_level',
variables=(one_level, bk),
func=lambda x, y: x + y)
result = load_variable_data_loader.recursively_compute_variable(
multi_level,
DatetimeNoLeap(5, 1, 1),
DatetimeNoLeap(5, 12, 31),
model=example_model,
intvl_in='monthly')
filepath = os.path.join(
os.path.split(ROOT_PATH)[0], 'netcdf', '00060101.sphum_monthly.nc')
expected = 3 * xr.open_dataset(filepath)['bk']
np.testing.assert_array_equal(result.values, expected.values)
def test_recursively_compute_grid_attr_error(load_variable_data_loader):
# Should fail because zsurf is not provided to the example_model object
zsurf = Var(name=ZSURF_STR, def_time=False, def_vert=False,
def_lon=True, def_lat=True)
with pytest.raises(AttributeError):
load_variable_data_loader.recursively_compute_variable(
zsurf, DatetimeNoLeap(5, 1, 1),
DatetimeNoLeap(5, 12, 31), model=example_model,
intvl_in='monthly')
def recursive_test_params():
basic_params = {
'proj': example_proj,
'model': example_model,
'run': example_run,
'var': condensation_rain,
'date_range': (datetime.datetime(4, 1,
1), datetime.datetime(6, 12, 31)),
'intvl_in': 'monthly',
'dtype_in_time': 'ts'
}
recursive_params = basic_params.copy()
recursive_condensation_rain = Var(name='recursive_condensation_rain',
variables=(precip, convection_rain),
func=lambda x, y: x - y,
def_time=True)
recursive_params['var'] = recursive_condensation_rain
yield (basic_params, recursive_params)
_clean_test_direcs()
precip_largescale, precip_convective : xarray.DataArrays
Precipitation from grid-scale condensation and from convective
parameterization, respectively.
Returns
-------
xarray.DataArray
"""
total = total_precip(precip_largescale, precip_convective)
# Mask using xarray's `where` method to prevent divide-by-zero.
return precip_convective / total.where(total)
precip_largescale = Var(
name='precip_largescale',
alt_names=('condensation_rain', ),
def_time=True,
description='Precipitation generated via grid-scale condensation',
)
precip_convective = Var(
name='precip_convective',
alt_names=('convection_rain', ),
def_time=True,
description='Precipitation generated by convective parameterization',
)
precip_total = Var(
name='precip_total',
def_time=True,
func=total_precip,
variables=(precip_largescale, precip_convective),
runs=[example_run],
load_grid_data=True,
grid_attrs={
LAND_MASK_STR: 'custom_land_mask',
LON_STR: 'custom_lon'
})
example_proj = Proj('example_proj',
direc_out=os.path.join(os.path.dirname(__file__),
'test-files'),
tar_direc_out=os.path.join(os.path.dirname(__file__),
'test-tar-files'),
models=[example_model])
var_not_time_defined = Var(
name='var_no_time_def',
def_time=False,
)
condensation_rain = Var(
name='condensation_rain',
alt_names=('prec_ls', ),
def_time=True,
description=('condensation rain'),
)
convection_rain = Var(
name='convection_rain',
alt_names=('prec_conv', ),
def_time=True,
description=('convection rain'),
)
'00060101.sphum_monthly.nc'),
os.path.join(
os.path.split(ROOT_PATH)[0],
'netcdf', 'im.landmask.nc')), ),
runs=[example_run])
example_proj = Proj('example_proj',
direc_out=os.path.join(os.path.dirname(__file__),
'test-files'),
tar_direc_out=os.path.join(os.path.dirname(__file__),
'test-tar-files'),
models=[example_model])
condensation_rain = Var(
name='condensation_rain',
alt_names=('prec_ls', ),
def_time=True,
description=('condensation rain'),
)
convection_rain = Var(
name='convection_rain',
alt_names=('prec_conv', ),
def_time=True,
description=('convection rain'),
)
precip = Var(name='total_precipitation',
def_time=True,
description=('total precipitation rate'),
func=total_precipitation,
variables=(convection_rain, condensation_rain))
base = GFDLDataLoader(
data_direc=rootdir,
data_dur=5,
data_start_date=datetime(1980, 1, 1),
data_end_date=datetime(1999, 12, 31),
)
example_run = Run(
name='relative humidity examination',
description='An examination of relative humidity 1980 => 2000',
data_loader=base)
example_model = Model(name='example_model',
runs=[example_run],
grid_file_paths=(os.path.join(rootdir, 'atmos', 'ts',
'monthly',
'zg_A1.199501-199912.nc')))
example_proj = Proj('example_proj',
direc_out=output_dir,
models=[example_model])
hur = Var(name='hur', def_time=True, description='Relative humidity')
globe = Region(name='globe',
description='Entire globe',
west_bound=0,
east_bound=360,
south_bound=-90,
north_bound=90,
do_land_mask=False)