Move forecast csv test to test folder (#209)

* Move forecast_csv test to test folder

* Remove TestGenerateForecast from forecast_csv

* Remove duplicate data in test_generate_forecast.py

* Remove extraneous files.

---------

Co-authored-by: Peter Dudfield <[email protected]>

quartz_solar_forecast/utils/forecast_csv.py

@@ -3,45 +3,56 @@
 from datetime import datetime, timedelta
 from quartz_solar_forecast.forecast import run_forecast
 from quartz_solar_forecast.pydantic_models import PVSite
-import unittest
-from unittest.mock import patch
 
 
 def generate_all_forecasts(
-        init_time_freq: int,
-        start: datetime,
-        end: datetime,
-        latitude: float,
-        longitude: float,
-        capacity_kwp: float) -> pd.DataFrame:
+    init_time_freq: int,
+    start: datetime,
+    end: datetime,
+    latitude: float,
+    longitude: float,
+    capacity_kwp: float,
+) -> pd.DataFrame:
 
     all_forecasts = pd.DataFrame()
 
     init_time = start
     while init_time <= end:
         print(f"Running forecast for initialization time: {init_time}")
-        predictions_df = forecast_for_site(latitude, longitude, capacity_kwp, init_time=init_time)
-        predictions_df['forecast_init_time'] = init_time
+        predictions_df = forecast_for_site(
+            latitude, longitude, capacity_kwp, init_time=init_time
+        )
+        predictions_df["forecast_init_time"] = init_time
         all_forecasts = pd.concat([all_forecasts, predictions_df])
         init_time += timedelta(hours=init_time_freq)
 
     return all_forecasts
 
 
-def forecast_for_site(latitude: float,
-                      longitude: float,
-                      capacity_kwp: float,
-                      model: str = "gb",
-                      init_time: datetime = None) -> pd.DataFrame:
+def forecast_for_site(
+    latitude: float,
+    longitude: float,
+    capacity_kwp: float,
+    model: str = "gb",
+    init_time: datetime = None,
+) -> pd.DataFrame:
 
     site = PVSite(latitude=latitude, longitude=longitude, capacity_kwp=capacity_kwp)
     predictions_df = run_forecast(site=site, model=model, ts=init_time)
     predictions_df.reset_index(inplace=True)
-    predictions_df.rename(columns={'index': 'datetime'}, inplace=True)
+    predictions_df.rename(columns={"index": "datetime"}, inplace=True)
     return predictions_df
 
 
-def write_out_forecasts(init_time_freq, start_datetime, end_datetime, site_name, latitude, longitude, capacity_kwp):
+def write_out_forecasts(
+    init_time_freq,
+    start_datetime,
+    end_datetime,
+    site_name,
+    latitude,
+    longitude,
+    capacity_kwp,
+):
     """
     Generates forecasts at specified intervals and saves them into a CSV file.
 
@@ -58,52 +69,15 @@ def write_out_forecasts(init_time_freq, start_datetime, end_datetime, site_name,
     start_date = start.date()
     end = datetime.strptime(end_datetime, "%Y-%m-%d %H:%M:%S")
     end_date = end.date()
-    all_forecasts = generate_all_forecasts(init_time_freq, start, end, latitude, longitude, capacity_kwp)
+    all_forecasts = generate_all_forecasts(
+        init_time_freq, start, end, latitude, longitude, capacity_kwp
+    )
 
-    output_dir = os.path.join(os.getcwd(), 'csv_forecasts')
+    output_dir = os.path.join(os.getcwd(), "csv_forecasts")
     if not os.path.exists(output_dir):
         os.makedirs(output_dir)
     output_file_name = f"forecast_{site_name}_{start_date}_{end_date}.csv"
     output_file_path = os.path.join(output_dir, output_file_name)
     all_forecasts.to_csv(output_file_path, index=False)
     print(f"Forecasts saved to {output_file_path}")
 
-class TestGenerateForecast(unittest.TestCase):
-    def setUp(self):
-        self.site_name = "TestCase"
-        self.latitude = 51.75
-        self.longitude = -1.25
-        self.capacity_kwp = 1.25
-        self.start_datetime = "2024-03-10 00:00:00"
-        self.end_datetime = "2024-03-11 00:00:00"
-        self.init_time_freq = 6
-        self.output_dir = os.path.join(os.getcwd(), 'csv_forecasts')
-        self.output_file_name = f"forecast_{self.site_name}_{self.start_datetime[:10]}_{self.end_datetime[:10]}.csv"
-        self.output_file_path = os.path.join(self.output_dir, self.output_file_name)
-
-    @patch('forecast_csv.run_forecast')
-    def test_generate_forecast(self, mock_run_forecast):
-        mock_df = pd.DataFrame({
-            'datetime': [datetime(2024, 3, 10, 0, 0) + timedelta(hours=6 * i) for i in range(4)],
-            'power_kw': [0.1, 0.5, 0.8, 0.6],
-            'forecast_init_time': [datetime(2024, 3, 10, 0, 0)] * 4
-        })
-        mock_run_forecast.return_value = mock_df
-
-        if not os.path.exists(self.output_dir):
-            os.makedirs(self.output_dir)
-
-        write_out_forecasts(self.init_time_freq,
-                          self.start_datetime, 
-                          self.end_datetime, 
-                          self.site_name, 
-                          self.latitude, 
-                          self.longitude, 
-                          self.capacity_kwp
-                        )
-
-        self.assertTrue(os.path.exists(self.output_file_path))
-        os.remove(self.output_file_path)
-
-if __name__ == '__main__':
-    unittest.main()

tests/test_generate_forecast.py

@@ -0,0 +1,55 @@
+import os
+import pandas as pd
+from datetime import datetime, timedelta
+import quartz_solar_forecast.forecast as forecast
+from quartz_solar_forecast.utils.forecast_csv import write_out_forecasts
+from quartz_solar_forecast.pydantic_models import PVSite
+
+def test_generate_forecast(monkeypatch):
+    site_name = "TestCase"
+    latitude = 51.75
+    longitude = -1.25
+    capacity_kwp = 1.25
+    start_datetime = "2024-03-10 00:00:00"
+    end_datetime = "2024-03-11 00:00:00"
+    init_time_freq = 6
+    output_dir = os.path.join(os.getcwd(), "csv_forecasts")
+    output_file_name = (
+        f"forecast_{site_name}_{start_datetime[:10]}_{end_datetime[:10]}.csv"
+    )
+    output_file_path = os.path.join(output_dir, output_file_name)
+
+    def mock_forecast(
+        site: PVSite,
+        model: str = "gb",
+        ts: datetime | str = None,
+        nwp_source: str = "icon",
+    ):
+        return pd.DataFrame(
+            {
+                "datetime": [
+                    datetime(2024, 3, 10, 0, 0) + timedelta(hours=6 * i)
+                    for i in range(4)
+                ],
+                "power_kw": [0.1, 0.5, 0.8, 0.6],
+                "forecast_init_time": [datetime(2024, 3, 10, 0, 0)] * 4,
+            }
+        )
+
+    monkeypatch.setattr(forecast, "run_forecast", mock_forecast)
+
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    write_out_forecasts(
+        init_time_freq,
+        start_datetime,
+        end_datetime,
+        site_name,
+        latitude,
+        longitude,
+        capacity_kwp,
+    )
+
+    assert os.path.exists(output_file_path)
+    os.remove(output_file_path)