Updating gotm version in tests (#117)

* Updating gotm version in tests

* Fixing 0D GOTM version

* updating gotm clone

* Adding runtime flag to gotm tests

* adding script to regenerate expected values for state var test

* Updating state vars expected results

* Updating gotm tests expected values and regen script

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"

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

github-actions/gotm-fabm-ersem/gotm_tut.py

@@ -6,6 +6,7 @@
 import sys
 import re
 
+
 try:
     import netCDF4 as nc
 except ImportError:

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)
+