Implement forecast generation and combining script (#75)

* script created

* Update README.md

* updated test cases

* added test case

* Calling lat lon cap using function declaration

* added forecast init time in csv

README.md

@@ -25,6 +25,22 @@ Which gives the following prediction
 
 ![https://github.com/openclimatefix/Open-Source-Quartz-Solar-Forecast/blob/main/predictions.png?raw=true](https://github.com/openclimatefix/Open-Source-Quartz-Solar-Forecast/blob/main/predictions.png?raw=true)
 
+## Generating Forecasts
+To generate solar forecasts and save them into a CSV file, follow these steps:
+1. Navigate to the scripts directory
+```bash
+cd scripts
+```
+2. Run the forecast_csv.py script with desired inputs
+```bash
+python forecast_csv.py
+```
+Replace the --init_time_freq, --start_datetime, --end_datetime, and --site_name with your desired forecast initialization frequency (in hours), start datetime, end datetime, and the name of the forecast or site, respectively.
+
+Output
+
+The script will generate solar forecasts at the specified intervals between the start and end datetimes. The results will be combined into a CSV file named using the site name, start and end datetimes, and the frequency of forecasts. This file will be saved in the scripts/csv_forecasts directory.
+
 ## Installation
 
 The source code is currently hosted on GitHub at: https://github.com/openclimatefix/Open-Source-Quartz-Solar-Forecast

scripts/forecast_csv.py

@@ -0,0 +1,97 @@
+import os
+import pandas as pd
+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_forecast(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.
+
+    Args:
+        init_time_freq (int): The frequency in hours at which the forecasts are generated.
+        start_datetime (str): The starting date and time for generating forecasts.
+        end_datetime (str): The ending date and time for generating forecasts.
+        site_name (str): The name of the site for which the forecasts are generated.
+        latitude (float): The latitude of the PV site.
+        longitude (float): The longitude of the PV site.
+        capacity_kwp (float): The capacity of the PV site in kilowatts peak (kWp).
+    """
+    start = datetime.strptime(start_datetime, "%Y-%m-%d %H:%M:%S")
+    start_date = start.date()
+    end = datetime.strptime(end_datetime, "%Y-%m-%d %H:%M:%S")
+    end_date = end.date()
+    all_forecasts = pd.DataFrame()
+    site = PVSite(latitude=latitude, longitude=longitude, capacity_kwp=capacity_kwp)
+
+    init_time = start
+    while init_time <= end:
+        print(f"Running forecast for initialization time: {init_time}")
+        predictions_df = run_forecast(site=site, ts=init_time.strftime("%Y-%m-%d %H:%M:%S"))
+        predictions_df.reset_index(inplace=True)  
+        predictions_df.rename(columns={'index': 'datetime'}, inplace=True) 
+        predictions_df['forecast_init_time'] = init_time  
+        all_forecasts = pd.concat([all_forecasts, predictions_df])
+        init_time += timedelta(hours=init_time_freq)
+
+    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}")
+
+if __name__ == "__main__":
+    # please change the site name, start_datetime and end_datetime, latitude, longitude and capacity_kwp as per your requirement
+    generate_forecast(
+        init_time_freq=6,
+        start_datetime="2024-03-10 00:00:00",
+        end_datetime="2024-03-11 00:00:00",
+        site_name="Test",
+        latitude=51.75,
+        longitude=-1.25,
+        capacity_kwp=1.25
+    )
+
+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)
+
+        generate_forecast(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()