From f68ecb48b2432a039ed12da5a40a327b0addcfb2 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Thu, 8 Jul 2021 16:25:30 +0200
Subject: [PATCH] /close #306 on pipeline success
---
mlair/run_modules/post_processing.py | 11 +++++++----
1 file changed, 7 insertions(+), 4 deletions(-)
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index 89a6f205..0d7bfeb4 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -85,6 +85,7 @@ class PostProcessing(RunEnvironment):
self.competitor_path = self.data_store.get("competitor_path")
self.competitors = to_list(self.data_store.get_default("competitors", default=[]))
self.forecast_indicator = "nn"
+ self.ahead_dim = "ahead"
self._run()
def _run(self):
@@ -172,7 +173,7 @@ class PostProcessing(RunEnvironment):
bootstrap_path = self.data_store.get("bootstrap_path")
forecast_path = self.data_store.get("forecast_path")
number_of_bootstraps = self.data_store.get("number_of_bootstraps", "postprocessing")
- dims = ["index", "ahead", "type"]
+ dims = ["index", self.ahead_dim, "type"]
for station in self.test_data:
logging.info(str(station))
X, Y = None, None
@@ -467,7 +468,8 @@ class PostProcessing(RunEnvironment):
"obs": observation,
"ols": ols_prediction}
all_predictions = self.create_forecast_arrays(full_index, list(target_data.indexes[window_dim]),
- time_dimension, **prediction_dict)
+ time_dimension, ahead_dim=self.ahead_dim,
+ **prediction_dict)
# save all forecasts locally
path = self.data_store.get("forecast_path")
@@ -618,7 +620,8 @@ class PostProcessing(RunEnvironment):
return index
@staticmethod
- def create_forecast_arrays(index: pd.DataFrame, ahead_names: List[Union[str, int]], time_dimension, **kwargs):
+ def create_forecast_arrays(index: pd.DataFrame, ahead_names: List[Union[str, int]], time_dimension,
+ ahead_dim="ahead", **kwargs):
"""
Combine different forecast types into single xarray.
@@ -631,7 +634,7 @@ class PostProcessing(RunEnvironment):
"""
keys = list(kwargs.keys())
res = xr.DataArray(np.full((len(index.index), len(ahead_names), len(keys)), np.nan),
- coords=[index.index, ahead_names, keys], dims=['index', 'ahead', 'type'])
+ coords=[index.index, ahead_names, keys], dims=['index', ahead_dim, 'type'])
for k, v in kwargs.items():
intersection = set(res.index.values) & set(v.indexes[time_dimension].values)
match_index = np.array(list(intersection))
--
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