From e0ad3fc9e746e7ca826362d8ce4922d16a2650cb Mon Sep 17 00:00:00 2001
From: Felix Kleinert <f.kleinert@fz-juelich.de>
Date: Fri, 15 Oct 2021 14:31:58 +0200
Subject: [PATCH] draft uncertainiy boots
---
mlair/helpers/statistics.py | 46 +++++++++++++++++++++++++++++++++++++
1 file changed, 46 insertions(+)
diff --git a/mlair/helpers/statistics.py b/mlair/helpers/statistics.py
index 87f780f9..546c2ff7 100644
--- a/mlair/helpers/statistics.py
+++ b/mlair/helpers/statistics.py
@@ -481,3 +481,49 @@ class SkillScores:
return monthly_mean
+
+def create_single_bootstrap_realization(data: xr.DataArray) -> xr.DataArray:
+ """
+ Return a bootstraped realization of data
+ :param data: data from which to draw ONE bootstrap realization
+ :return: bootstrapped realization of data
+ """
+
+ num_of_blocks = data.shape[0]
+ time_block_dim = data.dims[0]
+ boot_idx = np.random.choice(num_of_blocks, size=num_of_blocks, replace=True)
+ return data.isel({time_block_dim: boot_idx})
+
+
+def calculate_average(data: xr.DataArray, **kwargs) -> xr.DataArray:
+ """
+ Calculate mean of data
+ :param data: data for which to calculate mean
+ :return: mean of data
+ """
+ axis = kwargs.pop('axis', 0)
+ return data.mean(axis=axis, **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:
+ res_dims = [dim_name_boots]
+ dims = list(data.dims)
+ coords = {dim_name_boots: range(n_boots), dim_name_model: data.coords[dim_name_model] }
+ if len(dims) > 1:
+ res_dims = res_dims + dims[1:]
+ res = xr.DataArray(np.nan, dims=res_dims, coords=coords)
+ for boot in range(n_boots):
+ res[boot] = (calculate_average(
+ create_single_bootstrap_realization(data)
+ ))
+ return res
+
+
+
+
+
+
+
+
+
--
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