Add new tests

pyrte_rrtmgp/kernels/rrtmgp.py

@@ -9,8 +9,10 @@
     rrtmgp_compute_tau_rayleigh,
     rrtmgp_interpolation,
 )
+from pyrte_rrtmgp.utils import convert_xarray_args
 
 
+@convert_xarray_args
 def interpolation(
     neta: int,
     flavor: npt.NDArray,
@@ -111,6 +113,7 @@ def interpolation(
     return jtemp.T, fmajor.T, fminor.T, col_mix.T, tropo.T, jeta.T, jpress.T
 
 
+@convert_xarray_args
 def compute_planck_source(
     tlay,
     tlev,
@@ -208,6 +211,7 @@ def compute_planck_source(
     return sfc_src.T, lay_src.T, lev_src.T, sfc_src_jac.T
 
 
+@convert_xarray_args
 def compute_tau_absorption(
     idx_h2o,
     gpoint_flavor,
@@ -337,6 +341,7 @@ def compute_tau_absorption(
     return tau.T
 
 
+@convert_xarray_args
 def compute_tau_rayleigh(
     gpoint_flavor,
     band_lims_gpt,

pyrte_rrtmgp/rrtmgp_gas_optics.py

@@ -23,6 +23,8 @@
 @dataclass
 class GasOptics:
     tau: Optional[np.ndarray] = None
+    tau_rayleigh: Optional[np.ndarray] = None
+    tau_absorption: Optional[np.ndarray] = None
     g: Optional[np.ndarray] = None
     ssa: Optional[np.ndarray] = None
     lay_src: Optional[np.ndarray] = None
@@ -310,6 +312,7 @@ def compute_gas_taus(self):
             self._interpolated.jpress,
         )
 
+        self.gas_optics.tau_absorption = tau_absorption
         if self.source_is_internal:
             self.gas_optics.tau = tau_absorption
             self.gas_optics.ssa = np.full_like(tau_absorption, np.nan)
@@ -332,6 +335,7 @@ def compute_gas_taus(self):
                 self._interpolated.jtemp,
             )
 
+            self.gas_optics.tau_rayleigh = tau_rayleigh
             self.gas_optics.tau = tau_absorption + tau_rayleigh
             self.gas_optics.ssa = np.where(
                 self.gas_optics.tau > 2.0 * np.finfo(float).tiny,

pyrte_rrtmgp/utils.py

@@ -1,4 +1,5 @@
 import numpy as np
+import xarray as xr
 
 
 def get_usecols(solar_zenith_angle):
@@ -41,3 +42,21 @@ def compute_toa_flux(total_solar_irradiance, solar_source):
     toa_flux = np.stack([solar_source] * ncol)
     def_tsi = toa_flux.sum(axis=1)
     return (toa_flux.T * (total_solar_irradiance / def_tsi)).T
+
+
+def convert_xarray_args(func):
+    def wrapper(*args, **kwargs):
+        output_args = []
+        for x in args:
+            if isinstance(x, xr.DataArray):
+                output_args.append(x.data)
+            else:
+                output_args.append(x)
+        for k, v in kwargs.items():
+            if isinstance(v, xr.DataArray):
+                kwargs[k] = v.data
+            else:
+                kwargs[k] = v
+        return func(*output_args, **kwargs)
+
+    return wrapper

tests/test_python_frontend/test_gas_optics.py

@@ -0,0 +1,193 @@
+import os
+
+import numpy as np
+import pytest
+import xarray as xr
+from pyrte_rrtmgp import rrtmgp_gas_optics
+from pyrte_rrtmgp.kernels.rrtmgp import (
+    compute_planck_source,
+    compute_tau_absorption,
+    compute_tau_rayleigh,
+    interpolation,
+)
+
+from utils import convert_args_arrays
+
+ERROR_TOLERANCE = 1e-4
+
+rte_rrtmgp_dir = os.environ.get("RRTMGP_DATA", "rrtmgp-data")
+clear_sky_example_files = f"{rte_rrtmgp_dir}/examples/rfmip-clear-sky/inputs"
+
+rfmip = xr.load_dataset(
+    f"{clear_sky_example_files}/multiple_input4MIPs_radiation_RFMIP_UColorado-RFMIP-1-2_none.nc"
+)
+rfmip = rfmip.sel(expt=0)  # only one experiment
+kdist = xr.load_dataset(f"{rte_rrtmgp_dir}/rrtmgp-gas-lw-g256.nc")
+kdist_sw = xr.load_dataset(f"{rte_rrtmgp_dir}/rrtmgp-gas-sw-g224.nc")
+
+rrtmgp_gas_optics = kdist.gas_optics.load_atmosferic_conditions(rfmip)
+rrtmgp_gas_optics_sw = kdist_sw.gas_optics.load_atmosferic_conditions(rfmip)
+
+# Prepare the arguments for the interpolation function
+interpolation_args = [
+    len(kdist["mixing_fraction"]),
+    kdist.gas_optics.flavors_sets,
+    kdist["press_ref"].values,
+    kdist["temp_ref"].values,
+    kdist["press_ref_trop"].values.item(),
+    kdist.gas_optics.vmr_ref,
+    rfmip["pres_layer"].values,
+    rfmip["temp_layer"].values,
+    kdist.gas_optics.col_gas,
+]
+
+expected_output = (
+    kdist.gas_optics._interpolated.jtemp,
+    kdist.gas_optics._interpolated.fmajor,
+    kdist.gas_optics._interpolated.fminor,
+    kdist.gas_optics._interpolated.col_mix,
+    kdist.gas_optics._interpolated.tropo,
+    kdist.gas_optics._interpolated.jeta,
+    kdist.gas_optics._interpolated.jpress,
+)
+
+
+@pytest.mark.parametrize(
+    "args, expected",
+    [(i, expected_output) for i in convert_args_arrays(interpolation_args)],
+)
+def test_compute_interpoaltion(args, expected):
+    result = interpolation(*args)
+    assert len(result) == len(expected)
+    for r, e in zip(result, expected):
+        assert r.shape == e.shape
+        assert np.isclose(r, e, atol=ERROR_TOLERANCE).all()
+
+
+# Prepare the arguments for the compute_planck_source function
+planck_source_args = [
+    rfmip["temp_layer"].data,
+    rfmip["temp_level"].data,
+    rfmip["surface_temperature"].data,
+    kdist.gas_optics.top_at_1,
+    kdist.gas_optics._interpolated.fmajor,
+    kdist.gas_optics._interpolated.jeta,
+    kdist.gas_optics._interpolated.tropo,
+    kdist.gas_optics._interpolated.jtemp,
+    kdist.gas_optics._interpolated.jpress,
+    kdist["bnd_limits_gpt"].data.T,
+    kdist["plank_fraction"].data.transpose(0, 2, 1, 3),
+    kdist["temp_ref"].data.min(),
+    kdist["temp_ref"].data.max(),
+    kdist["totplnk"].data.T,
+    kdist.gas_optics.gpoint_flavor,
+]
+
+expected_output = (
+    rrtmgp_gas_optics.sfc_src,
+    rrtmgp_gas_optics.lay_src,
+    rrtmgp_gas_optics.lev_src,
+    rrtmgp_gas_optics.sfc_src_jac,
+)
+
+
+@pytest.mark.parametrize(
+    "args, expected",
+    [(i, expected_output) for i in convert_args_arrays(planck_source_args)],
+)
+def test_compute_planck_source(args, expected):
+    result = compute_planck_source(*args)
+    assert len(result) == len(expected)
+    for r, e in zip(result, expected):
+        assert r.shape == e.shape
+        assert np.isclose(r, e, atol=ERROR_TOLERANCE).all()
+
+
+# Prepare the arguments for the compute_tau_absorption function
+minor_gases_lower = kdist.gas_optics.extract_names(kdist["minor_gases_lower"].data)
+minor_gases_upper = kdist.gas_optics.extract_names(kdist["minor_gases_upper"].data)
+idx_minor_lower = kdist.gas_optics.get_idx_minor(
+    kdist.gas_optics.gas_names, minor_gases_lower
+)
+idx_minor_upper = kdist.gas_optics.get_idx_minor(
+    kdist.gas_optics.gas_names, minor_gases_upper
+)
+
+scaling_gas_lower = kdist.gas_optics.extract_names(kdist["scaling_gas_lower"].data)
+scaling_gas_upper = kdist.gas_optics.extract_names(kdist["scaling_gas_upper"].data)
+idx_minor_scaling_lower = kdist.gas_optics.get_idx_minor(
+    kdist.gas_optics.gas_names, scaling_gas_lower
+)
+idx_minor_scaling_upper = kdist.gas_optics.get_idx_minor(
+    kdist.gas_optics.gas_names, scaling_gas_upper
+)
+
+tau_absorption_args = [
+    kdist.gas_optics.idx_h2o,
+    kdist.gas_optics.gpoint_flavor,
+    kdist["bnd_limits_gpt"].values.T,
+    kdist["kmajor"].values,
+    kdist["kminor_lower"].values,
+    kdist["kminor_upper"].values,
+    kdist["minor_limits_gpt_lower"].values.T,
+    kdist["minor_limits_gpt_upper"].values.T,
+    kdist["minor_scales_with_density_lower"].values.astype(bool),
+    kdist["minor_scales_with_density_upper"].values.astype(bool),
+    kdist["scale_by_complement_lower"].values.astype(bool),
+    kdist["scale_by_complement_upper"].values.astype(bool),
+    idx_minor_lower,
+    idx_minor_upper,
+    idx_minor_scaling_lower,
+    idx_minor_scaling_upper,
+    kdist["kminor_start_lower"].values,
+    kdist["kminor_start_upper"].values,
+    kdist.gas_optics._interpolated.tropo,
+    kdist.gas_optics._interpolated.col_mix,
+    kdist.gas_optics._interpolated.fmajor,
+    kdist.gas_optics._interpolated.fminor,
+    rfmip["pres_layer"].values,
+    rfmip["temp_layer"].values,
+    kdist.gas_optics.col_gas,
+    kdist.gas_optics._interpolated.jeta,
+    kdist.gas_optics._interpolated.jtemp,
+    kdist.gas_optics._interpolated.jpress,
+]
+
+
+@pytest.mark.parametrize(
+    "args, expected",
+    [
+        (i, rrtmgp_gas_optics.tau_absorption)
+        for i in convert_args_arrays(tau_absorption_args)
+    ],
+)
+def test_compute_tau_absorption(args, expected):
+    result = compute_tau_absorption(*args)
+    assert np.isclose(result, expected, atol=ERROR_TOLERANCE).all()
+
+
+# Prepare the arguments for the compute_tau_rayleigh function
+tau_rayleigh_args = [
+    kdist_sw.gas_optics.gpoint_flavor,
+    kdist_sw["bnd_limits_gpt"].values.T,
+    np.stack([kdist_sw["rayl_lower"].values, kdist_sw["rayl_upper"].values], axis=-1),
+    kdist_sw.gas_optics.idx_h2o,
+    kdist_sw.gas_optics.col_gas[:, :, 0],
+    kdist_sw.gas_optics.col_gas,
+    kdist_sw.gas_optics._interpolated.fminor,
+    kdist_sw.gas_optics._interpolated.jeta,
+    kdist_sw.gas_optics._interpolated.tropo,
+    kdist_sw.gas_optics._interpolated.jtemp,
+]
+
+
+@pytest.mark.parametrize(
+    "args, expected",
+    [
+        (i, rrtmgp_gas_optics_sw.tau_rayleigh)
+        for i in convert_args_arrays(tau_rayleigh_args)
+    ],
+)
+def test_compute_tau_rayleigh(args, expected):
+    result = compute_tau_rayleigh(*args)
+    assert np.isclose(result, expected, atol=ERROR_TOLERANCE).all()

tests/test_python_frontend/utils.py

@@ -0,0 +1,24 @@
+import numpy as np
+import xarray as xr
+
+
+def convert_args_arrays(input_args, arrays_dtypes=[np.float64, np.float32]):
+    args_to_test = []
+    for dtype in arrays_dtypes:
+        args = []
+        for item in input_args:
+            if isinstance(item, np.ndarray) and item.dtype in arrays_dtypes:
+                output_item = item.astype(dtype)
+            else:
+                output_item = item
+            args.append(output_item)
+    args_to_test.append(args)
+    args = []
+    for item in input_args:
+        if isinstance(item, np.ndarray) and item.dtype in arrays_dtypes:
+            output_item = xr.DataArray(item)
+        else:
+            output_item = item
+        args.append(output_item)
+    args_to_test.append(args)
+    return args_to_test