From c0c08f35e1eab7c1772c0da784edc7f7778936ab Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Wed, 13 Nov 2024 16:42:16 +0100
Subject: [PATCH] Update sobol docstrings
---
.../post_processing/post_processing.py | 22 ++-----------------
.../surrogate_models/polynomial_chaos.py | 21 ++++++++++++++----
2 files changed, 19 insertions(+), 24 deletions(-)
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index abafb123d..e61de97c7 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -524,19 +524,8 @@ class PostProcessing:
# -------------------------------------------------------------------------
def sobol_indices(self, xlabel: str = 'Time [s]', plot_type: str = None, save:bool=True):
"""
- Provides Sobol indices as a sensitivity measure to infer the importance
- of the input parameters. See Eq. 27 in [1] for more details. For the
- case with Principal component analysis refer to [2].
-
- [1] Global sensitivity analysis: A flexible and efficient framework
- with an example from stochastic hydrogeology S. Oladyshkin, F.P.
- de Barros, W. Nowak https://doi.org/10.1016/j.advwatres.2011.11.001
-
- [2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
- component analysis and sparse polynomial chaos expansions for global
- sensitivity analysis and model calibration: Application to urban
- drainage simulation. Reliability Engineering & System Safety, 195,
- p.106737.
+ Visualizes and writes out Sobol' and Total Sobol' indices of the trained metamodel.
+ One file is created for each index and output key.
Parameters
----------
@@ -554,13 +543,6 @@ class PostProcessing:
AttributeError
MetaModel in given Engine needs to be of type 'pce' or 'apce'.
- Returns
- -------
- sobol_cell: dict
- Sobol indices.
- total_sobol: dict
- Total Sobol indices.
-
"""
# This function currently only supports PCE/aPCE
metamod = self.engine.MetaModel
diff --git a/src/bayesvalidrox/surrogate_models/polynomial_chaos.py b/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
index 498cb76e6..a9f72c2ea 100644
--- a/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
+++ b/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
@@ -935,8 +935,19 @@ class PCE(MetaModel):
def calculate_sobol(self, save = True):
"""
- Calculates Sobol' indices of all possible orders and the Total Sobol' indices
- based on the coefficients in the PCE.
+ Provides Sobol' indices as a sensitivity measure to infer the importance
+ of the input parameters. See Eq. 27 in [1] for more details. For the
+ case with Principal component analysis refer to [2].
+
+ [1] Global sensitivity analysis: A flexible and efficient framework
+ with an example from stochastic hydrogeology S. Oladyshkin, F.P.
+ de Barros, W. Nowak https://doi.org/10.1016/j.advwatres.2011.11.001
+
+ [2] Nagel, J.B., Rieckermann, J. and Sudret, B., 2020. Principal
+ component analysis and sparse polynomial chaos expansions for global
+ sensitivity analysis and model calibration: Application to urban
+ drainage simulation. Reliability Engineering & System Safety, 195,
+ p.106737.
Parameters
----------
@@ -944,8 +955,10 @@ class PCE(MetaModel):
Returns
-------
- sobol
- totalsobol
+ sobol : dict
+ Sobol' indices of all available orders.
+ totalsobol : dict
+ Total Sobol' indices
"""
# Extract the necessary variables
--
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