Add horizon_hours param to forecast object

tests/test_sdk.py

@@ -301,6 +301,26 @@ def test_historical_forecast_pandas(self):
         self.assertIn("point_time", df.columns)
         self.assertIn("value", df.columns)
         self.assertIn("generated_at", df.columns)
+
+    def test_horizon_hours(self):
+        json = self.forecast.get_forecast_json(region=REGION, horizon_hours=0)
+        self.assertIsInstance(json, dict)
+        self.assertIn("meta", json)
+        self.assertEqual(len(json["data"]), 1)
+        self.assertIn("point_time", json["data"][0])
+
+        json2 = self.forecast.get_forecast_json(region=REGION, horizon_hours=24)
+        self.assertIsInstance(json2, dict)
+        self.assertIn("meta", json2)
+        self.assertEqual(len(json2["data"]), 288)
+        self.assertIn("point_time", json2["data"][0])
+
+        json3 = self.forecast.get_forecast_json(region=REGION, horizon_hours=72)
+        self.assertIsInstance(json3, dict)
+        self.assertIn("meta", json3)
+        self.assertEqual(len(json3["data"]), 864)
+        self.assertIn("point_time", json3["data"][0])
+
 
 
 class TestWattTimeMaps(unittest.TestCase):

watttime/api.py

@@ -351,6 +351,7 @@ def get_forecast_json(
             Literal["co2_moer", "co2_aoer", "health_damage"]
         ] = "co2_moer",
         model_date: Optional[Union[str, date]] = None,
+        horizon_hours: int = 24,
     ) -> Dict:
         """
         Retrieves the most recent forecast data in JSON format based on the given region, signal type, and model date.
@@ -364,13 +365,18 @@ def get_forecast_json(
                 Valid options are "co2_moer", "co2_aoer", and "health_damage".
             model_date (str or date, optional): The date of the model version to use for the forecast data.
                 If not provided, the most recent model version will be used.
+            horizon_hours (int, optional): The number of hours to forecast. Defaults to 24. Minimum of 0 provides a "nowcast" created with the forecast, maximum of 72.
 
         Returns:
             List[dict]: A list of dictionaries representing the forecast data in JSON format.
         """
         if not self._is_token_valid():
             self._login()
-        params = {"region": region, "signal_type": signal_type}
+        params = {
+            "region": region,
+            "signal_type": signal_type,
+            "horizon_hours": horizon_hours,
+        }
 
         # No model_date will default to the most recent model version available
         if model_date is not None:
@@ -390,6 +396,7 @@ def get_forecast_pandas(
         ] = "co2_moer",
         model_date: Optional[Union[str, date]] = None,
         include_meta: bool = False,
+        horizon_hours: int = 24,
     ) -> pd.DataFrame:
         """
         Return a pd.DataFrame with point_time, and values.
@@ -403,7 +410,7 @@ def get_forecast_pandas(
         Returns:
             pd.DataFrame: _description_
         """
-        j = self.get_forecast_json(region, signal_type, model_date)
+        j = self.get_forecast_json(region, signal_type, model_date, horizon_hours)
         return pd.json_normalize(
             j, record_path="data", meta=["meta"] if include_meta else []
         )
@@ -417,6 +424,7 @@ def get_historical_forecast_json(
             Literal["co2_moer", "co2_aoer", "health_damage"]
         ] = "co2_moer",
         model_date: Optional[Union[str, date]] = None,
+        horizon_hours: int = 24,
     ) -> List[Dict[str, Any]]:
         """
         Retrieves the historical forecast data from the API as a list of dictionaries.
@@ -427,6 +435,7 @@ def get_historical_forecast_json(
             region (str): The region for which to retrieve the forecast data.
             signal_type (Optional[Literal["co2_moer", "co2_aoer", "health_damage"]]): The type of signal to retrieve. Defaults to "co2_moer".
             model_date (Optional[Union[str, date]]): The date of the model version to use. Defaults to None.
+            horizon_hours (int, optional): The number of hours to forecast. Defaults to 24. Minimum of 0 provides a "nowcast" created with the forecast, maximum of 72.
 
         Returns:
             List[Dict[str, Any]]: A list of dictionaries representing the forecast data.
@@ -439,7 +448,11 @@ def get_historical_forecast_json(
         url = "{}/v3/forecast/historical".format(self.url_base)
         headers = {"Authorization": "Bearer " + self.token}
         responses = []
-        params = {"region": region, "signal_type": signal_type}
+        params = {
+            "region": region,
+            "signal_type": signal_type,
+            horizon_hours: horizon_hours,
+        }
 
         start, end = self._parse_dates(start, end)
         chunks = self._get_chunks(start, end, chunk_size=timedelta(days=1))
@@ -473,6 +486,7 @@ def get_historical_forecast_pandas(
             Literal["co2_moer", "co2_aoer", "health_damage"]
         ] = "co2_moer",
         model_date: Optional[Union[str, date]] = None,
+        horizon_hours: int = 24,
     ) -> pd.DataFrame:
         """
         Retrieves the historical forecast data as a pandas DataFrame.
@@ -484,12 +498,13 @@ def get_historical_forecast_pandas(
             signal_type (Optional[Literal["co2_moer", "co2_aoer", "health_damage"]], optional): 
                 The type of signal for the historical forecast data. Defaults to "co2_moer".
             model_date (Optional[Union[str, date]], optional): The model date for the historical forecast data. Defaults to None.
+            horizon_hours (int, optional): The number of hours to forecast. Defaults to 24. Minimum of 0 provides a "nowcast" created with the forecast, maximum of 72.
 
         Returns:
             pd.DataFrame: A pandas DataFrame containing the historical forecast data.
         """
         json_list = self.get_historical_forecast_json(
-            start, end, region, signal_type, model_date
+            start, end, region, signal_type, model_date, horizon_hours
         )
         out = pd.DataFrame()
         for json in json_list: