From 0b853b17d106ffd0a5587a0d76aea17139d496bb Mon Sep 17 00:00:00 2001
From: Alexandra Udaltsova <43303448+AUdaltsova@users.noreply.github.com>
Date: Wed, 23 Oct 2024 12:10:08 +0100
Subject: [PATCH 1/2] Update backtest_sites.py

---
 scripts/backtest_sites.py | 26 +++++++++++++++++++-------
 1 file changed, 19 insertions(+), 7 deletions(-)

diff --git a/scripts/backtest_sites.py b/scripts/backtest_sites.py
index 3572daa3..e4b63223 100644
--- a/scripts/backtest_sites.py
+++ b/scripts/backtest_sites.py
@@ -118,21 +118,31 @@ 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)
 
 
@@ -429,8 +439,10 @@ 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")
+    data_pipeline['pv'] = data_pipeline['pv'].pad_forward_pv(
+        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(

From fdda5e31bf7770680b51cb72684d8d6b55b73445 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 23 Oct 2024 11:14:24 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 scripts/backtest_sites.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/scripts/backtest_sites.py b/scripts/backtest_sites.py
index e4b63223..65d63be2 100644
--- a/scripts/backtest_sites.py
+++ b/scripts/backtest_sites.py
@@ -118,11 +118,13 @@ class PadForwardPVIterDataPipe(IterDataPipe):
     to run out of data to slice for the forecast part.
     """
 
-    def __init__(self, pv_dp: IterDataPipe,
-                 forecast_duration: np.timedelta64,
-                 history_duration: np.timedelta64,
-                 time_resolution_minutes: np.timedelta64
-                ):
+    def __init__(
+        self,
+        pv_dp: IterDataPipe,
+        forecast_duration: np.timedelta64,
+        history_duration: np.timedelta64,
+        time_resolution_minutes: np.timedelta64,
+    ):
         """Init"""
 
         super().__init__()
@@ -137,7 +139,12 @@ def __iter__(self):
         """Iter"""
 
         for xr_data in self.pv_dp:
-            t_end = xr_data.time_utc.data[0] + self.history_duration + self.forecast_duration + self.time_resolution_minutes
+            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:
@@ -439,10 +446,10 @@ 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'),
-        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["pv"] = data_pipeline["pv"].pad_forward_pv(
+        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(