About
-----
-**The ADAO module provides data assimilation and optimization**
-features in Python or SALOME context (see
-http://www.salome-platform.org/). Briefly stated, Data Assimilation is
-a methodological framework to compute the optimal estimate of the
-inaccessible true value of a system state, eventually over time. It
-uses information coming from experimental measurements or observations,
-and from numerical *a priori* models, including information about their
-errors. Parts of the framework are also known under the names of
-*parameter estimation*, *inverse problems*, *Bayesian estimation*,
-*optimal interpolation*, etc. More details can be found in the full
-ADAO documentation (see https://www.salome-platform.org/).
+**The ADAO module provides data assimilation and optimization** features in
+Python or SALOME context (see http://www.salome-platform.org/). Briefly stated,
+Data Assimilation is a methodological framework to compute the optimal estimate
+of the inaccessible true value of a system state, eventually over time. It uses
+information coming from experimental measurements or observations, and from
+numerical *a priori* models, including information about their errors. Parts of
+the framework are also known under the names of *calibration*, *adjustment*,
+*state estimation*, *parameter estimation*, *parameter adjustment*, *inverse
+problems*, *Bayesian estimation*, *optimal interpolation*, *mathematical
+regularization*, *meta-heuristics for optimization*, *model reduction*, *data
+smoothing*, etc. More details can be found in the full ADAO documentation (see
+https://www.salome-platform.org/).
Only the use of ADAO text programming interface (API/TUI) is introduced
here. This interface gives ability to create a calculation object in a
from numpy import array, matrix
from adao import adaoBuilder
case = adaoBuilder.New()
- case.set( 'AlgorithmParameters', Algorithm='3DVAR' )
- case.set( 'Background', Vector=[0, 1, 2] )
- case.set( 'BackgroundError', ScalarSparseMatrix=1.0 )
- case.set( 'Observation', Vector=array([0.5, 1.5, 2.5]) )
- case.set( 'ObservationError', DiagonalSparseMatrix='1 1 1' )
- case.set( 'ObservationOperator', Matrix='1 0 0;0 2 0;0 0 3' )
- case.set( 'Observer', Variable="Analysis", Template="ValuePrinter" )
+ case.set( 'AlgorithmParameters', Algorithm = '3DVAR' )
+ case.set( 'Background', Vector = [0, 1, 2] )
+ case.set( 'BackgroundError', ScalarSparseMatrix = 1.0 )
+ case.set( 'Observation', Vector = array([0.5, 1.5, 2.5]) )
+ case.set( 'ObservationError', DiagonalSparseMatrix = '1 1 1' )
+ case.set( 'ObservationOperator', Matrix = '1 0 0;0 2 0;0 0 3' )
+ case.set( 'Observer', Variable = "Analysis", Template = "ValuePrinter" )
case.execute()
The result of running these commands in SALOME (either as a SALOME
<ADAO, a module for Data Assimilation and Optimization>
- Copyright (C) 2008-2021 EDF R&D
+ Copyright (C) 2008-2022 EDF R&D
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
See http://www.salome-platform.org/
-In addition, it is requested that any publication or presentation describing
+In addition, it is requested that any publication or presentation, describing
work using this module, or any commercial or non-commercial product using it,
-cite at least one of at least one of the references below with the current year
-added:
+cite at least one of the references below with the current year added:
* *ADAO, a module for Data Assimilation and Optimization*,
http://www.salome-platform.org/