changed default alkalinity to prognostic in main setup only

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

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])

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

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)

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