From 9ed8db81ff0cd508c5edd796581a90392c877496 Mon Sep 17 00:00:00 2001
From: lukas leufen <l.leufen@fz-juelich.de>
Date: Thu, 5 Mar 2020 15:03:27 +0100
Subject: [PATCH] simplified observation creation

---
 src/run_modules/post_processing.py | 14 +++-----------
 1 file changed, 3 insertions(+), 11 deletions(-)

diff --git a/src/run_modules/post_processing.py b/src/run_modules/post_processing.py
index 00d3d2d8..5105b5d8 100644
--- a/src/run_modules/post_processing.py
+++ b/src/run_modules/post_processing.py
@@ -195,19 +195,11 @@ class PostProcessing(RunEnvironment):
         getter = {"daily": "1D", "hourly": "1H"}
         return getter.get(self._sampling, None)
 
-    def _create_observation(self, data, observation, mean, std, transformation_method, normalised):
-        obs = data.observation.copy()
+    def _create_observation(self, data, _, mean, std, transformation_method, normalised):
+        obs = data.label.copy()
         if not normalised:
             obs = statistics.apply_inverse_transformation(obs, mean, std, transformation_method)
-        window_lead_time = self.data_store.get("window_lead_time", "general")
-        obs_w = []
-        for w in range(window_lead_time):
-            obs_w.append(obs.shift(datetime=-(w+1)))
-        if observation is None:
-            observation = data.label.copy()
-        observation.values = np.concatenate(obs_w, axis=0)
-        return observation
-
+        return obs
 
     def _create_ols_forecast(self, input_data, ols_prediction, mean, std, transformation_method, normalised):
         tmp_ols = self.ols_model.predict(input_data)
-- 
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