diff --git a/github-actions/fabm0d-gotm-ersem/fabm0d-gotm-ersem-build.sh b/github-actions/fabm0d-gotm-ersem/fabm0d-gotm-ersem-build.sh index cc7561b..0fa5bc1 100755 --- a/github-actions/fabm0d-gotm-ersem/fabm0d-gotm-ersem-build.sh +++ b/github-actions/fabm0d-gotm-ersem/fabm0d-gotm-ersem-build.sh @@ -19,6 +19,7 @@ git clone https://github.com/fabm-model/fabm.git echo "Cloning GOTM" git clone https://github.com/gotm-model/code.git gotm +# Od driver needs a stable version of GOTM, currently that is v6 cd gotm && git checkout v6.0 && git submodule update --init --recursive && cd .. echo "Checking out branch: $BRANCH" diff --git a/github-actions/gotm-fabm-ersem/expected.json b/github-actions/gotm-fabm-ersem/expected.json index ecd0ab6..3196f7b 100644 --- a/github-actions/gotm-fabm-ersem/expected.json +++ b/github-actions/gotm-fabm-ersem/expected.json @@ -1 +1 @@ -{"N1_p": [0.4000000059604645, 0.40233561396598816, 0.4107895791530609, 0.41989579796791077, 0.4281437397003174, 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b/github-actions/gotm-fabm-ersem/gotm-tut-config-setup.sh index 0f9a804..30e70b4 100755 --- a/github-actions/gotm-fabm-ersem/gotm-tut-config-setup.sh +++ b/github-actions/gotm-fabm-ersem/gotm-tut-config-setup.sh @@ -7,4 +7,4 @@ cp ersem/testcases/fabm-ersem-15.06-L4-ben-docdyn-iop.yaml ersem-setups/L4/fabm. cd ersem-setups/L4 echo "Running GOTM with repo configuration" -~/local/gotm/bin/gotm +~/local/gotm/bin/gotm --ignore_unknown_config diff --git a/github-actions/gotm-fabm-ersem/gotm_tut.py b/github-actions/gotm-fabm-ersem/gotm_tut.py index 5356be2..e82f086 100644 --- a/github-actions/gotm-fabm-ersem/gotm_tut.py +++ b/github-actions/gotm-fabm-ersem/gotm_tut.py @@ -6,6 +6,7 @@ import sys import re + try: import netCDF4 as nc except ImportError: diff --git a/github-actions/gotm-fabm-ersem/regen_expected_results.py b/github-actions/gotm-fabm-ersem/regen_expected_results.py new file mode 100644 index 0000000..8cb7a0b --- /dev/null +++ b/github-actions/gotm-fabm-ersem/regen_expected_results.py @@ -0,0 +1,75 @@ +""" +Script that regenerates expected results. You will need to install GOTM on your machine +first and use those results to regenerate the expected values +""" + + +import argparse +import json +import netCDF4 as nc +from numpy import ndarray, interp + +class NumpyEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, ndarray): + temp = [float(v) for v in obj.tolist()] + return temp + return json.JSONEncoder.default(self, obj) + +parser = argparse.ArgumentParser() +parser.add_argument('-p', '--data-path', type=str, required=True, + help='Path to output file from GOTM run') +args, _ = parser.parse_known_args() +data_path = args.data_path + +state_vars = \ + ["N1_p" , "N3_n" , "N4_n" , "N5_s" , "O2_o" , "O3_c" , "O3_bioalk" , "R1_c" , "R1_n" , + "R1_p" , "R2_c" , "R3_c" , "R4_c" , "R4_n" , "R4_p" , "R6_c" , "R6_n" , "R6_p" , + "R6_s" , "R8_c" , "R8_n" , "R8_p" , "R8_s" , "B1_c" , "B1_n" , "B1_p" , "P1_c" , + "P1_n" , "P1_p" , "P1_Chl" , "P1_s" , "P2_c" , "P2_n" , "P2_p" , "P2_Chl" , "P3_c" , + "P3_n" , "P3_p" , "P3_Chl" , "P4_c" , "P4_n" , "P4_p" , "P4_Chl" , "Z4_c" , "Z5_c" , + "Z5_n" , "Z5_p" , "Z6_c" , "Z6_n" , "Z6_p" , "L2_c" , "Q1_c" , "Q1_p" , "Q1_n" , + "Q6_c" , "Q6_p" , "Q6_n" , "Q6_s" , "Q6_pen_depth_c" , "Q6_pen_depth_n" , + "Q6_pen_depth_p" , "Q6_pen_depth_s" , "Q7_c" , "Q7_p" , "Q7_n" , "Q7_pen_depth_c" , + "Q7_pen_depth_n" , "Q7_pen_depth_p" , "Q17_c" , "Q17_p" , "Q17_n" , "bL2_c" , + "ben_col_D1m" , "ben_col_D2m" , "K1_p" , "K3_n" , "K4_n" , "K5_s" , "G2_o" , + "G2_o_deep" , "G3_c" , "ben_nit_G4n" , "H1_c" , "H2_c" , "Y2_c" , "Y3_c" , + "Y4_c"] + +gotm_vars_test = ["dates", "N1_p", "N3_n", "N5_s"] + +data_dict = {"expected": gotm_vars_test, "expected_state": state_vars} + +for key, items in data_dict.items(): + data = nc.Dataset(data_path, 'r') + expected_results = {} + for v in items: + if key == "expected": + if v == "dates": + times = data.variables['time'] + dates = nc.num2date(times[:], + units=times.units, + calendar=times.calendar) + dates = [str(d).split(" ")[0] for d in dates] + expected_results[v] = dates + else: + depth = 0.0 + var = data.variables[v] + zi = data.variables['zi'][:].squeeze() + z = data.variables['z'][:].squeeze() + var_time_series = [] + for i in range(var.shape[0]): + depth_offset = depth + zi[i, -1] + var_time_series.append(interp(depth_offset, z[i, :], var[i, :].squeeze())) + expected_results[v] = var_time_series + + elif data.variables[v].ndim == 4: + expected_results[v] = data.variables[v][:].squeeze()[-1, :] + elif data.variables[v].ndim == 3: + expected_results[v] = float(data.variables[v][:].squeeze()[-1]) + else: + raise RuntimeError + + with open(f'{key}.json', 'w') as f: + json.dump(expected_results, f, cls=NumpyEncoder) +