def test_error_zdim(self):
# Test error:
# catches error from _regular_interp linked to z_dim
with pytest.raises(RuntimeError):
linear_interpolation_remap(
self.dsfake["PRES"],
self.dsfake["TEMP"],
self.dsfake["Z_LEVELS"],
z_regridded_dim="Z_LEVELS",
)
def test_error_ds(self):
# Test error:
# catches error from linear_interpolation_remap linked to datatype
with pytest.raises(ValueError):
linear_interpolation_remap(
self.dsfake["PRES"],
self.dsfake,
self.dsfake["Z_LEVELS"],
z_dim="N_LEVELS",
z_regridded_dim="Z_LEVELS",
)
def test_interpolation(self):
# Run it with success:
dsi = linear_interpolation_remap(self.dsfake.PRES,
self.dsfake['TEMP'],
self.dsfake['Z_LEVELS'],
z_dim='N_LEVELS',
z_regridded_dim='Z_LEVELS')
assert 'remapped' in dsi.dims
def test_error_zdim(self):
# Test error:
# catches error from _regular_interp linked to z_dim
with pytest.raises(RuntimeError):
dsi = linear_interpolation_remap(self.dsfake.PRES,
self.dsfake['TEMP'],
self.dsfake['Z_LEVELS'],
z_regridded_dim='Z_LEVELS')
def test_interpolation_1d(self):
# Run it with success:
dsi = linear_interpolation_remap(
self.dsfake["PRES"].isel(N_PROF=0),
self.dsfake["TEMP"].isel(N_PROF=0),
self.dsfake["Z_LEVELS"],
z_regridded_dim="Z_LEVELS",
)
assert "remapped" in dsi.dims
def test_error_ds(self):
# Test error:
# catches error from linear_interpolation_remap linked to datatype
with pytest.raises(ValueError):
dsi = linear_interpolation_remap(self.dsfake.PRES,
self.dsfake,
self.dsfake['Z_LEVELS'],
z_dim='N_LEVELS',
z_regridded_dim='Z_LEVELS')
def interp_std_levels(self, std_lev):
""" Returns a new dataset interpolated to new inputs levels
Parameters
----------
list or np.array
Standard levels used for interpolation
Returns
-------
:class:`xarray.Dataset`
"""
if self._type != 'profile':
raise InvalidDatasetStructure(
"Method only available for a collection of profiles")
if (self._mode != 'standard'):
raise InvalidDatasetStructure(
"Method only available for the standard mode yet")
if (type(std_lev) is np.ndarray) | (type(std_lev) is list):
std_lev = np.array(std_lev)
if (np.any(sorted(std_lev) != std_lev)) | (np.any(std_lev < 0)):
raise ValueError(
'Standard levels must be a list or a numpy array of positive and sorted values'
)
else:
raise ValueError(
'Standard levels must be a list or a numpy array of positive and sorted values'
)
ds = self._obj
# Selecting profiles that have a max(pressure) > max(std_lev) to avoid extrapolation in that direction
# For levels < min(pressure), first level values of the profile are extended to surface.
i1 = (ds['PRES'].max('N_LEVELS') >= std_lev[-1])
dsp = ds.where(i1, drop=True)
# check if any profile is left, ie if any profile match the requested depth
if (len(dsp['N_PROF']) == 0):
raise Warning(
'None of the profiles can be interpolated (not reaching the requested depth range).'
)
return None
# add new vertical dimensions, this has to be in the datasets to apply ufunc later
dsp['Z_LEVELS'] = xr.DataArray(std_lev, dims={'Z_LEVELS': std_lev})
# init
ds_out = xr.Dataset()
# vars to interpolate
datavars = [
dv for dv in list(dsp.variables) if
set(['N_LEVELS', 'N_PROF']) == set(dsp[dv].dims) and 'QC' not in dv
]
# coords
coords = [dv for dv in list(dsp.coords)]
# vars depending on N_PROF only
solovars = [
dv for dv in list(dsp.variables)
if dv not in datavars and dv not in coords and 'QC' not in dv
]
for dv in datavars:
ds_out[dv] = linear_interpolation_remap(dsp.PRES,
dsp[dv],
dsp['Z_LEVELS'],
z_dim='N_LEVELS',
z_regridded_dim='Z_LEVELS')
ds_out = ds_out.rename({'remapped': 'PRES_INTERPOLATED'})
for sv in solovars:
ds_out[sv] = dsp[sv]
for co in coords:
ds_out.coords[co] = dsp[co]
ds_out = ds_out.drop_vars(['N_LEVELS', 'Z_LEVELS'])
ds_out = ds_out[np.sort(ds_out.data_vars)]
ds_out.attrs = self.attrs # Preserve original attributes
ds_out.argo._add_history('Interpolated on standard levels')
return ds_out