diff --git a/github-actions/fabm0d-gotm-ersem/aquarium_tut.py b/github-actions/fabm0d-gotm-ersem/aquarium_tut.py index 3d8128b..1aa7ea8 100644 --- a/github-actions/fabm0d-gotm-ersem/aquarium_tut.py +++ b/github-actions/fabm0d-gotm-ersem/aquarium_tut.py @@ -62,12 +62,13 @@ def plot_var(axes, x, y, label): # Plotting output data output_data = ["N1_p", "N3_n", "B1_c", "P2_c"] - fig, ax_arr = plt.subplots(len(input_data), 1) + fig, ax_arr = plt.subplots(len(output_data), 1) DPI = fig.get_dpi() fig.set_size_inches(1200.0/float(DPI),800.0/float(DPI)) for ax, label in zip(ax_arr, output_data): plot_var(ax, dates, data, label) fig.suptitle("Output data") + print(test_vars.keys()) plt.show() return test_vars diff --git a/github-actions/fabm0d-gotm-ersem/expected.json b/github-actions/fabm0d-gotm-ersem/expected.json index 813a310..6168664 100644 --- a/github-actions/fabm0d-gotm-ersem/expected.json +++ b/github-actions/fabm0d-gotm-ersem/expected.json @@ -1 +1 @@ -{"dates": ["2008-01-01", 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b/github-actions/fabm0d-gotm-ersem/test_aquarium.py @@ -84,5 +84,5 @@ def test_ncarbon_value(self): """ Checks the nanophytoplankton carbon (P2_c) values are the same """ - name = "B1_c" + name = "P2_c" assert np.allclose(self.expected[name], self.value_dict[name]) diff --git a/github-actions/gotm-fabm-ersem/expected.json b/github-actions/gotm-fabm-ersem/expected.json index 3196f7b..3217f0f 100644 --- a/github-actions/gotm-fabm-ersem/expected.json +++ b/github-actions/gotm-fabm-ersem/expected.json @@ -1 +1 @@ -{"dates": ["2007-01-01", "2007-01-02", "2007-01-03", "2007-01-04", "2007-01-05", "2007-01-06", "2007-01-07", "2007-01-08", "2007-01-09", "2007-01-10", "2007-01-11", "2007-01-12", "2007-01-13", "2007-01-14", "2007-01-15", "2007-01-16", "2007-01-17", "2007-01-18", "2007-01-19", "2007-01-20", "2007-01-21", "2007-01-22", "2007-01-23", "2007-01-24", "2007-01-25", "2007-01-26", "2007-01-27", "2007-01-28", "2007-01-29", "2007-01-30", "2007-01-31", "2007-02-01", "2007-02-02", 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1.8516147136688232, "bL2_c": 0.12351033091545105, "ben_col_D1m": 0.01548770535737276, "ben_col_D2m": 0.22482170164585114, "K1_p": 6.130862712860107, "K3_n": 1.2952054738998413, "K4_n": 9.093433380126953, "K5_s": 12.652621269226074, "G2_o": 0.36975228786468506, "G2_o_deep": -8.361115455627441, "G3_c": 287.12603759765625, "ben_nit_G4n": 14.878911972045898, "H1_c": 56.31911087036133, "H2_c": 102.78858184814453, "Y2_c": 2978.969482421875, "Y3_c": 1995.1578369140625, "Y4_c": 161.73666381835938} \ No newline at end of file diff --git a/github-actions/gotm-fabm-ersem/regen_expected_results.py b/github-actions/gotm-fabm-ersem/regen_expected_results.py deleted file mode 100644 index 8cb7a0b..0000000 --- a/github-actions/gotm-fabm-ersem/regen_expected_results.py +++ /dev/null @@ -1,75 +0,0 @@ -""" -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) - diff --git a/github-actions/gotm-fabm-ersem/test_state_variables.py b/github-actions/gotm-fabm-ersem/test_state_variables.py index 961dfff..0f3bc80 100644 --- a/github-actions/gotm-fabm-ersem/test_state_variables.py +++ b/github-actions/gotm-fabm-ersem/test_state_variables.py @@ -83,14 +83,6 @@ def test_O3_c_value(self): assert np.array_equal( self.expected[name], self.data.variables[name][:].squeeze()[-1, :]) - def test_O3_bioalk_value(self): - """ - Checks the O3_bioalk values are the same - """ - name = "O3_bioalk" - assert np.array_equal( - self.expected[name], self.data.variables[name][:].squeeze()[-1, :]) - def test_R1_c_value(self): """ Checks the R1_c values are the same diff --git a/github-actions/regen_expected_results.py b/github-actions/regen_expected_results.py new file mode 100644 index 0000000..76063ca --- /dev/null +++ b/github-actions/regen_expected_results.py @@ -0,0 +1,85 @@ +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 model run') +parser.add_argument('-m', '--model-run', type=str, required=True, + help='Type of model run that has been run', + choices=["gotm", "fabm0d"]) +args, _ = parser.parse_known_args() +data_path = args.data_path +model_run = args.model_run + +dir_mapping = {"fabm0d": "fabm0d-gotm-ersem", "gotm": "gotm-fabm-ersem"} +if model_run == "gotm": + 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" + ] + vars_of_interest = ["dates", "N1_p", "N3_n", "N5_s"] + data_dict = {"expected": vars_of_interest, "expected_state": state_vars} + +elif model_run == "fabm0d": + vars_of_interest = [ + "dates", "light_parEIR", "temp", "salt", "N1_p", "N3_n", "B1_c", "P2_c" + ] + + data_dict = {"expected": vars_of_interest} + +for key, items in data_dict.items(): + data = nc.Dataset(data_path, 'r') + expected_results = {} + for v in items: + try: + 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 + elif key == "expected" and model_run == "gotm": + 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() \ + if model_run == "fabm0d" else expected_results[v][-1, :] + elif data.variables[v].ndim == 3: + expected_results[v] = data.variables[v][:].squeeze() \ + if model_run == "fabm0d" else \ + float(data.variables[v][:].squeeze()[-1]) + else: + raise RuntimeError + except Exception as e: + print(f"Did not update {v} since {e}") + + with open(f'{dir_mapping[model_run]}/{key}.json', 'w') as f: + json.dump(expected_results, f, cls=NumpyEncoder) diff --git a/testcases/fabm-ersem-15.06-L4-ben-docdyn-iop.yaml b/testcases/fabm-ersem-15.06-L4-ben-docdyn-iop.yaml index bf2a39b..456e45f 100644 --- a/testcases/fabm-ersem-15.06-L4-ben-docdyn-iop.yaml +++ b/testcases/fabm-ersem-15.06-L4-ben-docdyn-iop.yaml @@ -54,12 +54,11 @@ instances: parameters: iswCO2: 1 # carbonate system diagnostics (0: off, 1: on), default = 1 iswASFLUX: 1 # air-sea CO2 exchange (0: none, 1: Nightingale et al. 2000, 2: Wanninkhof 1992 without chemical enhancement, 3: Wanninkhof 1992 with chemical enhancement, 4: Wanninkhof and McGillis 1999, 5: Wanninkhof 1992 switching to Wanninkhof and McGillis 1999, 6: Wan, default = 6 - iswtalk: 2 # alkalinity formulation (1-4: from salinity and temperature, 5: dynamic alkalinity), default = 5 + iswtalk: 5 # alkalinity formulation (1-4: from salinity and temperature, 5: dynamic alkalinity), default = 5 pHscale: 1 # pH scale (1: total, 0: SWS, -1: SWS backward compatible), default = 1 - iswbioalk: 1 # use bioalkalinity (0: off, 1: on), default = 1 initialization: c: 2130.0 # total dissolved inorganic carbon (mmol C/m^3) - bioalk: 0 # bioalkalinity (mmol/m^3) + TA: 2300.0 # total alkalinity (mmol/m^3) R1: long_name: labile dissolved organic matter model: ersem/pelagic_base