Tests for forecast data

recommender-back/src/tests/apis/forecast_test.py

@@ -0,0 +1,93 @@
+import unittest
+import numpy as np
+from datetime import datetime
+from unittest.mock import MagicMock, PropertyMock, patch
+from apis.weather import ForecastGrid, parse_forecast
+
+class ForecastTest(unittest.TestCase):
+    def setUp(self):
+        self.forecastgrid = ForecastGrid()
+        self.grid_by_datetime = MagicMock()
+        self.mock_grid = MagicMock()
+
+        self.grid_by_datetime.data = {
+            datetime(2023, 6, 19, 5, 0): self.mock_grid
+        }
+
+        self.mock_grid_data = PropertyMock(return_value={
+            datetime(2023, 6, 19, 5, 0): {
+                0: {
+                    'Mean sea level pressure': {
+                        'units': 'Pa',
+                        'data': np.array([[10300, 10500, 10600, 10700, 10800]])
+                    }
+                },
+                2: {
+                    '2 metre temperature': {
+                        'units': 'K',
+                        'data': np.array([[288.2, 287.3, 283.5, 291.4, 287.7]])
+                    }
+                },
+                10: {
+                    '10 metre wind speed': {
+                        'units': 'm s**-1',
+                        'data': np.array([[1.23, 1.7, 1.43, 1.42, 1.77]])
+                    }
+                }
+            }
+        })
+
+        type(self.mock_grid).data = self.mock_grid_data
+        type(self.mock_grid).latitudes = PropertyMock(return_value=np.array([[60.1, 60.2, 60.3, 60.4, 60.5]]))
+        type(self.mock_grid).longitudes = PropertyMock(return_value=np.array([[24.6, 24.7, 24.8, 24.9, 25.0]]))
+
+    @patch('apis.weather.download_stored_query')
+    def test_data_is_updated_correctly(self, mock_download_stored_query):
+        mock_download_stored_query.return_value = self.grid_by_datetime
+
+        self.forecastgrid.update_data()
+
+        # Check that data contains a grid object
+        self.assertIsInstance(self.forecastgrid.data, type(self.mock_grid))
+
+        # Check that coordinates are now pairs
+        np.testing.assert_array_equal(
+            self.forecastgrid.coordinates,
+            np.array([((60.1, 24.6), (60.2, 24.7), (60.3, 24.8), (60.4, 24.9), (60.5, 25.0))])
+        )
+
+        # Check that valid_times returns datetime dict_keys
+        expected_valid_times = self.grid_by_datetime.data.keys()
+        self.assertEqual(self.forecastgrid.valid_times, expected_valid_times)
+
+        # Check that all data levels match
+        expected_data_levels = []
+        for datetime_data in self.mock_grid.data.values():
+            data_levels = datetime_data.keys()
+            expected_data_levels.extend(data_levels)
+        self.assertListEqual(list(self.forecastgrid.data_levels), expected_data_levels)
+
+    def test_get_coordinates_returns_correct_list_of_pairs(self):
+        self.forecastgrid.coordinates = np.dstack((self.mock_grid.latitudes, self.mock_grid.longitudes))
+        coordinates = self.forecastgrid.get_coordinates()
+        for lat, lon in zip(self.mock_grid.latitudes.flatten(), self.mock_grid.longitudes.flatten()):
+            assert any(np.allclose([lat, lon], coord) for coord in coordinates)
+
+    def test_forecast_data_is_parsed_correctly(self):
+            data = [{'Dataset': '2 metre temperature', 'Unit': 'K', 'Data': 280.0},
+                    {'Dataset': '2 metre relative humidity', 'Unit': '%', 'Data': 64.013},
+                    {'Dataset': '10 metre U wind component', 'Unit': 'm s**-1', 'Data': 1.223},
+                    {'Dataset': '10 metre V wind component', 'Unit': 'm s**-1', 'Data': -4.7},
+                    {'Dataset': 'surface precipitation amount, rain, convective', 'Unit': 'kg m**-2', 'Data': 0.0231},
+                    {'Dataset': 'Total Cloud Cover', 'Unit': '%', 'Data': 23.2}]
+
+            expected_parsed_data = {'Air temperature': '6.9 °C',
+                                    'Humidity': '64.0 %',
+                                    'WindU': '1.2', 
+                                    'WindV': '-4.7',
+                                    'Precipication': '0.0',
+                                    'Total Cloud Cover': '23.2'}
+
+            parsed_data = parse_forecast(data)
+
+            self.assertEqual(parsed_data, expected_parsed_data)