Merge pull request #680 from PCMDI/multiple_write_bug

Multiple write bug

Src/cmor.c

@@ -6719,9 +6719,11 @@ int calculate_leadtime_coord(int var_id) {
     /* activate define mode */
     nc_redef(ncid);
     /* add leadtime */
-    if (ierr = nc_def_var(ncid, "leadtime", NC_DOUBLE, 1, &time_dim, &leadtime)) {
-        snprintf(msg, CMOR_MAX_STRING, "cannot add 'leadtime' variable");
-        cmor_handle_error(msg, CMOR_CRITICAL);
+    if (ierr = nc_inq_varid(ncid, "leadtime", &leadtime)) {
+        if (ierr = nc_def_var(ncid, "leadtime", NC_DOUBLE, 1, &time_dim, &leadtime)) {
+            snprintf(msg, CMOR_MAX_STRING, "cannot add 'leadtime' variable");
+            cmor_handle_error(msg, CMOR_CRITICAL);
+        }
     }
 
     /* variable attributes */

Test/test_python_forecast_coordinates.py

@@ -2,6 +2,7 @@
 import numpy as np
 import cmor
 import unittest
+import os
 
 from netCDF4 import Dataset
 
@@ -31,7 +32,7 @@
     "parent_activity_id": "no parent",
     "physics_index": "1",
     "realization_index": "1",
-    "source": "HadGEM3-GC31-LL",
+    "source": "HadGEM3-GC31-LL (2016): \naerosol: UKCA-GLOMAP-mode\natmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km)\natmosChem: none\nland: JULES-HadGEM3-GL7.1\nlandIce: none\nocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m)\nocnBgchem: none\nseaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude)",
     "source_id": "HadGEM3-GC31-LL",
     "source_type": "AGCM",
     "sub_experiment": "none",
@@ -59,6 +60,8 @@ def setUp(self):
         if error_flag:
             raise RuntimeError("CMOR dataset_json call failed")
 
+
+    def test_has_forcast_coordinates(self):
         # load MIP table
         mip_table = "CMIP6_Amon_leadtime.json"
         table_id = cmor.load_table(mip_table)
@@ -82,13 +85,101 @@ def setUp(self):
         reshaped_data = data.reshape((5, 5, 2, 1, 1))
         # write data
         cmor.write(tas_var_id, reshaped_data)
-        self.filename = cmor.close(tas_var_id, file_name=True)
+        filename = cmor.close(tas_var_id, file_name=True)
 
-    def test_has_forcast_coordinates(self):
-        ds = Dataset(self.filename)
+        ds = Dataset(filename)
         np.testing.assert_array_equal(ds.variables['reftime'][:].data, [10.0])
         np.testing.assert_array_equal(ds.variables['leadtime'][:].data, [20.0, 50.0])
         ds.close()
+        os.remove(filename)
+
+
+class TestWriteSlices(unittest.TestCase):
+
+    def setUp(self):
+        """
+        Write out a simple file using CMOR
+        """
+        # Set up CMOR
+        cmor.setup(inpath="TestTables", netcdf_file_action=cmor.CMOR_REPLACE,
+                   logfile="cmor.log", create_subdirectories=0)
+
+        # Define dataset using DATASET_INFO
+        with open("Test/input_leadtime.json", "w") as input_file_handle:
+            json.dump(DATASET_INFO, input_file_handle, sort_keys=True, indent=4)
+
+        # read dataset info
+        error_flag = cmor.dataset_json("Test/input_leadtime.json")
+        if error_flag:
+            raise RuntimeError("CMOR dataset_json call failed")
+
+
+    def test_multiple_writes(self):
+        # load MIP table
+        mip_table = "CMIP6_Amon.json"
+        table_id = cmor.load_table(mip_table)
+
+        # construct axes
+        time = cmor.axis(table_entry="time", units="days since 2000-01-01")
+        height2m = cmor.axis(table_entry="height2m", units="m", coord_vals=np.array((2.0,)))
+        latitude = cmor.axis(table_entry="latitude", units="degrees_north",
+                             coord_vals=np.array(range(5)),
+                             cell_bounds=np.array(range(6)))
+        longitude = cmor.axis(table_entry="longitude", units="degrees_east",
+                              coord_vals=np.array(range(5)),
+                              cell_bounds=np.array(range(6)))
+        axis_ids = [longitude, latitude, time, height2m]
+        tas_var_id = cmor.variable(table_entry="tas", axis_ids=axis_ids, units="K")
+        # write data
+        for year in range(2):
+            data = np.random.random(25*12)
+            reshaped_data = data.reshape((5, 5, 12, 1))
+            time_vals = [pt*30 + 360.0 * year + 15.0 for pt in range(12)]
+            time_bnds = [360.0 * year ] + [pt*30 + 360.0 * year + 30.0 for pt in range(12)]
+            cmor.write(tas_var_id, reshaped_data, time_vals=time_vals, time_bnds=time_bnds)
+
+        filename = cmor.close(tas_var_id, file_name=True)
+        ds = Dataset(filename)
+        np.testing.assert_array_equal(ds.variables['time'][:].data,  [15.,  45.,  75., 105., 135., 165., 195., 225., 255., 285., 315., 345., 375., 405., 435., 465., 495., 525., 555., 585., 615., 645., 675., 705.])
+        ds.close()
+        os.remove(filename)
+
+
+    def test_multiple_writes_with_leadtime(self):
+        # load MIP table
+        mip_table = "CMIP6_Amon_leadtime.json"
+        table_id = cmor.load_table(mip_table)
+
+        # construct axes
+        time = cmor.axis(table_entry="time", units="days since 2000-01-01")
+        height2m = cmor.axis(table_entry="height2m", units="m", coord_vals=np.array((2.0,)))
+        latitude = cmor.axis(table_entry="latitude", units="degrees_north",
+                             coord_vals=np.array(range(5)),
+                             cell_bounds=np.array(range(6)))
+        longitude = cmor.axis(table_entry="longitude", units="degrees_east",
+                              coord_vals=np.array(range(5)),
+                              cell_bounds=np.array(range(6)))
+        reftime = cmor.axis(table_entry="reftime1", units="days since 2000-01-01",
+                            coord_vals=np.array([10.0]))
+        axis_ids = [longitude, latitude, time, reftime, height2m]
+
+        tas_var_id = cmor.variable(table_entry="tas", axis_ids=axis_ids, units="K")
+        # write data
+        for year in range(2):
+            data = np.random.random(25 * 12)
+            reshaped_data = data.reshape((5, 5, 12, 1, 1))
+            time_vals = [pt * 30 + 360.0 * year + 15.0 for pt in range(12)]
+            time_bnds = [360.0 * year] + [pt * 30 + 360.0 * year + 30.0 for pt in range(12)]
+            cmor.write(tas_var_id, reshaped_data, time_vals=time_vals, time_bnds=time_bnds)
+
+        filename = cmor.close(tas_var_id, file_name=True)
+        ds = Dataset(filename)
+        np.testing.assert_array_equal(ds.variables['time'][:].data,
+                                      [15., 45., 75., 105., 135., 165., 195., 225., 255., 285., 315., 345., 375., 405.,
+                                       435., 465., 495., 525., 555., 585., 615., 645., 675., 705.])
+        ds.close()
+        os.remove(filename)
+
 
 if __name__ == '__main__':
     unittest.main()

TestTables/CMIP6_Amon.json

@@ -0,0 +1,36 @@
+{
+    "Header": {
+        "data_specs_version": "01.00.33", 
+        "cmor_version": "3.5", 
+        "table_id": "Table Amon", 
+        "realm": "atmos atmosChem", 
+        "table_date": "18 November 2020", 
+        "missing_value": "1e20", 
+        "int_missing_value": "-999", 
+        "product": "model-output", 
+        "approx_interval": "30.00000", 
+        "generic_levels": "alevel alevhalf", 
+        "mip_era": "CMIP6", 
+        "Conventions": "CF-1.7 CMIP-6.2"
+    }, 
+    "variable_entry": {
+        "tas": {
+            "frequency": "mon", 
+            "modeling_realm": "atmos", 
+            "standard_name": "air_temperature", 
+            "units": "K", 
+            "cell_methods": "area: time: mean", 
+            "cell_measures": "area: areacella", 
+            "long_name": "Near-Surface Air Temperature", 
+            "comment": "near-surface (usually, 2 meter) air temperature", 
+            "dimensions": "longitude latitude time height2m", 
+            "out_name": "tas", 
+            "type": "real", 
+            "positive": "", 
+            "valid_min": "", 
+            "valid_max": "", 
+            "ok_min_mean_abs": "", 
+            "ok_max_mean_abs": ""
+        }
+    }
+}