basic unittest for gridded timeseries

Author: mhidas

test_aodntools/base_test.py

@@ -51,14 +51,24 @@ def check_nan_values(self, dataset):
         "check that there are no NaN values in any variable (they should be fill values instead)"
         nan_vars = [(name, "contains NaN values")
                     for name, var in dataset.variables.items()
-                    if var.dtype in (np.dtype('float32'), np.dtype('float64')) and any(np.isnan(var[:]))
+                    if var.dtype in (np.dtype('float32'), np.dtype('float64')) and np.isnan(var[:]).any()
                     ]
         self.assertEqual([], nan_vars)
 
     def compare_variables(self, dataset, skip_vars=('source_file', 'instrument_id')):
         """Compare dimensions and values of all variables in dataset with those in self.EXPECTED_OUTPUT_FILE,
         except for variables listed in skip_vars.
         """
+
+        def _arrays_equal(testvar, expected):
+            """compare two numpy arrays, handling the case of scalar variables"""
+            if expected.shape == ():
+                if np.isclose(testvar, expected):
+                    return True
+            elif (np.isclose(testvar, expected)).all():
+                return True
+            return False
+
         differences = []
         with Dataset(self.EXPECTED_OUTPUT_FILE) as expected:
             for var in set(expected.variables.keys()) - set(skip_vars):
@@ -68,7 +78,7 @@ def compare_variables(self, dataset, skip_vars=('source_file', 'instrument_id'))
                     differences.append((var, "shapes differ"))
 
                 # compare the raw data arrays (not the masked_array)
-                if not all(np.isclose(dataset[var][:].data, expected[var][:].data)):
+                if not _arrays_equal(dataset[var][:].data, expected[var][:].data):
                     differences.append((var, "variable values differ"))
 
         self.assertEqual([], differences)

test_aodntools/timeseries_products/IMOS_ANMN-NRS_TZ_20181213_NRSROT_FV02_TEMP-gridded-timeseries_END-20190523_C-20230110.nc

@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+
+import os
+import unittest
+
+from netCDF4 import Dataset
+
+from test_aodntools.base_test import BaseTestCase
+from aodntools import __version__
+from aodntools.timeseries_products.gridded_timeseries import grid_variable
+
+
+TEST_ROOT = os.path.dirname(__file__)
+INPUT_FILE = 'IMOS_ANMN-NRS_STZ_20181213_NRSROT_FV02_hourly-timeseries_END-20190523_C-20220428.nc'
+
+
+class TestGriddedTimeseries(BaseTestCase):
+    EXPECTED_OUTPUT_FILE = os.path.join(
+        TEST_ROOT, 'IMOS_ANMN-NRS_TZ_20181213_NRSROT_FV02_TEMP-gridded-timeseries_END-20190523_C-20230110.nc'
+    )
+
+    def test_grid_variable(self):
+        output_file = grid_variable(INPUT_FILE, 'TEMP', input_dir=TEST_ROOT, output_dir='/tmp')
+
+        self.assertRegex(output_file,
+                         r'IMOS_ANMN-NRS_TZ_20181213_NRSROT_FV02_TEMP-gridded-timeseries_END-20190523_C-\d{8}\.nc'
+                         )
+
+        dataset = Dataset(output_file)
+        self.assertSetEqual(set(dataset.dimensions), {'TIME', 'DEPTH'})
+        self.assertSetEqual(set(dataset.variables.keys()),
+                            {'TIME', 'DEPTH', 'LATITUDE', 'LONGITUDE', 'TEMP', 'TEMP_count'})
+
+        # check metadata
+        self.assertEqual(__version__, dataset.generating_code_version)
+        self.assertIn(__version__, dataset.lineage)
+        self.assertIn('gridded_timeseries.py', dataset.lineage)
+        self.assertIn(INPUT_FILE, dataset.source_file)
+
+        self.compare_global_attributes(dataset)
+
+        self.check_nan_values(dataset)
+
+        self.compare_variables(dataset)
+
+
+if __name__ == '__main__':
+    unittest.main()