From df93b9fcb5bd22f578ca7d5223bd020c64c338d2 Mon Sep 17 00:00:00 2001
From: leufen1 <l.leufen@fz-juelich.de>
Date: Fri, 15 Oct 2021 15:18:51 +0200
Subject: [PATCH] include create_n_bootstrap_realizations in
estimate_sample_uncertainty, added left-over todos
---
mlair/helpers/statistics.py | 7 +++----
mlair/run_modules/post_processing.py | 5 +++++
2 files changed, 8 insertions(+), 4 deletions(-)
diff --git a/mlair/helpers/statistics.py b/mlair/helpers/statistics.py
index 9a0346e4..aa89da0e 100644
--- a/mlair/helpers/statistics.py
+++ b/mlair/helpers/statistics.py
@@ -525,8 +525,8 @@ def calculate_average(data: xr.DataArray, **kwargs) -> xr.DataArray:
return data.mean(**kwargs)
-def create_n_bootstrap_realizations(data: xr.DataArray, dim_name_time, dim_name_model, n_boots: int = 1000,
- dim_name_boots='boots') -> xr.DataArray:
+def create_n_bootstrap_realizations(data: xr.DataArray, dim_name_time: str, dim_name_model: str, n_boots: int = 1000,
+ dim_name_boots: str = 'boots') -> xr.DataArray:
"""
Create n bootstrap realizations and calculate averages across realizations
@@ -546,8 +546,7 @@ def create_n_bootstrap_realizations(data: xr.DataArray, dim_name_time, dim_name_
for boot in range(n_boots):
res[boot] = (calculate_average(
create_single_bootstrap_realization(data, dim_name_time=dim_name_time),
- dim=dim_name_time
- ))
+ dim=dim_name_time, skipna=True))
return res
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index e5e6b771..e7ed04b2 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -131,10 +131,15 @@ class PostProcessing(RunEnvironment):
self.plot()
def estimate_sample_uncertainty(self, separate_ahead=False):
+ #todo: set n_boots
+ #todo: visualize
+ #todo: write results on disk
block_length = self.data_store.get_default("uncertainty_estimate_block_length", default="1m")
evaluate_competitors = self.data_store.get_default("uncertainty_estimate_evaluate_competitors", default=True)
block_mse = self.calculate_block_mse(evaluate_competitors=evaluate_competitors, separate_ahead=separate_ahead,
block_length=block_length)
+ res = statistics.create_n_bootstrap_realizations(block_mse, self.index_dim, self.model_type_dim, n_boots=10)
+ res
def calculate_block_mse(self, evaluate_competitors=True, separate_ahead=False, block_length="1m"):
path = self.data_store.get("forecast_path")
--
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