[JTH] little change in binwaves swan example

Author: tausiaj

bluemath_tk/wrappers/swan/swan_example.py

@@ -5,177 +5,10 @@
 import xarray as xr
 from wavespectra.construct import construct_partition
 
-from bluemath_tk.waves.binwaves import (
-    generate_swan_cases,
-    process_kp_coefficients,
-    reconstruc_spectra,
-)
+from bluemath_tk.waves.binwaves import generate_swan_cases
 from bluemath_tk.wrappers.swan.swan_wrapper import SwanModelWrapper
 
 example_directions = [
-    1.5,
-    4.5,
-    7.5,
-    10.5,
-    13.5,
-    16.5,
-    19.5,
-    22.5,
-    25.5,
-    28.5,
-    31.5,
-    34.5,
-    37.5,
-    40.5,
-    43.5,
-    46.5,
-    49.5,
-    52.5,
-    55.5,
-    58.5,
-    61.5,
-    64.5,
-    67.5,
-    70.5,
-    73.5,
-    76.5,
-    79.5,
-    82.5,
-    85.5,
-    88.5,
-    91.5,
-    94.5,
-    97.5,
-    100.5,
-    103.5,
-    106.5,
-    109.5,
-    112.5,
-    115.5,
-    118.5,
-    121.5,
-    124.5,
-    127.5,
-    130.5,
-    133.5,
-    136.5,
-    139.5,
-    142.5,
-    145.5,
-    148.5,
-    151.5,
-    154.5,
-    157.5,
-    160.5,
-    163.5,
-    166.5,
-    169.5,
-    172.5,
-    175.5,
-    178.5,
-    181.5,
-    184.5,
-    187.5,
-    190.5,
-    193.5,
-    196.5,
-    199.5,
-    202.5,
-    205.5,
-    208.5,
-    211.5,
-    214.5,
-    217.5,
-    220.5,
-    223.5,
-    226.5,
-    229.5,
-    232.5,
-    235.5,
-    238.5,
-    241.5,
-    244.5,
-    247.5,
-    250.5,
-    253.5,
-    256.5,
-    259.5,
-    262.5,
-    265.5,
-    268.5,
-    271.5,
-    274.5,
-    277.5,
-    280.5,
-    283.5,
-    286.5,
-    289.5,
-    292.5,
-    295.5,
-    298.5,
-    301.5,
-    304.5,
-    307.5,
-    310.5,
-    313.5,
-    316.5,
-    319.5,
-    322.5,
-    325.5,
-    328.5,
-    331.5,
-    334.5,
-    337.5,
-    340.5,
-    343.5,
-    346.5,
-    349.5,
-    352.5,
-    355.5,
-    358.5,
-]
-example_frequencies = [
-    0.03,
-    0.033,
-    0.0363,
-    0.0399,
-    0.0438,
-    0.0482,
-    0.053,
-    0.0582,
-    0.064,
-    0.0704,
-    0.0774,
-    0.0851,
-    0.0935,
-    0.1028,
-    0.1131,
-    0.1243,
-    0.1367,
-    0.1503,
-    0.1652,
-    0.1816,
-    0.1997,
-    0.2195,
-    0.2413,
-    0.2653,
-    0.2917,
-    0.3207,
-    0.3526,
-    0.3876,
-    0.4262,
-    0.4685,
-    0.5151,
-    0.5663,
-    0.6226,
-    0.6845,
-    0.7525,
-    0.8273,
-    0.9096,
-    1.0,
-]
-
-laura_directions = [
     262.5,
     247.5,
     232.5,
@@ -201,7 +34,7 @@
     292.5,
     277.5,
 ]
-laura_frequencies = [
+example_frequencies = [
     0.03500004,
     0.03850004,
     0.04234991,
@@ -234,55 +67,6 @@
 ]
 
 
-def transform_CAWCR_WS(
-    cawcr_dataset: xr.Dataset,
-    subset_parameters: dict,
-    available_case_num: np.ndarray,
-) -> xr.Dataset:
-    """
-    Transform the wave spectra from CAWCR format to binwaves format.
-
-    Parameters
-    ----------
-    cawcr_dataset : xr.Dataset
-        The wave spectra dataset in CAWCR format.
-    subset_parameters : dict
-        A dictionary containing parameters for the subset processing.
-    available_case_num : np.ndarray
-        The available case numbers.
-
-    Returns
-    -------
-    xr.Dataset
-        The wave spectra dataset in binwaves format.
-    """
-
-    # First, reproject the wave spectra to the binwaves format
-    ds = cawcr_dataset.rename({"frequency": "freq", "direction": "dir"})
-    ds["efth"] = ds["efth"] * np.pi / 180.0
-    ds["dir"] = ds["dir"] - 180.0
-    ds["dir"] = np.where(ds["dir"] < 0, ds["dir"] + 360, ds["dir"])
-    ds = ds.sortby("dir").sortby("freq")
-
-    # Second, reproject into the available case numbers dimension
-    case_num_spectra = []
-    for case_num, case_dir, case_freq in zip(
-        available_case_num,
-        np.array(subset_parameters.get("dir"))[available_case_num],
-        np.array(subset_parameters.get("freq"))[available_case_num],
-    ):
-        case_num_spectra.append(
-            ds.efth.sel(freq=case_freq, dir=case_dir, method="nearest").expand_dims(
-                {"case_num": [case_num]}
-            )
-        )
-    ds_case_num = (
-        xr.concat(case_num_spectra, dim="case_num").drop_vars("dir").drop_vars("freq")
-    )
-
-    return ds, ds_case_num
-
-
 class BinWavesWrapper(SwanModelWrapper):
     """
     Wrapper example for the BinWaves model.
@@ -292,13 +76,13 @@ def build_case(self, case_dir: str, case_context: dict):
         input_spectrum = construct_partition(
             freq_name="jonswap",
             freq_kwargs={
-                "freq": laura_frequencies,
+                "freq": example_frequencies,
                 "fp": 1.0 / case_context.get("tp"),
                 "hs": case_context.get("hs"),
             },
             dir_name="cartwright",
             dir_kwargs={
-                "dir": laura_directions,
+                "dir": example_directions,
                 "dm": case_context.get("dir"),
                 "dspr": case_context.get("spr"),
             },
@@ -339,8 +123,8 @@ def build_cases(self, mode="one_by_one"):
     # Generate swan model parameters
     model_parameters = (
         generate_swan_cases(
-            directions_array=laura_directions,
-            frequencies_array=laura_frequencies,
+            directions_array=example_directions,
+            frequencies_array=example_frequencies,
         )
         .astype(float)
         .to_dataframe()
@@ -366,26 +150,3 @@ def build_cases(self, mode="one_by_one"):
     # postprocessed_ds = swan_wrapper.postprocess_cases()
     # postprocessed_ds.to_netcdf(op.join(swan_wrapper.output_dir, "waves_part.nc"))
     # print(postprocessed_ds)
-    # Get input and ouput spectra files from self.cases_dirs
-    input_files = [op.join(d, "input_spectra.bnd") for d in swan_wrapper.cases_dirs]
-    output_files = [op.join(d, "output.spec") for d in swan_wrapper.cases_dirs]
-    # Extract binwaves kp coeffs
-    kp_coeffs = process_kp_coefficients(
-        list_of_input_spectra=input_files,
-        list_of_output_spectra=output_files,
-    )
-    # Load interest spectra
-    _, offshore_spectra = transform_CAWCR_WS(
-        cawcr_dataset=xr.open_dataset(
-            "/home/tausiaj/GitHub-GeoOcean/BlueMath/test_data/ERA5_full.nc"
-        ),
-        subset_parameters=model_parameters,
-        available_case_num=kp_coeffs.case_num.values,
-    )
-    # Reconstruct spectra
-    onshore_spectra = reconstruc_spectra(
-        offshore_spectra=offshore_spectra,
-        kp_coeffs=kp_coeffs,
-    )
-    onshore_spectra.to_netcdf(op.join(swan_wrapper.output_dir, "onshore_spectra.nc"))
-    print(onshore_spectra)