update backtest_sites.py (#266)

* Update backtest_sites.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

scripts/backtest_sites.py

@@ -118,21 +118,38 @@ class PadForwardPVIterDataPipe(IterDataPipe):
     to run out of data to slice for the forecast part.
     """
 
-    def __init__(self, pv_dp: IterDataPipe, forecast_duration: np.timedelta64):
+    def __init__(
+        self,
+        pv_dp: IterDataPipe,
+        forecast_duration: np.timedelta64,
+        history_duration: np.timedelta64,
+        time_resolution_minutes: np.timedelta64,
+    ):
         """Init"""
 
         super().__init__()
         self.pv_dp = pv_dp
         self.forecast_duration = forecast_duration
+        self.history_duration = history_duration
+        self.time_resolution_minutes = time_resolution_minutes
+
+        self.min_seq_length = history_duration // time_resolution_minutes
 
     def __iter__(self):
         """Iter"""
 
         for xr_data in self.pv_dp:
-            t0 = xr_data.time_utc.data[int(xr_data.attrs["t0_idx"])]
-            pv_step = np.timedelta64(xr_data.attrs["sample_period_duration"])
-            t_end = t0 + self.forecast_duration + pv_step
-            time_idx = np.arange(xr_data.time_utc.data[0], t_end, pv_step)
+            t_end = (
+                xr_data.time_utc.data[0]
+                + self.history_duration
+                + self.forecast_duration
+                + self.time_resolution_minutes
+            )
+            time_idx = np.arange(xr_data.time_utc.data[0], t_end, self.time_resolution_minutes)
+
+            if len(xr_data.time_utc.data) < self.min_seq_length:
+                raise ValueError("Not enough PV data to predict")
+
             yield xr_data.reindex(time_utc=time_idx, fill_value=-1)
 
 
@@ -430,7 +447,9 @@ def get_datapipe(config_path: str) -> NumpyBatch:
 
     config = load_yaml_configuration(config_path)
     data_pipeline["pv"] = data_pipeline["pv"].pad_forward_pv(
-        forecast_duration=np.timedelta64(config.input_data.pv.forecast_minutes, "m")
+        forecast_duration=np.timedelta64(config.input_data.pv.forecast_minutes, "m"),
+        history_duration=np.timedelta64(config.input_data.pv.history_minutes, "m"),
+        time_resolution_minutes=np.timedelta64(config.input_data.pv.time_resolution_minutes, "m"),
     )
 
     data_pipeline = DictDatasetIterDataPipe(